# Qualitative Data Examples: How to Recognize and Use Non-Numerical Evidence ## Key Takeaways - Qualitative data, encompassing non-numerical evidence such as interview transcripts, field notes, and patient narratives, captures meaning, experience, and context, answering "how" and "why" questions, unlike quantitative data which addresses "how much." In life sciences, this includes verbatim patient accounts of symptomology or detailed clinical case notes. - Recognizing qualitative data involves identifying textual, visual, or auditory information that conveys meaning through language or imagery, such as direct quotations from semi-structured interviews detailing a patient's response to a new therapeutic regimen or observational notes on animal behavior in a controlled environment. - Theoretical frameworks, such as interpretivism or critical realism, are crucial for guiding the systematic analysis of qualitative data, ensuring that interpretations of participant experiences, like understanding the lived experience of chronic disease management, are grounded and transparent. - Purposive sampling, aiming for informational richness rather than statistical representativeness, is fundamental in qualitative research, exemplified by selecting participants with specific genetic markers or distinct disease progression patterns to illuminate a phenomenon. - Data collection in qualitative studies continues until theoretical saturation is achieved, meaning no new significant themes emerge from additional data, which is critical for comprehensive understanding, such as when interviews with individuals experiencing a rare zoonotic disease yield no novel insights. - Rigor in qualitative research is enhanced through strategies like triangulation of data sources (e.g., combining patient interviews with clinical records), member checking, and maintaining a detailed audit trail of analytical decisions, ensuring the trustworthiness of findings related to disease transmission or treatment efficacy. --- Qualitative data consists of non-numerical information that captures meaning, experience, context, and process. In life science research, this includes interview transcripts, field notes, observational records, open-ended questionnaire responses, and naturally occurring documents. Unlike quantitative data that answers questions of how much or how often, qualitative data answers questions of how, why, and in what way. This article provides concrete examples of qualitative data across research settings, explains how to recognize different types, and offers practical tools for documenting and analyzing non-numerical evidence in your own studies. ## What Counts as Qualitative Data Qualitative data is any evidence that describes qualities, characteristics, meanings, or experiences instead of measuring quantities. The definition used in mental health research contexts describes qualitative methods as the collection and analysis of non-numerical data to understand concepts and experiences [1]. This definition matters because it sets the boundary for what belongs in this category. Common forms of qualitative data include: - Verbatim interview transcripts from semi-structured or in-depth interviews - Field notes recorded during observation sessions - Open-ended responses on questionnaires - Diaries or journals kept by research participants - Clinical case notes and patient records - Photographs, video recordings, and audio recordings - Documents such as policies, letters, or meeting minutes - Story completion responses where participants write narratives in response to prompts The key distinction is that the data carries meaning through language, imagery, or narrative instead of through numerical values. A blood pressure reading is quantitative. A patient's description of what it feels like when their blood pressure is high is qualitative. ## Recognizing Qualitative Data in Life Science Research Qualitative data appears throughout life science research, often in forms that researchers may not immediately recognize as qualitative. Learning to identify these data types is the first step toward using them effectively. ### Interview Data Semi-structured interviews are among the most common sources of qualitative data in health research. In a study of adolescent fathers attending prenatal care services in Brazil, researchers used in-depth interviews with 14 expectant fathers aged up to 19 years [2]. The interview data captured reactions to a positive pregnancy test, concepts of fatherhood, imagined relationships with the baby, and changes in relationships with partners during pregnancy [2]. These transcripts represent qualitative data because they preserve the participants' own words and meanings. Interview data can be recognized by several features. The text is typically organized as dialogue with speaker labels. The content includes direct quotations, descriptive language, and personal accounts. The data often contains emotional expressions, hesitations, and contextual details that would be lost in numerical coding. ### Observational Field Notes Field notes are written records of observations made during data collection. In the adolescent father study, researchers used field diaries alongside interviews [2]. Field notes capture what the researcher sees, hears, and