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Varieties of Qualitative Research Methods pp 139–146 Cite as

Document Analysis

  • Benjamin Kutsyuruba 4  
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This chapter describes the document analysis approach. As a qualitative method, document analysis entails a systematic procedure for reviewing and evaluating documents through finding, selecting, appraising (making sense of), and synthesizing data contained within them. This chapter outlines the brief history, method and use of document analysis, provides an outline of its process, strengths and limitations, and application, and offers further readings, resources, and suggestions for student engagement activities.

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Additional Reading

Kutsyuruba, B. (2017). Examining education reforms through document analysis methodology. In I. Silova, A. Korzh, S. Kovalchuk, & N. Sobe (Eds.), Reimagining Utopias: Theory and method for educational research in post-socialist contexts (pp. 199–214). Sense.

Kutsyuruba, B., Christou, T., Heggie, L., Murray, J., & Deluca, C. (2015). Teacher collaborative inquiry in Ontario: An analysis of provincial and school board policies and support documents. Canadian Journal of Educational Administration and Policy, 172 , 1–38.

Kutsyuruba, B., Godden, L., & Tregunna, L. (2014). Curbing the early-career attrition: A pan-Canadian document analysis of teacher induction and mentorship programs. Canadian Journal of Educational Administration and Policy, 161 , 1–42.

Segeren, A., & Kutsyuruba, B. (2012). Twenty years and counting: An examination of the development of equity and inclusive education policy in Ontario (1990–2010). Canadian Journal of Educational Administration and Policy, 136 , 1–38.

Online Resources

Document Analysis: A How To Guide (12:27 min) https://www.youtube.com/watch?v=vOsE9saR_ck

Document Analysis with Philip Adu (1:16:40 min) https://youtu.be/bLKBffW5JPU

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Kutsyuruba, B. (2023). Document Analysis. In: Okoko, J.M., Tunison, S., Walker, K.D. (eds) Varieties of Qualitative Research Methods. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-031-04394-9_23

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The Basics of Document Analysis

what is document analysis in qualitative research

Document analysis is the process of reviewing or evaluating documents both printed and electronic in a methodical manner. The document analysis method, like many other qualitative research methods, involves examining and interpreting data to uncover meaning, gain understanding, and come to a conclusion.

What is Meant by Document Analysis?

Document analysis pertains to the process of interpreting documents for an assessment topic by the researcher as a means of giving voice and meaning. In Document Analysis as a Qualitative Research Method by Glenn A. Bowen , document analysis is described as, “... a systematic procedure for reviewing or evaluating documents—both printed and electronic (computer-based and Internet-transmitted) material. Like other analytical methods in qualitative research, document analysis requires that data be examined and interpreted in order to elicit meaning, gain understanding, and develop empirical knowledge.”

During the analysis of documents, the content is categorized into distinct themes, similar to the way transcripts from interviews or focus groups are analyzed. The documents may also be graded or scored using a rubric.

Document analysis is a social research method of great value, and it plays a crucial role in most triangulation methods, combining various methods to study a particular phenomenon.

>> View Webinar: How-To’s for Data Analysis

Documents fall into three main categories:

  • Personal Documents: A personal account of an individual's beliefs, actions, and experiences. The following are examples: e-mails, calendars, scrapbooks, Facebook posts, incident reports, blogs, duty logs, newspapers, and reflections or journals.
  • Public Records: Records of an organization's activities that are maintained continuously over time. These include mission statements, student transcripts, annual reports, student handbooks, policy manuals, syllabus, and strategic plans.
  • Physical Evidence: Artifacts or items found within a study setting, also referred to as artifacts. Among these are posters, flyers, agendas, training materials, and handbooks.

NVivo Demo Request

The qualitative researcher generally makes use of two or more resources, each using a different data source and methodology, to achieve convergence and corroboration. An important purpose of triangulating evidence is to establish credibility through a convergence of evidence. Corroboration of findings across data sets reduces the possibility of bias, by examining data gathered in different ways.

It is important to note that document analysis differs from content analysis as content analysis refers to more than documents. As part of their definition for content analysis, Columbia Mailman School of Public Health states that, “Sources of data could be from interviews, open-ended questions, field research notes, conversations, or literally any occurrence of communicative language (such as books, essays, discussions, newspaper headlines, speeches, media, historical documents).

How Do You Do Document Analysis?

In order for a researcher to obtain reliable results from document analysis, a detailed planning process must be undertaken. The following is an outline of an eight-step planning process that should be employed in all textual analysis including document analysis techniques.

  • Identify the texts you want to analyze such as samples, population, participants, and respondents.
  • You should consider how texts will be accessed, paying attention to any cultural or linguistic barriers.
  • Acknowledge and resolve biases.
  • Acquire appropriate research skills.
  • Strategize for ensuring credibility.
  • Identify the data that is being sought.
  • Take into account ethical issues.
  • Keep a backup plan handy.

what is document analysis in qualitative research

Researchers can use a wide variety of texts as part of their research, but the most common source is likely to be written material. Researchers often ask how many documents they should collect. There is an opinion that a wide selection of documents is preferable, but the issue should probably revolve more around the quality of the document than its quantity.

Why is Document Analysis Useful?

Different types of documents serve different purposes. They provide background information, indicate potential interview questions, serve as a mechanism for monitoring progress and tracking changes within a project, and allow for verification of any claims or progress made.

You can triangulate your claims about the phenomenon being studied using document analysis by using multiple sources and other research gathering methods.

Below are the advantages and disadvantages of document analysis

  • Document analysis may assist researchers in determining what questions to ask your interviewees, as well as provide insight into what to watch out for during your participant observation.
  • It is particularly useful to researchers who wish to focus on specific case studies
  • It is inexpensive and quick in cases where data is easily obtainable.
  • Documents provide specific and reliable data, unaffected by researchers' presence unlike with other research methods like participant observation.

Disadvantages

  • It is likely that the documents researchers obtain are not complete or written objectively, requiring researchers to adopt a critical approach and not assume their contents are reliable or unbiased.
  • There may be a risk of information overload due to the number of documents involved. Researchers often have difficulties determining what parts of each document are relevant to the topic being studied.
  • It may be necessary to anonymize documents and compare them with other documents.

How NVivo Can Help with Document Analysis

Analyzing copious amounts of data and information can be a daunting and time-consuming prospect. Luckily, qualitative data analysis tools like NVivo can help!

