testing hypothesis in qualitative research

What Is Hypothesis Testing In Qualitative Research?

Table of Contents

Hypothesis testing in qualitative research: understanding the basics.

Hypothesis testing in qualitative research is a nuanced approach that combines the exploration of human experiences with the rigor of scientific inquiry. Unlike quantitative research, which relies on numerical data, qualitative hypothesis testing delves into meanings, experiences, and perspectives to generate or test hypotheses. This allows researchers to gain a deeper understanding of human phenomena .

In qualitative research, hypothesis testing involves examining patterns and themes that emerge from qualitative data . Researchers may use methods such as interviews, focus groups, or observations to gather rich, detailed information. These insights can then be used to develop hypotheses or to test existing ones, making hypothesis testing in qualitative research an integrative process.

By incorporating hypothesis testing, qualitative research bridges the gap between narrative findings and scientific validity. This approach not only enhances the credibility of qualitative studies but also provides a robust framework for examining complex social phenomena. By understanding how qualitative hypothesis testing works, readers can appreciate its unique contribution to the field of research.

Fundamentals of Hypothesis Testing

Hypothesis testing in qualitative research involves creating and assessing assumptions to understand complex phenomena. It guides researchers in exploring patterns and relationships within data.

Definition and Purpose

Hypothesis testing is a method used to make decisions based on data analysis. In qualitative research, it isn’t about proving a hypothesis right or wrong but rather exploring and understanding data-driven insights.

It starts with forming an idea or assumption . This idea is tested through data collection and analysis. The goal is to see if the evidence supports the initial assumption.

Qualitative hypothesis testing helps strengthen research findings . It provides a structured way to understand relationships and patterns in the data. It is particularly useful when researchers want to explore new theories or concepts.

Types of Hypotheses in Qualitative Research

In qualitative research, there are two main types of hypotheses: descriptive and explanatory .

Descriptive hypotheses aim to describe the characteristics of a phenomenon. For example, a researcher might hypothesize that a specific teaching method improves student engagement.

Explanatory hypotheses seek to explain the relationships between different variables. An example might be hypothesizing that student engagement is higher in smaller classes due to more personalized attention.

By using these different types, researchers can gain a deeper understanding of the subject matter and provide insightful analysis through their studies.

The Role of Theory in Hypothesis Formation

Theory plays a crucial role in forming hypotheses. It helps researchers decide whether to use inductive or deductive reasoning and build a solid conceptual framework.

Inductive vs. Deductive Reasoning

Inductive and deductive reasoning are two approaches to hypothesis formation. Inductive reasoning starts from specific observations and moves to broader generalizations. This approach is useful in qualitative research, as patterns emerge from the data. Deductive reasoning , on the other hand, begins with a theory or general statement and examines specific cases to test it.

In qualitative research, inductive reasoning allows researchers to explore new theories. It focuses on the data at hand and looks for trends and patterns. Deductive reasoning is more common in quantitative research, but it can also be used in qualitative studies to test existing theories. Both methods have their place, depending on the research goals and context.

Building a Conceptual Framework

A conceptual framework provides a structure for hypothesis formation. It involves identifying key concepts and their relationships. This framework helps in formulating research questions and hypotheses based on existing theories.

For instance, in qualitative data analysis , theories guide the way data is interpreted. By understanding these theories, researchers can develop more focused and meaningful hypotheses. Conceptual frameworks also ensure consistency and direction in research, making hypotheses more precise and testable.

Building a framework involves reviewing literature, identifying theoretical models, and examining previous studies. This critical step lays a foundation for sound hypothesis testing.

Methodological Approaches

Hypothesis testing in qualitative research often leverages specific methodologies to explore complex phenomena. Three key approaches are case studies, ethnography, and grounded theory. Each method provides unique insights and ways to understand data.

Case Studies

Case studies involve in-depth examination of a single instance or event. They’re useful for exploring detailed aspects of a particular subject. This method allows researchers to gather rich, qualitative data.

In hypothesis testing, case studies can be used to test theories by applying them to real-world situations. Researchers collect detailed information through interviews, observations, and documents. This data helps in understanding the nuances of the case. Case studies are particularly effective in fields like psychology, education, and social sciences.