experiences in the research setting. These notes might describe body language, interactions between participants, environmental conditions, or unexpected events that occur during data collection. Field notes differ from interview transcripts because they are filtered through the researcher's perspective. They represent the researcher's observations instead of participant self-reports. Both forms are valid qualitative data, but they answer different questions and carry different limitations. ### Open-Ended Questionnaire Responses Open-ended questions on questionnaires produce qualitative data when they are designed and analyzed with qualitative research values in mind. A common error among students new to qualitative research is assuming that simply adding open-ended questions to a survey constitutes qualitative design [3]. Rigorous qualitative questionnaires require attention to qualitative ontology and a structured design process [3]. Open-ended responses become qualitative data when they are treated as meaningful text to be analyzed for themes and patterns instead of as items to be counted or categorized into predetermined codes. The distinction lies in the analytical approach, beyond the format of the question. ### Naturally Occurring Data Some qualitative data exists without any research intervention. Naturally occurring data includes documents, records, and communications that would exist regardless of the research study [4][5]. Examples include clinical notes, referral letters, policy documents, social media posts, and meeting minutes. This type of data offers the advantage of not being influenced by the research process, but it also presents challenges related to access, completeness, and ethical use [4][5]. ### Story Completion Responses Qualitative story completion is an innovative method where participants write a story in response to a hypothetical scenario, often in the third person with fictional characters [6]. This approach generates qualitative data that reveals social norms, expectations, and cultural scripts without requiring participants to report on their direct experiences [6]. The resulting narratives are analyzed as qualitative data for themes, assumptions, and narrative patterns. ## At a Glance: Qualitative Data Types and Recognition Features | Data Type | Recognition Features | Typical Research Use | Example Setting | |-----------|---------------------|---------------------|-----------------| | Interview transcripts | Dialogue format, speaker labels, direct quotations, personal accounts | Capturing participant perspectives and experiences | Adolescent fatherhood study using in-depth interviews [2] | | Field notes and diaries | Researcher perspective, descriptive language, contextual details | Documenting observations and research context | Field diaries used alongside interviews in prenatal care study [2] | | Open-ended questionnaire responses | Written text answers, varied length, participant vocabulary | Gathering written perspectives from larger samples | Qualitative questionnaires in medical education research [3] | | Naturally occurring documents | Existing records, no research intervention, authentic communication | Analyzing real-world communication and records | Clinical notes, policies, and correspondence [4][5] | | Story completion narratives | Third-person fictional stories, hypothetical scenarios | Revealing social norms and cultural expectations | Health research using story stems [6] | ## Core Principles for Working with Qualitative Data ### Theoretical Frameworks Guide Interpretation Qualitative data does not interpret itself. Researchers must use an explicit theoretical framework to guide the study, and this framework informs how researchers engage with the topic, report on their work with participants, describe key concepts, and address assumptions within the research questions and procedures [1]. Three commonly used frameworks in mental health research are positivism, interpretivism, and critical realism [1]. Positivism seeks objective facts and regularities. Interpretivism focuses on understanding meanings and experiences from the participant's perspective. Critical realism acknowledges that reality exists independently of perception but that our access to it is always mediated by interpretation. The choice of framework shapes every stage of qualitative research, from question development to data analysis and reporting [1]. ### Sampling Must Be Purposeful Qualitative research does not rely on random sampling. Instead, researchers use purposive and snowball sampling to identify participants who can provide rich information relevant to the research question. In a study of single-child households in western Iran, researchers used purposive and snowball sampling to identify parents with one child who no longer wanted more children [7]. The study included 29 couples and seven key informants, with data collection continuing until saturation was reached [7]. Sampling approaches in qualitative research are selected based on the research question and the need for diversity of experience [8]. The goal is not statistical representativeness but informational richness and the ability to illuminate the phenomenon under study. ### Data Collection Continues Until Saturation Qualitative studies