hypotheses qualitative research

The Ultimate Guide to Qualitative Research - Part 1: The Basics

• Introduction and overview

Basics of qualitative research

Types, aspects, examples, benefits and challenges, how qualitative research complements quantitative research, how is qualitative research reported.

• What is qualitative data?
• Examples of qualitative data
• Qualitative vs. quantitative research
• Mixed methods
• Qualitative research preparation
• Theoretical perspective
• Theoretical framework
• Literature reviews
• Research question
• Conceptual framework
• Conceptual vs. theoretical framework
• Data collection
• Qualitative research methods
• Focus groups
• Observational research
• Case studies
• Ethnographical research

Ethical considerations

• Confidentiality and privacy
• Power dynamics
• Reflexivity

What is qualitative research?

Qualitative research is an essential approach in various academic disciplines and professional fields, as it seeks to understand and interpret the meanings, experiences, and social realities of people in their natural settings. This type of research employs an array of qualitative methods to gather and analyze non-numerical data, such as words, images, and behaviors, and aims to generate in-depth and contextualized insights into the phenomena under study.

Qualitative research is designed to address research questions that focus on understanding the "why" and "how" of human behavior, experiences, and interactions, rather than just the "what" or "how many" that quantitative methods typically seek to answer. The main purpose of qualitative research is to gain a rich and nuanced understanding of people's perspectives, emotions, beliefs, and motivations in relation to specific issues, situations, or phenomena.

Characteristics of qualitative research

Several key characteristics distinguish qualitative research from other types of research, such as quantitative research:

Naturalistic settings : Qualitative researchers collect data in the real-world settings where the phenomena of interest occur, rather than in controlled laboratory environments. This allows researchers to observe and understand the participants' behavior, experiences, and social interactions in their natural context.

Inductive approach : Unlike quantitative research, which often follows a deductive approach , qualitative research begins with the collection of data and then seeks to develop theories, concepts, or themes that emerge from the data. This inductive approach enables researchers to stay open to new insights and unexpected findings.

Holistic perspective : Qualitative research aims to provide a comprehensive understanding of the phenomena under study by considering multiple dimensions, such as the social, cultural, historical, and psychological aspects that shape people's experiences and behavior.

Subjectivity and interpretation : Epistemology plays a crucial role in qualitative research. Researchers are encouraged to reflect on their biases, assumptions, and values , and to consider how these may influence their data collection, analysis, and interpretation.

Flexibility : Qualitative research methods are often flexible and adaptable, allowing researchers to refine their research questions , sampling strategies, or data collection techniques as new insights and perspectives emerge during the research process.

Key principles of qualitative research

Qualitative research is guided by several fundamental principles that shape its approach, methods, and analysis:

Empathy and reflexivity : Qualitative researchers strive to empathize with the participants and to understand their perspectives, experiences, and emotions from their viewpoint. This requires researchers to be attentive, open-minded, and sensitive to the participants' verbal and non-verbal cues. At the same, qualitative researchers critically reflect on their participants’ perspectives, experiences, and emotions to develop their findings and conclusions, instead of taking these at face value. In addition, it is important for the researcher to reflect on how their own role and viewpoint may be shaping the research.

Trustworthiness : Establishing trustworthiness in qualitative research involves demonstrating credibility, transferability, dependability, and confirmability. Researchers can enhance trustworthiness by using various strategies, such as triangulation, member checking , peer debriefing , and reflexivity .

Iterative analysis : Qualitative data analysis is an ongoing and iterative process, in which researchers continually review, compare, and revise their interpretations as they collect and analyze more data. This iterative process allows researchers to refine their understanding of the phenomena and to develop more robust and nuanced theories, concepts, or themes.

Rich description : Providing detailed, vivid, and context-sensitive descriptions of the data is essential in qualitative research. Rich descriptions help convey the complexity and nuances of the phenomena under study, and enable readers to assess the relevance and transferability of the findings to other settings or populations.

What are the common types of qualitative research?

Qualitative research is an umbrella term for various methodologies that focus on understanding and interpreting human experiences, behaviors, and social phenomena within their context. These approaches seek to gather in-depth, rich data through the analysis of language, actions, and expressions. Five common types of qualitative research are narrative research , phenomenology , grounded theory , ethnography , and case study .

Narrative research : This approach focuses on the stories and experiences of individuals, aiming to understand their lives and personal perspectives. Researchers can collect data through interviews, letters, diaries, or autobiographies, and analyze these narratives to identify recurring themes, patterns, and meanings . Narrative research can be valuable for exploring individual identities, cultural beliefs, and historical events.

Phenomenology : Phenomenology seeks to understand the essence of a particular phenomenon by analyzing the experiences and perceptions of individuals who have gone through that phenomenon . Researchers can explore participants' thoughts, feelings, and experiences through in-depth interviews, observations, or written materials. The goal is to describe the commonalities and variations in these experiences, ultimately revealing the underlying structures and meaning of the phenomenon under study.

Grounded theory : This inductive research method aims to generate new theories by systematically collecting and analyzing data. Researchers begin with an open-ended research question and gather data through observations, interviews, and document analysis . They then use a process of coding and constant comparison to identify patterns, categories, and relationships in the data. This iterative process continues until a comprehensive, grounded theory emerges that is based in the recollected data and explains the topic of interest.

Ethnography : Ethnographic research involves the in-depth study of a specific cultural or social group, focusing on understanding its members' behaviors, beliefs, and interactions. Researchers immerse themselves in the group's environment, often for extended periods, to observe and participate in daily activities. They can collect data through field notes, interviews, and document analysis, aiming to provide a holistic and nuanced understanding of the group's cultural practices and social dynamics.

Case study : A case study is an in-depth examination of a specific instance, event, organization, or individual within its real-life context. Researchers use multiple sources of data, such as interviews, observations, documents, and artifacts to build a rich, detailed understanding of the case. Case study research can be used to explore complex phenomena, generate new hypotheses , or evaluate the effectiveness of interventions or policies.

What are the purposes of qualitative research?

Qualitative research presents outcomes that emerge from the process of collecting and analyzing qualitative data. These outcomes often involve generating new theories, developing or challenging existing theories, and proposing practical implications based on actionable insights. The products of qualitative research contribute to a deeper understanding of human experiences, social phenomena, and cultural contexts. Qualitative research can also be a powerful complement to quantitative research.

Generating new theory : One of the primary goals of qualitative research is to develop new theories or conceptual frameworks that help explain previously unexplored or poorly understood phenomena. By conducting in-depth investigations and analyzing rich data, researchers can identify patterns, relationships, and underlying structures that form the basis of novel theoretical insights.

Developing or challenging existing theory : Qualitative research can also contribute to the refinement or expansion of existing theories by providing new perspectives, revealing previously unnoticed complexities, or highlighting areas where current theories may be insufficient or inaccurate. By examining the nuances and context-specific details of a phenomenon, researchers can generate evidence that supports, contradicts, or modifies existing theoretical frameworks .

Proposing practical implications : Qualitative research often yields actionable insights that can inform policy, practice, and intervention strategies. By delving into the lived experiences of individuals and communities, researchers can identify factors that contribute to or hinder the effectiveness of certain approaches, uncovering opportunities for improvement or innovation. The insights gained from qualitative research can be used to design targeted interventions, develop context-sensitive policies, or inform the professional practices of practitioners in various fields.

Enhancing understanding and empathy : Qualitative research promotes a deeper understanding of human experiences, emotions, and perspectives, fostering empathy and cultural sensitivity. By engaging with diverse voices and experiences, researchers can develop a more nuanced appreciation of the complexities of human behavior and social dynamics, ultimately contributing to more compassionate and inclusive societies.

Informing mixed-methods research : The products of qualitative research can also be used in conjunction with quantitative research, as part of a mixed-methods approach . Qualitative findings can help generate hypotheses for further testing, inform the development of survey instruments , or provide context and explanation for quantitative results. Combining the strengths of both approaches can lead to more robust and comprehensive understanding of complex research questions .

What are some examples of qualitative research?

Qualitative research can be conducted across various scientific fields, exploring diverse topics and phenomena. Here are six brief descriptions of qualitative studies that can provide researchers with ideas for their own projects:

Exploring the lived experiences of refugees : A phenomenological study could be conducted to investigate the lived experiences and coping strategies of refugees in a specific host country. By conducting in-depth interviews with refugees and analyzing their narratives , researchers can gain insights into the challenges they face, their resilience, and the factors that contribute to successful integration into their new communities.

Understanding the dynamics of online communities : An ethnographic study could be designed to explore the culture and social dynamics of a particular online community or social media platform. By immersing themselves in the virtual environment, researchers can observe patterns of interaction, communication styles, and shared values among community members, providing a nuanced understanding of the factors that influence online behavior and group dynamics.

Examining the impact of gentrification on local communities : A case study could be conducted to explore the impact of gentrification on a specific neighborhood or community. Researchers can collect data through interviews with residents, local business owners, and policymakers, as well as analyzing relevant documents and media coverage. The study can shed light on the effects of gentrification on housing affordability, social cohesion, and cultural identity, informing policy and urban planning decisions.

Studying the career trajectories of women in STEM fields : A narrative research project can be designed to investigate the career experiences and pathways of women in science, technology, engineering, and mathematics (STEM) fields. By collecting and analyzing the stories of women at various career stages, researchers can identify factors that contribute to their success, as well as barriers and challenges they face in male-dominated fields.

Evaluating the effectiveness of a mental health intervention : A qualitative study can be conducted to evaluate the effectiveness of a specific mental health intervention, such as a mindfulness-based program for reducing stress and anxiety. Researchers can gather data through interviews and focus groups with program participants, exploring their experiences, perceived benefits, and suggestions for improvement. The findings can provide valuable insights for refining the intervention and informing future mental health initiatives.

Investigating the role of social media in political activism : A qualitative study using document analysis and visual methods could explore the role of social media in shaping political activism and public opinion during a specific social movement or election campaign. By analyzing user-generated content, such as tweets, posts, images, and videos, researchers can examine patterns of communication, mobilization, and discourse, shedding light on the ways in which social media influences political engagement and democratic processes.

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What are common qualitative research methods?

Qualitative research methods are techniques used to collect, analyze, and interpret data in qualitative studies. These methods prioritize the exploration of meaning, context, and individual experiences. Common qualitative research methods include interviews, focus groups, observations, document analysis, and visual methods.

Interviews : Interviews involve one-on-one conversations between the researcher and the participant. They can be structured, semi-structured, or unstructured, depending on the level of guidance provided by the researcher. Interviews allow for in-depth exploration of participants' experiences, thoughts, and feelings, providing rich and detailed data for analysis.

Focus groups : Focus groups are group discussions facilitated by a researcher, usually consisting of 6-12 participants. They enable researchers to explore participants' collective perspectives, opinions, and experiences in a social setting. Focus groups can generate insights into group dynamics, cultural norms, and shared understandings, as participants interact and respond to each other's viewpoints.

Observations : Observational research involves the systematic collection of data through watching and recording people, events, or behaviors in their natural settings. Researchers can take on different roles, such as participant-observer or non-participant observer, depending on their level of involvement. Observations provide valuable information about context, social interactions, and non-verbal communication, which can help researchers understand the nuances of a particular phenomenon.

