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• Methodology

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

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

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

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

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

Table of contents

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

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

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

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

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

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

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• Unobtrusive data collection

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

• Transparent and replicable

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

• Highly flexible

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

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

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

• Time intensive

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

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

Next, you follow these five steps.

Step 1: Select the content you will analyse

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

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

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

Step 2: Define the units and categories of analysis

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

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

Step 3: Develop a set of rules for coding

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

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

Step 4: Code the text according to the rules

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

Step 5: Analyse the results and draw conclusions

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

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Luo, A. (2022, December 05). Content Analysis | A Step-by-Step Guide with Examples. Scribbr. Retrieved 9 September 2024, from https://www.scribbr.co.uk/research-methods/content-analysis-explained/

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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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19 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

Is it possible for you to list the journal articles and books or other sources you used to write this article? Thank you.

can you please send some samples of content analysis ?

can you kindly send some good examples done by using content analysis ?

This was very useful. can you please send me sample for qualitative content analysis. thank you

What a brilliant explanation! Kindly help with textbooks or blogs on the context analysis method such as discourse, thematic and semiotic analysis.

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Methodology

• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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McCombes, S. (2023, November 20). What Is a Case Study? | Definition, Examples & Methods. Scribbr. Retrieved September 11, 2024, from https://www.scribbr.com/methodology/case-study/

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Organizing Your Social Sciences Research Assignments

• Annotated Bibliography
• Analyzing a Scholarly Journal Article
• Group Presentations
• Dealing with Nervousness
• Using Visual Aids
• Grading Someone Else's Paper
• Types of Structured Group Activities
• Group Project Survival Skills
• Leading a Class Discussion
• Multiple Book Review Essay
• Reviewing Collected Works
• Writing a Case Analysis Paper
• Writing a Case Study
• About Informed Consent
• Writing Field Notes
• Writing a Policy Memo
• Writing a Reflective Paper
• Writing a Research Proposal
• Generative AI and Writing
• Acknowledgments

Definition and Introduction

Case analysis is a problem-based teaching and learning method that involves critically analyzing complex scenarios within an organizational setting for the purpose of placing the student in a “real world” situation and applying reflection and critical thinking skills to contemplate appropriate solutions, decisions, or recommended courses of action. It is considered a more effective teaching technique than in-class role playing or simulation activities. The analytical process is often guided by questions provided by the instructor that ask students to contemplate relationships between the facts and critical incidents described in the case.

Cases generally include both descriptive and statistical elements and rely on students applying abductive reasoning to develop and argue for preferred or best outcomes [i.e., case scenarios rarely have a single correct or perfect answer based on the evidence provided]. Rather than emphasizing theories or concepts, case analysis assignments emphasize building a bridge of relevancy between abstract thinking and practical application and, by so doing, teaches the value of both within a specific area of professional practice.

Given this, the purpose of a case analysis paper is to present a structured and logically organized format for analyzing the case situation. It can be assigned to students individually or as a small group assignment and it may include an in-class presentation component. Case analysis is predominately taught in economics and business-related courses, but it is also a method of teaching and learning found in other applied social sciences disciplines, such as, social work, public relations, education, journalism, and public administration.

Ellet, William. The Case Study Handbook: A Student's Guide . Revised Edition. Boston, MA: Harvard Business School Publishing, 2018; Christoph Rasche and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Analysis . Writing Center, Baruch College; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

How to Approach Writing a Case Analysis Paper

The organization and structure of a case analysis paper can vary depending on the organizational setting, the situation, and how your professor wants you to approach the assignment. Nevertheless, preparing to write a case analysis paper involves several important steps. As Hawes notes, a case analysis assignment “...is useful in developing the ability to get to the heart of a problem, analyze it thoroughly, and to indicate the appropriate solution as well as how it should be implemented” [p.48]. This statement encapsulates how you should approach preparing to write a case analysis paper.

Before you begin to write your paper, consider the following analytical procedures:

• Review the case to get an overview of the situation . A case can be only a few pages in length, however, it is most often very lengthy and contains a significant amount of detailed background information and statistics, with multilayered descriptions of the scenario, the roles and behaviors of various stakeholder groups, and situational events. Therefore, a quick reading of the case will help you gain an overall sense of the situation and illuminate the types of issues and problems that you will need to address in your paper. If your professor has provided questions intended to help frame your analysis, use them to guide your initial reading of the case.
• Read the case thoroughly . After gaining a general overview of the case, carefully read the content again with the purpose of understanding key circumstances, events, and behaviors among stakeholder groups. Look for information or data that appears contradictory, extraneous, or misleading. At this point, you should be taking notes as you read because this will help you develop a general outline of your paper. The aim is to obtain a complete understanding of the situation so that you can begin contemplating tentative answers to any questions your professor has provided or, if they have not provided, developing answers to your own questions about the case scenario and its connection to the course readings,lectures, and class discussions.
• Determine key stakeholder groups, issues, and events and the relationships they all have to each other . As you analyze the content, pay particular attention to identifying individuals, groups, or organizations described in the case and identify evidence of any problems or issues of concern that impact the situation in a negative way. Other things to look for include identifying any assumptions being made by or about each stakeholder, potential biased explanations or actions, explicit demands or ultimatums , and the underlying concerns that motivate these behaviors among stakeholders. The goal at this stage is to develop a comprehensive understanding of the situational and behavioral dynamics of the case and the explicit and implicit consequences of each of these actions.
• Identify the core problems . The next step in most case analysis assignments is to discern what the core [i.e., most damaging, detrimental, injurious] problems are within the organizational setting and to determine their implications. The purpose at this stage of preparing to write your analysis paper is to distinguish between the symptoms of core problems and the core problems themselves and to decide which of these must be addressed immediately and which problems do not appear critical but may escalate over time. Identify evidence from the case to support your decisions by determining what information or data is essential to addressing the core problems and what information is not relevant or is misleading.
• Explore alternative solutions . As noted, case analysis scenarios rarely have only one correct answer. Therefore, it is important to keep in mind that the process of analyzing the case and diagnosing core problems, while based on evidence, is a subjective process open to various avenues of interpretation. This means that you must consider alternative solutions or courses of action by critically examining strengths and weaknesses, risk factors, and the differences between short and long-term solutions. For each possible solution or course of action, consider the consequences they may have related to their implementation and how these recommendations might lead to new problems. Also, consider thinking about your recommended solutions or courses of action in relation to issues of fairness, equity, and inclusion.
• Decide on a final set of recommendations . The last stage in preparing to write a case analysis paper is to assert an opinion or viewpoint about the recommendations needed to help resolve the core problems as you see them and to make a persuasive argument for supporting this point of view. Prepare a clear rationale for your recommendations based on examining each element of your analysis. Anticipate possible obstacles that could derail their implementation. Consider any counter-arguments that could be made concerning the validity of your recommended actions. Finally, describe a set of criteria and measurable indicators that could be applied to evaluating the effectiveness of your implementation plan.

Use these steps as the framework for writing your paper. Remember that the more detailed you are in taking notes as you critically examine each element of the case, the more information you will have to draw from when you begin to write. This will save you time.

NOTE : If the process of preparing to write a case analysis paper is assigned as a student group project, consider having each member of the group analyze a specific element of the case, including drafting answers to the corresponding questions used by your professor to frame the analysis. This will help make the analytical process more efficient and ensure that the distribution of work is equitable. This can also facilitate who is responsible for drafting each part of the final case analysis paper and, if applicable, the in-class presentation.

Framework for Case Analysis . College of Management. University of Massachusetts; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Rasche, Christoph and Achim Seisreiner. Guidelines for Business Case Analysis . University of Potsdam; Writing a Case Study Analysis . University of Arizona Global Campus Writing Center; Van Ness, Raymond K. A Guide to Case Analysis . School of Business. State University of New York, Albany; Writing a Case Analysis . Business School, University of New South Wales.

Structure and Writing Style

A case analysis paper should be detailed, concise, persuasive, clearly written, and professional in tone and in the use of language . As with other forms of college-level academic writing, declarative statements that convey information, provide a fact, or offer an explanation or any recommended courses of action should be based on evidence. If allowed by your professor, any external sources used to support your analysis, such as course readings, should be properly cited under a list of references. The organization and structure of case analysis papers can vary depending on your professor’s preferred format, but its structure generally follows the steps used for analyzing the case.

Introduction

The introduction should provide a succinct but thorough descriptive overview of the main facts, issues, and core problems of the case . The introduction should also include a brief summary of the most relevant details about the situation and organizational setting. This includes defining the theoretical framework or conceptual model on which any questions were used to frame your analysis.

Following the rules of most college-level research papers, the introduction should then inform the reader how the paper will be organized. This includes describing the major sections of the paper and the order in which they will be presented. Unless you are told to do so by your professor, you do not need to preview your final recommendations in the introduction. U nlike most college-level research papers , the introduction does not include a statement about the significance of your findings because a case analysis assignment does not involve contributing new knowledge about a research problem.

Background Analysis

Background analysis can vary depending on any guiding questions provided by your professor and the underlying concept or theory that the case is based upon. In general, however, this section of your paper should focus on:

• Providing an overarching analysis of problems identified from the case scenario, including identifying events that stakeholders find challenging or troublesome,
• Identifying assumptions made by each stakeholder and any apparent biases they may exhibit,
• Describing any demands or claims made by or forced upon key stakeholders, and
• Highlighting any issues of concern or complaints expressed by stakeholders in response to those demands or claims.

These aspects of the case are often in the form of behavioral responses expressed by individuals or groups within the organizational setting. However, note that problems in a case situation can also be reflected in data [or the lack thereof] and in the decision-making, operational, cultural, or institutional structure of the organization. Additionally, demands or claims can be either internal and external to the organization [e.g., a case analysis involving a president considering arms sales to Saudi Arabia could include managing internal demands from White House advisors as well as demands from members of Congress].

Throughout this section, present all relevant evidence from the case that supports your analysis. Do not simply claim there is a problem, an assumption, a demand, or a concern; tell the reader what part of the case informed how you identified these background elements.

Identification of Problems

In most case analysis assignments, there are problems, and then there are problems . Each problem can reflect a multitude of underlying symptoms that are detrimental to the interests of the organization. The purpose of identifying problems is to teach students how to differentiate between problems that vary in severity, impact, and relative importance. Given this, problems can be described in three general forms: those that must be addressed immediately, those that should be addressed but the impact is not severe, and those that do not require immediate attention and can be set aside for the time being.

