Qualitative Content Analysis: Methods and Applications

Qualitative content analysis is a systematic method for interpreting textual, visual, or audio data by identifying patterns, categories, and themes through a structured coding process. For students, researchers, and life-science professionals, this method offers a transparent way to analyze interview transcripts, open-ended survey responses, clinical notes, policy documents, and media content. Unlike descriptive analysis, which summarizes basic features of data, content analysis examines texts in depth to reveal meaning, themes, and patterns. This article provides a practical protocol for conducting qualitative content analysis, including coding procedures, interpretation strategies, and a coding sheet template, with examples drawn from life science research.

At a Glance

Qualitative content analysis is one of several approaches to qualitative data analysis. The table below compares the main approaches to help you select the appropriate method for your research question.

The choice among these approaches depends on your research purpose. Conventional content analysis suits exploratory studies where existing theory is limited. Directed content analysis fits studies that build on prior theoretical work. Summative analysis works when you need to quantify specific terms before interpreting their meaning in context.

Understanding Qualitative Content Analysis

Qualitative content analysis is a research method for subjective interpretation of text data through systematic classification, coding, and theme identification. The method has roots in communication research but has been widely adopted across health sciences, education, and social sciences. Published literature shows conflicting opinions and unresolved issues regarding the meaning and use of concepts, procedures, and interpretation in qualitative content analysis. This ongoing debate means researchers must make explicit decisions about their analytical approach and document those decisions clearly.

The method differs from thematic analysis in important ways. Inductive content analysis, also called qualitative content analysis, is well suited to health-related research, particularly relatively small-scale studies conducted by health professionals undertaking research-focused degree courses. For those new to qualitative research, the methodological literature can be difficult to navigate because it employs a wide variety of terminology and gives different descriptions of when and how to carry it out. This article clarifies those distinctions and provides a step-by-step account of analyzing text.

Core Concepts and Terminology

Qualitative content analysis relies on several foundational concepts that researchers must understand before beginning analysis. These concepts include manifest and latent content, unit of analysis, meaning unit, condensation, abstraction, content area, code, category, and theme. Each concept serves a specific function in the analytical process.

Manifest content refers to the visible, surface-level components of the text. Latent content involves the underlying meaning that requires interpretation. The unit of analysis is the overall entity being studied, such as an interview transcript or a complete document. A meaning unit consists of words, sentences, or paragraphs that relate to the same central meaning. Condensation shortens the text while preserving the core meaning. Abstraction involves describing the condensed text at a higher logical level. A content area represents an explicit area of the text that corresponds to specific research questions. Codes are labels assigned to meaning units. Categories group codes that share common features. Themes express the underlying meaning across categories.

Trustworthiness in Qualitative Content Analysis

Trustworthiness in qualitative content analysis is established through credibility, dependability, and transferability. Credibility refers to confidence in the truth of the findings. Dependability concerns the stability of data over time and conditions. Transferability indicates the extent to which findings can be applied to other contexts.

Measures to achieve trustworthiness must be applied throughout the steps of the research procedure. Credibility can be strengthened through member checking, peer debriefing, and prolonged engagement with the data. Dependability benefits from maintaining an audit trail that documents analytical decisions. Transferability requires thick description of the research context and participants so readers can assess applicability to their own settings.

Planning Your Qualitative Content Analysis Study

Careful planning before data collection improves the quality of your analysis. The processes before and after collecting data, and finally reporting the data, should be planned in line with the purpose of the research. Before the content analysis process begins, you will need to use many data collection tools to gather data according to the research subject.

Defining Research Questions and Purpose

The first step in any qualitative content analysis is deciding your research questions and purposes. Clear research questions guide every subsequent decision, including sampling, data collection methods, and analytical approach. For example, a study exploring clinical nursing competency during epidemics might ask, "What competencies do nurses need to provide acceptable care during epidemic diseases?" This question directs attention to specific aspects of nursing practice and shapes the coding framework.

Research questions in qualitative content analysis typically address what, how, or why questions about human experience, social processes, or textual content. The questions should be broad enough to allow unexpected findings to emerge but focused enough to guide systematic analysis.