typically collect data until theoretical saturation is reached, meaning that new data no longer generates new insights or themes. In the divorce study from western Iran, researchers conducted semi-structured interviews with 16 key informants and 32 main participants, continuing until saturation [9]. The analysis produced 212 primary codes, 24 subcategories, and 3 categories [9]. Saturation is a practical decision point that requires judgment. Researchers must balance the need for comprehensive data against practical constraints of time, resources, and participant availability. ### Analysis Is Systematic and Transparent Qualitative data analysis requires a systematic approach. The Framework Method is one increasingly popular approach to managing and analyzing qualitative data in health research [10]. This method is appropriate for multidisciplinary research teams, including those involving clinicians, patients, and lay people, and it works well even when not all team members have previous qualitative research experience [10]. The Framework Method involves several stages: familiarization with the data, identifying a thematic framework, indexing, charting, mapping, and interpretation [10]. Used effectively with leadership from an experienced qualitative researcher, this method provides a systematic and flexible approach to analysis [10]. ## Practical Workflow for Using Qualitative Data ### Step 1: Define the Research Question Start with a question that genuinely requires qualitative data. Questions about meaning, experience, process, and context are appropriate for qualitative methods. Questions about frequency, quantity, or association are better suited to quantitative methods. If your question has both dimensions, consider a mixed methods design. ### Step 2: Select the Data Collection Method Choose the method that best fits your research question and context. Interviews work well for exploring individual experiences in depth. Focus groups capture group dynamics and shared perspectives. Open-ended questionnaires reach larger samples. Observation captures behavior in natural settings. Story completion reveals cultural scripts and social norms [6]. ### Step 3: Develop Data Collection Instruments Interview guides, observation protocols, and questionnaire items must be developed with attention to qualitative research values [3]. Questions should be open-ended, neutral, and designed to elicit rich responses. Pilot testing helps identify problems before full data collection begins. ### Step 4: Collect Data with Documentation Record interviews with participant permission and transcribe them verbatim. Write field notes promptly after observation sessions. Document the date, time, setting, participants, and any contextual factors that might influence interpretation. Maintain a research log that tracks decisions and rationales. ### Step 5: Manage and Organize Data Transcribe audio recordings, digitize documents, and organize materials by participant, date, and data type. Use qualitative data analysis software if appropriate, but remember that software supports analysis instead of performing it. The researcher remains responsible for interpretation. ### Step 6: Analyze Using a Systematic Method Select an analysis approach that fits your research question and framework. The Framework Method provides a structured approach that works well in teams [10]. Content analysis involves coding data into categories and themes. Narrative analysis focuses on the structure and meaning of stories. Thematic analysis identifies patterns across the data. ### Step 7: Report with Transparency Report your methods in enough detail that readers can assess the trustworthiness of your findings. Describe your sampling approach, data collection procedures, analysis methods, and the steps taken to enhance rigor. Reporting guidelines and resources can support transparent reporting of qualitative research [11]. ## Options and Tradeoffs in Qualitative Data Collection ### Interviews versus Questionnaires Interviews allow for depth, clarification, and follow-up questions. They are resource-intensive and require skilled interviewers. Questionnaires reach more participants at lower cost but cannot probe responses or clarify ambiguous answers. Qualitative questionnaires can produce rich findings when they prioritize qualitative research values and follow a rigorous design process [3]. ### Individual Interviews versus Focus Groups Individual interviews provide privacy and depth, which suits sensitive topics. Focus groups generate interaction and can reveal shared perspectives, but they may inhibit disclosure on sensitive subjects and can be dominated by vocal participants. ### Direct Data versus Naturally Occurring Data Direct data collection methods such as interviews and observation give researchers control over what is captured. Naturally occurring data avoids researcher influence but may be incomplete, difficult to access, or raise ethical concerns about consent [4][5]. ### Single Method versus Mixed Methods Mixed methods research offers powerful tools for investigating complex processes and systems [12]. Integration can occur at the study design level through exploratory sequential, explanatory sequential, or convergent designs [12]. At the methods level, integration