All of the problems you identify from the case should be identified in this section of your paper, with a description based on evidence explaining the problem variances. If the assignment asks you to conduct research to further support your assessment of the problems, include this in your explanation. Remember to cite those sources in a list of references. Use specific evidence from the case and apply appropriate concepts, theories, and models discussed in class or in relevant course readings to highlight and explain the key problems [or problem] that you believe must be solved immediately and describe the underlying symptoms and why they are so critical.

Alternative Solutions

This section is where you provide specific, realistic, and evidence-based solutions to the problems you have identified and make recommendations about how to alleviate the underlying symptomatic conditions impacting the organizational setting. For each solution, you must explain why it was chosen and provide clear evidence to support your reasoning. This can include, for example, course readings and class discussions as well as research resources, such as, books, journal articles, research reports, or government documents. In some cases, your professor may encourage you to include personal, anecdotal experiences as evidence to support why you chose a particular solution or set of solutions. Using anecdotal evidence helps promote reflective thinking about the process of determining what qualifies as a core problem and relevant solution .

Throughout this part of the paper, keep in mind the entire array of problems that must be addressed and describe in detail the solutions that might be implemented to resolve these problems.

Recommended Courses of Action

In some case analysis assignments, your professor may ask you to combine the alternative solutions section with your recommended courses of action. However, it is important to know the difference between the two. A solution refers to the answer to a problem. A course of action refers to a procedure or deliberate sequence of activities adopted to proactively confront a situation, often in the context of accomplishing a goal. In this context, proposed courses of action are based on your analysis of alternative solutions. Your description and justification for pursuing each course of action should represent the overall plan for implementing your recommendations.

For each course of action, you need to explain the rationale for your recommendation in a way that confronts challenges, explains risks, and anticipates any counter-arguments from stakeholders. Do this by considering the strengths and weaknesses of each course of action framed in relation to how the action is expected to resolve the core problems presented, the possible ways the action may affect remaining problems, and how the recommended action will be perceived by each stakeholder.

In addition, you should describe the criteria needed to measure how well the implementation of these actions is working and explain which individuals or groups are responsible for ensuring your recommendations are successful. In addition, always consider the law of unintended consequences. Outline difficulties that may arise in implementing each course of action and describe how implementing the proposed courses of action [either individually or collectively] may lead to new problems [both large and small].

Throughout this section, you must consider the costs and benefits of recommending your courses of action in relation to uncertainties or missing information and the negative consequences of success.

The conclusion should be brief and introspective. Unlike a research paper, the conclusion in a case analysis paper does not include a summary of key findings and their significance, a statement about how the study contributed to existing knowledge, or indicate opportunities for future research.

Begin by synthesizing the core problems presented in the case and the relevance of your recommended solutions. This can include an explanation of what you have learned about the case in the context of your answers to the questions provided by your professor. The conclusion is also where you link what you learned from analyzing the case with the course readings or class discussions. This can further demonstrate your understanding of the relationships between the practical case situation and the theoretical and abstract content of assigned readings and other course content.

Problems to Avoid

The literature on case analysis assignments often includes examples of difficulties students have with applying methods of critical analysis and effectively reporting the results of their assessment of the situation. A common reason cited by scholars is that the application of this type of teaching and learning method is limited to applied fields of social and behavioral sciences and, as a result, writing a case analysis paper can be unfamiliar to most students entering college.

After you have drafted your paper, proofread the narrative flow and revise any of these common errors:

• Unnecessary detail in the background section . The background section should highlight the essential elements of the case based on your analysis. Focus on summarizing the facts and highlighting the key factors that become relevant in the other sections of the paper by eliminating any unnecessary information.
• Analysis relies too much on opinion . Your analysis is interpretive, but the narrative must be connected clearly to evidence from the case and any models and theories discussed in class or in course readings. Any positions or arguments you make should be supported by evidence.
• Analysis does not focus on the most important elements of the case . Your paper should provide a thorough overview of the case. However, the analysis should focus on providing evidence about what you identify are the key events, stakeholders, issues, and problems. Emphasize what you identify as the most critical aspects of the case to be developed throughout your analysis. Be thorough but succinct.
• Writing is too descriptive . A paper with too much descriptive information detracts from your analysis of the complexities of the case situation. Questions about what happened, where, when, and by whom should only be included as essential information leading to your examination of questions related to why, how, and for what purpose.
• Inadequate definition of a core problem and associated symptoms . A common error found in case analysis papers is recommending a solution or course of action without adequately defining or demonstrating that you understand the problem. Make sure you have clearly described the problem and its impact and scope within the organizational setting. Ensure that you have adequately described the root causes w hen describing the symptoms of the problem.
• Recommendations lack specificity . Identify any use of vague statements and indeterminate terminology, such as, “A particular experience” or “a large increase to the budget.” These statements cannot be measured and, as a result, there is no way to evaluate their successful implementation. Provide specific data and use direct language in describing recommended actions.
• Unrealistic, exaggerated, or unattainable recommendations . Review your recommendations to ensure that they are based on the situational facts of the case. Your recommended solutions and courses of action must be based on realistic assumptions and fit within the constraints of the situation. Also note that the case scenario has already happened, therefore, any speculation or arguments about what could have occurred if the circumstances were different should be revised or eliminated.

Bee, Lian Song et al. "Business Students' Perspectives on Case Method Coaching for Problem-Based Learning: Impacts on Student Engagement and Learning Performance in Higher Education." Education & Training 64 (2022): 416-432; The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Georgallis, Panikos and Kayleigh Bruijn. "Sustainability Teaching using Case-Based Debates." Journal of International Education in Business 15 (2022): 147-163; Hawes, Jon M. "Teaching is Not Telling: The Case Method as a Form of Interactive Learning." Journal for Advancement of Marketing Education 5 (Winter 2004): 47-54; Georgallis, Panikos, and Kayleigh Bruijn. "Sustainability Teaching Using Case-based Debates." Journal of International Education in Business 15 (2022): 147-163; .Dean,  Kathy Lund and Charles J. Fornaciari. "How to Create and Use Experiential Case-Based Exercises in a Management Classroom." Journal of Management Education 26 (October 2002): 586-603; Klebba, Joanne M. and Janet G. Hamilton. "Structured Case Analysis: Developing Critical Thinking Skills in a Marketing Case Course." Journal of Marketing Education 29 (August 2007): 132-137, 139; Klein, Norman. "The Case Discussion Method Revisited: Some Questions about Student Skills." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 30-32; Mukherjee, Arup. "Effective Use of In-Class Mini Case Analysis for Discovery Learning in an Undergraduate MIS Course." The Journal of Computer Information Systems 40 (Spring 2000): 15-23; Pessoa, Silviaet al. "Scaffolding the Case Analysis in an Organizational Behavior Course: Making Analytical Language Explicit." Journal of Management Education 46 (2022): 226-251: Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Schweitzer, Karen. "How to Write and Format a Business Case Study." ThoughtCo. https://www.thoughtco.com/how-to-write-and-format-a-business-case-study-466324 (accessed December 5, 2022); Reddy, C. D. "Teaching Research Methodology: Everything's a Case." Electronic Journal of Business Research Methods 18 (December 2020): 178-188; Volpe, Guglielmo. "Case Teaching in Economics: History, Practice and Evidence." Cogent Economics and Finance 3 (December 2015). doi:https://doi.org/10.1080/23322039.2015.1120977.

Writing Tip

Ca se Study and Case Analysis Are Not the Same!

Confusion often exists between what it means to write a paper that uses a case study research design and writing a paper that analyzes a case; they are two different types of approaches to learning in the social and behavioral sciences. Professors as well as educational researchers contribute to this confusion because they often use the term "case study" when describing the subject of analysis for a case analysis paper. But you are not studying a case for the purpose of generating a comprehensive, multi-faceted understanding of a research problem. R ather, you are critically analyzing a specific scenario to argue logically for recommended solutions and courses of action that lead to optimal outcomes applicable to professional practice.

To avoid any confusion, here are twelve characteristics that delineate the differences between writing a paper using the case study research method and writing a case analysis paper:

• Case study is a method of in-depth research and rigorous inquiry ; case analysis is a reliable method of teaching and learning . A case study is a modality of research that investigates a phenomenon for the purpose of creating new knowledge, solving a problem, or testing a hypothesis using empirical evidence derived from the case being studied. Often, the results are used to generalize about a larger population or within a wider context. The writing adheres to the traditional standards of a scholarly research study. A case analysis is a pedagogical tool used to teach students how to reflect and think critically about a practical, real-life problem in an organizational setting.
• The researcher is responsible for identifying the case to study; a case analysis is assigned by your professor . As the researcher, you choose the case study to investigate in support of obtaining new knowledge and understanding about the research problem. The case in a case analysis assignment is almost always provided, and sometimes written, by your professor and either given to every student in class to analyze individually or to a small group of students, or students select a case to analyze from a predetermined list.
• A case study is indeterminate and boundless; a case analysis is predetermined and confined . A case study can be almost anything [see item 9 below] as long as it relates directly to examining the research problem. This relationship is the only limit to what a researcher can choose as the subject of their case study. The content of a case analysis is determined by your professor and its parameters are well-defined and limited to elucidating insights of practical value applied to practice.
• Case study is fact-based and describes actual events or situations; case analysis can be entirely fictional or adapted from an actual situation . The entire content of a case study must be grounded in reality to be a valid subject of investigation in an empirical research study. A case analysis only needs to set the stage for critically examining a situation in practice and, therefore, can be entirely fictional or adapted, all or in-part, from an actual situation.
• Research using a case study method must adhere to principles of intellectual honesty and academic integrity; a case analysis scenario can include misleading or false information . A case study paper must report research objectively and factually to ensure that any findings are understood to be logically correct and trustworthy. A case analysis scenario may include misleading or false information intended to deliberately distract from the central issues of the case. The purpose is to teach students how to sort through conflicting or useless information in order to come up with the preferred solution. Any use of misleading or false information in academic research is considered unethical.
• Case study is linked to a research problem; case analysis is linked to a practical situation or scenario . In the social sciences, the subject of an investigation is most often framed as a problem that must be researched in order to generate new knowledge leading to a solution. Case analysis narratives are grounded in real life scenarios for the purpose of examining the realities of decision-making behavior and processes within organizational settings. A case analysis assignments include a problem or set of problems to be analyzed. However, the goal is centered around the act of identifying and evaluating courses of action leading to best possible outcomes.
• The purpose of a case study is to create new knowledge through research; the purpose of a case analysis is to teach new understanding . Case studies are a choice of methodological design intended to create new knowledge about resolving a research problem. A case analysis is a mode of teaching and learning intended to create new understanding and an awareness of uncertainty applied to practice through acts of critical thinking and reflection.
• A case study seeks to identify the best possible solution to a research problem; case analysis can have an indeterminate set of solutions or outcomes . Your role in studying a case is to discover the most logical, evidence-based ways to address a research problem. A case analysis assignment rarely has a single correct answer because one of the goals is to force students to confront the real life dynamics of uncertainly, ambiguity, and missing or conflicting information within professional practice. Under these conditions, a perfect outcome or solution almost never exists.
• Case study is unbounded and relies on gathering external information; case analysis is a self-contained subject of analysis . The scope of a case study chosen as a method of research is bounded. However, the researcher is free to gather whatever information and data is necessary to investigate its relevance to understanding the research problem. For a case analysis assignment, your professor will often ask you to examine solutions or recommended courses of action based solely on facts and information from the case.
• Case study can be a person, place, object, issue, event, condition, or phenomenon; a case analysis is a carefully constructed synopsis of events, situations, and behaviors . The research problem dictates the type of case being studied and, therefore, the design can encompass almost anything tangible as long as it fulfills the objective of generating new knowledge and understanding. A case analysis is in the form of a narrative containing descriptions of facts, situations, processes, rules, and behaviors within a particular setting and under a specific set of circumstances.
• Case study can represent an open-ended subject of inquiry; a case analysis is a narrative about something that has happened in the past . A case study is not restricted by time and can encompass an event or issue with no temporal limit or end. For example, the current war in Ukraine can be used as a case study of how medical personnel help civilians during a large military conflict, even though circumstances around this event are still evolving. A case analysis can be used to elicit critical thinking about current or future situations in practice, but the case itself is a narrative about something finite and that has taken place in the past.
• Multiple case studies can be used in a research study; case analysis involves examining a single scenario . Case study research can use two or more cases to examine a problem, often for the purpose of conducting a comparative investigation intended to discover hidden relationships, document emerging trends, or determine variations among different examples. A case analysis assignment typically describes a stand-alone, self-contained situation and any comparisons among cases are conducted during in-class discussions and/or student presentations.

