Key Concepts in Qualitative Research & Mixed Methods

Qualitative studies, as we learned earlier in this course, use an inductive method. Meaning, they seek to understand a phenomenon, and then use an emergency design that evolves as the research takes place in order to finally produce a theory. Qualitative designs are also subjective and use an analysis of words to understand the meaning of viewpoints and realities of the participants.

Content:

• Defining Qualitative and Mixed Methods Research
• Qualitative Research Methodologies
• Mixed Methods Research Methodologies
• Analysis of Qualitative and Mixed Methods Study Data
• Quality Measures in Mixed and Qualitative Research Methods
• Goals and Distinctive Features of Qualitative and Mixed Methods Research
• Advantages of Qualitative and Mixed Methods Research
• Methodological Rigor in Qualitative and Mixed Methods Research
• Interprofessional Education of Teams Using Mixed Methods Research
• Appraisal of Mixed Methods Research

Objectives:

1. Differentiate between qualitative, quantitative, and mixed methods research by identifying key characteristics and appropriate applications in nursing research.
2. Analyze how various qualitative research methodologies (e.g., phenomenology, grounded theory, ethnography) contribute to understanding human experiences in healthcare.
3. Evaluate the role of reflexivity in qualitative research by demonstrating how researcher bias and positionality influence data collection and interpretation.
4. Apply coding and thematic analysis techniques to organize and interpret qualitative data from interview transcripts or case studies.
5. Develop a research question appropriate for a qualitative or mixed methods study and justify the chosen methodology based on the research problem.
6. Critique the trustworthiness of a qualitative study by assessing its credibility, transferability, dependability, and confirmability.
7. Integrate qualitative and quantitative findings in a mixed methods research design to provide a comprehensive understanding of a healthcare issue.
8. Propose ethical solutions to dilemmas in qualitative research, balancing participant confidentiality with professional responsibility in healthcare settings.

Key Terms:

Case study: A research strategy that involves an in-depth exploration of a particular case (individual, group, or organization) within its real-life context, using multiple sources of evidence to gain comprehensive insights.

Confirmability: A criterion in qualitative research that addresses the neutrality and objectivity of the findings, ensuring that the results are shaped by the participants’ responses and not researcher bias, motivations, or perspectives.

Convergent Design: A mixed methods design in which qualitative and quantitative data are collected separately but concurrently, analyzed independently, and then merged to compare and interpret the results.

Cross-validation: A method used to assess the reliability and validity of research findings by comparing data from different sources, researchers, or methods to confirm the consistency and robustness of the results.

Dependability: A measure of the consistency and reliability of qualitative research findings over time and across researchers, ensuring that the research process is logical, traceable, and clearly documented.

Embedded design: A mixed methods design in which one type of data (qualitative or quantitative) is embedded within a larger design to address a specific research question or to support the primary data collection and analysis.

Ethnography: A qualitative research method that studies cultures and communities by observing and interacting with participants in their natural environment, aiming to understand their social practices, behaviors, and beliefs.

Explanatory sequential design: A mixed methods design that begins with the collection and analysis of quantitative data, followed by the collection and analysis of qualitative data to explain or elaborate on the quantitative findings.

Exploratory sequential design: A mixed methods design that starts with qualitative data collection and analysis to explore a phenomenon, followed by quantitative data collection and analysis to test or further investigate the findings from the qualitative phase.

Grounded Theory: A qualitative research method aimed at developing a theory grounded in data systematically gathered and analyzed. It involves iterative data collection and analysis to build a theoretical framework directly from the data.

Mixed methods research: A research methodology that combines both qualitative and quantitative approaches in a single study to provide a more comprehensive understanding of a research problem.

Narrative research: A qualitative approach that explores and analyzes stories or personal accounts to understand how individuals make sense of their experiences and construct their identities.

Phenomenology: A qualitative research approach that seeks to understand and describe the lived experiences of individuals regarding a particular phenomenon, focusing on how they perceive and make sense of their experiences.

Qualitative coding: The process of categorizing and labeling qualitative data (such as interview transcripts) to identify patterns, themes, and relationships within the data.

Qualitative research: A research approach that explores and understands the meaning individuals or groups ascribe to social or human problems, focusing on the complexity of human experience through methods like interviews, observations, and textual analysis.

Thematic analysis: A method of analyzing qualitative data by identifying, analyzing, and reporting themes or patterns within the data, often used to organize and describe data in rich detail.

Transferability: A criterion of qualitative research that refers to the extent to which the findings can be applied or transferred to other contexts or settings beyond the study.

