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Case Study – Methods, Examples and Guide

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A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

• Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
• Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
• Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
• Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
• Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
• Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
• Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

• The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
• The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
• The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
• The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
• The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

• Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
• Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
• Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
• Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

• In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
• Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
• Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
• Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
• Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
• Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

• Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
• Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
• Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
• Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
• Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
• Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

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Qualitative study design: Case Studies

• Qualitative study design
• Phenomenology
• Grounded theory
• Ethnography
• Narrative inquiry
• Action research

Case Studies

• Field research
• Focus groups
• Observation
• Surveys & questionnaires
• Study Designs Home

In depth description of the experience of a single person, a family, a group, a community or an organisation.

An example of a qualitative case study is a life history which is the story of one specific person.  A case study may be done to highlight a specific issue by telling a story of one person or one group. 

• Oral recording

Ability to explore and describe, in depth, an issue or event. 

Develop an understanding of health, illness and health care in context. 

Single case can be used to develop or disprove a theory. 

Can be used as a model or prototype .  

Limitations

Labour intensive and generates large diverse data sets which can be hard to manage. 

Case studies are seen by many as a weak methodology because they only look at one person or one specific group and aren’t as broad in their participant selection as other methodologies. 

Example questions

This methodology can be used to ask questions about a specific drug or treatment and its effects on an individual.

• Does thalidomide cause birth defects?
• Does exposure to a pesticide lead to cancer?

Example studies

• Choi, T. S. T., Walker, K. Z., & Palermo, C. (2018). Diabetes management in a foreign land: A case study on Chinese Australians. Health & Social Care in the Community, 26(2), e225-e232. 
• Reade, I., Rodgers, W., & Spriggs, K. (2008). New Ideas for High Performance Coaches: A Case Study of Knowledge Transfer in Sport Science.  International Journal of Sports Science & Coaching , 3(3), 335-354. 
• Wingrove, K., Barbour, L., & Palermo, C. (2017). Exploring nutrition capacity in Australia's charitable food sector.  Nutrition & Dietetics , 74(5), 495-501. 
• Green, J., & Thorogood, N. (2018). Qualitative methods for health research (4th ed.). London: SAGE. 
• University of Missouri-St. Louis. Qualitative Research Designs. Retrieved from http://www.umsl.edu/~lindquists/qualdsgn.html     
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• Next: Field research >>
• Last Updated: Jul 3, 2024 11:46 AM
• URL: https://deakin.libguides.com/qualitative-study-designs

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• What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

Table of contents

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

• Provide new or unexpected insights into the subject
• Challenge or complicate existing assumptions and theories
• Propose practical courses of action to resolve a problem
• Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

• Exemplify a theory by showing how it explains the case under investigation
• Expand on a theory by uncovering new concepts and ideas that need to be incorporated
• Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

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The case study as a type of qualitative research

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Qualitative Research: Case Studies

• Getting Started
• Focus Groups
• Observation
• Case Studies
• Data Collection
• Cleaning Text
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How To Write a Case Study

How to undertake case study research

From: http://www.emeraldgrouppublishing.com/products/case_studies/index.htm

What is a case study?

• Attempts to shed light on a phenomena by studying a single case example.
• Focuses on an individual person, an event, a group, or an institution.
• Allows for in-depth examination by prolonged engagement or cultural immersion
• Explores processes and outcomes
• Investigates the context and setting of a situation
• Can involve a number of data gathering methods

Duke Resources

• Philanthropy Central from Sanford School of Public Policy Case Study Database Provides real-life case studies of philanthropic initiatives. There are currently more than 600 case studies linked to in the Database.

Suggested Readings

• McNabb, D. (2010).  Case reseach in public management.  NY: M.E.Sharpe.
• Samuels, D. (2013).  Case studies in comparative politics .  NY: Pearson Education.
• Stark, R. (1995). The  art of case study research, Thousand Oaks: Sage.
• Yin, R.K. (2009) Case study research: Design and methods. Los Angeles: Sage.
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• Last Updated: Mar 1, 2024 10:13 AM
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18 Qualitative Research Examples

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Qualitative research is an approach to scientific research that involves using observation to gather and analyze non-numerical, in-depth, and well-contextualized datasets.

It serves as an integral part of academic, professional, and even daily decision-making processes (Baxter & Jack, 2008).

Methods of qualitative research encompass a wide range of techniques, from in-depth personal encounters, like ethnographies (studying cultures in-depth) and autoethnographies (examining one’s own cultural experiences), to collection of diverse perspectives on topics through methods like interviewing focus groups (gatherings of individuals to discuss specific topics).

Qualitative Research Examples

1. ethnography.

Definition: Ethnography is a qualitative research design aimed at exploring cultural phenomena. Rooted in the discipline of anthropology , this research approach investigates the social interactions, behaviors, and perceptions within groups, communities, or organizations.

Ethnographic research is characterized by extended observation of the group, often through direct participation, in the participants’ environment. An ethnographer typically lives with the study group for extended periods, intricately observing their everyday lives (Khan, 2014).

It aims to present a complete, detailed and accurate picture of the observed social life, rituals, symbols, and values from the perspective of the study group.

Example of Ethnographic Research

Title: “ The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity “

Citation: Evans, J. (2010). The Everyday Lives of Men: An Ethnographic Investigation of Young Adult Male Identity. Peter Lang.

Overview: This study by Evans (2010) provides a rich narrative of young adult male identity as experienced in everyday life. The author immersed himself among a group of young men, participating in their activities and cultivating a deep understanding of their lifestyle, values, and motivations. This research exemplified the ethnographic approach, revealing complexities of the subjects’ identities and societal roles, which could hardly be accessed through other qualitative research designs.

Read my Full Guide on Ethnography Here

2. Autoethnography

Definition: Autoethnography is an approach to qualitative research where the researcher uses their own personal experiences to extend the understanding of a certain group, culture, or setting. Essentially, it allows for the exploration of self within the context of social phenomena.

Unlike traditional ethnography, which focuses on the study of others, autoethnography turns the ethnographic gaze inward, allowing the researcher to use their personal experiences within a culture as rich qualitative data (Durham, 2019).

The objective is to critically appraise one’s personal experiences as they navigate and negotiate cultural, political, and social meanings. The researcher becomes both the observer and the participant, intertwining personal and cultural experiences in the research.

Example of Autoethnographic Research

Title: “ A Day In The Life Of An NHS Nurse “

Citation: Osben, J. (2019). A day in the life of a NHS nurse in 21st Century Britain: An auto-ethnography. The Journal of Autoethnography for Health & Social Care. 1(1).

Overview: This study presents an autoethnography of a day in the life of an NHS nurse (who, of course, is also the researcher). The author uses the research to achieve reflexivity, with the researcher concluding: “Scrutinising my practice and situating it within a wider contextual backdrop has compelled me to significantly increase my level of scrutiny into the driving forces that influence my practice.”

Read my Full Guide on Autoethnography Here

3. Semi-Structured Interviews

Definition: Semi-structured interviews stand as one of the most frequently used methods in qualitative research. These interviews are planned and utilize a set of pre-established questions, but also allow for the interviewer to steer the conversation in other directions based on the responses given by the interviewee.

In semi-structured interviews, the interviewer prepares a guide that outlines the focal points of the discussion. However, the interview is flexible, allowing for more in-depth probing if the interviewer deems it necessary (Qu, & Dumay, 2011). This style of interviewing strikes a balance between structured ones which might limit the discussion, and unstructured ones, which could lack focus.

Example of Semi-Structured Interview Research

Title: “ Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review “

Citation: Puts, M., et al. (2014). Factors influencing adherence to cancer treatment in older adults with cancer: a systematic review. Annals of oncology, 25 (3), 564-577.

Overview: Puts et al. (2014) executed an extensive systematic review in which they conducted semi-structured interviews with older adults suffering from cancer to examine the factors influencing their adherence to cancer treatment. The findings suggested that various factors, including side effects, faith in healthcare professionals, and social support have substantial impacts on treatment adherence. This research demonstrates how semi-structured interviews can provide rich and profound insights into the subjective experiences of patients.

4. Focus Groups

Definition: Focus groups are a qualitative research method that involves organized discussion with a selected group of individuals to gain their perspectives on a specific concept, product, or phenomenon. Typically, these discussions are guided by a moderator.

During a focus group session, the moderator has a list of questions or topics to discuss, and participants are encouraged to interact with each other (Morgan, 2010). This interactivity can stimulate more information and provide a broader understanding of the issue under scrutiny. The open format allows participants to ask questions and respond freely, offering invaluable insights into attitudes, experiences, and group norms.

Example of Focus Group Research

Title: “ Perspectives of Older Adults on Aging Well: A Focus Group Study “

Citation: Halaweh, H., Dahlin-Ivanoff, S., Svantesson, U., & Willén, C. (2018). Perspectives of older adults on aging well: a focus group study. Journal of aging research .

Overview: This study aimed to explore what older adults (aged 60 years and older) perceived to be ‘aging well’. The researchers identified three major themes from their focus group interviews: a sense of well-being, having good physical health, and preserving good mental health. The findings highlight the importance of factors such as positive emotions, social engagement, physical activity, healthy eating habits, and maintaining independence in promoting aging well among older adults.

5. Phenomenology

Definition: Phenomenology, a qualitative research method, involves the examination of lived experiences to gain an in-depth understanding of the essence or underlying meanings of a phenomenon.

The focus of phenomenology lies in meticulously describing participants’ conscious experiences related to the chosen phenomenon (Padilla-Díaz, 2015).

In a phenomenological study, the researcher collects detailed, first-hand perspectives of the participants, typically via in-depth interviews, and then uses various strategies to interpret and structure these experiences, ultimately revealing essential themes (Creswell, 2013). This approach focuses on the perspective of individuals experiencing the phenomenon, seeking to explore, clarify, and understand the meanings they attach to those experiences.

Example of Phenomenology Research

Title: “ A phenomenological approach to experiences with technology: current state, promise, and future directions for research ”

Citation: Cilesiz, S. (2011). A phenomenological approach to experiences with technology: Current state, promise, and future directions for research. Educational Technology Research and Development, 59 , 487-510.

Overview: A phenomenological approach to experiences with technology by Sebnem Cilesiz represents a good starting point for formulating a phenomenological study. With its focus on the ‘essence of experience’, this piece presents methodological, reliability, validity, and data analysis techniques that phenomenologists use to explain how people experience technology in their everyday lives.

6. Grounded Theory

Definition: Grounded theory is a systematic methodology in qualitative research that typically applies inductive reasoning . The primary aim is to develop a theoretical explanation or framework for a process, action, or interaction grounded in, and arising from, empirical data (Birks & Mills, 2015).

In grounded theory, data collection and analysis work together in a recursive process. The researcher collects data, analyses it, and then collects more data based on the evolving understanding of the research context. This ongoing process continues until a comprehensive theory that represents the data and the associated phenomenon emerges – a point known as theoretical saturation (Charmaz, 2014).

Example of Grounded Theory Research

Title: “ Student Engagement in High School Classrooms from the Perspective of Flow Theory “

Citation: Shernoff, D. J., Csikszentmihalyi, M., Shneider, B., & Shernoff, E. S. (2003). Student engagement in high school classrooms from the perspective of flow theory. School Psychology Quarterly, 18 (2), 158–176.

Overview: Shernoff and colleagues (2003) used grounded theory to explore student engagement in high school classrooms. The researchers collected data through student self-reports, interviews, and observations. Key findings revealed that academic challenge, student autonomy, and teacher support emerged as the most significant factors influencing students’ engagement, demonstrating how grounded theory can illuminate complex dynamics within real-world contexts.

7. Narrative Research

Definition: Narrative research is a qualitative research method dedicated to storytelling and understanding how individuals experience the world. It focuses on studying an individual’s life and experiences as narrated by that individual (Polkinghorne, 2013).

In narrative research, the researcher collects data through methods such as interviews, observations , and document analysis. The emphasis is on the stories told by participants – narratives that reflect their experiences, thoughts, and feelings.

These stories are then interpreted by the researcher, who attempts to understand the meaning the participant attributes to these experiences (Josselson, 2011).

Example of Narrative Research

Title: “Narrative Structures and the Language of the Self”

Citation: McAdams, D. P., Josselson, R., & Lieblich, A. (2006). Identity and story: Creating self in narrative . American Psychological Association.

Overview: In this innovative study, McAdams et al. (2006) employed narrative research to explore how individuals construct their identities through the stories they tell about themselves. By examining personal narratives, the researchers discerned patterns associated with characters, motivations, conflicts, and resolutions, contributing valuable insights about the relationship between narrative and individual identity.

8. Case Study Research

Definition: Case study research is a qualitative research method that involves an in-depth investigation of a single instance or event: a case. These ‘cases’ can range from individuals, groups, or entities to specific projects, programs, or strategies (Creswell, 2013).

The case study method typically uses multiple sources of information for comprehensive contextual analysis. It aims to explore and understand the complexity and uniqueness of a particular case in a real-world context (Merriam & Tisdell, 2015). This investigation could result in a detailed description of the case, a process for its development, or an exploration of a related issue or problem.

Example of Case Study Research

Title: “ Teacher’s Role in Fostering Preschoolers’ Computational Thinking: An Exploratory Case Study “

Citation: Wang, X. C., Choi, Y., Benson, K., Eggleston, C., & Weber, D. (2021). Teacher’s role in fostering preschoolers’ computational thinking: An exploratory case study. Early Education and Development , 32 (1), 26-48.

Overview: This study investigates the role of teachers in promoting computational thinking skills in preschoolers. The study utilized a qualitative case study methodology to examine the computational thinking scaffolding strategies employed by a teacher interacting with three preschoolers in a small group setting. The findings highlight the importance of teachers’ guidance in fostering computational thinking practices such as problem reformulation/decomposition, systematic testing, and debugging.

Read about some Famous Case Studies in Psychology Here

9. Participant Observation

Definition: Participant observation has the researcher immerse themselves in a group or community setting to observe the behavior of its members. It is similar to ethnography, but generally, the researcher isn’t embedded for a long period of time.