NVivo’s AI-powered autocoding text analysis tool can help you efficiently analyze data and perform thematic analysis . By automatically detecting, grouping, and tagging noun phrases, you can quickly identify key themes throughout your documents – aiding in your evaluation.

Additionally, once you start coding part of your data, NVivo’s smart coding can take care of the rest for you by using machine learning to match your coding style. After your initial coding, you can run queries and create visualizations to expand on initial findings and gain deeper insights.

These features allow you to conduct data analysis on large amounts of documents – improving the efficiency of this qualitative research method. Learn more about these features in the webinar, NVivo 14: Thematic Analysis Using NVivo.

>> Watch Webinar NVivo 14: Thematic Analysis Using NVivo

Learn More About Document Analysis

Watch Twenty-Five Qualitative Researchers Share How-To's for Data Analysis

what is document analysis in qualitative research

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what is document analysis in qualitative research

Document Analysis - How to Analyze Text Data for Research

what is document analysis in qualitative research

Introduction

What is document analysis, where is document analysis used, how to perform document analysis, what is text analysis, atlas.ti as text analysis software.

In qualitative research , you can collect primary data through surveys , observations , or interviews , to name a few examples. In addition, you can rely on document analysis when the data already exists in secondary sources like books, public reports, or other archival records that are relevant to your research inquiry.

In this article, we will look at the role of document analysis, the relationship between document analysis and text analysis, and how text analysis software like ATLAS.ti can help you conduct qualitative research.

what is document analysis in qualitative research

Document analysis is a systematic procedure used in qualitative research to review and interpret the information embedded in written materials. These materials, often referred to as “documents,” can encompass a wide range of physical and digital sources, such as newspapers, diaries, letters, policy documents, contracts, reports, transcripts, and many others.

At its core, document analysis involves critically examining these sources to gather insightful data and understand the context in which they were created. Research can perform sentiment analysis , text mining, and text categorization, to name a few methods. The goal is not just to derive facts from the documents, but also to understand the underlying nuances, motivations, and perspectives that they represent. For instance, a historical researcher may examine old letters not just to get a chronological account of events, but also to understand the emotions, beliefs, and values of people during that era.

Benefits of document analysis

There are several advantages to using document analysis in research:

  • Authenticity : Since documents are typically created for purposes other than research, they can offer an unobtrusive and genuine insight into the topic at hand, without the potential biases introduced by direct observation or interviews.
  • Availability : Documents, especially those in the public domain, are widely accessible, making it easier for researchers to source information.
  • Cost-effectiveness : As these documents already exist, researchers can save time and resources compared to other data collection methods.

However, document analysis is not without challenges. One must ensure the documents are authentic and reliable. Furthermore, the researcher must be adept at discerning between objective facts and subjective interpretations present in the document.

Document analysis is a versatile method in qualitative research that offers a lens into the intricate layers of meaning, context, and perspective found within textual materials. Through careful and systematic examination, it unveils the richness and depth of the information housed in documents, providing a unique dimension to research findings.

what is document analysis in qualitative research

Document analysis is employed in a myriad of sectors, serving various purposes to generate actionable insights. Whether it's understanding customer sentiments or gleaning insights from historical records, this method offers valuable information. Here are some examples of how document analysis is applied.

Analyzing surveys and their responses

A common use of document analysis in the business world revolves around customer surveys . These surveys are designed to collect data on the customer experience, seeking to understand how products or services meet or fall short of customer expectations.

By analyzing customer survey responses , companies can identify areas of improvement, gauge satisfaction levels, and make informed decisions to enhance the customer experience. Even if customer service teams designed a survey for a specific purpose, text analytics of the responses can focus on different angles to gather insights for new research questions.

Examining customer feedback through social media posts

In today's digital age, social media is a goldmine of customer feedback. Customers frequently share their experiences, both positive and negative, on platforms like Twitter, Facebook, and Instagram.

Through document analysis of social media posts, companies can get a real-time pulse of their customer sentiments. This not only helps in immediate issue resolution but also in shaping product or service strategies to align with customer preferences.

Interpreting customer support tickets

Another rich source of data is customer support tickets. These tickets often contain detailed descriptions of issues faced by customers, their frustrations, or sometimes their appreciation for assistance received.

By employing document analysis on these tickets, businesses can detect patterns, identify recurring issues, and work towards streamlining their support processes. This ensures a smoother and more satisfying customer experience.

Historical research and social studies

Beyond the world of business, document analysis plays a pivotal role in historical and social research. Scholars analyze old manuscripts, letters, and other archival materials to construct a narrative of past events, cultures, and civilizations.

As a result, document analysis is an ideal method for historical research since generating new data is less feasible than turning to existing sources for analysis. Researchers can not only examine historical narratives but also how those narratives were constructed in their own time.

what is document analysis in qualitative research

Turn to ATLAS.ti for your data analysis needs

Try out our powerful data analysis tools with a free trial to make the most out of your data today.

Performing document analysis is a structured process that ensures researchers can derive meaningful, qualitative insights by organizing source material into structured data . Here's a brief outline of the process:

  • Define the research question
  • Choose relevant documents
  • Prepare and organize the documents
  • Begin initial review and coding
  • Analyze and interpret the data
  • Present findings and draw conclusions

The process in detail

Before diving into the documents, it's crucial to have a clear research question or objective. This serves as the foundation for the entire analysis and guides the selection and review of documents. A well-defined question will focus the research, ensuring that the document analysis is targeted and relevant.

The next step is to identify and select documents that align with the research question. It's vital to ensure that these documents are credible, reliable, and pertinent to the research inquiry. The chosen materials can vary from official reports, personal diaries, to digital resources like social media data , depending on the nature of the research.

Once the documents are selected, they need to be organized in a manner that facilitates smooth analysis. This could mean categorizing documents by themes, chronology, or source types. Digital tools and data analysis software , such as ATLAS.ti, can assist in this phase, making the organization more efficient and helping researchers locate specific data when needed.

what is document analysis in qualitative research

With everything in place, the researcher starts an initial review of the documents. During this phase, the emphasis is on identifying patterns, themes, or specific information relevant to the research question.

Coding involves assigning labels or tags to sections of the text to categorize the information. This step is iterative, and codes can be refined as the researcher delves deeper.

After coding, interesting patterns across codes can be analyzed. Here, researchers seek to draw meaningful connections between codes, identify overarching themes, and interpret the data in the context of the research question .