occurs through connecting, building, merging, or embedding [12]. At the interpretation level, integration occurs through narrative, data transformation, or joint display [12]. ## Records and Measurements for Qualitative Research ### Research Logs Maintain a research log that documents all decisions made during the study. This includes sampling decisions, changes to data collection instruments, analytical choices, and any challenges encountered. The log provides an audit trail that supports the trustworthiness of the research. ### Data Inventories Create an inventory of all data collected, including participant identifiers, dates, data types, and file locations. This supports data management and enables the research team to track progress toward saturation. ### Codebooks Develop a codebook that defines each code used in analysis, provides examples, and documents any changes to coding definitions over time. The codebook supports consistency across team members and provides transparency for readers. ### Analysis Memos Write memos throughout the analysis process that capture emerging insights, questions, and connections between codes. Memos document the analytical journey and provide material for the final report. ### Field Notes Template Use a structured template for field notes that includes sections for descriptive observations, reflective notes, and methodological notes. This structure supports consistency and completeness. ## Common Failure Patterns in Qualitative Data Work ### Treating Open-Ended Questions as Qualitative by Default A common error is assuming that open-ended questions alone constitute qualitative research design [3]. Open-ended questions produce qualitative data only when they are designed and analyzed according to qualitative research values. Without this foundation, the data may be thin, unfocused, or analyzed in ways that do not honor qualitative principles [3]. ### Reaching Saturation Too Early or Too Late Ending data collection before saturation is reached produces incomplete findings. Continuing beyond saturation wastes resources and may burden participants unnecessarily. Both errors reflect inadequate attention to the iterative nature of qualitative data collection. ### Ignoring Negative Cases Qualitative analysis must account for all data, including cases that do not fit emerging themes. Studies that ignore contradictory evidence produce conclusions that are not supported by the full dataset. A hierarchy of evidence for qualitative health research gives more weight to studies that analyze all data according to conceptual themes and account for all data in the analysis [13]. ### Failing to Document Analytical Decisions Qualitative analysis involves many judgment calls. When these decisions are not documented, the research cannot be audited or replicated. Transparent documentation supports the credibility of the findings. ### Overgeneralizing from Small Samples Qualitative findings are not statistically generalizable. The hierarchy of evidence for qualitative health research distinguishes between descriptive studies that provide helpful lists of quotations and generalizable studies that use conceptual frameworks with appropriately diversified samples [13]. Researchers must be careful not to claim more for their findings than the methods support. ## Quality and Welfare Considerations ### Enhancing Rigor Several strategies enhance the rigor of qualitative research. Using multiple data sources or methods allows triangulation. Member checking involves returning findings to participants for verification. Peer debriefing provides external review of the analysis. Maintaining an audit trail documents the research process. The hierarchy of evidence for assessing qualitative health research provides explicit criteria for evaluating the contribution of qualitative studies [13]. The least reliable studies are single case studies, followed by descriptive studies that provide lists of quotations without detailed analysis [13]. Conceptual studies that analyze all data according to conceptual themes receive more weight, though they may be limited by a lack of diversity in the sample [13]. Generalizable studies that use conceptual frameworks to derive appropriately diversified samples and account for all data provide the best evidence for practice [13]. ### Ethical and Welfare Considerations Qualitative research often involves collecting sensitive personal information. Researchers must protect participant confidentiality, obtain informed consent, and minimize any harm that might result from participation. In health research, patient and public involvement can lead to higher quality research that is more relatable to and helps empower the public [14]. However, the literature describing approaches to and impact of patient and public involvement in low and middle income countries is sparse, and most studies do not provide evidence of effectiveness or elaborate on the impact of involvement [14]. ### Intersectionality in Qualitative Research Intersectionality theory has emerged as a critical theoretical and methodological approach in health research [15]. This approach examines how multiple social identities and systems of oppression interact to shape health experiences [15]. Researchers using intersectionality must consider