Introduction

As both a science and an art, nursing relies heavily on research to inform and guide practice. While quantitative research offers invaluable insights into measurable outcomes, qualitative research provides a deeper understanding of the experiences, perceptions, and meanings behind these outcomes. In addition, mixed methods research—an approach that integrates both qualitative and quantitative methods—allows researchers to explore complex healthcare issues from multiple perspectives, offering a more comprehensive understanding. This chapter will explore the key concepts in qualitative and mixed methods research, focusing on their definitions, methodologies, analysis techniques, quality measures, and the importance of methodological rigor. This exploration will highlight these research approaches’ distinctive features and advantages and their relevance in interprofessional education and practice.

Defining Qualitative and Mixed Methods Research

Qualitative research is an approach aimed at exploring and understanding the meaning individuals or groups ascribe to a social or human problem. Unlike quantitative research, which focuses on numerical measurement and statistical analysis, qualitative research delves into non-numerical data, such as text, video, or audio, to uncover insights into concepts, opinions, or experiences. The primary goal of qualitative research is to gain a rich, contextualized understanding of the phenomena under study, often by capturing the complexity of human experience in its natural setting. Examining the quality of something infers a level of subjectivity. Qualitative research generally aims for an in-depth understanding of the experiences of others, which is subjective in nature (McCusker & Gunaydin, 2015).

A key concept is to remember that qualitative research is generally not generalizable, as we are not testing a hypothesis and not making inferences based on data. However, qualitative research often revolves around the concept of transferability. Transferability is established by providing evidence that the research study’s findings could be applicable to other contexts, situations, times, and populations.

In contrast, mixed methods research involves the deliberate combination of qualitative and quantitative research methods within a single study or a series of studies (See Figure 8.1). This approach allows researchers to harness the strengths of both qualitative and quantitative methods, offering a more nuanced and comprehensive exploration of research questions. Mixed methods research is particularly valuable in healthcare, where complex, multifaceted problems often require the integration of diverse perspectives to understand the issues at hand fully.

As an example of a qualitative study, an analysis of staff perspectives of a cardiology short stay unit was conducted (Narasimhan et al., 2009). In this pilot study, the researchers wanted to know more about staff perceptions about length of stay, the discharge process, transfer of medical information, and workplace satisfaction. The researchers interviewed the cardiologists, nurses, and other staff to find out more about these perceptions. The results will be used to address improvement of the department.

Figure Above: Mixed Methods Research Combines Qualitative and Quantitative Methodology

Quantifying Qualitative Research: When Words Become Numbers

While qualitative research is primarily focused on exploring experiences, perspectives, and meanings, researchers often use numerical techniques to help analyze patterns in qualitative data. This process, known as quantification of qualitative data, involves coding responses into categories, counting frequencies, or using software to identify recurring themes. For example, in a study on patient experiences with chronic pain, researchers may categorize interview responses into themes like “emotional distress,” “coping strategies,” and “healthcare satisfaction,” then track how often each theme appears. Although these numbers aid in organizing and interpreting qualitative data, the research remains qualitative because it prioritizes depth, context, and subjective meaning over statistical generalization.

Foundational Assumptions of Qualitative Methodology

The overarching assumptions in qualitative methodology include:

1. Truth is fluid. Meaning, it is flexible and holistic.
2. Some aspects of humanity and the human experience is best examined with qualitative methods so that we can have a deeper understanding of a person’s experience and viewpoints.

In general, qualitative design (methodology):

• Is flexible and capable to changing as the study progresses, depending on what is learned during the data collection.
• It often uses various data collection strategies in order to collect rich data.
• Is holistic in nature, with the goal of understanding of the whole.
• Researchers are involved, reflexive, and can interact with participants during data collection.

Qualitative Research Methodologies

Qualitative research encompasses a variety of methodologies, each with its unique approach to understanding human experiences and social phenomena. Unlike quantitative research, which focuses on numerical data and statistical analysis, qualitative research seeks to explore the depth, complexity, and meaning behind people’s lived experiences. These methodologies provide researchers with diverse tools to capture narratives, cultural influences, and the processes that shape behaviors and perceptions. Common qualitative research approaches include phenomenology, grounded theory, ethnography, narrative research, case studies, action research, participatory research, autoethnography, critical discourse analysis, and grounded visualization. Each of these methodologies contributes valuable insights into nursing practice, patient care, and healthcare systems by emphasizing the voices, experiences, and interactions of individuals and communities.

Phenomenology

One of the most well-known qualitative methodologies is phenomenology, which focuses on exploring individuals’ lived experiences. Phenomenology is the study of structures of consciousness as experienced from the first-person point of view (Stanford, 2013). Phenomenologists seek to understand how people perceive and make sense of their experiences in the world. For instance, in nursing, phenomenology might be used to study the lived experiences of patients with chronic illnesses, providing insights into how they cope with their conditions and the impact of these experiences on their lives.

Application in Nursing: Exploring the lived experiences of patients with chronic illnesses to understand their coping mechanisms and the impact of these experiences on their lives.