This is where the hidden insights and deeper understanding emerge, as researchers juxtapose various pieces of information and infer meaning from them.

Finally, after the intensive process of document analysis, the researcher consolidates their findings, crafting a narrative or report that presents the results. This might also involve visual representations like charts or graphs, especially when demonstrating patterns or trends.

Drawing conclusions involves synthesizing the insights gained from the analysis and offering answers or perspectives in relation to the original research question.

Ultimately, document analysis is a meticulous and iterative procedure. But with a clear plan and systematic approach, it becomes a potent tool in the researcher's arsenal, allowing them to uncover profound insights from textual data.

what is document analysis in qualitative research

Text analysis, often referenced alongside document analysis, is a method that focuses on extracting meaningful information from textual data. While document analysis revolves around reviewing and interpreting data from various sources, text analysis hones in on the intricate details within these documents, enabling a deeper understanding. Both these methods are vital in fields such as linguistics, literature, social sciences, and business analytics.

In the context of document analysis, text analysis emerges as a nuanced exploration of the textual content. After documents have been sourced, be it from books, articles, social networks, or any other medium, they undergo a preprocessing phase. Here, irrelevant information is eliminated, errors are rectified, and the text may be translated or converted to ensure uniformity.

This cleaned text is then tokenized into smaller units like words or phrases, facilitating a granular review. Techniques specific to text analysis, such as topic modeling to determine discussed subjects or pattern recognition to identify trends, are applied.

The derived insights can be visualized using tools like graphs or charts, offering a clearer understanding of the content's depth. Interpretation follows, allowing researchers to draw actionable insights or theoretical conclusions based on both the broader document context and the specific text analysis.

Merging text analysis with document analysis presents unique challenges. With the proliferation of digital content, managing vast data sets becomes a significant hurdle. The inherent variability of language, laden with cultural nuances, idioms, and sometimes sarcasm, can make precise interpretation elusive.

Many text analysis tools exist that can facilitate the analytical process. ATLAS.ti offers a well-rounded, useful solution as a text analytics software . In this section, we'll highlight some of the tools that can help you conduct document analysis.

Word Frequencies

A word cloud can be a powerful text analytics tool to understand the nature of human language as it pertains to a particular context. Researchers can perform text mining on their unstructured text data to get a sense of what is being discussed. The Word Frequencies tool can also parse out specific parts of speech, facilitating more granular text extraction.

what is document analysis in qualitative research

Sentiment Analysis

The Sentiment Analysis tool employs natural language processing (NLP) and machine learning to analyze text based on sentiment and facilitate natural language understanding. This is important for tasks such as, for example, analyzing customer reviews and assessing customer satisfaction, because you can quickly categorize large numbers of customer data records by their positive or negative sentiment.

AI Coding relies on massive amounts of training data to interpret text and automatically code large amounts of qualitative data. Rather than read each and every document line by line, you can turn to AI Coding to process your data and devote time to the more essential tasks of analysis such as critical reflection and interpretation.

These text analytics tools can be a powerful complement to research. When you're conducting document analysis to understand the meaning of text, AI Coding can help with providing a code structure or organization of data that helps to identify deeper insights.

what is document analysis in qualitative research

AI Summaries

Dealing with large numbers of discrete documents can be a daunting task if done manually, especially if each document in your data set is lengthy and complicated. Simplifying the meaning of documents down to their essential insights can help researchers identify patterns in the data.

AI Summaries fills this role by using natural language processing algorithms to simplify data to its salient points. Text generated by AI Summaries are stored in memos attached to documents to illustrate pathways to coding and analysis or to highlight how the data conveys meaning.

Take advantage of ATLAS.ti's analysis tools with a free trial

Let our powerful data analysis interface make the most out of your data. Download a free trial today.

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Qualitative Research Journal

ISSN : 1443-9883

Article publication date: 3 August 2009

This article examines the function of documents as a data source in qualitative research and discusses document analysis procedure in the context of actual research experiences. Targeted to research novices, the article takes a nuts‐and‐bolts approach to document analysis. It describes the nature and forms of documents, outlines the advantages and limitations of document analysis, and offers specific examples of the use of documents in the research process. The application of document analysis to a grounded theory study is illustrated.

  • Content analysis
  • Grounded theory
  • Thematic analysis
  • Triangulation

Bowen, G.A. (2009), "Document Analysis as a Qualitative Research Method", Qualitative Research Journal , Vol. 9 No. 2, pp. 27-40. https://doi.org/10.3316/QRJ0902027

Emerald Group Publishing Limited

Copyright © 2009, Emerald Group Publishing Limited

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Document Analysis as a Qualitative Research Method

Profile image of Glenn A Bowen

2009, Qualitative Research Journal

This article examines the function of documents as a data source in qualitative research and discusses document analysis procedure in the context of actual research experiences. Targeted to research novices, the article takes a nuts-and-bolts approach to document analysis. It describes the nature and forms of documents, outlines the advantages and limitations of document analysis, and offers specific examples of the use of documents in the research process. The application of document analysis to a grounded theory study is illustrated.

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what is document analysis in qualitative research

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  • What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

  • How does social media shape body image in teenagers?
  • How do children and adults interpret healthy eating in the UK?
  • What factors influence employee retention in a large organization?
  • How is anxiety experienced around the world?
  • How can teachers integrate social issues into science curriculums?

Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

  • Observations: recording what you have seen, heard, or encountered in detailed field notes.
  • Interviews:  personally asking people questions in one-on-one conversations.
  • Focus groups: asking questions and generating discussion among a group of people.
  • Surveys : distributing questionnaires with open-ended questions.
  • Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
  • You take field notes with observations and reflect on your own experiences of the company culture.
  • You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
  • You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

  • Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
  • Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
  • Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
  • Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
  • Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

  • Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

  • Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

  • Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

  • Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

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Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

  • Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

  • Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

  • Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

  • Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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.