Document analysis : Document analysis is the examination of written or visual materials, such as letters, diaries, reports, newspaper articles, photographs, or videos. This method can provide insights into historical or cultural contexts, individual perspectives, and organizational processes. Researchers may use content analysis, discourse analysis, or other analytic techniques to interpret the meaning and significance of these documents.

Visual methods : Visual methods involve the use of visual materials, such as photographs, drawings, or videos, to explore and represent participants' experiences and perspectives. Techniques like photo elicitation, where participants are asked to take or select photographs related to the research topic and discuss their meaning, can encourage reflection and stimulate discussion. Visual methods can be particularly useful in capturing non-verbal information, promoting cross-cultural understanding, and engaging with hard-to-reach populations.

Importance of qualitative research and qualitative data analysis

Qualitative research and qualitative data analysis play a vital role in advancing knowledge, informing policies, and improving practices in various fields, such as education, healthcare, business, and social work. The unique insights and in-depth understanding generated through qualitative research can accomplish a number of goals.

Inform decision-making

Qualitative research helps decision-makers better understand the needs, preferences, and concerns of different stakeholders, such as customers, employees, or community members. This can lead to more effective and tailored policies, programs, or interventions that address real-world challenges.

Enhance innovation

By exploring people's experiences, motivations, and aspirations, qualitative research can uncover new ideas, opportunities, and trends that can drive innovation in products, services, or processes.

Foster empathy and cultural competence

Qualitative research can increase our empathy and understanding of diverse populations, cultures, and contexts. This can enhance our ability to communicate, collaborate, and work effectively with people from different backgrounds.

Complement quantitative research

Qualitative research can complement quantitative research by providing rich contextual information and in-depth insights into the underlying mechanisms, processes, or factors that may explain the patterns or relationships observed in quantitative data.

Facilitate social change

Qualitative research can give voice to marginalized or underrepresented groups, highlight social injustices or inequalities, and inspire actions and reforms that promote social change and well-being.

Challenges of conducting qualitative research

While qualitative research offers valuable insights and understanding of human experiences, it also presents some challenges that researchers must navigate. Acknowledging and addressing these challenges can help ensure the rigor, credibility, and relevance of qualitative research. In this section, we will discuss some common challenges that researchers may encounter when conducting qualitative research and offer suggestions on how to overcome them.

Subjectivity and bias

One of the primary challenges in qualitative research is managing subjectivity and potential biases that may arise from the researcher's personal beliefs, values, and experiences. Since qualitative research relies on the researcher's interpretation of the data , there is a risk that the researcher's subjectivity may influence the findings.

Researchers can minimize the impact of subjectivity and bias by maintaining reflexivity , or ongoing self-awareness and critical reflection on their role, assumptions, and influences in the research process. This may involve keeping a reflexive journal, engaging in peer debriefing , and discussing potential biases with research participants during member checking .

Data collection and quality

Collecting high-quality data in qualitative research can be challenging, particularly when dealing with sensitive topics, hard-to-reach populations, or complex social phenomena. Ensuring the trustworthiness of qualitative data collection is essential to producing credible and meaningful findings.

Researchers can enhance data quality by employing various strategies, such as purposive or theoretical sampling, triangulation of data sources, methods or researchers, and establishing rapport and trust with research participants.

Data analysis and interpretation

The analysis and interpretation of qualitative data can be a complex, time-consuming, and sometimes overwhelming process. Researchers must make sense of large amounts of diverse and unstructured data, while also ensuring the rigor, transparency, and consistency of their analysis.

Researchers can facilitate data analysis and interpretation by adopting systematic and well-established approaches, such as thematic analysis , grounded theory , or content analysis . Utilizing qualitative data analysis software , like ATLAS.ti, can also help manage and analyze data more efficiently and rigorously.

Qualitative research often involves exploring sensitive issues or working with vulnerable populations, which raises various ethical considerations , such as privacy, confidentiality , informed consent , and potential harm to participants.

Researchers should be familiar with the ethical guidelines and requirements of their discipline, institution, or funding agency, and should obtain ethical approval from relevant review boards or committees before conducting the research. Researchers should also maintain open communication with participants, respect their autonomy and dignity, and protect their well-being throughout the research process.

Generalizability and transferability

Qualitative research typically focuses on in-depth exploration of specific cases or contexts, which may limit the generalizability or transferability of the findings to other settings or populations. However, the goal of qualitative research is not to produce statistically generalizable results but rather to provide a rich, contextualized, and nuanced understanding of the phenomena under study.

Researchers can enhance the transferability of their findings by providing rich descriptions of the research context, participants, and methods, and by discussing the potential applicability or relevance of the findings to other settings or populations. Readers can then assess the transferability of the findings based on the similarity of their own context to the one described in the research.

By addressing these challenges and adopting rigorous and transparent research practices, qualitative researchers can contribute valuable and meaningful insights that advance knowledge, inform policies, and improve practices in various fields and contexts.

Qualitative and quantitative research approaches are often seen as distinct and even opposing paradigms. However, these two approaches can be complementary, providing a more comprehensive understanding of complex social phenomena when combined. In this section, we will discuss how qualitative research can complement quantitative research and enhance the overall depth, breadth, and rigor of research findings.

Exploring and understanding context

Quantitative research excels at identifying patterns, trends, and relationships among variables using numerical data, while qualitative research provides rich and nuanced insights into the context, meaning, and underlying processes that shape these patterns or relationships. By integrating qualitative research with quantitative research, researchers can explore not only the "what" or "how many" but also the "why" and "how" of the phenomena under study.

For example, a quantitative study in health services research might reveal a correlation between social media usage and mental health outcomes, while a qualitative study could help explain the reasons behind this correlation by exploring users' experiences, motivations, and perceptions of social media. Qualitative and quantitative data in this case complement each other to contribute to a more robust theory and more informed policy implications.

Generating and refining hypotheses

Qualitative research can inform the development and refinement of hypotheses for quantitative research by identifying new concepts, variables, or relationships that emerge from the data. This can lead to more focused, relevant, and innovative quantitative research questions and hypotheses. For instance, a qualitative study on employee motivation might uncover the importance of meaningful work and supportive relationships with supervisors as key factors influencing motivation. These findings could then be incorporated into a quantitative study to test the relationships between these factors and employee motivation.

Validating and triangulating findings

Combining qualitative and quantitative research methods can enhance the credibility and trustworthiness of research findings through validation and triangulation. Validation involves comparing the findings from different methods to assess their consistency and convergence, while triangulation involves using multiple methods, data sources, or researchers to gain a more comprehensive understanding of the phenomena under study.

For example, a researcher might use both quantitative surveys and qualitative interviews in a mixed methods research design to assess the effectiveness of a health intervention. If both methods yield similar findings, this can increase confidence in the results. If the findings differ, the researcher can further investigate the reasons for these discrepancies and refine their understanding of the intervention's effectiveness.

Enhancing communication and dissemination

Qualitative research can enhance the communication and dissemination of quantitative research findings by providing vivid narratives, case studies, or examples that bring the data to life and make it more accessible and engaging for diverse audiences, such as policymakers, practitioners, or the public.

For example, a quantitative study on the impact of a community-based program might report the percentage of participants who experienced improvements in various outcomes. By adding qualitative data, such as quotes or stories from participants, the researcher can illustrate the human impact of the program and make the findings more compelling and relatable.

In conclusion, qualitative research can complement and enrich quantitative research in various ways, leading to a more comprehensive, contextualized, and rigorous understanding of complex social phenomena. By integrating qualitative and quantitative research methods, researchers can harness the strengths of both approaches to produce more robust, relevant, and impactful findings that inform theory, policy, and practice.

Qualitative research findings are typically reported in various formats, depending on the audience, purpose, and context of the research. Common ways to report qualitative research include dissertations, journal articles, market research reports, and needs assessment reports. Each format has its own structure and emphasis, tailored to meet the expectations and requirements of its target audience.

Dissertations and theses : Doctoral,master's, or bachelor students often conduct qualitative research as part of their dissertation or thesis projects. In this format, researchers provide a comprehensive account of their research questions , methodology, data collection , data analysis , and findings. Dissertations are expected to make a significant contribution to the existing body of knowledge and demonstrate the researcher's mastery of the subject matter.

Journal articles : Researchers frequently disseminate their qualitative research findings through articles published in academic journals . These articles are typically structured in a way that includes an introduction, literature review, methodology, results, and discussion sections. In addition, articles often undergo a peer-review process before being published in the academic journal. Journal articles focus on communicating the study's purpose, methods, and findings in a concise and coherent manner, providing enough detail for other researchers to evaluate the rigor and validity of the research so that they can cite the article and build on it in their own studies.

Market research reports : Market research often employs qualitative methods to gather insights into consumer behavior, preferences, and attitudes. Market research reports present the findings of these studies to clients, typically businesses or organizations interested in understanding their target audience or market trends. These reports focus on providing actionable insights and recommendations based on the qualitative data, helping clients make informed decisions and develop effective marketing strategies.

Needs assessment reports : Needs assessment is a process used to identify gaps or areas of improvement in a specific context, such as healthcare, education, or social services. Qualitative research methods can be used to collect data on the needs, challenges, and experiences of the target population. Needs assessment reports present the findings of this research, highlighting the identified needs and providing recommendations for addressing them. These reports are used by organizations and policymakers to inform the development and implementation of targeted interventions and policies.

Other formats : In addition to the aforementioned formats, qualitative research findings can also be reported in conference presentations, white papers, policy briefs, blog posts, or multimedia presentations. The choice of format depends on the target audience and the intended purpose of the research, as well as the researcher's preferences and resources. Regardless of the format, it is important for researchers to present their findings in a clear, accurate, and engaging manner, ensuring that their work is accessible and relevant to their audience.

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Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

“Not everything that can be counted counts, and not everything that counts can be counted“ (Albert Einstein)

Qualitative research is a process used for the systematic collection, analysis, and interpretation of non-numerical data (Punch, 2013). 

Qualitative research can be used to: (i) gain deep contextual understandings of the subjective social reality of individuals and (ii) to answer questions about experience and meaning from the participant’s perspective (Hammarberg et al., 2016).

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research focuses on thematic and contextual information.

Characteristics of Qualitative Research 

Reality is socially constructed.

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the context of the research setting (Scarduzio, 2017).

Why Conduct Qualitative Research? 

In order to gain a deeper understanding of how people experience the world, individuals are studied in their natural setting. This enables the researcher to understand a phenomenon close to how participants experience it. 

Qualitative research allows researchers to gain an in-depth understanding, which is difficult to attain using quantitative methods. 

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

This helps to further investigate and understand quantitative data by discovering reasons for the outcome of a study – answering the why question behind statistics. 

The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively (Busetto et al., 2020).

To design hypotheses, theory must be researched using qualitative methods to find out what is important in order to begin research. 

For example, by conducting interviews or focus groups with key stakeholders to discover what is important to them. 

Examples of qualitative research questions include: 

• How does stress influence young adults’ behavior?
• What factors influence students’ school attendance rates in developed countries?
• How do adults interpret binge drinking in the UK?
• What are the psychological impacts of cervical cancer screening in women?
• How can mental health lessons be integrated into the school curriculum? 

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.

Observations

This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.

Ethnography

Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

 This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.