The researcher, being a participant, engages in daily activities, interactions, and events as a way of conducting a detailed study of a particular social phenomenon (Kawulich, 2005).

The method involves long-term engagement in the field, maintaining detailed records of observed events, informal interviews, direct participation, and reflexivity. This approach allows for a holistic view of the participants’ lived experiences, behaviours, and interactions within their everyday environment (Dewalt, 2011).

Example of Participant Observation Research

Title: Conflict in the boardroom: a participant observation study of supervisory board dynamics

Citation: Heemskerk, E. M., Heemskerk, K., & Wats, M. M. (2017). Conflict in the boardroom: a participant observation study of supervisory board dynamics. Journal of Management & Governance , 21 , 233-263.

Overview: This study examined how conflicts within corporate boards affect their performance. The researchers used a participant observation method, where they actively engaged with 11 supervisory boards and observed their dynamics. They found that having a shared understanding of the board’s role called a common framework, improved performance by reducing relationship conflicts, encouraging task conflicts, and minimizing conflicts between the board and CEO.

10. Non-Participant Observation

Definition: Non-participant observation is a qualitative research method in which the researcher observes the phenomena of interest without actively participating in the situation, setting, or community being studied.

This method allows the researcher to maintain a position of distance, as they are solely an observer and not a participant in the activities being observed (Kawulich, 2005).

During non-participant observation, the researcher typically records field notes on the actions, interactions, and behaviors observed , focusing on specific aspects of the situation deemed relevant to the research question.

This could include verbal and nonverbal communication , activities, interactions, and environmental contexts (Angrosino, 2007). They could also use video or audio recordings or other methods to collect data.

Example of Non-Participant Observation Research

Title: Mental Health Nurses’ attitudes towards mental illness and recovery-oriented practice in acute inpatient psychiatric units: A non-participant observation study

Citation: Sreeram, A., Cross, W. M., & Townsin, L. (2023). Mental Health Nurses’ attitudes towards mental illness and recovery‐oriented practice in acute inpatient psychiatric units: A non‐participant observation study. International Journal of Mental Health Nursing .

Overview: This study investigated the attitudes of mental health nurses towards mental illness and recovery-oriented practice in acute inpatient psychiatric units. The researchers used a non-participant observation method, meaning they observed the nurses without directly participating in their activities. The findings shed light on the nurses’ perspectives and behaviors, providing valuable insights into their attitudes toward mental health and recovery-focused care in these settings.

11. Content Analysis

Definition: Content Analysis involves scrutinizing textual, visual, or spoken content to categorize and quantify information. The goal is to identify patterns, themes, biases, or other characteristics (Hsieh & Shannon, 2005).

Content Analysis is widely used in various disciplines for a multitude of purposes. Researchers typically use this method to distill large amounts of unstructured data, like interview transcripts, newspaper articles, or social media posts, into manageable and meaningful chunks.

When wielded appropriately, Content Analysis can illuminate the density and frequency of certain themes within a dataset, provide insights into how specific terms or concepts are applied contextually, and offer inferences about the meanings of their content and use (Duriau, Reger, & Pfarrer, 2007).

Example of Content Analysis

Title: Framing European politics: A content analysis of press and television news .

Citation: Semetko, H. A., & Valkenburg, P. M. (2000). Framing European politics: A content analysis of press and television news. Journal of Communication, 50 (2), 93-109.

Overview: This study analyzed press and television news articles about European politics using a method called content analysis. The researchers examined the prevalence of different “frames” in the news, which are ways of presenting information to shape audience perceptions. They found that the most common frames were attribution of responsibility, conflict, economic consequences, human interest, and morality.

Read my Full Guide on Content Analysis Here

12. Discourse Analysis

Definition: Discourse Analysis, a qualitative research method, interprets the meanings, functions, and coherence of certain languages in context.

Discourse analysis is typically understood through social constructionism, critical theory , and poststructuralism and used for understanding how language constructs social concepts (Cheek, 2004).

Discourse Analysis offers great breadth, providing tools to examine spoken or written language, often beyond the level of the sentence. It enables researchers to scrutinize how text and talk articulate social and political interactions and hierarchies.

Insight can be garnered from different conversations, institutional text, and media coverage to understand how topics are addressed or framed within a specific social context (Jorgensen & Phillips, 2002).

Example of Discourse Analysis

Title: The construction of teacher identities in educational policy documents: A critical discourse analysis

Citation: Thomas, S. (2005). The construction of teacher identities in educational policy documents: A critical discourse analysis. Critical Studies in Education, 46 (2), 25-44.

Overview: The author examines how an education policy in one state of Australia positions teacher professionalism and teacher identities. While there are competing discourses about professional identity, the policy framework privileges a  narrative that frames the ‘good’ teacher as one that accepts ever-tightening control and regulation over their professional practice.

Read my Full Guide on Discourse Analysis Here

13. Action Research

Definition: Action Research is a qualitative research technique that is employed to bring about change while simultaneously studying the process and results of that change.

This method involves a cyclical process of fact-finding, action, evaluation, and reflection (Greenwood & Levin, 2016).

Typically, Action Research is used in the fields of education, social sciences , and community development. The process isn’t just about resolving an issue but also developing knowledge that can be used in the future to address similar or related problems.

The researcher plays an active role in the research process, which is normally broken down into four steps: 

• developing a plan to improve what is currently being done
• implementing the plan
• observing the effects of the plan, and
• reflecting upon these effects (Smith, 2010).

Example of Action Research

Title: Using Digital Sandbox Gaming to Improve Creativity Within Boys’ Writing

Citation: Ellison, M., & Drew, C. (2020). Using digital sandbox gaming to improve creativity within boys’ writing. Journal of Research in Childhood Education , 34 (2), 277-287.

Overview: This was a research study one of my research students completed in his own classroom under my supervision. He implemented a digital game-based approach to literacy teaching with boys and interviewed his students to see if the use of games as stimuli for storytelling helped draw them into the learning experience.

Read my Full Guide on Action Research Here

14. Semiotic Analysis

Definition: Semiotic Analysis is a qualitative method of research that interprets signs and symbols in communication to understand sociocultural phenomena. It stems from semiotics, the study of signs and symbols and their use or interpretation (Chandler, 2017).

In a Semiotic Analysis, signs (anything that represents something else) are interpreted based on their significance and the role they play in representing ideas.

This type of research often involves the examination of images, sounds, and word choice to uncover the embedded sociocultural meanings. For example, an advertisement for a car might be studied to learn more about societal views on masculinity or success (Berger, 2010).

Example of Semiotic Research

Title: Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia

Citation: Symes, C. (2023). Shielding the learned body: a semiotic analysis of school badges in New South Wales, Australia. Semiotica , 2023 (250), 167-190.

Overview: This study examines school badges in New South Wales, Australia, and explores their significance through a semiotic analysis. The badges, which are part of the school’s visual identity, are seen as symbolic representations that convey meanings. The analysis reveals that these badges often draw on heraldic models, incorporating elements like colors, names, motifs, and mottoes that reflect local culture and history, thus connecting students to their national identity. Additionally, the study highlights how some schools have shifted from traditional badges to modern logos and slogans, reflecting a more business-oriented approach.

15. Qualitative Longitudinal Studies

Definition: Qualitative Longitudinal Studies are a research method that involves repeated observation of the same items over an extended period of time.

Unlike a snapshot perspective, this method aims to piece together individual histories and examine the influences and impacts of change (Neale, 2019).

Qualitative Longitudinal Studies provide an in-depth understanding of change as it happens, including changes in people’s lives, their perceptions, and their behaviors.

For instance, this method could be used to follow a group of students through their schooling years to understand the evolution of their learning behaviors and attitudes towards education (Saldaña, 2003).

Example of Qualitative Longitudinal Research

Title: Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study

Citation: Hackett, J., Godfrey, M., & Bennett, M. I. (2016). Patient and caregiver perspectives on managing pain in advanced cancer: a qualitative longitudinal study.  Palliative medicine ,  30 (8), 711-719.

Overview: This article examines how patients and their caregivers manage pain in advanced cancer through a qualitative longitudinal study. The researchers interviewed patients and caregivers at two different time points and collected audio diaries to gain insights into their experiences, making this study longitudinal.

Read my Full Guide on Longitudinal Research Here

16. Open-Ended Surveys

Definition: Open-Ended Surveys are a type of qualitative research method where respondents provide answers in their own words. Unlike closed-ended surveys, which limit responses to predefined options, open-ended surveys allow for expansive and unsolicited explanations (Fink, 2013).

Open-ended surveys are commonly used in a range of fields, from market research to social studies. As they don’t force respondents into predefined response categories, these surveys help to draw out rich, detailed data that might uncover new variables or ideas.

For example, an open-ended survey might be used to understand customer opinions about a new product or service (Lavrakas, 2008).

Contrast this to a quantitative closed-ended survey, like a Likert scale, which could theoretically help us to come up with generalizable data but is restricted by the questions on the questionnaire, meaning new and surprising data and insights can’t emerge from the survey results in the same way.

Example of Open-Ended Survey Research

Title: Advantages and disadvantages of technology in relationships: Findings from an open-ended survey

Citation: Hertlein, K. M., & Ancheta, K. (2014). Advantages and disadvantages of technology in relationships: Findings from an open-ended survey.  The Qualitative Report ,  19 (11), 1-11.

Overview: This article examines the advantages and disadvantages of technology in couple relationships through an open-ended survey method. Researchers analyzed responses from 410 undergraduate students to understand how technology affects relationships. They found that technology can contribute to relationship development, management, and enhancement, but it can also create challenges such as distancing, lack of clarity, and impaired trust.

17. Naturalistic Observation

Definition: Naturalistic Observation is a type of qualitative research method that involves observing individuals in their natural environments without interference or manipulation by the researcher.

Naturalistic observation is often used when conducting research on behaviors that cannot be controlled or manipulated in a laboratory setting (Kawulich, 2005).

It is frequently used in the fields of psychology, sociology, and anthropology. For instance, to understand the social dynamics in a schoolyard, a researcher could spend time observing the children interact during their recess, noting their behaviors, interactions, and conflicts without imposing their presence on the children’s activities (Forsyth, 2010).

Example of Naturalistic Observation Research

Title: Dispositional mindfulness in daily life: A naturalistic observation study

Citation: Kaplan, D. M., Raison, C. L., Milek, A., Tackman, A. M., Pace, T. W., & Mehl, M. R. (2018). Dispositional mindfulness in daily life: A naturalistic observation study. PloS one , 13 (11), e0206029.

Overview: In this study, researchers conducted two studies: one exploring assumptions about mindfulness and behavior, and the other using naturalistic observation to examine actual behavioral manifestations of mindfulness. They found that trait mindfulness is associated with a heightened perceptual focus in conversations, suggesting that being mindful is expressed primarily through sharpened attention rather than observable behavioral or social differences.

Read my Full Guide on Naturalistic Observation Here

18. Photo-Elicitation

Definition: Photo-elicitation utilizes photographs as a means to trigger discussions and evoke responses during interviews. This strategy aids in bringing out topics of discussion that may not emerge through verbal prompting alone (Harper, 2002).

Traditionally, Photo-Elicitation has been useful in various fields such as education, psychology, and sociology. The method involves the researcher or participants taking photographs, which are then used as prompts for discussion.

For instance, a researcher studying urban environmental issues might invite participants to photograph areas in their neighborhood that they perceive as environmentally detrimental, and then discuss each photo in depth (Clark-Ibáñez, 2004).

Example of Photo-Elicitation Research

Title: Early adolescent food routines: A photo-elicitation study

Citation: Green, E. M., Spivak, C., & Dollahite, J. S. (2021). Early adolescent food routines: A photo-elicitation study. Appetite, 158 .

Overview: This study focused on early adolescents (ages 10-14) and their food routines. Researchers conducted in-depth interviews using a photo-elicitation approach, where participants took photos related to their food choices and experiences. Through analysis, the study identified various routines and three main themes: family, settings, and meals/foods consumed, revealing how early adolescents view and are influenced by their eating routines.

Features of Qualitative Research

Qualitative research is a research method focused on understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

Some key features of this method include:

• Naturalistic Inquiry: Qualitative research happens in the natural setting of the phenomena, aiming to understand “real world” situations (Patton, 2015). This immersion in the field or subject allows the researcher to gather a deep understanding of the subject matter.
• Emphasis on Process: It aims to understand how events unfold over time rather than focusing solely on outcomes (Merriam & Tisdell, 2015). The process-oriented nature of qualitative research allows researchers to investigate sequences, timing, and changes.
• Interpretive: It involves interpreting and making sense of phenomena in terms of the meanings people assign to them (Denzin & Lincoln, 2011). This interpretive element allows for rich, nuanced insights into human behavior and experiences.
• Holistic Perspective: Qualitative research seeks to understand the whole phenomenon rather than focusing on individual components (Creswell, 2013). It emphasizes the complex interplay of factors, providing a richer, more nuanced view of the research subject.
• Prioritizes Depth over Breadth: Qualitative research favors depth of understanding over breadth, typically involving a smaller but more focused sample size (Hennink, Hutter, & Bailey, 2020). This enables detailed exploration of the phenomena of interest, often leading to rich and complex data.

Qualitative vs Quantitative Research

Qualitative research centers on exploring and understanding the meaning individuals or groups attribute to a social or human problem (Creswell, 2013).

It involves an in-depth approach to the subject matter, aiming to capture the richness and complexity of human experience.

Examples include conducting interviews, observing behaviors, or analyzing text and images.

There are strengths inherent in this approach. In its focus on understanding subjective experiences and interpretations, qualitative research can yield rich and detailed data that quantitative research may overlook (Denzin & Lincoln, 2011).

Additionally, qualitative research is adaptive, allowing the researcher to respond to new directions and insights as they emerge during the research process.