A key benefit is the ability to generate a comprehensive view of the subject. However, the method requires careful planning and a structured approach to ensure the data is reliable.

Ethnography

Ethnography focuses on studying cultures and communities by immersing the researcher in the environment. This approach is valuable for understanding behaviors, traditions, and interactions from an insider perspective.

In hypothesis testing, ethnography helps in forming and examining hypotheses about cultural practices. Researchers live within the community and participate in daily activities. This method relies heavily on participant observation and interviews.

Ethnography is widely used in anthropology, sociology, and healthcare research. It provides deep insights into the social context of the studied group. Yet, it demands significant time and effort, as well as maintaining ethical considerations throughout the study.

Grounded Theory

Grounded theory involves developing theories based on data collected during research. Unlike other methods, it doesn’t start with a preconceived hypothesis. Instead, hypotheses are generated from patterns observed in the data.

This approach is suitable for exploring areas where existing theories are insufficient. Researchers collect and analyze data simultaneously, allowing emerging themes to shape the study. Techniques such as coding and memo-writing are integral to grounded theory.

Grounded theory is often used in fields like sociology, psychology, and nursing. It provides a systematic way to build new theories grounded in real-world data. The iterative process ensures that findings are closely tied to the observed phenomena, making the results highly relevant and applicable.

Data Collection and Analysis

Data collection in qualitative research involves gathering in-depth insights about a research problem. Methods may include interviews, focus groups, and observations. Interviews can be structured, semi-structured, or unstructured, each offering different levels of flexibility.

Focus groups encourage discussion among participants to provide a range of perspectives. Observations allow researchers to see actual behaviors and interactions in real-life settings.

Common Data Collection Methods:

• Interviews: One-on-one conversations to gather detailed information.
• Focus Groups: Group discussions to explore collective views.
• Observations: Watching and recording behaviors in natural settings.

Data analysis in qualitative research is iterative and ongoing. Unlike quantitative analysis, qualitative data analysis involves identifying patterns and themes. This process can be time-consuming but provides deep understanding.

Researchers often use coding to organize data. Coding involves labeling segments of data with descriptive tags. These tags help identify recurring themes and patterns. Once coded, data can be grouped to facilitate analysis.

Steps in Data Analysis:

• Transcription: Convert audio or video recordings into text.
• Coding: Assign labels to data segments.
• Theming: Identify patterns and group coded data into themes.
• Interpretation: Analyze themes to draw conclusions.

Using tools like software can aid in the coding and analysis process. Software such as NVivo or Atlas.ti helps manage large volumes of data efficiently. These tools assist in keeping the analysis organized and systematic.

Qualitative data collection and analysis require careful planning and execution. By using the appropriate methods and tools, researchers can gain valuable insights into their research questions.

Interpreting Results and Drawing Conclusions

Interpreting results in qualitative hypothesis testing involves ensuring the findings are credible and identifying the study’s limitations. This helps provide a reliable understanding of the research outcomes.

Credibility and Trustworthiness

Credibility is about how believable and convincing the findings are. Researchers should check if the data truly reflects what participants said and did.

To ensure trustworthiness, researchers use methods like * triangulation , which involves using multiple data sources or methods to confirm the findings. Member checks allow participants to review the findings and confirm their accuracy. Detailed documentation of the research process also helps others assess the study’s reliability.

Reflexivity involves researchers reflecting on their biases and how they might affect the research. Being open about potential biases adds to the credibility of the findings.

Limitations of Qualitative Hypothesis Testing

Every study has its limitations . In qualitative hypothesis testing, it’s important to acknowledge these to give a clear picture of how reliable the findings are.

Sample size can be a limitation. Qualitative studies often have smaller samples, which might not represent the broader population.

Subjectivity is another limitation. Researchers’ interpretations can introduce bias. To minimize this, using a clear and systematic approach is key.

Generalizability is often limited in qualitative research. The findings might not apply to other settings or groups. Researchers should be clear about this scope and context.

Recognizing these limitations helps set realistic expectations about the findings and their applicability.

Frequently Asked Questions

This section addresses key differences in hypothesis testing between qualitative and quantitative research, its formulation in qualitative studies, and its role in qualitative research design.

How does hypothesis testing differ between qualitative and quantitative research?

