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Significance of the Study – Examples and Writing Guide

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Significance of the Study

Definition:

Significance of the study in research refers to the potential importance, relevance, or impact of the research findings. It outlines how the research contributes to the existing body of knowledge, what gaps it fills, or what new understanding it brings to a particular field of study.

In general, the significance of a study can be assessed based on several factors, including:

• Originality : The extent to which the study advances existing knowledge or introduces new ideas and perspectives.
• Practical relevance: The potential implications of the study for real-world situations, such as improving policy or practice.
• Theoretical contribution: The extent to which the study provides new insights or perspectives on theoretical concepts or frameworks.
• Methodological rigor : The extent to which the study employs appropriate and robust methods and techniques to generate reliable and valid data.
• Social or cultural impact : The potential impact of the study on society, culture, or public perception of a particular issue.

Types of Significance of the Study

The significance of the Study can be divided into the following types:

Theoretical Significance

Theoretical significance refers to the contribution that a study makes to the existing body of theories in a specific field. This could be by confirming, refuting, or adding nuance to a currently accepted theory, or by proposing an entirely new theory.

Practical Significance

Practical significance refers to the direct applicability and usefulness of the research findings in real-world contexts. Studies with practical significance often address real-life problems and offer potential solutions or strategies. For example, a study in the field of public health might identify a new intervention that significantly reduces the spread of a certain disease.

Significance for Future Research

This pertains to the potential of a study to inspire further research. A study might open up new areas of investigation, provide new research methodologies, or propose new hypotheses that need to be tested.

How to Write Significance of the Study

Here’s a guide to writing an effective “Significance of the Study” section in research paper, thesis, or dissertation:

• Background : Begin by giving some context about your study. This could include a brief introduction to your subject area, the current state of research in the field, and the specific problem or question your study addresses.
• Identify the Gap : Demonstrate that there’s a gap in the existing literature or knowledge that needs to be filled, which is where your study comes in. The gap could be a lack of research on a particular topic, differing results in existing studies, or a new problem that has arisen and hasn’t yet been studied.
• State the Purpose of Your Study : Clearly state the main objective of your research. You may want to state the purpose as a solution to the problem or gap you’ve previously identified.
• Contributes to the existing body of knowledge.
• Addresses a significant research gap.
• Offers a new or better solution to a problem.
• Impacts policy or practice.
• Leads to improvements in a particular field or sector.
• Identify Beneficiaries : Identify who will benefit from your study. This could include other researchers, practitioners in your field, policy-makers, communities, businesses, or others. Explain how your findings could be used and by whom.
• Future Implications : Discuss the implications of your study for future research. This could involve questions that are left open, new questions that have been raised, or potential future methodologies suggested by your study.

Significance of the Study in Research Paper

The Significance of the Study in a research paper refers to the importance or relevance of the research topic being investigated. It answers the question “Why is this research important?” and highlights the potential contributions and impacts of the study.

The significance of the study can be presented in the introduction or background section of a research paper. It typically includes the following components:

• Importance of the research problem: This describes why the research problem is worth investigating and how it relates to existing knowledge and theories.
• Potential benefits and implications: This explains the potential contributions and impacts of the research on theory, practice, policy, or society.
• Originality and novelty: This highlights how the research adds new insights, approaches, or methods to the existing body of knowledge.
• Scope and limitations: This outlines the boundaries and constraints of the research and clarifies what the study will and will not address.

Suppose a researcher is conducting a study on the “Effects of social media use on the mental health of adolescents”.

The significance of the study may be:

“The present study is significant because it addresses a pressing public health issue of the negative impact of social media use on adolescent mental health. Given the widespread use of social media among this age group, understanding the effects of social media on mental health is critical for developing effective prevention and intervention strategies. This study will contribute to the existing literature by examining the moderating factors that may affect the relationship between social media use and mental health outcomes. It will also shed light on the potential benefits and risks of social media use for adolescents and inform the development of evidence-based guidelines for promoting healthy social media use among this population. The limitations of this study include the use of self-reported measures and the cross-sectional design, which precludes causal inference.”

Significance of the Study In Thesis

The significance of the study in a thesis refers to the importance or relevance of the research topic and the potential impact of the study on the field of study or society as a whole. It explains why the research is worth doing and what contribution it will make to existing knowledge.

For example, the significance of a thesis on “Artificial Intelligence in Healthcare” could be:

• With the increasing availability of healthcare data and the development of advanced machine learning algorithms, AI has the potential to revolutionize the healthcare industry by improving diagnosis, treatment, and patient outcomes. Therefore, this thesis can contribute to the understanding of how AI can be applied in healthcare and how it can benefit patients and healthcare providers.
• AI in healthcare also raises ethical and social issues, such as privacy concerns, bias in algorithms, and the impact on healthcare jobs. By exploring these issues in the thesis, it can provide insights into the potential risks and benefits of AI in healthcare and inform policy decisions.
• Finally, the thesis can also advance the field of computer science by developing new AI algorithms or techniques that can be applied to healthcare data, which can have broader applications in other industries or fields of research.

Significance of the Study in Research Proposal

The significance of a study in a research proposal refers to the importance or relevance of the research question, problem, or objective that the study aims to address. It explains why the research is valuable, relevant, and important to the academic or scientific community, policymakers, or society at large. A strong statement of significance can help to persuade the reviewers or funders of the research proposal that the study is worth funding and conducting.

Here is an example of a significance statement in a research proposal:

Title : The Effects of Gamification on Learning Programming: A Comparative Study

Significance Statement:

This proposed study aims to investigate the effects of gamification on learning programming. With the increasing demand for computer science professionals, programming has become a fundamental skill in the computer field. However, learning programming can be challenging, and students may struggle with motivation and engagement. Gamification has emerged as a promising approach to improve students’ engagement and motivation in learning, but its effects on programming education are not yet fully understood. This study is significant because it can provide valuable insights into the potential benefits of gamification in programming education and inform the development of effective teaching strategies to enhance students’ learning outcomes and interest in programming.

Examples of Significance of the Study

Here are some examples of the significance of a study that indicates how you can write this into your research paper according to your research topic:

Research on an Improved Water Filtration System : This study has the potential to impact millions of people living in water-scarce regions or those with limited access to clean water. A more efficient and affordable water filtration system can reduce water-borne diseases and improve the overall health of communities, enabling them to lead healthier, more productive lives.

Study on the Impact of Remote Work on Employee Productivity : Given the shift towards remote work due to recent events such as the COVID-19 pandemic, this study is of considerable significance. Findings could help organizations better structure their remote work policies and offer insights on how to maximize employee productivity, wellbeing, and job satisfaction.

Investigation into the Use of Solar Power in Developing Countries : With the world increasingly moving towards renewable energy, this study could provide important data on the feasibility and benefits of implementing solar power solutions in developing countries. This could potentially stimulate economic growth, reduce reliance on non-renewable resources, and contribute to global efforts to combat climate change.

Research on New Learning Strategies in Special Education : This study has the potential to greatly impact the field of special education. By understanding the effectiveness of new learning strategies, educators can improve their curriculum to provide better support for students with learning disabilities, fostering their academic growth and social development.

Examination of Mental Health Support in the Workplace : This study could highlight the impact of mental health initiatives on employee wellbeing and productivity. It could influence organizational policies across industries, promoting the implementation of mental health programs in the workplace, ultimately leading to healthier work environments.

Evaluation of a New Cancer Treatment Method : The significance of this study could be lifesaving. The research could lead to the development of more effective cancer treatments, increasing the survival rate and quality of life for patients worldwide.

When to Write Significance of the Study

The Significance of the Study section is an integral part of a research proposal or a thesis. This section is typically written after the introduction and the literature review. In the research process, the structure typically follows this order:

• Title – The name of your research.
• Abstract – A brief summary of the entire research.
• Introduction – A presentation of the problem your research aims to solve.
• Literature Review – A review of existing research on the topic to establish what is already known and where gaps exist.
• Significance of the Study – An explanation of why the research matters and its potential impact.

In the Significance of the Study section, you will discuss why your study is important, who it benefits, and how it adds to existing knowledge or practice in your field. This section is your opportunity to convince readers, and potentially funders or supervisors, that your research is valuable and worth undertaking.

Advantages of Significance of the Study

The Significance of the Study section in a research paper has multiple advantages:

• Establishes Relevance: This section helps to articulate the importance of your research to your field of study, as well as the wider society, by explicitly stating its relevance. This makes it easier for other researchers, funders, and policymakers to understand why your work is necessary and worth supporting.
• Guides the Research: Writing the significance can help you refine your research questions and objectives. This happens as you critically think about why your research is important and how it contributes to your field.
• Attracts Funding: If you are seeking funding or support for your research, having a well-written significance of the study section can be key. It helps to convince potential funders of the value of your work.
• Opens up Further Research: By stating the significance of the study, you’re also indicating what further research could be carried out in the future, based on your work. This helps to pave the way for future studies and demonstrates that your research is a valuable addition to the field.
• Provides Practical Applications: The significance of the study section often outlines how the research can be applied in real-world situations. This can be particularly important in applied sciences, where the practical implications of research are crucial.
• Enhances Understanding: This section can help readers understand how your study fits into the broader context of your field, adding value to the existing literature and contributing new knowledge or insights.

Limitations of Significance of the Study

The Significance of the Study section plays an essential role in any research. However, it is not without potential limitations. Here are some that you should be aware of:

• Subjectivity: The importance and implications of a study can be subjective and may vary from person to person. What one researcher considers significant might be seen as less critical by others. The assessment of significance often depends on personal judgement, biases, and perspectives.
• Predictability of Impact: While you can outline the potential implications of your research in the Significance of the Study section, the actual impact can be unpredictable. Research doesn’t always yield the expected results or have the predicted impact on the field or society.
• Difficulty in Measuring: The significance of a study is often qualitative and can be challenging to measure or quantify. You can explain how you think your research will contribute to your field or society, but measuring these outcomes can be complex.
• Possibility of Overstatement: Researchers may feel pressured to amplify the potential significance of their study to attract funding or interest. This can lead to overstating the potential benefits or implications, which can harm the credibility of the study if these results are not achieved.
• Overshadowing of Limitations: Sometimes, the significance of the study may overshadow the limitations of the research. It is important to balance the potential significance with a thorough discussion of the study’s limitations.
• Dependence on Successful Implementation: The significance of the study relies on the successful implementation of the research. If the research process has flaws or unexpected issues arise, the anticipated significance might not be realized.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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How To Write Significance of the Study (With Examples) 

Whether you’re writing a research paper or thesis, a portion called Significance of the Study ensures your readers understand the impact of your work. Learn how to effectively write this vital part of your research paper or thesis through our detailed steps, guidelines, and examples.

Related: How to Write a Concept Paper for Academic Research

Table of Contents

What is the significance of the study.

The Significance of the Study presents the importance of your research. It allows you to prove the study’s impact on your field of research, the new knowledge it contributes, and the people who will benefit from it.

Related: How To Write Scope and Delimitation of a Research Paper (With Examples)

Where Should I Put the Significance of the Study?

The Significance of the Study is part of the first chapter or the Introduction. It comes after the research’s rationale, problem statement, and hypothesis.

Related: How to Make Conceptual Framework (with Examples and Templates)

Why Should I Include the Significance of the Study?

The purpose of the Significance of the Study is to give you space to explain to your readers how exactly your research will be contributing to the literature of the field you are studying 1 . It’s where you explain why your research is worth conducting and its significance to the community, the people, and various institutions.

How To Write Significance of the Study: 5 Steps

Below are the steps and guidelines for writing your research’s Significance of the Study.

1. Use Your Research Problem as a Starting Point

Your problem statement can provide clues to your research study’s outcome and who will benefit from it 2 .

Ask yourself, “How will the answers to my research problem be beneficial?”. In this manner, you will know how valuable it is to conduct your study. 

Let’s say your research problem is “What is the level of effectiveness of the lemongrass (Cymbopogon citratus) in lowering the blood glucose level of Swiss mice (Mus musculus)?”

Discovering a positive correlation between the use of lemongrass and lower blood glucose level may lead to the following results:

• Increased public understanding of the plant’s medical properties;
• Higher appreciation of the importance of lemongrass  by the community;
• Adoption of lemongrass tea as a cheap, readily available, and natural remedy to lower their blood glucose level.

Once you’ve zeroed in on the general benefits of your study, it’s time to break it down into specific beneficiaries.

2. State How Your Research Will Contribute to the Existing Literature in the Field

Think of the things that were not explored by previous studies. Then, write how your research tackles those unexplored areas. Through this, you can convince your readers that you are studying something new and adding value to the field.

3. Explain How Your Research Will Benefit Society

In this part, tell how your research will impact society. Think of how the results of your study will change something in your community. 

For example, in the study about using lemongrass tea to lower blood glucose levels, you may indicate that through your research, the community will realize the significance of lemongrass and other herbal plants. As a result, the community will be encouraged to promote the cultivation and use of medicinal plants.

4. Mention the Specific Persons or Institutions Who Will Benefit From Your Study

Using the same example above, you may indicate that this research’s results will benefit those seeking an alternative supplement to prevent high blood glucose levels.

5. Indicate How Your Study May Help Future Studies in the Field

You must also specifically indicate how your research will be part of the literature of your field and how it will benefit future researchers. In our example above, you may indicate that through the data and analysis your research will provide, future researchers may explore other capabilities of herbal plants in preventing different diseases.

Tips and Warnings

• Think ahead . By visualizing your study in its complete form, it will be easier for you to connect the dots and identify the beneficiaries of your research.
• Write concisely. Make it straightforward, clear, and easy to understand so that the readers will appreciate the benefits of your research. Avoid making it too long and wordy.
• Go from general to specific . Like an inverted pyramid, you start from above by discussing the general contribution of your study and become more specific as you go along. For instance, if your research is about the effect of remote learning setup on the mental health of college students of a specific university , you may start by discussing the benefits of the research to society, to the educational institution, to the learning facilitators, and finally, to the students.
• Seek help . For example, you may ask your research adviser for insights on how your research may contribute to the existing literature. If you ask the right questions, your research adviser can point you in the right direction.
• Revise, revise, revise. Be ready to apply necessary changes to your research on the fly. Unexpected things require adaptability, whether it’s the respondents or variables involved in your study. There’s always room for improvement, so never assume your work is done until you have reached the finish line.

Significance of the Study Examples

This section presents examples of the Significance of the Study using the steps and guidelines presented above.

Example 1: STEM-Related Research

Research Topic: Level of Effectiveness of the Lemongrass ( Cymbopogon citratus ) Tea in Lowering the Blood Glucose Level of Swiss Mice ( Mus musculus ).

Significance of the Study .

This research will provide new insights into the medicinal benefit of lemongrass ( Cymbopogon citratus ), specifically on its hypoglycemic ability.

Through this research, the community will further realize promoting medicinal plants, especially lemongrass, as a preventive measure against various diseases. People and medical institutions may also consider lemongrass tea as an alternative supplement against hyperglycemia. 

Moreover, the analysis presented in this study will convey valuable information for future research exploring the medicinal benefits of lemongrass and other medicinal plants.  

Example 2: Business and Management-Related Research

Research Topic: A Comparative Analysis of Traditional and Social Media Marketing of Small Clothing Enterprises.

Significance of the Study:

By comparing the two marketing strategies presented by this research, there will be an expansion on the current understanding of the firms on these marketing strategies in terms of cost, acceptability, and sustainability. This study presents these marketing strategies for small clothing enterprises, giving them insights into which method is more appropriate and valuable for them. 

Specifically, this research will benefit start-up clothing enterprises in deciding which marketing strategy they should employ. Long-time clothing enterprises may also consider the result of this research to review their current marketing strategy.

Furthermore, a detailed presentation on the comparison of the marketing strategies involved in this research may serve as a tool for further studies to innovate the current method employed in the clothing Industry.

Example 3: Social Science -Related Research.

Research Topic:  Divide Et Impera : An Overview of How the Divide-and-Conquer Strategy Prevailed on Philippine Political History.

Significance of the Study :

Through the comprehensive exploration of this study on Philippine political history, the influence of the Divide et Impera, or political decentralization, on the political discernment across the history of the Philippines will be unraveled, emphasized, and scrutinized. Moreover, this research will elucidate how this principle prevailed until the current political theatre of the Philippines.

In this regard, this study will give awareness to society on how this principle might affect the current political context. Moreover, through the analysis made by this study, political entities and institutions will have a new approach to how to deal with this principle by learning about its influence in the past.

In addition, the overview presented in this research will push for new paradigms, which will be helpful for future discussion of the Divide et Impera principle and may lead to a more in-depth analysis.

Example 4: Humanities-Related Research

Research Topic: Effectiveness of Meditation on Reducing the Anxiety Levels of College Students.

Significance of the Study: 

This research will provide new perspectives in approaching anxiety issues of college students through meditation. 

Specifically, this research will benefit the following:

 Community – this study spreads awareness on recognizing anxiety as a mental health concern and how meditation can be a valuable approach to alleviating it.

Academic Institutions and Administrators – through this research, educational institutions and administrators may promote programs and advocacies regarding meditation to help students deal with their anxiety issues.

Mental health advocates – the result of this research will provide valuable information for the advocates to further their campaign on spreading awareness on dealing with various mental health issues, including anxiety, and how to stop stigmatizing those with mental health disorders.

Parents – this research may convince parents to consider programs involving meditation that may help the students deal with their anxiety issues.

Students will benefit directly from this research as its findings may encourage them to consider meditation to lower anxiety levels.

Future researchers – this study covers information involving meditation as an approach to reducing anxiety levels. Thus, the result of this study can be used for future discussions on the capabilities of meditation in alleviating other mental health concerns.

Frequently Asked Questions

1. what is the difference between the significance of the study and the rationale of the study.

Both aim to justify the conduct of the research. However, the Significance of the Study focuses on the specific benefits of your research in the field, society, and various people and institutions. On the other hand, the Rationale of the Study gives context on why the researcher initiated the conduct of the study.

Let’s take the research about the Effectiveness of Meditation in Reducing Anxiety Levels of College Students as an example. Suppose you are writing about the Significance of the Study. In that case, you must explain how your research will help society, the academic institution, and students deal with anxiety issues through meditation. Meanwhile, for the Rationale of the Study, you may state that due to the prevalence of anxiety attacks among college students, you’ve decided to make it the focal point of your research work.

2. What is the difference between Justification and the Significance of the Study?

In Justification, you express the logical reasoning behind the conduct of the study. On the other hand, the Significance of the Study aims to present to your readers the specific benefits your research will contribute to the field you are studying, community, people, and institutions.

Suppose again that your research is about the Effectiveness of Meditation in Reducing the Anxiety Levels of College Students. Suppose you are writing the Significance of the Study. In that case, you may state that your research will provide new insights and evidence regarding meditation’s ability to reduce college students’ anxiety levels. Meanwhile, you may note in the Justification that studies are saying how people used meditation in dealing with their mental health concerns. You may also indicate how meditation is a feasible approach to managing anxiety using the analysis presented by previous literature.

3. How should I start my research’s Significance of the Study section?

– This research will contribute… – The findings of this research… – This study aims to… – This study will provide… – Through the analysis presented in this study… – This study will benefit…

Moreover, you may start the Significance of the Study by elaborating on the contribution of your research in the field you are studying.

4. What is the difference between the Purpose of the Study and the Significance of the Study?

The Purpose of the Study focuses on why your research was conducted, while the Significance of the Study tells how the results of your research will benefit anyone.

Suppose your research is about the Effectiveness of Lemongrass Tea in Lowering the Blood Glucose Level of Swiss Mice . You may include in your Significance of the Study that the research results will provide new information and analysis on the medical ability of lemongrass to solve hyperglycemia. Meanwhile, you may include in your Purpose of the Study that your research wants to provide a cheaper and natural way to lower blood glucose levels since commercial supplements are expensive.

5. What is the Significance of the Study in Tagalog?

In Filipino research, the Significance of the Study is referred to as Kahalagahan ng Pag-aaral.

• Draft your Significance of the Study. Retrieved 18 April 2021, from http://dissertationedd.usc.edu/draft-your-significance-of-the-study.html
• Regoniel, P. (2015). Two Tips on How to Write the Significance of the Study. Retrieved 18 April 2021, from https://simplyeducate.me/2015/02/09/significance-of-the-study/

Written by Jewel Kyle Fabula

in Career and Education , Juander How

Jewel Kyle Fabula

Jewel Kyle Fabula is a Bachelor of Science in Economics student at the University of the Philippines Diliman. His passion for learning mathematics developed as he competed in some mathematics competitions during his Junior High School years. He loves cats, playing video games, and listening to music.

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How To Write a Significance Statement for Your Research

A significance statement is an essential part of a research paper. It explains the importance and relevance of the study to the academic community and the world at large. To write a compelling significance statement, identify the research problem, explain why it is significant, provide evidence of its importance, and highlight its potential impact on future research, policy, or practice. A well-crafted significance statement should effectively communicate the value of the research to readers and help them understand why it matters.

Updated on May 4, 2023

A significance statement is a clearly stated, non-technical paragraph that explains why your research matters. It’s central in making the public aware of and gaining support for your research.

Write it in jargon-free language that a reader from any field can understand. Well-crafted, easily readable significance statements can improve your chances for citation and impact and make it easier for readers outside your field to find and understand your work.

Read on for more details on what a significance statement is, how it can enhance the impact of your research, and, of course, how to write one.

What is a significance statement in research?

A significance statement answers the question: How will your research advance scientific knowledge and impact society at large (as well as specific populations)? 

You might also see it called a “Significance of the study” statement. Some professional organizations in the STEM sciences and social sciences now recommended that journals in their disciplines make such statements a standard feature of each published article. Funding agencies also consider “significance” a key criterion for their awards.

Read some examples of significance statements from the Proceedings of the National Academy of Sciences (PNAS) here .