However, there are also limitations. Because of the interpretive nature of this research, findings may not be generalizable to a broader population (Marshall & Rossman, 2014). Well-designed quantitative research, on the other hand, can be generalizable.

Moreover, the reliability and validity of qualitative data can be challenging to establish due to its subjective nature, unlike quantitative research, which is ideally more objective.

Compare Qualitative and Quantitative Research Methodologies in This Guide Here

In conclusion, qualitative research methods provide distinctive ways to explore social phenomena and understand nuances that quantitative approaches might overlook. Each method, from Ethnography to Photo-Elicitation, presents its strengths and weaknesses but they all offer valuable means of investigating complex, real-world situations. The goal for the researcher is not to find a definitive tool, but to employ the method best suited for their research questions and the context at hand (Almalki, 2016). Above all, these methods underscore the richness of human experience and deepen our understanding of the world around us.

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Qualitative case study data analysis: an example from practice

Affiliation.

• 1 School of Nursing and Midwifery, National University of Ireland, Galway, Republic of Ireland.
• PMID: 25976531
• DOI: 10.7748/nr.22.5.8.e1307

Aim: To illustrate an approach to data analysis in qualitative case study methodology.

Background: There is often little detail in case study research about how data were analysed. However, it is important that comprehensive analysis procedures are used because there are often large sets of data from multiple sources of evidence. Furthermore, the ability to describe in detail how the analysis was conducted ensures rigour in reporting qualitative research.

Data sources: The research example used is a multiple case study that explored the role of the clinical skills laboratory in preparing students for the real world of practice. Data analysis was conducted using a framework guided by the four stages of analysis outlined by Morse ( 1994 ): comprehending, synthesising, theorising and recontextualising. The specific strategies for analysis in these stages centred on the work of Miles and Huberman ( 1994 ), which has been successfully used in case study research. The data were managed using NVivo software.

Review methods: Literature examining qualitative data analysis was reviewed and strategies illustrated by the case study example provided. Discussion Each stage of the analysis framework is described with illustration from the research example for the purpose of highlighting the benefits of a systematic approach to handling large data sets from multiple sources.

Conclusion: By providing an example of how each stage of the analysis was conducted, it is hoped that researchers will be able to consider the benefits of such an approach to their own case study analysis.

Implications for research/practice: This paper illustrates specific strategies that can be employed when conducting data analysis in case study research and other qualitative research designs.

Keywords: Case study data analysis; case study research methodology; clinical skills research; qualitative case study methodology; qualitative data analysis; qualitative research.

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• Rigour in qualitative case-study research. Houghton C, Casey D, Shaw D, Murphy K. Houghton C, et al. Nurse Res. 2013 Mar;20(4):12-7. doi: 10.7748/nr2013.03.20.4.12.e326. Nurse Res. 2013. PMID: 23520707
• Selection, collection and analysis as sources of evidence in case study research. Houghton C, Casey D, Smyth S. Houghton C, et al. Nurse Res. 2017 Mar 22;24(4):36-41. doi: 10.7748/nr.2017.e1482. Nurse Res. 2017. PMID: 28326917
• Qualitative case study methodology in nursing research: an integrative review. Anthony S, Jack S. Anthony S, et al. J Adv Nurs. 2009 Jun;65(6):1171-81. doi: 10.1111/j.1365-2648.2009.04998.x. Epub 2009 Apr 3. J Adv Nurs. 2009. PMID: 19374670 Review.
• Avoiding and identifying errors in health technology assessment models: qualitative study and methodological review. Chilcott J, Tappenden P, Rawdin A, Johnson M, Kaltenthaler E, Paisley S, Papaioannou D, Shippam A. Chilcott J, et al. Health Technol Assess. 2010 May;14(25):iii-iv, ix-xii, 1-107. doi: 10.3310/hta14250. Health Technol Assess. 2010. PMID: 20501062 Review.
• Genital Cosmetic Surgery in Women of Different Generations: A Qualitative Study. Yıldırım Bayraktar BN, Ada G, Hamlacı Başkaya Y, Ilçioğlu K. Yıldırım Bayraktar BN, et al. Aesthetic Plast Surg. 2024 Aug 15. doi: 10.1007/s00266-024-04290-w. Online ahead of print. Aesthetic Plast Surg. 2024. PMID: 39145811
• The lived experiences of fatigue among patients receiving haemodialysis in Oman: a qualitative exploration. Al-Naamani Z, Gormley K, Noble H, Santin O, Al Omari O, Al-Noumani H, Madkhali N. Al-Naamani Z, et al. BMC Nephrol. 2024 Jul 29;25(1):239. doi: 10.1186/s12882-024-03647-2. BMC Nephrol. 2024. PMID: 39075347 Free PMC article.
• How a National Organization Works in Partnership With People Who Have Lived Experience in Mental Health Improvement Programs: Protocol for an Exploratory Case Study. Robertson C, Hibberd C, Shepherd A, Johnston G. Robertson C, et al. JMIR Res Protoc. 2024 Apr 19;13:e51779. doi: 10.2196/51779. JMIR Res Protoc. 2024. PMID: 38640479 Free PMC article.
• Implementation of an office-based addiction treatment model for Medicaid enrollees: A mixed methods study. Treitler P, Enich M, Bowden C, Mahone A, Lloyd J, Crystal S. Treitler P, et al. J Subst Use Addict Treat. 2024 Jan;156:209212. doi: 10.1016/j.josat.2023.209212. Epub 2023 Nov 5. J Subst Use Addict Treat. 2024. PMID: 37935350
• Using the quadruple aim to understand the impact of virtual delivery of care within Ontario community health centres: a qualitative study. Bhatti S, Dahrouge S, Muldoon L, Rayner J. Bhatti S, et al. BJGP Open. 2022 Dec 20;6(4):BJGPO.2022.0031. doi: 10.3399/BJGPO.2022.0031. Print 2022 Dec. BJGP Open. 2022. PMID: 36109022 Free PMC article.
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Aug 19, 2024 • 17 minutes read

Qualitative research examples: How to unlock, rich, descriptive insights

Qualitative research uncovers in-depth user insights, but what does it look like? Here are seven methods and examples to help you get the data you need.

Armin Tanovic

Behind every what, there’s a why . Qualitative research is how you uncover that why. It enables you to connect with users and understand their thoughts, feelings, wants, needs, and pain points.

There’s many methods for conducting qualitative research, and many objectives it can help you pursue—you might want to explore ways to improve NPS scores, combat reduced customer retention, or understand (and recreate) the success behind a well-received product. The common thread? All these metrics impact your business, and qualitative research can help investigate and improve that impact.

In this article, we’ll take you through seven methods and examples of qualitative research, including when and how to use them.

Qualitative UX research made easy

Conduct qualitative research with Maze, analyze data instantly, and get rich, descriptive insights that drive decision-making.

7 Qualitative research methods: An overview

There are various qualitative UX research methods that can help you get in-depth, descriptive insights. Some are suited to specific phases of the design and development process, while others are more task-oriented.

Here’s our overview of the most common qualitative research methods. Keep reading for their use cases, and detailed examples of how to conduct them.

1. User interviews

A user interview is a one-on-one conversation between a UX researcher, designer or Product Manager and a target user to understand their thoughts, perspectives, and feelings on a product or service. User interviews are a great way to get non-numerical data on individual experiences with your product, to gain a deeper understanding of user perspectives.

Interviews can be structured, semi-structured, or unstructured . Structured interviews follow a strict interview script and can help you get answers to your planned questions, while semi and unstructured interviews are less rigid in their approach and typically lead to more spontaneous, user-centered insights.

When to use user interviews

Interviews are ideal when you want to gain an in-depth understanding of your users’ perspectives on your product or service, and why they feel a certain way.

Interviews can be used at any stage in the product design and development process, being particularly helpful during:

• The discovery phase: To better understand user needs, problems, and the context in which they use your product—revealing the best potential solutions
• The design phase: To get contextual feedback on mockups, wireframes, and prototypes, helping you pinpoint issues and the reasons behind them
• Post-launch: To assess if your product continues to meet users’ shifting expectations and understand why or why not

How to conduct user interviews: The basics

• Draft questions based on your research objectives
• Recruit relevant research participants and schedule interviews
• Conduct the interview and transcribe responses
• Analyze the interview responses to extract insights
• Use your findings to inform design, product, and business decisions

💡 A specialized user interview tool makes interviewing easier. With Maze Interview Studies , you can recruit, host, and analyze interviews all on one platform.

User interviews: A qualitative research example

Let’s say you’ve designed a recruitment platform, called Tech2Talent , that connects employers with tech talent. Before starting the design process, you want to clearly understand the pain points employers experience with existing recruitment tools'.

You draft a list of ten questions for a semi-structured interview for 15 different one-on-one interviews. As it’s semi-structured, you don’t expect to ask all the questions—the script serves as more of a guide.

One key question in your script is: “Have tech recruitment platforms helped you find the talent you need in the past?”

Most respondents answer with a resounding and passionate ‘no’ with one of them expanding:

“For our company, it’s been pretty hit or miss honestly. They let just about anyone make a profile and call themselves tech talent. It’s so hard sifting through serious candidates. I can’t see any of their achievements until I invest time setting up an interview.”

You begin to notice a pattern in your responses: recruitment tools often lack easily accessible details on talent profiles.

You’ve gained contextual feedback on why other recruitment platforms fail to solve user needs.

2. Focus groups

A focus group is a research method that involves gathering a small group of people—around five to ten users—to discuss a specific topic, such as their’ experience with your new product feature. Unlike user interviews, focus groups aim to capture the collective opinion of a wider market segment and encourage discussion among the group.

When to use focus groups

You should use focus groups when you need a deeper understanding of your users’ collective opinions. The dynamic discussion among participants can spark in-depth insights that might not emerge from regular interviews.

Focus groups can be used before, during, and after a product launch. They’re ideal:

• Throughout the problem discovery phase: To understand your user segment’s pain points and expectations, and generate product ideas
• Post-launch: To evaluate and understand the collective opinion of your product’s user experience
• When conducting market research: To grasp usage patterns, consumer perceptions, and market opportunities for your product

How to conduct focus group studies: The basics

• Draft prompts to spark conversation, or a series of questions based on your UX research objectives
• Find a group of five to ten users who are representative of your target audience (or a specific user segment) and schedule your focus group session
• Conduct the focus group by talking and listening to users, then transcribe responses
• Analyze focus group responses and extract insights
• Use your findings to inform design decisions

The number of participants can make it difficult to take notes or do manual transcriptions. We recommend using a transcription or a specialized UX research tool , such as Maze, that can automatically create ready-to-share reports and highlight key user insights.

Focus groups: A qualitative research example

You’re a UX researcher at FitMe , a fitness app that creates customized daily workouts for gym-goers. Unlike many other apps, FitMe takes into account the previous day’s workout and aims to create one that allows users to effectively rest different muscles.

However, FitMe has an issue. Users are generating workouts but not completing them. They’re accessing the app, taking the necessary steps to get a workout for the day, but quitting at the last hurdle.

Time to talk to users.

You organize a focus group to get to the root of the drop-off issue. You invite five existing users, all of whom have dropped off at the exact point you’re investigating, and ask them questions to uncover why.

A dialog develops:

Participant 1: “Sometimes I’ll get a workout that I just don’t want to do. Sure, it’s a good workout—but I just don’t want to physically do it. I just do my own thing when that happens.”

Participant 2: “Same here, some of them are so boring. I go to the gym because I love it. It’s an escape.”

Participant 3: “Right?! I get that the app generates the best one for me on that specific day, but I wish I could get a couple of options.”

Participant 4: “I’m the same, there are some exercises I just refuse to do. I’m not coming to the gym to do things I dislike.”

Conducting the focus groups and reviewing the transcripts, you realize that users want options. A workout that works for one gym-goer doesn’t necessarily work for the next.

A possible solution? Adding the option to generate a new workout (that still considers previous workouts)and the ability to blacklist certain exercises, like burpees.

3. Ethnographic research

Ethnographic research is a research method that involves observing and interacting with users in a real-life environment. By studying users in their natural habitat, you can understand how your product fits into their daily lives.

Ethnographic research can be active or passive. Active ethnographic research entails engaging with users in their natural environment and then following up with methods like interviews. Passive ethnographic research involves letting the user interact with the product while you note your observations.

When to use ethnographic research

Ethnographic research is best suited when you want rich insights into the context and environment in which users interact with your product. Keep in mind that you can conduct ethnographic research throughout the entire product design and development process —from problem discovery to post-launch. However, it’s mostly done early in the process:

• Early concept development: To gain an understanding of your user's day-to-day environment. Observe how they complete tasks and the pain points they encounter. The unique demands of their everyday lives will inform how to design your product.
• Initial design phase: Even if you have a firm grasp of the user’s environment, you still need to put your solution to the test. Conducting ethnographic research with your users interacting with your prototype puts theory into practice.

How to conduct ethnographic research:

• Recruit users who are reflective of your audience
• Meet with them in their natural environment, and tell them to behave as they usually would
• Take down field notes as they interact with your product
• Engage with your users, ask questions, or host an in-depth interview if you’re doing an active ethnographic study
• Collect all your data and analyze it for insights

While ethnographic studies provide a comprehensive view of what potential users actually do, they are resource-intensive and logistically difficult. A common alternative is diary studies. Like ethnographic research, diary studies examine how users interact with your product in their day-to-day, but the data is self-reported by participants.

⚙️ Recruiting participants proving tough and time-consuming? Maze Panel makes it easy, with 400+ filters to find your ideal participants from a pool of 3 million participants.

Ethnographic research: A qualitative research example

You're a UX researcher for a project management platform called ProFlow , and you’re conducting an ethnographic study of the project creation process with key users, including a startup’s COO.

The first thing you notice is that the COO is rushing while navigating the platform. You also take note of the 46 tabs and Zoom calls opened on their monitor. Their attention is divided, and they let out an exasperated sigh as they repeatedly hit “refresh” on your website’s onboarding interface.