Grounded Theory

Another prominent qualitative methodology is grounded theory, which involves systematically collecting and analyzing data to develop a theory. Unlike other approaches where a theory is proposed before data collection, grounded theory is “grounded” in the data, emerging organically from the research process (Tie, Birks, & Francis, 2019). This methodology is particularly useful when existing theories do not adequately explain the phenomena being studied. For example, grounded theory could be applied in nursing research to develop a theory on how nurses in emergency departments manage patient triage under high-pressure conditions.

Application in Nursing: Developing a theory on how nurses in emergency departments manage patient triage under pressure.

Ethnography

Ethnography involves the in-depth study of people and cultures through direct observation and interaction within their natural environments. It is used commonly in nursing to describe cultures (Brown, 2017). Ethnographers immerse themselves in the community or group they are studying, often spending extended periods of time observing and participating in the daily lives of their subjects. In nursing, ethnography might be used to explore different communities’ cultural practices and beliefs regarding health and wellness, providing insights that can inform culturally sensitive care practices.

Application in Nursing: Understanding the cultural practices of different communities regarding health and wellness, providing insights that can inform culturally sensitive care practices.

Narrative Research

Narrative methodology focuses on the stories individuals tell about their lives and experiences. This approach is based on the understanding that storytelling is a fundamental way people make sense of their experiences and convey their identity. Narrative research in nursing might involve analyzing the stories of cancer survivors to understand their journey and how the illness has shaped their sense of self. When the narrative is revolved around history, this could be considered historical research which is a retrospective examination of events to explain and understand the situations further (Schmidt & Brown, 2019).

Application in Nursing: Analyzing the stories of cancer survivors to understand their journey and the impact of illness on their identity and sense of self.

Case Study

Case study research involves an in-depth exploration of a single case within its real-life context, an individual, group, or event. It is a comprehensive investigation of individuals or groups of people to gain insight into a specific situation (Brown, 2017). This methodology is particularly useful for studying complex phenomena in detail. In nursing, a case study might be used to examine the implementation of a new nursing protocol in a specific hospital unit, offering insights into the challenges and successes encountered during the process.

Application in Nursing: A detailed examination of implementing a new nursing protocol in a specific hospital unit, offering insights into the challenges and successes encountered during the process.

Action Research

Action research is a participatory approach that involves researchers and participants working collaboratively to identify problems, implement changes, and assess their impact. It is particularly valuable in healthcare settings where continuous improvement is needed. In nursing, action research can be used to evaluate and refine patient care practices by involving nurses in identifying challenges and developing solutions.

Application in Nursing: Collaborating with nurses to improve patient handoff procedures, leading to better communication and patient safety.

Participatory Research

Participatory research emphasizes collaboration between researchers and participants, ensuring that those affected by the study actively contribute to the research process. This approach is often used in community health settings, where researchers work alongside patients, families, or communities to address health disparities. In nursing, participatory research might focus on empowering patients with chronic illnesses to co-develop self-management strategies tailored to their needs.

Application in Nursing: Engaging diabetic patients in designing a patient-centered education program to improve disease management.

Autoethnography

Autoethnography blends personal narrative with cultural analysis, allowing researchers to use their own experiences as a lens to explore broader social or healthcare phenomena. This method is particularly useful for self-reflection on nursing practice, patient interactions, or the emotional toll of caregiving. In nursing, autoethnography might be used to examine a nurse’s experience of burnout and resilience in a high-stress hospital environment.

Application in Nursing: A nurse reflecting on their personal experience with compassion fatigue and resilience, providing insights into coping mechanisms and support systems.

Critical Discourse Analysis

Critical discourse analysis (CDA) examines language, communication, and power structures in healthcare settings. This method is valuable for analyzing policies, patient-provider interactions, and media portrayals of health issues. In nursing, CDA can be used to study how nurses communicate with patients from diverse backgrounds and identify ways to reduce healthcare disparities.

Application in Nursing: Analyzing how nurses communicate with non-English-speaking patients to improve culturally competent care.

Grounded Visualization

Grounded visualization combines qualitative research with visual elements such as photographs, drawings, or videos to capture and analyze experiences. This method is particularly useful when studying complex emotions or interactions in healthcare settings. In nursing, grounded visualization might involve analyzing images created by patients with chronic pain to understand their emotional and physical struggles.

Application in Nursing: Using patient-generated drawings to explore the lived experience of chronic pain and its impact on daily life.

Table Above: Types of Qualitative Research

Reflexivity and Examining Beliefs in Qualitative Research

Reflexivity is a critical component of qualitative research that enhances rigor by encouraging researchers to continuously examine their own beliefs, biases, and assumptions throughout the research process. Since qualitative research often involves interpreting human experiences, researchers must recognize how their perspectives might influence data collection, analysis, and conclusions. Engaging in reflexivity helps ensure transparency and credibility, as researchers actively reflect on how their positionality—such as personal experiences, cultural background, or professional knowledge—shapes their interpretation of findings.