Depending upon the specific journal or funding agency’s requirements, your statement may be around 100 words and answer these questions:

1. What’s the purpose of this research?

2. What are its key findings?

3. Why do they matter?

4. Who benefits from the research results?

Readers will want to know: “What is interesting or important about this research?” Keep asking yourself that question.

Where to place the significance statement in your manuscript

Most journals ask you to place the significance statement before or after the abstract, so check with each journal’s guide. 

This article is focused on the formal significance statement, even though you’ll naturally highlight your project’s significance elsewhere in your manuscript. (In the introduction, you’ll set out your research aims, and in the conclusion, you’ll explain the potential applications of your research and recommend areas for future research. You’re building an overall case for the value of your work.)

Developing the significance statement

The main steps in planning and developing your statement are to assess the gaps to which your study contributes, and then define your work’s implications and impact.

Identify what gaps your study fills and what it contributes

Your literature review was a big part of how you planned your study. To develop your research aims and objectives, you identified gaps or unanswered questions in the preceding research and designed your study to address them.

Go back to that lit review and look at those gaps again. Review your research proposal to refresh your memory. Ask:

• How have my research findings advanced knowledge or provided notable new insights?
• How has my research helped to prove (or disprove) a hypothesis or answer a research question?
• Why are those results important?

Consider your study’s potential impact at two levels: 

• What contribution does my research make to my field?
• How does it specifically contribute to knowledge; that is, who will benefit the most from it?

Define the implications and potential impact

As you make notes, keep the reasons in mind for why you are writing this statement. Whom will it impact, and why?

The first audience for your significance statement will be journal reviewers when you submit your article for publishing. Many journals require one for manuscript submissions. Study the author’s guide of your desired journal to see its criteria ( here’s an example ). Peer reviewers who can clearly understand the value of your research will be more likely to recommend publication. 

Second, when you apply for funding, your significance statement will help justify why your research deserves a grant from a funding agency . The U.S. National Institutes of Health (NIH), for example, wants to see that a project will “exert a sustained, powerful influence on the research field(s) involved.” Clear, simple language is always valuable because not all reviewers will be specialists in your field.

Third, this concise statement about your study’s importance can affect how potential readers engage with your work. Science journalists and interested readers can promote and spread your work, enhancing your reputation and influence. Help them understand your work.

You’re now ready to express the importance of your research clearly and concisely. Time to start writing.

How to write a significance statement: Key elements 

When drafting your statement, focus on both the content and writing style.

• In terms of content, emphasize the importance, timeliness, and relevance of your research results. 
• Write the statement in plain, clear language rather than scientific or technical jargon. Your audience will include not just your fellow scientists but also non-specialists like journalists, funding reviewers, and members of the public. 

Follow the process we outline below to build a solid, well-crafted, and informative statement. 

Get started

Some suggested opening lines to help you get started might be:

• The implications of this study are… 
• Building upon previous contributions, our study moves the field forward because…
• Our study furthers previous understanding about…

Alternatively, you may start with a statement about the phenomenon you’re studying, leading to the problem statement.

Include these components

Next, draft some sentences that include the following elements. A good example, which we’ll use here, is a significance statement by Rogers et al. (2022) published in the Journal of Climate .

1. Briefly situate your research study in its larger context . Start by introducing the topic, leading to a problem statement. Here’s an example:

‘Heatwaves pose a major threat to human health, ecosystems, and human systems.”

2. State the research problem.

“Simultaneous heatwaves affecting multiple regions can exacerbate such threats. For example, multiple food-producing regions simultaneously undergoing heat-related crop damage could drive global food shortages.”

3. Tell what your study does to address it.

“We assess recent changes in the occurrence of simultaneous large heatwaves.”

4. Provide brief but powerful evidence to support the claims your statement is making , Use quantifiable terms rather than vague ones (e.g., instead of “This phenomenon is happening now more than ever,” see below how Rogers et al. (2022) explained it). This evidence intensifies and illustrates the problem more vividly:

“Such simultaneous heatwaves are 7 times more likely now than 40 years ago. They are also hotter and affect a larger area. Their increasing occurrence is mainly driven by warming baseline temperatures due to global heating, but changes in weather patterns contribute to disproportionate increases over parts of Europe, the eastern United States, and Asia.

5. Relate your study’s impact to the broader context , starting with its general significance to society—then, when possible, move to the particular as you name specific applications of your research findings. (Our example lacks this second level of application.) 

“Better understanding the drivers of weather pattern changes is therefore important for understanding future concurrent heatwave characteristics and their impacts.”

Refine your English

Don’t understate or overstate your findings – just make clear what your study contributes. When you have all the elements in place, review your draft to simplify and polish your language. Even better, get an expert AJE edit . Be sure to use “plain” language rather than academic jargon.

• Avoid acronyms, scientific jargon, and technical terms 
• Use active verbs in your sentence structure rather than passive voice (e.g., instead of “It was found that...”, use “We found...”)
• Make sentence structures short, easy to understand – readable
• Try to address only one idea in each sentence and keep sentences within 25 words (15 words is even better)
• Eliminate nonessential words and phrases (“fluff” and wordiness)

Enhance your significance statement’s impact

Always take time to review your draft multiple times. Make sure that you:

• Keep your language focused
• Provide evidence to support your claims
• Relate the significance to the broader research context in your field

After revising your significance statement, request feedback from a reading mentor about how to make it even clearer. If you’re not a native English speaker, seek help from a native-English-speaking colleague or use an editing service like AJE to make sure your work is at a native level.

Understanding the significance of your study

Your readers may have much less interest than you do in the specific details of your research methods and measures. Many readers will scan your article to learn how your findings might apply to them and their own research. 

Different types of significance

Your findings may have different types of significance, relevant to different populations or fields of study for different reasons. You can emphasize your work’s statistical, clinical, or practical significance. Editors or reviewers in the social sciences might also evaluate your work’s social or political significance.

Statistical significance means that the results are unlikely to have occurred randomly. Instead, it implies a true cause-and-effect relationship.

Clinical significance means that your findings are applicable for treating patients and improving quality of life.

Practical significance is when your research outcomes are meaningful to society at large, in the “real world.” Practical significance is usually measured by the study’s  effect size . Similarly, evaluators may attribute social or political significance to research that addresses “real and immediate” social problems.

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What is the Significance of the Study?

• By DiscoverPhDs
• August 25, 2020

• what the significance of the study means,
• why it’s important to include in your research work,
• where you would include it in your paper, thesis or dissertation,
• how you write one
• and finally an example of a well written section about the significance of the study.

What does Significance of the Study mean?

The significance of the study is a written statement that explains why your research was needed. It’s a justification of the importance of your work and impact it has on your research field, it’s contribution to new knowledge and how others will benefit from it.

Why is the Significance of the Study important?

The significance of the study, also known as the rationale of the study, is important to convey to the reader why the research work was important. This may be an academic reviewer assessing your manuscript under peer-review, an examiner reading your PhD thesis, a funder reading your grant application or another research group reading your published journal paper. Your academic writing should make clear to the reader what the significance of the research that you performed was, the contribution you made and the benefits of it.

How do you write the Significance of the Study?

When writing this section, first think about where the gaps in knowledge are in your research field. What are the areas that are poorly understood with little or no previously published literature? Or what topics have others previously published on that still require further work. This is often referred to as the problem statement.

The introduction section within the significance of the study should include you writing the problem statement and explaining to the reader where the gap in literature is.

Then think about the significance of your research and thesis study from two perspectives: (1) what is the general contribution of your research on your field and (2) what specific contribution have you made to the knowledge and who does this benefit the most.

For example, the gap in knowledge may be that the benefits of dumbbell exercises for patients recovering from a broken arm are not fully understood. You may have performed a study investigating the impact of dumbbell training in patients with fractures versus those that did not perform dumbbell exercises and shown there to be a benefit in their use. The broad significance of the study would be the improvement in the understanding of effective physiotherapy methods. Your specific contribution has been to show a significant improvement in the rate of recovery in patients with broken arms when performing certain dumbbell exercise routines.

This statement should be no more than 500 words in length when written for a thesis. Within a research paper, the statement should be shorter and around 200 words at most.

Significance of the Study: An example

Building on the above hypothetical academic study, the following is an example of a full statement of the significance of the study for you to consider when writing your own. Keep in mind though that there’s no single way of writing the perfect significance statement and it may well depend on the subject area and the study content.

Here’s another example to help demonstrate how a significance of the study can also be applied to non-technical fields:

The significance of this research lies in its potential to inform clinical practices and patient counseling. By understanding the psychological outcomes associated with non-surgical facial aesthetics, practitioners can better guide their patients in making informed decisions about their treatment plans. Additionally, this study contributes to the body of academic knowledge by providing empirical evidence on the effects of these cosmetic procedures, which have been largely anecdotal up to this point.

The statement of the significance of the study is used by students and researchers in academic writing to convey the importance of the research performed; this section is written at the end of the introduction and should describe the specific contribution made and who it benefits.

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Chapter 5. Sampling

Introduction.

Most Americans will experience unemployment at some point in their lives. Sarah Damaske ( 2021 ) was interested in learning about how men and women experience unemployment differently. To answer this question, she interviewed unemployed people. After conducting a “pilot study” with twenty interviewees, she realized she was also interested in finding out how working-class and middle-class persons experienced unemployment differently. She found one hundred persons through local unemployment offices. She purposefully selected a roughly equal number of men and women and working-class and middle-class persons for the study. This would allow her to make the kinds of comparisons she was interested in. She further refined her selection of persons to interview:

I decided that I needed to be able to focus my attention on gender and class; therefore, I interviewed only people born between 1962 and 1987 (ages 28–52, the prime working and child-rearing years), those who worked full-time before their job loss, those who experienced an involuntary job loss during the past year, and those who did not lose a job for cause (e.g., were not fired because of their behavior at work). ( 244 )

The people she ultimately interviewed compose her sample. They represent (“sample”) the larger population of the involuntarily unemployed. This “theoretically informed stratified sampling design” allowed Damaske “to achieve relatively equal distribution of participation across gender and class,” but it came with some limitations. For one, the unemployment centers were located in primarily White areas of the country, so there were very few persons of color interviewed. Qualitative researchers must make these kinds of decisions all the time—who to include and who not to include. There is never an absolutely correct decision, as the choice is linked to the particular research question posed by the particular researcher, although some sampling choices are more compelling than others. In this case, Damaske made the choice to foreground both gender and class rather than compare all middle-class men and women or women of color from different class positions or just talk to White men. She leaves the door open for other researchers to sample differently. Because science is a collective enterprise, it is most likely someone will be inspired to conduct a similar study as Damaske’s but with an entirely different sample.

This chapter is all about sampling. After you have developed a research question and have a general idea of how you will collect data (observations or interviews), how do you go about actually finding people and sites to study? Although there is no “correct number” of people to interview, the sample should follow the research question and research design. You might remember studying sampling in a quantitative research course. Sampling is important here too, but it works a bit differently. Unlike quantitative research, qualitative research involves nonprobability sampling. This chapter explains why this is so and what qualities instead make a good sample for qualitative research.

Quick Terms Refresher

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.
• Sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).
• Sample size is how many individuals (or units) are included in your sample.

The “Who” of Your Research Study

After you have turned your general research interest into an actual research question and identified an approach you want to take to answer that question, you will need to specify the people you will be interviewing or observing. In most qualitative research, the objects of your study will indeed be people. In some cases, however, your objects might be content left by people (e.g., diaries, yearbooks, photographs) or documents (official or unofficial) or even institutions (e.g., schools, medical centers) and locations (e.g., nation-states, cities). Chances are, whatever “people, places, or things” are the objects of your study, you will not really be able to talk to, observe, or follow every single individual/object of the entire population of interest. You will need to create a sample of the population . Sampling in qualitative research has different purposes and goals than sampling in quantitative research. Sampling in both allows you to say something of interest about a population without having to include the entire population in your sample.

We begin this chapter with the case of a population of interest composed of actual people. After we have a better understanding of populations and samples that involve real people, we’ll discuss sampling in other types of qualitative research, such as archival research, content analysis, and case studies. We’ll then move to a larger discussion about the difference between sampling in qualitative research generally versus quantitative research, then we’ll move on to the idea of “theoretical” generalizability, and finally, we’ll conclude with some practical tips on the correct “number” to include in one’s sample.

Sampling People

To help think through samples, let’s imagine we want to know more about “vaccine hesitancy.” We’ve all lived through 2020 and 2021, and we know that a sizable number of people in the United States (and elsewhere) were slow to accept vaccines, even when these were freely available. By some accounts, about one-third of Americans initially refused vaccination. Why is this so? Well, as I write this in the summer of 2021, we know that some people actively refused the vaccination, thinking it was harmful or part of a government plot. Others were simply lazy or dismissed the necessity. And still others were worried about harmful side effects. The general population of interest here (all adult Americans who were not vaccinated by August 2021) may be as many as eighty million people. We clearly cannot talk to all of them. So we will have to narrow the number to something manageable. How can we do this?

First, we have to think about our actual research question and the form of research we are conducting. I am going to begin with a quantitative research question. Quantitative research questions tend to be simpler to visualize, at least when we are first starting out doing social science research. So let us say we want to know what percentage of each kind of resistance is out there and how race or class or gender affects vaccine hesitancy. Again, we don’t have the ability to talk to everyone. But harnessing what we know about normal probability distributions (see quantitative methods for more on this), we can find this out through a sample that represents the general population. We can’t really address these particular questions if we only talk to White women who go to college with us. And if you are really trying to generalize the specific findings of your sample to the larger population, you will have to employ probability sampling , a sampling technique where a researcher sets a selection of a few criteria and chooses members of a population randomly. Why randomly? If truly random, all the members have an equal opportunity to be a part of the sample, and thus we avoid the problem of having only our friends and neighbors (who may be very different from other people in the population) in the study. Mathematically, there is going to be a certain number that will be large enough to allow us to generalize our particular findings from our sample population to the population at large. It might surprise you how small that number can be. Election polls of no more than one thousand people are routinely used to predict actual election outcomes of millions of people. Below that number, however, you will not be able to make generalizations. Talking to five people at random is simply not enough people to predict a presidential election.

In order to answer quantitative research questions of causality, one must employ probability sampling. Quantitative researchers try to generalize their findings to a larger population. Samples are designed with that in mind. Qualitative researchers ask very different questions, though. Qualitative research questions are not about “how many” of a certain group do X (in this case, what percentage of the unvaccinated hesitate for concern about safety rather than reject vaccination on political grounds). Qualitative research employs nonprobability sampling . By definition, not everyone has an equal opportunity to be included in the sample. The researcher might select White women they go to college with to provide insight into racial and gender dynamics at play. Whatever is found by doing so will not be generalizable to everyone who has not been vaccinated, or even all White women who have not been vaccinated, or even all White women who have not been vaccinated who are in this particular college. That is not the point of qualitative research at all. This is a really important distinction, so I will repeat in bold: Qualitative researchers are not trying to statistically generalize specific findings to a larger population . They have not failed when their sample cannot be generalized, as that is not the point at all.

In the previous paragraph, I said it would be perfectly acceptable for a qualitative researcher to interview five White women with whom she goes to college about their vaccine hesitancy “to provide insight into racial and gender dynamics at play.” The key word here is “insight.” Rather than use a sample as a stand-in for the general population, as quantitative researchers do, the qualitative researcher uses the sample to gain insight into a process or phenomenon. The qualitative researcher is not going to be content with simply asking each of the women to state her reason for not being vaccinated and then draw conclusions that, because one in five of these women were concerned about their health, one in five of all people were also concerned about their health. That would be, frankly, a very poor study indeed. Rather, the qualitative researcher might sit down with each of the women and conduct a lengthy interview about what the vaccine means to her, why she is hesitant, how she manages her hesitancy (how she explains it to her friends), what she thinks about others who are unvaccinated, what she thinks of those who have been vaccinated, and what she knows or thinks she knows about COVID-19. The researcher might include specific interview questions about the college context, about their status as White women, about the political beliefs they hold about racism in the US, and about how their own political affiliations may or may not provide narrative scripts about “protective whiteness.” There are many interesting things to ask and learn about and many things to discover. Where a quantitative researcher begins with clear parameters to set their population and guide their sample selection process, the qualitative researcher is discovering new parameters, making it impossible to engage in probability sampling.

Looking at it this way, sampling for qualitative researchers needs to be more strategic. More theoretically informed. What persons can be interviewed or observed that would provide maximum insight into what is still unknown? In other words, qualitative researchers think through what cases they could learn the most from, and those are the cases selected to study: “What would be ‘bias’ in statistical sampling, and therefore a weakness, becomes intended focus in qualitative sampling, and therefore a strength. The logic and power of purposeful sampling like in selecting information-rich cases for study in depth. Information-rich cases are those from which one can learn a great deal about issues of central importance to the purpose of the inquiry, thus the term purposeful sampling” ( Patton 2002:230 ; emphases in the original).

Before selecting your sample, though, it is important to clearly identify the general population of interest. You need to know this before you can determine the sample. In our example case, it is “adult Americans who have not yet been vaccinated.” Depending on the specific qualitative research question, however, it might be “adult Americans who have been vaccinated for political reasons” or even “college students who have not been vaccinated.” What insights are you seeking? Do you want to know how politics is affecting vaccination? Or do you want to understand how people manage being an outlier in a particular setting (unvaccinated where vaccinations are heavily encouraged if not required)? More clearly stated, your population should align with your research question . Think back to the opening story about Damaske’s work studying the unemployed. She drew her sample narrowly to address the particular questions she was interested in pursuing. Knowing your questions or, at a minimum, why you are interested in the topic will allow you to draw the best sample possible to achieve insight.

Once you have your population in mind, how do you go about getting people to agree to be in your sample? In qualitative research, it is permissible to find people by convenience. Just ask for people who fit your sample criteria and see who shows up. Or reach out to friends and colleagues and see if they know anyone that fits. Don’t let the name convenience sampling mislead you; this is not exactly “easy,” and it is certainly a valid form of sampling in qualitative research. The more unknowns you have about what you will find, the more convenience sampling makes sense. If you don’t know how race or class or political affiliation might matter, and your population is unvaccinated college students, you can construct a sample of college students by placing an advertisement in the student paper or posting a flyer on a notice board. Whoever answers is your sample. That is what is meant by a convenience sample. A common variation of convenience sampling is snowball sampling . This is particularly useful if your target population is hard to find. Let’s say you posted a flyer about your study and only two college students responded. You could then ask those two students for referrals. They tell their friends, and those friends tell other friends, and, like a snowball, your sample gets bigger and bigger.

Researcher Note

Gaining Access: When Your Friend Is Your Research Subject

My early experience with qualitative research was rather unique. At that time, I needed to do a project that required me to interview first-generation college students, and my friends, with whom I had been sharing a dorm for two years, just perfectly fell into the sample category. Thus, I just asked them and easily “gained my access” to the research subject; I know them, we are friends, and I am part of them. I am an insider. I also thought, “Well, since I am part of the group, I can easily understand their language and norms, I can capture their honesty, read their nonverbal cues well, will get more information, as they will be more opened to me because they trust me.” All in all, easy access with rich information. But, gosh, I did not realize that my status as an insider came with a price! When structuring the interview questions, I began to realize that rather than focusing on the unique experiences of my friends, I mostly based the questions on my own experiences, assuming we have similar if not the same experiences. I began to struggle with my objectivity and even questioned my role; am I doing this as part of the group or as a researcher? I came to know later that my status as an insider or my “positionality” may impact my research. It not only shapes the process of data collection but might heavily influence my interpretation of the data. I came to realize that although my inside status came with a lot of benefits (especially for access), it could also bring some drawbacks.

—Dede Setiono, PhD student focusing on international development and environmental policy, Oregon State University

The more you know about what you might find, the more strategic you can be. If you wanted to compare how politically conservative and politically liberal college students explained their vaccine hesitancy, for example, you might construct a sample purposively, finding an equal number of both types of students so that you can make those comparisons in your analysis. This is what Damaske ( 2021 ) did. You could still use convenience or snowball sampling as a way of recruitment. Post a flyer at the conservative student club and then ask for referrals from the one student that agrees to be interviewed. As with convenience sampling, there are variations of purposive sampling as well as other names used (e.g., judgment, quota, stratified, criterion, theoretical). Try not to get bogged down in the nomenclature; instead, focus on identifying the general population that matches your research question and then using a sampling method that is most likely to provide insight, given the types of questions you have.

There are all kinds of ways of being strategic with sampling in qualitative research. Here are a few of my favorite techniques for maximizing insight:

• Consider using “extreme” or “deviant” cases. Maybe your college houses a prominent anti-vaxxer who has written about and demonstrated against the college’s policy on vaccines. You could learn a lot from that single case (depending on your research question, of course).
• Consider “intensity”: people and cases and circumstances where your questions are more likely to feature prominently (but not extremely or deviantly). For example, you could compare those who volunteer at local Republican and Democratic election headquarters during an election season in a study on why party matters. Those who volunteer are more likely to have something to say than those who are more apathetic.
• Maximize variation, as with the case of “politically liberal” versus “politically conservative,” or include an array of social locations (young vs. old; Northwest vs. Southeast region). This kind of heterogeneity sampling can capture and describe the central themes that cut across the variations: any common patterns that emerge, even in this wildly mismatched sample, are probably important to note!
• Rather than maximize the variation, you could select a small homogenous sample to describe some particular subgroup in depth. Focus groups are often the best form of data collection for homogeneity sampling.
• Think about which cases are “critical” or politically important—ones that “if it happens here, it would happen anywhere” or a case that is politically sensitive, as with the single “blue” (Democratic) county in a “red” (Republican) state. In both, you are choosing a site that would yield the most information and have the greatest impact on the development of knowledge.
• On the other hand, sometimes you want to select the “typical”—the typical college student, for example. You are trying to not generalize from the typical but illustrate aspects that may be typical of this case or group. When selecting for typicality, be clear with yourself about why the typical matches your research questions (and who might be excluded or marginalized in doing so).
• Finally, it is often a good idea to look for disconfirming cases : if you are at the stage where you have a hypothesis (of sorts), you might select those who do not fit your hypothesis—you will surely learn something important there. They may be “exceptions that prove the rule” or exceptions that force you to alter your findings in order to make sense of these additional cases.