  • Chi square goodness of fit test
  • Degrees of freedom
  • Null hypothesis
  • Discourse analysis
  • Control groups
  • Mixed methods research
  • Non-probability sampling
  • Quantitative research
  • Inclusion and exclusion criteria

Research bias

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

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

  • Grounded theory involves collecting data in order to develop new theories.
  • Ethnography involves immersing yourself in a group or organization to understand its culture.
  • Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
  • Phenomenological research involves investigating phenomena through people’s lived experiences.
  • Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

  • Prepare and organize your data.
  • Review and explore your data.
  • Develop a data coding system.
  • Assign codes to the data.
  • Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Research Method

Home » Qualitative Research – Methods, Analysis Types and Guide

Qualitative Research – Methods, Analysis Types and Guide

Table of Contents

Qualitative Research

Qualitative Research

Qualitative research is a type of research methodology that focuses on exploring and understanding people’s beliefs, attitudes, behaviors, and experiences through the collection and analysis of non-numerical data. It seeks to answer research questions through the examination of subjective data, such as interviews, focus groups, observations, and textual analysis.

Qualitative research aims to uncover the meaning and significance of social phenomena, and it typically involves a more flexible and iterative approach to data collection and analysis compared to quantitative research. Qualitative research is often used in fields such as sociology, anthropology, psychology, and education.

Qualitative Research Methods

Types of Qualitative Research

Qualitative Research Methods are as follows:

One-to-One Interview

This method involves conducting an interview with a single participant to gain a detailed understanding of their experiences, attitudes, and beliefs. One-to-one interviews can be conducted in-person, over the phone, or through video conferencing. The interviewer typically uses open-ended questions to encourage the participant to share their thoughts and feelings. One-to-one interviews are useful for gaining detailed insights into individual experiences.

Focus Groups

This method involves bringing together a group of people to discuss a specific topic in a structured setting. The focus group is led by a moderator who guides the discussion and encourages participants to share their thoughts and opinions. Focus groups are useful for generating ideas and insights, exploring social norms and attitudes, and understanding group dynamics.

Ethnographic Studies

This method involves immersing oneself in a culture or community to gain a deep understanding of its norms, beliefs, and practices. Ethnographic studies typically involve long-term fieldwork and observation, as well as interviews and document analysis. Ethnographic studies are useful for understanding the cultural context of social phenomena and for gaining a holistic understanding of complex social processes.

Text Analysis

This method involves analyzing written or spoken language to identify patterns and themes. Text analysis can be quantitative or qualitative. Qualitative text analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Text analysis is useful for understanding media messages, public discourse, and cultural trends.

This method involves an in-depth examination of a single person, group, or event to gain an understanding of complex phenomena. Case studies typically involve a combination of data collection methods, such as interviews, observations, and document analysis, to provide a comprehensive understanding of the case. Case studies are useful for exploring unique or rare cases, and for generating hypotheses for further research.

Process of Observation

This method involves systematically observing and recording behaviors and interactions in natural settings. The observer may take notes, use audio or video recordings, or use other methods to document what they see. Process of observation is useful for understanding social interactions, cultural practices, and the context in which behaviors occur.

Record Keeping

This method involves keeping detailed records of observations, interviews, and other data collected during the research process. Record keeping is essential for ensuring the accuracy and reliability of the data, and for providing a basis for analysis and interpretation.

This method involves collecting data from a large sample of participants through a structured questionnaire. Surveys can be conducted in person, over the phone, through mail, or online. Surveys are useful for collecting data on attitudes, beliefs, and behaviors, and for identifying patterns and trends in a population.

Qualitative data analysis is a process of turning unstructured data into meaningful insights. It involves extracting and organizing information from sources like interviews, focus groups, and surveys. The goal is to understand people’s attitudes, behaviors, and motivations

Qualitative Research Analysis Methods

Qualitative Research analysis methods involve a systematic approach to interpreting and making sense of the data collected in qualitative research. Here are some common qualitative data analysis methods:

Thematic Analysis

This method involves identifying patterns or themes in the data that are relevant to the research question. The researcher reviews the data, identifies keywords or phrases, and groups them into categories or themes. Thematic analysis is useful for identifying patterns across multiple data sources and for generating new insights into the research topic.

Content Analysis

This method involves analyzing the content of written or spoken language to identify key themes or concepts. Content analysis can be quantitative or qualitative. Qualitative content analysis involves close reading and interpretation of texts to identify recurring themes, concepts, and patterns. Content analysis is useful for identifying patterns in media messages, public discourse, and cultural trends.

Discourse Analysis

This method involves analyzing language to understand how it constructs meaning and shapes social interactions. Discourse analysis can involve a variety of methods, such as conversation analysis, critical discourse analysis, and narrative analysis. Discourse analysis is useful for understanding how language shapes social interactions, cultural norms, and power relationships.

Grounded Theory Analysis

This method involves developing a theory or explanation based on the data collected. Grounded theory analysis starts with the data and uses an iterative process of coding and analysis to identify patterns and themes in the data. The theory or explanation that emerges is grounded in the data, rather than preconceived hypotheses. Grounded theory analysis is useful for understanding complex social phenomena and for generating new theoretical insights.

Narrative Analysis

This method involves analyzing the stories or narratives that participants share to gain insights into their experiences, attitudes, and beliefs. Narrative analysis can involve a variety of methods, such as structural analysis, thematic analysis, and discourse analysis. Narrative analysis is useful for understanding how individuals construct their identities, make sense of their experiences, and communicate their values and beliefs.

Phenomenological Analysis

This method involves analyzing how individuals make sense of their experiences and the meanings they attach to them. Phenomenological analysis typically involves in-depth interviews with participants to explore their experiences in detail. Phenomenological analysis is useful for understanding subjective experiences and for developing a rich understanding of human consciousness.

Comparative Analysis

This method involves comparing and contrasting data across different cases or groups to identify similarities and differences. Comparative analysis can be used to identify patterns or themes that are common across multiple cases, as well as to identify unique or distinctive features of individual cases. Comparative analysis is useful for understanding how social phenomena vary across different contexts and groups.