• Define a Research Question : To start content analysis, a clear research question should be developed.
• Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
• Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
• Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
• Code the Data : Follow the coding rules to categorize data into themes.
• Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.

• Define the Research Question : Develop a relevant research question to frame the analysis.
• Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
• Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
• Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.

• Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
• Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
• Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
• Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
• Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
• Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.

• Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
• Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
• Indexing : Apply the framework systematically to the entire study data.
• Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
• Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 

Co-coding 

Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

Flexibility

Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 

Limitations

Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 

Subjectivity 

The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

• Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
• Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
• Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

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7.4 Qualitative Research

Learning objectives.

• List several ways in which qualitative research differs from quantitative research in psychology.
• Describe the strengths and weaknesses of qualitative research in psychology compared with quantitative research.
• Give examples of qualitative research in psychology.

What Is Qualitative Research?

This book is primarily about quantitative research . Quantitative researchers typically start with a focused research question or hypothesis, collect a small amount of data from each of a large number of individuals, describe the resulting data using statistical techniques, and draw general conclusions about some large population. Although this is by far the most common approach to conducting empirical research in psychology, there is an important alternative called qualitative research. Qualitative research originated in the disciplines of anthropology and sociology but is now used to study many psychological topics as well. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the experience of their research participants.

Consider, for example, a study by researcher Per Lindqvist and his colleagues, who wanted to learn how the families of teenage suicide victims cope with their loss (Lindqvist, Johansson, & Karlsson, 2008). They did not have a specific research question or hypothesis, such as, What percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from their perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected.

The Purpose of Qualitative Research

Again, this book is primarily about quantitative research in psychology. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior. This is how we know that people have a strong tendency to obey authority figures, for example, or that female college students are not substantially more talkative than male college students. But while quantitative research is good at providing precise answers to specific research questions, it is not nearly as good at generating novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation.

But the relative weaknesses of quantitative research are the relative strengths of qualitative research. Qualitative research can help researchers to generate new and interesting research questions and hypotheses. The research of Lindqvist and colleagues, for example, suggests that there may be a general relationship between how unexpected a suicide is and how consumed the family is with trying to understand why the teen committed suicide. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Among qualitative researchers, this is often referred to as “thick description” (Geertz, 1973). Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the “lived experience” of the research participants. Lindqvist and colleagues, for example, describe how all the families spontaneously offered to show the interviewer the victim’s bedroom or the place where the suicide occurred—revealing the importance of these physical locations to the families. It seems unlikely that a quantitative study would have discovered this.

Data Collection and Analysis in Qualitative Research

As with correlational research, data collection approaches in qualitative research are quite varied and can involve naturalistic observation, archival data, artwork, and many other things. But one of the most common approaches, especially for psychological research, is to conduct interviews . Interviews in qualitative research tend to be unstructured—consisting of a small number of general questions or prompts that allow participants to talk about what is of interest to them. The researcher can follow up by asking more detailed questions about the topics that do come up. Such interviews can be lengthy and detailed, but they are usually conducted with a relatively small sample. This was essentially the approach used by Lindqvist and colleagues in their research on the families of suicide survivors. Small groups of people who participate together in interviews focused on a particular topic or issue are often referred to as focus groups . The interaction among participants in a focus group can sometimes bring out more information than can be learned in a one-on-one interview. The use of focus groups has become a standard technique in business and industry among those who want to understand consumer tastes and preferences. The content of all focus group interviews is usually recorded and transcribed to facilitate later analyses.

Another approach to data collection in qualitative research is participant observation. In participant observation , researchers become active participants in the group or situation they are studying. The data they collect can include interviews (usually unstructured), their own notes based on their observations and interactions, documents, photographs, and other artifacts. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. An example of participant observation comes from a study by sociologist Amy Wilkins (published in Social Psychology Quarterly ) on a college-based religious organization that emphasized how happy its members were (Wilkins, 2008). Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

Data Analysis in Quantitative Research

Although quantitative and qualitative research generally differ along several important dimensions (e.g., the specificity of the research question, the type of data collected), it is the method of data analysis that distinguishes them more clearly than anything else. To illustrate this idea, imagine a team of researchers that conducts a series of unstructured interviews with recovering alcoholics to learn about the role of their religious faith in their recovery. Although this sounds like qualitative research, imagine further that once they collect the data, they code the data in terms of how often each participant mentions God (or a “higher power”), and they then use descriptive and inferential statistics to find out whether those who mention God more often are more successful in abstaining from alcohol. Now it sounds like quantitative research. In other words, the quantitative-qualitative distinction depends more on what researchers do with the data they have collected than with why or how they collected the data.

But what does qualitative data analysis look like? Just as there are many ways to collect data in qualitative research, there are many ways to analyze data. Here we focus on one general approach called grounded theory (Glaser & Strauss, 1967). This approach was developed within the field of sociology in the 1960s and has gradually gained popularity in psychology. Remember that in quantitative research, it is typical for the researcher to start with a theory, derive a hypothesis from that theory, and then collect data to test that specific hypothesis. In qualitative research using grounded theory, researchers start with the data and develop a theory or an interpretation that is “grounded in” those data. They do this in stages. First, they identify ideas that are repeated throughout the data. Then they organize these ideas into a smaller number of broader themes. Finally, they write a theoretical narrative —an interpretation—of the data in terms of the themes that they have identified. This theoretical narrative focuses on the subjective experience of the participants and is usually supported by many direct quotations from the participants themselves.

As an example, consider a study by researchers Laura Abrams and Laura Curran, who used the grounded theory approach to study the experience of postpartum depression symptoms among low-income mothers (Abrams & Curran, 2009). Their data were the result of unstructured interviews with 19 participants. Table 7.1 “Themes and Repeating Ideas in a Study of Postpartum Depression Among Low-Income Mothers” shows the five broad themes the researchers identified and the more specific repeating ideas that made up each of those themes. In their research report, they provide numerous quotations from their participants, such as this one from “Destiny:”

Well, just recently my apartment was broken into and the fact that his Medicaid for some reason was cancelled so a lot of things was happening within the last two weeks all at one time. So that in itself I don’t want to say almost drove me mad but it put me in a funk.…Like I really was depressed. (p. 357)

Their theoretical narrative focused on the participants’ experience of their symptoms not as an abstract “affective disorder” but as closely tied to the daily struggle of raising children alone under often difficult circumstances.

Table 7.1 Themes and Repeating Ideas in a Study of Postpartum Depression Among Low-Income Mothers

The Quantitative-Qualitative “Debate”

Given their differences, it may come as no surprise that quantitative and qualitative research in psychology and related fields do not coexist in complete harmony. Some quantitative researchers criticize qualitative methods on the grounds that they lack objectivity, are difficult to evaluate in terms of reliability and validity, and do not allow generalization to people or situations other than those actually studied. At the same time, some qualitative researchers criticize quantitative methods on the grounds that they overlook the richness of human behavior and experience and instead answer simple questions about easily quantifiable variables.

In general, however, qualitative researchers are well aware of the issues of objectivity, reliability, validity, and generalizability. In fact, they have developed a number of frameworks for addressing these issues (which are beyond the scope of our discussion). And in general, quantitative researchers are well aware of the issue of oversimplification. They do not believe that all human behavior and experience can be adequately described in terms of a small number of variables and the statistical relationships among them. Instead, they use simplification as a strategy for uncovering general principles of human behavior.

Many researchers from both the quantitative and qualitative camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). (In fact, the studies by Lindqvist and colleagues and by Abrams and Curran both combined quantitative and qualitative approaches.) One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. Again, while a qualitative study might suggest that families who experience an unexpected suicide have more difficulty resolving the question of why, a well-designed quantitative study could test a hypothesis by measuring these specific variables for a large sample. A second approach to combining quantitative and qualitative research is referred to as triangulation . The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge and how can they be reconciled?

Key Takeaways

• Qualitative research is an important alternative to quantitative research in psychology. It generally involves asking broader research questions, collecting more detailed data (e.g., interviews), and using nonstatistical analyses.
• Many researchers conceptualize quantitative and qualitative research as complementary and advocate combining them. For example, qualitative research can be used to generate hypotheses and quantitative research to test them.
• Discussion: What are some ways in which a qualitative study of girls who play youth baseball would be likely to differ from a quantitative study on the same topic?

Abrams, L. S., & Curran, L. (2009). “And you’re telling me not to stress?” A grounded theory study of postpartum depression symptoms among low-income mothers. Psychology of Women Quarterly, 33 , 351–362.

Geertz, C. (1973). The interpretation of cultures . New York, NY: Basic Books.

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

Lindqvist, P., Johansson, L., & Karlsson, U. (2008). In the aftermath of teenage suicide: A qualitative study of the psychosocial consequences for the surviving family members. BMC Psychiatry, 8 , 26. Retrieved from http://www.biomedcentral.com/1471-244X/8/26 .

Todd, Z., Nerlich, B., McKeown, S., & Clarke, D. D. (2004) Mixing methods in psychology: The integration of qualitative and quantitative methods in theory and practice . London, UK: Psychology Press.

Wilkins, A. (2008). “Happier than Non-Christians”: Collective emotions and symbolic boundaries among evangelical Christians. Social Psychology Quarterly, 71 , 281–301.

Research Methods in Psychology Copyright © 2016 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

Qualitative Study

Affiliations.

• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. 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." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. 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. 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. 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. 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." 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. 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.” 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. 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. 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."

Research Paradigm

Research paradigms are the assumptions, norms, and standards underpinning different research approaches. Essentially, research paradigms are the "worldviews" that inform research. 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. 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. 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. 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. 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.” 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.”

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. 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:

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.

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. 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. The Standards for Reporting Qualitative Research (SRQR) is a checklist covering a more comprehensive range of qualitative research.

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.

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Qualitative Research: Model and Hypotheses Refinement

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Despite the active discussion of firms policies to foster innovation generation, few researchers have engaged in analysing RRs, their characteristics and key drivers, be it from a management or a research perspective. On the other hand, conceptual elaborations have been made within the DC literature, and “the dynamic capability framework is drawing support and increased validity by researchers, empirical studies of dynamic capabilities remain relatively rare”. Scattered research has emerged in recent years stating the increased relevance of DCs in firms and conceptually investigating the notion of DCs, hitherto “we have little theoretical or empirical evidence on which to base any suggestions as to how dynamic capabilities can be deliberately built”.

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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on 4 April 2022 by Pritha Bhandari . Revised on 30 January 2023.

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

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

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

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

Table of contents

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

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

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

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

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

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

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

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

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

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

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

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

• Flexibility

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

• Natural settings

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

• Meaningful insights

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

• Generation of new ideas

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

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

• Unreliability

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

• Subjectivity

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

• Limited generalisability

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

• Labour-intensive

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

There are five common approaches to qualitative research :

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

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

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

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

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

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What Is A Research (Scientific) Hypothesis? A plain-language explainer + examples

By:  Derek Jansen (MBA)  | Reviewed By: Dr Eunice Rautenbach | June 2020

If you’re new to the world of research, or it’s your first time writing a dissertation or thesis, you’re probably noticing that the words “research hypothesis” and “scientific hypothesis” are used quite a bit, and you’re wondering what they mean in a research context .

“Hypothesis” is one of those words that people use loosely, thinking they understand what it means. However, it has a very specific meaning within academic research. So, it’s important to understand the exact meaning before you start hypothesizing. 

