role of the researcher dissertation

The Role of the Qualitative Researcher

In the following, we'll explore how the researcher conducting qualitative research becomes responsible for maintaining the rigor and credibility of various aspects of the research. In a way, this is analogous to the role statistics, validated and reliable instruments, and standardized measures and methods play in quantitative research.

After reviewing this document, you will be able to:

• Compare the role of the qualitative researcher with the role of standardized instruments, measures, and methods in quantitative analysis.
• Monitoring and reducing bias,
• Developing competence in one's methods,
• Collecting the data,
• Analyzing the data, and
• Presenting the findings.

Integrity of the Research is the Issue

Recall from other qualitative courses that qualitative researchers are as concerned about the integrity of their research as quantitative researchers, but they face different challenges. Before examining how the researcher is key to research integrity in qualitative research, let's note some terminology differences between the methodologies. The below provides them at a glance. These are terms related to research integrity:

In Quantitative: designs, validity, reliability, and generalizability (or external validity) are based on the integrity of the design, and of the methods, and instruments used, and only to a lesser extent to the person of the researcher.

In Qualitative: on the other hand, credibility, dependability, and transferability rely on the person and performance of the researcher.

This is why we talk about the role of the researcher in qualitative research.

The Integrity of the Research Equals The Integrity of the Researcher

Of course, this is true of both quantitative and qualitative research. Researchers make errors, and these threaten the validity, reliability, and utility of their studies.

Qualitative researchers, however, lack many of the protections against errors that the statistical methods, standardized measures, and classical designs afford. They must rely on their own competence, openness, and honesty. That is, on their person. Thus, their role, the role of the researcher is more open to scrutiny.

Role of Researcher: Monitoring and Reducing Bias

Bias is a source of error. When a quantitative researcher administers a standardized test, bias is less a problem than when a qualitative researcher has a conversation with a participant. Why?

The researcher's ideas—about the study, her knowledge, about the topic from the literature review, hopes for the study, and simply human distractibility—crop up constantly and can distort what she hears. Confirmation bias—(the name for this) afflicts quantitative researchers, too, but more often when they are analyzing data and seeing what they are disposed to see. Qualitative researchers, whose human brains are trained to find meaning in everything, encounter confirmation bias in every interaction with both participants and data.

Therefore, monitoring and reducing one's disposition to interpret too quickly is an essential part of the researcher's role. Qualitative researchers have evolved a variety of methods for this, such as the famous phenomenological reduction and epoché, but every design within qualitative methodology requires an explicit description of how the researcher will remain conscious of his or her previous knowledge and dispositions and how he or she will control the intrusion of bias.

For example, many qualitative researchers practice mindfulness meditation as a means to become aware when their thoughts are about previous knowledge rather than open and receptive to the information from the participant.

Role of Researcher: Developing Competence in Methods

Many novice researchers think they are competent to do qualitative research. Unfortunately, they are usually wrong.

Qualitative methods, like quantitative methods, require implementing specialized skills correctly. Competence in these skills is required at all these points:

• Explaining the study without biasing the potential participants.
• Conducting interviews properly, according to the design.
• Making appropriate field observations.
• Selecting appropriate artifacts, images, journal portions, and so on.
• Handling data per design.
• Analyzing and interpreting the data per the design.

This competence is not taught in most methods courses; novice researchers are often expected to obtain training and practice on their own. What should they do?

Here are some ideas, although they are not prescriptions and you may find many other ways to develop competence.

The first step: is to self-assess your competence. Assume you do not have competence in each of the skill areas unless you have demonstrated it to someone who knows. If you perform interviews of clients, for example, but have never been taught to do interviews for research, assume you do not have the competence until a researcher who uses interviews tells you that you do.

The next step: is to talk with your mentor— about a plan to get training. For example, many learners who need to demonstrate competence in qualitative interviews do a few practice interviews and ask their mentors to critique their technique. The coaching not only amounts to a kind of training, but the mentor can then attest to the researcher's baseline competence. Another common plan is to attend training workshops in the actual design—such as grounded theory—conducted in research organizations or universities.

For each skill set your design requires you to have, including practicing the analysis methods, create a training plan that includes demonstrating competence to someone.

Is this more work? Maybe so, maybe not. If you were conducting a multiple regression analysis and did not know how to do that, you'd have to learn it, practice it, and demonstrate your competence to someone. So, it's all a matter of perspective.

Role of Researcher: Collecting and Analyzing Data

There are far too many complications in collecting and analyzing qualitative data to cover in this presentation. Have you ever:

• Wired someone with a microphone and inadvertently touched a sensitive body part?
• Sat in a schoolyard to make field observations amid the chaos and swirl of 200 hundred children at recess and known where to start?
• Been confronted with 500 pages of a single-spaced transcript and, known where to start?
• Brought a straying interviewee back to the topic in a way that not only did not offend but actually improved rapport?
• Asked questions that didn't betray what you think the answer should be?
• Sorted through 10,000 sentences or 500 pictures to identify which ones should be retained as data and which ones could be discarded?
• Recognized when you have an actual finding. In other words, can you spot a finding in qualitative analysis?