To establish rigor, qualitative researchers should also focus on examining the beliefs of both themselves and their participants. This involves critically analyzing how personal and societal beliefs influence the study topic, participant responses, and researcher interpretations. Strategies to enhance reflexivity include maintaining reflexive journals, engaging in peer debriefing, and incorporating participant validation techniques to ensure findings are accurately represented. By integrating reflexivity and belief examination, researchers strengthen the trustworthiness of their study and provide a more authentic representation of lived experiences.

Mixed Methods Research Methodologies

To review, mixed methods research combines quantitative and qualitative research methods and techniques for collecting and analyzing data that increase the understanding gained from the research data, offering a comprehensive perspective on research problems (Parker et al., 2017). Remember, not all research is simply qualitative or quantitative. Research in which both types of methodology is utilized is called mixed-methods research. Mixed methods is a research approach whereby researchers collect and analyze both quantitative and qualitative data within the same study.

Growth of mixed methods research in nursing and healthcare has occurred at a time of internationally increasing complexity in healthcare delivery. Mixed methods research draws on potential strengths of both qualitative and quantitative methods, allowing researchers to explore diverse perspectives and uncover relationships that exist between the intricate layers of our multifaceted research questions. As providers and policy makers strive to ensure quality and safety for patients and families, researchers can use mixed methods to explore contemporary healthcare trends and practices across increasingly diverse practice settings (Shorten & Smith, 2017).

Convergent Design

One common mixed methods approach is the convergent design, in which qualitative and quantitative data are collected simultaneously but analyzed separately. The results are then compared or combined to draw overarching conclusions. For instance, a study investigating the effectiveness of a health intervention might collect survey data to measure outcomes while also conducting interviews to understand participants’ experiences with the intervention. The convergent design allows researchers to validate findings across different methods and fully understand the intervention’s impact.

Application in Nursing: A study investigating the effectiveness of a health intervention might collect survey data to measure outcomes while also conducting interviews to understand participants’ experiences with the intervention.

Explanatory Sequential Design

Another mixed methods approach is the explanatory sequential design, which involves two distinct phases. First, quantitative data is collected and analyzed to identify patterns or relationships. This is followed by qualitative data collection to explain or build upon the initial findings. For example, in nursing research, a survey might be conducted to identify factors associated with job satisfaction among nurses, followed by focus groups to explore the reasons behind these factors in depth. The explanatory sequential design is particularly useful for studies where the initial quantitative results raise questions that require further exploration through qualitative methods.

Application in Nursing: For example, in nursing research, a survey might be conducted to identify factors associated with job satisfaction among nurses, followed by focus groups to explore in depth the reasons behind these factors.

Exploratory Sequential Design

In contrast, the exploratory sequential design begins with qualitative data collection to explore a phenomenon, followed by quantitative data collection to test or quantify the findings. This approach is useful when little is known about a research topic, and the qualitative phase informs the development of quantitative measures. In nursing, an exploratory sequential design might involve conducting interviews with nurses to identify key challenges in using a new electronic health record system, followed by a survey to assess the prevalence of these challenges across a larger sample.

Application in Nursing: An exploratory sequential design might involve conducting interviews with nurses to identify key challenges in using a new electronic health record system, followed by a survey to assess the prevalence of these challenges across a larger sample.

Image Above: Basic Mixed Methods Research Designs. Adapted from Creswell, J.W. (2014). A concise introduction to mixed methods research. SAGE Publications.

Embedded Design

Finally, the embedded design integrates qualitative and quantitative data within a single study, but one type of data plays a supportive role. For instance, a large-scale quantitative study on patient outcomes might embed qualitative interviews to provide additional context or explanations for the quantitative findings. This design allows researchers to enhance the depth of their analysis by incorporating multiple data types, even when the primary focus is on one method.

Application in Nursing: A large-scale quantitative study on patient outcomes might embed qualitative interviews to provide additional context or explanations for the quantitative findings.

Analysis of Qualitative and Mixed Methods Study Data

Analysis of qualitative data is quite different than that of quantitative data. The following section describes commonly used data analysis strategies used in qualitative methods. The intent is to introduce terms and how these relate to qualitative analysis.

Qualitative analysis varies depending on the research design used. For example, phenomenological studies use interpretive analysis, which focuses on understanding lived experiences by identifying significant statements and clustering them into themes. In contrast, narrative research often employs thematic and structural analysis, examining the way individuals construct their personal stories. Likewise, grounded theory follows a structured coding process (open, axial, and selective coding) to develop a theory from the data, but axial coding is not necessarily used in other qualitative approaches. It is essential to align the analysis method with the qualitative research design to ensure rigor and accuracy in interpreting data.