The Case Analysis . Fred Meijer Center for Writing and Michigan Authors. Grand Valley State University; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Ramsey, V. J. and L. D. Dodge. "Case Analysis: A Structured Approach." Exchange: The Organizational Behavior Teaching Journal 6 (November 1981): 27-29; Yin, Robert K. Case Study Research and Applications: Design and Methods . 6th edition. Thousand Oaks, CA: Sage, 2017; Crowe, Sarah et al. “The Case Study Approach.” BMC Medical Research Methodology 11 (2011):  doi: 10.1186/1471-2288-11-100; Yin, Robert K. Case Study Research: Design and Methods . 4th edition. Thousand Oaks, CA: Sage Publishing; 1994.

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The Use of Qualitative Content Analysis in Case Study Research

• Florian Kohlbacher Wirtschaftsuniversität Wien

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Home » Content Analysis – Methods, Types and Examples

Content Analysis – Methods, Types and Examples

Table of Contents

Content Analysis

Definition:

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

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

Types of Content Analysis

There are generally two types of content analysis:

Quantitative Content Analysis

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

Qualitative Content Analysis

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

Methods of Content Analysis

There are several methods of content analysis, including:

Conceptual Analysis

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

Content Analysis by Frequency

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

Comparative Analysis

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

Discourse Analysis

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

Narrative Analysis

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

Content Analysis Conducting Guide

Here is a basic guide to conducting a content analysis:

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

Applications of Content Analysis

Content analysis has numerous applications across different fields, including:

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

Examples of Content Analysis

Here are some examples of content analysis:

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

Purpose of Content Analysis

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

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

When to use Content Analysis

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

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

Characteristics of Content Analysis

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

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

Advantages of Content Analysis

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

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

Limitations of Content Analysis

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

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Design Review

A discussion of qualitative & quantitative research design, analyzability & a qualitative content analysis case study.

The following is a modified excerpt from Applied Qualitative Research Design: A Total Quality Framework Approach (Roller & Lavrakas, 2015, pp. 284-285).

Purpose & Scope The primary purpose of this primary qualitative content analysis study was to extend the existing literature on the portrayal of women’s roles in print media by examining the imagery and themes depicted of heterosexual college-educated women who leave the workforce to devote themselves to being stay-at-home mothers (a phenomenon referred to as “opting out”) across a wide, diverse range of print publications. More specifically, this research set out to investigate two areas of media coverage: the content (e.g., the women who are portrayed in the media and how they are described) and the context (e.g., the types of media and articles).

This study examined a 16-year period from 1988 to 2003. This 16-year period was chosen because 1988 was the earliest date on which the researchers had access to a searchable database for sampling, and 2003 was the year that the term “opting out” (referring to women leaving the workforce to become full-time mothers) became popular. The researchers identified 51 articles from 30 publications that represented a wide diversity of large-circulation print media. The researchers acknowledged that the sample “underrepresents articles appearing in small-town outlets” (p. 502).

Analyzability There are two aspects of the TQF Analyzability component — processing and verification. In terms of processing, the content data obtained by Kuperberg and Stone from coding revealed three primary patterns or themes in the depiction of women who opt out: “family first, child-centric”; “the mommy elite”; and “making choices.” The researchers discuss these themes at some length and support their findings by way of research literature and other references. In some instances, they report that their findings were in contrast to the literature (which presented an opportunity for future research in this area). Their final interpretation of the data includes their overall assertion that print media depict “traditional images of heterosexual women” (p. 510).

Important to the integrity of the analysis process, the researchers absorbed themselves in the sampled articles and, in doing so, identified inconsistencies in the research outcomes. For example, a careful reading of the articles revealed that many of the women depicted as stay-at-home mothers were actually employed in some form of paid work from home. The researchers also enriched the discussion of their findings by giving the reader some context relevant to the publications and articles. For example, they revealed that 45 of the 51 articles were from general interest newspapers or magazines, a fact that supports their research objective of analyzing print media that reach large, diverse audiences.

In terms of verification, the researchers performed a version of deviant case analysis in which they investigated contrary evidence to the assertion made by many articles that there is a growing trend in the proportion of women opting out. Citing research studies from the literature as well as actual trend data, the researchers stated that the articles’ claim that women were increasingly opting out had weak support.

Kuperberg, A., & Stone, P. (2008). The media depiction of women who opt out. Gender & Society , 22 (4), 497–517.

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Sales CRM Terms

What is Case Study Analysis? (Explained With Examples)

Oct 11, 2023

Case Study Analysis is a widely used research method that examines in-depth information about a particular individual, group, organization, or event. It is a comprehensive investigative approach that aims to understand the intricacies and complexities of the subject under study. Through the analysis of real-life scenarios and inquiry into various data sources, Case Study Analysis provides valuable insights and knowledge that can be used to inform decision-making and problem-solving strategies.

1°) What is Case Study Analysis?

Case Study Analysis is a research methodology that involves the systematic investigation of a specific case or cases to gain a deep understanding of the subject matter. This analysis encompasses collecting and analyzing various types of data, including qualitative and quantitative information. By examining multiple aspects of the case, such as its context, background, influences, and outcomes, researchers can draw meaningful conclusions and provide valuable insights for various fields of study.

When conducting a Case Study Analysis, researchers typically begin by selecting a case or multiple cases that are relevant to their research question or area of interest. This can involve choosing a specific organization, individual, event, or phenomenon to study. Once the case is selected, researchers gather relevant data through various methods, such as interviews, observations, document analysis, and artifact examination.

The data collected during a Case Study Analysis is then carefully analyzed and interpreted. Researchers use different analytical frameworks and techniques to make sense of the information and identify patterns, themes, and relationships within the data. This process involves coding and categorizing the data, conducting comparative analysis, and drawing conclusions based on the findings.

One of the key strengths of Case Study Analysis is its ability to provide a rich and detailed understanding of a specific case. This method allows researchers to delve deep into the complexities and nuances of the subject matter, uncovering insights that may not be captured through other research methods. By examining the case in its natural context, researchers can gain a holistic perspective and explore the various factors and variables that contribute to the case.

1.1 - Definition of Case Study Analysis

Case Study Analysis can be defined as an in-depth examination and exploration of a particular case or cases to unravel relevant details and complexities associated with the subject being studied. It involves a comprehensive and detailed analysis of various factors and variables that contribute to the case, aiming to answer research questions and uncover insights that can be applied in real-world scenarios.

When conducting a Case Study Analysis, researchers employ a range of research methods and techniques to collect and analyze data. These methods can include interviews, surveys, observations, document analysis, and experiments, among others. By using multiple sources of data, researchers can triangulate their findings and ensure the validity and reliability of their analysis.

Furthermore, Case Study Analysis often involves the use of theoretical frameworks and models to guide the research process. These frameworks provide a structured approach to analyzing the case and help researchers make sense of the data collected. By applying relevant theories and concepts, researchers can gain a deeper understanding of the underlying factors and dynamics at play in the case.

1.2 - Advantages of Case Study Analysis

Case Study Analysis offers numerous advantages that make it a popular research method across different disciplines. One significant advantage is its ability to provide rich and detailed information about a specific case, allowing researchers to gain a holistic understanding of the subject matter. Additionally, Case Study Analysis enables researchers to explore complex issues and phenomena in their natural context, capturing the intricacies and nuances that may not be captured through other research methods.

Moreover, Case Study Analysis allows researchers to investigate rare or unique cases that may not be easily replicated or studied through experimental methods. This method is particularly useful when studying phenomena that are complex, multifaceted, or involve multiple variables. By examining real-world cases, researchers can gain insights that can be applied to similar situations or inform future research and practice.

Furthermore, this research method allows for the analysis of multiple sources of data, such as interviews, observations, documents, and artifacts, which can contribute to a comprehensive and well-rounded examination of the case. Case Study Analysis also facilitates the exploration and identification of patterns, trends, and relationships within the data, generating valuable insights and knowledge for future reference and application.

1.3 - Disadvantages of Case Study Analysis

While Case Study Analysis offers various advantages, it also comes with certain limitations and challenges. One major limitation is the potential for researcher bias, as the interpretation of data and findings can be influenced by preconceived notions and personal perspectives. Researchers must be aware of their own biases and take steps to minimize their impact on the analysis.

Additionally, Case Study Analysis may suffer from limited generalizability, as it focuses on specific cases and contexts, which might not be applicable or representative of broader populations or situations. The findings of a case study may not be easily generalized to other settings or individuals, and caution should be exercised when applying the results to different contexts.