Applications of Qualitative Research

Qualitative research has many applications across different fields and industries. Here are some examples of how qualitative research is used:

  • Market Research: Qualitative research is often used in market research to understand consumer attitudes, behaviors, and preferences. Researchers conduct focus groups and one-on-one interviews with consumers to gather insights into their experiences and perceptions of products and services.
  • Health Care: Qualitative research is used in health care to explore patient experiences and perspectives on health and illness. Researchers conduct in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
  • Education: Qualitative research is used in education to understand student experiences and to develop effective teaching strategies. Researchers conduct classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
  • Social Work : Qualitative research is used in social work to explore social problems and to develop interventions to address them. Researchers conduct in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
  • Anthropology : Qualitative research is used in anthropology to understand different cultures and societies. Researchers conduct ethnographic studies and observe and interview members of different cultural groups to gain insights into their beliefs, practices, and social structures.
  • Psychology : Qualitative research is used in psychology to understand human behavior and mental processes. Researchers conduct in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
  • Public Policy : Qualitative research is used in public policy to explore public attitudes and to inform policy decisions. Researchers conduct focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

How to Conduct Qualitative Research

Here are some general steps for conducting qualitative research:

  • Identify your research question: Qualitative research starts with a research question or set of questions that you want to explore. This question should be focused and specific, but also broad enough to allow for exploration and discovery.
  • Select your research design: There are different types of qualitative research designs, including ethnography, case study, grounded theory, and phenomenology. You should select a design that aligns with your research question and that will allow you to gather the data you need to answer your research question.
  • Recruit participants: Once you have your research question and design, you need to recruit participants. The number of participants you need will depend on your research design and the scope of your research. You can recruit participants through advertisements, social media, or through personal networks.
  • Collect data: There are different methods for collecting qualitative data, including interviews, focus groups, observation, and document analysis. You should select the method or methods that align with your research design and that will allow you to gather the data you need to answer your research question.
  • Analyze data: Once you have collected your data, you need to analyze it. This involves reviewing your data, identifying patterns and themes, and developing codes to organize your data. You can use different software programs to help you analyze your data, or you can do it manually.
  • Interpret data: Once you have analyzed your data, you need to interpret it. This involves making sense of the patterns and themes you have identified, and developing insights and conclusions that answer your research question. You should be guided by your research question and use your data to support your conclusions.
  • Communicate results: Once you have interpreted your data, you need to communicate your results. This can be done through academic papers, presentations, or reports. You should be clear and concise in your communication, and use examples and quotes from your data to support your findings.

Examples of Qualitative Research

Here are some real-time examples of qualitative research:

  • Customer Feedback: A company may conduct qualitative research to understand the feedback and experiences of its customers. This may involve conducting focus groups or one-on-one interviews with customers to gather insights into their attitudes, behaviors, and preferences.
  • Healthcare : A healthcare provider may conduct qualitative research to explore patient experiences and perspectives on health and illness. This may involve conducting in-depth interviews with patients and their families to gather information on their experiences with different health care providers and treatments.
  • Education : An educational institution may conduct qualitative research to understand student experiences and to develop effective teaching strategies. This may involve conducting classroom observations and interviews with students and teachers to gather insights into classroom dynamics and instructional practices.
  • Social Work: A social worker may conduct qualitative research to explore social problems and to develop interventions to address them. This may involve conducting in-depth interviews with individuals and families to understand their experiences with poverty, discrimination, and other social problems.
  • Anthropology : An anthropologist may conduct qualitative research to understand different cultures and societies. This may involve conducting ethnographic studies and observing and interviewing members of different cultural groups to gain insights into their beliefs, practices, and social structures.
  • Psychology : A psychologist may conduct qualitative research to understand human behavior and mental processes. This may involve conducting in-depth interviews with individuals to explore their thoughts, feelings, and experiences.
  • Public Policy: A government agency or non-profit organization may conduct qualitative research to explore public attitudes and to inform policy decisions. This may involve conducting focus groups and one-on-one interviews with members of the public to gather insights into their perspectives on different policy issues.

Purpose of Qualitative Research

The purpose of qualitative research is to explore and understand the subjective experiences, behaviors, and perspectives of individuals or groups in a particular context. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research aims to provide in-depth, descriptive information that can help researchers develop insights and theories about complex social phenomena.

Qualitative research can serve multiple purposes, including:

  • Exploring new or emerging phenomena : Qualitative research can be useful for exploring new or emerging phenomena, such as new technologies or social trends. This type of research can help researchers develop a deeper understanding of these phenomena and identify potential areas for further study.
  • Understanding complex social phenomena : Qualitative research can be useful for exploring complex social phenomena, such as cultural beliefs, social norms, or political processes. This type of research can help researchers develop a more nuanced understanding of these phenomena and identify factors that may influence them.
  • Generating new theories or hypotheses: Qualitative research can be useful for generating new theories or hypotheses about social phenomena. By gathering rich, detailed data about individuals’ experiences and perspectives, researchers can develop insights that may challenge existing theories or lead to new lines of inquiry.
  • Providing context for quantitative data: Qualitative research can be useful for providing context for quantitative data. By gathering qualitative data alongside quantitative data, researchers can develop a more complete understanding of complex social phenomena and identify potential explanations for quantitative findings.

When to use Qualitative Research

Here are some situations where qualitative research may be appropriate:

  • Exploring a new area: If little is known about a particular topic, qualitative research can help to identify key issues, generate hypotheses, and develop new theories.
  • Understanding complex phenomena: Qualitative research can be used to investigate complex social, cultural, or organizational phenomena that are difficult to measure quantitatively.
  • Investigating subjective experiences: Qualitative research is particularly useful for investigating the subjective experiences of individuals or groups, such as their attitudes, beliefs, values, or emotions.
  • Conducting formative research: Qualitative research can be used in the early stages of a research project to develop research questions, identify potential research participants, and refine research methods.
  • Evaluating interventions or programs: Qualitative research can be used to evaluate the effectiveness of interventions or programs by collecting data on participants’ experiences, attitudes, and behaviors.

Characteristics of Qualitative Research

Qualitative research is characterized by several key features, including:

  • Focus on subjective experience: Qualitative research is concerned with understanding the subjective experiences, beliefs, and perspectives of individuals or groups in a particular context. Researchers aim to explore the meanings that people attach to their experiences and to understand the social and cultural factors that shape these meanings.
  • Use of open-ended questions: Qualitative research relies on open-ended questions that allow participants to provide detailed, in-depth responses. Researchers seek to elicit rich, descriptive data that can provide insights into participants’ experiences and perspectives.
  • Sampling-based on purpose and diversity: Qualitative research often involves purposive sampling, in which participants are selected based on specific criteria related to the research question. Researchers may also seek to include participants with diverse experiences and perspectives to capture a range of viewpoints.
  • Data collection through multiple methods: Qualitative research typically involves the use of multiple data collection methods, such as in-depth interviews, focus groups, and observation. This allows researchers to gather rich, detailed data from multiple sources, which can provide a more complete picture of participants’ experiences and perspectives.
  • Inductive data analysis: Qualitative research relies on inductive data analysis, in which researchers develop theories and insights based on the data rather than testing pre-existing hypotheses. Researchers use coding and thematic analysis to identify patterns and themes in the data and to develop theories and explanations based on these patterns.
  • Emphasis on researcher reflexivity: Qualitative research recognizes the importance of the researcher’s role in shaping the research process and outcomes. Researchers are encouraged to reflect on their own biases and assumptions and to be transparent about their role in the research process.