Quantitative research uses hypothesis testing to measure and analyze numerical data. It focuses on statistical significance. Qualitative research, on the other hand, explores more abstract and subjective data. It may use hypothesis testing less formally to understand patterns or themes.

Can you provide examples of how a hypothesis is formulated in qualitative studies?

In qualitative studies, a hypothesis might explore how social factors affect individual behavior. For instance, one might hypothesize that community support positively impacts mental health among teenagers. These hypotheses are tested by observing and analyzing participants’ experiences and perspectives.

What role does the null hypothesis play in qualitative research design?

The null hypothesis suggests there is no relationship between variables. In qualitative research, it might not be used as rigidly as in quantitative studies. Researchers focus more on exploring relationships and gaining insights rather than strictly proving or disproving a null hypothesis.

How is hypothesis testing of qualitative data conducted?

Hypothesis testing in qualitative data involves coding and categorizing data to identify themes. Researchers look for patterns and relationships within these themes. They may gather narratives, conduct interviews, or observe behaviors to support or refine their hypotheses.

In what ways can qualitative research include hypotheses?

Qualitative research can start with hypotheses to guide the study. These hypotheses help frame research questions and focus the investigation. They can evolve as more data is collected. Hypotheses provide a starting point for exploring complex social and behavioral phenomena.

What are the characteristics of a good research hypothesis in qualitative studies?

A good qualitative research hypothesis is clear and specific. It addresses a particular aspect of the study and is open to exploration. It should be flexible to accommodate new findings and insights. The hypothesis must also be relevant to the research context and questions.

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StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

StatPearls [Internet].

Qualitative study.

Steven Tenny ; Janelle M. Brannan ; Grace D. Brannan .

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Last Update: September 18, 2022 .

• Introduction

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. [1] Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." [2] Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. [2] One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. [3] Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. [4] While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

Qualitative Research Approaches

Ethnography

Ethnography as a research design originates in social and cultural anthropology and involves the researcher being directly immersed in the participant’s environment. [2] Through this immersion, the ethnographer can use a variety of data collection techniques to produce a comprehensive account of the social phenomena that occurred during the research period. [2] That is to say, the researcher’s aim with ethnography is to immerse themselves into the research population and come out of it with accounts of actions, behaviors, events, etc, through the eyes of someone involved in the population. Direct involvement of the researcher with the target population is one benefit of ethnographic research because it can then be possible to find data that is otherwise very difficult to extract and record.

Grounded theory

Grounded Theory is the "generation of a theoretical model through the experience of observing a study population and developing a comparative analysis of their speech and behavior." [5] Unlike quantitative research, which is deductive and tests or verifies an existing theory, grounded theory research is inductive and, therefore, lends itself to research aimed at social interactions or experiences. [3] [2] In essence, Grounded Theory’s goal is to explain how and why an event occurs or how and why people might behave a certain way. Through observing the population, a researcher using the Grounded Theory approach can then develop a theory to explain the phenomena of interest.

Phenomenology

Phenomenology is the "study of the meaning of phenomena or the study of the particular.” [5] At first glance, it might seem that Grounded Theory and Phenomenology are pretty similar, but the differences can be seen upon careful examination. At its core, phenomenology looks to investigate experiences from the individual's perspective. [2] Phenomenology is essentially looking into the "lived experiences" of the participants and aims to examine how and why participants behaved a certain way from their perspective. Herein lies one of the main differences between Grounded Theory and Phenomenology. Grounded Theory aims to develop a theory for social phenomena through an examination of various data sources. In contrast, Phenomenology focuses on describing and explaining an event or phenomenon from the perspective of those who have experienced it.

Narrative research

One of qualitative research’s strengths lies in its ability to tell a story, often from the perspective of those directly involved in it. Reporting on qualitative research involves including details and descriptions of the setting involved and quotes from participants. This detail is called a "thick" or "rich" description and is a strength of qualitative research. Narrative research is rife with the possibilities of "thick" description as this approach weaves together a sequence of events, usually from just one or two individuals, hoping to create a cohesive story or narrative. [2] While it might seem like a waste of time to focus on such a specific, individual level, understanding one or two people’s narratives for an event or phenomenon can help to inform researchers about the influences that helped shape that narrative. The tension or conflict of differing narratives can be "opportunities for innovation." [2]