Moreover, Case Study Analysis can require significant time and resources due to its in-depth nature and the need for meticulous data collection and analysis. This can pose challenges for researchers working with limited budgets or tight deadlines. However, the thoroughness and depth of the analysis often outweigh the resource constraints, as the insights gained from a well-conducted case study can be highly valuable.

Finally, ethical considerations also play a crucial role in Case Study Analysis, as researchers must ensure the protection of participant confidentiality and privacy. Researchers must obtain informed consent from participants and take measures to safeguard their identities and personal information. Ethical guidelines and protocols should be followed to ensure the rights and well-being of the individuals involved in the case study.

2°) Examples of Case Study Analysis

Real-world examples of Case Study Analysis demonstrate the method's practical application and showcase its usefulness across various fields. The following examples provide insights into different scenarios where Case Study Analysis has been employed successfully.

2.1 - Example in a Startup Context

In a startup context, a Case Study Analysis might explore the factors that contributed to the success of a particular startup company. It would involve examining the organization's background, strategies, market conditions, and key decision-making processes. This analysis could reveal valuable lessons and insights for aspiring entrepreneurs and those interested in understanding the intricacies of startup success.

2.2 - Example in a Consulting Context

In the consulting industry, Case Study Analysis is often utilized to understand and develop solutions for complex business problems. For instance, a consulting firm might conduct a Case Study Analysis on a company facing challenges in its supply chain management. This analysis would involve identifying the underlying issues, evaluating different options, and proposing recommendations based on the findings. This approach enables consultants to apply their expertise and provide practical solutions to their clients.

2.3 - Example in a Digital Marketing Agency Context

Within a digital marketing agency, Case Study Analysis can be used to examine successful marketing campaigns. By analyzing various factors such as target audience, message effectiveness, channel selection, and campaign metrics, this analysis can provide valuable insights into the strategies and tactics that contribute to successful marketing initiatives. Digital marketers can then apply these insights to optimize future campaigns and drive better results for their clients.

2.4 - Example with Analogies

Case Study Analysis can also be utilized with analogies to investigate specific scenarios and draw parallels to similar situations. For instance, a Case Study Analysis could explore the response of different countries to natural disasters and draw analogies to inform disaster management strategies in other regions. These analogies can help policymakers and researchers develop more effective approaches to mitigate the impact of disasters and protect vulnerable populations.

In conclusion, Case Study Analysis is a powerful research method that provides a comprehensive understanding of a particular individual, group, organization, or event. By analyzing real-life cases and exploring various data sources, researchers can unravel complexities, generate valuable insights, and inform decision-making processes. With its advantages and limitations, Case Study Analysis offers a unique approach to gaining in-depth knowledge and practical application across numerous fields.

About the author

Arnaud Belinga

"i wrote this article"

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Content Analysis

Last updated 22 Mar 2021

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Content analysis is a method used to analyse qualitative data (non-numerical data). In its most common form it is a technique that allows a researcher to take qualitative data and to transform it into quantitative data (numerical data). The technique can be used for data in many different formats, for example interview transcripts, film, and audio recordings.

The researcher conducting a content analysis will use ‘coding units’ in their work. These units vary widely depending on the data used, but an example would be the number of positive or negative words used by a mother to describe her child’s behaviour or the number of swear words in a film.

The procedure for a content analysis is shown below:

Strengths of content analysis

It is a reliable way to analyse qualitative data as the coding units are not open to interpretation and so are applied in the same way over time and with different researchers

It is an easy technique to use and is not too time consuming

It allows a statistical analysis to be conducted if required as there is usually quantitative data as a result of the procedure

Weaknesses of content analysis

Causality cannot be established as it merely describes the data

As it only describes the data it cannot extract any deeper meaning or explanation for the data patterns arising.

• Content Analysis

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A hands-on guide to doing content analysis

Christen erlingsson.

a Department of Health and Caring Sciences, Linnaeus University, Kalmar 391 82, Sweden

Petra Brysiewicz

b School of Nursing & Public Health, University of KwaZulu-Natal, Durban 4041, South Africa

Associated Data

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including the emergency care context in Africa. Novice qualitative researchers are often daunted by the prospect of qualitative data analysis and thus may experience much difficulty in the data analysis process. Our objective with this manuscript is to provide a practical hands-on example of qualitative content analysis to aid novice qualitative researchers in their task.

African relevance

• • Qualitative research is useful to deepen the understanding of the human experience.
• • Novice qualitative researchers may benefit from this hands-on guide to content analysis.
• • Practical tips and data analysis templates are provided to assist in the analysis process.

Introduction

There is a growing recognition for the important role played by qualitative research and its usefulness in many fields, including emergency care research. An increasing number of health researchers are currently opting to use various qualitative research approaches in exploring and describing complex phenomena, providing textual accounts of individuals’ “life worlds”, and giving voice to vulnerable populations our patients so often represent. Many articles and books are available that describe qualitative research methods and provide overviews of content analysis procedures [1] , [2] , [3] , [4] , [5] , [6] , [7] , [8] , [9] , [10] . Some articles include step-by-step directions intended to clarify content analysis methodology. What we have found in our teaching experience is that these directions are indeed very useful. However, qualitative researchers, especially novice researchers, often struggle to understand what is happening on and between steps, i.e., how the steps are taken.

As research supervisors of postgraduate health professionals, we often meet students who present brilliant ideas for qualitative studies that have potential to fill current gaps in the literature. Typically, the suggested studies aim to explore human experience. Research questions exploring human experience are expediently studied through analysing textual data e.g., collected in individual interviews, focus groups, documents, or documented participant observation. When reflecting on the proposed study aim together with the student, we often suggest content analysis methodology as the best fit for the study and the student, especially the novice researcher. The interview data are collected and the content analysis adventure begins. Students soon realise that data based on human experiences are complex, multifaceted and often carry meaning on multiple levels.

For many novice researchers, analysing qualitative data is found to be unexpectedly challenging and time-consuming. As they soon discover, there is no step-wise analysis process that can be applied to the data like a pattern cutter at a textile factory. They may become extremely annoyed and frustrated during the hands-on enterprise of qualitative content analysis.

The novice researcher may lament, “I’ve read all the methodology but don’t really know how to start and exactly what to do with my data!” They grapple with qualitative research terms and concepts, for example; differences between meaning units, codes, categories and themes, and regarding increasing levels of abstraction from raw data to categories or themes. The content analysis adventure may now seem to be a chaotic undertaking. But, life is messy, complex and utterly fascinating. Experiencing chaos during analysis is normal. Good advice for the qualitative researcher is to be open to the complexity in the data and utilise one’s flow of creativity.

Inspired primarily by descriptions of “conventional content analysis” in Hsieh and Shannon [3] , “inductive content analysis” in Elo and Kyngäs [5] and “qualitative content analysis of an interview text” in Graneheim and Lundman [1] , we have written this paper to help the novice qualitative researcher navigate the uncertainty in-between the steps of qualitative content analysis. We will provide advice and practical tips, as well as data analysis templates, to attempt to ease frustration and hopefully, inspire readers to discover how this exciting methodology contributes to developing a deeper understanding of human experience and our professional contexts.

Overview of qualitative content analysis

Synopsis of content analysis.

A common starting point for qualitative content analysis is often transcribed interview texts. The objective in qualitative content analysis is to systematically transform a large amount of text into a highly organised and concise summary of key results. Analysis of the raw data from verbatim transcribed interviews to form categories or themes is a process of further abstraction of data at each step of the analysis; from the manifest and literal content to latent meanings ( Fig. 1 and Table 1 ).

Example of analysis leading to higher levels of abstraction; from manifest to latent content.

Glossary of terms as used in this hands-on guide to doing content analysis. *

The initial step is to read and re-read the interviews to get a sense of the whole, i.e., to gain a general understanding of what your participants are talking about. At this point you may already start to get ideas of what the main points or ideas are that your participants are expressing. Then one needs to start dividing up the text into smaller parts, namely, into meaning units. One then condenses these meaning units further. While doing this, you need to ensure that the core meaning is still retained. The next step is to label condensed meaning units by formulating codes and then grouping these codes into categories. Depending on the study’s aim and quality of the collected data, one may choose categories as the highest level of abstraction for reporting results or you can go further and create themes [1] , [2] , [3] , [5] , [8] .

Content analysis as a reflective process

You must mould the clay of the data , tapping into your intuition while maintaining a reflective understanding of how your own previous knowledge is influencing your analysis, i.e., your pre-understanding. In qualitative methodology, it is imperative to vigilantly maintain an awareness of one’s pre-understanding so that this does not influence analysis and/or results. This is the difficult balancing task of keeping a firm grip on one’s assumptions, opinions, and personal beliefs, and not letting them unconsciously steer your analysis process while simultaneously, and knowingly, utilising one’s pre-understanding to facilitate a deeper understanding of the data.

Content analysis, as in all qualitative analysis, is a reflective process. There is no “step 1, 2, 3, done!” linear progression in the analysis. This means that identifying and condensing meaning units, coding, and categorising are not one-time events. It is a continuous process of coding and categorising then returning to the raw data to reflect on your initial analysis. Are you still satisfied with the length of meaning units? Do the condensed meaning units and codes still “fit” with each other? Do the codes still fit into this particular category? Typically, a fair amount of adjusting is needed after the first analysis endeavour. For example: a meaning unit might need to be split into two meaning units in order to capture an additional core meaning; a code modified to more closely match the core meaning of the condensed meaning unit; or a category name tweaked to most accurately describe the included codes. In other words, analysis is a flexible reflective process of working and re-working your data that reveals connections and relationships. Once condensed meaning units are coded it is easier to get a bigger picture and see patterns in your codes and organise codes in categories.

Content analysis exercise

The synopsis above is representative of analysis descriptions in many content analysis articles. Although correct, such method descriptions still do not provide much support for the novice researcher during the actual analysis process. Aspiring to provide guidance and direction to support the novice, a practical example of doing the actual work of content analysis is provided in the following sections. This practical example is based on a transcribed interview excerpt that was part of a study that aimed to explore patients’ experiences of being admitted into the emergency centre ( Fig. 2 ).

Excerpt from interview text exploring “Patient’s experience of being admitted into the emergency centre”

This content analysis exercise provides instructions, tips, and advice to support the content analysis novice in a) familiarising oneself with the data and the hermeneutic spiral, b) dividing up the text into meaning units and subsequently condensing these meaning units, c) formulating codes, and d) developing categories and themes.