You conclude the session with an interview and ask, “How easy or difficult did you find using ProFlow to coordinate a project?”

The COO answers: “Look, the whole reason we turn to project platforms is because we need to be quick on our feet. I’m doing a million things so I need the process to be fast and simple. The actual project management is good, but creating projects and setting up tables is way too complicated.”

You realize that ProFlow ’s project creation process takes way too much time for professionals working in fast-paced, dynamic environments. To solve the issue, propose a quick-create option that enables them to move ahead with the basics instead of requiring in-depth project details.

4. Qualitative observation

Qualitative observation is a similar method to ethnographic research, though not as deep. It involves observing your users in a natural or controlled environment and taking notes as they interact with a product. However, be sure not to interrupt them, as this compromises the integrity of the study and turns it into active ethnographic research.

When to qualitative observation

Qualitative observation is best when you want to record how users interact with your product without anyone interfering. Much like ethnographic research, observation is best done during:

• Early concept development: To help you understand your users' daily lives, how they complete tasks, and the problems they deal with. The observations you collect in these instances will help you define a concept for your product.
• Initial design phase: Observing how users deal with your prototype helps you test if they can easily interact with it in their daily environments

How to conduct qualitative observation:

• Recruit users who regularly use your product
• Meet with users in either their natural environment, such as their office, or within a controlled environment, such as a lab
• Observe them and take down field notes based on what you notice

Qualitative observation: An qualitative research example

You’re conducting UX research for Stackbuilder , an app that connects businesses with tools ideal for their needs and budgets. To determine if your app is easy to use for industry professionals, you decide to conduct an observation study.

Sitting in with the participant, you notice they breeze past the onboarding process, quickly creating an account for their company. Yet, after specifying their company’s budget, they suddenly slow down. They open links to each tool’s individual page, confusingly switching from one tab to another. They let out a sigh as they read through each website.

Conducting your observation study, you realize that users find it difficult to extract information from each tool’s website. Based on your field notes, you suggest including a bullet-point summary of each tool directly on your platform.

5. Case study research

Case studies are a UX research method that provides comprehensive and contextual insights into a real-world case over a long period of time. They typically include a range of other qualitative research methods, like interviews, observations, and ethnographic research. A case study allows you to form an in-depth analysis of how people use your product, helping you uncover nuanced differences between your users.

When to use case studies

Case studies are best when your product involves complex interactions that need to be tracked over a longer period or through in-depth analysis. You can also use case studies when your product is innovative, and there’s little existing data on how users interact with it.

As for specific phases in the product design and development process:

• Initial design phase: Case studies can help you rigorously test for product issues and the reasons behind them, giving you in-depth feedback on everything between user motivations, friction points, and usability issues
• Post-launch phase: Continuing with case studies after launch can give you ongoing feedback on how users interact with the product in their day-to-day lives. These insights ensure you can meet shifting user expectations with product updates and future iterations

How to conduct case studies:

• Outline an objective for your case study such as examining specific user tasks or the overall user journey
• Select qualitative research methods such as interviews, ethnographic studies, or observations
• Collect and analyze your data for comprehensive insights
• Include your findings in a report with proposed solutions

Case study research: A qualitative research example

Your team has recently launched Pulse , a platform that analyzes social media posts to identify rising digital marketing trends. Pulse has been on the market for a year, and you want to better understand how it helps small businesses create successful campaigns.

To conduct your case study, you begin with a series of interviews to understand user expectations, ethnographic research sessions, and focus groups. After sorting responses and observations into common themes you notice a main recurring pattern. Users have trouble interpreting the data from their dashboards, making it difficult to identify which trends to follow.

With your synthesized insights, you create a report with detailed narratives of individual user experiences, common themes and issues, and recommendations for addressing user friction points.

Some of your proposed solutions include creating intuitive graphs and summaries for each trend study. This makes it easier for users to understand trends and implement strategic changes in their campaigns.

6. Secondary research

Secondary research is a research method that involves collecting and analyzing documents, records, and reviews that provide you with contextual data on your topic. You’re not connecting with participants directly, but rather accessing pre-existing available data. For example, you can pull out insights from your UX research repository to reexamine how they apply to your new UX research objective.

Strictly speaking, it can be both qualitative and quantitative—but today we focus on its qualitative application.

When to use secondary research

Record keeping is particularly useful when you need supplemental insights to complement, validate, or compare current research findings. It helps you analyze shifting trends amongst your users across a specific period. Some other scenarios where you need record keeping include:

• Initial discovery or exploration phase: Secondary research can help you quickly gather background information and data to understand the broader context of a market
• Design and development phase: See what solutions are working in other contexts for an idea of how to build yours

Secondary research is especially valuable when your team faces budget constraints, tight deadlines, or limited resources. Through review mining and collecting older findings, you can uncover useful insights that drive decision-making throughout the product design and development process.

How to conduct secondary research:

• Outline your UX research objective
• Identify potential data sources for information on your product, market, or target audience. Some of these sources can include: a. Review websites like Capterra and G2 b. Social media channels c. Customer service logs and disputes d. Website reviews e. Reports and insights from previous research studies f. Industry trends g. Information on competitors
• Analyze your data by identifying recurring patterns and themes for insights

Secondary research: A qualitative research example

SafeSurf is a cybersecurity platform that offers threat detection, security audits, and real-time reports. After conducting multiple rounds of testing, you need a quick and easy way to identify remaining usability issues. Instead of conducting another resource-intensive method, you opt for social listening and data mining for your secondary research.

Browsing through your company’s X, you identify a recurring theme: many users without a background in tech find SafeSurf ’s reports too technical and difficult to read. Users struggle with understanding what to do if their networks are breached.

After checking your other social media channels and review sites, the issue pops up again.

With your gathered insights, your team settles on introducing a simplified version of reports, including clear summaries, takeaways, and step-by-step protocols for ensuring security.

By conducting secondary research, you’ve uncovered a major usability issue—all without spending large amounts of time and resources to connect with your users.

7. Open-ended surveys

Open-ended surveys are a type of unmoderated UX research method that involves asking users to answer a list of qualitative research questions designed to uncover their attitudes, expectations, and needs regarding your service or product. Open-ended surveys allow users to give in-depth, nuanced, and contextual responses.

When to use open-ended surveys

User surveys are an effective qualitative research method for reaching a large number of users. You can use them at any stage of the design and product development process, but they’re particularly useful:

• When you’re conducting generative research : Open-ended surveys allow you to reach a wide range of users, making them especially useful during initial research phases when you need broad insights into user experiences
• When you need to understand customer satisfaction: Open-ended customer satisfaction surveys help you uncover why your users might be dissatisfied with your product, helping you find the root cause of their negative experiences
• In combination with close-ended surveys: Get a combination of numerical, statistical insights and rich descriptive feedback. You’ll know what a specific percentage of your users think and why they think it.

How to conduct open-ended surveys:

• Design your survey and draft out a list of survey questions
• Distribute your surveys to respondents
• Analyze survey participant responses for key themes and patterns
• Use your findings to inform your design process

Open-ended surveys: A qualitative research example

You're a UX researcher for RouteReader , a comprehensive logistics platform that allows users to conduct shipment tracking and route planning. Recently, you’ve launched a new predictive analytics feature that allows users to quickly identify and prepare for supply chain disruptions.

To better understand if users find the new feature helpful, you create an open-ended, in-app survey.

The questions you ask your users:

• “What has been your experience with our new predictive analytics feature?"
• “Do you find it easy or difficult to rework your routes based on our predictive suggestions?”
• “Does the predictive analytics feature make planning routes easier? Why or why not?”

Most of the responses are positive. Users report using the predictive analytics feature to make last-minute adjustments to their route plans, and some even rely on it regularly. However, a few users find the feature hard to notice, making it difficult to adjust their routes on time.

To ensure users have supply chain insights on time, you integrate the new feature into each interface so users can easily spot important information and adjust their routes accordingly.

💡 Surveys are a lot easier with a quality survey tool. Maze’s Feedback Surveys solution has all you need to ensure your surveys get the insights you need—including AI-powered follow-up and automated reports.

Qualitative research vs. quantitative research: What’s the difference?

Alongside qualitative research approaches, UX teams also use quantitative research methods. Despite the similar names, the two are very different.

Here are some of the key differences between qualitative research and quantitative research .

Before selecting either qualitative or quantitative methods, first identify what you want to achieve with your UX research project. As a general rule of thumb, think qualitative data collection for in-depth understanding and quantitative studies for measurement and validation.

Conduct qualitative research with Maze

You’ll often find that knowing the what is pointless without understanding the accompanying why . Qualitative research helps you uncover your why.

So, what about how —how do you identify your 'what' and your 'why'?

The answer is with a user research tool like Maze.

Maze is the leading user research platform that lets you organize, conduct, and analyze both qualitative and quantitative research studies—all from one place. Its wide variety of UX research methods and advanced AI capabilities help you get the insights you need to build the right products and experiences faster.

Frequently asked questions about qualitative research examples

What is qualitative research?

Qualitative research is a research method that aims to provide contextual, descriptive, and non-numerical insights on a specific issue. Qualitative research methods like interviews, case studies, and ethnographic studies allow you to uncover the reasoning behind your user’s attitudes and opinions.

Can a study be both qualitative and quantitative?

Absolutely! You can use mixed methods in your research design, which combines qualitative and quantitative approaches to gain both descriptive and statistical insights.

For example, user surveys can have both close-ended and open-ended questions, providing comprehensive data like percentages of user views and descriptive reasoning behind their answers.

Is qualitative or quantitative research better?

The choice between qualitative and quantitative research depends upon your research goals and objectives.

Qualitative research methods are better suited when you want to understand the complexities of your user’s problems and uncover the underlying motives beneath their thoughts, feelings, and behaviors. Quantitative research excels in giving you numerical data, helping you gain a statistical view of your user's attitudes, identifying trends, and making predictions.

What are some approaches to qualitative research?

There are many approaches to qualitative studies. An approach is the underlying theory behind a method, and a method is a way of implementing the approach. Here are some approaches to qualitative research:

• Grounded theory: Researchers study a topic and develop theories inductively
• Phenomenological research: Researchers study a phenomenon through the lived experiences of those involved
• Ethnography: Researchers immerse themselves in organizations to understand how they operate

Qualitative research examples

UserTesting

Qualitative research is a powerful tool that helps you unlock insights into the user experience—quintessential to building effective products and services. It provides a deeper understanding of complex behaviors, needs, and motivations. But what is qualitative research, and when is it ideal to use it? Let’s explore its methodologies and implementation with a few qualitative research examples.

What is qualitative research?

Qualitative research is a behavioral research method that seeks to understand the undertones, motivations, and subjective interpretations inherent in human behavior. It involves gathering nonnumerical data, such as text, audio, and video, allowing you to explore nuances and patterns that quantitative data can’t capture.

Instead of focusing on how many or how much, qualitative research questions delve into the why and how. This approach is instrumental in gaining a comprehensive understanding of a particular context, issue, or phenomenon from the perspective of those experiencing it. Examples of qualitative research questions include “How did you feel when you first used our product?” and “Could you describe your experience when you purchased a product from our website?”

Qualitative research methodology

Qualitative research design employs a variety of methodologies to collect and analyze data. The primary objective is to gather detailed and nuanced insights rather than generalizable findings. Steps include the following:

• Formulating research questions:  Qualitative research begins by identifying specific research questions to guide the study. These questions should align with the research objectives and provide a clear focus for data collection and analysis.  
• Selection of participants:  Participant selection is a critical step in qualitative research. You must recruit participants who provide relevant and diverse perspectives on the research topic. It involves purposive sampling, where participants are chosen based on their knowledge or experiences related to the research questions. ​​​​​​
• Data collection:  Qualitative research uses various methods to collect data, such as interviews, focus groups, observation, and document analysis. You often employ multiple methods to comprehensively understand the research topic.
• Data analysis:  Once the data is collected, it’s analyzed to identify recurring themes, patterns, and meanings. This analysis uses coding, thematic analysis, and constant comparison. The goal is to uncover the underlying perspectives of the participant.
• Interpretation and reporting:  This is the final step in which findings are synthesized and interpreted, revealing their significance to the research questions. You can present your findings through descriptive narratives, quotes, and illustrative examples to provide a rich understanding of the research topic. 

Types of qualitative research methods

The best qualitative research method primarily depends on your research questions and objectives. Different methods uncover different discernments.

One-on-one interviews

You often use one-on-one interviews to delve deep into a topic or understand individual experiences or perspectives. An interviewer asks a participant open-ended questions to understand their perspective, thoughts, feelings, and experiences regarding a specific topic, product, or service. Read about open ended vs closed ended questions to learn which questions will be most effective in an interview.

Say you’re developing a new electric vehicle mode. You can conduct one-on-one interviews to understand user experiences, probing into aspects such as comfort, design, driving experience, and more.

Focus groups

In-person or remote focus groups involve a small group of people (usually 6–10) discussing a given topic or question under the guidance of a moderator. This method is beneficial when you want to understand group dynamics or collective views. The interaction among group members can disclose awarenesses that may not arise in one-on-one interviews.

In the gaming industry, for example, you can use focus groups to explore player reactions to a new game design. You can encourage group interaction to spark discussions about usability, game mechanics, graphics, storyline, and other aspects.

Case study research

Case study research provides an in-depth analysis of a particular case (an individual, group, organization, event, etc.) within its real-life context. It’s a valuable method for exploring something in-depth and in its natural setting.

For instance, a healthcare case study could explore implementing a new electronic health record system in a hospital, focusing on challenges, successes, and lessons learned.

Ethnographic research

Ethnographic research (or an ethnographic stud y) involves an immersive investigation into a group’s behaviors, culture, and practices. It requires you to engage directly with the participants over a prolonged period in their natural environment. It can help uncover how people interact with products or services in natural settings.