In addition to all these sampling variations, there is the theoretical approach taken by grounded theorists in which the researcher samples comparative people (or events) on the basis of their potential to represent important theoretical constructs. The sample, one can say, is by definition representative of the phenomenon of interest. It accompanies the constant comparative method of analysis. In the words of the funders of Grounded Theory , “Theoretical sampling is sampling on the basis of the emerging concepts, with the aim being to explore the dimensional range or varied conditions along which the properties of the concepts vary” ( Strauss and Corbin 1998:73 ).

When Your Population is Not Composed of People

I think it is easiest for most people to think of populations and samples in terms of people, but sometimes our units of analysis are not actually people. They could be places or institutions. Even so, you might still want to talk to people or observe the actions of people to understand those places or institutions. Or not! In the case of content analyses (see chapter 17), you won’t even have people involved at all but rather documents or films or photographs or news clippings. Everything we have covered about sampling applies to other units of analysis too. Let’s work through some examples.

Case Studies

When constructing a case study, it is helpful to think of your cases as sample populations in the same way that we considered people above. If, for example, you are comparing campus climates for diversity, your overall population may be “four-year college campuses in the US,” and from there you might decide to study three college campuses as your sample. Which three? Will you use purposeful sampling (perhaps [1] selecting three colleges in Oregon that are different sizes or [2] selecting three colleges across the US located in different political cultures or [3] varying the three colleges by racial makeup of the student body)? Or will you select three colleges at random, out of convenience? There are justifiable reasons for all approaches.

As with people, there are different ways of maximizing insight in your sample selection. Think about the following rationales: typical, diverse, extreme, deviant, influential, crucial, or even embodying a particular “pathway” ( Gerring 2008 ). When choosing a case or particular research site, Rubin ( 2021 ) suggests you bear in mind, first, what you are leaving out by selecting this particular case/site; second, what you might be overemphasizing by studying this case/site and not another; and, finally, whether you truly need to worry about either of those things—“that is, what are the sources of bias and how bad are they for what you are trying to do?” ( 89 ).

Once you have selected your cases, you may still want to include interviews with specific people or observations at particular sites within those cases. Then you go through possible sampling approaches all over again to determine which people will be contacted.

Content: Documents, Narrative Accounts, And So On

Although not often discussed as sampling, your selection of documents and other units to use in various content/historical analyses is subject to similar considerations. When you are asking quantitative-type questions (percentages and proportionalities of a general population), you will want to follow probabilistic sampling. For example, I created a random sample of accounts posted on the website studentloanjustice.org to delineate the types of problems people were having with student debt ( Hurst 2007 ). Even though my data was qualitative (narratives of student debt), I was actually asking a quantitative-type research question, so it was important that my sample was representative of the larger population (debtors who posted on the website). On the other hand, when you are asking qualitative-type questions, the selection process should be very different. In that case, use nonprobabilistic techniques, either convenience (where you are really new to this data and do not have the ability to set comparative criteria or even know what a deviant case would be) or some variant of purposive sampling. Let’s say you were interested in the visual representation of women in media published in the 1950s. You could select a national magazine like Time for a “typical” representation (and for its convenience, as all issues are freely available on the web and easy to search). Or you could compare one magazine known for its feminist content versus one antifeminist. The point is, sample selection is important even when you are not interviewing or observing people.

Goals of Qualitative Sampling versus Goals of Quantitative Sampling

We have already discussed some of the differences in the goals of quantitative and qualitative sampling above, but it is worth further discussion. The quantitative researcher seeks a sample that is representative of the population of interest so that they may properly generalize the results (e.g., if 80 percent of first-gen students in the sample were concerned with costs of college, then we can say there is a strong likelihood that 80 percent of first-gen students nationally are concerned with costs of college). The qualitative researcher does not seek to generalize in this way . They may want a representative sample because they are interested in typical responses or behaviors of the population of interest, but they may very well not want a representative sample at all. They might want an “extreme” or deviant case to highlight what could go wrong with a particular situation, or maybe they want to examine just one case as a way of understanding what elements might be of interest in further research. When thinking of your sample, you will have to know why you are selecting the units, and this relates back to your research question or sets of questions. It has nothing to do with having a representative sample to generalize results. You may be tempted—or it may be suggested to you by a quantitatively minded member of your committee—to create as large and representative a sample as you possibly can to earn credibility from quantitative researchers. Ignore this temptation or suggestion. The only thing you should be considering is what sample will best bring insight into the questions guiding your research. This has implications for the number of people (or units) in your study as well, which is the topic of the next section.

What is the Correct “Number” to Sample?

Because we are not trying to create a generalizable representative sample, the guidelines for the “number” of people to interview or news stories to code are also a bit more nebulous. There are some brilliant insightful studies out there with an n of 1 (meaning one person or one account used as the entire set of data). This is particularly so in the case of autoethnography, a variation of ethnographic research that uses the researcher’s own subject position and experiences as the basis of data collection and analysis. But it is true for all forms of qualitative research. There are no hard-and-fast rules here. The number to include is what is relevant and insightful to your particular study.

That said, humans do not thrive well under such ambiguity, and there are a few helpful suggestions that can be made. First, many qualitative researchers talk about “saturation” as the end point for data collection. You stop adding participants when you are no longer getting any new information (or so very little that the cost of adding another interview subject or spending another day in the field exceeds any likely benefits to the research). The term saturation was first used here by Glaser and Strauss ( 1967 ), the founders of Grounded Theory. Here is their explanation: “The criterion for judging when to stop sampling the different groups pertinent to a category is the category’s theoretical saturation . Saturation means that no additional data are being found whereby the sociologist can develop properties of the category. As he [or she] sees similar instances over and over again, the researcher becomes empirically confident that a category is saturated. [They go] out of [their] way to look for groups that stretch diversity of data as far as possible, just to make certain that saturation is based on the widest possible range of data on the category” ( 61 ).

It makes sense that the term was developed by grounded theorists, since this approach is rather more open-ended than other approaches used by qualitative researchers. With so much left open, having a guideline of “stop collecting data when you don’t find anything new” is reasonable. However, saturation can’t help much when first setting out your sample. How do you know how many people to contact to interview? What number will you put down in your institutional review board (IRB) protocol (see chapter 8)? You may guess how many people or units it will take to reach saturation, but there really is no way to know in advance. The best you can do is think about your population and your questions and look at what others have done with similar populations and questions.

Here are some suggestions to use as a starting point: For phenomenological studies, try to interview at least ten people for each major category or group of people . If you are comparing male-identified, female-identified, and gender-neutral college students in a study on gender regimes in social clubs, that means you might want to design a sample of thirty students, ten from each group. This is the minimum suggested number. Damaske’s ( 2021 ) sample of one hundred allows room for up to twenty-five participants in each of four “buckets” (e.g., working-class*female, working-class*male, middle-class*female, middle-class*male). If there is more than one comparative group (e.g., you are comparing students attending three different colleges, and you are comparing White and Black students in each), you can sometimes reduce the number for each group in your sample to five for, in this case, thirty total students. But that is really a bare minimum you will want to go. A lot of people will not trust you with only “five” cases in a bucket. Lareau ( 2021:24 ) advises a minimum of seven or nine for each bucket (or “cell,” in her words). The point is to think about what your analyses might look like and how comfortable you will be with a certain number of persons fitting each category.

Because qualitative research takes so much time and effort, it is rare for a beginning researcher to include more than thirty to fifty people or units in the study. You may not be able to conduct all the comparisons you might want simply because you cannot manage a larger sample. In that case, the limits of who you can reach or what you can include may influence you to rethink an original overcomplicated research design. Rather than include students from every racial group on a campus, for example, you might want to sample strategically, thinking about the most contrast (insightful), possibly excluding majority-race (White) students entirely, and simply using previous literature to fill in gaps in our understanding. For example, one of my former students was interested in discovering how race and class worked at a predominantly White institution (PWI). Due to time constraints, she simplified her study from an original sample frame of middle-class and working-class domestic Black and international African students (four buckets) to a sample frame of domestic Black and international African students (two buckets), allowing the complexities of class to come through individual accounts rather than from part of the sample frame. She wisely decided not to include White students in the sample, as her focus was on how minoritized students navigated the PWI. She was able to successfully complete her project and develop insights from the data with fewer than twenty interviewees. [1]

But what if you had unlimited time and resources? Would it always be better to interview more people or include more accounts, documents, and units of analysis? No! Your sample size should reflect your research question and the goals you have set yourself. Larger numbers can sometimes work against your goals. If, for example, you want to help bring out individual stories of success against the odds, adding more people to the analysis can end up drowning out those individual stories. Sometimes, the perfect size really is one (or three, or five). It really depends on what you are trying to discover and achieve in your study. Furthermore, studies of one hundred or more (people, documents, accounts, etc.) can sometimes be mistaken for quantitative research. Inevitably, the large sample size will push the researcher into simplifying the data numerically. And readers will begin to expect generalizability from such a large sample.

To summarize, “There are no rules for sample size in qualitative inquiry. Sample size depends on what you want to know, the purpose of the inquiry, what’s at stake, what will be useful, what will have credibility, and what can be done with available time and resources” ( Patton 2002:244 ).

How did you find/construct a sample?

Since qualitative researchers work with comparatively small sample sizes, getting your sample right is rather important. Yet it is also difficult to accomplish. For instance, a key question you need to ask yourself is whether you want a homogeneous or heterogeneous sample. In other words, do you want to include people in your study who are by and large the same, or do you want to have diversity in your sample?

For many years, I have studied the experiences of students who were the first in their families to attend university. There is a rather large number of sampling decisions I need to consider before starting the study. (1) Should I only talk to first-in-family students, or should I have a comparison group of students who are not first-in-family? (2) Do I need to strive for a gender distribution that matches undergraduate enrollment patterns? (3) Should I include participants that reflect diversity in gender identity and sexuality? (4) How about racial diversity? First-in-family status is strongly related to some ethnic or racial identity. (5) And how about areas of study?

As you can see, if I wanted to accommodate all these differences and get enough study participants in each category, I would quickly end up with a sample size of hundreds, which is not feasible in most qualitative research. In the end, for me, the most important decision was to maximize the voices of first-in-family students, which meant that I only included them in my sample. As for the other categories, I figured it was going to be hard enough to find first-in-family students, so I started recruiting with an open mind and an understanding that I may have to accept a lack of gender, sexuality, or racial diversity and then not be able to say anything about these issues. But I would definitely be able to speak about the experiences of being first-in-family.

—Wolfgang Lehmann, author of “Habitus Transformation and Hidden Injuries”

Examples of “Sample” Sections in Journal Articles

Think about some of the studies you have read in college, especially those with rich stories and accounts about people’s lives. Do you know how the people were selected to be the focus of those stories? If the account was published by an academic press (e.g., University of California Press or Princeton University Press) or in an academic journal, chances are that the author included a description of their sample selection. You can usually find these in a methodological appendix (book) or a section on “research methods” (article).

Here are two examples from recent books and one example from a recent article:

Example 1 . In It’s Not like I’m Poor: How Working Families Make Ends Meet in a Post-welfare World , the research team employed a mixed methods approach to understand how parents use the earned income tax credit, a refundable tax credit designed to provide relief for low- to moderate-income working people ( Halpern-Meekin et al. 2015 ). At the end of their book, their first appendix is “Introduction to Boston and the Research Project.” After describing the context of the study, they include the following description of their sample selection:

In June 2007, we drew 120 names at random from the roughly 332 surveys we gathered between February and April. Within each racial and ethnic group, we aimed for one-third married couples with children and two-thirds unmarried parents. We sent each of these families a letter informing them of the opportunity to participate in the in-depth portion of our study and then began calling the home and cell phone numbers they provided us on the surveys and knocking on the doors of the addresses they provided.…In the end, we interviewed 115 of the 120 families originally selected for the in-depth interview sample (the remaining five families declined to participate). ( 22 )

Was their sample selection based on convenience or purpose? Why do you think it was important for them to tell you that five families declined to be interviewed? There is actually a trick here, as the names were pulled randomly from a survey whose sample design was probabilistic. Why is this important to know? What can we say about the representativeness or the uniqueness of whatever findings are reported here?

Example 2 . In When Diversity Drops , Park ( 2013 ) examines the impact of decreasing campus diversity on the lives of college students. She does this through a case study of one student club, the InterVarsity Christian Fellowship (IVCF), at one university (“California University,” a pseudonym). Here is her description:

I supplemented participant observation with individual in-depth interviews with sixty IVCF associates, including thirty-four current students, eight former and current staff members, eleven alumni, and seven regional or national staff members. The racial/ethnic breakdown was twenty-five Asian Americans (41.6 percent), one Armenian (1.6 percent), twelve people who were black (20.0 percent), eight Latino/as (13.3 percent), three South Asian Americans (5.0 percent), and eleven people who were white (18.3 percent). Twenty-nine were men, and thirty-one were women. Looking back, I note that the higher number of Asian Americans reflected both the group’s racial/ethnic composition and my relative ease about approaching them for interviews. ( 156 )

How can you tell this is a convenience sample? What else do you note about the sample selection from this description?

Example 3. The last example is taken from an article published in the journal Research in Higher Education . Published articles tend to be more formal than books, at least when it comes to the presentation of qualitative research. In this article, Lawson ( 2021 ) is seeking to understand why female-identified college students drop out of majors that are dominated by male-identified students (e.g., engineering, computer science, music theory). Here is the entire relevant section of the article:

Method Participants Data were collected as part of a larger study designed to better understand the daily experiences of women in MDMs [male-dominated majors].…Participants included 120 students from a midsize, Midwestern University. This sample included 40 women and 40 men from MDMs—defined as any major where at least 2/3 of students are men at both the university and nationally—and 40 women from GNMs—defined as any may where 40–60% of students are women at both the university and nationally.… Procedure A multi-faceted approach was used to recruit participants; participants were sent targeted emails (obtained based on participants’ reported gender and major listings), campus-wide emails sent through the University’s Communication Center, flyers, and in-class presentations. Recruitment materials stated that the research focused on the daily experiences of college students, including classroom experiences, stressors, positive experiences, departmental contexts, and career aspirations. Interested participants were directed to email the study coordinator to verify eligibility (at least 18 years old, man/woman in MDM or woman in GNM, access to a smartphone). Sixteen interested individuals were not eligible for the study due to the gender/major combination. ( 482ff .)

What method of sample selection was used by Lawson? Why is it important to define “MDM” at the outset? How does this definition relate to sampling? Why were interested participants directed to the study coordinator to verify eligibility?

Final Words

I have found that students often find it difficult to be specific enough when defining and choosing their sample. It might help to think about your sample design and sample recruitment like a cookbook. You want all the details there so that someone else can pick up your study and conduct it as you intended. That person could be yourself, but this analogy might work better if you have someone else in mind. When I am writing down recipes, I often think of my sister and try to convey the details she would need to duplicate the dish. We share a grandmother whose recipes are full of handwritten notes in the margins, in spidery ink, that tell us what bowl to use when or where things could go wrong. Describe your sample clearly, convey the steps required accurately, and then add any other details that will help keep you on track and remind you why you have chosen to limit possible interviewees to those of a certain age or class or location. Imagine actually going out and getting your sample (making your dish). Do you have all the necessary details to get started?

Table 5.1. Sampling Type and Strategies

Further Readings

Fusch, Patricia I., and Lawrence R. Ness. 2015. “Are We There Yet? Data Saturation in Qualitative Research.” Qualitative Report 20(9):1408–1416.

Saunders, Benjamin, Julius Sim, Tom Kinstone, Shula Baker, Jackie Waterfield, Bernadette Bartlam, Heather Burroughs, and Clare Jinks. 2018. “Saturation in Qualitative Research: Exploring Its Conceptualization and Operationalization.”  Quality & Quantity  52(4):1893–1907.

• Rubin ( 2021 ) suggests a minimum of twenty interviews (but safer with thirty) for an interview-based study and a minimum of three to six months in the field for ethnographic studies. For a content-based study, she suggests between five hundred and one thousand documents, although some will be “very small” ( 243–244 ). ↵

The process of selecting people or other units of analysis to represent a larger population. In quantitative research, this representation is taken quite literally, as statistically representative.  In qualitative research, in contrast, sample selection is often made based on potential to generate insight about a particular topic or phenomenon.

The actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).  Sampling frames can differ from the larger population when specific exclusions are inherent, as in the case of pulling names randomly from voter registration rolls where not everyone is a registered voter.  This difference in frame and population can undercut the generalizability of quantitative results.

The specific group of individuals that you will collect data from.  Contrast population.

The large group of interest to the researcher.  Although it will likely be impossible to design a study that incorporates or reaches all members of the population of interest, this should be clearly defined at the outset of a study so that a reasonable sample of the population can be taken.  For example, if one is studying working-class college students, the sample may include twenty such students attending a particular college, while the population is “working-class college students.”  In quantitative research, clearly defining the general population of interest is a necessary step in generalizing results from a sample.  In qualitative research, defining the population is conceptually important for clarity.

A sampling strategy in which the sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the sample.  This is often done through a lottery or other chance mechanisms (e.g., a random selection of every twelfth name on an alphabetical list of voters).  Also known as random sampling .

The selection of research participants or other data sources based on availability or accessibility, in contrast to purposive sampling .

A sample generated non-randomly by asking participants to help recruit more participants the idea being that a person who fits your sampling criteria probably knows other people with similar criteria.

Broad codes that are assigned to the main issues emerging in the data; identifying themes is often part of initial coding . 

A form of case selection focusing on examples that do not fit the emerging patterns. This allows the researcher to evaluate rival explanations or to define the limitations of their research findings. While disconfirming cases are found (not sought out), researchers should expand their analysis or rethink their theories to include/explain them.

A methodological tradition of inquiry and approach to analyzing qualitative data in which theories emerge from a rigorous and systematic process of induction.  This approach was pioneered by the sociologists Glaser and Strauss (1967).  The elements of theory generated from comparative analysis of data are, first, conceptual categories and their properties and, second, hypotheses or generalized relations among the categories and their properties – “The constant comparing of many groups draws the [researcher’s] attention to their many similarities and differences.  Considering these leads [the researcher] to generate abstract categories and their properties, which, since they emerge from the data, will clearly be important to a theory explaining the kind of behavior under observation.” (36).

The result of probability sampling, in which a sample is chosen to represent (numerically) the larger population from which it is drawn by random selection.  Each person in the population has an equal chance of making it into the random sample.  This is often done through a lottery or other chance mechanisms (e.g., the random selection of every twelfth name on an alphabetical list of voters).  This is typically not required in qualitative research but rather essential for the generalizability of quantitative research.

A form of case selection or purposeful sampling in which cases that are unusual or special in some way are chosen to highlight processes or to illuminate gaps in our knowledge of a phenomenon.   See also extreme case .

The point at which you can conclude data collection because every person you are interviewing, the interaction you are observing, or content you are analyzing merely confirms what you have already noted.  Achieving saturation is often used as the justification for the final sample size.

The accuracy with which results or findings can be transferred to situations or people other than those originally studied.  Qualitative studies generally are unable to use (and are uninterested in) statistical generalizability where the sample population is said to be able to predict or stand in for a larger population of interest.  Instead, qualitative researchers often discuss “theoretical generalizability,” in which the findings of a particular study can shed light on processes and mechanisms that may be at play in other settings.  See also statistical generalization and theoretical generalization .

A term used by IRBs to denote all materials aimed at recruiting participants into a research study (including printed advertisements, scripts, audio or video tapes, or websites).  Copies of this material are required in research protocols submitted to IRB.

Introduction to Qualitative Research Methods Copyright © 2023 by Allison Hurst is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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31 Interpretation In Qualitative Research: What, Why, How

Allen Trent, College of Education, University of Wyoming

Jeasik Cho, Department of Educational Studies, University of Wyoming

• Published: 02 September 2020
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This chapter addresses a wide range of concepts related to interpretation in qualitative research, examines the meaning and importance of interpretation in qualitative inquiry, and explores the ways methodology, data, and the self/researcher as instrument interact and impact interpretive processes. Additionally, the chapter presents a series of strategies for qualitative researchers engaged in the process of interpretation and closes by presenting a framework for qualitative researchers designed to inform their interpretations. The framework includes attention to the key qualitative research concepts transparency, reflexivity, analysis, validity, evidence, and literature. Four questions frame the chapter: What is interpretation, and why are interpretive strategies important in qualitative research? How do methodology, data, and the researcher/self impact interpretation in qualitative research? How do qualitative researchers engage in the process of interpretation? And, in what ways can a framework for interpretation strategies support qualitative researchers across multiple methodologies and paradigms?

“ All human knowledge takes the form of interpretation.” In this seemingly simple statement, the late German philosopher Walter Benjamin asserted that all knowledge is mediated and constructed. In doing so, he situates himself as an interpretivist, one who believes that human subjectivity, individuals’ characteristics, feelings, opinions, and experiential backgrounds impact observations, analysis of these observations, and resultant knowledge/truth constructions. Hammersley ( 2013 ) noted,

People—unlike atoms … actively interpret or make sense of their environment and of themselves; the ways in which they do this are shaped by the particular cultures in which they live; and these distinctive cultural orientations will strongly influence not only what they believe but also what they do. (p. 26)

Contrast this perspective with positivist claims that knowledge is based exclusively on external facts, objectively observed and recorded. Interpretivists, then, acknowledge that if positivistic notions of knowledge and truth are inadequate to explain social phenomena, then positivist, hard science approaches to research (i.e., the scientific method and its variants) are also inadequate and can even have a detrimental impact. According to Polyani (1967), “The ideal of exact science would turn out to be fundamentally misleading and possibly a source of devastating fallacies” (as cited in Packer, 2018 , p. 71). So, although the literature often contrasts quantitative and qualitative research as largely a difference in kinds of data employed (numerical vs. linguistic), instead, the primary differentiation is in the foundational, paradigmatic assumptions about truth, knowledge, and objectivity.