Advantages of Qualitative Research

Qualitative research offers several advantages over other research methods, including:

  • Depth and detail: Qualitative research allows researchers to gather rich, detailed data that provides a deeper understanding of complex social phenomena. Through in-depth interviews, focus groups, and observation, researchers can gather detailed information about participants’ experiences and perspectives that may be missed by other research methods.
  • Flexibility : Qualitative research is a flexible approach that allows researchers to adapt their methods to the research question and context. Researchers can adjust their research methods in real-time to gather more information or explore unexpected findings.
  • Contextual understanding: Qualitative research is well-suited to exploring the social and cultural context in which individuals or groups are situated. Researchers can gather information about cultural norms, social structures, and historical events that may influence participants’ experiences and perspectives.
  • Participant perspective : Qualitative research prioritizes the perspective of participants, allowing researchers to explore subjective experiences and understand the meanings that participants attach to their experiences.
  • Theory development: Qualitative research can contribute to the development of new theories and insights about complex social phenomena. By gathering rich, detailed data and using inductive data analysis, researchers can develop new theories and explanations that may challenge existing understandings.
  • Validity : Qualitative research can offer high validity by using multiple data collection methods, purposive and diverse sampling, and researcher reflexivity. This can help ensure that findings are credible and trustworthy.

Limitations of Qualitative Research

Qualitative research also has some limitations, including:

  • Subjectivity : Qualitative research relies on the subjective interpretation of researchers, which can introduce bias into the research process. The researcher’s perspective, beliefs, and experiences can influence the way data is collected, analyzed, and interpreted.
  • Limited generalizability: Qualitative research typically involves small, purposive samples that may not be representative of larger populations. This limits the generalizability of findings to other contexts or populations.
  • Time-consuming: Qualitative research can be a time-consuming process, requiring significant resources for data collection, analysis, and interpretation.
  • Resource-intensive: Qualitative research may require more resources than other research methods, including specialized training for researchers, specialized software for data analysis, and transcription services.
  • Limited reliability: Qualitative research may be less reliable than quantitative research, as it relies on the subjective interpretation of researchers. This can make it difficult to replicate findings or compare results across different studies.
  • Ethics and confidentiality: Qualitative research involves collecting sensitive information from participants, which raises ethical concerns about confidentiality and informed consent. Researchers must take care to protect the privacy and confidentiality of participants and obtain informed consent.

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  • Afr J Emerg Med
  • v.7(3); 2017 Sep

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 ).

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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 ).

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Object name is gr2.jpg

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

Enago Academy

Choosing the Right Analytical Approach: Thematic analysis vs. content analysis for data interpretation

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In research, choosing the right approach to understand data is crucial for deriving meaningful insights. Researchers often struggle to pick the best method of data analysis because not all analytical approaches are equal when interpreting data. This is where content analysis and thematic analysis prove invaluable. These methodologies help systematically organize and interpret data to uncover subtle themes and patterns, transforming volumes of texts into impactful findings when applied skillfully.

Table of Contents

What Is Content Analysis?

Content analysis is defined as a data analysis method that adopts a systematic approach to examine textual, visual, or verbal communication data. Quantifying words, concepts, themes, phrases, characters, or sentences within a dataset helps draw meaningful conclusions from the information. This approach uncovers how often certain information appears, giving insights into the context and content of communication.

Example of content analysis: Analyze terms like “sustainability” and associated words (e.g., “quality”) in online reviews to understand how consumers weigh environmental concerns against product attributes.

What Is Thematic Analysis?

Thematic analysis involves identifying, analyzing, and reporting themes or patterns within qualitative data. It emphasizes broad themes from different sources that represent fundamental aspects of a phenomenon.

Example of thematic analysis: In a study on student feedback, codes such as satisfaction and frustration were grouped into themes of positive and negative experiences, offering insights into overall perceptions of educational quality.

Similarities and Differences Between Content Analysis and Thematic Analysis

Content analysis and thematic analysis are two qualitative data analysis approaches that share some key similarities. They both involve examining written information by breaking down the text into smaller content units. Despite employing distinct approaches, their shared objective is to generate fresh insights and knowledge from qualitative data sources. Both methods require a comprehensive understanding of the data through an iterative process. Using these methods, theoretical claims in the research can be made.

To gain a comprehensive understanding of the applications of content analysis and thematic analysis, it’s beneficial to explore their differences.

Given these key differences and similarities, it is crucial for researchers to understand both these aspects to select the most appropriate approach based on their research questions, data characteristics, and goals. Making an informed choice allows one to employ the technique that will best elucidate insights, generate new knowledge, and align with the overall research aims . Recognizing their nuances is key to unlocking their full potential for precise data interpretation.

Welcome to your Quiz on Choosing the Right Analytical Approach: Content Analysis vs. Thematic Analysis

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Advantages and Disadvantages of Content Analysis and Thematic Analysis

By conducting a thoughtful evaluation of the pros and cons of both the methods, we can decide on which method to opt for. Here are the advantages and disadvantages of content analysis and thematic analysis:

Choosing the Right Method for Data Analysis: Content analysis or thematic analysis

Qualitative research relies on extracting meaning from complex data. However, not all analytical approaches are created equal. Researchers must select methods strategically based on the requirement of their research study. They need to understand the key differences between both the approached right from the research design stage. This knowledge guides the choice of optimal data analysis method aligned to the study goals. It helps researchers gain clarity regarding the technique to employ.

An insightful interpretation begins with the right analytical lens. By distinguishing approaches early on, researchers give themselves the best chance to unveil the crux of the data – paving the way for impactful qualitative discoveries.

How to Conduct Content Analysis

To fully unlock the potential of content analysis in understanding and comprehending data, researchers must follow systematic steps. Here are the steps for conducting content analysis:

How to Conduct Content Analysis

How to Conduct Thematic Analysis

After deciding to employ thematic analysis, you can follow the six-step process for conducting thematic analysis outlined by Braun and Clarke.