A gaming organization may choose to study players in their natural gaming environments (such as home, game cafes, or e-sport tournaments) to understand their gaming habits, social interactions, and responses to specific features. These insights can inform the development of more engaging and user-friendly games.

Process of observation

The process of observation typically doesn’t involve the same level of immersion as ethnographic research. You observe and record behavior related to a specific context or activity. It can be in natural settings (naturalistic observation) or a controlled environment. It’s more about observing and recording specific behaviors or situations rather than cultural norms or dynamics.

For example, a consumer technology organization could observe how users interact with a new software interface, noting challenges, efficiencies, and overall user experience.

Record keeping

Record keeping refers to collecting and analyzing documents, records, and artifacts that provide an understanding of the study area. Record keeping allows you to access historical and contextual data that can be examined and reexamined. It’s a nonobtrusive method, meaning it doesn’t involve direct contact with the participants, nor does it affect or alter the situation you’re studying.

An online retailer might examine shopping cart abandonment records to identify at what point in the buying process customers tend to drop off. This information can help streamline the checkout process and improve conversion rates.

Qualitative research: Data collection and analysis

Data collection and analysis in qualitative research are closely linked processes that help generate meaningful and useful results.

Data collection

Data collection involves gathering rich, detailed materials to explain and understand the subject. These include interview transcripts, meeting notes, personal diaries, and photographs. 

There are various qualitative data collection methods to consider depending on your research questions and the context of your study. For example, you could use one-on-one interviews to understand personal user experiences with a financial services app. A moderated focus group may be more appropriate to discuss user preferences in a new media and entertainment platform.

Data analysis

Once data are collected, the analysis process begins. It’s where you extract patterns, themes, and insights from the collected data. It’s one of the most critical aspects of qualitative research, turning raw, unstructured data into valuable insights.

Qualitative data analysis usually takes place with several steps, such as:

• Organizing and preparing the data for analysis
• Reading through the data
• Coding the data
• Generating themes or categories
• Interpreting the findings and 
• Representing the data

Your choice of qualitative data analysis method depends on your research questions and the data type you collected. Common analysis methods include thematic, content, discourse, and narrative analysis. Some research platforms provide AI features that can do much of this analysis for researchers to speed up insight gathering.

When to use qualitative research

Qualitative techniques are ideal for understanding human experiences and perspectives. Here are common situations where qualitative research is invaluable:

• Exploring customer motivations, needs, behaviors, and pain points
• Gathering in-depth user feedback on products and services
• Understanding decision-making and buyer journeys
• Discovering barriers to adoption and satisfaction
• Developing hypotheses for future quantitative research
• Testing concepts , interfaces, or designs
• Identifying problems and improvement opportunities
• Learning about group norms, cultures, and social interactions
• Collecting evidence to develop theories and models
• Capturing complex, nuanced insights beyond numbers

Qualitative research methods vs. quantitative research methods

Qualitative and quantitative research  differ in their approach to data collection, analysis, and the nature of the findings. Here are some key differences:

• Data collection:  Qualitative research uses in-depth interviews , focus groups, observations, and analysis of documents to gather data. In contrast, quantitative research relies on structured surveys, experiments, and standard measurements.
• Analysis:  Qualitative research involves analyzing textual or visual data through coding, categorization, and theme identification techniques. Quantitative research uses statistical analysis to examine numerical data for patterns, correlations, and trends.
• Sample size:  Qualitative research typically involves smaller sample sizes, often selected through purposive sampling to ensure diversity and relevance. Quantitative research uses larger sample sizes to ensure statistical power and generalizability.
• Generalizability:  Qualitative research seeks in-depth insight into specific contexts or groups and does not prioritize generalizability. On the other hand, quantitative research seeks to draw conclusions that apply to a broader context.
• Findings:  Qualitative research generates descriptive and explanatory results that provide a deeper understanding of phenomena. Quantitative research produces numerical data that allows for statistical inferences and comparisons.
• Theory development:  Qualitative research often contributes to theory development by generating new concepts, theories, or frameworks based on the rich and context-specific data collected. However, quantitative research tests preexisting theories and hypotheses using statistical models.

Advantages and strengths of qualitative research

Qualitative research enriches your research process and outcomes, making it an invaluable tool in many fields, including UX research, marketing, and digital product development. 

In-depth understanding

Qualitative research provides a rich, detailed, in-depth understanding of the research subject.  Proactive qualitative research  takes this further with ongoing data collection, allowing organizations to continuously capture insights and adapt strategies based on evolving user needs.

Contextual data

Qualitative research collects contextually relevant data. It captures nuances that might be missed in numerically-based quantitative data, allowing you to understand the contexts in which behaviors and interactions occur.

Flexibility

The methods used in qualitative research, like interviews and focus groups, enable you to explore different topics in depth and adapt your approach based on the participants’ responses.

Human perspective

Qualitative research lets you capture human experiences and thoughts. It’s advantageous in fields such as UX research, where the human perspective is critical. 

Hypothesis generation

The exploratory nature of qualitative research helps you identify new areas for exploration or generate hypotheses you can test using quantitative methods.

Trendspotting

Qualitative research reveals trends in thought and opinions, diving deeper into the problem. This is helpful when trying to understand behaviors, culture, and user interactions.

Disadvantages and limitations of qualitative research

While qualitative research offers many advantages, it’s essential to acknowledge its limitations. 

Time-consuming

Collecting and analyzing qualitative data, particularly from in-depth interviews or focus groups, requires significant time investment.

Qualitative research relies on the skills and judgment of the researcher, introducing potential bias into the research process. The researcher may actively shape the research by posing questions, interpreting data, and influencing the findings.

Requires skilled researchers

The quality of qualitative research heavily depends on the researcher’s skills, experience, and perspective. A less experienced researcher may overlook important nuances, potentially affecting the depth and accuracy of the findings.

Lacks generalizability

Qualitative research often involves a smaller, nonrepresentative sample size than quantitative research. Therefore, the findings may not be generalizable to a larger context.

Limited numeric representation

Qualitative research usually focuses on words, observations, or experiences, so it doesn’t provide the numeric estimates often desired in research studies.

Challenging to replicate and standardize

Qualitative research’s inherent flexibility and context dependence make it challenging to repeat the study under the same conditions. This flexibility can often make it hard to standardize. Researchers approach and conduct the study in various ways, leading to inconsistent results and interpretations.

Difficult to measure reliability and validity

Assessing reliability and validity is more difficult with qualitative research since it relies on subjective human interpretation and has few established metrics and statistical tools compared to quantitative research. Triangulation and member checking add credibility but lack the discreteness of quantitative measures. However, there have been advancement s in the measurement of qualitative research that help to quantify its impact. 

Qualitative research gives you the opportunity to dive deep into human behavior, experiences, and perceptions. It offers a prolific, intricate perspective that quantifiable data alone can’t provide. Combine qualitative research methodologies with techniques like  A/B testing  to gain a more holistic understanding of user experiences and preferences. 

Despite its limitations, the depth and richness of data procured through qualitative research are undeniable assets. By understanding and utilizing its diverse methods, you will uncover detailed insights from your target audience and enhance your products or services to meet their needs. 

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Learn how to gather the user feedback you need to build best-in-class products. 

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Why is qualitative research important?

Qualitative research delves into subjective experiences and social contexts, providing in-depth insights and understanding. It provides a deep understanding of individuals’ needs, motivations, and preferences, allowing organizations to develop products and services that meet customer expectations.

What’s the difference between quantitative and qualitative methods?

Quantitative methods focus on numerical data and statistical analysis, aiming for generalizability and objectivity. Qualitative methods explore meanings, experiences, and behaviors, seeking in-depth understanding and detailed descriptions.

What are the main qualitative research approaches?

The main qualitative research approaches include one-on-one interviews, focus groups, case study research, ethnographic research, observation, and record-keeping. Each approach offers unique benefits and applications.

What is data collection?

Data collection in qualitative research involves gathering information through various methods such as interviews, focus groups, observations, and document analysis. It’s a critical step in generating meaningful insights and understanding human experiences.

How do you analyze qualitative data?

What are the ethical considerations in qualitative research.

Ethical considerations refer to the protection of participants’ rights, privacy, and confidentiality. You must obtain informed consent, maintain anonymity, and handle sensitive information responsibly. Additionally, maintaining transparency, addressing power imbalances, and conducting research unbiased and respectfully are vital ethical considerations in qualitative research.

How can I incorporate qualitative research into my study or project?

To incorporate qualitative research into your study, you must first define your research objectives to guide the choice of methodology. Next, choose a suitable qualitative method, such as interviews or focus groups. Then, collect and analyze the data using appropriate techniques and, finally, interpret and present the findings clearly and meaningfully. Remember to be mindful of the ethical considerations throughout the process.

How do you effectively communicate and present qualitative research findings to stakeholders?

For a quality presentation, create engaging visual representations, such as infographics or data visualizations, and use storytelling techniques to highlight key insights. Also, prepare concise and informative reports and organize interactive presentations or workshops to facilitate discussion and understanding.

How do you translate qualitative research findings into actionable insights?

Identify key themes linked to research goals and propose strategic solutions to address core needs and barriers. These solutions should be tailored to specific needs.

How can I ensure the validity and reliability of qualitative research findings?

About the author(s).

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• Published: 02 September 2024

A qualitative dynamic analysis of the relationship between tourism and human development

• Pablo Juan Cárdenas-García   ORCID: orcid.org/0000-0002-1779-392X 1 ,
• Juan Gabriel Brida   ORCID: orcid.org/0000-0002-2319-5790 2 &
• Verónica Segarra   ORCID: orcid.org/0000-0003-0436-3303 2  

Humanities and Social Sciences Communications volume  11 , Article number:  1125 ( 2024 ) Cite this article

Metrics details

• Development studies

This study analyzes the dynamic relationship between tourism and human development in a sample of 123 countries between 1995–2019 using a symbolic time series methodological analysis, with the number of international tourist arrivals per capita as the tourism measurement variable and the Human Development Index as the development measurement variable. The objective was to determine if a higher level of tourism specialization is related to a higher level of economic development. The definition of economic regime is used and the concept of the distance between the dynamic trajectories of the different countries analyzed is introduced to create a minimum spanning tree. In this way, groups of countries are identified that display similar behavior in terms of tourism specialization and levels of human development. The results suggest that countries with a high level of tourism specialization have a higher level of development as compared to those in which tourism has a lower specific weight. However, the largest group of countries identified is characterized by low levels of tourism specialization and economic development, which appears to translate into a poverty trap. Therefore, policies related to tourism activity expansion should be created since higher tourism levels have been linked to higher levels of human development. In the case of less developed countries, however, these projects should be financed by international organizations so that these countries can escape the poverty trap in which they are currently found.

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Introduction.

Traditionally, the Gross Domestic Product per capita (GDP per capita) is considered the go-to variable to determine a population’s economic development and is restricted exclusively to an economic measure (Todaro and Smith, 2020 ). Recently, however, studies on development have begun incorporating other noneconomic factors, such as education and health. These factors, together with the economic criteria, provide a baseline for measuring a population’s development in broader terms (World Bank, 1991 ; Lee, 2017 ). In the search for economic activities that enable economic growth and improve the level of economic development, many countries have been especially interested in tourist activity since it is an economic activity that has a strong potential for job creation, the generation of foreign currency, and revenue increase. In short, it may be able to boost economic growth in host regions (Brida et al., 2020 ). In some cases, the development of tourism has been found to contribute to reducing inequality (Chi, 2020 ; Nguyen et al. ( 2021 )) or reducing poverty (Garza-Rodriguez ( 2019 ); Folarin, Adeniyi ( 2019 )).

In fact, what is actually important in economic policies is not only the promotion of a country’s economic growth but also, the channeling of this economic growth into improved economic development in the territory (Croes, 2012 ). This latter concept is much broader and it serves to satisfy the needs and demands of the resident population, improving its quality of life (Ranis et al., 2000 ).

In terms of the analysis of the relationship between tourism and economic growth, many studies have researched this connection. Most of them agree that a causal relationship exists between both variables, that tourism influences growth (Balaguer and Cantavella-Jordá, 2002 ; Brida et al., 2016 ), that the economic cycle influences the development of tourism (Antonakakis et al., ( 2015 ); Sokhanvar et al., 2018 ), and that there is a bidirectional relationship between tourism and economic growth (Bojanic and Lo, 2016 ; Hussain-Shahzad et al. ( 2017 )).

Given that a relationship between tourism and economic growth has been proven in the economies of host countries and national governments, despite a lack of sufficient empirical evidence, various international organizations have been promoting tourism activity as a tool to facilitate the population’s development in those host regions that attract tourist flows to their territory (OECD, 2010 ; UNCTAD - United Nations Conference on Trade and Development ( 2011 )). Such has been the case with the relationship between tourism and economic growth, with the suggestion that tourism is a tool for economic development (Cárdenas-García and Pulido-Fernández, 2019 ).

Many studies have already analyzed the relationship between tourism and GDP per capita, finding long-term equilibrium relationships between the expansion of tourism and economic growth, whereby a higher level of tourists received means higher levels of economic growth (Akadiri et al., 2017 ). As previously mentioned, the economic development of a population, in a broad sense, and in addition to the economic variables, has to be linked to additional variables with a multidimensional content (Wahyuningsih et al., 2020 ). In this scenario, although some studies have measured development in a broader sense (Andergassen and Candela, 2013 ; Banerjee et al. 2018 ; Bojanic and Lo, 2016 ; Li et al., 2018 ), there is a clear lack of analysis of the relationship between tourism and economic development as a multidimensional variable.

In this regard, human development, and its measurement through the Human Development Index (HDI), is a multidimensional variable related to the living conditions of the resident population (income, education, and health), which has been used on many occasions (more than level of poverty or income inequality) to measure a country’s level of development (Cárdenas-García et al., 2015 ; Chattopadhyay et al., 2021 ; Croes et al., 2021 ). The link between tourism and human development arises from the economic growth generated by the expansion of tourist activity. This economic growth is used to develop policies that will improve the education and health levels of the host population (Alcalá-Ordóñez and Segarra, 2023 ).