Research Hypothesis 101

• What is a hypothesis ?
• What is a research hypothesis (scientific hypothesis)?
• Requirements for a research hypothesis
• Definition of a research hypothesis
• The null hypothesis

What is a hypothesis?

Let’s start with the general definition of a hypothesis (not a research hypothesis or scientific hypothesis), according to the Cambridge Dictionary:

Hypothesis: an idea or explanation for something that is based on known facts but has not yet been proved.

In other words, it’s a statement that provides an explanation for why or how something works, based on facts (or some reasonable assumptions), but that has not yet been specifically tested . For example, a hypothesis might look something like this:

Hypothesis: sleep impacts academic performance.

This statement predicts that academic performance will be influenced by the amount and/or quality of sleep a student engages in – sounds reasonable, right? It’s based on reasonable assumptions , underpinned by what we currently know about sleep and health (from the existing literature). So, loosely speaking, we could call it a hypothesis, at least by the dictionary definition.

But that’s not good enough…

Unfortunately, that’s not quite sophisticated enough to describe a research hypothesis (also sometimes called a scientific hypothesis), and it wouldn’t be acceptable in a dissertation, thesis or research paper . In the world of academic research, a statement needs a few more criteria to constitute a true research hypothesis .

What is a research hypothesis?

A research hypothesis (also called a scientific hypothesis) is a statement about the expected outcome of a study (for example, a dissertation or thesis). To constitute a quality hypothesis, the statement needs to have three attributes – specificity , clarity and testability .

Let’s take a look at these more closely.

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Hypothesis Essential #1: Specificity & Clarity

A good research hypothesis needs to be extremely clear and articulate about both what’ s being assessed (who or what variables are involved ) and the expected outcome (for example, a difference between groups, a relationship between variables, etc.).

Let’s stick with our sleepy students example and look at how this statement could be more specific and clear.

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.

As you can see, the statement is very specific as it identifies the variables involved (sleep hours and test grades), the parties involved (two groups of students), as well as the predicted relationship type (a positive relationship). There’s no ambiguity or uncertainty about who or what is involved in the statement, and the expected outcome is clear.

Contrast that to the original hypothesis we looked at – “Sleep impacts academic performance” – and you can see the difference. “Sleep” and “academic performance” are both comparatively vague , and there’s no indication of what the expected relationship direction is (more sleep or less sleep). As you can see, specificity and clarity are key.

Hypothesis Essential #2: Testability (Provability)

A statement must be testable to qualify as a research hypothesis. In other words, there needs to be a way to prove (or disprove) the statement. If it’s not testable, it’s not a hypothesis – simple as that.

For example, consider the hypothesis we mentioned earlier:

Hypothesis: Students who sleep at least 8 hours per night will, on average, achieve higher grades in standardised tests than students who sleep less than 8 hours a night.  

We could test this statement by undertaking a quantitative study involving two groups of students, one that gets 8 or more hours of sleep per night for a fixed period, and one that gets less. We could then compare the standardised test results for both groups to see if there’s a statistically significant difference. 

Again, if you compare this to the original hypothesis we looked at – “Sleep impacts academic performance” – you can see that it would be quite difficult to test that statement, primarily because it isn’t specific enough. How much sleep? By who? What type of academic performance?

So, remember the mantra – if you can’t test it, it’s not a hypothesis 🙂

Defining A Research Hypothesis

You’re still with us? Great! Let’s recap and pin down a clear definition of a hypothesis.

A research hypothesis (or scientific hypothesis) is a statement about an expected relationship between variables, or explanation of an occurrence, that is clear, specific and testable.

So, when you write up hypotheses for your dissertation or thesis, make sure that they meet all these criteria. If you do, you’ll not only have rock-solid hypotheses but you’ll also ensure a clear focus for your entire research project.

What about the null hypothesis?

You may have also heard the terms null hypothesis , alternative hypothesis, or H-zero thrown around. At a simple level, the null hypothesis is the counter-proposal to the original hypothesis.

For example, if the hypothesis predicts that there is a relationship between two variables (for example, sleep and academic performance), the null hypothesis would predict that there is no relationship between those variables.

At a more technical level, the null hypothesis proposes that no statistical significance exists in a set of given observations and that any differences are due to chance alone.

And there you have it – hypotheses in a nutshell. 

If you have any questions, be sure to leave a comment below and we’ll do our best to help you. If you need hands-on help developing and testing your hypotheses, consider our private coaching service , where we hold your hand through the research journey.

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16 Comments

Very useful information. I benefit more from getting more information in this regard.

Very great insight,educative and informative. Please give meet deep critics on many research data of public international Law like human rights, environment, natural resources, law of the sea etc

In a book I read a distinction is made between null, research, and alternative hypothesis. As far as I understand, alternative and research hypotheses are the same. Can you please elaborate? Best Afshin

This is a self explanatory, easy going site. I will recommend this to my friends and colleagues.

Very good definition. How can I cite your definition in my thesis? Thank you. Is nul hypothesis compulsory in a research?

It’s a counter-proposal to be proven as a rejection

Please what is the difference between alternate hypothesis and research hypothesis?

It is a very good explanation. However, it limits hypotheses to statistically tasteable ideas. What about for qualitative researches or other researches that involve quantitative data that don’t need statistical tests?

In qualitative research, one typically uses propositions, not hypotheses.

could you please elaborate it more

I’ve benefited greatly from these notes, thank you.

This is very helpful

well articulated ideas are presented here, thank you for being reliable sources of information

Excellent. Thanks for being clear and sound about the research methodology and hypothesis (quantitative research)

I have only a simple question regarding the null hypothesis. – Is the null hypothesis (Ho) known as the reversible hypothesis of the alternative hypothesis (H1? – How to test it in academic research?

this is very important note help me much more

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A Practical Guide to Conversation Research: How to Study What People Say to Each Other Michael Yeomans, F. Katelynn Boland, Hanne Collins, Nicole Abi-Esber, and Alison Wood Brooks  

Conversation—a verbal interaction between two or more people—is a complex, pervasive, and consequential human behavior. Conversations have been studied across many academic disciplines. However, advances in recording and analysis techniques over the last decade have allowed researchers to more directly and precisely examine conversations in natural contexts and at a larger scale than ever before, and these advances open new paths to understand humanity and the social world. Existing reviews of text analysis and conversation research have focused on text generated by a single author (e.g., product reviews, news articles, and public speeches) and thus leave open questions about the unique challenges presented by interactive conversation data (i.e., dialogue). In this article, we suggest approaches to overcome common challenges in the workflow of conversation science, including recording and transcribing conversations, structuring data (to merge turn-level and speaker-level data sets), extracting and aggregating linguistic features, estimating effects, and sharing data. This practical guide is meant to shed light on current best practices and empower more researchers to study conversations more directly—to expand the community of conversation scholars and contribute to a greater cumulative scientific understanding of the social world. 

Open-Science Guidance for Qualitative Research: An Empirically Validated Approach for De-Identifying Sensitive Narrative Data Rebecca Campbell, McKenzie Javorka, Jasmine Engleton, Kathryn Fishwick, Katie Gregory, and Rachael Goodman-Williams  

The open-science movement seeks to make research more transparent and accessible. To that end, researchers are increasingly expected to share de-identified data with other scholars for review, reanalysis, and reuse. In psychology, open-science practices have been explored primarily within the context of quantitative data, but demands to share qualitative data are becoming more prevalent. Narrative data are far more challenging to de-identify fully, and because qualitative methods are often used in studies with marginalized, minoritized, and/or traumatized populations, data sharing may pose substantial risks for participants if their information can be later reidentified. To date, there has been little guidance in the literature on how to de-identify qualitative data. To address this gap, we developed a methodological framework for remediating sensitive narrative data. This multiphase process is modeled on common qualitative-coding strategies. The first phase includes consultations with diverse stakeholders and sources to understand reidentifiability risks and data-sharing concerns. The second phase outlines an iterative process for recognizing potentially identifiable information and constructing individualized remediation strategies through group review and consensus. The third phase includes multiple strategies for assessing the validity of the de-identification analyses (i.e., whether the remediated transcripts adequately protect participants’ privacy). We applied this framework to a set of 32 qualitative interviews with sexual-assault survivors. We provide case examples of how blurring and redaction techniques can be used to protect names, dates, locations, trauma histories, help-seeking experiences, and other information about dyadic interactions. 

Impossible Hypotheses and Effect-Size Limits Wijnand van Tilburg and Lennert van Tilburg

Psychological science is moving toward further specification of effect sizes when formulating hypotheses, performing power analyses, and considering the relevance of findings. This development has sparked an appreciation for the wider context in which such effect sizes are found because the importance assigned to specific sizes may vary from situation to situation. We add to this development a crucial but in psychology hitherto underappreciated contingency: There are mathematical limits to the magnitudes that population effect sizes can take within the common multivariate context in which psychology is situated, and these limits can be far more restrictive than typically assumed. The implication is that some hypothesized or preregistered effect sizes may be impossible. At the same time, these restrictions offer a way of statistically triangulating the plausible range of unknown effect sizes. We explain the reason for the existence of these limits, illustrate how to identify them, and offer recommendations and tools for improving hypothesized effect sizes by exploiting the broader multivariate context in which they occur. 

It’s All About Timing: Exploring Different Temporal Resolutions for Analyzing Digital-Phenotyping Data Anna Langener, Gert Stulp, Nicholas Jacobson, Andrea Costanzo, Raj Jagesar, Martien Kas, and Laura Bringmann  

The use of smartphones and wearable sensors to passively collect data on behavior has great potential for better understanding psychological well-being and mental disorders with minimal burden. However, there are important methodological challenges that may hinder the widespread adoption of these passive measures. A crucial one is the issue of timescale: The chosen temporal resolution for summarizing and analyzing the data may affect how results are interpreted. Despite its importance, the choice of temporal resolution is rarely justified. In this study, we aim to improve current standards for analyzing digital-phenotyping data by addressing the time-related decisions faced by researchers. For illustrative purposes, we use data from 10 students whose behavior (e.g., GPS, app usage) was recorded for 28 days through the Behapp application on their mobile phones. In parallel, the participants actively answered questionnaires on their phones about their mood several times a day. We provide a walk-through on how to study different timescales by doing individualized correlation analyses and random-forest prediction models. By doing so, we demonstrate how choosing different resolutions can lead to different conclusions. Therefore, we propose conducting a multiverse analysis to investigate the consequences of choosing different temporal resolutions. This will improve current standards for analyzing digital-phenotyping data and may help combat the replications crisis caused in part by researchers making implicit decisions. 

Calculating Repeated-Measures Meta-Analytic Effects for Continuous Outcomes: A Tutorial on Pretest–Posttest-Controlled Designs David R. Skvarc, Matthew Fuller-Tyszkiewicz  

Meta-analysis is a statistical technique that combines the results of multiple studies to arrive at a more robust and reliable estimate of an overall effect or estimate of the true effect. Within the context of experimental study designs, standard meta-analyses generally use between-groups differences at a single time point. This approach fails to adequately account for preexisting differences that are likely to threaten causal inference. Meta-analyses that take into account the repeated-measures nature of these data are uncommon, and so this article serves as an instructive methodology for increasing the precision of meta-analyses by attempting to estimate the repeated-measures effect sizes, with particular focus on contexts with two time points and two groups (a between-groups pretest–posttest design)—a common scenario for clinical trials and experiments. In this article, we summarize the concept of a between-groups pretest–posttest meta-analysis and its applications. We then explain the basic steps involved in conducting this meta-analysis, including the extraction of data and several alternative approaches for the calculation of effect sizes. We also highlight the importance of considering the presence of within-subjects correlations when conducting this form of meta-analysis.   