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. [4] It is valuable for qualitative and quantitative researchers to understand what paradigm they are working within because understanding the theoretical basis of research paradigms allows researchers to understand the strengths and weaknesses of the approach being used and adjust accordingly. Different paradigms have different ontologies and epistemologies. Ontology is defined as the "assumptions about the nature of reality,” whereas epistemology is defined as the "assumptions about the nature of knowledge" that inform researchers' work. [2] It is essential to understand the ontological and epistemological foundations of the research paradigm researchers are working within to allow for a complete understanding of the approach being used and the assumptions that underpin the approach as a whole. Further, researchers must understand their own ontological and epistemological assumptions about the world in general because their assumptions about the world will necessarily impact how they interact with research. A discussion of the research paradigm is not complete without describing positivist, postpositivist, and constructivist philosophies.

Positivist versus postpositivist

To further understand qualitative research, we must discuss positivist and postpositivist frameworks. Positivism is a philosophy that the scientific method can and should be applied to social and natural sciences. [4] Essentially, positivist thinking insists that the social sciences should use natural science methods in their research. It stems from positivist ontology, that there is an objective reality that exists that is wholly independent of our perception of the world as individuals. Quantitative research is rooted in positivist philosophy, which can be seen in the value it places on concepts such as causality, generalizability, and replicability.

Conversely, postpositivists argue that social reality can never be one hundred percent explained, but could be approximated. [4] Indeed, qualitative researchers have been insisting that there are “fundamental limits to the extent to which the methods and procedures of the natural sciences could be applied to the social world,” and therefore, postpositivist philosophy is often associated with qualitative research. [4] An example of positivist versus postpositivist values in research might be that positivist philosophies value hypothesis-testing, whereas postpositivist philosophies value the ability to formulate a substantive theory.

Constructivist

Constructivism is a subcategory of postpositivism. Most researchers invested in postpositivist research are also constructivist, meaning they think there is no objective external reality that exists but instead that reality is constructed. Constructivism is a theoretical lens that emphasizes the dynamic nature of our world. "Constructivism contends that individuals' views are directly influenced by their experiences, and it is these individual experiences and views that shape their perspective of reality.” [6]  constructivist thought focuses on how "reality" is not a fixed certainty and how experiences, interactions, and backgrounds give people a unique view of the world. Constructivism contends, unlike positivist views, that there is not necessarily an "objective"reality we all experience. This is the ‘relativist’ ontological view that reality and our world are dynamic and socially constructed. Therefore, qualitative scientific knowledge can be inductive as well as deductive.” [4]

So why is it important to understand the differences in assumptions that different philosophies and approaches to research have? Fundamentally, the assumptions underpinning the research tools a researcher selects provide an overall base for the assumptions the rest of the research will have. It can even change the role of the researchers. [2] For example, is the researcher an "objective" observer, such as in positivist quantitative work? Or is the researcher an active participant in the research, as in postpositivist qualitative work? Understanding the philosophical base of the study undertaken allows researchers to fully understand the implications of their work and their role within the research and reflect on their positionality and bias as it pertains to the research they are conducting.

Data Sampling 

The better the sample represents the intended study population, the more likely the researcher is to encompass the varying factors. The following are examples of participant sampling and selection: [7]

• Purposive sampling- selection based on the researcher’s rationale for being the most informative.
• Criterion sampling selection based on pre-identified factors.
• Convenience sampling- selection based on availability.
• Snowball sampling- the selection is by referral from other participants or people who know potential participants.
• Extreme case sampling- targeted selection of rare cases.
• Typical case sampling selection based on regular or average participants. 

Data Collection and Analysis

Qualitative research uses several techniques, including interviews, focus groups, and observation. [1] [2] [3] Interviews may be unstructured, with open-ended questions on a topic, and the interviewer adapts to the responses. Structured interviews have a predetermined number of questions that every participant is asked. It is usually one-on-one and appropriate for sensitive topics or topics needing an in-depth exploration. Focus groups are often held with 8-12 target participants and are used when group dynamics and collective views on a topic are desired. Researchers can be participant-observers to share the experiences of the subject or non-participants or detached observers.