This article analyzes the relationship between tourism and economic growth, measuring the economic growth of the countries in the broadest possible sense, with a link to the concept of human development (Cárdenas-García et al., 2015 ). As a novelty, a wide set of countries is used for this analysis. This overcomes the limitations of prior works that analyzed the relationship between tourism and human development using small country samples (Chattopadhyay et al., 2021 ).

Although distinct works have already analyzed the relationship between tourism and economic development, they tended to focus on the application of econometric tests to determine the type of causal relationship existing between these variables (Alcalá-Ordóñez and Segarra, 2023 ). This work takes a distinct approach, analyzing the qualitative dynamic behavior arising between tourism and human development. Different country groups are identified that have similar behavior within the group and, simultaneously, with differences as compared to the other groups. Thus it is possible to verify the relationship existing between tourism and human development in each of these country groups, to determine if a higher level of tourism specialization is linked to a higher level of human development.

This approach does not attempt to determine if a causal relationship exists by which tourism precedes the level of development. Rather, this approach of grouping countries aims to determine if, at similar levels of development, the country groups with a higher level of tourism specialization display higher levels of human development. This would suggest that tourism activity is an economic activity that promotes human development to a greater extent than other economic activities.

In this context, this study analyzes the dynamic relationship between tourism and economic development, considering development as a multidimensional variable. It uses a data panel consisting of 123 countries for the period between 1995–2019 and considers the diversity of countries in terms of tourism development and their economic development dynamics. To perform this dynamic analysis, the concept of economic regime is introduced (Brida, 2008 ; Cristelli et al., 2015 , Brida et al., 2020 ), and symbolic time series are used (Risso ( 2018 )).

This article contributes to the empirical literature examining the relationship between tourism and economic development. It analyzes the qualitative dynamic behavior of the countries without considering any particular model. Therefore, this analysis enables the identification of groups of countries with similar dynamics, for which economic models of the same type can be identified. The results of this study indicate that there are different groups of countries displaying similar dynamic behavior in terms of both tourism and development. These groups are characterized by their level of tourism specialization and economic development. Therefore, it is interesting to note the heterogeneity existing in the relationship between tourism and development, as well as the consequences that this situation has for both the empirical analysis and the political implications.

The rest of the document is organized as follows: the following section reviews the literature on the subject under study, section “Data” presents the data used, section “Methodology” details the methodology applied, section “Results” presents the results obtained, section “Discussion” includes a discussion of the paper, and, finally, section “Conclusions and policy implications” outlines the final conclusions and policy implications of the work.

Literature review

Economic growth versus economic development.

Traditionally, studies on development have focused on economic growth and have been based on the premise that the efficient allocation of resources maximizes growth and that the expansion of growth and consumption is a measure of population welfare (Easterly, 2002 ). However, the emergence of new studies at the end of the last century, beginning with the works by Sen ( 1990 , 1999 ), resulted in a change of focus for studies on development. They moved from an exclusive view of development linked to economic growth to the inclusion of new factors that connect it to the population’s living conditions (Croes et al., 2018 ).

Economic growth and development are distinct concepts that do not need to be linked. In other words, increased economic growth does not necessarily imply improved economic development (Croes et al., 2021 ). However, it is also true that economic growth, and the revenue generated, can be used to improve a population’s living conditions through better health care, infrastructures, and education (Banerjee et al., 2018 ; Cárdenas-García and Pulido-Fernández, 2019 ).

In this regard, the first studies to analyze the relationship between tourist activity and the economies of host countries focused exclusively on the relationship between tourism and economic growth, using a traditional view of development that is linked to economic variables.

Tourism and economic growth

Numerous studies have analyzed the relationship between tourism and economic growth. Therefore, it is a highly relevant research area in the economic analysis of tourist activity, with three streams of perfectly defined results in which these works may be grouped (Alcalá-Ordóñez et al., 2023; Brida et al., 2016 ).

Firstly, different studies have determined that tourism development drives economic growth, identified under the tourism-led economic growth hypothesis. Both the first study to analyze this causal relationship (Balaguer and Cantavella-Jorda, 2002), as well as the later studies (Brida et al., 2016 ; Castro-Nuño et al., 2013 ; Lin et al., 2019 , Pérez-Rodríguez et al., 2021 ; Ridderstaat et al., 2016 ), have confirmed the existence of this relationship.

Secondly, other studies determined that the evolution of the economic cycle has an influence on the development of tourism, identified under the economic-driven tourism growth. These studies indicate that those economies with a greater level of investment, stability in the price level, or lower level of unemployment determine the development of tourism (Antonakakis et al. ( 2015 ); Rivera, 2017 ; Sokhanvar et al., 2018 ; Tang, Tan ( 2018 )).

Finally, a third wave of studies determined that the relationship between the development of tourism and economic growth has a bidirectional character. These studies note that the relationship between both variables is a causal bidirectional relationship (Antonakakis et al., 2019 ; Bojanic and Lo, 2016 ; Chingarande and Saayman, 2018 ; Hussain-Shahzad et al. ( 2017 ); Ridderstaat et al., 2013 ).

Human Development as a measure of development

Since the end of the last century, the scientific literature has shown that the concept of development cannot be linked exclusively to variables of economic content. Instead, development should be considered along with other non-economic factors that are related to the population’s living conditions. Therefore, it is a multidimensional concept (Alcalá-Ordóñez and Segarra, 2023 ).

When measuring development using a multidimensional perspective, this concept is often linked to human development (Cárdenas et al., 2015 ; Chattopadhyay et al., 2021 ). In this regard, the HDI is a multidimensional indicator that, in addition to considering variables of economic content, in this case per capita income, also incorporates other non-economic factors, specifically, life expectancy and educational level of the population (United Nations Development Program, 2022 ).

The HDI offers some major advantages as a measure of development over other indicators, providing a more complete vision of society’s progress and focusing not only on economic factors but also on factors related to the population’s living conditions. This makes it possible to identify inequalities that need to be addressed to promote more equitable and sustainable development (Sharma et al., 2020 ; Tan et al., 2019 ). Moreover, since it was created by the United Nations Development Program for a large group of countries, it permits homogenous comparison-making between a broad base of countries at a global level (Cárdenas-García and Pulido-Fernández, 2019 ).

Tourism and human development

The expansion of tourism activity can influence the level of human development (Croes et al., 2021 ). The common link between these two variables is the economic impact generated by the expansion of tourist activity since this is a linked process, whereby a higher level of tourists results in an increase in income generated and thus, a higher level of economic growth (Brida et al., 2016 ). Countries can take advantage of this higher level of economic growth to develop specific policies aimed at improving the living conditions of the host population, thereby improving human development (Eluwole et al., 2022 ).

This link between tourism and human development has also been highlighted by the United Nations Tourism in its Millennium Development Goals of 2000, which declared that factors such as health and education are very important in economic development. It was suggested that tourism may improve human development given that it has an influence on these non-economic factors (UN Tourism, 2006 ).

The triple component of the HDI, the most frequently used indicator to measure economic development, has been considered in most of the studies analyzing the relationship between tourism and economic development (Alcalá-Ordóñez and Segarra, 2023 ).

Distinct studies have attempted to determine whether tourism is a tool for economic growth in host countries, although most of the studies have exclusively used economic content to measure the concept of development (Wahyuningsih et al., 2020 ). Therefore, there is a major lack of empirical studies that consider whether tourism influences development and that do so while considering development to be a multidimensional variable encompassing other factors (beyond those associated with the economy).

Some of these studies have outlined that the expansion of tourism has led to an increase in the level of development for host countries. This suggests that tourism has a positive unidirectional relationship with the living conditions of the population (Meyer and Meyer, 2016 ). Fahimi et al. ( 2018 ), examining microstates, found evidence supporting the idea that the expansion of tourism leads to an improvement in human capital. Other studies have also noted that this causal relationship between tourism and development exists, but only in developed countries (Banerjee et al., 2018 ; Bojanic and Lo, 2016 ). Some studies have suggested that only the least developed countries have benefited from the tourism industry in terms of increased economic development ratios (Cárdenas-García et al., 2015 ).

However, although it has been indicated that tourism influences economic growth, some authors have noted that tourism does not have an influence on the development of host countries (Rivera, 2017 ), or simply, that the expansion of this activity does not have any effect on human development (Croes et al., 2021 ).

As an intermediate position between these two schools of thought, some works have suggested that tourism has a positive influence on the development of the resident population, but this causal relationship is only found when certain factors exist in the host countries, such as infrastructure, environment, technology, and human capital (Andergassen and Candela, 2013 ; Cárdenas-García and Pulido-Fernández, 2019 ; Li et al., 2018 ).

Along these same lines, in a study using panel data from 133 countries, Chattopadhyay et al. ( 2021 ) determined that, although no global relationship exists between tourism and human development for all countries, the specific characteristics of each country (level of growth, degree of urbanization, or commercial openness) are determinants for tourism to improve human development levels.

Finally, other studies in the scientific literature have looked to determine whether the relationship between tourism and development is a bidirectional causal relationship, with papers affirming the existence of this relationship between tourism and development (Pulido-Fernández and Cárdenas-García, 2021 ).

Therefore, when examining the few studies that have analyzed the relationship between tourism and development, it may be concluded that contradictory and biased results exist. This may be due to the characteristics of the samples chosen, the variables used, and the methodology employed. Currently, there is no defined school of thought in the scientific literature with regard to the ability of tourism to improve living conditions for the resident population. This contrasts with the conclusions drawn regarding the relationship between tourism and economic growth.

This gap in the scientific literature provides an opportunity for new empirical studies that can analyze the relationship between tourism and development.

In this study, data from different sources of information were used with the objective of analyzing the relationship between tourism and economic development, in accordance with the methodology proposed in the following section. The data used in the present study are available for a total of 123 countries, covering all geographical areas worldwide. The specific data for these countries are as follows, including a web link to the availability of the data to provide greater transparency:

Tourist activity. The number of international tourists received was used as a variable for measuring tourist activity. For those countries for which this data was unavailable, the number of international visitors received was used, based on annual information provided by the United Nations Tourism between 1995 and the present (UN Tourism, 2022 ).

Data on international tourists received at a country level are available at https://www.unwto.org/tourism-data/global-and-regional-tourism-performance

Economic development. The HDI, developed by the United Nations Development Program and available annually from 1990 to the present day, was used as a variable for measuring economic development (United Nations Development Program, 2022 ).

Data from the HDI for each country are available at https://hdr.undp.org/data-center/human-development-index#/indicies/HDI

Total population. The de facto population was used as a measurement variable and counts all residents regardless of their legal status or citizenship. This information was provided by the World Bank and is available from 1960 to the present day, on an annual basis (World Bank, 2022 ).

Data on the population of the distinct countries are available and accessible at https://data.worldbank.org/indicator/sp.pop.totl .

Based on the data indicated above, the initial variables are transformed, specifically, in the case of tourism, through the use of the relativized per capita variable. A descriptive summary of the variables used in the analysis is presented in Table 1 . Finally, two variables have been used to analyze the relationship between tourism and economic development:

International tourists per inhabitant received in the country (number of international tourists / total population of the country), as a measure of tourism specialization. The unit of this variable is established at a relative value, by dividing the number of tourists by the population.

HDI of the country, as a measure of economic development. The unit of this variable is established at a relative value for each country, which, in all cases, is between 0 (lowest level of human development) and 1 (highest level of human development).

Regarding the tourist sector, the measurement of tourism is a subject that has generated great interest, and, on many occasions, the selection of different indicators leads to different results (Song and Wu, 2021 ). As a result, the results of the empirical analysis may be affected by the indicators used to represent the tourist demand (Fonseca and Sanchez-Rivero, 2020 ), with there being important differences between studies with respect to the tourism indicator. According to Rosselló-Nadal, He ( 2020 ), tourist arrivals or tourism expenditure are frequently used to measure tourist demand; however, when looking at the literature, differences in the results are found depending on the indicator considered. Indeed, in their study, which looked at 191 countries between 1998–2016, the authors found evidence that estimates may differ depending on the indicator used for the tourism demand of a destination (international tourist arrivals, or international tourist expenditure in this case). Other studies use indicators that do not measure the degree of tourist activity of a destination, as is the case for the number of tourist arrivals, the expenses, or the revenues. Instead, they consider an indicator that measures the degree of specialization that an economy has in tourism, for example, international tourist arrivals in per capita terms or expenditure or income as a percentage of GDP or exports. This work uses the number of international tourist arrivals, in relation to the population, and thus obtains the degree of tourism specialization of a destination (such as Dritsakis, 2012 ; Tang and Abosedra, 2016 ).

With regard to the measurement of economic development, the arrival of the HDI has resulted in a notable improvement in terms of GDP per capita, which is traditionally used to measure the progress of a country linked only to economic aspects (Lind, 2019 ). In fact, the HDI includes other noneconomic factors as it measures three key dimensions of development: a long and healthy life, being well-informed, and having a decent standard of living. This is why this index was created from the geometric mean of the normalized indices for each of the three dimensions indicated: (i) health: life expectancy at birth; (ii) education: years of schooling for adults and expected years of schooling for children; and (iii) standard of living: Gross National Income per capita (United Nations Development Program, 2022 ). Therefore, since the emergence of this index, there have been increasingly more studies that have incorporated HDI as a measurement of economic development. This variable has been shown to represent development better than other variables that are based exclusively on economic factors (Anand and Sen, 2000 ; Jalil and Kamaruddin, 2018 ; Ngoo and Tey, 2019 ; Ogwang and Abdou, 2003 ; Sajith and Malathi, 2020 ).