Selecting the Analytical Approach

Your research purpose determines whether you use conventional, directed, or summative content analysis. Conventional content analysis is appropriate when existing theory or research literature on a phenomenon is limited. Codes emerge directly from the data during analysis. Directed content analysis starts with existing theory or prior research to identify initial coding categories. This approach validates or extends a theoretical framework. Summative content analysis begins with counting words or manifest content to explore usage patterns before interpreting the underlying context.

A study of job stress experiences among pediatric ward nurses used qualitative content analysis to identify underlying meanings and patterns of these experiences. The researchers conducted individual in-depth interviews with nine pediatric ward nurses who had at least one year of experience, excluding nurses in the initial adaptation phase of clinical practice. The analysis identified five categories of job stress experiences, demonstrating how conventional content analysis can reveal multifaceted phenomena shaped by clinical, relational, organizational, and sociocultural contexts.

Determining the Unit of Analysis

The unit of analysis is the entity you will analyze, such as individual interviews, focus group transcripts, documents, or media articles. Your research questions and data collection methods determine the appropriate unit. In interview-based studies, each transcript typically serves as a unit of analysis. In document analysis, each document or a defined section of a document may constitute the unit.

For a solo researcher, determining the unit of analysis is a critical early decision. A proposed 11-step process for single coding based on an inductive-dominant approach includes determining the unit of analysis as step three, after collecting texts and before engaging in partial inductive coding. This sequencing ensures that the analytical focus is clear before coding begins.

Data Collection Methods for Content Analysis

Qualitative content analysis can work with various data types, including interview transcripts, open-ended survey responses, observation notes, self-evaluation reports, news articles, syllabi, and clinical documents. The data collection method must align with your research questions and provide sufficient depth for meaningful analysis.

Interviews and Focus Groups

Semi-structured interviews are a common data source for qualitative content analysis. A study on clinical nursing competency during epidemics used semi-structured interviews with 12 nurses actively engaged in providing patient care during the COVID-19 pandemic. The interviews were conducted from October 2022 to March 2023, and the data analysis process followed five steps suggested by Graneheim and Lundman. After analysis, 159 competencies were derived from the interviews and categorized into 11 subcategories and three categories: clinical nursing skills in epidemics, knowledge of epidemics, and soft skills for nurses in epidemics.

In-depth and semi-structured individual interviews also served as the data collection method in a study identifying indexes and factors affecting successful evaluation of disaster preparedness exercises. The researchers used purposeful sampling and interviewed 25 health professionals in the field of disasters. The data were analyzed using directed content analysis, producing 24 initial codes, 5 subcategories, and 2 main categories under the original theme of "exercise immediate feedback."

Document and Media Analysis

Content analysis is well suited to analyzing documents and media content. A study assessing advocacy content and skills taught to Master of Public Health students analyzed 98 course syllabi submitted to the Council on Education for Public Health between 2019 and 2021. The syllabi were analyzed using MAXQDA Qualitative Data Analysis Software with a two-coder approach. The analysis found that most advocacy courses were survey, health policy, or health care delivery courses, covering policy, policy communication, coalition-building, lobbying, community organizing, and media advocacy skills.

A software-assisted qualitative content analysis of news articles provides another example of document-based analysis. This approach demonstrates how researchers can use software tools to manage and analyze large volumes of textual data while maintaining systematic coding procedures.

Observation and Self-Report Data

Content analysis can also apply to observation notes and self-evaluation reports. A methodological case study in science education assumed that interviews were conducted with students and teachers, the researcher used a semi-structured observation form, and open-ended self-evaluation reports were prepared in which students evaluated themselves. The study provided explanations about how data obtained from student-teacher interviews, observation, and student self-assessment reports should be followed according to content analysis stages.

Step-by-Step Protocol for Qualitative Content Analysis

The following protocol synthesizes published guidance into a practical workflow. The steps are adapted from the Clinical-Qualitative Content Analysis technique, which comprises seven steps: editing material for analysis, floating reading, construction of the units of analysis, construction of codes of meaning, general refining of the codes and construction of categories, discussion, and validity. Additional guidance from a proposed 16-step method for directed qualitative content analysis and an 11-step process for solo researchers informs this protocol.