This chapter is about interpretation and the strategies that qualitative researchers use to interpret a wide variety of “texts.” Knowledge, we assert, is constructed, both individually (constructivism) and socially (constructionism). We accept this as our starting point. Our aim here is to share our perspective on a broad set of concepts associated with the interpretive, or meaning-making, process. Although it may happen at different times and in different ways, interpretation is part of almost all qualitative research.

Qualitative research is an umbrella term that encompasses a wide array of paradigmatic views, goals, and methods. Still, there are key unifying elements that include a generally constructionist epistemological standpoint, attention to primarily linguistic data, and generally accepted protocols or syntax for conducting research. Typically, qualitative researchers begin with a starting point—a curiosity, a problem in need of solutions, a research question, and/or a desire to better understand a situation from the “native” perspectives of the individuals who inhabit that context. This is what anthropologists call the emic , or insider’s, perspective. Olivier de Sardan ( 2015 ) wrote, “It evokes the meaning that social facts have for the actors concerned. It is opposed to the term etic , which, at times, designates more external or ‘objective’ data, and, at others, the researcher’s interpretive analysis” (p. 65).

From this starting point, researchers determine the appropriate kinds of data to collect, engage in fieldwork as participant observers to gather these data, organize the data, look for patterns, and attempt to understand the emic perspectives while integrating their own emergent interpretations. Researchers construct meaning from data by synthesizing research “findings,” “assertions,” or “theories” that can be shared so that others may also gain insights from the conducted inquiry. This interpretive process has a long history; hermeneutics, the theory of interpretation, blossomed in the 17th century in the form of biblical exegesis (Packer, 2018 ).

Although there are commonalities that cut across most forms of qualitative research, this is not to say that there is an accepted, linear, standardized approach. To be sure, there are an infinite number of variations and nuances in the qualitative research process. For example, some forms of inquiry begin with a firm research question; others start without even a clear focus for study. Grounded theorists begin data analysis and interpretation very early in the research process, whereas some case study researchers, for example, may collect data in the field for a period of time before seriously considering the data and its implications. Some ethnographers may be a part of the context (e.g., observing in classrooms), but they may assume more observer-like roles, as opposed to actively participating in the context. Alternatively, action researchers, in studying issues related to their own practice, are necessarily situated toward the participant end of the participant–observer continuum.

Our focus here is on one integrated part of the qualitative research process, interpretation, the hermeneutic process of collective and individual “meaning making.” Like Willig ( 2017 ), we believe “interpretation is at the heart of qualitative research because qualitative research is concerned with meaning and the process of meaning-making … qualitative data … needs to be given meaning by the researcher” (p. 276). As we discuss throughout this chapter, researchers take a variety of approaches to interpretation in qualitative work. Four general questions guide our explorations:

What is interpretation, and why are interpretive strategies important in qualitative research?

How do methodology, data, and the researcher/self impact interpretation in qualitative research?

How do qualitative researchers engage in the process of interpretation?

In what ways can a framework for interpretation strategies support qualitative researchers across multiple methodological and paradigmatic views?

We address each of these guiding questions in our attempt to explicate our interpretation of “interpretation” and, as educational researchers, we include examples from our own work to illustrate some key concepts.

What Is Interpretation, and Why Are Interpretive Strategies Important in Qualitative Research?

Qualitative researchers and those writing about qualitative methods often intertwine the terms analysis and interpretation . For example, Hubbard and Power ( 2003 ) described data analysis as “bringing order, structure, and meaning to the data” (p. 88). To us, this description combines analysis with interpretation. Although there is nothing wrong with this construction, our understanding aligns more closely with Mills’s ( 2018 ) claim that, “put simply, analysis involves summarizing what’s in the data, whereas interpretation involves making sense of—finding meaning in—that data” (p. 176). Hesse-Biber ( 2017 ) also separated out the essential process of interpretation. She described the steps in qualitative analysis and interpretation as data preparation, data exploration, and data reduction (all part of Mills’s “analysis” processes), followed by interpretation (pp. 307–328). Willig ( 2017 ) elaborated: analysis, she claims, is “sober and systematic,” whereas interpretation is associated with “creativity and the imagination … interpretation is seen as stimulating, it is interesting and it can be illuminating” (p. 276). For the purpose of this chapter, we will adhere to Mills’s distinction, understanding analysis as summarizing and organizing and interpretation as meaning making. Unavoidably, these closely related processes overlap and interact, but our focus will be primarily on the more complex of these endeavors, interpretation. Interpretation, in this sense, is in part translation, but translation is not an objective act. Instead, translation necessarily involves selectivity and the ascribing of meaning. Qualitative researchers “aim beneath manifest behavior to the meaning events have for those who experience them” (Eisner, 1991 , p. 35). The presentation of these insider/emic perspectives, coupled with researchers’ own interpretations, is a hallmark of qualitative research.

Qualitative researchers have long borrowed from extant models for fieldwork and interpretation. Approaches from anthropology and the arts have become especially prominent. For example, Eisner’s ( 1991 ) form of qualitative inquiry, educational criticism , draws heavily on accepted models of art criticism. T. Barrett ( 2011 ), an authority on art criticism, described interpretation as a complex set of processes based on a set of principles. We believe many of these principles apply as readily to qualitative research as they do to critique. The following principles, adapted from T. Barrett’s principles of interpretation (2011), inform our examination:

Qualitative phenomena have “aboutness” : All social phenomena have meaning, but meanings in this context can be multiple, even contradictory.

Interpretations are persuasive arguments : All interpretations are arguments, and qualitative researchers, like critics, strive to build strong arguments grounded in the information, or data, available.

  Some interpretations are better than others : Barrett noted that “some interpretations are better argued, better grounded with evidence, and therefore more reasonable, more certain, and more acceptable than others.” This contradicts the argument that “all interpretations are equal,” heard in the common refrain, “Well, that’s just your interpretation.”

There can be different, competing, and contradictory interpretations of the same phenomena : As noted at the beginning of this chapter, we acknowledge that subjectivity matters, and, unavoidably, it impacts one’s interpretations. As Barrett noted, “Interpretations are often based on a worldview.”

Interpretations are not (and cannot be) “right,” but instead, they can be more or less reasonable, convincing, and informative : There is never one “true” interpretation, but some interpretations are more compelling than others.

Interpretations can be judged by coherence, correspondence, and inclusiveness : Does the argument/interpretation make sense (coherence)? Does the interpretation fit the data (correspondence)? Have all data been attended to, including outlier data that do not necessarily support identified themes (inclusiveness)?

Interpretation is ultimately a communal endeavor : Initial interpretations may be incomplete, nearsighted, and/or narrow, but eventually these interpretations become richer, broader, and more inclusive. Feminist revisionist history projects are an exemplary case. Over time, the writing, art, and cultural contributions of countless women, previously ignored, diminished, or distorted, have come to be accepted as prominent contributions given serious consideration.

So, meaning is conferred; interpretations are socially constructed arguments; multiple interpretations are to be expected; and some interpretations are better than others. As we discuss later in this chapter, what makes an interpretation “better” often hinges on the purpose/goals of the research in question. Interpretations designed to generate theory, or generalizable rules, will be better for responding to research questions aligned with the aims of more traditional quantitative/positivist research, whereas interpretations designed to construct meanings through social interaction, to generate multiple perspectives, and to represent the context-specific perspectives of the research participants are better for researchers constructing thick, contextually rich descriptions, stories, or narratives. The former relies on more atomistic interpretive strategies, whereas the latter adheres to a more holistic approach (Willis, 2007 ). Both approaches to analysis/interpretation are addressed in more detail later in this chapter.

At this point, readers might ask, Why does interpretation matter, anyway? Our response to this question involves the distinctive nature of interpretation and the ability of the interpretive process to put unique fingerprints on an otherwise relatively static set of data. Once interview data are collected and transcribed (and we realize that even the process of transcription is, in part, interpretive), documents are collected, and observations are recorded, qualitative researchers could just, in good faith and with fidelity, represent the data in as straightforward ways as possible, allowing readers to “see for themselves” by sharing as much actual data (e.g., the transcribed words of the research participants) as possible. This approach, however, includes analysis, what we have defined as summarizing and organizing data for presentation, but it falls short of what we reference and define as interpretation—attempting to explain the meaning of others’ words and actions. According to Lichtman ( 2013 ),

While early efforts at qualitative research might have stopped at description, it is now more generally accepted that a qualitative researcher goes beyond pure description.… Many believe that it is the role of the researcher to bring understanding, interpretation, and meaning. (p. 17)

Because we are fond of the arts and arts-based approaches to qualitative research, an example from the late jazz drummer, Buddy Rich, seems fitting. Rich explains the importance of having the flexibility to interpret: “I don’t think any arranger should ever write a drum part for a drummer, because if a drummer can’t create his own interpretation of the chart, and he plays everything that’s written, he becomes mechanical; he has no freedom.” The same is true for qualitative researchers: without the freedom to interpret, the researcher merely regurgitates, attempting to share with readers/reviewers exactly what the research subjects shared with him or her. It is only through interpretation that the researcher, as collaborator with unavoidable subjectivities, is able to construct unique, contextualized meaning. Interpretation, then, in this sense, is knowledge construction.

In closing this section, we will illustrate the analysis-versus-interpretation distinction with the following transcript excerpt. In this study, the authors (Trent & Zorko, 2006 ) were studying student teaching from the perspective of K–12 students. This quote comes from a high school student in a focus group interview. She is describing a student teacher she had:

The right-hand column contains codes or labels applied to parts of the transcript text. Coding will be discussed in more depth later in this chapter, but for now, note that the codes are mostly summarizing the main ideas of the text, sometimes using the exact words of the research participant. This type of coding is a part of what we have called analysis—organizing and summarizing the data. It is a way of beginning to say “what is” there. As noted, though, most qualitative researchers go deeper. They want to know more than what is; they also ask, What does it mean? This is a question of interpretation.

Specific to the transcript excerpt, researchers might next begin to cluster the early codes into like groups. For example, the teacher “felt targeted,” “assumed kids were going to behave inappropriately,” and appeared to be “overwhelmed.” A researcher might cluster this group of codes in a category called “teacher feelings and perceptions” and may then cluster the codes “could not control class” and “students off task” into a category called “classroom management.” The researcher then, in taking a fresh look at these categories and the included codes, may begin to conclude that what is going on in this situation is that the student teacher does not have sufficient training in classroom management models and strategies and may also be lacking the skills she needs to build relationships with her students. These then would be interpretations, persuasive arguments connected to the study’s data. In this specific example, the researchers might proceed to write a memo about these emerging interpretations. In this memo, they might more clearly define their early categories and may also look through other data to see if there are other codes or categories that align with or overlap this initial analysis. They may write further about their emergent interpretations and, in doing so, may inform future data collection in ways that will allow them to either support or refute their early interpretations. These researchers will also likely find that the processes of analysis and interpretation are inextricably intertwined. Good interpretations very often depend on thorough and thoughtful analyses.

How Do Methodology, Data, and the Researcher/Self Impact Interpretation in Qualitative Research?

Methodological conventions guide interpretation and the use of interpretive strategies. For example, in grounded theory and in similar methodological traditions, “formal analysis begins early in the study and is nearly completed by the end of data collection” (Bogdan & Biklen, 2007 , p. 73). Alternatively, for researchers from other traditions, for example, case study researchers, “formal analysis and theory development [interpretation] do not occur until after the data collection is near complete” (p. 73).

Researchers subscribing to methodologies that prescribe early data analysis and interpretation may employ methods like analytic induction or the constant comparison method. In using analytic induction, researchers develop a rough definition of the phenomena under study; collect data to compare to this rough definition; modify the definition as needed, based on cases that both fit and do not fit the definition; and, finally, establish a clear, universal definition (theory) of the phenomena (Robinson, 1951, cited in Bogdan & Biklen, 2007 , p. 73). Generally, those using a constant comparison approach begin data collection immediately; identify key issues, events, and activities related to the study that then become categories of focus; collect data that provide incidents of these categories; write about and describe the categories, accounting for specific incidents and seeking others; discover basic processes and relationships; and, finally, code and write about the categories as theory, “grounded” in the data (Glaser, 1965 ). Although processes like analytic induction and constant comparison can be listed as steps to follow, in actuality, these are more typically recursive processes in which the researcher repeatedly goes back and forth between the data and emerging analyses and interpretations.

In addition to methodological conventions that prescribe data analysis early (e.g., grounded theory) or later (e.g., case study) in the inquiry process, methodological approaches also impact the general approach to analysis and interpretation. Ellingson ( 2011 ) situated qualitative research methodologies on a continuum spanning “science”-like approaches on one end juxtaposed with “art”-like approaches on the other.

Researchers pursuing a more science-oriented approach seek valid, reliable, generalizable knowledge; believe in neutral, objective researchers; and ultimately claim single, authoritative interpretations. Researchers adhering to these science-focused, postpositivistic approaches may count frequencies, emphasize the validity of the employed coding system, and point to intercoder reliability and random sampling as criteria that bolster the research credibility. Researchers at or near the science end of the continuum might employ analysis and interpretation strategies that include “paired comparisons,” “pile sorts,” “word counts,” identifying “key words in context,” and “triad tests” (Bernard, Wutich, & Ryan, 2017 , pp. 112, 381, 113, 170). These researchers may ultimately seek to develop taxonomies or other authoritative final products that organize and explain the collected data.

For example, in a study we conducted about preservice teachers’ experiences learning to teach second-language learners, the researchers collected larger data sets and used a statistical analysis package to analyze survey data, and the resultant findings included descriptive statistics. These survey results were supported with open-ended, qualitative data. For example, one of the study’s findings was that “a strong majority of candidates (96%) agreed that an immersion approach alone will not guarantee academic or linguistic success for second language learners.” In narrative explanations, one preservice teacher, representative of many others, remarked, “There has to be extra instructional efforts to help their students learn English … they won’t learn English by merely sitting in the classrooms” (Cho, Rios, Trent, & Mayfield, 2012 , p. 75).

Methodologies on the art side of Ellingson’s ( 2011 ) continuum, alternatively, “value humanistic, openly subjective knowledge, such as that embodied in stories, poetry, photography, and painting” (p. 599). Analysis and interpretation in these (often more contemporary) methodological approaches do not strive for “social scientific truth,” but instead are formulated to “enable us to learn about ourselves, each other, and the world through encountering the unique lens of a person’s (or a group’s) passionate rendering of a reality into a moving, aesthetic expression of meaning” (p. 599). For these “artistic/interpretivists, truths are multiple, fluctuating and ambiguous” (p. 599). Methodologies taking more subjective approaches to analysis and interpretation include autoethnography, testimonio, performance studies, feminist theorists/researchers, and others from related critical methodological forms of qualitative practice. More specifically arts-based approaches include poetic inquiry, fiction-based research, music as method, and dance and movement as inquiry (Leavy, 2017 ). Interpretation in these approaches is inherent. For example, “ interpretive poetry is understood as a method of merging the participant’s words with the researcher’s perspective” (Leavy, 2017 , p. 82).

As an example, one of us engaged in an artistic inquiry with a group of students in an art class for elementary teachers. We called it “Dreams as Data” and, among the project aims, we wanted to gather participants’ “dreams for education in the future” and display these dreams in an accessible, interactive, artistic display (see Trent, 2002 ). The intent was not to statistically analyze the dreams/data; instead, it was more universal. We wanted, as Ellingson ( 2011 , p. 599) noted, to use participant responses in ways that “enable us to learn about ourselves, each other, and the world.” The decision was made to leave responses intact and to share the whole/raw data set in the artistic display in ways that allowed the viewers to holistically analyze and interpret for themselves. Additionally, the researcher (Trent, 2002 ) collaborated with his students to construct their own contextually situated interpretations of the data. The following text is an excerpt from one participant’s response:

Almost a century ago, John Dewey eloquently wrote about the need to imagine and create the education that ALL children deserve, not just the richest, the Whitest, or the easiest to teach. At the dawn of this new century, on some mornings, I wake up fearful that we are further away from this ideal than ever.… Collective action, in a critical, hopeful, joyful, anti-racist and pro-justice spirit, is foremost in my mind as I reflect on and act in my daily work.… Although I realize the constraints on teachers and schools in the current political arena, I do believe in the power of teachers to stand next to, encourage, and believe in the students they teach—in short, to change lives. (Trent, 2002 , p. 49)

In sum, researchers whom Ellingson ( 2011 ) characterized as being on the science end of the continuum typically use more detailed or atomistic strategies to analyze and interpret qualitative data, whereas those toward the artistic end most often employ more holistic strategies. Both general approaches to qualitative data analysis and interpretation, atomistic and holistic, will be addressed later in this chapter.

As noted, qualitative researchers attend to data in a wide variety of ways depending on paradigmatic and epistemological beliefs, methodological conventions, and the purpose/aims of the research. These factors impact the kinds of data collected and the ways these data are ultimately analyzed and interpreted. For example, life history or testimonio researchers conduct extensive individual interviews, ethnographers record detailed observational notes, critical theorists may examine documents from pop culture, and ethnomethodologists may collect videotapes of interaction for analysis and interpretation.

In addition to the wide range of data types that are collected by qualitative researchers (and most qualitative researchers collect multiple forms of data), qualitative researchers, again influenced by the factors noted earlier, employ a variety of approaches to analyzing and interpreting data. As mentioned earlier in this chapter, some advocate for a detailed/atomistic, fine-grained approach to data (see, e.g., Bernard et al., 2017 ); others prefer a more broad-based, holistic, “eyeballing” of the data. According to Willis ( 2007 ), “Eyeballers reject the more structured approaches to analysis that break down the data into small units and, from the perspective of the eyeballers, destroy the wholeness and some of the meaningfulness of the data” (p. 298).

Regardless, we assert, as illustrated in Figure 31.1 , that as the process evolves, data collection becomes less prominent later in the process, as interpretation and making sense/meaning of the data becomes more prominent. It is through this emphasis on interpretation that qualitative researchers put their individual imprints on the data, allowing for the emergence of multiple, rich perspectives. This space for interpretation allows researchers the freedom Buddy Rich alluded to in his quote about interpreting musical charts. Without this freedom, Rich noted that the process would simply be “mechanical.” Furthermore, allowing space for multiple interpretations nourishes the perspectives of many others in the community. Writer and theorist Meg Wheatley explained, “Everyone in a complex system has a slightly different interpretation. The more interpretations we gather, the easier it becomes to gain a sense of the whole.” In qualitative research, “there is no ‘getting it right’ because there could be many ‘rights’ ” (as cited in Lichtman, 2013 ).

Increasing Role of Interpretation in Data Analysis

In addition to the roles methodology and data play in the interpretive process, perhaps the most important is the role of the self/the researcher in the interpretive process. According to Lichtman ( 2013 ), “Data are collected, information is gathered, settings are viewed, and realities are constructed through his or her eyes and ears … the qualitative researcher interprets and makes sense of the data” (p. 21). Eisner ( 1991 ) supported the notion of the researcher “self as instrument,” noting that expert researchers know not simply what to attend to, but also what to neglect. He describes the researcher’s role in the interpretive process as combining sensibility , the ability to observe and ascertain nuances, with schema , a deep understanding or cognitive framework of the phenomena under study.

J. Barrett ( 2007 ) described self/researcher roles as “transformations” (p. 418) at multiple points throughout the inquiry process: early in the process, researchers create representations through data generation, conducting observations and interviews and collecting documents and artifacts. Then,

transformation occurs when the “raw” data generated in the field are shaped into data records by the researcher. These data records are produced through organizing and reconstructing the researcher’s notes and transcribing audio and video recordings in the form of permanent records that serve as the “evidentiary warrants” of the generated data. The researcher strives to capture aspects of the phenomenal world with fidelity by selecting salient aspects to incorporate into the data record. (J. Barrett, 2007 , p. 418)

Transformation continues when the researcher codes, categorizes, and explores patterns in the data (the process we call analysis).

Transformations also involve interpreting what the data mean and relating these interpretations to other sources of insight about the phenomena, including findings from related research, conceptual literature, and common experience.… Data analysis and interpretation are often intertwined and rely upon the researcher’s logic, artistry, imagination, clarity, and knowledge of the field under study. (J. Barrett, 2007 , p. 418)

We mentioned the often-blended roles of participation and observation earlier in this chapter. The role(s) of the self/researcher are often described as points along a participant–observer continuum (see, e.g., Bogdan & Biklen, 2007 ). On the far observer end of this continuum, the researcher situates as detached, tries to be inconspicuous (so as not to impact/disrupt the phenomena under study), and approaches the studied context as if viewing it from behind a one-way mirror. On the opposite, participant end, the researcher is completely immersed and involved in the context. It would be difficult for an outsider to distinguish between researcher and subjects. For example, “some feminist researchers and postmodernists take a political stance and have an agenda that places the researcher in an activist posture. These researchers often become quite involved with the individuals they study and try to improve their human condition” (Lichtman, 2013 , p. 17).