Reliability and Feasibility of Linear Mixed Models in Fully Crossed Experimental Designs Michele Scandola, Emmanuele Tidoni  

The use of linear mixed models (LMMs) is increasing in psychology and neuroscience research In this article, we focus on the implementation of LMMs in fully crossed experimental designs. A key aspect of LMMs is choosing a random-effects structure according to the experimental needs. To date, opposite suggestions are present in the literature, spanning from keeping all random effects (maximal models), which produces several singularity and convergence issues, to removing random effects until the best fit is found, with the risk of inflating Type I error (reduced models). However, defining the random structure to fit a nonsingular and convergent model is not straightforward. Moreover, the lack of a standard approach may lead the researcher to make decisions that potentially inflate Type I errors. After reviewing LMMs, we introduce a step-by-step approach to avoid convergence and singularity issues and control for Type I error inflation during model reduction of fully crossed experimental designs. Specifically, we propose the use of complex random intercepts (CRIs) when maximal models are overparametrized. CRIs are multiple random intercepts that represent the residual variance of categorical fixed effects within a given grouping factor. We validated CRIs and the proposed procedure by extensive simulations and a real-case application. We demonstrate that CRIs can produce reliable results and require less computational resources. Moreover, we outline a few criteria and recommendations on how and when scholars should reduce overparametrized models. Overall, the proposed procedure provides clear solutions to avoid overinflated results using LMMs in psychology and neuroscience.   

Understanding Meta-Analysis Through Data Simulation With Applications to Power Analysis Filippo Gambarota, Gianmarco Altoè  

Meta-analysis is a powerful tool to combine evidence from existing literature. Despite several introductory and advanced materials about organizing, conducting, and reporting a meta-analysis, to our knowledge, there are no introductive materials about simulating the most common meta-analysis models. Data simulation is essential for developing and validating new statistical models and procedures. Furthermore, data simulation is a powerful educational tool for understanding a statistical method. In this tutorial, we show how to simulate equal-effects, random-effects, and metaregression models and illustrate how to estimate statistical power. Simulations for multilevel and multivariate models are available in the Supplemental Material available online. All materials associated with this article can be accessed on OSF ( https://osf.io/54djn/ ).   

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Qualitative Methods in Health Care Research

Vishnu renjith.

School of Nursing and Midwifery, Royal College of Surgeons Ireland - Bahrain (RCSI Bahrain), Al Sayh Muharraq Governorate, Bahrain

Renjulal Yesodharan

1 Department of Mental Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Judith A. Noronha

2 Department of OBG Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Elissa Ladd

3 School of Nursing, MGH Institute of Health Professions, Boston, USA

Anice George

4 Department of Child Health Nursing, Manipal College of Nursing Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, India

Healthcare research is a systematic inquiry intended to generate robust evidence about important issues in the fields of medicine and healthcare. Qualitative research has ample possibilities within the arena of healthcare research. This article aims to inform healthcare professionals regarding qualitative research, its significance, and applicability in the field of healthcare. A wide variety of phenomena that cannot be explained using the quantitative approach can be explored and conveyed using a qualitative method. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research. The greatest strength of the qualitative research approach lies in the richness and depth of the healthcare exploration and description it makes. In health research, these methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

Introduction

Healthcare research is a systematic inquiry intended to generate trustworthy evidence about issues in the field of medicine and healthcare. The three principal approaches to health research are the quantitative, the qualitative, and the mixed methods approach. The quantitative research method uses data, which are measures of values and counts and are often described using statistical methods which in turn aids the researcher to draw inferences. Qualitative research incorporates the recording, interpreting, and analyzing of non-numeric data with an attempt to uncover the deeper meanings of human experiences and behaviors. Mixed methods research, the third methodological approach, involves collection and analysis of both qualitative and quantitative information with an objective to solve different but related questions, or at times the same questions.[ 1 , 2 ]

In healthcare, qualitative research is widely used to understand patterns of health behaviors, describe lived experiences, develop behavioral theories, explore healthcare needs, and design interventions.[ 1 , 2 , 3 ] Because of its ample applications in healthcare, there has been a tremendous increase in the number of health research studies undertaken using qualitative methodology.[ 4 , 5 ] This article discusses qualitative research methods, their significance, and applicability in the arena of healthcare.

Qualitative Research

Diverse academic and non-academic disciplines utilize qualitative research as a method of inquiry to understand human behavior and experiences.[ 6 , 7 ] According to Munhall, “Qualitative research involves broadly stated questions about human experiences and realities, studied through sustained contact with the individual in their natural environments and producing rich, descriptive data that will help us to understand those individual's experiences.”[ 8 ]

Significance of Qualitative Research

The qualitative method of inquiry examines the 'how' and 'why' of decision making, rather than the 'when,' 'what,' and 'where.'[ 7 ] Unlike quantitative methods, the objective of qualitative inquiry is to explore, narrate, and explain the phenomena and make sense of the complex reality. Health interventions, explanatory health models, and medical-social theories could be developed as an outcome of qualitative research.[ 9 ] Understanding the richness and complexity of human behavior is the crux of qualitative research.

Differences between Quantitative and Qualitative Research

The quantitative and qualitative forms of inquiry vary based on their underlying objectives. They are in no way opposed to each other; instead, these two methods are like two sides of a coin. The critical differences between quantitative and qualitative research are summarized in Table 1 .[ 1 , 10 , 11 ]

Differences between quantitative and qualitative research

Qualitative Research Questions and Purpose Statements

Qualitative questions are exploratory and are open-ended. A well-formulated study question forms the basis for developing a protocol, guides the selection of design, and data collection methods. Qualitative research questions generally involve two parts, a central question and related subquestions. The central question is directed towards the primary phenomenon under study, whereas the subquestions explore the subareas of focus. It is advised not to have more than five to seven subquestions. A commonly used framework for designing a qualitative research question is the 'PCO framework' wherein, P stands for the population under study, C stands for the context of exploration, and O stands for the outcome/s of interest.[ 12 ] The PCO framework guides researchers in crafting a focused study question.

Example: In the question, “What are the experiences of mothers on parenting children with Thalassemia?”, the population is “mothers of children with Thalassemia,” the context is “parenting children with Thalassemia,” and the outcome of interest is “experiences.”

The purpose statement specifies the broad focus of the study, identifies the approach, and provides direction for the overall goal of the study. The major components of a purpose statement include the central phenomenon under investigation, the study design and the population of interest. Qualitative research does not require a-priori hypothesis.[ 13 , 14 , 15 ]

Example: Borimnejad et al . undertook a qualitative research on the lived experiences of women suffering from vitiligo. The purpose of this study was, “to explore lived experiences of women suffering from vitiligo using a hermeneutic phenomenological approach.” [ 16 ]

Review of the Literature

In quantitative research, the researchers do an extensive review of scientific literature prior to the commencement of the study. However, in qualitative research, only a minimal literature search is conducted at the beginning of the study. This is to ensure that the researcher is not influenced by the existing understanding of the phenomenon under the study. The minimal literature review will help the researchers to avoid the conceptual pollution of the phenomenon being studied. Nonetheless, an extensive review of the literature is conducted after data collection and analysis.[ 15 ]

Reflexivity

Reflexivity refers to critical self-appraisal about one's own biases, values, preferences, and preconceptions about the phenomenon under investigation. Maintaining a reflexive diary/journal is a widely recognized way to foster reflexivity. According to Creswell, “Reflexivity increases the credibility of the study by enhancing more neutral interpretations.”[ 7 ]

Types of Qualitative Research Designs

The qualitative research approach encompasses a wide array of research designs. The words such as types, traditions, designs, strategies of inquiry, varieties, and methods are used interchangeably. The major types of qualitative research designs are narrative research, phenomenological research, grounded theory research, ethnographic research, historical research, and case study research.[ 1 , 7 , 10 ]

Narrative research

Narrative research focuses on exploring the life of an individual and is ideally suited to tell the stories of individual experiences.[ 17 ] The purpose of narrative research is to utilize 'story telling' as a method in communicating an individual's experience to a larger audience.[ 18 ] The roots of narrative inquiry extend to humanities including anthropology, literature, psychology, education, history, and sociology. Narrative research encompasses the study of individual experiences and learning the significance of those experiences. The data collection procedures include mainly interviews, field notes, letters, photographs, diaries, and documents collected from one or more individuals. Data analysis involves the analysis of the stories or experiences through “re-storying of stories” and developing themes usually in chronological order of events. Rolls and Payne argued that narrative research is a valuable approach in health care research, to gain deeper insight into patient's experiences.[ 19 ]

Example: Karlsson et al . undertook a narrative inquiry to “explore how people with Alzheimer's disease present their life story.” Data were collected from nine participants. They were asked to describe about their life experiences from childhood to adulthood, then to current life and their views about the future life. [ 20 ]

Phenomenological research

Phenomenology is a philosophical tradition developed by German philosopher Edmond Husserl. His student Martin Heidegger did further developments in this methodology. It defines the 'essence' of individual's experiences regarding a certain phenomenon.[ 1 ] The methodology has its origin from philosophy, psychology, and education. The purpose of qualitative research is to understand the people's everyday life experiences and reduce it into the central meaning or the 'essence of the experience'.[ 21 , 22 ] The unit of analysis of phenomenology is the individuals who have had similar experiences of the phenomenon. Interviews with individuals are mainly considered for the data collection, though, documents and observations are also useful. Data analysis includes identification of significant meaning elements, textural description (what was experienced), structural description (how was it experienced), and description of 'essence' of experience.[ 1 , 7 , 21 ] The phenomenological approach is further divided into descriptive and interpretive phenomenology. Descriptive phenomenology focuses on the understanding of the essence of experiences and is best suited in situations that need to describe the lived phenomenon. Hermeneutic phenomenology or Interpretive phenomenology moves beyond the description to uncover the meanings that are not explicitly evident. The researcher tries to interpret the phenomenon, based on their judgment rather than just describing it.[ 7 , 21 , 22 , 23 , 24 ]

Example: A phenomenological study conducted by Cornelio et al . aimed at describing the lived experiences of mothers in parenting children with leukemia. Data from ten mothers were collected using in-depth semi-structured interviews and were analyzed using Husserl's method of phenomenology. Themes such as “pivotal moment in life”, “the experience of being with a seriously ill child”, “having to keep distance with the relatives”, “overcoming the financial and social commitments”, “responding to challenges”, “experience of faith as being key to survival”, “health concerns of the present and future”, and “optimism” were derived. The researchers reported the essence of the study as “chronic illness such as leukemia in children results in a negative impact on the child and on the mother.” [ 25 ]