The time scale considered in this study covers the period between 1995–2019, in order to perform the broadest possible time analysis. On the one hand, there is an initial time restriction in terms of the data, given that the first data available on international tourist arrivals, provided by the United Nations Tourism, refer to the 1995 fiscal year. On the other hand, the data for the 2019 fiscal year are the latest in the time series analyzed. Therefore, the consequences of the COVID-19 crisis, which may have had a different impact at the country level, as well as the level of recovery in international tourist arrivals, do not affect the results of this work.

Methodology

In this work, an analysis is carried out involving the dynamics of two variables: tourism specialization and the HDI. Each of the countries considered in the analysis is represented by a two-dimensional time series of coordinates of these two variables.

In order to compare these dynamics and thereby find homogenous country groups sharing similar dynamics, it was first necessary to introduce a metric permitting this comparison. A fundamental issue in this analysis is that the units of measurement used for each variable are different and the relationship between them is unknown since tourism is measured in the number of tourists per inhabitant while the HDI is an index that varies between 0 and 1. Therefore, the frequently used Euclidean metrics are not valid for this analysis. For this reason, in this study, the problem was analyzed within the framework of complex systems by introducing the concept of “regimes”.

In economic literature, the term “regime” is used to characterize a type of behavior exhibited by one economy, which can be qualitatively distinguished from the “regime” that characterizes another economy. In this way, one regime is distinguished and differentiated from another, so that the economy as a whole may be considered a system of multiple regimes. Intuitively, an “economic regime” may be considered a set of rules governing the economy as a system and determining certain qualitative behaviors (Boehm and Punzo, 2001 ).

Regime changes, on the other hand, are associated with qualitative changes in the dynamics of an economy. Identifying and characterizing these regimes is a complex issue. For example, when working with mathematical models, a commonly used criterion is through Markov partitions (see Adler, 1998 ). Another widely used criterion when working with data is the division of the state space using various statistical indicators, such as the mean, median, etc. (see Brida and Punzo, 2003 ).

Firstly, a distance between countries was calculated to compare their trajectories; secondly, a symbolic time series analysis was used and the concept of “regime” was incorporated; as a result, the original two-dimensional series was transformed into a one-dimensional symbolic series. Then, a metric allowing for the comparison of the dynamic trajectories of the different countries was introduced; finally, a cluster analysis was performed to group the countries based on their dynamics.

The symbolic time series analysis methodology, still quite undeveloped in the field of economics, has been used in some previous works, such as that by Brida et al. ( 2020 ) that analyzes the relationship between tourism and economic growth. All analyses have been performed using RStudio software.

Time series symbolization

To identify the qualitatively relevant characteristics, the concepts of regime and regime dynamics were introduced (Brida, 2008 ; Brida et al., 2020 ). Each regime had its own economic performance model that made it qualitatively different from the rest. The partitioning of the space of tourism states and the development was established by means of annual averages of international arrivals per capita (x) and the HDI (y). The space was divided into four regions, which were determined by the annual averages of tourism and economic development, \({\bar{x}}_{t}\) and \({\bar{y}}_{t}\) respectively, with \(t=1,\ldots ,25\) . Using this partitioning of the states space into regimes, two types of dynamics are distinguished: one within each of the regimes and one of change between regimes. While the dynamic observed in each regime determines a performance model that differs from the models that act in the others, the dynamics of change from one region to another indicate where an economy is at each temporal moment. This dynamic describes performance in terms of tourism specialization and economic development in a qualitative way.

A change of regime of course signals some qualitative transformation. To explore these qualitative changes for every country, let us substitute a bi-dimensional time series \(\left\{\left({x}_{1},{y}_{1}\right),\,\left({x}_{2},{y}_{2}\right),\,\ldots ,\,\left({x}_{{\rm{T}}},{y}_{{\rm{T}}}\right)\right\}\) , by a sequence of symbols: \(s=\left\{{s}_{1},{s}_{2},\ldots ,{s}_{T}\right\}\) , such that \({s}_{t}=j\) if and only if \(\left({x}_{t},{y}_{t}\right)\) belongs to a selected state space region, \(\,{R}_{j}\) . It is defined four regions in the following way:

Regime 1: countries with above-average HDI and tourism specialization. In this regime, the most developed economies specializing in tourism are expected to be found. The majority of European countries are expected to be found in this regime; countries in other regions with a high level of tourism specialization could also be included.

Regime 2: countries with high HDI and low tourism specialization. In this regime, the most developed economies, but in which tourism activity has a less important weight in their economic base, are expected to be found. Some large countries such as the US and Germany are expected to be found in this regime. Other countries may also be found here even if they do not present similar levels of development as European countries, for example, they have higher levels in relative terms (above the sample average).

Regime 3: countries with low HDI and low tourism specialization. In this regime, economies with a lower level of development and where tourism activity is not relevant to their economic activity, are expected to be found. Countries such as China, other Asian countries, countries on the African continent, and countries in South America are expected to be included in this regime.

Regime 4: countries with low HDI and high tourism specialization. Countries with a lower level of development and a high level of tourism specialization, such as Caribbean countries and some island countries, are expected to be found in this regime.

Once the one-dimensional symbolic series is obtained, a metric is introduced that allows comparing the dynamics of the countries, and which in turn allows for obtaining homogeneous groups. Given the symbolic sequences \({\left\{{s}_{{it}}\right\}}_{t=1}^{t=T}\) and \({\{{s}_{{jt}}\}}_{t=1}^{t=T}\) the distance between two countries, i and j is given by.

Intuitively, the distance between two countries measures the number of years of regime non-coincidence during the period. If the distance between two countries is zero, the countries have been in the same regime for the entire period. On the contrary, if the distance between two countries is T, the countries have not coincided for any time during the analyzed period. If the distance between two countries is α, it means that they have not coincided for α years during the period. In other words, they have coincided for T-α years.

Using the defined distance, the hierarchical tree was created using the nearest neighbor cluster analysis method (Mantegna, 1999 ; Mantegna and Stanley, 2000 ). Using the algorithm by Kruskal ( 1956 ), the minimum spanning tree (MST) was created. This tree was created progressively, joining all the countries from the sample using a minimum distance. According to this algorithm, in the first step, the two countries whose series had the shortest distances were connected. In the second step, the countries with the second shortest distance were connected. This pattern continued until all countries were connected in one tree.

Symbolic time series analysis

Figure 1 shows the point cloud corresponding to 2019, with the respective averages of each variable. Each point represents a country in this year with its coordinates (Tourism, HDI). As is expected, the points are distributed in the four regions, showing that qualitatively the countries perform differently. A clustering in the second and third quadrants can be observed, indicating a clustering in the sections with a low level of tourism specialization, and, in turn, there are not many countries in the fourth quadrant. In other words, few countries have been considered to have a high level of tourism specialization but low levels of development, in the last year (Belize, Fiji, Jamaica, Saint Lucia, the Maldives, and Samoa).

Cloud of points of the 123 countries for the year 2019.

Table 2 shows the percentage of time spent by each of the 123 countries analyzed in each of the previously defined regimes, showing that the large majority of the countries (80 countries) remained in the same regime for the entire period or, at least, for three-quarters of the period analyzed in the same regime (16 countries). In this regard, using the symbolization of the series, 4 clear groups were identified, made up of countries that remained in the same regime for the entire period:

Group 1: made up of countries that are in regime 1 for the entire period (high level of tourism specialization and high level of development): Austria, Bahamas, Barbados, Switzerland, Cyprus, Spain, France, Greece, Hong Kong, Ireland, Iceland, Italy, Luxembourg, Malta, Netherlands, Norway, Portugal, and Singapore.

Group 2: made up of countries that are in regime 2 for the entire period (low level of tourism specialization and high level of development): Germany, Argentina, Australia, Chile, South Korea, Costa Rica, Cuba, the United States, Russia, Iran, Japan, Kazakhstan, Kuwait, Mexico, Panama, United Kingdom, Romania, Trinidad and Tobago, and Ukraine.

Group 3: made up of countries that are in regime 3 for the entire period (low level of tourism specialization and low level of development): Azerbaijan, Benin, Bangladesh, Bolivia, Central African Republic, China, Congo, Algeria, Egypt, Gambia, Guatemala, Guyana, Honduras, Haiti, Indonesia, India, Cambodia, Laos, Lesotho, Morocco, Mali, Myanmar, Mongolia, Malawi, Namibia, Niger, Nicaragua, Nepal, Philippines, Papua New Guinea, Paraguay, Sudan, Sierra Leone, El Salvador, Togo, Tuvalu, Tanzania, Uganda, Vietnam, Zambia, and Zimbabwe.

Finally, Group 4, made up of Belize and the Maldives, which are in regime 4 for the entire period (high level of tourism specialization and low level of development):

It is worth noting that according to the results obtained, regime changes can be difficult to observe. This could be a result of the fact that a regime change implies a structural change in the economy and in such a period as the one analyzed in this study (25 years), the observation of a structural change may be circumstantial in nature. In other words, the timing of structural changes seems to be slower than the tick of the chosen clock; in this case, an annual tick.

Within the group of countries that always remain in regime 1, two groups of countries can be identified. One of the groups is that in which tourism is an essential sector for the economy (like in the case of the Bahamas or Barbados, which have tourism contribution rates to GDP of above 25%), and in which tourism seems to have an influence in the high level of development. The other group is that in which, while tourism is not necessarily an essential sector for the economy, due to the existence of other economic activities, it is an important sector for development (such as Spain or Portugal, with tourism contribution rates to GDP of above 10%).

Within the group of countries that always remain in regime 2, there are fundamentally countries in which tourism has a marginal weight in relation to the level of population (like in the case of Germany, the US, and Japan), due to the lack of or little exploitation of the country’s tourism resources, which would result in development seeming to be related to other economic activities.

Within the group of countries that always remain in regime 3, there is a large group consisting of 41 countries (a third of the sample) that seem to be in a poverty trap, due to the low level of development and low level of tourism specialization. This is in such a way that the low level of development hinders the expansion of tourism activity, and, in turn, this lack of tourism development makes it difficult to increase the levels of development.

Finally, within the group of countries that always remain in regime 4, there are only two countries found, which are characterized by a high level of tourism specialization but have not transformed this into an improvement in development, possibly due to the existence of certain factors that hinder this relationship.

Therefore, the first issue to note is the little mobility that countries have in terms of their classification between the different regimes, given that 80 countries (two-thirds of the sample) remained in the same regime during the 25 years analyzed, which seems to show that the variables are somewhat stable, and thus justifies the fact that no major changes were observed during the period analyzed. This behavior reveals that the homogeneity in the tourism and development dynamic is the rule and not the exception.

In fact, only 27 countries, out of the 123 countries analyzed, are in a different regime for at least a quarter of the period: Albania, Armenia, Bulgaria, Brazil, Botswana, Canada, Colombia, Slovakia, Eswatini, Finland, Fiji, Hungary, Jamaica, Jordan, Lithuania, Latvia, Moldova, Malaysia, New Zealand, Peru, Saint Lucia, Sweden, Thailand, Tonga, Tunisia, Turkey, and Samoa.

In this regard, Fig. 2 shows the time evolution of the symbolic series for some selected countries. As can be noted, there are some countries, like Brazil, that always have a low level of tourism specialization and alternate between periods of high and low economic development, with it seeming as though there is consolidation as being a low HDI country in recent years (until 2002, Brazil had an above average level of development but, after it was hit by a crisis, the country moved to the low development regime. Then, in 2013, it managed to return to the high HDI regime, albeit temporarily as in 2016, in the midst of a political and economic crisis, it returned to the low development regime, where it currently remains). This is similar to what happened in Fiji, insofar as it was almost always specialized in tourism and alternated HDI, consolidating itself in Regime 4 of the low HDI. As such, it seems as though certain countries define their behavior according to the degree of tourism specialization; in this case, not particularly specialized countries.

Top panel: Brazil (left) and Fiji (right). Bottom panel: Latvia (left) and Eswatini (right).

However, the behavior of Latvia or Eswatini seems to be determined by HDI and not by tourism specialization. As to be expected, Latvia remained always in regimes 1 and 2 with a high HDI while Eswatini remained in regimes 3 and 4 with a low HDI. In both cases, they alternated periods of high and low specialization in tourism.

Grouping homogeneous countries

In the case analyzed, there are many countries with zero distance. These are the countries that have the same symbolic representation, that is, the regimes dynamics are coincidental given that these countries always remain in the same regime. Therefore, there are three groups that start to form with countries that have zero distance (countries that are always placed in regimes 1, 2, and 3), and a small group, formed by Belize and the Maldives, which are the only countries that remained in regime 4 for the entire period analyzed. According to this algorithm, 6 groups were obtained, while some countries were not included in any of the groups as they were considered to be “outliers”.

Specifically, there was a graph with 123 nodes corresponding to each country and 122 links; however, given that there were several countries with the same dynamic (the distance between these countries is zero), each of these groups is represented in a single node; that is, the countries that always remained in regime 1 were considered together as one single node, with the same happening for the remaining three groups of countries with identical dynamics (groups 2, 3, and 4). Therefore, in this case, there is a node representing 18 countries from group A and another node (both pink) that represents multiple countries; the Czech Republic, Estonia, Croatia, Mauritius, and Slovenia, which all share the same dynamic (they always remain in regime 1, except in 1995). There is a node representing 19 countries from group B (light blue), another node representing 41 countries from group C (green), and a final node representing Belize and Maldives in group D. In this way, 80 countries are represented in four nodes. To complete the tree, 38 other nodes, each corresponding to a country, were established. Using Kruskal’s algorithm ( 1956 ), the MST is built, in which all nodes are connected in a single tree from the minimum distances. In this way, a tree is created having links that connect the nodes to represent the minimum distances between them (a longer arrow indicates a longer distance).

Figure 3 shows the MST. It is worth noting the central position that these multiple nodes have within the groups, that is, nodes that represent a group of countries with the same dynamics. The structure of the MST seems to be almost linear; moreover, while group C (green) is the most numerous, it is also the most compact of the large groups.