We assert that most researchers fall somewhere between these poles. We believe that complete detachment is both impossible and misguided. In doing so, we, along with many others, acknowledge (and honor) the role of subjectivity, the researcher’s beliefs, opinions, biases, and predispositions. Positivist researchers seeking objective data and accounts either ignore the impact of subjectivity or attempt to drastically diminish/eliminate its impact. Even qualitative researchers have developed methods to avoid researcher subjectivity affecting research data collection, analysis, and interpretation. For example, foundational phenomenologist Husserl ( 1913/1962 ) developed the concept of bracketing , what Lichtman describes as “trying to identify your views on the topic and then putting them aside” (2013, p. 22). Like Slotnick and Janesick ( 2011 ), we ultimately claim “it is impossible to bracket yourself” (p. 1358). Instead, we take a balanced approach, like Eisner, understanding that subjectivity allows researchers to produce the rich, idiosyncratic, insightful, and yet data-based interpretations and accounts of lived experience that accomplish the primary purposes of qualitative inquiry. Eisner ( 1991 ) wrote, “Rather than regarding uniformity and standardization as the summum bonum, educational criticism [Eisner’s form of qualitative research] views unique insight as the higher good” (p. 35). That said, we also claim that, just because we acknowledge and value the role of researcher subjectivity, researchers are still obligated to ground their findings in reasonable interpretations of the data. Eisner ( 1991 ) explained:

This appreciation for personal insight as a source of meaning does not provide a license for freedom. Educational critics must provide evidence and reasons. But they reject the assumption that unique interpretation is a conceptual liability in understanding, and they see the insights secured from multiple views as more attractive than the comforts provided by a single right one. (p. 35)

Connected to this participant–observer continuum is the way the researcher positions him- or herself in relation to the “subjects” of the study. Traditionally, researchers, including early qualitative researchers, anthropologists, and ethnographers, referenced those studied as subjects . More recently, qualitative researchers better understand that research should be a reciprocal process in which both researcher and the foci of the research should derive meaningful benefit. Researchers aligned with this thinking frequently use the term participants to describe those groups and individuals included in a study. Going a step further, some researchers view research participants as experts on the studied topic and as equal collaborators in the meaning-making process. In these instances, researchers often use the terms co-researchers or co-investigators .

The qualitative researcher, then, plays significant roles throughout the inquiry process. These roles include transforming data, collaborating with research participants or co-researchers, determining appropriate points to situate along the participant–observer continuum, and ascribing personal insights, meanings, and interpretations that are both unique and justified with data exemplars. Performing these roles unavoidably impacts and changes the researcher. Slotnick and Janesick ( 2011 ) noted, “Since, in qualitative research the individual is the research instrument through which all data are passed, interpreted, and reported, the scholar’s role is constantly evolving as self evolves” (p. 1358).

As we note later, key in all this is for researchers to be transparent about the topics discussed in the preceding section: What methodological conventions have been employed and why? How have data been treated throughout the inquiry to arrive at assertions and findings that may or may not be transferable to other idiosyncratic contexts? And, finally, in what ways has the researcher/self been situated in and impacted the inquiry? Unavoidably, we assert, the self lies at the critical intersection of data and theory, and, as such, two legs of this stool, data and researcher, interact to create the third, theory.

How Do Qualitative Researchers Engage in the Process of Interpretation?

Theorists seem to have a propensity to dichotomize concepts, pulling them apart and placing binary opposites on the far ends of conceptual continuums. Qualitative research theorists are no different, and we have already mentioned some of these continua in this chapter. For example, in the previous section, we discussed the participant–observer continuum. Earlier, we referenced both Willis’s ( 2007 ) conceptualization of atomistic versus holistic approaches to qualitative analysis and interpretation and Ellingson’s ( 2011 ) science–art continuum. Each of these latter two conceptualizations inform how qualitative researchers engage in the process of interpretation.

Willis ( 2007 ) shared that the purpose of a qualitative project might be explained as “what we expect to gain from research” (p. 288). The purpose, or what we expect to gain, then guides and informs the approaches researchers might take to interpretation. Some researchers, typically positivist/postpositivist, conduct studies that aim to test theories about how the world works and/or how people behave. These researchers attempt to discover general laws, truths, or relationships that can be generalized. Others, less confident in the ability of research to attain a single, generalizable law or truth, might seek “local theory.” These researchers still seek truths, but “instead of generalizable laws or rules, they search for truths about the local context … to understand what is really happening and then to communicate the essence of this to others” (Willis, 2007 , p. 291). In both these purposes, researchers employ atomistic strategies in an inductive process in which researchers “break the data down into small units and then build broader and broader generalizations as the data analysis proceeds” (p. 317). The earlier mentioned processes of analytic induction, constant comparison, and grounded theory fit within this conceptualization of atomistic approaches to interpretation. For example, a line-by-line coding of a transcript might begin an atomistic approach to data analysis.

Alternatively, other researchers pursue distinctly different aims. Researchers with an objective description purpose focus on accurately describing the people and context under study. These researchers adhere to standards and practices designed to achieve objectivity, and their approach to interpretation falls within the binary atomistic/holistic distinction.

The purpose of hermeneutic approaches to research is to “understand the perspectives of humans. And because understanding is situational, hermeneutic research tends to look at the details of the context in which the study occurred. The result is generally rich data reports that include multiple perspectives” (Willis, 2007 , p. 293).

Still other researchers see their purpose as the creation of stories or narratives that utilize “a social process that constructs meaning through interaction … it is an effort to represent in detail the perspectives of participants … whereas description produces one truth about the topic of study, storytelling may generate multiple perspectives, interpretations, and analyses by the researcher and participants” (Willis, 2007 , p. 295).

In these latter purposes (hermeneutic, storytelling, narrative production), researchers typically employ more holistic strategies. According to Willis ( 2007 ), “Holistic approaches tend to leave the data intact and to emphasize that meaning must be derived for a contextual reading of the data rather than the extraction of data segments for detailed analysis” (p. 297). This was the case with the Dreams as Data project mentioned earlier.

We understand the propensity to dichotomize, situate concepts as binary opposites, and create neat continua between these polar descriptors. These sorts of reduction and deconstruction support our understandings and, hopefully, enable us to eventually reconstruct these ideas in meaningful ways. Still, in reality, we realize most of us will, and should, work in the middle of these conceptualizations in fluid ways that allow us to pursue strategies, processes, and theories most appropriate for the research task at hand. As noted, Ellingson ( 2011 ) set up another conceptual continuum, but, like ours, her advice was to “straddle multiple points across the field of qualitative methods” (p. 595). She explained, “I make the case for qualitative methods to be conceptualized as a continuum anchored by art and science, with vast middle spaces that embody infinite possibilities for blending artistic, expository, and social scientific ways of analysis and representation” (p. 595).

We explained at the beginning of this chapter that we view analysis as organizing and summarizing qualitative data and interpretation as constructing meaning. In this sense, analysis allows us to describe the phenomena under study. It enables us to succinctly answer what and how questions and ensures that our descriptions are grounded in the data collected. Descriptions, however, rarely respond to questions of why . Why questions are the domain of interpretation, and, as noted throughout this text, interpretation is complex. Gubrium and Holstein ( 2000 ) noted, “Traditionally, qualitative inquiry has concerned itself with what and how questions … qualitative researchers typically approach why questions cautiously, explanation is tricky business” (p. 502). Eisner ( 1991 ) described this distinctive nature of interpretation: “It means that inquirers try to account for [interpretation] what they have given account of ” (p. 35).

Our focus here is on interpretation, but interpretation requires analysis, because without clear understandings of the data and its characteristics, derived through systematic examination and organization (e.g., coding, memoing, categorizing), “interpretations” resulting from inquiry will likely be incomplete, uninformed, and inconsistent with the constructed perspectives of the study participants. Fortunately for qualitative researchers, we have many sources that lead us through analytic processes. We earlier mentioned the accepted processes of analytic induction and the constant comparison method. These detailed processes (see, e.g., Bogdan & Biklen, 2007 ) combine the inextricably linked activities of analysis and interpretation, with analysis more typically appearing as earlier steps in the process and meaning construction—interpretation—happening later.

A wide variety of resources support researchers engaged in the processes of analysis and interpretation. Saldaña ( 2011 ), for example, provided a detailed description of coding types and processes. He showed researchers how to use process coding (uses gerunds, “-ing” words to capture action), in vivo coding (uses the actual words of the research participants/ subjects), descriptive coding (uses nouns to summarize the data topics), versus coding (uses “vs” to identify conflicts and power issues), and values coding (identifies participants’ values, attitudes, and/or beliefs). To exemplify some of these coding strategies, we include an excerpt from a transcript of a meeting of a school improvement committee. In this study, the collaborators were focused on building “school community.” This excerpt illustrates the application of a variety of codes described by Saldaña to this text:

To connect and elaborate the ideas developed in coding, Saldaña ( 2011 ) suggested researchers categorize the applied codes, write memos to deepen understandings and illuminate additional questions, and identify emergent themes. To begin the categorization process, Saldaña recommended all codes be “classified into similar clusters … once the codes have been classified, a category label is applied to them” (p. 97). So, in continuing with the study of school community example coded here, the researcher might create a cluster/category called “Value of Collaboration” and in this category might include the codes “relationships,” “building community,” and “effective strategies.”

Having coded and categorized a study’s various data forms, a typical next step for researchers is to write memos or analytic memos . Writing analytic memos allows the researcher(s) to

set in words your interpretation of the data … an analytic memo further articulates your … thinking processes on what things may mean … as the study proceeds, however, initial and substantive analytic memos can be revisited and revised for eventual integration into the report itself. (Saldaña, 2011 , p. 98)

In the study of student teaching from K–12 students’ perspectives (Trent & Zorko, 2006 ), we noticed throughout our analysis a series of focus group interview quotes coded “names.” The following quote from a high school student is representative of many others:

I think that, ah, they [student teachers] should like know your face and your name because, uh, I don’t like it if they don’t and they’ll just like … cause they’ll blow you off a lot easier if they don’t know, like our new principal is here … he is, like, he always, like, tries to make sure to say hi even to the, like, not popular people if you can call it that, you know, and I mean, yah, and the people that don’t usually socialize a lot, I mean he makes an effort to know them and know their name like so they will cooperate better with him.

Although we did not ask the focus groups a specific question about whether student teachers knew the K–12 students’ names, the topic came up in every focus group interview. We coded the above excerpt and the others “knowing names,” and these data were grouped with others under the category “relationships.” In an initial analytic memo about this, the researchers wrote,

STUDENT TEACHING STUDY—MEMO #3 “Knowing Names as Relationship Building” Most groups made unsolicited mentions of student teachers knowing, or not knowing, their names. We haven’t asked students about this, but it must be important to them because it always seems to come up. Students expected student teachers to know their names. When they did, students noticed and seemed pleased. When they didn’t, students seemed disappointed, even annoyed. An elementary student told us that early in the semester, “she knew our names … cause when we rose [sic] our hands, she didn’t have to come and look at our name tags … it made me feel very happy.” A high schooler, expressing displeasure that his student teacher didn’t know students’ names, told us, “They should like know your name because it shows they care about you as a person. I mean, we know their names, so they should take the time to learn ours too.” Another high school student said that even after 3 months, she wasn’t sure the student teacher knew her name. Another student echoed, “Same here.” Each of these students asserted that this (knowing students’ names) had impacted their relationship with the student teacher. This high school student focus group stressed that a good relationship, built early, directly impacts classroom interaction and student learning. A student explained it like this: “If you get to know each other, you can have fun with them … they seem to understand you more, you’re more relaxed, and learning seems easier.”

As noted in these brief examples, coding, categorizing, and writing memos about a study’s data are all accepted processes for data analysis and allow researchers to begin constructing new understandings and forming interpretations of the studied phenomena. We find the qualitative research literature to be particularly strong in offering support and guidance for researchers engaged in these analytic practices. In addition to those already noted in this chapter, we have found the following resources provide practical, yet theoretically grounded approaches to qualitative data analysis. For more detailed, procedural, or atomistic approaches to data analysis, we direct researchers to Miles and Huberman’s classic 1994 text, Qualitative Data Analysis , and Bernard et al.’s 2017 book Analyzing Qualitative Data: Systematic Approaches. For analysis and interpretation strategies falling somewhere between the atomistic and holistic poles, we suggest Hesse-Biber and Leavy’s ( 2011 ) chapter, “Analysis and Interpretation of Qualitative Data,” in their book, The Practice of Qualitative Research (second edition); Lichtman’s chapter, “Making Meaning From Your Data,” in her 2013 book Qualitative Research in Education: A User’s Guide (third edition); and “Processing Fieldnotes: Coding and Memoing,” a chapter in Emerson, Fretz, and Shaw’s ( 1995 ) book, Writing Ethnographic Fieldwork . Each of these sources succinctly describes the processes of data preparation, data reduction, coding and categorizing data, and writing memos about emergent ideas and findings. For more holistic approaches, we have found Denzin and Lincoln’s ( 2007 ) Collecting and Interpreting Qualitative Materials and Ellis and Bochner’s ( 2000 ) chapter “Autoethnography, Personal Narrative, Reflexivity” to both be very informative. Finally, Leavy’s 2017 book, Method Meets Art: Arts-Based Research Practice , provides support and guidance to researchers engaged in arts-based research.

Even after reviewing the multiple resources for treating data included here, qualitative researchers might still be wondering, But exactly how do we interpret? In the remainder of this section and in the concluding section of this chapter, we more concretely provide responses to this question and, in closing, we propose a framework for researchers to utilize as they engage in the complex, ambiguous, and yet exciting process of constructing meanings and new understandings from qualitative sources.

These meanings and understandings are often presented as theory, but theories in this sense should be viewed more as “guides to perception” as opposed to “devices that lead to the tight control or precise prediction of events” (Eisner, 1991 , p. 95). Perhaps Erickson’s ( 1986 ) concept of assertions is a more appropriate aim for qualitative researchers. He claimed that assertions are declarative statements; they include a summary of the new understandings, and they are supported by evidence/data. These assertions are open to revision and are revised when disconfirming evidence requires modification. Assertions, theories, or other explanations resulting from interpretation in research are typically presented as “findings” in written research reports. Belgrave and Smith ( 2002 ) emphasized the importance of these interpretations (as opposed to descriptions): “The core of the report is not the events reported by the respondent, but rather the subjective meaning of the reported events for the respondent” (p. 248).

Mills ( 2018 ) viewed interpretation as responding to the question, So what? He provided researchers a series of concrete strategies for both analysis and interpretation. Specific to interpretation, Mills (pp. 204–207) suggested a variety of techniques, including the following:

“ Extend the analysis ”: In doing so, researchers ask additional questions about the research. The data appear to say X , but could it be otherwise? In what ways do the data support emergent finding X ? And, in what ways do they not?

“ Connect findings with personal experience ”: Using this technique, researchers share interpretations based on their intimate knowledge of the context, the observed actions of the individuals in the studied context, and the data points that support emerging interpretations, as well as their awareness of discrepant events or outlier data. In a sense, the researcher is saying, “Based on my experiences in conducting this study, this is what I make of it all.”

“ Seek the advice of ‘critical’ friends ”: In doing so, researchers utilize trusted colleagues, fellow researchers, experts in the field of study, and others to offer insights, alternative interpretations, and the application of their own unique lenses to a researcher’s initial findings. We especially like this strategy because we acknowledge that, too often, qualitative interpretation is a “solo” affair.

“ Contextualize findings in the literature ”: This allows researchers to compare their interpretations to those of others writing about and studying the same/similar phenomena. The results of this contextualization may be that the current study’s findings correspond with the findings of other researchers. The results might, alternatively, differ from the findings of other researchers. In either instance, the researcher can highlight his or her unique contributions to our understanding of the topic under study.

“ Turn to theory ”: Mills defined theory as “an analytical and interpretive framework that helps the researcher make sense of ‘what is going on’ in the social setting being studied.” In turning to theory, researchers search for increasing levels of abstraction and move beyond purely descriptive accounts. Connecting to extant or generating new theory enables researchers to link their work to the broader contemporary issues in the field.

Other theorists offer additional advice for researchers engaged in the act of interpretation. Richardson ( 1995 ) reminded us to account for the power dynamics in the researcher–researched relationship and notes that, in doing so, we can allow for oppressed and marginalized voices to be heard in context. Bogdan and Biklen ( 2007 ) suggested that researchers engaged in interpretation revisit foundational writing about qualitative research, read studies related to the current research, ask evaluative questions (e.g., Is what I’m seeing here good or bad?), ask about implications of particular findings/interpretations, think about the audience for interpretations, look for stories and incidents that illustrate a specific finding/interpretation, and attempt to summarize key interpretations in a succinct paragraph. All these suggestions can be pertinent in certain situations and with particular methodological approaches. In the next and closing section of this chapter, we present a framework for interpretive strategies we believe will support, guide, and be applicable to qualitative researchers across multiple methodologies and paradigms.

In What Ways Can a Framework for Interpretation Strategies Support Qualitative Researchers across Multiple Methodological and Paradigmatic Views?

The process of qualitative research is often compared to a journey, one without a detailed itinerary and ending, but with general direction and aims and yet an open-endedness that adds excitement and thrives on curiosity. Qualitative researchers are travelers. They travel physically to field sites; they travel mentally through various epistemological, theoretical, and methodological grounds; they travel through a series of problem-finding, access, data collection, and data analysis processes; and, finally—the topic of this chapter—they travel through the process of making meaning of all this physical and cognitive travel via interpretation.

Although travel is an appropriate metaphor to describe the journey of qualitative researchers, we will also use “travel” to symbolize a framework for qualitative research interpretation strategies. By design, this framework applies across multiple paradigmatic, epistemological, and methodological traditions. The application of this framework is not formulaic or highly prescriptive; it is also not an anything-goes approach. It falls, and is applicable, between these poles, giving concrete (suggested) direction to qualitative researchers wanting to make the most of the interpretations that result from their research and yet allowing the necessary flexibility for researchers to employ the methods, theories, and approaches they deem most appropriate to the research problem(s) under study.

TRAVEL, a Comprehensive Approach to Qualitative Interpretation

In using the word TRAVEL as a mnemonic device, our aim is to highlight six essential concepts we argue all qualitative researchers should attend to in the interpretive process: transparency, reflexivity, analysis, validity, evidence, and literature. The importance of each is addressed here.

Transparency , as a research concept seems, well, transparent. But, too often, we read qualitative research reports and are left with many questions: How were research participants and the topic of study selected/excluded? How were the data collected, when, and for how long? Who analyzed and interpreted these data? A single researcher? Multiple? What interpretive strategies were employed? Are there data points that substantiate these interpretations/findings? What analytic procedures were used to organize the data prior to making the presented interpretations? In being transparent about data collection, analysis, and interpretation processes, researchers allow reviewers/readers insight into the research endeavor, and this transparency leads to credibility for both researcher and researcher’s claims. Altheide and Johnson ( 2011 ) explained,

There is great diversity of qualitative research.… While these approaches differ, they also share an ethical obligation to make public their claims, to show the reader, audience, or consumer why they should be trusted as faithful accounts of some phenomenon. (p. 584)

This includes, they noted, articulating

what the different sources of data were, how they were interwoven, and … how subsequent interpretations and conclusions are more or less closely tied to the various data … the main concern is that the connection be apparent, and to the extent possible, transparent. (p. 590)

In the Dreams as Data art and research project noted earlier, transparency was addressed in multiple ways. Readers of the project write-up were informed that interpretations resulting from the study, framed as themes , were a result of collaborative analysis that included insights from both students and instructor. Viewers of the art installation/data display had the rare opportunity to see all participant responses. In other words, viewers had access to the entire raw data set (see Trent, 2002 ). More frequently, we encounter only research “findings” already distilled, analyzed, and interpreted in research accounts, often by a single researcher. Allowing research consumers access to the data to interpret for themselves in the Dreams project was an intentional attempt at transparency.

Reflexivity , the second of our concepts for interpretive researcher consideration, has garnered a great deal of attention in qualitative research literature. Some have called this increased attention the reflexive turn (see, e.g., Denzin & Lincoln, 2004 ).

Although you can find many meanings for the term reflexivity, it is usually associated with a critical reflection on the practice and process of research and the role of the researcher. It concerns itself with the impact of the researcher on the system and the system on the researcher. It acknowledges the mutual relationships between the researcher and who and what is studied … by acknowledging the role of the self in qualitative research, the researcher is able to sort through biases and think about how they affect various aspects of the research, especially interpretation of meanings. (Lichtman, 2013 , p. 165)

As with transparency, attending to reflexivity allows researchers to attach credibility to presented findings. Providing a reflexive account of researcher subjectivity and the interactions of this subjectivity within the research process is a way for researchers to communicate openly with their audience. Instead of trying to exhume inherent bias from the process, qualitative researchers share with readers the value of having a specific, idiosyncratic positionality. As a result, situated, contextualized interpretations are viewed as an asset, as opposed to a liability.

LaBanca ( 2011 ), acknowledging the often solitary nature of qualitative research, called for researchers to engage others in the reflexive process. Like many other researchers, LaBanca utilized a researcher journal to chronicle reflexive thoughts, explorations, and understandings, but he took it a step farther. Realizing the value of others’ input, LaBanca posts his reflexive journal entries on a blog (what he calls an online reflexivity blog ) and invites critical friends, other researchers, and interested members of the community to audit his reflexive moves, providing insights, questions, and critique that inform his research and study interpretations.

We agree this is a novel approach worth considering. We, too, understand that multiple interpreters will undoubtedly produce multiple interpretations, a richness of qualitative research. So, we suggest researchers consider bringing others in before the production of the report. This could be fruitful in multiple stages of the inquiry process, but especially in the complex, idiosyncratic processes of reflexivity and interpretation. We are both educators and educational researchers. Historically, each of these roles has tended to be constructed as an isolated endeavor, the solitary teacher, the solo researcher/fieldworker. As noted earlier and in the analysis section that follows, introducing collaborative processes to what has often been a solitary activity offers much promise for generating rich interpretations that benefit from multiple perspectives.

Being consciously reflexive throughout our practice as researchers has benefitted us in many ways. In a study of teacher education curricula designed to prepare preservice teachers to support second-language learners, we realized hard truths that caused us to reflect on and adapt our own practices as teacher educators. Reflexivity can inform a researcher at all parts of the inquiry, even in early stages. For example, one of us was beginning a study of instructional practices in an elementary school. The communicated methods of the study indicated that the researcher would be largely an observer. Early fieldwork revealed that the researcher became much more involved as a participant than anticipated. Deep reflection and writing about the classroom interactions allowed the researcher to realize that the initial purpose of the research was not being accomplished, and the researcher believed he was having a negative impact on the classroom culture. Reflexivity in this instance prompted the researcher to leave the field and abandon the project as it was just beginning. Researchers should plan to openly engage in reflexive activities, including writing about their ongoing reflections and subjectivities. Including excerpts of this writing in research account supports our earlier recommendation of transparency.