how this framework applies throughout the research process, including study conceptualization, participant recruitment, data collection, and data analysis [15]. ## Limitations of Qualitative Data ### Not Statistically Generalizable Qualitative findings describe the experiences and meanings of the specific participants and settings studied. They do not provide statistical estimates for larger populations. This limitation is inherent to the method and does not diminish the value of qualitative findings for understanding processes and meanings. ### Resource Intensive Qualitative data collection and analysis require substantial time and skill. Interviews must be conducted, transcribed, and analyzed. Field notes require disciplined writing. Analysis requires careful reading and rereading of the data. These demands can be challenging for researchers new to qualitative methods. ### Researcher Influence The researcher is the primary instrument in qualitative research. Personal biases, assumptions, and interactions with participants can influence the data collected and the interpretations made. Reflexivity, or ongoing attention to the researcher's influence, is essential. ### Context Bound Qualitative findings are tied to their context. Findings from one setting may not transfer to another setting with different characteristics. Researchers must describe their context in enough detail that readers can assess the relevance of findings to other settings. ## Professional Escalation Criteria ### When to Seek Experienced Qualitative Supervision Researchers new to qualitative methods should seek guidance from an experienced qualitative researcher when designing their study. The Framework Method is appropriate for use in research teams even when not all members have previous experience, but it requires the leadership of an experienced qualitative researcher [10]. If your team lacks this expertise, seek consultation before proceeding. ### When to Reconsider the Research Design If your research question is fundamentally about frequency or quantity, qualitative methods alone will not answer it. Consider whether a mixed methods design would better address your question. Mixed methods research offers powerful tools for investigating complex processes and systems [12]. ### When to Escalate Ethical Concerns If data collection reveals participant distress, safety concerns, or ethical issues that exceed your expertise, escalate to the appropriate institutional review board, ethics committee, or professional support service. Participant welfare takes priority over data collection goals. ### When to Seek Reporting Guidance If you are uncertain about how to report your qualitative study, consult reporting guidelines and resources available through the EQUATOR Network [11]. Transparent reporting supports the use of your findings by others. ## Frequently Asked Questions ### What is the difference between qualitative and quantitative data? Qualitative data is non-numerical information that captures meanings, experiences, and processes. Quantitative data is numerical information that measures quantities, frequencies, and associations. Qualitative methods are defined as the collection and analysis of non-numerical data to understand concepts and experiences [1]. The choice between them depends on the research question. ### How do I know if my open-ended survey responses are qualitative data? Open-ended survey responses become qualitative data when they are designed and analyzed according to qualitative research values [3]. Simply adding open-ended questions to a survey does not make the study qualitative [3]. The responses must be treated as meaningful text to be analyzed for themes and patterns, and the questionnaire must follow a rigorous design process [3]. ### What is the Framework Method for qualitative data analysis? The Framework Method is a systematic approach to managing and analyzing qualitative data in health research [10]. It involves familiarization with the data, identifying a thematic framework, indexing, charting, mapping, and interpretation [10]. The method is appropriate for multidisciplinary research teams and works well even when not all team members have previous qualitative research experience [10]. ### How many participants do I need for a qualitative study? Qualitative studies do not use statistical power calculations to determine sample size. Instead, data collection continues until theoretical saturation is reached, meaning that new data no longer generates new insights. Studies vary widely in sample size. A study of adolescent fathers included 14 participants [2], while a study of divorce included 16 key informants and 32 main participants [9]. The appropriate sample size depends on the research question, the richness of the data, and the diversity needed. ### Can I combine qualitative and quantitative data in one study? Yes. Mixed methods research offers powerful tools for investigating complex processes and systems [12]. Integration can occur at the study design level through exploratory sequential, explanatory sequential, or convergent designs [12]. At the methods level, integration occurs through connecting, building, merging, or embedding [12]. At the interpretation level, integration occurs through narrative, data transformation, or