Grounded Theory Research

Grounded theory has its base in sociology and propagated by two sociologists, Barney Glaser, and Anselm Strauss.[ 26 ] The primary purpose of grounded theory is to discover or generate theory in the context of the social process being studied. The major difference between grounded theory and other approaches lies in its emphasis on theory generation and development. The name grounded theory comes from its ability to induce a theory grounded in the reality of study participants.[ 7 , 27 ] Data collection in grounded theory research involves recording interviews from many individuals until data saturation. Constant comparative analysis, theoretical sampling, theoretical coding, and theoretical saturation are unique features of grounded theory research.[ 26 , 27 , 28 ] Data analysis includes analyzing data through 'open coding,' 'axial coding,' and 'selective coding.'[ 1 , 7 ] Open coding is the first level of abstraction, and it refers to the creation of a broad initial range of categories, axial coding is the procedure of understanding connections between the open codes, whereas selective coding relates to the process of connecting the axial codes to formulate a theory.[ 1 , 7 ] Results of the grounded theory analysis are supplemented with a visual representation of major constructs usually in the form of flow charts or framework diagrams. Quotations from the participants are used in a supportive capacity to substantiate the findings. Strauss and Corbin highlights that “the value of the grounded theory lies not only in its ability to generate a theory but also to ground that theory in the data.”[ 27 ]

Example: Williams et al . conducted a grounded theory research to explore the nature of relationship between the sense of self and the eating disorders. Data were collected form 11 women with a lifetime history of Anorexia Nervosa and were analyzed using the grounded theory methodology. Analysis led to the development of a theoretical framework on the nature of the relationship between the self and Anorexia Nervosa. [ 29 ]

Ethnographic research

Ethnography has its base in anthropology, where the anthropologists used it for understanding the culture-specific knowledge and behaviors. In health sciences research, ethnography focuses on narrating and interpreting the health behaviors of a culture-sharing group. 'Culture-sharing group' in an ethnography represents any 'group of people who share common meanings, customs or experiences.' In health research, it could be a group of physicians working in rural care, a group of medical students, or it could be a group of patients who receive home-based rehabilitation. To understand the cultural patterns, researchers primarily observe the individuals or group of individuals for a prolonged period of time.[ 1 , 7 , 30 ] The scope of ethnography can be broad or narrow depending on the aim. The study of more general cultural groups is termed as macro-ethnography, whereas micro-ethnography focuses on more narrowly defined cultures. Ethnography is usually conducted in a single setting. Ethnographers collect data using a variety of methods such as observation, interviews, audio-video records, and document reviews. A written report includes a detailed description of the culture sharing group with emic and etic perspectives. When the researcher reports the views of the participants it is called emic perspectives and when the researcher reports his or her views about the culture, the term is called etic.[ 7 ]

Example: The aim of the ethnographic study by LeBaron et al . was to explore the barriers to opioid availability and cancer pain management in India. The researchers collected data from fifty-nine participants using in-depth semi-structured interviews, participant observation, and document review. The researchers identified significant barriers by open coding and thematic analysis of the formal interview. [ 31 ]

Historical research

Historical research is the “systematic collection, critical evaluation, and interpretation of historical evidence”.[ 1 ] The purpose of historical research is to gain insights from the past and involves interpreting past events in the light of the present. The data for historical research are usually collected from primary and secondary sources. The primary source mainly includes diaries, first hand information, and writings. The secondary sources are textbooks, newspapers, second or third-hand accounts of historical events and medical/legal documents. The data gathered from these various sources are synthesized and reported as biographical narratives or developmental perspectives in chronological order. The ideas are interpreted in terms of the historical context and significance. The written report describes 'what happened', 'how it happened', 'why it happened', and its significance and implications to current clinical practice.[ 1 , 10 ]

Example: Lubold (2019) analyzed the breastfeeding trends in three countries (Sweden, Ireland, and the United States) using a historical qualitative method. Through analysis of historical data, the researcher found that strong family policies, adherence to international recommendations and adoption of baby-friendly hospital initiative could greatly enhance the breastfeeding rates. [ 32 ]

Case study research

Case study research focuses on the description and in-depth analysis of the case(s) or issues illustrated by the case(s). The design has its origin from psychology, law, and medicine. Case studies are best suited for the understanding of case(s), thus reducing the unit of analysis into studying an event, a program, an activity or an illness. Observations, one to one interviews, artifacts, and documents are used for collecting the data, and the analysis is done through the description of the case. From this, themes and cross-case themes are derived. A written case study report includes a detailed description of one or more cases.[ 7 , 10 ]

Example: Perceptions of poststroke sexuality in a woman of childbearing age was explored using a qualitative case study approach by Beal and Millenbrunch. Semi structured interview was conducted with a 36- year mother of two children with a history of Acute ischemic stroke. The data were analyzed using an inductive approach. The authors concluded that “stroke during childbearing years may affect a woman's perception of herself as a sexual being and her ability to carry out gender roles”. [ 33 ]

Sampling in Qualitative Research

Qualitative researchers widely use non-probability sampling techniques such as purposive sampling, convenience sampling, quota sampling, snowball sampling, homogeneous sampling, maximum variation sampling, extreme (deviant) case sampling, typical case sampling, and intensity sampling. The selection of a sampling technique depends on the nature and needs of the study.[ 34 , 35 , 36 , 37 , 38 , 39 , 40 ] The four widely used sampling techniques are convenience sampling, purposive sampling, snowball sampling, and intensity sampling.

Convenience sampling

It is otherwise called accidental sampling, where the researchers collect data from the subjects who are selected based on accessibility, geographical proximity, ease, speed, and or low cost.[ 34 ] Convenience sampling offers a significant benefit of convenience but often accompanies the issues of sample representation.

Purposive sampling

Purposive or purposeful sampling is a widely used sampling technique.[ 35 ] It involves identifying a population based on already established sampling criteria and then selecting subjects who fulfill that criteria to increase the credibility. However, choosing information-rich cases is the key to determine the power and logic of purposive sampling in a qualitative study.[ 1 ]

Snowball sampling

The method is also known as 'chain referral sampling' or 'network sampling.' The sampling starts by having a few initial participants, and the researcher relies on these early participants to identify additional study participants. It is best adopted when the researcher wishes to study the stigmatized group, or in cases, where findings of participants are likely to be difficult by ordinary means. Respondent ridden sampling is an improvised version of snowball sampling used to find out the participant from a hard-to-find or hard-to-study population.[ 37 , 38 ]

Intensity sampling

The process of identifying information-rich cases that manifest the phenomenon of interest is referred to as intensity sampling. It requires prior information, and considerable judgment about the phenomenon of interest and the researcher should do some preliminary investigations to determine the nature of the variation. Intensity sampling will be done once the researcher identifies the variation across the cases (extreme, average and intense) and picks the intense cases from them.[ 40 ]

Deciding the Sample Size

A-priori sample size calculation is not undertaken in the case of qualitative research. Researchers collect the data from as many participants as possible until they reach the point of data saturation. Data saturation or the point of redundancy is the stage where the researcher no longer sees or hears any new information. Data saturation gives the idea that the researcher has captured all possible information about the phenomenon of interest. Since no further information is being uncovered as redundancy is achieved, at this point the data collection can be stopped. The objective here is to get an overall picture of the chronicle of the phenomenon under the study rather than generalization.[ 1 , 7 , 41 ]

Data Collection in Qualitative Research

The various strategies used for data collection in qualitative research includes in-depth interviews (individual or group), focus group discussions (FGDs), participant observation, narrative life history, document analysis, audio materials, videos or video footage, text analysis, and simple observation. Among all these, the three popular methods are the FGDs, one to one in-depth interviews and the participant observation.

FGDs are useful in eliciting data from a group of individuals. They are normally built around a specific topic and are considered as the best approach to gather data on an entire range of responses to a topic.[ 42 Group size in an FGD ranges from 6 to 12. Depending upon the nature of participants, FGDs could be homogeneous or heterogeneous.[ 1 , 14 ] One to one in-depth interviews are best suited to obtain individuals' life histories, lived experiences, perceptions, and views, particularly while exporting topics of sensitive nature. In-depth interviews can be structured, unstructured, or semi-structured. However, semi-structured interviews are widely used in qualitative research. Participant observations are suitable for gathering data regarding naturally occurring behaviors.[ 1 ]

Data Analysis in Qualitative Research

Various strategies are employed by researchers to analyze data in qualitative research. Data analytic strategies differ according to the type of inquiry. A general content analysis approach is described herewith. Data analysis begins by transcription of the interview data. The researcher carefully reads data and gets a sense of the whole. Once the researcher is familiarized with the data, the researcher strives to identify small meaning units called the 'codes.' The codes are then grouped based on their shared concepts to form the primary categories. Based on the relationship between the primary categories, they are then clustered into secondary categories. The next step involves the identification of themes and interpretation to make meaning out of data. In the results section of the manuscript, the researcher describes the key findings/themes that emerged. The themes can be supported by participants' quotes. The analytical framework used should be explained in sufficient detail, and the analytic framework must be well referenced. The study findings are usually represented in a schematic form for better conceptualization.[ 1 , 7 ] Even though the overall analytical process remains the same across different qualitative designs, each design such as phenomenology, ethnography, and grounded theory has design specific analytical procedures, the details of which are out of the scope of this article.

Computer-Assisted Qualitative Data Analysis Software (CAQDAS)

Until recently, qualitative analysis was done either manually or with the help of a spreadsheet application. Currently, there are various software programs available which aid researchers to manage qualitative data. CAQDAS is basically data management tools and cannot analyze the qualitative data as it lacks the ability to think, reflect, and conceptualize. Nonetheless, CAQDAS helps researchers to manage, shape, and make sense of unstructured information. Open Code, MAXQDA, NVivo, Atlas.ti, and Hyper Research are some of the widely used qualitative data analysis software.[ 14 , 43 ]

Reporting Guidelines

Consolidated Criteria for Reporting Qualitative Research (COREQ) is the widely used reporting guideline for qualitative research. This 32-item checklist assists researchers in reporting all the major aspects related to the study. The three major domains of COREQ are the 'research team and reflexivity', 'study design', and 'analysis and findings'.[ 44 , 45 ]

Critical Appraisal of Qualitative Research

Various scales are available to critical appraisal of qualitative research. The widely used one is the Critical Appraisal Skills Program (CASP) Qualitative Checklist developed by CASP network, UK. This 10-item checklist evaluates the quality of the study under areas such as aims, methodology, research design, ethical considerations, data collection, data analysis, and findings.[ 46 ]

Ethical Issues in Qualitative Research

A qualitative study must be undertaken by grounding it in the principles of bioethics such as beneficence, non-maleficence, autonomy, and justice. Protecting the participants is of utmost importance, and the greatest care has to be taken while collecting data from a vulnerable research population. The researcher must respect individuals, families, and communities and must make sure that the participants are not identifiable by their quotations that the researchers include when publishing the data. Consent for audio/video recordings must be obtained. Approval to be in FGDs must be obtained from the participants. Researchers must ensure the confidentiality and anonymity of the transcripts/audio-video records/photographs/other data collected as a part of the study. The researchers must confirm their role as advocates and proceed in the best interest of all participants.[ 42 , 47 , 48 ]

Rigor in Qualitative Research

The demonstration of rigor or quality in the conduct of the study is essential for every research method. However, the criteria used to evaluate the rigor of quantitative studies are not be appropriate for qualitative methods. Lincoln and Guba (1985) first outlined the criteria for evaluating the qualitative research often referred to as “standards of trustworthiness of qualitative research”.[ 49 ] The four components of the criteria are credibility, transferability, dependability, and confirmability.