Step 1: Prepare and Edit the Material

Begin by transcribing interviews or converting documents into analyzable text. Edit the material to remove identifying information and ensure accuracy of transcription. This step also involves organizing the data in a consistent format that facilitates coding. For interview data, verify that the transcript accurately reflects the recording and note any non-verbal cues that may be relevant to interpretation.

Step 2: Conduct Floating Reading

Read through the entire dataset without coding to gain an overall sense of the content. This floating reading allows you to immerse yourself in the data and develop familiarity with the material. During this step, note initial impressions, recurring topics, and potential areas of interest. This reading provides the foundation for subsequent analytical decisions.

Step 3: Construct Units of Analysis

Divide the text into meaning units that relate to the same central meaning. A meaning unit can be a word, a sentence, or a paragraph, depending on the richness of the data and the level of analysis required. The goal is to identify segments of text that can stand alone as meaningful units for coding. For example, in a study of hemodialysis patients and family experiences of perceived social support, the researchers analyzed interview data to reveal patient problems regarding social support and identified many problems of supporting patients by health team members, family members, and organizations.

Step 4: Construct Codes of Meaning

Assign codes to each meaning unit. Codes are labels that capture the essence of the meaning unit in a condensed form. During initial coding, stay close to the data and use descriptive labels. In conventional content analysis, codes emerge from the data without predefined categories. In directed content analysis, codes may be derived from existing theory or prior research.

For a solo researcher, an 11-step process suggests engaging in partial inductive coding of the target texts before developing a temporary coding schema. This approach involves coding a portion of the text, analyzing frequent words found in the text, and using these frequent words and inductive coding to create a temporary coding schema. The researcher then collaborates with multiple experts or researchers to verify the temporary coding schema before coding the entire text.

Step 5: Refine Codes and Construct Categories

Review all codes and group those that share common features into categories. This step involves comparing codes across the dataset, merging similar codes, and distinguishing between codes that represent different concepts. Categories should be internally consistent and externally distinct. The process of abstraction moves from concrete codes to more abstract categories.

In the study of clinical nursing competency during epidemics, the researchers derived 159 competencies from interviews that were categorized into 11 subcategories and three categories. This hierarchical structure demonstrates how codes are refined and organized into meaningful categories that address the research questions.

Step 6: Discuss and Interpret Findings

Interpret the categories in relation to your research questions and the broader literature. This step involves moving beyond description to explain what the findings mean. The discussion should consider the context of the data, the theoretical framework guiding the analysis, and the implications for practice or policy.

Interpretation in qualitative content analysis can be understood through communication theory. The meaning of the text is not simply in the words themselves but in the relationship between the text and its context. Researchers must consider both manifest and latent content when interpreting findings.

Step 7: Establish Validity

Assess the validity of your analysis through various strategies. These may include member checking, where participants review and confirm the findings, peer debriefing, where colleagues review the analytical process, and audit trails, where you document all analytical decisions. The clinical-qualitative analysis presupposes and involves a critical reflection on the processes carried out at each step. This reflection is an extremely rich process if carried out collectively and in dialogue with other researchers with some proficiency in qualitative methods.

Coding Sheet Template for Qualitative Content Analysis

A coding sheet helps maintain consistency and transparency throughout the analysis process. The template below provides a structured format for recording codes, categories, and supporting evidence.

The coding sheet should be maintained throughout the analysis process and updated as codes are refined and categories are developed. Each entry should include sufficient context to allow another researcher to understand the coding decision.

Directed Qualitative Content Analysis

Directed content analysis differs from conventional analysis in its starting point. This approach begins with existing theory or prior research to identify initial coding categories. The method is used to validate or extend a theoretical framework. A proposed 16-step method of data analysis for directed qualitative content analysis provides a detailed description of analytical steps that covers the current gap in knowledge regarding the practical process of qualitative data analysis.