Early in this chapter, for the purposes of discussion and examination, we defined analysis as “summarizing and organizing” data in a qualitative study and interpretation as “meaning making.” Although our focus has been on interpretation as the primary topic, the importance of good analysis cannot be underestimated, because without it, resultant interpretations are likely incomplete and potentially uninformed. Comprehensive analysis puts researchers in a position to be deeply familiar with collected data and to organize these data into forms that lead to rich, unique interpretations, and yet interpretations that are clearly connected to data exemplars. Although we find it advantageous to examine analysis and interpretation as different but related practices, in reality, the lines blur as qualitative researchers engage in these recursive processes.

We earlier noted our affinity for a variety of approaches to analysis (see, e.g., Hesse-Biber & Leavy, 2011 ; Lichtman, 2013 ; or Saldaña, 2011 ). Emerson et al. ( 1995 ) presented a grounded approach to qualitative data analysis: In early stages, researchers engage in a close, line-by-line reading of data/collected text and accompany this reading with open coding , a process of categorizing and labeling the inquiry data. Next, researchers write initial memos to describe and organize the data under analysis. These analytic phases allow the researcher(s) to prepare, organize, summarize, and understand the data, in preparation for the more interpretive processes of focused coding and the writing up of interpretations and themes in the form of integrative memos .

Similarly, Mills ( 2018 ) provided guidance on the process of analysis for qualitative action researchers. His suggestions for organizing and summarizing data include coding (labeling data and looking for patterns); identifying themes by considering the big picture while looking for recurrent phrases, descriptions, or topics; asking key questions about the study data (who, what, where, when, why, and how); developing concept maps (graphic organizers that show initial organization and relationships in the data); and stating what’s missing by articulating what data are not present (pp. 179–189).

Many theorists, like Emerson et al. ( 1995 ) and Mills ( 2018 ) noted here, provide guidance for individual researchers engaged in individual data collection, analysis, and interpretation; others, however, invite us to consider the benefits of collaboratively engaging in these processes through the use of collaborative research and analysis teams. Paulus, Woodside, and Ziegler ( 2008 ) wrote about their experiences in collaborative qualitative research: “Collaborative research often refers to collaboration among the researcher and the participants. Few studies investigate the collaborative process among researchers themselves” (p. 226).

Paulus et al. ( 2008 ) claimed that the collaborative process “challenged and transformed our assumptions about qualitative research” (p. 226). Engaging in reflexivity, analysis, and interpretation as a collaborative enabled these researchers to reframe their views about the research process, finding that the process was much more recursive, as opposed to following a linear progression. They also found that cooperatively analyzing and interpreting data yielded “collaboratively constructed meanings” as opposed to “individual discoveries.” And finally, instead of the traditional “individual products” resulting from solo research, collaborative interpretation allowed researchers to participate in an “ongoing conversation” (p. 226).

These researchers explained that engaging in collaborative analysis and interpretation of qualitative data challenged their previously held assumptions. They noted,

through collaboration, procedures are likely to be transparent to the group and can, therefore, be made public. Data analysis benefits from an iterative, dialogic, and collaborative process because thinking is made explicit in a way that is difficult to replicate as a single researcher. (Paulus et al., 2008 , p. 236)

They shared that, during the collaborative process, “we constantly checked our interpretation against the text, the context, prior interpretations, and each other’s interpretations” (p. 234).

We, too, have engaged in analysis similar to these described processes, including working on research teams. We encourage other researchers to find processes that fit with the methodology and data of a particular study, use the techniques and strategies most appropriate, and then cite the utilized authority to justify the selected path. We urge traditionally solo researchers to consider trying a collaborative approach. Generally, we suggest researchers be familiar with a wide repertoire of practices. In doing so, they will be in better positions to select and use strategies most appropriate for their studies and data. Succinctly preparing, organizing, categorizing, and summarizing data sets the researcher(s) up to construct meaningful interpretations in the forms of assertions, findings, themes, and theories.

Researchers want their findings to be sound, backed by evidence, and justifiable and to accurately represent the phenomena under study. In short, researchers seek validity for their work. We assert that qualitative researchers should attend to validity concepts as a part of their interpretive practices. We have previously written and theorized about validity, and, in doing so, we have highlighted and labeled what we consider two distinctly different approaches, transactional and transformational (Cho & Trent, 2006 ). We define transactional validity in qualitative research as an interactive process occurring among the researcher, the researched, and the collected data, one that is aimed at achieving a relatively higher level of accuracy. Techniques, methods, and/or strategies are employed during the conduct of the inquiry. These techniques, such as member checking and triangulation, are seen as a medium with which to ensure an accurate reflection of reality (or, at least, participants’ constructions of reality). Lincoln and Guba’s ( 1985 ) widely known notion of trustworthiness in “naturalistic inquiry” is grounded in this approach. In seeking trustworthiness, researchers attend to research credibility, transferability, dependability, and confirmability. Validity approaches described by Maxwell ( 1992 ) as “descriptive” and “interpretive” also proceed in the usage of transactional processes.

For example, in the write-up of a study on the facilitation of teacher research, one of us (Trent, 2012 ) wrote about the use of transactional processes:

“Member checking is asking the members of the population being studied for their reaction to the findings” (Sagor, 2000 , p. 136). Interpretations and findings of this research, in draft form, were shared with teachers (for member checking) on multiple occasions throughout the study. Additionally, teachers reviewed and provided feedback on the final draft of this article. (p. 44)

This member checking led to changes in some resultant interpretations (called findings in this particular study) and to adaptations of others that shaped these findings in ways that made them both richer and more contextualized.

Alternatively, in transformational approaches, validity is not so much something that can be achieved solely by employing certain techniques. Transformationalists assert that because traditional or positivist inquiry is no longer seen as an absolute means to truth in the realm of human science, alternative notions of validity should be considered to achieve social justice, deeper understandings, broader visions, and other legitimate aims of qualitative research. In this sense, it is the ameliorative aspects of the research that achieve (or do not achieve) its validity. Validity is determined by the resultant actions prompted by the research endeavor.

Lather ( 1993 ), Richardson ( 1997 ), and others (e.g., Lenzo, 1995 ; Scheurich, 1996 ) proposed a transgressive approach to validity that emphasized a higher degree of self-reflexivity. For example, Lather proposed a “catalytic validity” described as “the degree to which the research empowers and emancipates the research subjects” (Scheurich, 1996 , p. 4). Beverley ( 2000 , p. 556) proposed testimonio as a qualitative research strategy. These first-person narratives find their validity in their ability to raise consciousness and thus provoke political action to remedy problems of oppressed peoples (e.g., poverty, marginality, exploitation).

We, too, have pursued research with transformational aims. In the earlier mentioned study of preservice teachers’ experiences learning to teach second-language learners (Cho et al., 2012 ), our aims were to empower faculty members, evolve the curriculum, and, ultimately, better serve preservice teachers so that they might better serve English-language learners in their classrooms. As program curricula and activities have changed as a result, we claim a degree of transformational validity for this research.

Important, then, for qualitative researchers throughout the inquiry, but especially when engaged in the process of interpretation, is to determine the type(s) of validity applicable to the study. What are the aims of the study? Providing an “accurate” account of studied phenomena? Empowering participants to take action for themselves and others? The determination of this purpose will, in turn, inform researchers’ analysis and interpretation of data. Understanding and attending to the appropriate validity criteria will bolster researcher claims to meaningful findings and assertions.

Regardless of purpose or chosen validity considerations, qualitative research depends on evidence . Researchers in different qualitative methodologies rely on different types of evidence to support their claims. Qualitative researchers typically utilize a variety of forms of evidence including texts (written notes, transcripts, images, etc.), audio and video recordings, cultural artifacts, documents related to the inquiry, journal entries, and field notes taken during observations of social contexts and interactions. Schwandt ( 2001 ) wrote,

Evidence is essential to justification, and justification takes the form of an argument about the merit(s) of a given claim. It is generally accepted that no evidence is conclusive or unassailable (and hence, no argument is foolproof). Thus, evidence must often be judged for its credibility, and that typically means examining its source and the procedures by which it was produced [thus the need for transparency discussed earlier]. (p. 82)

Altheide and Johnson ( 2011 ) drew a distinction between evidence and facts:

Qualitative researchers distinguish evidence from facts. Evidence and facts are similar but not identical. We can often agree on facts, e.g., there is a rock, it is harder than cotton candy. Evidence involves an assertion that some facts are relevant to an argument or claim about a relationship. Since a position in an argument is likely tied to an ideological or even epistemological position, evidence is not completely bound by facts, but it is more problematic and subject to disagreement. (p. 586)

Inquirers should make every attempt to link evidence to claims (or findings, interpretations, assertions, conclusions, etc.). There are many strategies for making these connections. Induction involves accumulating multiple data points to infer a general conclusion. Confirmation entails directly linking evidence to resultant interpretations. Testability/falsifiability means illustrating that evidence does not necessarily contradict the claim/interpretation and so increases the credibility of the claim (Schwandt, 2001 ). In the study about learning to teach second-language learners, for example, a study finding (Cho et al., 2012 ) was that “as a moral claim , candidates increasingly [in higher levels of the teacher education program] feel more responsible and committed to … [English language learners]” (p. 77). We supported this finding with a series of data points that included the following preservice teacher response: “It is as much the responsibility of the teacher to help teach second-language learners the English language as it is our responsibility to teach traditional English speakers to read or correctly perform math functions.” Claims supported by evidence allow readers to see for themselves and to both examine researcher assertions in tandem with evidence and form further interpretations of their own.

Some postmodernists reject the notion that qualitative interpretations are arguments based on evidence. Instead, they argue that qualitative accounts are not intended to faithfully represent that experience, but instead are designed to evoke some feelings or reactions in the reader of the account (Schwandt, 2001 ). We argue that, even in these instances where transformational validity concerns take priority over transactional processes, evidence still matters. Did the assertions accomplish the evocative aims? What evidence/arguments were used to evoke these reactions? Does the presented claim correspond with the study’s evidence? Is the account inclusive? In other words, does it attend to all evidence or selectively compartmentalize some data while capitalizing on other evidentiary forms?

Researchers, we argue, should be both transparent and reflexive about these questions and, regardless of research methodology or purpose, should share with readers of the account their evidentiary moves and aims. Altheide and Johnson ( 2011 ) called this an evidentiary narrative and explain:

Ultimately, evidence is bound up with our identity in a situation.… An “evidentiary narrative” emerges from a reconsideration of how knowledge and belief systems in everyday life are tied to epistemic communities that provide perspectives, scenarios, and scripts that reflect symbolic and social moral orders. An “evidentiary narrative” symbolically joins an actor, an audience, a point of view (definition of a situation), assumptions, and a claim about a relationship between two or more phenomena. If any of these factors are not part of the context of meaning for a claim, it will not be honored, and thus, not seen as evidence. (p. 686)

In sum, readers/consumers of a research account deserve to know how evidence was treated and viewed in an inquiry. They want and should be aware of accounts that aim to evoke versus represent, and then they can apply their own criteria (including the potential transferability to their situated context). Renowned ethnographer and qualitative research theorist Harry Wolcott ( 1990 ) urged researchers to “let readers ‘see’ for themselves” by providing more detail rather than less and by sharing primary data/evidence to support interpretations. In the end, readers do not expect perfection. Writer Eric Liu ( 2010 ) explained, “We don’t expect flawless interpretation. We expect good faith. We demand honesty.”

Last, in this journey through concepts we assert are pertinent to researchers engaged in interpretive processes, we include attention to the literature . In discussing literature, qualitative researchers typically mean publications about the prior research conducted on topics aligned with or related to a study. Most often, this research/literature is reviewed and compiled by researchers in a section of the research report titled “Literature Review.” It is here we find others’ studies, methods, and theories related to our topics of study, and it is here we hope the assertions and theories that result from our studies will someday reside.

We acknowledge the value of being familiar with research related to topics of study. This familiarity can inform multiple phases of the inquiry process. Understanding the extant knowledge base can inform research questions and topic selection, data collection and analysis plans, and the interpretive process. In what ways do the interpretations from this study correspond with other research conducted on this topic? Do findings/interpretations corroborate, expand, or contradict other researchers’ interpretations of similar phenomena? In any of these scenarios (correspondence, expansion, contradiction), new findings and interpretations from a study add to and deepen the knowledge base, or literature, on a topic of investigation.

For example, in our literature review for the study of student teaching, we quickly determined that the knowledge base and extant theories related to the student teaching experience were immense, but also quickly realized that few, if any, studies had examined student teaching from the perspective of the K–12 students who had the student teachers. This focus on the literature related to our topic of student teaching prompted us to embark on a study that would fill a gap in this literature: Most of the knowledge base focused on the experiences and learning of the student teachers themselves. Our study, then, by focusing on the K–12 students’ perspectives, added literature/theories/assertions to a previously untapped area. The “literature” in this area (at least we would like to think) is now more robust as a result.

In another example, a research team (Trent et al., 2003 ) focused on institutional diversity efforts, mined the literature, found an appropriate existing (a priori) set of theories/assertions, and then used the existing theoretical framework from the literature as a framework to analyze data, in this case, a variety of institutional activities related to diversity.

Conducting a literature review to explore extant theories on a topic of study can serve a variety of purposes. As evidenced in these examples, consulting the literature/extant theory can reveal gaps in the literature. A literature review might also lead researchers to existing theoretical frameworks that support analysis and interpretation of their data (as in the use of the a priori framework example). Finally, a review of current theories related to a topic of inquiry might confirm that much theory already exists, but that further study may add to, bolster, and/or elaborate on the current knowledge base.

Guidance for researchers conducting literature reviews is plentiful. Lichtman ( 2013 ) suggested researchers conduct a brief literature review, begin research, and then update and modify the literature review as the inquiry unfolds. She suggested reviewing a wide range of related materials (not just scholarly journals) and additionally suggested that researchers attend to literature on methodology, not just the topic of study. She also encouraged researchers to bracket and write down thoughts on the research topic as they review the literature, and, important for this chapter, that researchers “integrate your literature review throughout your writing rather than using a traditional approach of placing it in a separate chapter” (p. 173).

We agree that the power of a literature review to provide context for a study can be maximized when this information is not compartmentalized apart from a study’s findings. Integrating (or at least revisiting) reviewed literature juxtaposed alongside findings can illustrate how new interpretations add to an evolving story. Eisenhart ( 1998 ) expanded the traditional conception of the literature review and discussed the concept of an interpretive review . By taking this interpretive approach, Eisenhart claimed that reviews, alongside related interpretations/findings on a specific topic, have the potential to allow readers to see the studied phenomena in entirely new ways, through new lenses, revealing heretofore unconsidered perspectives. Reviews that offer surprising and enriching perspectives on meanings and circumstances “shake things up, break down boundaries, and cause things (or thinking) to expand” (p. 394). Coupling reviews of this sort with current interpretations will “give us stories that startle us with what we have failed to notice” (p. 395).

In reviews of research studies, it can certainly be important to evaluate the findings in light of established theories and methods [the sorts of things typically included in literature reviews]. However, it also seems important to ask how well the studies disrupt conventional assumptions and help us to reconfigure new, more inclusive, and more promising perspectives on human views and actions. From an interpretivist perspective, it would be most important to review how well methods and findings permit readers to grasp the sense of unfamiliar perspectives and actions. (Eisenhart, 1998 , p. 397)

Though our interpretation-related journey in this chapter nears an end, we are hopeful it is just the beginning of multiple new conversations among ourselves and in concert with other qualitative researchers. Our aims have been to circumscribe interpretation in qualitative research; emphasize the importance of interpretation in achieving the aims of the qualitative project; discuss the interactions of methodology, data, and the researcher/self as these concepts and theories intertwine with interpretive processes; describe some concrete ways that qualitative inquirers engage the process of interpretation; and, finally, provide a framework of interpretive strategies that may serve as a guide for ourselves and other researchers.

In closing, we note that the TRAVEL framework, construed as a journey to be undertaken by researchers engaged in interpretive processes, is not designed to be rigid or prescriptive, but instead is designed to be a flexible set of concepts that will inform researchers across multiple epistemological, methodological, and theoretical paradigms. We chose the concepts of transparency, reflexivity, analysis, validity, evidence, and literature (TRAVEL) because they are applicable to the infinite journeys undertaken by qualitative researchers who have come before and to those who will come after us. As we journeyed through our interpretations of interpretation, we have discovered new things about ourselves and our work. We hope readers also garner insights that enrich their interpretive excursions. Happy travels!

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What is Qualitative Observation? Definition, Types, Examples

Appinio Research · 03.05.2024 · 30min read

Have you ever wondered how researchers gain deep insights into human behavior and social interactions? Qualitative observation offers a fascinating window into the complexities of everyday life, allowing researchers to immerse themselves in natural settings and observe people in their element. From bustling city streets to quiet coffee shops, qualitative observation captures the nuances, subtleties, and context-specific dynamics that shape human experiences. In this guide, we'll explore the definition, purpose, methods, and applications of qualitative observation, providing practical insights and tips for conducting meaningful research and understanding the world around us. Whether you're a student delving into the realm of social sciences or a curious individual eager to explore the intricacies of human behavior, this guide will equip you with the knowledge and skills to navigate the fascinating terrain of qualitative observation with confidence and clarity.

What is Qualitative Observation?

Qualitative observation is a research method used to gather detailed insights into human behavior, experiences, and interactions through direct observation in natural settings. Unlike quantitative methods that rely on numerical data and statistical analysis , qualitative observation focuses on capturing the richness, complexity, and context of social phenomena through descriptive and interpretive means.

The primary purpose of qualitative observation is to deepen understanding and generate insights into the subjective experiences, perspectives, and behaviors of individuals and groups within their natural environments. By immersing researchers in the context of the study, qualitative observation facilitates the exploration of social dynamics, cultural norms, and contextual factors that shape human behavior.

Importance of Qualitative Observation in Research

• Richness of Insights : Qualitative observation allows researchers to explore the depth and complexity of human behavior and interactions in real-world settings, providing rich and detailed insights that may not be captured through quantitative methods alone.
• Contextual Understanding : By immersing researchers in the natural environments of participants, qualitative observation enables the exploration of behavior within its social, cultural, and environmental contexts. This contextual understanding is essential for interpreting and making sense of human actions and interactions.
• Theory Development : Qualitative observation contributes to theory development by generating new hypotheses, concepts , and frameworks grounded in empirical evidence and real-world observations. It allows researchers to uncover patterns , themes, and relationships that inform theoretical perspectives and models.
• Informing Practice : In addition to its role in academic research, qualitative observation has practical applications in various fields, including education, healthcare , business, and social services. It informs decision-making, program development, and policy formulation by providing insights into people's needs, preferences , and experiences.
• Enhanced Validity : Qualitative observation enhances the validity and credibility of research findings by complementing quantitative data with rich, contextualized insights. Triangulation of data sources and methods increases the robustness of research conclusions and reduces the risk of bias or misinterpretation.
• Personal Development : Beyond its role in academic and professional contexts, qualitative observation offers opportunities for personal growth and development. Engaging in observation and reflection fosters empathy, cultural competence, and critical thinking skills, enhancing researchers' capacity to navigate diverse social contexts and understand human behavior.

Qualitative observation is not only a valuable research method but also a powerful tool for understanding and navigating the complexities of human behavior and social interactions in everyday life. Its emphasis on context, depth, and interpretation makes it a versatile and indispensable approach in both research and practice.

Understanding Qualitative Observation

Qualitative observation serves as a fundamental research method across various disciplines, providing rich insights into human behavior, social interactions, and cultural dynamics. To effectively utilize qualitative observation, it's crucial to understand its underlying principles and the different types of observation methods available.

Qualitative Observation Principles

Qualitative observation operates on several fundamental principles that shape the approach to data collection , analysis, and interpretation:

• Subjectivity vs. Objectivity : Unlike quantitative methods that aim for objectivity and generalizability, qualitative observation acknowledges the subjective nature of human experiences. Researchers recognize their role as active participants in the research process, influencing the interpretation of data through their perspectives and biases.
• Contextual Understanding : Qualitative observation emphasizes the importance of understanding behavior within its social, cultural, and environmental contexts. By immersing themselves in the natural settings of participants, researchers gain a deeper appreciation for the factors that shape human actions and interactions.
• Holistic Perspective : Qualitative observation adopts a holistic approach to studying phenomena, focusing on the interconnectedness of various elements within a given context. Researchers seek to capture the complexity and nuance of human experiences, considering multiple layers of meaning and interpretation.
• Inductive Reasoning : Qualitative observation often employs inductive reasoning, allowing patterns and themes to emerge from the data rather than imposing preconceived hypotheses or theories. This open-ended approach enables researchers to explore new insights and perspectives that may challenge existing paradigms.

Types of Qualitative Observation

Qualitative observation encompasses a diverse range of methods, each offering unique advantages and considerations for data collection and analysis:

• Participant Observation : In participant observation, researchers immerse themselves in the natural settings of participants, actively engaging in social interactions and activities. By becoming part of the environment under study, researchers gain insider perspectives and access to rich, contextualized data. This method is particularly well-suited for studying cultural practices, group dynamics, and everyday behaviors.
• Naturalistic Observation : Naturalistic observation involves observing people in their natural environments without intervention or manipulation by the researcher. Researchers adopt a passive role, simply observing and documenting behaviors as they naturally occur. This method provides authentic insights into real-world behaviors and interactions, free from artificial constraints or biases.
• Structured Observation : Structured observation involves defining specific behaviors, events, or criteria for observation in advance. Researchers develop structured protocols or checklists to guide data collection, ensuring consistency and reliability across observations. While less flexible than participant and naturalistic observation, this method allows for standardized data collection and comparison across different contexts or groups.