Credibility refers to confidence in the 'truth value' of the data and its interpretation. It is used to establish that the findings are true, credible and believable. Credibility is similar to the internal validity in quantitative research.[ 1 , 50 , 51 ] The second criterion to establish the trustworthiness of the qualitative research is transferability, Transferability refers to the degree to which the qualitative results are applicability to other settings, population or contexts. This is analogous to the external validity in quantitative research.[ 1 , 50 , 51 ] Lincoln and Guba recommend authors provide enough details so that the users will be able to evaluate the applicability of data in other contexts.[ 49 ] The criterion of dependability refers to the assumption of repeatability or replicability of the study findings and is similar to that of reliability in quantitative research. The dependability question is 'Whether the study findings be repeated of the study is replicated with the same (similar) cohort of participants, data coders, and context?'[ 1 , 50 , 51 ] Confirmability, the fourth criteria is analogous to the objectivity of the study and refers the degree to which the study findings could be confirmed or corroborated by others. To ensure confirmability the data should directly reflect the participants' experiences and not the bias, motivations, or imaginations of the inquirer.[ 1 , 50 , 51 ] Qualitative researchers should ensure that the study is conducted with enough rigor and should report the measures undertaken to enhance the trustworthiness of the study.

Conclusions

Qualitative research studies are being widely acknowledged and recognized in health care practice. This overview illustrates various qualitative methods and shows how these methods can be used to generate evidence that informs clinical practice. Qualitative research helps to understand the patterns of health behaviors, describe illness experiences, design health interventions, and develop healthcare theories. The ultimate strength of the qualitative research approach lies in the richness of the data and the descriptions and depth of exploration it makes. Hence, qualitative methods are considered as the most humanistic and person-centered way of discovering and uncovering thoughts and actions of human beings.

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• Open access
• Published: 09 May 2024

Exploring factors affecting the unsafe behavior of health care workers’ in using respiratory masks during COVID-19 pandemic in Iran: a qualitative study

• Azadeh Tahernejad 1 ,
• Sanaz Sohrabizadeh   ORCID: orcid.org/0000-0002-9170-178X 1 &
• Somayeh Tahernejad 2  

BMC Health Services Research volume  24 , Article number:  608 ( 2024 ) Cite this article

146 Accesses

Metrics details

The use of respiratory masks has been one of the most important measures to prevent the spread of COVID-19 among health care workers during the COVID-19 pandemic. Therefore, correct and safe use of breathing masks is vital. The purpose of this study was to exploring factors affecting the unsafe behavior of health care workers’ in using respiratory masks during the COVID-19 pandemic in Iran.

This study was carried out using the conventional qualitative content analysis. Participants were the number of 26 health care workers selected by purposive sampling method. Data collection was conducted through in-depth semi-structured interviews. Data analysis was done using the content analysis approach of Graneheim and Lundman. This study aligns with the Consolidated Criteria for Reporting Qualitative Research (COREQ) checklist and was conducted between December 2021 and April 2022.

The factors affecting the unsafe behavior of health care workers while using respiratory masks were divided into 3 main categories and 8 sub-categories. Categories included discomfort and pain (four sub-categories of headache and dizziness, skin discomfort, respiratory discomfort, feeling hot and thirsty), negative effect on performance (four sub-categories of effect on physical function, effect on cognitive function, system function vision, and hearing), and a negative effect on the mental state (two subcategories of anxiety and depression).

The findings can help identify and analyze possible scenarios to reduce unsafe behaviors at the time of using breathing masks. The necessary therapeutic and preventive interventions regarding the complications of using masks, as well as planning to train personnel for the correct use of masks with minimal health effects are suggested.

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The COVID-19 pandemic has brought unprecedented challenges to healthcare systems worldwide, requiring Health Care Workers (HCWs) to adopt strict infection control measures to protect themselves [ 1 ]. Among these measures, the proper use of respiratory masks plays a crucial role in preventing the transmission of the virus [ 2 ]. Iran was among the initial countries impacted by COVID-19. In Iran, as in many other countries, HCWs have been at the forefront of the battle against COVID-19, facing various challenges in utilizing respiratory masks effectively [ 3 ]. Over 7.6 million Iranians have been infected by the SARS-CoV-2 virus, with more than 146,480 reported deaths as of August 2023 [ 4 ]. Amid the COVID-19 pandemic, Iran’s healthcare system experienced significant impacts as well [ 5 ].

Despite the passage of several years since the onset of the COVID-19 pandemic, new variant of the virus continues to emerge worldwide. It is crucial to be prepared for future pandemics and similar biological disasters.

Due to the SARS-CoV-2 virus transmission via respiratory droplets, the use of masks and personal protective equipment is essential [ 6 ]. The World Health Organization recommended the use of medical masks, such as surgical masks, for HCWs during the COVID-19 pandemic [ 7 ]. These masks are designed to provide a barrier to respiratory droplets and help reduce the transmission of the virus [ 8 ].

Few studies have been devoted to negative aspects of using respiratory masks in human being. The physiological and adverse effects of using PPE have been investigated in a systematic review study [ 9 ]. In another review study, of skin problems related to the use of respiratory masks were studied [ 10 ]. Also, in some studies, a significant relationship has been found between the time of using masks and the severity of the adverse effects of using masks [ 11 ]. In all the above studies, questionnaires have been used to check the prevalence of these adverse effects among HCWs.

Incorrect use of masks is considered as the unsafe behaviors of HCWs. In some studies, unsafe behaviors are defined as disobeying an accepted safe method while working with the capability of causing an accident [ 12 ]. Since the reasons for unsafe behavior are complex and multifaceted, their prevention requires a clear understanding of important and influential factors. In various studies about the prevalence of unsafe behaviors in work environments, several factors such as individual characteristics, psychological aspects, safety conditions, perceived risk, and stress have been introduced as effective factors in demonstrating the unsafe behaviors [ 12 , 13 , 14 ]. However, the findings are still unable to provide a deep understanding of the underlying causes and motivations contributing to unsafe behaviors.

In the present study, unsafe behaviors while using respiratory masks is defined as the behaviors that are seen by some HCWs, which reduce the effectiveness of respiratory masks due to improper placement on the face or hand contact with the mask [ 15 ]. Some researchers in their studies indicated that other unknown factors are also effective in the unsafe behaviors [ 14 ]. However, the findings are still unable to provide a deep understanding of the underlying causes and motivations contributing to unsafe behaviors. Qualitative studies are needed to answer these questions and determine its causes. Hence, the present study is aimed to explore the factors affecting the unsafe behavior of HCWs while using respiratory masks during the COVID-19 pandemic through a qualitative study.

Study design

This study was carried out using conventional qualitative content analysis (item 9 in COREQ checklist). The interviews explored HCWs’ experiences regarding factors affecting the unsafe behavior in using respiratory masks during covid-19 pandemic in Iran. This research adheres to the guidelines outlined in the Consolidated Criteria for Reporting Qualitative Research (COREQ).

This study was conducted in government and non-government hospitals in Tehran, Mashhad and Rafsanjan that admitted patients with COVID-19 disease. The authors’ place of work and access to participants were important causes of choosing the settings. Moreover, these hospitals experienced a large amount of patients seeking healthcare during the Covid-19 pandemic. This study was performed between December 2021 and April 2022.

Participants

In this study, interviews were performed with healthcare workers (HCWs) including nurses, physicians and hospital workers who had direct contact with patients that used masks for more than 4 h in each work shift. Also, participants frequently utilized surgical masks. Among them, few employed filter masks or a combination of both types. The inclusion criteria were people with experience of using respiratory masks for more than one year and the ability to express their experiences and point of views. The sole exclusion criterion of the current study was a lack of interest in further participation. The participants were selected using purposive sampling method (item 10 in COREQ checklist) in which the researcher selected the most informed people who could explain their experiences regarding the research topic [ 16 ]. The number of participants was determined based on the data saturation principle in which no new concepts were obtained. Data saturation was achieved after 24 interviews, and to ensure saturation, two more interviews were also performed. Finally, the total number of participants was 26 people (items 12–13 in COREQ checklist).

Data gathering

Data collection was performed through in-depth face to face (item 11 in COREQ checklist) semi-structured interviews. The first author, who received training in qualitative research methods, conducted all the interviews (items 1–5 in COREQ checklist). The participants were presented with information about the research topic, objectives, and the researchers’ identities. The researcher thoroughly described the study procedure to those who consented to participate, and written informed consent was obtained from all participants (items 6–8 in COREQ checklist). The data was gathered in the workplace of the participants. Additionally, demographic data of the participants was documented (items 14–16 in COREQ checklist). At first, 5 unstructured interviews were done to extract the primary concept, and then, 21 semi-structured interviews were conducted using the interview guide. The interviews were done in a quiet and comfortable place. The interviews started with simple and general topics and were gradually directed to specific questions based on the answers. Some of the questions were: Based on your experience, what factors are effective in not using your mask safely?

New concepts were extracted from each interview, and this process continued until data saturation was reached. After obtaining permission from the participants to record the interviews, the implementation of the interviews was done immediately after the completion of each interview to increase the accuracy of the obtained data. The duration of the interviews was between 15 and 40 min (30 min on average). Field notes were made during or after the interview and transcripts were returned to participants for the comments and corrections (items 17–23 in COREQ checklist).

Data analysis

Data analysis was done using the five-step content analysis approach of Graneheim and Lundman [ 17 ]. Immediately after conducting each interview, the recorded file of the interview was transcribed in Word software. The interview text was read several times and based on the research question, all the content related to the participants’ experiences were extracted in the form of meaning units. In addition, notes were written in the margins of the text and then, the abstracted meaning units were designated as the code. Subsequently, the compiled codes were categorized into subcategories according to similarities. This process was repeated for all transcribed interviews until the main categories were established. The whole data analysis process was carried out by the researchers. Direct quotes from the interviews included in the results section to elucidate the codes, categories, and themes. (items 24–32 in COREQ checklist).

Trustworthiness

The strategies of transferability, dependability, credibility outlined by Lincoln and Guba were employed to achieve data trustworthiness [ 18 ]. Credibility and dependability were established through data triangulation approach, which involved interviews and field notes. Furthermore, peer check and member check were applied for ensuring credibility. To obtain member check, the transcribed interviews and codes were shared with some participants to receive their feedbacks. In the case of peer check, the research team and independent experts were verified the extracted codes and sub-categories. Data transferability and Confirmability were met through the detailed explanation of the research stages and process.

Women were 50% of all participants and the highest frequency of education was bachelor’s degree ( n  = 17). Furthermore, the highest amount of work experience was 22 years (Table  1 ).

In the present study, 689 initial codes were identified in the initial writing, and after removing duplicate codes and cleaning, the number of final codes included 132 codes. After reviewing and analyzing the data, the factors affecting the unsafe behavior of HCWs while using respiratory masks were divided into 3 main categories and 8 sub-categories (Table  2 ). Categories included discomfort and pain (four sub-categories of headache and dizziness, skin discomfort, respiratory discomfort, feeling hot and thirsty), negative effect on performance (four sub-categories of effect on physical function, effect on cognitive function, system function vision and hearing), and a negative effect on the mental state (two subcategories of anxiety and depression).