An example of directed content analysis used Victor Vroom's expectancy theory to study resuscitation team members' motivation for cardiopulmonary resuscitation. The directed approach allowed the researchers to test whether the theory explained motivation in this specific context and to identify aspects of motivation not captured by the theory.

The directed qualitative content analysis method proposed in the literature is described as a reliable, transparent, and comprehensive method for qualitative researchers. It can increase the rigor of qualitative data analysis, make the comparison of the findings of different studies possible, and yield practical results.

Using Software for Qualitative Content Analysis

Software tools can support qualitative content analysis by managing large volumes of data, facilitating coding, and enabling systematic retrieval of coded segments. A study analyzing public health syllabi used MAXQDA Qualitative Data Analysis Software with a two-coder approach. The software supported systematic coding and enabled the researchers to calculate aggregate frequencies of advocacy content and skills.

A software-assisted qualitative content analysis of news articles demonstrates how software can support the analysis of media content. The approach allows researchers to handle large datasets while maintaining systematic coding procedures.

For a solo researcher, software can help manage the coding process and calculate intra-rater reliability. The 11-step process for single coding suggests that after coding the entire text based on the coding schema, the researcher should wait a certain period and then re-code the text to calculate intra-rater reliability. Software can facilitate this process by allowing the researcher to compare coding across time.

Common Failure Patterns in Qualitative Content Analysis

Several common errors can compromise the quality of qualitative content analysis. Being aware of these failure patterns helps researchers avoid them.

Confusing Descriptive and Content Analysis

Descriptive analysis and content analysis are different concepts that are often confused. Descriptive analysis is a statistically descriptive method that summarizes the basic features of the data. Content analysis is a method in which meaning, themes, and patterns are revealed by examining texts or qualitative data in depth. Although both are analysis methods used in research, they have different purposes and techniques. Researchers must be clear about which approach they are using and apply the appropriate procedures.

Inadequate Documentation of Analytical Decisions

Qualitative content analysis requires transparency about how codes and categories were developed. Failure to document analytical decisions undermines the trustworthiness of the findings. An audit trail that records coding decisions, category development, and interpretation steps allows others to assess the rigor of the analysis.

Insufficient Attention to Trustworthiness

Trustworthiness in qualitative content analysis is established through credibility, dependability, and transferability. Researchers who neglect these measures risk producing findings that lack credibility. Measures to achieve trustworthiness must be applied throughout the steps of the research procedure, not added at the end.

Coding Without Interpretation

Coding is a means to an end, not the end itself. The purpose of content analysis is to interpret meaning, themes, and patterns in the data. Researchers who stop at coding without moving to interpretation fail to address their research questions. The discussion and validity steps in the analytical process ensure that findings are interpreted in light of the research context and existing literature.

Applications in Life Science Research

Qualitative content analysis has broad applications in life science research. The method is used to explore patient experiences, understand professional competencies, evaluate educational programs, and analyze health policy documents.

Clinical Nursing Competency During Epidemics

A qualitative content analysis study determined the required clinical competencies for nurses during epidemics. The study used semi-structured interviews with 12 nurses actively engaged in providing patient care during the COVID-19 pandemic. The analysis identified 159 competencies categorized into 11 subcategories and three categories: clinical nursing skills in epidemics, knowledge of epidemics, and soft skills for nurses in epidemics. The findings suggest that nurses need a wide range of competencies to address professional expectations regarding providing acceptable care during epidemics. Knowing these competencies can help nursing managers prepare nurses for crises such as the COVID-19 pandemic.

Job Stress Among Pediatric Ward Nurses

A study exploring job stress experiences among Korean pediatric ward nurses used qualitative content analysis to identify underlying meanings and patterns. Data were collected through individual in-depth interviews with nine pediatric ward nurses who had at least one year of experience. Five categories of job stress experiences were identified: cognitive and emotional burden related to the distinctive characteristics of children, difficulties maintaining positive relationships with caregivers, difficulties arising from job demands and organizational culture, additional stress from caring for children from multicultural families, and growth through overcoming stress. The findings highlight the need for integrated educational and organizational strategies that address emotional labor, strengthen resilience, and promote equitable family-centered pediatric nursing care.