Each type of qualitative observation offers distinct advantages and challenges, and researchers must carefully consider the appropriateness of each method based on their research goals, context, and ethical considerations.

Qualitative observation is an invaluable tool for gaining deep insights into human behavior and social interactions. With Appinio , conducting qualitative research becomes a seamless and efficient process, allowing researchers to collect real-time consumer insights in minutes without the hassle. By leveraging our intuitive platform and global reach, researchers can unlock a wealth of qualitative data to inform their decision-making and drive business success. Say goodbye to lengthy research processes and hello to actionable insights at your fingertips.

Ready to experience the power of Appinio for yourself? Book a demo today and discover how our platform can revolutionize your qualitative research endeavors!

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How to Prepare for Qualitative Observation?

Before embarking on qualitative observation, thorough planning and preparation are essential to ensure the success and ethical integrity of your study.

1. Define Research Objectives

Defining clear and specific research objectives is the cornerstone of any qualitative observation study. Your research objectives serve as guiding principles that shape your study's scope, focus, and direction.

• Identify Research Questions : Start by identifying the key questions you want to address through your qualitative observation. What phenomena are you interested in exploring? What specific aspects of human behavior or social interactions do you want to investigate?
• Clarify Purpose and Scope : Clearly articulate the purpose and scope of your study. What do you hope to achieve through your observation? Are you aiming to generate new insights, test existing theories, or explore a particular phenomenon in depth?
• Consider Practical Constraints : Take into account any practical constraints or limitations that may impact your research objectives, such as time, resources, and access to participants or observation settings. Set realistic goals that align with the available resources and logistical considerations.

2. Select Observation Methods

Once you've defined your research objectives, the next step is to select the most appropriate observation methods to achieve your goals. Qualitative observation offers a variety of techniques, each with its own strengths and considerations.

• Research Objectives : Choose observation methods that align with your research objectives and questions. Consider whether you need to immerse yourself in the environment as a participant, observe behaviors from a distance, or use structured protocols for data collection.
• Context and Setting : Take into account the specific context and setting of your study. Are you observing individuals in a naturalistic environment, such as a public space or workplace, or are you conducting observations within a controlled setting, such as a laboratory or simulated environment?
• Ethical Considerations : Consider the ethical implications of different observation methods, particularly in terms of privacy, consent, and potential risks to participants. Ensure that your chosen methods adhere to ethical guidelines and respect the rights and dignity of participants.

3. Identify Observation Settings

Identifying the appropriate observation settings is crucial for ensuring the validity and relevance of your observations. The observation setting should provide access to the phenomena of interest while allowing for naturalistic and unobtrusive observation.

• Access and Permissions : Obtain necessary permissions and access to the observation settings, whether they are public spaces, private institutions, or community settings. Seek cooperation from relevant stakeholders, such as facility managers, organizational leaders, or community members.
• Naturalistic Environments : Whenever possible, choose observation settings that reflect the natural environments where the phenomena of interest naturally occur. This could include public spaces, workplaces, classrooms, homes, or other community settings.
• Variety and Diversity : Consider the importance of sampling diverse observation settings to capture a range of experiences, perspectives, and contexts. Avoid over-reliance on a single setting or context, as this may limit the generalizability and richness of your observations.

4. Establish Ethical Guidelines

Given the potential impact on participants' privacy, autonomy, and well-being, ethical considerations are paramount in qualitative observation research. Establishing clear ethical guidelines helps ensure the ethical conduct of your study and protect the rights of participants.

• Informed Consent : Obtain informed consent from participants before initiating any observation activities. Clearly explain the purpose, procedures, risks, and benefits of the study, and allow participants to make an informed decision about their participation.
• Confidentiality and Anonymity : Protect participants' privacy and anonymity by keeping their identities and personal information confidential. Avoid using identifying information in your observations or reporting unless participants explicitly consent.
• Respect for Autonomy : Respect the autonomy and agency of participants throughout the research process. Allow participants to withdraw from the study at any time without consequence and ensure that their decisions are respected without coercion or undue influence.
• Minimization of Harm : Take proactive measures to minimize any potential harm or discomfort to participants arising from the observation process. Be attentive to signs of distress or discomfort and take appropriate steps to address them, including providing support or discontinuing the observation if necessary.

By carefully planning and preparing for qualitative observation, you can lay the foundation for a rigorous, ethical, and insightful study that contributes valuable insights to your field of inquiry.

How to Conduct Qualitative Observation?

Once you've completed the planning phase, it's time to immerse yourself in the field and start collecting data through qualitative observation. Let's take a look at the essential aspects of conducting qualitative observation.

Immersion in the Environment

Immersing yourself in the observation environment is crucial for gaining a deep understanding of the context, culture, and dynamics at play. To effectively immerse yourself:

• Engage with the Environment : Actively participate in the activities and interactions occurring within the observation setting. Immerse yourself in the daily routines, rituals, and social dynamics to gain insider perspectives and insights.
• Observe Unobtrusively : While actively engaging with the environment, strive to maintain a balance between active participation and unobtrusive observation. Avoid drawing undue attention to yourself or disrupting the natural flow of interactions.
• Build Trust and Familiarity : Take the time to build trust and familiarity with the participants and stakeholders in the observation setting. Be approachable, respectful, and non-judgmental in your interactions, allowing participants to feel comfortable and open in your presence.

Building Rapport with Participants

Establishing rapport with participants is essential for gaining their cooperation and obtaining rich, meaningful data. To build rapport effectively:

• Demonstrate Genuine Interest : Show genuine curiosity and interest in the participants' lives, experiences, and perspectives. Listen actively, ask open-ended questions , and express empathy and understanding.
• Respect Cultural Sensitivities : Be mindful of cultural norms, values, and sensitivities that may influence your interactions with participants. Respect their cultural practices, traditions, and beliefs, and avoid imposing your own cultural biases or assumptions.
• Be Transparent and Ethical : Be transparent about the purpose and objectives of your study, as well as the role of participants in the observation process. Ensure that participants understand their rights, including the option to withdraw from the study at any time.

Recording Observations

During observation sessions, recording detailed and accurate observations is essential for capturing the richness and complexity of human behavior and interactions. To record observations effectively:

• Use Multiple Data Collection Methods : Employ a combination of data collection methods , such as field notes, audio recordings, video recordings, photographs, or sketches, to capture different aspects of the observation setting.
• Document Contextual Details : Record not only what is happening but also the context, nuances, and subtleties of interactions. Note the physical environment, social dynamics, non-verbal cues, and emotional expressions that contribute to the overall context of the observation.
• Maintain Objectivity and Neutrality : Strive to maintain objectivity and neutrality in your observations, avoiding personal biases or interpretations. Record observations objectively without filtering or distorting the data to fit preconceived notions or expectations.

Managing Observer Bias

Observer bias refers to the tendency of researchers to interpret observations in a way that aligns with their preconceived beliefs or expectations. To manage observer bias effectively:

• Reflect on Personal Biases : Reflect on your own biases, assumptions, and perspectives that may influence your observations and interpretations. Be aware of how your background, experiences, and beliefs shape your perceptions of the observation setting and participants.
• Seek Diverse Perspectives : Involve multiple observers or researchers in the observation process to mitigate individual biases and enhance the reliability and validity of the observations. Compare and discuss observations to identify and address any discrepancies or biases.
• Triangulate Data Sources : Triangulate your observations with other data sources, such as interviews, surveys , or existing literature, to corroborate findings and minimize the impact of observer bias. Use multiple perspectives and sources of evidence to validate your interpretations.

By immersing yourself in the observation environment, building rapport with participants, recording detailed observations, and managing observer bias, you can conduct qualitative observation effectively and ethically, generating valuable insights into human behavior and social interactions.

Qualitative Observation Examples

Examples of qualitative observation abound in various contexts, offering valuable insights into human behavior, social interactions, and cultural dynamics. Here are a few illustrative examples to showcase the diverse applications and approaches of qualitative observation:

Ethnographic Studies

Ethnographic studies involve immersive, long-term observation of a specific group or community within its natural environment. For example, an ethnographer might live in a tribal community for an extended period, observing their daily routines, rituals, and social interactions. Through participant observation, interviews , and field notes, ethnographers gain deep insights into the cultural beliefs, practices, and norms of the community.

Classroom Observations

In education, qualitative observation plays a crucial role in understanding classroom dynamics, teaching practices, and student behaviors. Researchers may observe classroom activities, interactions between teachers and students, and instructional strategies to identify effective teaching methods and areas for improvement. By capturing the complexity of the learning environment, qualitative observation informs educational policy, curriculum development, and teacher training initiatives.

Workplace Observations

Qualitative observation is also valuable in studying organizational behavior and dynamics within the workplace. Researchers may observe employee interactions, communication patterns, and leadership styles to understand organizational culture, team dynamics, and factors influencing employee satisfaction and productivity. Workplace observations inform management practices, employee training programs, and organizational development strategies to foster a positive work environment and enhance performance.

Clinical Observations

In healthcare settings , qualitative observation studies patient-provider interactions, healthcare delivery processes, and patient experiences. Clinicians and researchers may observe medical consultations, treatment procedures, and patient interactions to identify barriers to effective care, communication challenges, and opportunities for patient-centered interventions. Clinical observations contribute to improving healthcare quality, patient satisfaction , and health outcomes.

Urban Planning and Design

In urban planning and design, qualitative observation helps researchers understand the built environment's impact on human behavior and well-being. Urban planners may observe pedestrian movement patterns, public space utilization, and community interactions to inform the design of cities, neighborhoods, and public infrastructure. Qualitative observation contributes to creating inclusive, accessible, and sustainable urban environments that enhance the quality of life for residents.

These examples demonstrate the versatility and significance of qualitative observation in generating insights, informing decision-making, and addressing complex social phenomena across diverse fields and contexts. Whether studying cultural practices in remote villages, classroom dynamics in schools, or patient experiences in healthcare settings, qualitative observation offers a powerful lens through which to explore the intricacies of human behavior and society.

How to Analyze Qualitative Data from Observation?

Analyzing qualitative data from observation involves systematically organizing, interpreting, and making sense of the rich and nuanced information gathered during the observation process.

Data Coding and Categorization

Data coding and categorization are fundamental processes in qualitative data analysis , enabling researchers to organize and structure the raw data into meaningful units for analysis. To effectively code and categorize qualitative data:

• Open Coding : Begin by engaging in open coding, where you systematically review and categorize the data into initial codes or categories based on recurring patterns, themes, or concepts. This process involves breaking down the data into smaller units and identifying key concepts or ideas.
• Axial Coding : Once you have generated initial codes, use axial coding to establish relationships and connections between codes. Look for patterns, associations, and linkages between different codes, grouping them into broader categories or themes.
• Selective Coding : Finally, engage in selective coding to refine and prioritize the most salient and significant codes or themes that capture the essence of the data. Selective coding involves identifying core themes or concepts that emerge as central to the phenomenon under study and integrating them into a coherent narrative.

Identifying Patterns and Themes

Identifying patterns and themes is a central aspect of qualitative data analysis , allowing researchers to uncover underlying meanings, insights, and relationships within the data. To identify patterns and themes effectively:

• Thematic Analysis : Conduct thematic analysis to systematically identify and explore recurring patterns, themes, or concepts within the data. This involves reviewing the coded data, looking for commonalities, variations, and outliers, and organizing them into meaningful clusters or themes.
• Constant Comparison : Engage in continuous comparison , where you continually compare and contrast different segments of the data to identify similarities and differences. This iterative process allows themes to emerge organically from the data rather than imposing preconceived categories or frameworks.
• Contextual Interpretation : Interpret the identified patterns and themes within the broader context of the observation setting, participants' experiences, and relevant theoretical frameworks. Consider the socio-cultural, historical, and environmental factors that may influence the emergence and significance of the themes.

Integrating Qualitative and Quantitative Data (if applicable)

In some research studies, it may be appropriate to integrate qualitative observation data with quantitative data from other sources to gain a comprehensive understanding of the phenomenon under study. To integrate qualitative and quantitative data effectively:

• Mixed Methods Approach : Adopt a mixed methods approach, where qualitative observation data are triangulated with quantitative data collected through surveys, experiments , or secondary sources. This integration allows for a more holistic and nuanced analysis of the research problem, providing multiple perspectives and insights.
• Complementary Analysis : Analyze qualitative and quantitative data separately to identify unique insights and patterns within each dataset. Then, integrate the findings through comparison, contrast, or synthesis to identify convergent or divergent themes and trends.
• Data Transformation : Transform qualitative observation data into quantitative metrics or variables for comparative analysis with quantitative data. This may involve quantifying qualitative codes or themes into numerical scores or categories for statistical analysis.

Ensuring Data Trustworthiness and Reliability

Ensuring the trustworthiness and reliability of qualitative data is essential for establishing the validity and credibility of the research findings. To ensure data trustworthiness and reliability:

• Credibility : Enhance credibility by employing rigorous data collection and analysis techniques, maintaining detailed documentation of the research process, and engaging in member checking, where participants review and validate the findings to ensure accuracy and authenticity.
• Transferability : Enhance transferability by providing rich, detailed descriptions of the observation setting, participants, and data collection procedures, allowing readers to assess the applicability of the findings to other contexts or populations.
• Dependability : Enhance dependability by ensuring transparency and consistency in the research process, including clear documentation of data collection methods, coding procedures, and analytical decisions. Engage in peer debriefing and external audits to verify the reliability of the findings.
• Confirmability : Enhance confirmability by maintaining reflexivity throughout the research process, acknowledging and addressing personal biases or assumptions that may influence the interpretation of the data. Use transparent and systematic approaches to data analysis, allowing for independent verification by other researchers.

By systematically analyzing qualitative data from observation, researchers can uncover meaningful patterns, themes, and insights that provide rich insights into human behavior, social interactions, and cultural dynamics. By ensuring the trustworthiness and reliability of the data, researchers can generate robust and credible findings that contribute to the body of knowledge in their respective fields.

How to Report Qualitative Observation Findings?

Reporting findings from qualitative observation is a critical step in the research process. It enables researchers to communicate their insights, interpretations, and conclusions to the broader academic community and stakeholders. We'll explore the key considerations for effectively reporting findings from qualitative observation studies.

1. Choose the Right Format

Choosing the proper format for reporting your qualitative observation findings depends on various factors, including the nature of the research, the preferences of the target audience, and the intended impact of the study. Popular formats include:

• Research Papers : Academic journals are a common platform for reporting qualitative observation findings. Research papers typically follow a standardized structure, including an abstract, introduction, methods, results, discussion, and conclusion sections. Choose a journal that specializes in qualitative research and aligns with the scope and focus of your study.
• Reports : Reports provide a more comprehensive and detailed overview of the research findings, including background information, methods, results, and implications. Reports may be distributed to stakeholders, funding agencies, or organizational partners interested in the study outcomes.
• Presentations : Presentations offer an opportunity to disseminate key findings and insights to a broader audience in a concise and engaging format. Consider presenting your findings at academic conferences, professional meetings, or community forums to share your research with peers and stakeholders.

2. Structure the Narrative

Structuring the narrative of your qualitative observation report is essential for guiding readers through the research process and facilitating understanding and interpretation of the findings. When structuring the narrative, make sure to include these elements:

• Introduction : Provide an overview of the research problem, objectives, and significance of the study. Briefly summarize the research design , methods, and approach to qualitative observation.
• Methods : Describe the methods used to conduct qualitative observation, including the observation setting, participants , data collection procedures, and ethical considerations. Provide sufficient detail to allow readers to assess the rigor and credibility of the study.
• Results : Present the key findings and insights derived from qualitative observation. Organize the results thematically, highlighting recurring patterns, themes, or categories that emerged from the data. Use illustrative examples and quotes to support your interpretations.
• Discussion : Interpret and discuss the implications of the findings in relation to the research objectives, theoretical frameworks, and existing literature. Explore the significance of the findings, their practical implications, and areas for further research.
• Conclusion : Summarize the study's main findings and conclusions, emphasizing their relevance and contributions to the field. Reflect on the research's strengths and limitations and offer recommendations for future research or practice.

3. Incorporate Quotes and Examples

Incorporating quotes and examples from the qualitative observation data adds depth, richness, and authenticity to your report, helping to illustrate key themes, insights, and interpretations.

• Selecting Representative Quotes : Choose quotes that capture the essence of participants' experiences, perspectives, and emotions. Select quotes that are vivid, compelling, and representative of the broader themes or patterns identified in the data.
• Providing Contextual Information : Provide contextual information to accompany the quotes, including details about the participant, the observation setting, and the specific context in which the quote was obtained. This helps readers understand the significance and relevance of the quote within the broader narrative of the study.
• Interpreting Quotes : Interpret and analyze the quotes within the discussion section of your report, providing insights into their meanings, implications, and contributions to the overall findings. Avoid simply presenting quotes without analysis or interpretation, as this may limit the depth of understanding for readers.

4. Address Limitations and Future Directions

Every research study, including qualitative observation, has its limitations and areas for improvement. Acknowledging and addressing these limitations is essential for maintaining transparency and credibility in your reporting.

• Limitations : Identify and discuss any limitations or challenges encountered during the research process, such as sample size constraints, data collection biases, or contextual constraints. Be honest and transparent about the limitations of the study and their potential impact on the validity and generalizability of the findings.
• Future Directions : Based on your study's findings, suggest potential avenues for future research or areas for further exploration. Consider unanswered questions, emerging themes, or areas of controversy that warrant further investigation. Offer recommendations for methodological improvements or alternative approaches to address the limitations identified.

By carefully choosing a suitable format, structuring the narrative effectively, incorporating quotes and examples, and addressing limitations and future directions, you can create a compelling and informative report that effectively communicates the findings of your qualitative observation study to the broader academic community and stakeholders.

Conclusion for Qualitative Observation

Qualitative observation is a powerful tool for understanding the richness and complexity of human behavior and social interactions. By immersing researchers in natural settings and allowing them to observe people in their everyday environments, qualitative observation offers unique insights that complement quantitative methods. From uncovering cultural norms and social dynamics to informing policy decisions and program development, qualitative observation has diverse applications across various fields, including academia, healthcare, business, and social services. Embracing the principles of subjectivity, contextuality, and inductive reasoning, qualitative observation empowers researchers to explore the depth and nuance of human experiences, ultimately contributing to our collective understanding of the world around us. Furthermore, as we continue to navigate an increasingly interconnected and diverse world, the importance of qualitative observation only grows. Its emphasis on context, perspective, and interpretation enables researchers to bridge disciplinary boundaries, engage with diverse communities, and address complex social issues. By fostering empathy, cultural competence, and critical thinking skills, qualitative observation not only advances knowledge and scholarship but also promotes social justice, equity, and inclusion.

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• Open access
• Published: 08 May 2024

Exploring dental professionals’ outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan

• Wajiha Qamar 1 ,
• Nadia Khaleeq 2 ,
• Anita Nisar 3 ,
• Sahibzadi Fatima Tariq 4 &
• Mehreen Lajber 5  

BMC Oral Health volume  24 , Article number:  542 ( 2024 ) Cite this article

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Metrics details

The purpose of this study is to explore the perspectives, familiarity, and readiness of dental faculty members regarding the integration and application of artificial intelligence (AI) in dentistry, with a focus on the possible effects on dental education and clinical practice.

Methodology

In a mix-method cross-sectional quantitative and quantitative study conducted between June 1st and August 30th, 2023, the perspectives of faculty members from a public sector dental college in Pakistan regarding the function of AI were explored. This study used qualitative as well as quantitative techniques to analyse faculty’s viewpoints on the subject. The sample size was comprised of twenty-three faculty members. The quantitative data was analysed using descriptive statistics, while the qualitative data was analysed using theme analysis.

Position-specific differences in faculty familiarity underscore the value of individualized instruction. Surprisingly few had ever come across AI concepts in their professional lives. Nevertheless, many acknowledged that AI had the potential to improve patient outcomes. The majority thought AI would improve dentistry education. Participants suggested a few dental specialties where AI could be useful.

The study emphasizes the significance of addressing in dental professionals’ knowledge gaps about AI. The promise of AI in dentistry calls for specialized training and teamwork between academic institutions and AI developers. Graduates of dentistry programs who use AI are better prepared to navigate shifting environments. The study highlights the positive effects of AI and the value of faculty involvement in maximizing its potential for better dental education and practice.

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Introduction

The concept of artificial intelligence (AI) emerged as a breakthrough across various domains in an era of rapid technological advancement [ 1 ]. It has revolutionized industries and sectors around the world, aiming to create intelligent machines that can follow human cognitive capabilities. Its impact is remarkable, especially in the context of evolving global scenario. AI possesses the ability to improve diagnosis precision, effectiveness of treatment, and overall patient care, making healthcare one of the sectors where its significance is becoming more and more evident [ 2 ].

The application of AI into healthcare systems has already started transforming medical practices around the world through accurate diagnosis and tailored treatment plans [ 2 ]. In this regard, the prospect of AI cannot be overlooked in the field of dentistry, which is frequently viewed as an essential aspect of healthcare. Dental practitioners are currently employing AI-driven solutions to streamline the processes involved in diagnosis, treatment, and patient care [ 3 ]. AI assists with image analysis, predictive modelling, and decision support through various applications, improving clinical results [ 3 ].

Given its merits and use, the potential of AI in dentistry is particularly interesting. AI has proven to be helpful for addressing disparities in healthcare across a wide range of geographic and economic situations [ 4 ]. Delivering high-quality dental treatment to populations in countries that are developing like Pakistan presents particular difficulties. Due to its capacity to fill knowledge and scarcity of resources, AI now plays a more significant part in diagnostic and treatment planning [ 4 ]. AI can help dentists making informed choices that have a positive impact on patient outcomes by providing specific information and recommendations [ 5 ].