How To Conduct Thematic Analysis

Regardless of the data analysis method, it is crucial to grasp the functioning of both these approaches. This understanding enables you to make an informed decision about the most suitable method and accelerates your research process. Are you struggling to decide which is the best data analysis method for your research? Seek professional assistance for informed decisions and a smoother research journey.

Frequently Asked Questions

Content analysis quantifies and analyzes specific words, themes, or concepts within qualitative data, facilitating meaningful insights.

Thematic analysis serves the purpose of identifying significant patterns, or themes, within data, with the aim of addressing research objectives.

The main difference between content analysis and thematic analysis lies in their approach towards a dataset. Content analysis focuses on determining the frequency of categories, while thematic analysis the aim is to identify themes and organize the analysis cohesively.

The main stages of thematic analysis include: familiarization, initial coding, search for themes, refining themes, defining themes, and reporting.

The main stages of content analysis include: preparation, organization, analysis and interpretation.

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Analysis and coding example: qualitative data.

The following is an example of how to engage in a three step analytic process of coding, categorizing, and identifying themes within the data presented. Note that different researchers would come up with different results based on their specific research questions, literature review findings, and theoretical perspective.

There are many ways cited in the literature to analyze qualitative data. The specific analytic plan in this exercise involved a constant comparative (Glaser & Strauss, 1967) approach that included a three-step process of open coding, categorizing, and synthesizing themes. The constant comparative process involved thinking about how these comments were interrelated. Intertwined within this three step process, this example engages in content analysis techniques as described by Patton (1987) through which coherent and salient themes and patterns are identified throughout the data. This is reflected in the congruencies and incongruencies reflected in the memos and relational matrix.

Step 1: Open Coding

Codes for the qualitative data are created through a line by line analysis of the comments. Codes would be based on the research questions, literature review, and theoretical perspective articulated. Numbering the lines is helpful so that the researcher can make notes regarding which comments they might like to quote in their report.

It is also useful to include memos to remind yourself of what you were thinking and allow you to reflect on the initial interpretations as you engage in the next two analytic steps. In addition, memos will be a reminder of issues that need to be addressed if there is an opportunity for follow up data collection. This technique allows the researcher time to reflect on how his/her biases might affect the analysis. Using different colored text for memos makes it easy to differentiate thoughts from the data.

Many novice researchers forgo this step.  Rather, they move right into arranging the entire statements into the various categories that have been pre-identified. There are two problems with the process. First, since the categories have been listed open coding, it is unclear from where the categories have been derived. Rather, when a researcher uses the open coding process, he/she look at each line of text individually and without consideration for the others. This process of breaking the pieces down and then putting them back together through analysis ensures that the researcher consider all for the data equally and limits the bias that might introduced. In addition, if a researcher is coding interviews or other significant amounts of qualitative data it will likely become overwhelming as the researcher tries to organize and remember from which context each piece of data came.

Step 2: Categorizing

To categorize the codes developed in Step 1 , list the codes and group them by similarity.  Then, identify an appropriate label for each group. The following table reflects the result of this activity.

Step 3: Identification of Themes

In this step, review the categories as well as the memos to determine the themes that emerge.   In the discussion below, three themes emerged from the synthesis of the categories. Relevant quotes from the data are included that exemplify the essence of the themes.These can be used in the discussion of findings. The relational matrix demonstrates the pattern of thinking of the researcher as they engaged in this step in the analysis. This is similar to an axial coding strategy.

Note that this set of data is limited and leaves some questions in mind. In a well-developed study, this would just be a part of the data collected and there would be other data sets and/or opportunities to clarify/verify some of the interpretations made below.  In addition, since there is no literature review or theoretical statement, there are no reference points from which to draw interferences in the data. Some assumptions were made for the purposes of this demonstration in these areas.

T h eme 1:  Professional Standing

Individual participants have articulated issues related to their own professional position. They are concerned about what and when they will teach, their performance, and the respect/prestige that they have within the school. For example, they are concerned about both their physical environment and the steps that they have to take to ensure that they have the up to date tools that they need. They are also concerned that their efforts are being acknowledged, sometimes in relation to their peers and their beliefs that they are more effective.

Selected quotes:

  • Some teachers are carrying the weight for other teachers. (demonstrates that they think that some of their peers are not qualified.)
  • We need objective observations and feedback from the principal (demonstrates that they are looking for acknowledgement for their efforts.  Or this could be interpreted as a belief that their peers who are less qualified should be acknowledged).
  • There is a lack of support for individual teachers

Theme 2:  Group Dynamics and Collegiality

Rationale: There are groups or clicks that have formed. This seems to be the basis for some of the conflict.  This conflict is closely related to the status and professional standing themes. This theme however, has more to do with the group issues while the first theme is an individual perspective. Some teachers and/or subjects are seen as more prestigious than others.  Some of this is related to longevity. This creates jealously and inhibits collegiality. This affects peer-interaction, instruction, and communication.

  • Grade level teams work against each other rather than together.
  • Each team of teachers has stereotypes about the other teams.
  • There is a division between the old and new teachers

Theme 3:  Leadership Issues

Rationale: There seems to be a lack of leadership and shared understanding of the general direction in which the school will go. This is also reflected in a lack of two way communications.  There doesn’t seem to be information being offered by the leadership of the school, nor does there seem to be an opportunity for individuals to share their thoughts, let alone decision making. There seems to be a lack of intervention in the conflict from leadership.

  • Decisions are made on inaccurate information.
  • We need consistent decisions about school rules

Coding Example - Category - Relationships - Themes

Glaser, B.G., & Strauss, A.  (1967).   The discovery of grounded theory:  Strategies for qualitative research . Chicago, IL: Aldine.

Patton, M. Q.  (1987).   How to use qualitative methods in evaluation .  Newbury Park, CA:  Sage Publications.

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The registered nurse experience delivering care to a difficult patient: a qualitative study.

Stacy L. Nilsen , University of San Diego Follow

Date of Award

Degree name.

PhD Nursing

Dissertation Committee

Cynthia D. Connelly, PhD, RN, FAAN, Chairperson; Jane M. Georges, PhD, RN, Committee Member; Christine Nibbelink, PhD, RN, Committee Member; Julie Graham, PhD, APRN, ACCNS-AG, Committee Member

difficult patient, qualitative, therapeutic engagement, altruism

Background : Nurses provide care for patients they classify as “difficult.” The phenomenon of the difficult patient is described in nursing research and is associated with a broad spectrum of behaviors ranging from non-compliant to physical violence. Despite the recommendation from bioethicists that the etiology is multifactorial, nursing research focuses on interventions to manage the patient’s behaviors. Locating the source of the difficult patient only within the patient control may create an environment where the nurse is socialized to take an authoritarian approach to care without reflection on the nurse/patient relationship dynamics. An extended exploration was needed to understand the influence of the difficult patient within the context of the nurse/patient relationship and the impact on care delivery.