(Nodes: Pink group A/Light blue group B/Green group C/Yellow group D/Orange group E/Blue group F/Red Outliers. Distances according to arrow color: black 1/red 2/light blue 3/green 4/blue 5/orange 6/pink 7/gray 8/violet 9).

Figure 4 shows the geographic distribution of the different groups. There are 6 groups (3 large and 3 small), while some countries are not included in any of these groups, as they are considered to be “outliers”:

Group A: Albania, Austria, Belgium, Bulgaria, Bahamas, Barbados, Switzerland, Cyprus, Czech Republic, Denmark, Spain, Estonia, Finland, France, Greece, Hong Kong, Croatia, Hungary, Iceland, Ireland, Italy, Lithuania, Luxembourg, Latvia, Malta, Mauritius, Malaysia, Netherlands, Norway, New Zealand, Portugal, Qatar, Singapore, Slovakia, Slovenia, and Uruguay. This group is made up of countries that predominantly remained in regime 1, that is, in general, these are countries with a high tourism specialization and high economic development.

Group B: Argentina, Australia, Brazil, Chile, Colombia, Costa Rica, Cuba, Germany, Ecuador, United Kingdom, Iran, Israel, Jordan, Japan, Kazakhstan, South Korea, Kuwait, Sri Lanka, Mexico, North Macedonia, Panama, Peru, Poland, Romania, Russia, Tonga, Trinidad and Tobago, Ukraine, United States. This group is made up of countries that predominantly remained in regime 2, that is, in general, these are countries with a low tourism specialization and high economic development.

Group C: Azerbaijan, Benin, Bangladesh, Bolivia, Central African Republic, China, Congo, Dominican Republic, Algeria, Egypt, Gambia, Guatemala, Guyana, Honduras, Haiti, Indonesia, India, Cambodia, Laos, Lesotho, Mali, Morocco, Myanmar, Mongolia, Malawi, Namibia, Niger, Nicaragua, Nepal, Philippines, Papua New Guinea, Paraguay, Sudan, Sierra Leone, El Salvador, Togo, Tuvalu, Tanzania, Uganda, Vietnam, South Africa, Zambia, and Zimbabwe. This group is made up of countries that remained the majority of the time in regime 3, that is, in general, these are countries with a low tourism specialization and low economic development. With the exception of the Dominican Republic and South Africa (96% and 92%, respectively), all countries remained in regime 3 for the entire period.

Group D: Belize and the Maldives. This group is made up of the two countries that always remained in regime 4, that is, in general, these are countries with a high tourism specialization and low economic development.

Group E: Armenia, Moldova, Thailand, and Turkey. This group has the particular characteristic of having low tourism specialization throughout the period but alternating between a high level of development (regime 2) and a low level of development (regime 3).

Group F: Botswana, Jamaica, and Tunisia. This group is made up of countries that fundamentally remained in regime 4, that is, these are countries with a high tourism specialization and low economic development, however, unlike group D, they moved during the period analyzed through other regimes.

Outliers: Canada, Fiji, Saint Lucia, Sweden, Eswatini, and Samoa. These countries presented different dynamics and were not integrated into any of the previously-defined groups.

(Note: Pink: group A/Light blue: group B/Green: group C/Yellow: group D/Orange: group E/Blue: group F/Red: Outliers).

As can be seen, group A, which consists of countries with a high tourism specialization and high economic development, is basically made up of European countries, some Asian countries, and Uruguay (the only country in the Americas to be part of this group).

The countries in group B, that is, those countries with a good level of economic development, but a low specialization in the sector, are more geographically dispersed. This group consists of some European countries (in particular, Eastern European countries), a large part of Latin America and the Caribbean, as well as the US, Australia, and some Asian countries.

Group C, that is, those countries with a low tourism specialization and low economic development, consists of the vast majority of African countries, as well as a significant number of Asian countries, in addition to Bolivia and Paraguay in Latin America, as well as some countries in Central America.

The countries in Group D, that is, those countries that had a high tourism specialization but a low level of economic development throughout the period analyzed, as well as those in Group F, which were also in this regime for most of the period, do not have a uniform geographic pattern, since they are located on different continents.

Finally, the countries in Group E, that is, those countries with a low tourism specialization and alternating levels of economic development, are also geographically dispersed between Europe and Asia.

As can be seen in Table 3 both Group A and Group B are made up of countries with a high level of development; however, the countries in Group A, which also have a high level of tourism specialization, on average, have a significantly higher level of development than the countries in Group B, where the level of tourism specialization is low. These results appear to show that in terms of those countries specialized in tourism (Group A), the link with development is higher than for those countries that have achieved high levels of development due to the development of other economic activities.

Similar results can be found when comparing the data from Group C (countries with a low level of tourism specialization and low level of economic development) with the data from Groups D and F (countries with a high level of tourism specialization and low level of economic development). This is because, despite the level of development being low in all the countries, in the Group D and F countries, the level of development is significantly higher than in Group C countries. This appears to show that for those countries specialized in tourism (Groups D and F), the link with development is greater than for those countries that rely on other sectors as the basis of their economy.

Tourism’s relevance lies not only in its contribution to economic growth but also in the fact that the improved economic growth generated by the expansion of tourism activity may translate into improved living conditions for the host population. Due to this chained process, many countries have opted for this economic activity with the aim of improving income, education, and health. In short, they hope to increase their levels of human development.

Although distinct works have analyzed the relationship between tourism and human development by applying causality tests to determine the type of relationship between these variables, this study adopts a different approach. It analyzes the qualitative dynamic behavior between tourism and human development, to identify clusters of countries that display similar behavior with regard to this relationship.

Firstly, it is necessary to note the little movement there is of the countries between the different regimes, which indicates great stability, given that 80 countries (two-thirds of the sample) remained in the same regime throughout the entire period analyzed (1995–2019). These results regarding the stability of the countries in the different regimes differ significantly from the results obtained in other studies that have used the same technique for the analysis of the dynamic relationship between variables (Brida et al., 2020 ). This is because even when there is a movement of the countries between regimes, this happens, at most, between two or three regimes (Jordan and Samoa are the only exceptions, passing through all four regimes).

Furthermore, the results appear to show that groups of countries with a higher level of tourism specialization have higher levels of human development. Therefore, tourism is configured as an effective tool to improve development levels, as previously stated in works such as that of Cárdenas-García et al. ( 2015 ) conducting a joint analysis with data from 144 countries or Bojanic and Lo ( 2016 ), whose global analysis referred to a sample of 187 countries.

Specifically, these results are found both in the group of countries with the highest level of development, (countries of Group A versus the countries of Group B), as previously revealed in works such as that of Meyer and Meyer ( 2016 ) analyzing South Africa and that of Tan et al. ( 2019 ) analyzing Malaysia. These results were also found in the case of countries with a lower level of development (countries of Group D and F as compared to the countries of Group C), as previously suggested by works, such as that of Sharma et al. ( 2020 ) examining India or Croes ( 2012 ) analyzing Nicaragua.

However, despite these majority results, countries have been identified that, despite having an important tourism specialization (Belize, Botswana, Jamaica, Maldives, and Tunisia), had a low level of human development. This has not allowed for the high level of tourism specialization to become a tool to improve the living conditions of the population in these countries.

This exception may be due to the link between tourism and human development, which, in addition to being affected by the level of tourism specialization, also depends on the destination’s characteristics. These characteristics include the provision of infrastructure, the level of education, and the existing investment climate in the receiving countries, as previously suggested by Cárdenas-García and Pulido-Fernández ( 2019 ), or by the level of economic growth, the development of the urbanization process, or the degree of commercial openness of the receiving countries, as identified by Chattopadhyay et al. ( 2021 ).

Conclusions and policy implications

Distinct international organizations have shown that what is really important is not the contribution of tourism to economic growth, but rather, that this economic growth generated by the expansion of tourism activity permits the improvement of living conditions of the host population (EC, 2018 ; IADB - Inter-American Development Bank ( 2020 ); UNCTAD - United Nations Conference on Trade and Development ( 2020 )).

Given the importance of economic development for the host countries, empirical studies that analyze the relationship between tourism and economic development have begun to emerge. These works mainly link the multidimensional concept of development with human development, measured by the HDI. Here, the link between tourism and human development is produced through the economic growth generated by the expansion of tourism activity. This economic growth is used to develop policies to improve the host population’s education and health levels.

However, few such studies exist, and the scientific literature does not reveal a defined trend with regard to this relationship. Furthermore, most of these existing works rely on causality analyses to determine whether there is a relationship between tourism and human development. They do not analyze whether having a higher degree of tourism specialization, for groups of countries with similar levels of development, implies a higher level of human development, which would suggest that tourism promotes development to a greater extent than other economic activities.

Due to the methodology used, this empirical work cannot determine the type of relationship existing between tourism and development, that is, whether there is a unidirectional or bidirectional relationship between both variables. However, it does allow us to determine if countries with a higher level of tourism specialization have a higher level of development than those specializing in other productive activities.

This study aimed to contribute to the empirical discussion about the relationship between tourism and development through the use of a non-parametric and non-linear approach; specifically, the qualitative dynamic behavior of these two variables was compared using the definition of economic regime and clustering tools based on the concept of hierarchical and MST (Mantegna, 1999 ; Kruskal, 1956 ).

The results seem to indicate that tourism is an economic activity that can promote human development more than other economic activities. Indeed, at similar levels of human development, both in the case of countries with a high level of development (countries in Group A versus countries in Group B) and in the case of countries with a low level of development (countries in Groups D-F versus countries in Group C), the country groups with a higher level of tourism specialization have higher human development values than those countries specialized in other productive activities.

Therefore, public administrations should develop specific actions to increase the level of tourism specialization since tourism is a strategic tool that improves human development levels, as compared to other economic activities. It is necessary to invest in the improvement and expansion of tourism infrastructure, including the improvement of transportation systems in host destinations, increasing and improving the supply of accommodations and basic tourism-related services. Moreover, an attractive offer should be provided, both in terms of resources and attraction factors. This includes complementary services to attract a greater number of tourist flows, while developing destination promotion campaigns and, therefore, ensuring greater tourism specialization.

It should also be noted that, of the identified country groups, the most numerous one is that which includes countries from Group C, which is made up of 43 countries (approximately a third of the sample). This cluster is characterized by low tourism specialization and a low level of economic development, which seems to translate into a poverty trap, given that the low level of development prevents the expansion of the tourism activity, and, in turn, this lack of tourism development makes it difficult to increase the levels of development.

Policies should be developed that consider the lack of financial resources of these countries to carry out investment projects. International organizations and institutions linked to development, such as the United Nations Development Program, Inter-American Development Bank, or World Bank, should finance specific projects so that these countries may receive investments related to the improvement and expansion of tourism infrastructure, so as to improve human development through this activity. Suitable regulatory frameworks should be established in these countries, to encourage public-private collaboration for the development of tourism projects. In this way, private investments could make up for the lack of public financing in these destinations.

The analysis performed in this work has also identified groups of countries that, despite their high degree of tourism specialization, do not have high levels of human development (Belize, Botswana, Jamaica, Maldives, and Tunisia). This highlights the importance of identifying factors or characteristics that provide the destination with ideal initial conditions to permit the economic impacts generated by the expansion of tourism to be channeled into an improvement in human development. In addition to being conditioned by the host country’s level of tourism specialization, the link between tourism and human development also depends on infrastructure provision, education level, investment climate, urbanization level, and the degree of commercial openness. Although this current of scientific literature has not been widely studied, it has been addressed by some works analyzing the relationship between tourism and human development (Cárdenas-García and Pulido-Fernández, 2019 ; Chattopadhyay et al., 2021 ).

Policies established by public administrations should consider a dual objective: on the one hand, investing in the improvement and expansion of the tourism infrastructure and, on the other hand, increasing and improving the factors found to be determinant in configuring tourism as a tool for human development. Given that there are entities investing in projects linked to tourism aimed at improving the living conditions of the resident population, the failure to act on the determinant factors of this relationship could result in inefficient policies in terms of the allocation of resources linked to improved development.

Finally, this study has certain limitations, including the variables used to measure tourism specialization and economic development. With regard to tourism, it has been shown that changing the indicator used leads to differences in the results obtained. In terms of economic development, while other factors such as poverty level, quality of life, or income inequality are related to development, human development, and its measurement through HDI, is the most frequently used indicator to measure it. Moreover, the short period analyzed (1995–2019) is another limitation. There is a restriction in the initial period used since it is the first year in which data were available on development and this may determine the small variability between countries among the different regimes. Another limitation lies in the fact that it does not analyze the characteristics of the destination as a determinant in the relationship between tourism and human development, in accordance with the new current of the scientific literature. In terms of methodology, the choice of the measure used for the symbolization of the series can affect the results. For example, the mean may be influenced by outliers in the data, and this can be relevant for certain variables, such as tourism, which displays a high degree of variation. It would be interesting to perform the same exercise using other measures for the symbolization of the series, such as the truncated mean, the median, or some type of threshold.

Future lines of research may highlight the fact that this study consists of an analysis at the country level, although it is clear that the impacts of tourism are produced in the territory at the regional and local levels. As a result, it may be interesting to replicate this work at the regional level using different countries as an analysis, depending on the availability of such data.

Moreover, as a continuation of this study, in addition to the degree of tourism specialization, it may be interesting to analyze the type of tourism received by each of the groups of countries that have been identified. In other words, to examine whether the characteristics of the type of tourism received (accommodations, motivations, or level of expenditure) in each cluster also determine the relationship between tourism and human development. Furthermore, it may be interesting to introduce the influence of other factors on the relationship between tourism and development into the analysis of this relationship, as discussed previously in the limitations.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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Pablo Juan Cárdenas-García

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Cárdenas-García, P.J., Brida, J.G. & Segarra, V. A qualitative dynamic analysis of the relationship between tourism and human development. Humanit Soc Sci Commun 11 , 1125 (2024). https://doi.org/10.1057/s41599-024-03663-5

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DOI : https://doi.org/10.1057/s41599-024-03663-5

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