Pain and discomfort

Some of the participants reported that the reason for improper and unsafe use of the mask is feeling pain and discomfort, and the reasons include the four subcategories of headache and dizziness, skin discomfort, respiratory discomfort, discomfort caused by heat and thirst.

Skin disorders

The side effects of the mask on the skin are of the important factors in this category. Thus, some participants, due effects of the mask to their skin, limited the use of the mask or did not use it correctly. Among the skin problems experienced by the participants were acne and skin sensitivities, which in some cases required drug treatments. The subcategory of skin sensitivities such as itching and burning was mentioned by more than 70% of the samples as the most important cause of discomfort.

“…I can’t help touching my mask. After half an hour when I put on the new mask, my face, especially my nose, starts to itch badly and I often have to blow my nose from under the mask or over the mask with my fingers, palm or the back of my hand…” (P1)

Respiratory disorders

Most of the participants in the study noted to problems such as difficulty in breathing, heart palpitations, carbon dioxide and unpleasant smell inside the mask as the most important respiratory problems. Therefore, it can be one of the important reasons for removing the mask and unsafe behavior in using the mask.

“… at any opportunity, I remove my mask to take a breath…” (P15)

Feeling hot and thirsty

Temperature discomfort, especially in long-term use and when people had to use two masks, was mentioned as an annoying factor.

“… the heat inside the mask bothers me a lot, I sweat and the mask gets wet… no matter how much water I drink, I still feel thirsty…” (P6)

Unfitness of mask with the individual’s face

Another important point extracted from the interviews was the importance of when to use the mask. In this way, as the time of using the mask increased, the person’s feeling of discomfort due to the mismatch between the belt and the mask increased, because the feeling of pressure and pain on the nose, behind the ears, and the face usually occurs several hours after wearing the mask. Several participants reported experiencing discomfort and headaches after wearing the mask. Although These headaches were often short-term and didn’t have long-term complications according to the participants’ reports, they could affect the work performance of HCWs and their behavior in the correct use of respiratory masks.

“…. After a while, the mask puts pressure on my nose and parts of my head and face. Sometimes I touch and move it unintentionally…” (P3) “… if I don’t move the mask on my face, I get a headache because the mask strap puts pressure on my head and nose…” (P21)

Effects on performance

The participants reported that wearing a mask for a long time is one of their important problems in performing their duties, and one of the main categories extracted from this study is the effects on performance, which includes the physical, cognitive, vision and hearing performance.

Effects on physical performance

The effect on the physical performance of HCWs had less effect on their unsafe behavior in using masks than other cases. But when masks were used for a long time and people were more physically tired, sometimes people removed the mask to increase their ability to perform physical work.

“…when I wear a mask, it becomes difficult for me to walk and do physical work, as if I am short of breath…” (P17)

Effects on cognitive function

It was the most frequent subcategory. Because when people feel uncomfortable, their attention decreases and part of the working memory is involved in feeling uncomfortable. Of course, it should be noted that many of the participants in the present study reported the decrease in alertness to be an effective factor in reducing their cognitive performance.

“…When I take off the mask, I can focus better on my work. Especially when I wear it in longer times, I get tired. Many times, I move the mask to finish my job faster…” (P8)

Based on the participants’ point of views, data perception (understanding information through the visual and auditory systems) decreases while using the mask. However, the negative effect of mask on the visual performance affects the unsafe behavior of the HCWs in the incorrect use of the mask and moving it on the face more than other cases. Most of the people who used glasses reported the steam condensation under the glasses as an important cause of discomfort and interference of the mask with their work duties.

“…Using glasses with a mask is really annoying. I have eye pain and burning, and there is always a fog in front of my eyes…” (P2)

Effects on mental status

Among the other main categories extracted in this study is the effects on mental status, which includes the subcategories of depression and anxiety. The negative effect of the mask on the mental state unconsciously affects the person’s behavior in using the respiratory mask.

Some of the participants in this study reported feeling anxious while wearing the mask for various reasons. Therefore, they refuse to wear masks, although they have no justification for doing so. In many cases, the participants in this study expressed that during higher psychological stress, they suffer more from wearing masks and tend to wear them improperly.

“… Sometimes I distractedly take off my mask so that the other person hears my voice better. However, there are many patients, So I am afraid of getting infected. Sometimes I have to speak loudly and this makes me furious … I worry about making a mistake or misunderstanding the conversation, and …” (P4)

One of the most important factors mentioned as a cause of depression was harder communication with colleagues and patients while wearing a mask. This occurs by increasing the physical and mental workload and placing people in social isolation. In this situation, HCWs sometimes consciously take off their masks, so that they can communicate with each other more conveniently.

“…When I wear a mask, I get tired when talking to others. I prefer not to talk to my colleague. Sometimes I don’t pay attention, I take the mask down so they can understand me …” (P5)

To the best of our knowledge, this research is one of the first qualitative studies to extract the experiences of HCWs for explaining the factors affecting the unsafe behavior of HCWs in using respiratory masks during the COVID-19 pandemic in Iran. Although many reasons can cause the unsafe behavior of HCWs in the correct use of respiratory masks in the hospital, according to the present results, three main categories include discomfort and pain, effects on performance, effects on mental status. Skin and respiratory discomforts and the negative effect of the mask on cognitive functions are among the most important factors affecting the unsafe behavior of HCWs in the field of correct use of respiratory masks.

Based on the present study, the participants experienced discomfort and pain while using the mask, and this was one of the important factors of unsafe use of respiratory masks. Discomfort while wearing masks has been confirmed in several studies [ 19 ]. Additionally, in a similar study, researchers found that wearing face masks during the COVID-19 era heightens the discomfort experienced by HCWs [ 20 ]. Some studies have delved into these discomforts in greater detail. For example, the prevalence of skin disorders among HCWs using PPE during the COVID-19 pandemic was reported to be significant [ 21 ]. Some researchers also reported significant prevalence of respiratory disorders and headaches when using PPE [ 22 ]. The findings of a study suggested that a novel form of headache has emerged among HCWs when using a mask during the COVID-19 pandemic. Both exacerbation of existing headaches and the onset of new headaches have been observed to rise with mask usage, irrespective of the use duration [ 23 ]. In some studies, a significant percentage of people reported feeling thirsty and dehydrated after long-term use of respiratory masks [ 24 ]. Several studies reported disturbing rates of perspiration from prolonged use of respiratory masks [ 25 , 26 , 27 ]. A similar study reported that prolonged exposure to masks and protective gear, especially among HCWs, can lead to various issues such as acne, skin irritation, cognitive impairment, and headaches [ 28 ]. According to the results of the present study, discomfort often causes HCWs to move the mask and disturb the correct fitness of the mask on their face.

The results of the present study indicated that respiratory masks have the ability to hinder the work performance of their users. Various studies have confirmed the adverse effect of respiratory masks on HCWs performance. A similar research indicated that respiratory masks reduce physical performance [ 29 ]. Several studies have highlighted the issue of mask users’ ability to see and read being hindered by fogging of glasses [ 22 , 27 , 30 ]. The feel of weakness to perform cognitive tasks has also been reported in various studies [ 31 , 32 ]. An increase in physical fatigue has been mentioned in some studies as an adverse effect of respiratory masks [ 27 , 31 ]. A research showed the effect of respiratory mask on hearing and visual performance [ 33 ]. Another study reported that high-protection respiratory masks reduced physiological and psychological ability, especially if the workers perform physical work [ 34 ].

The third category is related to the negative impact on the psychological state of HCWs. Some studies noted the use of some PPE, including respiratory masks, as one of the possible reasons for the increase of mental health problems among HCWs [ 35 , 36 ]. Before the prevalence of the COVID-19 virus, the hypothesis of the negative effect of respiratory masks on the mental state of people was investigated and confirmed by some studies [ 37 ]. Furthermore, one study reported that wearing respiratory masks leads to an increase in anxiety [ 38 ].

The non-ergonomic nature of respiratory masks (the lack of suitability of masks for people for long-term use) can affect the effectiveness of respiratory masks by encouraging people to perform unsafe behaviors in using respiratory masks [ 39 ]. An important point was that the attitude and knowledge of health care works regarding the use of respiratory masks were not identified as the cause of unsafe behavior of HCWs. However, this factor has been reported in some previous studies as a reason for people not using PPE properly [ 40 ]. The COVID-19 pandemic situation and the extensive information collected about this pandemic may improve the level of awareness and the attitude of the HCWs.

The escalation in infection rates among HCWs, despite receiving training and utilizing personal protective equipment, served as a catalyst for this research endeavor. So far, there has been a deficiency in the context-specific research that could offer a more profound understanding of this issue. Therefore, the outcomes of this qualitative study may prove beneficial in enhancing the design and execution of respiratory protection programs for HCWs in infectious hospital departments or during similar pandemics.

Implications for nursing practice

It is expected that the findings of this study can provide a better understanding of the factors influencing the unsafe behavior of HCWs while using masks. Furthermore, it can be used as a preliminary study to evaluate the effectiveness of safety and infection control programs in hospitals in the COVID-19 pandemic and similar disasters in the future.

Discomfort and pain, effects on performance, and effects on mental status are important factors for unsafe behavior of HCWs’ in using respiratory masks. Our results could contribute to the identification and analysis of possible scenarios to reduce unsafe behaviors in the use of respiratory masks. Accordingly, it is recommended to provide the necessary therapeutic and preventive interventions regarding the complications of using masks. Planning to reduce the side effects of masks and training personnel on the correct use of masks with minimal health effects are recommended as well.

Limitations

The physical and cognitive workload of HCWs which increased during the COVID-19 pandemic [ 41 ], had possible impacts on the work ability of the staff [ 42 ]. Therefore, their explanation about the negative effects of wearing masks may be affected by their specific working conditions.

Data availability

The datasets used during the current study are available from the corresponding author on reasonable request.

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Azadeh Tahernejad & Sanaz Sohrabizadeh

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All authors have read and approved the manuscript. AT, SS, ST are responsible for the overall conceptualization and oversight of the study, including study design, data interpretation, and manuscript write-up. AT is responsible for the first draft. All authors reviewed and provided feedback on the manuscript prior to submission.

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This study was approved by the ethics committee of the Shahid Beheshti University of Medical Sciences, Tehran, Iran (ethical code: IR.SBMU.PHNS.REC.1401.108). All the participants signed the written informed consent. Accordingly, all participants were informed about the research objectives, confidentiality of their personal information, and the possibility of their leaving or declining the interview sessions at any time. In addition, all methods were carried out in accordance with relevant guidelines and regulations in the Declaration of Helsinki.

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Tahernejad, A., Sohrabizadeh, S. & Tahernejad, S. Exploring factors affecting the unsafe behavior of health care workers’ in using respiratory masks during COVID-19 pandemic in Iran: a qualitative study. BMC Health Serv Res 24 , 608 (2024). https://doi.org/10.1186/s12913-024-11000-4

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Received : 03 September 2023

Accepted : 16 April 2024

Published : 09 May 2024

DOI : https://doi.org/10.1186/s12913-024-11000-4

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