Coding

Qualitative data analysis involves organizing and interpreting non-numerical information to identify patterns, themes, and meanings. A key aspect of qualitative data analysis is coding, where the researcher systematically categorizes the data into themes or concepts that emerge from the data. Line by line coding of the transcript is done to identify reappearing concepts in the data (Schmidt & Brown, 2019). Coding is the process of labeling and organizing your qualitative data to identify different themes and the relationships between them. When coding, you assign labels to words or phrases that represent important (and recurring) themes in each response.

Open coding is the grouping of data into main categories (Schmidt & Brown, 2019). With open coding, you break your data into discrete parts and create “codes” to label them. As its name would imply, open-coding is meant to open you up to new theoretical possibilities, as you first engage with your qualitative data.

After open coding is completed, the categories are analyzed (Schmidt & Brown, 2019).  Axial coding is when the researcher begins to draw connections between ideas in their research.

This process is iterative, with the researcher continually refining the codes as new insights are gained. For instance, in a study exploring patient experiences with chronic pain, the researcher might identify codes such as “emotional impact,” “coping strategies,” and “interaction with healthcare providers,” which are then used to develop a thematic understanding of the data. There are some software programs that analyze qualitative data in transcripts to look for themes or commonly appearing concepts (Schmidt & Brown, 2019). Researchers can also do this manually. Some use index cards, tally marks, and other methods to note common themes/patterns (Leibold, 2020).

Thematic Analysis

Thematic analysis is a common approach in qualitative research, where the researcher identifies and interprets patterns or themes within the data. These themes represent the core ideas that emerge from the data and provide insights into the research question. In the context of nursing, thematic analysis might be used to explore themes related to patient satisfaction with care, such as “communication with nurses,” “perceived competence,” and “emotional support.”

Thematic analysis is flexible and can be applied to various types of qualitative data, such as interviews, focus groups, or written texts. The process involves coding the data, identifying significant themes, and interpreting these themes to understand how they relate to the research question. The researcher’s role in thematic analysis is crucial, as the process is interpretive and requires a deep understanding of the context and content of the data.

For example, in a study exploring nurses’ experiences with burnout, a researcher might identify themes such as “emotional exhaustion,” “lack of support,” and “coping strategies.” Thematic analysis would involve not just identifying these themes, but also interpreting how they interact and contribute to the overall experience of burnout.

 Key Steps in Thematic Analysis:

1. 1. Familiarization with Data: Immerse yourself in the data by reading and re-reading it to gain a deep understanding of the content.
   2. Initial Coding: Generate initial codes by identifying key features of the data that are relevant to the research question. These codes are labels for important segments of the data.
   3. Searching for Themes: Group the initial codes into broader themes that represent significant patterns across the data. A theme is a coherent and meaningful pattern in the data that relates to the research question.
   4. Reviewing Themes: Refine the themes by reviewing and checking if they work in relation to the coded extracts and the entire dataset. Some themes may be combined, refined, or discarded.
   5. Defining and Naming Themes: Clearly define each theme, ensuring it captures the essence of what the data conveys. Provide each theme with a descriptive name.
   6. Writing Up: Write a report that tells the story of the data, illustrating the themes with examples and showing how they answer the research question. The write-up should provide a coherent and persuasive narrative of the data’s meaning.

While thematic analysis is widely used, its application varies based on the qualitative research approach. In narrative research, themes are drawn from the structure and sequence of stories, focusing on how individuals construct meaning through their narratives. In ethnographic studies, thematic analysis incorporates cultural interpretation, linking patterns in data to broader social and cultural contexts. Case study research may apply thematic analysis across multiple data sources (interviews, observations, documents) to develop a comprehensive understanding of the case. By recognizing these distinctions, researchers ensure that their analytic method remains true to the research design, enhancing the validity of qualitative findings.

Figure Above: Thematic Analysis

Content Analysis

The researcher systematically categorizes verbal or behavioral data to classify and interpret the content in content analysis. This approach is often used to analyze textual data, such as interview transcripts or open-ended survey responses. Content analysis allows for both quantitative and qualitative interpretation of data, as it involves counting the frequency of certain themes or categories and exploring their meanings. For example, a researcher studying patient feedback might use content analysis to quantify how often certain issues, like “waiting times” or “communication with staff,” are mentioned. By analyzing the frequency of these themes, the researcher can identify common concerns and areas for improvement.

 Key Steps in Content Analysis:

1. 1. Data Collection: Gather textual, visual, or audio data relevant to the research question.
   2. Coding: Develop a coding scheme, where data is categorized into themes or concepts. Codes can be predetermined or emerge from the data.
   3. Quantification: Count the frequency of each code or theme to identify patterns and trends in the data.
   4. Interpretation: Analyze the patterns to draw conclusions about the underlying meaning, trends, or social phenomena the data represents.
   5. Reporting: Present the findings with both quantitative data (e.g., frequency counts) and qualitative interpretations.