Familiarising oneself with the data and the hermeneutic spiral

An important initial phase in the data analysis process is to read and re-read the transcribed interview while keeping your aim in focus. Write down your initial impressions. Embrace your intuition. What is the text talking about? What stands out? How did you react while reading the text? What message did the text leave you with? In this analysis phase, you are gaining a sense of the text as a whole.

You may ask why this is important. During analysis, you will be breaking down the whole text into smaller parts. Returning to your notes with your initial impressions will help you see if your “parts” analysis is matching up with your first impressions of the “whole” text. Are your initial impressions visible in your analysis of the parts? Perhaps you need to go back and check for different perspectives. This is what is referred to as the hermeneutic spiral or hermeneutic circle. It is the process of comparing the parts to the whole to determine whether impressions of the whole verify the analysis of the parts in all phases of analysis. Each part should reflect the whole and the whole should be reflected in each part. This concept will become clearer as you start working with your data.

Dividing up the text into meaning units and condensing meaning units

You have now read the interview a number of times. Keeping your research aim and question clearly in focus, divide up the text into meaning units. Located meaning units are then condensed further while keeping the central meaning intact ( Table 2 ). The condensation should be a shortened version of the same text that still conveys the essential message of the meaning unit. Sometimes the meaning unit is already so compact that no further condensation is required. Some content analysis sources warn researchers against short meaning units, claiming that this can lead to fragmentation [1] . However, our personal experience as research supervisors has shown us that a greater problem for the novice is basing analysis on meaning units that are too large and include many meanings which are then lost in the condensation process.

Suggestion for how the exemplar interview text can be divided into meaning units and condensed meaning units ( condensations are in parentheses ).

Formulating codes

The next step is to develop codes that are descriptive labels for the condensed meaning units ( Table 3 ). Codes concisely describe the condensed meaning unit and are tools to help researchers reflect on the data in new ways. Codes make it easier to identify connections between meaning units. At this stage of analysis you are still keeping very close to your data with very limited interpretation of content. You may adjust, re-do, re-think, and re-code until you get to the point where you are satisfied that your choices are reasonable. Just as in the initial phase of getting to know your data as a whole, it is also good to write notes during coding on your impressions and reactions to the text.

Suggestions for coding of condensed meaning units.

Developing categories and themes

The next step is to sort codes into categories that answer the questions who , what , when or where? One does this by comparing codes and appraising them to determine which codes seem to belong together, thereby forming a category. In other words, a category consists of codes that appear to deal with the same issue, i.e., manifest content visible in the data with limited interpretation on the part of the researcher. Category names are most often short and factual sounding.

In data that is rich with latent meaning, analysis can be carried on to create themes. In our practical example, we have continued the process of abstracting data to a higher level, from category to theme level, and developed three themes as well as an overarching theme ( Table 4 ). Themes express underlying meaning, i.e., latent content, and are formed by grouping two or more categories together. Themes are answering questions such as why , how , in what way or by what means? Therefore, theme names include verbs, adverbs and adjectives and are very descriptive or even poetic.

Suggestion for organisation of coded meaning units into categories and themes.

Some reflections and helpful tips

Understand your pre-understandings.

While conducting qualitative research, it is paramount that the researcher maintains a vigilance of non-bias during analysis. In other words, did you remain aware of your pre-understandings, i.e., your own personal assumptions, professional background, and previous experiences and knowledge? For example, did you zero in on particular aspects of the interview on account of your profession (as an emergency doctor, emergency nurse, pre-hospital professional, etc.)? Did you assume the patient’s gender? Did your assumptions affect your analysis? How about aspects of culpability; did you assume that this patient was at fault or that this patient was a victim in the crash? Did this affect how you analysed the text?

Staying aware of one’s pre-understandings is exactly as difficult as it sounds. But, it is possible and it is requisite. Focus on putting yourself and your pre-understandings in a holding pattern while you approach your data with an openness and expectation of finding new perspectives. That is the key: expect the new and be prepared to be surprised. If something in your data feels unusual, is different from what you know, atypical, or even odd – don’t by-pass it as “wrong”. Your reactions and intuitive responses are letting you know that here is something to pay extra attention to, besides the more comfortable condensing and coding of more easily recognisable meaning units.

Use your intuition

Intuition is a great asset in qualitative analysis and not to be dismissed as “unscientific”. Intuition results from tacit knowledge. Just as tacit knowledge is a hallmark of great clinicians [11] , [12] ; it is also an invaluable tool in analysis work [13] . Literally, take note of your gut reactions and intuitive guidance and remember to write these down! These notes often form a framework of possible avenues for further analysis and are especially helpful as you lift the analysis to higher levels of abstraction; from meaning units to condensed meaning units, to codes, to categories and then to the highest level of abstraction in content analysis, themes.

Aspects of coding and categorising hard to place data

All too often, the novice gets overwhelmed by interview material that deals with the general subject matter of the interview, but doesn’t seem to answer the research question. Don’t be too quick to consider such text as off topic or dross [6] . There is often data that, although not seeming to match the study aim precisely, is still important for illuminating the problem area. This can be seen in our practical example about exploring patients’ experiences of being admitted into the emergency centre. Initially the participant is describing the accident itself. While not directly answering the research question, the description is important for understanding the context of the experience of being admitted into the emergency centre. It is very common that participants will “begin at the beginning” and prologue their narratives in order to create a context that sets the scene. This type of contextual data is vital for gaining a deepened understanding of participants’ experiences.

In our practical example, the participant begins by describing the crash and the rescue, i.e., experiences leading up to and prior to admission to the emergency centre. That is why we have chosen in our analysis to code the condensed meaning unit “Ambulance staff looked worried about all the blood” as “In the ambulance” and place it in the category “Reliving the rescue”. We did not choose to include this meaning unit in the categories specifically about admission to the emergency centre itself. Do you agree with our coding choice? Would you have chosen differently?

Another common problem for the novice is deciding how to code condensed meaning units when the unit can be labelled in several different ways. At this point researchers usually groan and wish they had thought to ask one of those classic follow-up questions like “Can you tell me a little bit more about that?” We have examples of two such coding conundrums in the exemplar, as can be seen in Table 3 (codes we conferred on) and Table 4 (codes we reached consensus on). Do you agree with our choices or would you have chosen different codes? Our best advice is to go back to your impressions of the whole and lean into your intuition when choosing codes that are most reasonable and best fit your data.

A typical problem area during categorisation, especially for the novice researcher, is overlap between content in more than one initial category, i.e., codes included in one category also seem to be a fit for another category. Overlap between initial categories is very likely an indication that the jump from code to category was too big, a problem not uncommon when the data is voluminous and/or very complex. In such cases, it can be helpful to first sort codes into narrower categories, so-called subcategories. Subcategories can then be reviewed for possibilities of further aggregation into categories. In the case of a problematic coding, it is advantageous to return to the meaning unit and check if the meaning unit itself fits the category or if you need to reconsider your preliminary coding.

It is not uncommon to be faced by thorny problems such as these during coding and categorisation. Here we would like to reiterate how valuable it is to have fellow researchers with whom you can discuss and reflect together with, in order to reach consensus on the best way forward in your data analysis. It is really advantageous to compare your analysis with meaning units, condensations, coding and categorisations done by another researcher on the same text. Have you identified the same meaning units? Do you agree on coding? See similar patterns in the data? Concur on categories? Sometimes referred to as “researcher triangulation,” this is actually a key element in qualitative analysis and an important component when striving to ensure trustworthiness in your study [14] . Qualitative research is about seeking out variations and not controlling variables, as in quantitative research. Collaborating with others during analysis lets you tap into multiple perspectives and often makes it easier to see variations in the data, thereby enhancing the quality of your results as well as contributing to the rigor of your study. It is important to note that it is not necessary to force consensus in the findings but one can embrace these variations in interpretation and use that to capture the richness in the data.

Yet there are times when neither openness, pre-understanding, intuition, nor researcher triangulation does the job; for example, when analysing an interview and one is simply confused on how to code certain meaning units. At such times, there are a variety of options. A good starting place is to re-read all the interviews through the lens of this specific issue and actively search for other similar types of meaning units you might have missed. Another way to handle this is to conduct further interviews with specific queries that hopefully shed light on the issue. A third option is to have a follow-up interview with the same person and ask them to explain.

Additional tips

It is important to remember that in a typical project there are several interviews to analyse. Codes found in a single interview serve as a starting point as you then work through the remaining interviews coding all material. Form your categories and themes when all project interviews have been coded.

When submitting an article with your study results, it is a good idea to create a table or figure providing a few key examples of how you progressed from the raw data of meaning units, to condensed meaning units, coding, categorisation, and, if included, themes. Providing such a table or figure supports the rigor of your study [1] and is an element greatly appreciated by reviewers and research consumers.

During the analysis process, it can be advantageous to write down your research aim and questions on a sheet of paper that you keep nearby as you work. Frequently referring to your aim can help you keep focused and on track during analysis. Many find it helpful to colour code their transcriptions and write notes in the margins.

Having access to qualitative analysis software can be greatly helpful in organising and retrieving analysed data. Just remember, a computer does not analyse the data. As Jennings [15] has stated, “… it is ‘peopleware,’ not software, that analyses.” A major drawback is that qualitative analysis software can be prohibitively expensive. One way forward is to use table templates such as we have used in this article. (Three analysis templates, Templates A, B, and C, are provided as supplementary online material ). Additionally, the “find” function in word processing programmes such as Microsoft Word (Redmond, WA USA) facilitates locating key words, e.g., in transcribed interviews, meaning units, and codes.

Lessons learnt/key points

From our experience with content analysis we have learnt a number of important lessons that may be useful for the novice researcher. They are:

• • A method description is a guideline supporting analysis and trustworthiness. Don’t get caught up too rigidly following steps. Reflexivity and flexibility are just as important. Remember that a method description is a tool helping you in the process of making sense of your data by reducing a large amount of text to distil key results.
• • It is important to maintain a vigilant awareness of one’s own pre-understandings in order to avoid bias during analysis and in results.
• • Use and trust your own intuition during the analysis process.
• • If possible, discuss and reflect together with other researchers who have analysed the same data. Be open and receptive to new perspectives.
• • Understand that it is going to take time. Even if you are quite experienced, each set of data is different and all require time to analyse. Don’t expect to have all the data analysis done over a weekend. It may take weeks. You need time to think, reflect and then review your analysis.
• • Keep reminding yourself how excited you have felt about this area of research and how interesting it is. Embrace it with enthusiasm!
• • Let it be chaotic – have faith that some sense will start to surface. Don’t be afraid and think you will never get to the end – you will… eventually!