Purpose/Aims : The purpose of this study was to describe how acute care hospital nurses made decisions and delivered nursing care in the context of the difficult patient.

Methods : A qualitative descriptive study using a constructivist approach was used. Informants were acute care hospital nurses with experience providing nursing care to difficult patients. Purposeful sampling with maximum variation sampling was used to select informants with diverse characteristics. Data collection included individual, semi-structured interviews, and document review of nursing notes of patients described as difficult. The sample size was seven registered nurses and thirty-seven documents. Data analysis was completed using inductive thematic analysis.

Findings : The difficult patient was described with the theme the Spectrum of the Difficult Patient. Nurse decision making in the context of the difficult patient was expressed with the themes Navigating Control and Limit Setting, Effects of Violence, Understanding and Altruistic Care, Respecting Autonomy and Moral Distress, and Role Support.

Potential Implications : Repeated exposure to difficult behaviors remains a role dissatisfier. This study presented an evolving nursing understanding of the potential for biased provision of care with the use of stigmatizing label. The social context of providing nursing care to a difficult patient is changing from an authoritarian approach to one of altruistic collaboration. Further professional development in therapeutic engagement was identified as a need. Organizations can decrease the burden of care to difficult patients through resource allocation and structural support with embedded clinical role models.

Document Type

Dissertation: USD Users Only

Digital USD Citation

Nilsen, Stacy L., "The Registered Nurse Experience Delivering Care to a Difficult Patient: A Qualitative Study" (2023). Dissertations . 966. https://digital.sandiego.edu/dissertations/966

Copyright held by the author

Available for download on Thursday, April 24, 2025

Since April 26, 2023

https://doi.org/10.22371/07.2023.007

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Behavioral Interventions to Improve Work Search Among UI Claimants: Results from North Carolina and Washington

Publication info, research methodology, country, state or territory, description.

This report describes studies in two states – North Carolina and Washington – to test new communication methods to increase Unemployment Insurance (UI) claimants' compliance with various "work search" requirements. The requirements include contacting a certain number of employers per week, recording employer contact information, position sought, and results of the contact, and maintaining records on the work search activities. The communication methods tested included, among others, pop-up alerts and emails, as well as clearly stated information in "tip sheets" about the types of employer contacts that counted and how to document work search activities.

The new approaches were devised after an analysis of the barriers to compliance and identification of possible solutions using behavioral principles, as part of a broader initiative, the Behavioral Interventions in Labor Programs Evaluation project, under the Department of Labor's Chief Evaluation Office. This project also involved collaboration with the Employment and Training Administration, as well as the two states' UI agencies.

Two separate studies, using multi-method, multi-phased approaches, were conducted. In Washington, a randomized control trial (RCT) involving 26,967 claimants compared knowledge and work search behaviors between a treatment group who received the intervention and a control group who did not. Outcomes were assessed using administrative data as well as information from phone interviews with staff. In North Carolina, an RCT involving 24,416 claimants explored the communications’ influence on self-attested work search compliance, while a series of quasi-experimental design (QED) studies explored other outcomes. Outcomes were assessed using administrative data as well as information from phone interviews with staff and in-person focus groups with staff and claimants. However, the COVID-19 pandemic limited some aspects of data collection and prevented the possibility of improving features of the interventions and further testing, as is typical in behavioral trials.

The quantitative data in both states showed no statistically significant impact on claimants’ knowledge of work search requirements, as measured through self-attestation of work search or work search behavior. However, qualitative findings suggested areas for improving the interventions to increase claimants' understanding of certain aspects of the requirements, such as how to record and provide documentation of work search activities – which could inform future research.

The trials demonstrated the feasibility of quickly deploying behaviorally-informed communications and measuring their effectiveness with administrative data. State partners reported the collaborative design process to apply behavioral principles was useful. In North Carolina, state partners have scaled up the use of the behavioral intervention materials from the trial with new claimants and have demonstrated interest in applying behavioral insights to improve operations. In Washington state, staff reported that learning to apply behavioral insights has increased their ability to quickly revise communications going forward.

ORIGINAL RESEARCH article

This article is part of the research topic.

The Politics of the Commercial Determinants of Health

Capture and corporate cooptation: The role of the Mexican Foundation for Health in public health policy

  • 1 EL PODER DEL CONSUMIDOR, A.C., Mexico

The final, formatted version of the article will be published soon.

Introduction: Corporate capture responds to efforts to strengthen regulation or prohibition of commercial determinants of health (tobacco, alcohol, sugar-sweetened beverages, ultraprocessed products (UPFs), commercial milk formula, and pharmaceuticals), in an attempt to interfere with public health policies that threaten the commercial, economic, and political interests of major industries. This manuscript proposes the characterization of the corporate capture of public health in Mexico, exercised through the Mexican Foundation for Health (FUNSALUD).Methodology: An analysis of FUNSALUD and its stakeholders was carried out under the framework of commercial determinants of health, using a qualitative methodology, and executed in five stages: document analysis from 1985 to 2021; identification and characterization of stakeholders; semi-structured interviews; classification of corporate strategies; and relationship mapping.Results: Actors in the pharmaceutical, food, tobacco, alcohol, commercial milk formula, and sugar-sweetened beverage industries were identified as corporate members of FUNSALUD.We identify six corporate strategies used to interfere in public health and food policies, highlighting the role of a revolving door bureaucracy in the case of the Ministry of Health.The Mexican Foundation for Health has functioned as a front organization created by the private sector to influence public policy decision-making, protect corporate interests, and oppose international recommendations to combat non-communicable diseases.

Keywords: commercial determinants of health, proprietary health facilities, Conflict of Interest, Health Policy, Nutrition Policy

Received: 01 Jun 2022; Accepted: 15 Feb 2024.

Copyright: © 2024 Torres, Calvillo and Zúñiga. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mr. Christian Torres, EL PODER DEL CONSUMIDOR, A.C., Coyoacán, Mexico

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