Narrative Analysis

 Narrative analysis is a qualitative research method that focuses on the stories people tell to understand how they make sense of their experiences and the world around them. This approach is based on the idea that individuals construct meaning through narrative or storytelling, and these narratives provide insight into their identities, values, and beliefs.

In narrative analysis, the researcher collects stories, often through interviews, and then examines these stories to understand how they are structured and what they reveal about the narrator’s experiences. Unlike other forms of qualitative analysis that might focus on coding and categorizing data into themes, narrative analysis pays close attention to the structure, content, and context of the stories themselves.

For example, a researcher might study the stories of cancer survivors to understand how they narrate their journey through illness, how they construct their identity as survivors, and what meanings they attach to their experiences. The researcher would analyze not just what the participants say, but how they say it—the plot structure, the characters involved, and the moral or lesson of the story.

Key Steps in Narrative Analysis:

1. 1. Data Collection: Gather narratives through interviews, personal stories, or written accounts.
   2. Data Analysis: Analyze the structure of the narrative (beginning, middle, end), the use of language, and the roles of different characters in the story.
   3. Interpretation: Understand how the narrative reflects the individual’s identity, values, and social context.

Discourse Analysis

 Discourse analysis is a qualitative research method that studies how language is used in texts and conversations to construct meaning and social reality. Unlike narrative analysis, which focuses on personal stories, discourse analysis examines the broader context of communication, including how power, social norms, and ideologies are embedded in language.

Discourse analysis involves studying written or spoken language in its social context to understand how social structures shape and reshape it. Researchers might analyze anything from media articles to political speeches to everyday conversations to explore how language influences perceptions and behavior.

For example, in nursing, a researcher might use discourse analysis to study how healthcare professionals discuss patient care in team meetings. The analysis might reveal how certain discourses (e.g., medical versus holistic) dominate and how these shape the care provided to patients.

Key Steps in Discourse Analysis:

1. 1. Data Collection: Collect texts or transcripts of conversations, interviews, or media content.
   2. Data Analysis: Examine language use, focusing on how words, phrases, and rhetorical structures construct meaning and reflect power relations.
   3. Interpretation: Understand how discourse shapes social reality, influences behavior, and reinforces or challenges social norms.

Grounded Theory Analysis

 Grounded theory analysis is a qualitative research method that involves generating a theory directly from the data. Unlike traditional research approaches, where the researcher starts with a hypothesis, grounded theory begins with data collection. The theory emerges as the researcher analyzes the data, making it “grounded” in the actual experiences of participants.

The grounded theory process is iterative and involves constant comparison of data. Researchers collect data, code it into categories, and continually compare new data with existing categories. This process leads to the refinement of categories and the eventual development of a theory that explains the phenomenon under study.

For example, a researcher might use grounded theory to study how nurses cope with stress in the workplace. By collecting data from interviews with nurses, coding the data into categories such as “support systems” and “coping mechanisms,” and continually refining these categories, the researcher might develop a theory about the role of social support in managing workplace stress.

Key Steps in Grounded Theory Analysis:

1. 1. Data Collection: Collect data through interviews, observations, or other qualitative methods.
   2. Open Coding: Break down the data into discrete parts and code it into categories.
   3. Axial Coding: Relate categories to each other to identify patterns and relationships.
   4. Selective Coding: Integrate and refine categories to develop a core category that forms the basis of the emerging theory.
   5. Theory Development: Develop a theory that explains the relationships between the categories and the overall phenomenon.

Figure Above: Common Methods of Qualitative Data Analysis

Interpretive Analysis in Phenomenology

In phenomenological research, the focus is on uncovering the meaning of lived experiences. Unlike thematic analysis, which simply identifies patterns, interpretive phenomenological analysis (IPA) seeks to deeply explore the essence of experience through reflection and interpretation. Researchers use hermeneutic analysis, which involves reading and re-reading transcripts, highlighting meaningful phrases, and interpreting their significance within the participant’s context. This process requires bracketing (setting aside biases) and engaging in reflection to ensure that the researcher does not impose their own assumptions on the data.

For example, a phenomenological study on nurses’ experiences with patient death may involve analyzing how nurses describe emotional responses, coping mechanisms, and the meaning they assign to end-of-life care. The researcher does not just categorize themes but interprets the deeper significance of these experiences in shaping nursing identity and emotional resilience.

In mixed methods research, the analysis involves the integration of both qualitative and quantitative data. This can be done through various techniques, such as merging, where qualitative and quantitative data are combined in a single analysis, or connecting, where one set of data informs the analysis of the other. For example, in a mixed methods study on patient adherence to medication, the researcher might quantify survey results to measure adherence rates while also conducting interviews to understand the reasons behind these rates. The integration of both types of data provides a richer understanding of the research problem.