Peer review under responsibility of African Federation for Emergency Medicine.

Appendix A Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10.1016/j.afjem.2017.08.001 .

Appendix A. Supplementary data

• Open access
• Published: 09 September 2024

A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan

• Matthew Seah 1 &
• Miho Iwakuma 1  

BMC Public Health volume  24 , Article number:  2447 ( 2024 ) Cite this article

Metrics details

The COVID-19 pandemic has spurred the growth of a global infodemic. In order to combat the COVID-19 infodemic, it is necessary to understand what kinds of misinformation are spreading. Furthermore, various local factors influence how the infodemic manifests in different countries. Therefore, understanding how and why infodemics differ between countries is a matter of interest for public health. This study aims to elucidate and compare the types of COVID-19 misinformation produced from the infodemic in the US and Japan.

COVID-19 fact-checking articles were obtained from the two largest publishers of fact-checking articles in each language. 1,743 US articles and 148 Japanese articles in their respective languages were gathered, with articles published between 23 January 2020 and 4 November 2022. Articles were analyzed using the free text mining software KH Coder. Exploration of frequently-occurring words and groups of related words was carried out. Based on agglomeration plots and prior research, eight categories of misinformation were created. Lastly, coding rules were created for these eight categories, and a chi-squared test was performed to compare the two datasets.

Overall, the most frequent words in both languages were related to health-related terms, but the Japan dataset had more words referring to foreign countries. Among the eight categories, differences with chi-squared p  ≤ 0.01 were found after Holm-Bonferroni p value adjustment for the proportions of misinformation regarding statistics (US 40.0% vs. JP 25.7%, ϕ 0.0792); origin of the virus and resultant discrimination (US 7.0% vs. JP 20.3%, ϕ 0.1311); and COVID-19 disease severity, treatment, or testing (US 32.6% vs. JP 45.9%, ϕ 0.0756).

Conclusions

Local contextual factors were found that likely influenced the infodemic in both countries; representations of these factors include societal polarization in the US and the HPV vaccine scare in Japan. It is possible that Japan’s relative resistance to misinformation affects the kinds of misinformation consumed, directing attention away from conspiracy theories and towards health-related issues. However, more studies need to be done to verify whether misinformation resistance affects misinformation consumption patterns this way.

Peer Review reports

Introduction

The COVID-19 pandemic has brought into the spotlight the growing infodemic : the “excessive amount of unfiltered information concerning a problem such that the solution is made more difficult” [ 1 ]. Between the mainstream media, statements made by politicians, social media platforms, instant messaging services, and changing guidelines released by official institutions, the typical person is constantly inundated with a barrage of information that presents both the challenge of discerning reliable information, as well as the option to take fringe or pseudoscientific theories as the truth. This represents a public health concern, as COVID-19 misinformation or “fake news” may spread anti-vaccine views or promote racial discrimination [ 2 ].

A multi-pronged approach is necessary to mitigate the impact of the infodemic, as no single intervention can achieve the breadth required to match the scale of the worldwide flow of information. Eysenbach proposes four pillars of infodemic management in his 2020 paper: infoveillance and infodemiology (surveillance of information supply and demand, as well as its quality); building eHealth literacy; improving the translation of knowledge between academia and larger outlets such as policymakers, mainstream media, and social media; and the peer-review process and fact-checking [ 3 ].

“Fact-checking” refers to the process of evaluating a statement for its factual accuracy or whether it has been framed in a misleading manner due to omission of context. Fact-checking has its origins in American TV segments devoted to checking the accuracy of statements made by American presidential candidates [ 4 ], though most current fact-checking content is produced by websites such as Snopes or FactCheck.org in the form of articles or videos.

Fact-checking alone cannot be the ultimate counter to misinformation – not only does it have limited effects on correcting perceptions of misinformation due to the strong biases and emotions involved when interacting with such information [ 4 , 5 ], the local politics of truth [ 6 ], i.e. the historical and cultural contexts of the region, inform behavior and beliefs to a significant degree; for instance, close-contact burial practices in parts of west Africa stricken by ebola [ 7 ], or vaccine hesitancy in Japan following the HPV vaccine scare in 2013 [ 8 ]. Interventions targeting an infodemic need to take into account the nature and context of the region to be effective.

One of the few extant studies comparing the COVID-19 infodemics and national contexts across countries was published by Zeng et al. [ 9 ], in which they analyzed fact-checking article contents from the US, China, India, Germany, and France. Some key findings included the fact that non-health misinformation (e.g. regarding politics, or the origin of the virus) is nearly twice as common as health misinformation (e.g. COVID-19 being “just a cold”); Germany is relatively resilient to misinformation compared to the US or India owing to its low societal polarization and high trust in the news media; misinformation regarding the spread of COVID-19 or travel restrictions is common in China, likely due to China being the early epicenter of the pandemic as well as large-scale travel movements that occur around Chinese New Year; and wedge-driving misinformation along religious lines is common in India owing to the longstanding conflict between the nation’s Muslim and Hindu populations.

Although there is already an abundance of cross-cultural research between the US and Japan, a comparative study of infodemics in these countries has yet to be done, and much has changed in the time since the publication of the Zeng paper – noteworthy developments including the progress made in global vaccination campaigns [ 10 ], and the emergence of the highly transmissible delta and omicron variants [ 11 ]. Furthermore, the national contexts of the US and Japan differ to a notable extent, in geographical, sociocultural, and historical terms, making it reasonable to expect differences in the types of misinformation that would gather more traction. Therefore, this research aims to provide an updated understanding of the COVID-19 infodemics in the US and Japan through a quantitative content analysis of the types of misinformation that appear in fact-checking articles.

Methodology

Data selection and gathering.

In order to find the types of COVID-19 misinformation that gathered significant traction in the US and Japan, COVID-19 fact-checking articles were gathered from the top two largest fact-checking publishers: Politifact and FactCheck.org for the US, and Buzzfeed and InFact for Japan. All articles were written in their respective countries’ languages (English for the US, Japanese for Japan). A summary of the data sources used is shown in Table  1 below. Articles included were published between 23 January 2020 and 4 November 2022.

Article URLs were scraped from the COVID-19 sections of each source in Python, using the Selenium library in Chrome 108.0.5359.124. Following this, a separate program was used to visit the listed URLs and scrape the article contents using the news-please library [ 16 ]. (Source codes can be accessed at https://github.com/seahmatthew/KyotoU-PublicHealth2023 .)

Data analysis in KH coder

The open-source quantitative text analysis program KH Coder [ 17 ], developed by Koichi Higuchi at Ritsumeikan university, was used to analyze the article contents, with the US and Japan datasets in separate projects. As of January 2023, there are 5,761 published research articles which make use of KH Coder [ 18 ], many of which cover health-related research topics. Its strengths include functions for statistical analysis (e.g., term frequency) of large data files, as well as the KWIC Concordance function [ 19 ] which provides the capability to easily refer to the original data from any given result.

Word Frequency [ 19 ] was used to obtain an overview of the data as a preliminary step. Following this, Hierarchal Cluster Analysis [ 19 ] was used to explore groups of related words, and also to build the lists of terms to force pickup (such as “toilet paper” or “Moderna”) which would not be picked up by default, and irrelevant terms to force ignore (such as “website” or “article”), which introduce noise due to appearing very frequently but not being indicative of any relevant themes. This took a process of trial and error especially when building the force ignore lists, as blocking certain seemingly irrelevant terms would sometimes turn out to hide an otherwise useable article.

After substantive force pickup/ignore lists had been built for each languages, the lists were compared to ensure that relevant keywords were ignored in both languages, although words that appear frequently as syntactic features in each language (such as “pants [on] fire” or “subject”) were not duplicated in the same way.

Next, Hierarchal Cluster Analysis was re-run using the finalized force pickup/ignore lists to gather the terms to form the document coding files. For the U.S. dataset, the minimum Term Frequency (TF) was set to 90, Document Frequency (DF) to 1, and only nouns, proper nouns, and terms from the force pickup list were analyzed to minimize noise. For the Japan dataset, the minimum TF was set to 10, DF to 1, and only nouns, proper nouns, location names, and terms from the force pickup list were analyzed. For both datasets, the Ward method and Jaccard frequency were used, with the number of clusters shown being auto-chosen.

Based on the agglomeration plot turning points from the Hierarchal Cluster analyses, the prior Zeng paper [ 9 ], and familiarity with the data, it was decided to split the data into eight categories. From the categories and keywords found, coding files were built for the US and Japan datasets and applied to obtain the frequencies for each category. Articles could be assigned to multiple categories, and manual sorting was used to classify articles through a first pass after automatic sorting. Articles that failed to be classified in any category after both automatic and manual sorting were assigned to a separate Miscellaneous category.

After the code frequencies for each language had been obtained, chi-squared tests were carried out to test whether there were differences in the frequencies across countries. Holm-Bonferroni adjustment was used to adjust the p values.

The agglomeration plots produced from the Hierarchal Cluster analyses are shown below in Fig.  1 . The turning points show that somewhere in the range of seven categories would be ideal, but considering prior research and familiarity with the data, it was decided to generate eight categories.

Agglomeration plots produced by Hierarchal Cluster Analysis of the US (left) and Japan (right) datasets

The coding files created based on the categories and keywords found are shown in Table  2 . A total of eight categories were created: government policy; resource shortages; statistics; measures to stem the spread of infection; masks and transmission; origin of the virus and resultant discrimination; COVID-19 disease severity, treatment, or testing; and vaccine efficacy, contents, or safety. Each category contains a set of keywords in its respective language that results in close association; for instance, “lockdown”, “quarantine”, and “border” associate highly with articles about measures taken to stem the spread of infection.

A summary of the top 50 words with the highest tf (term frequency) is shown in Table  3 . Both the U.S. and Japan lists are topped by words pertaining to vaccination, masks, cases and testing, likely because these words are likely to appear across a broad range of categories. For instance, words pertaining to vaccination could appear in both articles about supposed deleterious health effects of vaccination, as well as articles about vaccination program plans or vaccine-related conspiracy theories.

A summary of the code frequencies, chi-squared test p values, and relevant excerpts from the data is provided below in Table  4 . Articles that contained none of the eight predetermined codes are grouped in the “Miscellaneous” category. Chi-squared tests were carried out to compare the code frequencies across datasets, and p value correction was done using the Holm-Bonferroni method. Three categories stood out due to their relatively low p values and relatively high effect sizes: statistics, the origin of the virus and resultant discrimination, and COVID-19 severity, treatment, and testing.