While quantitative research design prescribes a controlled environment for data collection, qualitative data collection may be in a central location or the participants' environment, depending on the study goals and design. Qualitative research could amount to a large amount of data. Data is transcribed, which may then be coded manually or using computer-assisted qualitative data analysis software or CAQDAS such as ATLAS.ti or NVivo. [8] [9] [10]

After the coding process, qualitative research results could be in various formats. It could be a synthesis and interpretation presented with excerpts from the data. [11] Results could also be in the form of themes and theory or model development.

Dissemination

The healthcare team can use two reporting standards to standardize and facilitate the dissemination of qualitative research outcomes. The Consolidated Criteria for Reporting Qualitative Research or COREQ is a 32-item checklist for interviews and focus groups. [12] The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research. [13]

Applications

Many times, a research question will start with qualitative research. The qualitative research will help generate the research hypothesis, which can be tested with quantitative methods. After the data is collected and analyzed with quantitative methods, a set of qualitative methods can be used to dive deeper into the data to better understand what the numbers truly mean and their implications. The qualitative techniques can then help clarify the quantitative data and also help refine the hypothesis for future research. Furthermore, with qualitative research, researchers can explore poorly studied subjects with quantitative methods. These include opinions, individual actions, and social science research.

An excellent qualitative study design starts with a goal or objective. This should be clearly defined or stated. The target population needs to be specified. A method for obtaining information from the study population must be carefully detailed to ensure no omissions of part of the target population. A proper collection method should be selected that will help obtain the desired information without overly limiting the collected data because, often, the information sought is not well categorized or obtained. Finally, the design should ensure adequate methods for analyzing the data. An example may help better clarify some of the various aspects of qualitative research.

A researcher wants to decrease the number of teenagers who smoke in their community. The researcher could begin by asking current teen smokers why they started smoking through structured or unstructured interviews (qualitative research). The researcher can also get together a group of current teenage smokers and conduct a focus group to help brainstorm factors that may have prevented them from starting to smoke (qualitative research).

In this example, the researcher has used qualitative research methods (interviews and focus groups) to generate a list of ideas of why teens start to smoke and factors that may have prevented them from starting to smoke. Next, the researcher compiles this data. The research found that, hypothetically, peer pressure, health issues, cost, being considered "cool," and rebellious behavior all might increase or decrease the likelihood of teens starting to smoke.

The researcher creates a survey asking teen participants to rank how important each of the above factors is in either starting smoking (for current smokers) or not smoking (for current nonsmokers). This survey provides specific numbers (ranked importance of each factor) and is thus a quantitative research tool.

The researcher can use the survey results to focus efforts on the one or two highest-ranked factors. Let us say the researcher found that health was the primary factor that keeps teens from starting to smoke, and peer pressure was the primary factor that contributed to teens starting smoking. The researcher can go back to qualitative research methods to dive deeper into these for more information. The researcher wants to focus on keeping teens from starting to smoke, so they focus on the peer pressure aspect.

The researcher can conduct interviews and focus groups (qualitative research) about what types and forms of peer pressure are commonly encountered, where the peer pressure comes from, and where smoking starts. The researcher hypothetically finds that peer pressure often occurs after school at the local teen hangouts, mostly in the local park. The researcher also hypothetically finds that peer pressure comes from older, current smokers who provide the cigarettes.

The researcher could further explore this observation made at the local teen hangouts (qualitative research) and take notes regarding who is smoking, who is not, and what observable factors are at play for peer pressure to smoke. The researcher finds a local park where many local teenagers hang out and sees that the smokers tend to hang out in a shady, overgrown area of the park. The researcher notes that smoking teenagers buy their cigarettes from a local convenience store adjacent to the park, where the clerk does not check identification before selling cigarettes. These observations fall under qualitative research.

If the researcher returns to the park and counts how many individuals smoke in each region, this numerical data would be quantitative research. Based on the researcher's efforts thus far, they conclude that local teen smoking and teenagers who start to smoke may decrease if there are fewer overgrown areas of the park and the local convenience store does not sell cigarettes to underage individuals.