One useful tool in mixed methods research is the joint display, which involves creating tables or graphs that integrate qualitative and quantitative data in a visually coherent way. For example, a joint display might present quantitative survey results on patient satisfaction alongside qualitative quotes from patient interviews, allowing the researcher to compare the different types of data.

Quality Measures in Mixed and Qualitative Research Methods

Ensuring the quality and credibility of research findings is critical in both qualitative and mixed-methods research. In qualitative research, the concept of trustworthiness is used to evaluate the rigor and validity of the study. Trustworthiness encompasses several key criteria, including credibility, which refers to the confidence in the truth of the findings. Credibility can be enhanced through techniques such as triangulation, where multiple data sources or methods are used to corroborate the findings, and member checking, where participants are asked to review and verify the accuracy of the findings.

Transferability is another important criterion in qualitative research, referring to the extent to which the findings can be applied or generalized to other contexts. While qualitative research does not aim for generalization in the same way as quantitative research, providing detailed descriptions of the research context and participants can help readers assess the applicability of the findings to their own settings.

Dependability involves the stability of the data over time and under different conditions. Researchers can enhance dependability by providing a clear and detailed account of the research process, including how data was collected, analyzed, and interpreted.

Confirmability refers to the degree to which the findings are shaped by the participants and not by researcher bias. Maintaining an audit trail, a detailed record of all research decisions and steps, can help ensure confirmability.

Validity and Rigor

In mixed-methods research, quality measures focus on the integration of qualitative and quantitative components. Researchers must ensure that the methods are effectively combined and that the findings from both approaches are mutually reinforcing. Validity in mixed methods research refers to the extent to which the results are trustworthy and credible. This can be assessed through cross-validation, where the findings from one method are used to validate the findings from the other.

Rigor in mixed methods research involves the thoroughness and accuracy of the study. This includes careful planning and execution of both the qualitative and quantitative components, as well as clear documentation of the integration process. Transparency is key, as researchers must clearly document how and why they chose to integrate the methods and how they interpreted the combined data.

Goals and Distinctive Features of Qualitative and Mixed Methods Research

The goals of qualitative and mixed methods research differ in significant ways, reflecting their distinct approaches to understanding human experience and social phenomena. The primary goal of qualitative research is to explore and understand complex phenomena that cannot be easily quantified. Qualitative research seeks to uncover the meanings, perspectives, and experiences of individuals, often focusing on the richness and depth of the data rather than generalizability. For example, a qualitative study might explore how patients with chronic illness navigate their daily lives, providing insights into their challenges, coping strategies, and support needs.

In contrast, mixed-methods research aims to provide a more comprehensive understanding of a research problem by integrating qualitative and quantitative approaches. Mixed methods research allows researchers to validate findings across different methods, offering a more robust and nuanced understanding of the issues being studied. For instance, a mixed methods study might combine a quantitative survey of patient satisfaction with qualitative interviews to explore the reasons behind the survey results. This approach captures the breadth of the phenomenon and probes into the underlying reasons and meanings.

The distinctive features of qualitative research include its emphasis on rich, detailed data, flexibility in data collection, and a focus on context. Qualitative research often involves open-ended data collection methods, such as interviews and observations, allowing researchers to adapt their approach as new insights emerge. This flexibility is particularly valuable in exploring complex, dynamic phenomena that may not be fully understood at the outset of the study.

Mixed methods research, on the other hand, is characterized by its combination of breadth and depth. By integrating quantitative and qualitative data, mixed methods research offers a more holistic view of the research problem. This approach is particularly valuable in healthcare, where complex issues often require multiple perspectives to fully understand the different dimensions of the problem. Mixed methods research also involves triangulation, where multiple data sources or methods are used to confirm and validate the findings, enhancing the credibility and reliability of the study.

Advantages of Qualitative and Mixed Methods Research

Both qualitative and mixed methods research offer unique advantages that make them valuable tools in nursing research. The primary advantage of qualitative research is its ability to provide deep insights into complex issues. By capturing the richness of participants’ experiences and perspectives, qualitative research can uncover nuances and subtleties that might be overlooked in quantitative research. This depth of understanding is particularly important in healthcare, where patients’ experiences, beliefs, and values play a critical role in shaping their health behaviors and outcomes.

Another advantage of qualitative research is its flexibility. Qualitative researchers can adapt their data collection methods as new insights emerge, allowing for a more responsive and dynamic approach to exploring the research problem. This flexibility is particularly valuable in exploratory research, where the research questions and objectives may evolve as the study progresses.

Mixed methods research offers the advantage of combining the strengths of both qualitative and quantitative research. By integrating these approaches, mixed methods research can provide a more comprehensive and nuanced understanding of the research problem. This approach allows researchers to explore different dimensions of the problem, validate findings across methods, and offer a more robust and credible interpretation of the data.