Social Support in Hemodialysis Patients

A qualitative research study used a grounded theory approach written as a content analysis form to study hemodialysis patients and family experiences of perceived social support. Three nurses, four general practitioners, a specialist, and two family members participated in interviews. The analysis produced 113 categories and four main themes from 993 first codes. Social support was explored based on the implications of five general themes including perceived threats caused by disease complications, searching for social support, accessible social support, beliefs and values, and perceived social support. The core variable of the research was acceptance of the reality of the conditions caused by the disease. The findings suggest that individual aspects of patient experiences must be considered if social support is to be given.

Verbal Hallucinations Content Analysis

A study of auditory verbal hallucinations argued for the importance of looking at content to get a fuller understanding of the hallucinatory experience. Guided by Lacanian psychoanalysis, the researchers conducted thematic and narrative analysis on interviews with 10 schizophrenic patients about their hallucinations. They discerned five themes in the data based on Lacanian theory: parenthood and authority, sexuality and relationships, gender identity, life in the light of death, and what does the other want. The analysis showed that hallucinations can be thematically and narratively organized and that hallucinatory contents are not random but are about existential issues embedded in a life narrative.

Public Health Advocacy Education

A qualitative content analysis assessed advocacy content and skills taught to Master of Public Health students by analyzing 98 course syllabi submitted to the Council on Education for Public Health between 2019 and 2021. The analysis found that most advocacy courses were survey, health policy, or health care delivery courses, covering policy, policy communication, coalition-building, lobbying, community organizing, and media advocacy skills. Only 7 percent of courses prioritized advocacy skill instruction, and 10 percent addressed how to advocate in an equitable way. The study concluded that defining public health advocacy and essential skills is crucial and that issuing competency guidelines, supporting advocacy faculty, offering standardized training, and expanding experiential learning are important first steps.

Disaster Exercise Evaluation

A qualitative content analysis identified indexes and factors affecting successful evaluation of disaster preparedness exercises in the hot wash stage. Data were collected through in-depth and semi-structured individual interviews with 25 health professionals in the field of disasters. The data were analyzed using directed content analysis, producing 24 initial codes, 5 subcategories, and 2 main categories of "evaluation and exercise debriefing" and "modification of programs and promotion of exercise operational functions" under the original theme of "exercise immediate feedback." The study can be considered a suitable standard guide for health care organizations to evaluate disaster exercises successfully in the hot wash stage.

Limitations and Considerations

Qualitative content analysis has limitations that researchers must acknowledge. The method requires careful attention to trustworthiness, and the quality of findings depends on the rigor of the analytical process.

Sample Size and Generalizability

Qualitative content analysis typically uses smaller samples than quantitative research. The goal is not statistical generalizability but transferability, where the reader assesses whether findings apply to other contexts. For example, the study of job stress among pediatric ward nurses used nine participants, which is appropriate for qualitative research but does not support statistical generalization.

Researcher Subjectivity

The researcher is the primary instrument in qualitative content analysis. This subjectivity can be a strength, allowing deep engagement with the data, but it also requires safeguards. Strategies such as peer debriefing, member checking, and audit trails help manage researcher bias.

Time and Resource Requirements

Qualitative content analysis is labor-intensive. Transcribing interviews, coding data, and developing categories require substantial time. A solo researcher must plan carefully to manage the workload. The 11-step process for single coding provides a systematic approach that supports solo researchers in conducting rigorous analysis.

Reporting Standards

Existing reporting guidelines for qualitative research, including the Consolidated Criteria for Reporting Qualitative Research (COREQ), provide minimal guidance for documenting the use of large language models in qualitative research. As technology evolves, researchers must consider how to report their methods transparently. The EQUATOR Network provides access to reporting guidelines for health research, and researchers should consult relevant guidelines when preparing their manuscripts.