joint display [12]. ### What is qualitative story completion? Qualitative story completion is a research method where participants write a story in response to a hypothetical story stem, often in the third person and involving fictional characters [6]. This approach generates data about social norms and cultural expectations without requiring participants to report on their direct experiences [6]. The method is being increasingly used in health research [6]. ### How do I assess the quality of qualitative research? The hierarchy of evidence for assessing qualitative health research provides explicit criteria for evaluating qualitative studies [13]. The least reliable studies are single case studies, followed by descriptive studies that provide lists of quotations without detailed analysis [13]. Conceptual studies that analyze all data according to conceptual themes receive more weight [13]. Generalizable studies that use conceptual frameworks with appropriately diversified samples and account for all data provide the best evidence for practice [13]. ### What is naturally occurring data in qualitative research? Naturally occurring data is qualitative data that exists without any research intervention [4][5]. Examples include clinical notes, policy documents, and other records that would exist regardless of the research study [4][5]. This type of data offers the advantage of not being influenced by the research process, but it presents challenges related to access, completeness, and ethical use [4][5]. ## Related Articles - [Research Assistant Careers in Life Science](/blog/careers/research-assistant-careers-in-life-science) - [Data Science Careers in Biology: Building a Transferable Evidence Portfolio](/blog/careers/data-science-careers-in-biology-building-a-transferable-evidence-portfolio) - [Git for Research Projects: Version Control for Code, Data, and Analysis Notes](/blog/guides/git-for-research-projects-version-control-for-code-data-and-analysis-notes) - [Data Visualization for Biological Research: Choosing Figures That Preserve Meaning](/blog/guides/data-visualization-for-biological-research-choosing-figures-that-preserve-meanin) - [Biomedical Science Research Building](/blog/guides/biomedical-science-research-building) ## Related Clinical & Scientific Guides * [Observational vs. Experimental Studies: How to Tell Them Apart](/blog/guides/observational-vs-experimental-studies-how-to-tell-them-apart) * [Astrocyte Single Cell Rna Seq](/blog/guides/astrocyte-single-cell-rna-seq) * [Structural Genes](/blog/guides/structural-genes) ## References and Further Reading - [Theoretical frameworks used to inform qualitative mental health research: a focus on positivism, interpretivism and critical realism](https://doi.org/10.1192/bja.2024.66). BJ Psych Advances, 2024. - [When Adolescents Become Dads: A Clinical-Qualitative Study on the Experience of Male Adolescents Attending a Prenatal Care Service.](https://doi.org/10.1007/s10995-026-04302-y). 2026. - [Using Qualitative Questionnaires in Medical Education Research.](https://pubmed.ncbi.nlm.nih.gov/38737397). Perspectives on medical education, 2024. - [Reviewing Empirical Examples of Health Research Using Naturally Occurring Data](https://doi.org/10.1007/978-3-319-94839-3_12). Using Naturally Occurring Data in Qualitative Health Research, 2018. - [Using Naturally Occurring Data in Qualitative Health Research: A Practical Guide](https://doi.org/10.1007/978-3-319-94839-3). Using Naturally Occurring Data in Qualitative Health Research A Practical Guide, 2018. - [Qualitative Story Completion: Opportunities and Considerations for Health Research.](https://pubmed.ncbi.nlm.nih.gov/36718765). Qualitative health research, 2023. - [Exploring the factors behind single-child households in Iranian families: a qualitative study.](https://doi.org/10.1017/s0021932026100601). 2026. - [Approaches to sampling and case selection in qualitative research: Examples in the geography of health](https://doi.org/10.1016/S0277-9536%2899%2900350-0). Social Science and Medicine, 2000. - [Explaining the reasons for the divorce of young couples in Western Iran: A qualitative Study from the Beneficiary's point of view.](https://doi.org/10.1371/journal.pone.0354355). 2026. - [Using the framework method for the analysis of qualitative data in multi-disciplinary health research.](https://pubmed.ncbi.nlm.nih.gov/24047204). BMC medical research methodology, 2013. - [EQUATOR Network](https://www.equator-network.org/). EQUATOR Network. - [Achieving integration in mixed methods designs-principles and practices.](https://pubmed.ncbi.nlm.nih.gov/24279835). Health services research, 2013. - [A hierarchy of evidence for assessing qualitative health research.](https://pubmed.ncbi.nlm.nih.gov/17161753). Journal of clinical epidemiology, 2007. - [Patient and public involvement in health research in low and middle-income countries: a systematic review.](https://pubmed.ncbi.nlm.nih.gov/31076471). BMJ open, 2019. - [Considerations for Employing Intersectionality in Qualitative Health Research](https://doi.org/10.1016/j.socscimed.2020.113138). Social Science & Medicine (1967), 2020. > This article is educational and does not replace institutional policy, professional advice, or applicable safety and regulatory requirements.