Another advantage of mixed methods research is its ability to address complex research questions that require multiple perspectives. By combining qualitative and quantitative data, mixed methods research can capture both the breadth and depth of the problem, offering a more holistic view of the issues being studied. This is particularly valuable in healthcare research, where multiple factors—such as biological, psychological, and social determinants—often interact to influence health outcomes.

Methodological Rigor in Qualitative and Mixed Methods Research

Ensuring methodological rigor is critical in both qualitative and mixed methods research. In qualitative research, rigor is achieved through techniques such as triangulation, member checking, and audit trails. Triangulation involves using multiple data sources, methods, or researchers to validate the findings, enhancing the credibility and reliability of the study. For example, in a qualitative study on patient experiences with chronic illness, the researcher might triangulate data from interviews, observations, and patient diaries to ensure a comprehensive and accurate understanding of the phenomenon.

Member checking involves sharing the findings with participants to verify their accuracy and ensure that the interpretations align with the participants’ perspectives. This technique enhances the credibility of the study by ensuring that the findings are grounded in the participants’ experiences rather than the researcher’s biases. An audit trail involves maintaining a detailed record of all research decisions, data collection procedures, and data analysis steps, allowing others to assess the trustworthiness of the study.

In mixed methods research, methodological rigor is achieved through careful integration of qualitative and quantitative methods. This involves ensuring that the methods are effectively combined and that the findings from both approaches are mutually reinforcing. Integration can take various forms, such as merging qualitative and quantitative data in a single analysis or connecting the findings from one method to inform the analysis of the other. Cross-validation is another technique used to ensure rigor in mixed methods research, where the findings from one method are used to validate the findings from the other.

Transparency is also crucial in mixed methods research, as researchers must clearly document how and why they chose to integrate the methods and how they interpreted the combined data. This involves providing a detailed account of the research process, including the rationale for using a mixed methods approach, the specific techniques used to integrate the data, and the strategies employed to ensure the validity and reliability of the findings.

Interprofessional Education of Teams Using Mixed Methods Research

Mixed methods research is particularly valuable in interprofessional education, where diverse perspectives are crucial for understanding complex healthcare issues. Interprofessional education involves collaboration among professionals from different disciplines, each contributing their expertise to the research process. This collaborative approach is essential in healthcare, where complex problems often require input from multiple disciplines, such as nursing, medicine, pharmacy, and social work.

By integrating qualitative and quantitative data, mixed methods research allows interprofessional teams to explore healthcare issues from multiple perspectives, enhancing their understanding of the problem and informing the development of more effective interventions. For example, a mixed methods study on patient safety might involve nurses, physicians, and pharmacists, each contributing their perspective on the factors that influence safety in healthcare settings. The integration of qualitative and quantitative data allows the team to capture both the broad patterns and the nuanced experiences that shape patient safety.

Mixed methods research also enhances problem-solving within interprofessional teams by combining the analytical rigor of quantitative research with the depth of qualitative insights. This approach allows teams to explore complex problems in a comprehensive and nuanced way, offering a more holistic view of the issues and informing the development of more effective solutions.

Appraisal of Mixed Methods Research

When appraising mixed methods research, several key criteria should be considered. Relevance is one of the most important factors, as it assesses how well the study addresses the research question and contributes to the existing body of knowledge. In addition, the methodological transparency of the study is crucial, as it ensures that the research process is clearly documented and that the integration of qualitative and quantitative methods is justified and well-executed.

The quality of integration is another important criterion in appraising mixed methods research. This involves assessing how well the qualitative and quantitative data are combined and whether the integration enhances the overall understanding of the research problem. For example, a well-integrated mixed methods study might use qualitative data to provide context and explanation for quantitative findings, offering a more comprehensive and nuanced interpretation of the results.

Rigor and validity are also essential considerations in the appraisal of mixed methods research. Rigor refers to the thoroughness and accuracy of the study, while validity assesses the credibility and trustworthiness of the findings. These criteria can be evaluated through techniques such as cross-validation, triangulation, and member checking, which help ensure that the study’s conclusions are well-supported by the data.

 In summary, qualitative and mixed methods research are essential tools in the nurse’s EBP toolkit, offering insights that go beyond what can be measured with numbers alone. By understanding and applying these methods, nurses can contribute to the development of evidence-based practices that are both comprehensive and contextually relevant. Whether exploring patient experiences or integrating diverse perspectives, the skills learned in this chapter will be invaluable in your journey as a nurse researcher.

EBP Poster Application! Continue polishing up your Results and Discussion section of your posters. Keep looking for more articles. Continue to update your Synthesis of Evidence Table. Keep those Google Docs active with lots of contributions. Remember, as you are working on your poster template, if you are using Google Docs, start working on transferring your content directly onto a PPT version of the template so that your formatting stays intact. Google Docs PPT version often skews the formatting in transitioning it to a PPT.

References & Attribution

“Green check mark” by rawpixel licensed CC0.

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