Professional Escalation Criteria

Researchers should seek additional support or consultation when they encounter specific challenges in qualitative content analysis. Consider consulting with experienced qualitative researchers or methodologists when you face any of the following situations.

When to Seek Methodological Consultation

Consult a methodologist or experienced qualitative researcher when you are uncertain about the appropriate analytical approach for your research questions. The choice between conventional, directed, and summative content analysis has significant implications for your findings. A methodologist can help you match your approach to your research purpose.

Seek consultation when you encounter difficulties achieving trustworthiness in your analysis. If you are unable to establish credibility, dependability, or transferability, an experienced researcher can help you identify strategies to strengthen your findings.

When to Consider Additional Training

Consider additional training in qualitative methods when you are new to qualitative research. The methodological literature on inductive content analysis can be difficult to navigate because it employs a wide variety of terminology and gives different descriptions of when and how to carry it out. Formal training or mentorship can help you navigate this complexity.

When to Escalate to Institutional Support

Escalate to institutional support when your research involves sensitive topics or vulnerable populations. Your institution may have specific requirements for ethical approval, data storage, or participant protection. Consult your institutional review board or ethics committee early in the research process.

Records and Documentation

Maintaining thorough records is essential for the trustworthiness of qualitative content analysis. The following records support the credibility and dependability of your findings.

Audit Trail

An audit trail documents all analytical decisions, including how codes were developed, how categories were constructed, and how interpretations were made. The audit trail should include dated entries that record your reasoning at each step of the analysis. This documentation allows others to assess the rigor of your analysis and supports the dependability of your findings.

Coding Sheets

Coding sheets record the codes assigned to meaning units, the categories developed, and the supporting evidence from the data. The coding sheet template provided earlier in this article offers a structured format for this documentation. Maintain coding sheets throughout the analysis process and update them as codes are refined.

Analysis Memos

Analysis memos capture your reflections on the data, emerging patterns, and analytical decisions. These memos support the development of categories and themes and provide a record of your interpretive process. Write memos regularly throughout the analysis, noting connections between codes, questions that arise, and insights about the data.

Frequently Asked Questions

What is the difference between qualitative content analysis and thematic analysis?

Qualitative content analysis and thematic analysis are distinct methods with different purposes and techniques. Inductive content analysis, also called qualitative content analysis, is well suited to health-related research, particularly relatively small-scale studies. Thematic analysis focuses on identifying and interpreting patterns of meaning across the dataset. Content analysis involves systematic coding and categorization of text, while thematic analysis emphasizes the development of themes that capture the essence of participant experiences. The choice between methods depends on your research questions and analytical goals.

How do I choose between conventional, directed, and summative content analysis?

The choice depends on your research purpose and the existing literature. Conventional content analysis is appropriate when existing theory or research literature on a phenomenon is limited, and codes emerge directly from the data. Directed content analysis starts with existing theory or prior research to identify initial coding categories, validating or extending a theoretical framework. Summative content analysis begins with counting words or manifest content to explore usage patterns before interpreting the underlying context. Consider your research questions and the state of knowledge in your field when selecting an approach.

What is the minimum sample size for qualitative content analysis?

There is no fixed minimum sample size for qualitative content analysis. The appropriate sample size depends on your research questions, the richness of the data, and the purpose of the study. Studies using qualitative content analysis have used as few as nine participants, as in the study of job stress among pediatric ward nurses, and as many as 25 health professionals in the disaster exercise evaluation study. The goal is data saturation, where additional data no longer yields new insights. Researchers should continue data collection until saturation is achieved.

How do I ensure trustworthiness in qualitative content analysis?

Trustworthiness is established through credibility, dependability, and transferability. Credibility can be strengthened through member checking, peer debriefing, and prolonged engagement with the data. Dependability benefits from maintaining an audit trail that documents analytical decisions. Transferability requires thick description of the research context and participants so readers can assess applicability to their own settings. Measures to achieve trustworthiness must be applied throughout the steps of the research procedure.

Can I use software for qualitative content analysis?