Versions of Tables  2 and 3 , and 4 with the original Japanese text are available in Supp_012024.docx.

The effect sizes ϕ for each category are shown below in Table  5 . Only the category on the origin of the virus and resultant discrimination showed an effect size exceeding 0.1, a small effect. The two categories of statistics, and COVID-19 severity, treatment, and testing showed the next-highest effect sizes of > 0.07. Hence, these three categories were chosen for further discussion.

Similarities and differences between US and Japan categories

Selective reading of articles with high tf (term frequency) for the chosen categories produced a handful of similarities and differences. Within the statistics category (which was more common in the US dataset, 40.1% vs. 25.7%, ϕ 0.0792), misinformation from both countries tended to downplay the severity of the COVID-19 mortality rate, or otherwise make factually false statistical assertions. US misinformation tended to make more (invalid) comparisons to influenza, and there were false assertions that the US was performing statistically better in terms of mortality rate than other countries, while Japanese misinformation contained more assertions that vaccines increase mortality rate. Many of the US articles in this category were based on quotes from then-President Donald Trump.

Within the category regarding the origin of the virus and resultant discrimination (which was more common in the Japan dataset, 20.3% vs. 7.0%, ϕ 0.1311), misinformation from both countries asserted that COVID-19 was artificially made in the Wuhan Institute of Virology. However, US misinformation tended to focus on federal funding for the institute, and some articles tied the origin of the pandemic to Chinese meat-eating practices. Japanese misinformation focused more on Chinese people within Japan itself, such as warning of incoming tourist swarms or Chinese nationals taking up space in hospitals.

Within the category of COVID-19 severity, treatment, or testing (which was more common in the Japan dataset, 46.0% vs. 32.6%, ϕ 0.0756), both countries had misinformation about treatments for COVID-19, as well as about testing kits. While both countries mentioned ivermectin, hydroxychloroquine and marijuana as COVID-19 treatments were exclusive to the US dataset, while green tea and hot water were exclusive to the Japan dataset. More US articles tended to downplay the severity of infection by likening it to the flu. There were pieces of misinformation in the US that stemmed from misinterpretation of test kits, while there were Japanese assertions that COVID-19 test kits are faulty or ineffective.

Overall, non-health misinformation appeared more frequently than health misinformation, echoing findings from other studies analyzing fact-checking articles [ 9 ] or social media posts [ 20 ].

In addition, while the category frequencies for masks and transmission did not appear to differ, the contents of articles in these categories showed differences: articles from the US dataset tended to be regarding misinformation on the effectiveness of masks as a means for preventing transmission, while articles from the Japan dataset tended to be on ancillary topics, such as the country of manufacture of masks, or mask shortages. Mask-wearing as a means for preventing disease transmission while sick is an established aspect of Japanese culture [ 21 ].

National contextual factors that affect misinformation consumption

As outlined above, there are some differences in the contents of the COVID-19 misinformation circulating in the US and Japan. A few of the numerous contextual factors that may have influenced these differences will be described further below.

Importantly, it should not be assumed that a cause-and-effect relationship is at play, as a myriad of factors influence consumer (and macro-level) information-seeking habits. For instance, on the micro level, there are consumer culture factors that influence patterns of consumption, such as social influences or social class [ 22 ]; on the macro level, society-level factors such as the quality of official communications can affect attitudes towards health measures [ 23 ]. Some evidence also exists to suggest that in certain countries, the demand for certain kinds of misinformation fluctuates based on the epidemic curve [ 9 ]. While a comprehensive list of every potential influencing factor would be beyond the scope of this research, it can be seen that local context can indeed influence information-seeking habits. Understanding the concerns and mindsets of those grappling with the infodemic should be a priority in determining what countermeasures to take (e.g., targeted messaging, rapid response, etc.).

On the topic of the high prevalence of political figures involved in US misinformation, a survey conducted by the Reuters Institute for the Study of Journalism in 2020 [ 24 ] found that American information-seeking habits surrounding COVID-19 are strongly tied to political affiliation. Left-leaning respondents were likely to trust the news media and unlikely to trust the government; the opposite was true for right-leaning participants. Trump was himself a major direct source of COVID-19 misinformation [ 25 ], and many of the erroneous claims he made are reflected in the data, especially in the Statistics and Origin categories. The significant sway a person’s political beliefs hold over their information-seeking behavior in the US is likely to be associated with the country’s highly polarized political climate. This finding of the high frequency of misinformation from politicians in the US is echoed in the Zeng paper [ 9 ], and the same paper found that this connection between societal polarization and political misinformation was also clear in India.

In the Japanese dataset, articles pertaining to the origin of COVID-19 from China were much more frequent and pointed in general; as opposed to US articles which mostly addressed conspiracy theories of American funding for the Wuhan Institute of Virology or the animal origins of the virus, articles in this category in the Japan dataset tended to focus directly on Chinese nationals, either as disproportionate occupants of Japanese medical institutions, or as spreaders of COVID-19 inbound from China. Japan’s relative geographical proximity to China and popularity as a Chinese tourist destination, as well as existing anti-Chinese sentiment that has been worsening progressively since the 1980s [ 26 ], may explain to some extent the personal nature of Japanese misinformation in this category.

At first glance, it may seem surprising that both the US and Japan have similar proportions of articles discussing vaccine efficacy, contents, or safety, especially given the heavy role US political figures played in leading supporters to act contrary to evidence-based findings [ 27 ]. In an article published in the Japanese journal Chiryo in 2021, the founders of HPV vaccine awareness group MinPapi describe how vaccine hesitancy in Japan may have been exacerbated by the human papillomavirus (HPV) vaccine side effect scare in 2013 [ 28 ]; years later, addressing vaccine hesitancy through their new website CoviNavi continues to be a challenge.

Additionally, a 2021 survey conducted in Japan showed that Japanese respondents were uncertain in general about what sources of COVID-19 information they could trust [ 20 ]. 24.7% of respondents believed there was no information source they could trust, and only 26.0% of respondents felt they could trust health experts. This stands in stark contrast to the results from the aforementioned Reuters study, where over 80% of American respondents on both sides of the political spectrum felt they could trust health experts. This difference in response to the infodemic – picking sides, as opposed to being assailed by uncertainty – may actually help to explain why vaccine misinformation is relatively common in both countries; one possible interpretation is that a limited segment of the American audience consumes vaccine misinformation in greater per capita amounts, while a more general segment of the Japanese audience consumes vaccine misinformation in lower per capita amounts.

Disinformation resilience and its effects on misinformation consumption

In a 2020 paper, Humprecht et al. outline a framework for cross-national comparisons of disinformation (henceforth “misinformation”) resilience : the degree to which online misinformation is likely to receive exposure and be spread [ 29 ]. Political factors limiting misinformation resilience include societal polarization, and frequency of populist communication; media-related factors include low trust in news media, weak public news services, and audience fragmentation; economic factors include a large advertisement market size, and high social media usage. Using this framework in a comparison of the US with 16 other mainly European countries, the authors found that the US scored the lowest in misinformation resilience, owing to its fragmented media landscape, large ad market, low trust in news, highly polarized society, and frequent populist communication.

In comparison to the US, Japan scores notably lower in terms of populist communication [ 30 ]; NHK, the public broadcasting network, attains comparable viewership to other networks [ 31 ] as opposed to American public broadcasters with one- to two-thirds the viewership of major American TV networks [ 32 , 33 ]; major TV news networks in Japan attain roughly two times the viewer share of US TV network providers, with Yahoo! News dominating the online news market with over 50% weekly usage [ 34 ]. While a formal comparison has yet to be done in the literature, these factors suggest that Japan may be more resilient to misinformation than the US. It is possible that this affected the sizes of the datasets that could be obtained, leading to the US dataset being more than ten times as large than the Japan dataset.

While it stands to reason that increased misinformation resilience would lead to lower spread and consumption of misinformation, its effect on the types of misinformation consumed is less clear. In the Zeng study [ 9 ], Germany stood out as one of the studied countries with high misinformation resilience; compared to the other countries which tended to contain high proportions of articles on political conspiracy theories, lockdown measures, or transmission methods, misinformation from Germany was centered on COVID-19 treatment and vaccines, similarly to the Japan dataset used in this report. If we consider the nature of rumors and misinformation as an answer-seeking response to a perceived external threat [ 35 ], one possible interpretation of this pattern is that increased misinformation resilience in the midst of the pandemic contributes to lower distraction with non-key issues – the key issue in this context being the health impact of COVID-19 and how it can be avoided or treated. The “Miscellaneous” category is mostly comprised of articles on these non-key issues , including those bordering on absurdity or conspiracy; while this category was not notably differently sized between the US and Japan datasets, the Japan data had a noticeably lower proportion of misinformation along the lines of the “deity of death” US article.

Strengths and limitations of this study

In comparison to prior studies which used fact-checking articles as data, this study uses a larger sample size for the US dataset and offers a Japanese dataset for the first time. In particular, using KH Coder allowed for multiple categories to be assigned to a single article, which reflects the data more accurately than other studies [ 9 ] that are limited to a single category for each article. Additionally, quantitative content analysis using KH Coder allowed for counting the term frequencies in the large datasets, as well as for referring back to the original data when needed using the KWIK Concordance function.

However, as to the limitations of the study, the span of misinformation covered in this report is limited to that selected by the editorial teams in a “gatekeeping” process [ 36 ] for the four online news sources used; in particular, fact-checking in Japan is a relatively new endeavor, with the InFact team and website notably smaller than established fact-checking organizations from the US. This has negative implications for the generalizability of the Japan data, and a larger future dataset would likely give richer results. In addition, since the categorization processes were carried out automatically, there may be a handful of data points that have not been categorized correctly. More studies should be done to further verify the relationship between the misinformation resistance of a country and the types of misinformation that spread within it. Future studies of this nature will have larger and more varied datasets to work with, whether they are about COVID-19 or any other infodemic. Finally, the effect sizes found for the sections discussed here are all of small magnitude, meaning that it should not be inferred that certain segments of misinformation should receive disproportionate amounts of focus in countries that seem vulnerable to that kind of misinformation.

Practical implications

In combination with aggregated data from other countries, data on the types of misinformation which are comparatively common in the country provides policymakers a reference point when allocating resources to tackling misinformation, through means such as rapid-response messaging [ 37 ]. Of course, this data should be weighed against the actual likely impact of said misinformation spreading in the populace; any given piece vaccine misinformation is likely to do more harm overall than a wild claim of a vaccination center bearing a logo of a “deity of death”.