The researcher could try to have the parks department reassess the shady areas to make them less conducive to smokers or identify how to limit the sales of cigarettes to underage individuals by the convenience store. The researcher would then cycle back to qualitative methods of asking at-risk populations their perceptions of the changes and what factors are still at play, and quantitative research that includes teen smoking rates in the community and the incidence of new teen smokers, among others. [14] [15]

Qualitative research functions as a standalone research design or combined with quantitative research to enhance our understanding of the world. Qualitative research uses techniques including structured and unstructured interviews, focus groups, and participant observation not only to help generate hypotheses that can be more rigorously tested with quantitative research but also to help researchers delve deeper into the quantitative research numbers, understand what they mean, and understand what the implications are. Qualitative research allows researchers to understand what is going on, especially when things are not easily categorized. [16]

• Issues of Concern

As discussed in the sections above, quantitative and qualitative work differ in many ways, including the evaluation criteria. There are four well-established criteria for evaluating quantitative data: internal validity, external validity, reliability, and objectivity. Credibility, transferability, dependability, and confirmability are the correlating concepts in qualitative research. [4] [11] The corresponding quantitative and qualitative concepts can be seen below, with the quantitative concept on the left and the qualitative concept on the right:

• Internal validity: Credibility
• External validity: Transferability
• Reliability: Dependability
• Objectivity: Confirmability

In conducting qualitative research, ensuring these concepts are satisfied and well thought out can mitigate potential issues from arising. For example, just as a researcher will ensure that their quantitative study is internally valid, qualitative researchers should ensure that their work has credibility. 

Indicators such as triangulation and peer examination can help evaluate the credibility of qualitative work.

• Triangulation: Triangulation involves using multiple data collection methods to increase the likelihood of getting a reliable and accurate result. In our above magic example, the result would be more reliable if we interviewed the magician, backstage hand, and the person who "vanished." In qualitative research, triangulation can include telephone surveys, in-person surveys, focus groups, and interviews and surveying an adequate cross-section of the target demographic.
• Peer examination: A peer can review results to ensure the data is consistent with the findings.

A "thick" or "rich" description can be used to evaluate the transferability of qualitative research, whereas an indicator such as an audit trail might help evaluate the dependability and confirmability.

• Thick or rich description:  This is a detailed and thorough description of details, the setting, and quotes from participants in the research. [5] Thick descriptions will include a detailed explanation of how the study was conducted. Thick descriptions are detailed enough to allow readers to draw conclusions and interpret the data, which can help with transferability and replicability.
• Audit trail: An audit trail provides a documented set of steps of how the participants were selected and the data was collected. The original information records should also be kept (eg, surveys, notes, recordings).

One issue of concern that qualitative researchers should consider is observation bias. Here are a few examples:

• Hawthorne effect: The effect is the change in participant behavior when they know they are being observed. Suppose a researcher wanted to identify factors that contribute to employee theft and tell the employees they will watch them to see what factors affect employee theft. In that case, one would suspect employee behavior would change when they know they are being protected.
• Observer-expectancy effect: Some participants change their behavior or responses to satisfy the researcher's desired effect. This happens unconsciously for the participant, so it is essential to eliminate or limit the transmission of the researcher's views.
• Artificial scenario effect: Some qualitative research occurs in contrived scenarios with preset goals. In such situations, the information may not be accurate because of the artificial nature of the scenario. The preset goals may limit the qualitative information obtained.
• Clinical Significance

Qualitative or quantitative research helps healthcare providers understand patients and the impact and challenges of the care they deliver. Qualitative research provides an opportunity to generate and refine hypotheses and delve deeper into the data generated by quantitative research. Qualitative research is not an island apart from quantitative research but an integral part of research methods to understand the world around us. [17]

• Enhancing Healthcare Team Outcomes

Qualitative research is essential for all healthcare team members as all are affected by qualitative research. Qualitative research may help develop a theory or a model for health research that can be further explored by quantitative research. Much of the qualitative research data acquisition is completed by numerous team members, including social workers, scientists, nurses, etc. Within each area of the medical field, there is copious ongoing qualitative research, including physician-patient interactions, nursing-patient interactions, patient-environment interactions, healthcare team function, patient information delivery, etc. 

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Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Janelle Brannan declares no relevant financial relationships with ineligible companies.

Disclosure: Grace Brannan declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

• Cite this Page Tenny S, Brannan JM, Brannan GD. Qualitative Study. [Updated 2022 Sep 18]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan-.

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