Additionally, it is impractical to overlook how AI is affecting both clinical practice as well as dental education. The incorporation of AI into the curriculum could provide fresh graduates the understanding and abilities they need to successfully navigate the changing dentistry practice landscape [ 6 ]. As a result, it is critical for dental faculty to comprehend, accept, and successfully incorporate AI concepts into their curricula. The inclusion of AI-related themes in the curriculum could enhance students’ ability to take leverage of technological advancements in their future careers, which will result in a more knowledgeable and skilled dental workforce [ 6 , 7 ].

Dental faculty’s comprehension of AI is essential as the field of dentistry advances. However, it has been recognized that incorporating AI into dental education necessitates an appropriate approach [ 5 ]. In addition to having a thorough understanding of AI, faculty members must be able to develop and roll out programs that reflect how AI is employed in dental practice. Dental education demands a strategic capacity-enhancement program that can assist professionals negotiate the complex world of AI integration [ 6 ]. The faculty members who implement this program should be better able to understand the ethical implications, clinical significance, and practical application of AI in the field of dentistry.

Many studies are focusing on AI-powered solutions for automated detection of oral disorders, treatment planning optimization, and enhancement of interaction with patients as they explore the impact of AI on dentistry and dental education [ 3 ]. Furthermore, cooperative initiatives are being made to create thorough AI-focused curricula for dentistry schools, ensuring that future dental professionals have the knowledge to use full advantage of AI’s potential in their current positions. These works together add to the body of knowledge that emphasizes the revolutionary effects of AI in dentistry and emphasizes the importance of faculty involvement and comprehension of this development. Considering the existing knowledge and factors listed above, this study aims to review the dental faculty members’ perceptions and comprehension of the use of AI in dentistry. This study intends to provide insight into the current level of AI integration in dental education through studying the knowledge, attitudes, and readiness of faculty members to use AI. Additionally, it aims to emphasize the requirement for an established program for capacity training that enables dental faculty to successfully harness AI for the improvement of dental teaching and practice.

Materials and methods

The cross-sectional quantitative study was carried out from 1st June to 30th August 2023 at a public sector dental college located in the Khyber Pakhtunkhwa province of Pakistan, which is recognized for graduating more than 175 students per year. Prior to commencing the study, approval was sought and obtained from the ethical review committee of the institute. The committee’s approval was obtained after presenting the objectives, methodology, and scope of the study for their review and consideration.

To ensure a representative sample, a simple random sampling technique was used. A comprehensive list of all prospective faculty members was compiled, encompassing individuals from various positions, including postgraduate trainees, lecturers, assistants, associates, and professors across both basic and clinical sciences departments of the dental college. Each prospective participant was assigned a unique number to facilitate randomization. Using a random number generator technique, 35 people at random were selected from the compiled list. Out of these 35 participants, only 23 participated in the study, yielding a response rate of 65.71%.

The study’s participation was voluntary, and the researchers individually interviewed those selected to ensure uniformity and consistency. Despite careful application of randomization to ensure an unbiased selection process, it is crucial to recognize the potential of selection bias resulting from voluntary participation. Self-selection bias may be introduced because individuals who freely choose to engage can have different perspectives or motives than those who decline. To minimize that, the researchers thoroughly explained the objectives and approach of the study during these interviews. Furthermore, participants’ rights and the study’s objective were properly disclosed, and ethical principles including informed consent were rigorously followed. Participants received assurance of anonymity, and individual privacy was protected through the aggregation and group presentation of data. In addition, participants were made aware that the data collected will be shared and used to guide policy choices, so advancing dental knowledge.

For this study, a specially designed questionnaire addressing a variety of topics about AI’s importance in dentistry was developed. The process of developing the questionnaire was informed by a thorough review of relevant research papers and articles in the domain. Rigorous efforts were made to validate the questionnaire’s relevance and appropriateness for the setting of research, reducing any concerns regarding its reliability, even though formal validation procedures were not feasible due to financial constraints. The purpose of the questionnaire was to gather opinions, knowledge, and suggestions from dental professionals about the use of AI in dentistry. Qualitative methods were also employed to augment the quantitative data that was gathered. Thematic analysis was conducted to analyse qualitative responses and further understand participant viewpoints.

Utilising statistical techniques, the quantitative data was gathered and analysed, enabling an in-depth analysis of participants’ perspectives about artificial intelligence in dentistry. The integration of quantitative and qualitative methodologies enabled a comprehensive investigation of faculty attitudes towards artificial intelligence in dentistry, resulting in a more comprehensive comprehension of the topic.

Based on the findings from our study, we discovered that participants’ familiarity with AI ideas and their use in dentistry differed depending on their position. The overall awareness rate was 39.14% ( n  = 9), however there were notable variances when looked down by position. As shown in Fig.  1 , while lecturers showed a familiarity rate of 25% ( n  = 3), postgraduate trainees claimed no familiarity. Among the participants, Associate Professors had the highest familiarity rate, at 66% ( n  = 3), while Assistant Professors displayed a slightly lower familiarity rate of 57% ( n  = 7). Further discussion and assessment revealed that only a small percentage of participants (8.7%, n  = 2) reported having had any exposure to the concepts of artificial intelligence during their academic and professional careers. Participants with exposure to or understanding of AI concepts conveyed their perspective on AI application in dentistry, suggesting that integrating Computer-aided design (CAD) and Computer-aided manufacturing (CAM) and Exocad with AI-enabled Cone beam computed tomography (CBCT) interpretation as a promising way to improve the efficiency of diagnosis and treatment planning. They acknowledged these possible advantages, but they also recognised a lack of familiarity AI in dentistry and uncertain of its applicability or usefulness in their field. Nonetheless, the participants expressed a positive outlook about artificial intelligence’s potential to advance several facets of dental care, highlighting the technology’s ability to improve treatment planning and diagnostic accuracy, particularly about dentofacial abnormalities, orthodontic interventions, and prosthetic treatments.

Awareness of AI concepts among various academic designations

On the other hand, none of the participants learned any AI principles while working in their respective fields. When explored they provided diverse perspectives about potential applications of AI in dentistry, highlighting the technology’s ability to facilitate treatment and planning procedures, patient record management, training medical and dental students, and microscope-assisted histology and pathology diagnosis. While some participants were uncertain or uninformed about AI in dentistry, others recognised how it may improve radiological results and treatment planning. They emphasized how it may help with a range of dental procedures, including orthognathic surgery, and increase the accuracy of diagnostics. It’s interesting to note that a sizable percentage of participants (82.6%, n  = 19) recognized the value of AI applications in improving patient outcomes. The potential benefits of AI technology in dentistry were also emphasised by the participants such as to streamline diagnostic procedures, establish orthodontic and surgical treatment, and analyse radiological data. They also underlined the potential applications of AI in prosthetics, education, postoperative care, and preventative interventions. Some, meanwhile, voiced concerns or doubts about AI’s complete integration and efficacy in specific dental treatments.

Regarding the range of AI applications in dentistry, participants provided a range of viewpoints, from a targeted use in particular departments to a more general potential across the board. Some highlighted that AI is mostly useful in surgical settings, particularly for intricate extraction techniques. Others emphasised its importance in the planning of orthodontic treatments, where AI might improve accuracy and expedite procedures. Moreover, a portion of participants supported the use of AI in research projects, arguing that its analytical powers may help with data interpretation and analysis. On the other hand, other participants expressed their belief that AI technologies had the potential to completely transform the dental field. They envisioned AI being integrated into different practice areas and specialisations to maximise patient results and care. These different points of view highlight the continuous discussion in the dental community over the extent of AI’s influence and its potential to influence dentistry’s future.

Only 34.7% ( n  = 8) of the respondents said that basic sciences were another area where AI’s effectiveness was recognised, in addition to clinical sciences. During further discussion, it was revealed that the participants perceived that AI-driven educational technologies may be integrated into lectures to improve teaching methods, automated laboratory procedures can be used to accelerate studies, and AI can be used for microscopic examination and diagnosis of slides. Participants also emphasized how AI could enhance teaching and learning in dentistry education, especially in areas like anatomy and physiology, by using creative teaching strategies and interactive platforms.

Furthermore, a sizable majority (73.9%, n  = 17) thought that the incorporation of AI may improve undergraduate dental education. The need for faculty-focused AI programs was acknowledged by everyone who participated. This highlighted the need for faculty in the field of dentistry to receive training that is specifically geared toward AI. A variety of topics and areas that participants felt should be included in a successful training programme were highlighted. These include the use of AI for research and undergraduate teaching, with an emphasis on the basic and clinical sciences. The significance of incorporating AI principles into methods of teaching, lectures, research projects, lab work, and clinical practice was also emphasised by the participants. A few responders also emphasised how important it is to comprehend the fundamental ideas behind AI and how it can be applied to certain fields like radiography, operational dentistry, and oral medicine. Fig.  2 provides an overview of AI applications in dentistry: A perspective of dental faculty.

AI applications in dentistry: a viewpoint from dental faculty

To ensure the efficacy of the tailored training programme in improving faculty competency in AI applications for dentistry, participants made several recommendations. One suggestion was to develop an extensive curriculum for undergraduate students that included AI’s function in both basic and clinical sciences. Additionally, they promoted the inclusion of AI principles in laboratory work, research, and education, with a focus on real-world applications. Participants also suggested specialised training courses centred on AI-related research, introducing pertinent software gradually after covering basic topics. In addition, suggestions included integrating AI training into faculty capacity development initiatives and holding interactive workshops and debates on the fundamentals of AI. The need of working with AI teachers was emphasised to gain more knowledge and understanding of how AI is used in dentistry. The perspectives of the participants areas of dentistry where AI are considered advantageous differed and are demonstrated in Fig.  3 . One of the many fields where AI has the potential to be useful in the future is “entire dentistry”, according to 43.5% ( n  = 10) of participants. However, 21.7% ( n  = 5) of respondents specifically mentioned that the use of AI is limited to orthodontic treatment planning, while 17.4% ( n  = 4) of respondents added that AI might only be useful for difficult surgical and extraction procedures.

AI applications limited to the following dental disciplines

These results highlight the diverse levels of understanding of artificial intelligence (AI) concepts among dental specialties, the recognition of AI’s potential to enhance patient outcomes, and the significance of incorporating AI into dental education and clinical practice. The study also identifies several dental fields where AI is thought to have significant impact.

The findings of the chi-square test suggest a significant association between participants’ positions and their familiarity with AI concepts (χ² = 37.21, df = 3, p  < 0.001). This underscores the importance of considering professional roles when exploring attitudes and understanding towards AI in dentistry.

We investigated dental faculty knowledge, exposure to, and attitudes regarding AI in the field of dentistry. The findings provided interesting insights concerning the interactions of these professionals in different roles with AI concepts.

The results showed significant variations in positions’ familiarity with AI. The associate professors displayed the highest familiarity, at 66%, while lecturers had the lowest, at 25%, emphasizing the importance of experience. The lack of familiarity indicated by postgraduate trainees highlights educational gaps. Our results are consistent with similar research from an adjacent region, which identified similar patterns of low AI comprehension among dental professionals [ 8 , 9 , 10 ]. To address the awareness disparity, both studies underlined the necessity for AI education and its integration with dental courses [ 9 ].

Additionally, just 8.7% of our indicated they had encountered AI concepts at some point in their academic or professional careers. Despite several confessing to being inexperienced with the AI, participants cautiously see potential in AI for dentistry, notably in enhancing diagnostic and treatment planning [ 10 ]. Our study emphasizes the significance of comprehensive AI education integration into dental courses and ongoing professional development programs [ 10 ]. Furthermore, given that AI concepts are seldom taught in the workplace, it raises concerns about how effectively society will be able to adopt new technology. Most communication channels will eventually be transformed by AI, necessitating the need for dental schools to educate current students about these significant advances in technology [ 11 ]. In addition to opening opportunities to train dental professionals to employ AI approaches in clinical practice, its adoption holds huge potential for improving healthcare and educational opportunities [ 6 , 12 ]. The dental community and regulatory authorities must implement the suggested criteria to properly digitalize dentistry education with AI technology. Research projects that could involve academic, business, and government collaboration can improve knowledge and use of AI education in the workplace.

Considering the rapid advancement of AI in healthcare sectors, including dentistry, this lack of exposure is particularly alarming. For procedures like diagnostics, individualized treatment plans, and predictive analytics, other sectors have embraced AI [ 3 ]. The incorporation of AI in the field of dentistry in blooming in recent years, unlike other industries [ 13 ].

Despite having minimal exposure to and knowledge with AI, a significant percentage of participants (82.6%) were aware of its potential to enhance patient outcomes. This implies a theoretical comprehension of the advantages of AI, even though it might not directly translate to implementation in practice. This confidence in AI’s potential to improve patient outcomes is consistent with findings from related studies in the medical field [ 4 , 6 , 13 , 14 ]. A recent study by Bajwa et al. (2021) found that a sizable percentage of healthcare professionals believe AI could contribute to better patient care. However, our study adds depth by exploring the differences in familiarity based on various positions within the dental profession.

Surprisingly, our study revealed a positive assessment of AI’s contribution to dental education, despite participants’ limited understanding and background in AI concepts. Most participants (73.9%) thought that using AI may improve undergraduate dental education. This finding supports the growing body of evidence that shows AI has the capability to transform education through targeted instruction, data-driven insights, and interactive simulations [ 4 , 9 , 13 , 15 ].

Only 34.7% of the participants thought that AI could be utilized in basic sciences, demonstrating the need for a greater awareness of AI’s more general applicability. These concerns are similar to those raised in research that revealed challenges in describing the transdisciplinary character of AI [ 9 ]. To fully embrace AI’s potential, professionals must understand how it may be applied across a variety of fields.

The potential of AI in dental education has been highlighted in studies, which is consistent with our participants’ positive thoughts toward its incorporation into undergraduate the dental profession education [ 16 ]. However, our work adds to this body of knowledge by highlighting the necessity of faculty members receiving specialized AI training to ensure successful implementation.

One of the most important conclusions from our study is that all participants acknowledged that faculty-focused AI training programs are essential. This emphasizes how crucial it is for academics to be knowledgeable about AI to integrate it into the curriculum and educate a new generation of AI-savvy the dental professionals. Tailored training programmes that meet the unique demands of different academic levels and are based on in-depth assessments of training needs are crucial. These courses ought to be designed to improve their capacity to use AI to dentistry in a productive way.

Our study distinguishes itself by focusing on the position-based variations in AI familiarity when comparing our results with those of earlier studies carried out in comparable regions and among comparable populations. Our research stands out from others since we have concentrated on the critical requirement for faculty development that is specifically geared toward AI.

While highlighting current circumstances, our study additionally highlights the significance of ongoing research to monitor the progression of AI awareness and integration in dentistry. An ongoing assessment of dental professionals’ readiness to use AI technologies is critical as these technologies advance. Following the implementation of faculty-focused training programs, this can entail conducting follow-up research to evaluate changes in familiarity rates, perceptions, and real AI integration.

The study’s conclusion underlines that there exists significant difference in familiarity with AI concepts among dental professionals based on their respective positions within the profession. Despite varying levels of familiarity, a consensus among participants acknowledges the potential of AI applications in improving patient outcomes. There may be a training and education gap in dentistry given the limited exposure to AI concepts during academic and professional careers. Therefore, it’s imperative to develop a comprehensive curricula that highlight real-world applications and integrate AI’s role in both basic and clinical sciences for undergraduate students.

The integration of AI is seen advantageous in several dental fields, such as clinical practice, fundamental sciences, and dental education for undergraduates. This calls for, applying AI concepts to research, teaching, and lab work while emphasising useful applications and real-world situations. Moreover, all the participants concur that faculty-focused AI programmes are essential, highlighting the need of specialised training programmes to equip dental professionals for the integration of AI into clinical practice and instruction such as interactive seminars and debates to foster understanding as well as AI training in faculty capacity building programmes. Lastly, it is crucial to provide specialised training classes on AI-related research; after going over the fundamentals, progressively introduce relevant software.

Limitations

It’s critical to recognise several limitations when assessing the implications of our study’s findings since they might affect how the data are interpreted and used more broadly. Among these some are:

The limited sample size of the study, which originated from a single dental institution in a specific region of Pakistan, could make it challenging to generalise the results to a wider population of dental professionals.

Due to the voluntary nature of participation, selection bias may be introduced, which might affect participant perspectives and attitudes as well as the sample’s overall representativeness.

The study’s cross-sectional approach makes it difficult to establish causal links and makes it difficult to observe changes in views regarding artificial intelligence in dentistry over time.

Data availability

The datasets generated and/or analysed during the current study are not publicly available as we assured all participants that their data would be handled with the utmost confidentiality and under no circumstances would it be shared with any other parties. However, the datasets created and/or analysed during the current study are not publicly available but are nonetheless available from the corresponding author upon reasonable request.

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Department of Oral Biology at Bacha Khan College of Dentistry, Mardan, Pakistan

Wajiha Qamar

Department of Community Dentistry, Institute of Public Health & Social Sciences, Khyber Medical University, Peshawar, Pakistan

Nadia Khaleeq

Senior Registrar at Department of Periodontology Rehman College of Dentistry, Peshawar, Pakistan

Anita Nisar

Assistant Professor at Department of Oral Pathology Rehman College of Dentistry, Peshawar, Pakistan

Sahibzadi Fatima Tariq

Department of Medical Education at Bacha Khan Medical College, Mardan, Pakistan

Mehreen Lajber

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Contributions

1. Wajiha Qamar (WQ) was involved in the conception of the study, in-depth literature review, drafting the article, data collection, analysis, reviews and edits, and journal correspondence management, 2. Nadia Khaleeq (NK) actively contributed to the literature review, contributed to the initial manuscript draft, data collection, analysis, conducting revisions, and participating in the editing process, 3. Anita Nisar (AN) actively participated in the literature review, data collection, contributed to the initial draft carried out reviews and edits, and made ensured the article matched to the journal’s requirements, 4. Sahibzadi Fatima Tariq (SFT) & Mehreen Lajber (ML) actively participated in the process of doing a literature review, data collection, analysis, contributing to the original draft, took part in review and editing, managing reference, and carrying out the final review to make sure everything complied with the journal’s guidelines.

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Correspondence to Wajiha Qamar .

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We requested and received the ethical review committee of the institute where the research was conducted after presenting the objectives, approach, and scope of the study. The study was approved from the Ethics Committee of Bacha Khan College of Dentistry Mardan, as indicated by notification number 204/BKCD, dated June 1, 2023. Informed consent was taken from all the participants of the study before commencing the study. The objectives of the research were thoroughly explained to every participant during the study. Participants were also made aware that their participation was entirely optional. Furthermore, it was made clear that no other individuals would have access to any information.

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Qamar, W., Khaleeq, N., Nisar, A. et al. Exploring dental professionals’ outlook on the future of dental care amidst the integration of artificial intelligence in dentistry: a pilot study in Pakistan. BMC Oral Health 24 , 542 (2024). https://doi.org/10.1186/s12903-024-04305-7

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DOI : https://doi.org/10.1186/s12903-024-04305-7

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Research on internal quality testing method of dry longan based on terahertz imaging detection technology

• Original Paper
• Published: 11 May 2024

Cite this article

• Jun Hu   ORCID: orcid.org/0000-0003-0027-7993 1 ,
• Hao Wang 1 ,
• Yongqi Zhou 1 ,
• Shimin Yang 1 ,
• Haohao Lv 1 &
• Liang Yang 1  

Longan is a kind of nut with rich nutritional value and homologous function of medicine and food. The quality of longan directly affects its curative effect, and its fullness is the key index to evaluate its quality. However, the internal information of longan cannot be obtained from the outside. Therefore, rapid non-destructive testing of internal quality of dry longan is of great significance. In this paper, rapid non-destructive testing of longan internal fullness based on terahertz transmission imaging technology was carried out. This study takes longan as the research object. Firstly, the terahertz transmission images of longans with different fullness were collected, and the terahertz spectral signals of different regions of interest were extracted for analysis. Then, three qualitative discriminant models, support vector machine (SVM), Random forest (RF) and linear discriminant analysis (LDA), were established to explore the optimal discriminant model and realize the distinction of different regional categories of longan. Finally, the collected longan terahertz transmission image is processed, and the number of white pixels in the connected domain is calculated by using Otsu threshold segmentation and image inversion. The fullness of longan can be achieved by calculating the ratio of core and pulp to the pixel of the shell. The LDA discriminant model had the best prediction effect. It could not only identify the spectral data of background region, shell region, core region, but also reach 98.57% accuracy for the spectral data of pulp region. The maximum error between the measured fullness and the actual fullness of the terahertz image processed by Otsu threshold segmentation is less than 3.11%. Terahertz imaging technique can realize rapid non-destructive detection of longan fullness and recognition of different regions. This study provides an effective scheme for selecting the quality of longan.

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Acknowledgements

National Youth Natural Science Foundation of China (32302261); Jiangxi Ganpo Talented Support Plan -Young science and technology talent Lift Project (2023QT04); Jiangxi Provincial Youth Science Fund Project (20224BAB215042); National Key R&D Program of China (2022YFD2001805).

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School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang, 330013, Jiangxi, China

Jun Hu, Hao Wang, Yongqi Zhou, Shimin Yang, Haohao Lv & Liang Yang

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Jun Hu: Investigation, Writing-review and editing, Experimental scheme design, Formal analysis. Hao Wang: Writing-original draft, Formal analysis. Yongqi Zhou: Experiment. Shimin Yang, Haohao Lv: Review and editing. Liang Yang: Formal analysis.

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Correspondence to Jun Hu .

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We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. Jun Hu, Hao Wang, Yongqi Zhou, Shimin Yang, Haohao Lv, Liang Yang declare that they have no conflict of interest.

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Hu, J., Wang, H., Zhou, Y. et al. Research on internal quality testing method of dry longan based on terahertz imaging detection technology. Food Measure (2024). https://doi.org/10.1007/s11694-024-02583-x

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Received : 29 December 2023

Accepted : 18 April 2024

Published : 11 May 2024

DOI : https://doi.org/10.1007/s11694-024-02583-x

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