Yes, software tools can support qualitative content analysis by managing large volumes of data, facilitating coding, and enabling systematic retrieval of coded segments. A study analyzing public health syllabi used MAXQDA Qualitative Data Analysis Software with a two-coder approach. Software can also support solo researchers by facilitating the coding process and enabling calculation of intra-rater reliability. However, software is a tool that supports analysis, not a substitute for the researcher's analytical judgment.

How do I handle data from multiple sources in content analysis?

Content analysis can integrate data from multiple sources, including interviews, observations, and self-evaluation reports. A methodological case study in science education demonstrated how data obtained from student-teacher interviews, observation, and student self-assessment reports could be analyzed according to content analysis stages. The key is to organize the data systematically and ensure that your analytical approach is consistent across data sources. Maintain clear records of which data source each code and category comes from.

What is the role of theory in qualitative content analysis?

The role of theory depends on the analytical approach. In conventional content analysis, codes emerge from the data without predefined categories, and theory may be developed from the findings. In directed content analysis, existing theory guides the initial coding categories, and the analysis validates or extends the theoretical framework. The study of resuscitation team members' motivation used Victor Vroom's expectancy theory as the guiding framework. Theory can also inform interpretation, as demonstrated by the study of verbal hallucinations guided by Lacanian psychoanalysis.

How do I report qualitative content analysis findings?

Report your findings with sufficient detail to allow readers to assess the trustworthiness of the analysis. Describe your analytical approach, the steps you followed, and the measures you took to establish trustworthiness. Provide quotes from the data to support your codes, categories, and themes. Consult reporting guidelines available through the EQUATOR Network when preparing your manuscript. The EQUATOR Network provides access to reporting guidelines for health research, and the Consolidated Criteria for Reporting Qualitative Research (COREQ) is relevant for qualitative studies.

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References and Further Reading

• Research Data Framework. National Institute of Standards and Technology.
• EQUATOR Network. EQUATOR Network.
• Experimental Design Assistant. NC3Rs.
• NCBI Literature Resources. National Center for Biotechnology Information.
• PubMed. National Library of Medicine.
• Qualitative content analysis in nursing research: concepts, procedures and measures to achieve trustworthiness.. Nurse education today, 2004.
• Seven steps for qualitative treatment in health research: the Clinical-Qualitative Content Analysis.. Ciencia & saude coletiva, 2021.
• Clinical nursing competency during epidemics: a qualitative content analysis.. BMC nursing, 2024.
• Charting the Advocacy Landscape: A Qualitative Content Analysis of Syllabi in Public Health Graduate Education.. Journal of public health management and practice : JPHMP, 2024.
• A qualitative content analysis for determining indexes and factors affecting for evaluation of disaster exercises immediate feedback stage.. Journal of education and health promotion, 2022.
• Content Matters, a Qualitative Analysis of Verbal Hallucinations.. Frontiers in psychology, 2018.
• Directed qualitative content analysis: the description and elaboration of its underpinning methods and data analysis process.. Journal of research in nursing : JRN, 2018.
• Content analysis and qualitative study of hemodialysis patients, family experience and perceived social support.. Iranian Red Crescent medical journal, 2014.
• The use and methodological reporting of large language models in qualitative research: a scoping review.. 2026.
• Job stress experiences among Korean pediatric ward nurses: a qualitative content analysis.. 2026.
• A methodological approach to content analysis of qualitative data in science education: An applied example. Pedagogical Perspective, 2025.
• A Software-Assisted Qualitative Content Analysis of News Articles: Example and Reflections. 2015.
• Proposal of a Qualitative Content Analysis Process for a Solo Researcher. Libri, 2023.
• Inductive content analysis: A guide for beginning qualitative researchers. Focus on Health Professional Education A Multi-Professional Journal, 2022.
• A Study on Professional Competence of Future Teacher Students as an Example of a Study Using Qualitative Content Analysis. 2015.
• Qualitative Content Analysis-A Step-by-Step Guide for Beginners to the Method, Theories, Epistemology, Ontology, and Rigour. Qualitative Report, 2025.
• Automation of qualitative content analysis: A Proposal. Forum Qualitative Sozialforschung, 2019.

This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.