This research also opens up new avenues for further research – for instance, research to verify whether modifying our taking a culturally-relevant approach to tackling misinformation results in better correction outcomes. One possible example would be altering the tone of messaging to be firmer and more succinct in an environment like Japan, where misinformation likely spreads out of uncertainty instead of certainty in misinformation, while a more indirect approach may be more effective in places like the United States where misinformed beliefs are grounded in certainty.

Using quantitative content analysis, this study shows the similarities and differences in the COVID-19 infodemics in US and Japan since the start of the pandemic. Differences were found in the proportion of articles mentioning statistics, the origin of the virus and resultant discrimination, and COVID-19 severity, treatment and testing, though the effect sizes were seen to be small.

Several facets of national context appear to support the trends seen in the data, such as the history of the HPV vaccine in Japan leading to increased distrust of COVID-19 vaccines. In addition, application of a misinformation resilience framework appears to show that in countries with higher resilience, distracting non-key issues such as conspiracy theories attract less attention compared to key issues , which refer to COVID-19 health impacts and other health information in the context of the pandemic. Understanding the types of misinformation in circulation gives policymakers and educators direction in developing strategies to counter this misinformation.

Lastly, it should be reiterated that fact-checking, even when done through appropriate channels in a culturally relevant manner, cannot be relied upon as the sole measure with which to combat an infodemic. Not only does fact-checking have heavily limited effects on correcting misinformed beliefs [ 4 , 5 ], a deluge of fact-checking information may even backfire by contributing to information overload and avoidance in the intended audience [ 38 ], or by simply acting as a dissemination channel for the misinformation that would not have been spread otherwise [ 36 ]. Fact-checking has a place as one of the pillars of infodemic management – there is a need to uphold journalistic integrity, and to provide a reliable source for a more invested, informed reader subset. The other pillars of infoveillance and infodemiology, the gradual process of building eHealth literacy in the populace, and providing clear, timely translations of scientific findings to actionable messages need to be upheld in tandem as a long-term strategy for decreasing the impact of misinformation [ 3 ].

Data availability

The dataset supporting the conclusions of this article is available in the GitHub repository, https://doi.org/10.5281/zenodo.8282744 at https://github.com/seahmatthew/KyotoU-PublicHealth2023 [ 39 ].

Abbreviations

Coronavirus disease 2019

Human papillomavirus

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Seah, M., Iwakuma, M. A quantitative content analysis of topical characteristics of the online COVID-19 infodemic in the United States and Japan. BMC Public Health 24 , 2447 (2024). https://doi.org/10.1186/s12889-024-19813-y

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Integrating ecological value and charismatic species habitats to prioritize habitats for conservation: A case study from Greater Yellowstone

Expanding human pressure has reduced natural habitats globally and motivated strategies to conserve remaining natural habitats. Decisions about conservation on private lands, however, are typically made by local stakeholders who are motivated by the elements of nature they most highly value. Thus, national prioritization for conservation should be complemented by local analysis of species or habitats that most influence local landowner decisions. We demonstrate within the Greater Yellowstone Ecosystem how quantitative mapping of wildlife species that are highly valued by local residents can be integrated with indices of ecosystem integrity to prioritize private lands for conservation. We found that natural vegetation cover (NVC) comprised 81% of the private lands. Some watersheds have lost 6% of NVC since 2001 and developed lands now cover >40% of their areas. Locations high in ecological value, elk habitat, and grizzly habitat occurred in different biophysical settings. Consequently, only 2% of the NVC supports high levels of all three biodiversity measures and 26% of this area was within conservation easements. The remaining areas of high biodiversity value that are unprotected are priorities for conservation. We suggest that national-scale conservation planning will be most effective on private lands if additional within-ecoregion analyses are done on the elements of biodiversity that are most valued by local people.

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Web Technology to Support Work Processes in Energy Policy Research - A Case Study with Energy Efficiency Standards

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This paper focuses on a process to design and build a web-based system to assist staff in day-to-day management and contemporaneous documentation of their work. Other groups that want to use web technology to support their work could apply the approach presented here, but the design itself pertains to a particular set of issues in a unique context. Each user must apply the approach to identify their objectives and design a site to meet them. The main question that the Energy Efficiency Standards Group addressed was: "How can we facilitate documentation of interim results and final products while conducting a complex, interdependent set of analyses by multiple authors under time pressures for delivering a final product?" The approach to address this question includes categorization of the components of the work, discussions with staff, development of infrastructure support for documentation, implementation of the documentation process and integration with the workflow, and follow- up with staff. The search for a solution raised a number of issues such as the need for a thorough understanding of the work, consensus building by inclusion of key staff, and deliverable scheduling to allow for contemporaneous documentation. Documentation results vary among the product analyses, from extensive internal and external use to much slower adoption. Complaints include the length of the input forms and pressure from clients to deliver results. But with repeated demand for interim output, the need for thorough contemporaneous documentation still remains. Accordingly, as problems arise there is continued commitment among the staff to address them.

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Afghanistan

Afghanistan: Comparative Drought Analysis, September 2024

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According to a 2018 World Bank report, roughly 70% of the population of Afghanistan live in rural areas where the majority of livelihoods rely on agriculture and livestock, signaling that large parts of the population of Afghanistan are particularly vulnerable to drought. Drought and its impacts have played a major role in driving needs in Afghanistan. Alongside the triple-dip La Niña event that began at the end of 2020 and continued until 2023, Afghanistan experienced one of the most severe droughts in its history. The impact of the drought, combined with other natural disasters, COVID-19, armed conflict, and the collapse of the representative government in August 2021, has led the country into humanitarian crisis.

The drought has exacerbated food insecurity, affected livelihoods, and limited access to water in Afghanistan. It has also acted as a push factor for displacement within the country. According to the Whole of Afghanistan Assessment (WoAA) conducted by REACH in 2023, about 67% of households reported being affected by drought in the 12 months preceding the data collection. Over the three consecutive dry years from 2021 to 2023, agricultural drought was reported to have severely impacted on food security. According to WoAA data, the percentage of the population experiencing poor food consumption increased from 38% in 2021 to 42% in 2022, and decreased in 2023 to 28%, as drought conditions improved. The percentage of households with acceptable food consumption never exceeded 30% during these years.

In addition, according to the Humanitarian Situation Monitoring (HSM) Key Informant survey conducted in September 2023, approximately 58% of key informants reported drought as the primary cause of displacement in their settlements over the six months preceding the data collection.

The Comparative Drought Analysis (CPDA) conducted in Afghanistan during the first and second quarters of 2024 aims to fill information gaps, at the province level, on the impact of drought on communities' food security, livelihoods, displacement, WASH (water, sanitation, and hygiene), and health. It also aims to provide insights into the environmental impacts of drought through remote sensing data. Specifically, the study will enhance the development of drought severity monitoring systems, allowing for real-time monitoring of drought severity in Afghanistan.

The methodology and scope of this study were developed by REACH and endorsed by WFP Afghanistan. The study uses free available remote sensing-driven climate data to examine the characteristics of meteorological indicators during dry and wet years. In addition to remote sensing climate data, already available assessment data collected by REACH and other actors in Afghanistan are utilized as well.

Remote sensing data included CHIRPS, MODIS, FEWS NET, Era5, Sentinel-2, and other sources used to calculate drought indicators. Assessment data collected by various organizations over different years were combined and analyzed to monitor changes in related sectors. Data from REACH's WoAA, a nationwide multi-sectoral household survey, were used extensively. Additionally, data from other assessments, including Humanitarian Situation Monitoring (HSM) by REACH, Vulnerability Assessment and Mapping (VAM) by WFP, seasonal calendars from FEWS NET, and acute watery diarrhea cases from WHO, were integrated into the analysis.

Drought indicators in this study were derived from remote sensing data, as access to meteorological ground station data collected by government departments was not accessible. Therefore, the results of the drought indicator analysis have limitations. Additionally, freely available climate datasets have coarse precision, which, while suitable for large-scale geographic scopes, is limited for localized studies.

Nationwide Multi Sectoral Needs Assessment data in Afghanistan is only available since 2021. Accordingly, this limits the ability to track the evolution of needs in communities before that year. Most of the alignment between drought remote sensing data and WoAA assessment data is found between 2021-2022, when the multi sectoral needs data for admin1 level (provinces) is available and at the same time drought condition overshadowed the whole country.

Key Findings

Since 1999, corresponding to the scope of this study, Afghanistan has experienced several drought events with varying severity and geographical impact. Dry weather in Afghanistan is significantly influenced by La Niña events in the eastern Pacific Ocean. Additionally, climate change and global warming contribute to the severity and impact of droughts on communities, particularly by diminishing permanent glaciers and snowpacks. The impacts of drought vary based on the type of drought, topography, and livelihood of the affected areas. Typically, meteorological drought impacts are visible in the upper river basins or mountainous regions of the country, including the Central Highlands and northwestern provinces. Additionally, rainfed and agro-pastoral livelihoods are more sensitive to meteorological drought.

At the beginning of the 21st century, from 2000 to 2002, the country experienced a multi-year drought. Another multi-year drought occurred recently from 2021 to 2023. During these extended drought periods, the country faced hydrological droughts as a result of prolonged meteorological droughts. Hydrological droughts impacted the entire country, but irrigated livelihoods, mostly in the lower and flat parts of the river basins, were more severely affected.

Droughts in Afghanistan have damaged agriculture and livestock, which has further led to increased food prices. In addition, in agro-pastoral communities during drought years, the value of livestock decreased, negatively affecting the purchasing power of these communities. Overall, droughts have disrupted the supply and demand of commodities in the communities.

Drought has emerged as a driver of food insecurity and a deterioration of coping strategies in the country by damaging food sources. The number of people consuming less food increased during drought years. Furthermore, the number of households practicing emergency livelihood coping strategies increased considerably during these years. Although community resilience varies based on the livelihoods practiced, provinces practicing agro-pastoral livelihoods found particularly central highland region, and those provinces with more drought-resistant livelihoods such as forest-based livelihoods in the southeastern region, have shown more stability during drought conditions.

The number of households using unprotected water sources increased generally across the country as droughts extended. Additionally, the number of households traveling longer distances to access water also increased. The incidence of acute watery diarrhea saw a substantial increase during drought years. Water scarcity seems to relate to the type of drought: communities in upper river basins report more challenges in accessing water during meteorological droughts, while lower river basin provinces report more water scarcity during prolonged droughts when hydrological droughts occur.

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