These are but a few of the challenges that the qualitative researcher faces. Are you ready? Probably not. What should you do?

• Acknowledge that you are a novice. A dissertation is an apprenticeship or internship in research. No one expects the apprentice or intern to be a master.
• Committee members.
• Other dissertators, those in your mentor's courseroom, but also others around the world. Read similar dissertations and write to their authors asking for tips and tricks. Authors love knowing that someone is reading their work.
• Professional researchers. These professionals are scholars, and they will help, at least many of them will. Two or three e-mails that yield excellent advice—and perhaps an ongoing relationship—are well worth the investment of anxiety and time.

Role of Researcher: Presenting the Findings

Most of us present findings in writing. While a few will also present their findings in posters and oral presentations, everyone in Track 3 will at least present them in writing.

Develop and demonstrate competence in writing!

Dr. James Meredith of the Capella Writing Program points out that you have to write your way out of the doctoral program.

Capella makes an extraordinary effort to provide support and instruction in scholarly writing, primarily through the Capella Writing Program and the Online Writing Center. Failing to take advantage of all these resources will result in your findings being sent back to you for revision. Why waste the time? Right now, you can and should start to make use of:

• The Scholarly Communications Guide; it's available in the Dissertation Research Seminar courseroom for you.
• Review and get familiar with the Dissertation Chapter Four Guide (qualitative or quantitative); this too is available in the Dissertation Milestone Resources area on iGuide. It offers a conventional way to structure the findings chapter of the dissertation. By learning it now, you'll have in mind a set of ideas about what sort of competence in writing and in analyzing your data you'll need at this point in the project.
• Resources for writing in the Capella Writing Program; these are broad and deep—you will be ignoring a treasure that would help you succeed if you fail to take advantage of these.
• And, perhaps most important, read dissertations and articles; read dozens in your specific methodology and design (for example, phenomenology or grounded theory). Get to know what other novice researchers are doing and how well they are doing it. Open your mind to learning from them, and remain critical of their errors and foibles: we all have them. Make it your goal to absorb the style and conventions of writers using your methodology and design.
• Learn APA style; again, Dr. Meredith reminds us that the correct use of APA format and style is an automatic claim to credibility! Remember that the converse is also true: APA errors, or even ignoring the format and style rules, automatically deprive your writing of credibility and trustworthiness.

We've covered the importance of evaluating your own role as the researcher, in the various elements of a qualitative study:

• Monitoring and reducing bias.
• Developing competence in one's methods.
• Collecting the data.
• Analyzing the data.

Doc. reference: phd_t3_u06s1_qualrole.html

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• Dissertation Content

A doctoral dissertation makes an original contribution to knowledge, as defined in a discipline or an interdisciplinary domain and addresses a significant researchable problem. Not all problems are researchable and not all are significant. Problems that can be solved by a mere descriptive exercise are not appropriate for the PhD dissertation. Acceptable problems are those that:

• pose a puzzle to the field at a theoretical, methodological, or policy level;
• make analytical demands for solution, rather than mere cataloging or descriptive demands; and
• can yield to a reasonable research methodology.

The doctoral dissertation advisor, reading committee, and oral exam committee provide further guidance and details with regard to dissertation content and format. General formatting and submission guidelines are published by the University Registrar. The American Psychological Association (APA) publication guidelines normally apply to GSE doctoral dissertations, but is not required if the advisor and relevant committees determine that an alternative, and academically acceptable, protocol is more appropriate.

Published Papers and Multiple Authorship

The inclusion of published papers in a dissertation is the prerogative of the major department.  Where published papers or ready-for-publication papers are included, the following criteria must be met:

1. There must be an introductory chapter that integrates the general theme of the research and the relationship between the chapters.  The introduction may also include a review of the literature relevant to the dissertation topic that does not appear in the chapters.

2. Multiple authorship of a published paper should be addressed by clearly designating, in an introduction, the role that the dissertation author had in the research and production of the published paper.  The student must have a major contribution to the research and writing of papers included in the dissertation.

3. There must be adequate referencing of where individual papers have been published.

4. Written permission must be obtained for all copyrighted materials; letters of permission must be uploaded electronically in PDF form when submitting the dissertation.  Please see the following website for more information on the use of copyrighted materials: http://library.stanford.edu/using/copyright-reminder .

5. The submitted material must be in a form that is legible and reproducible as required by these specifications.  The Office of the University Registrar will approve a dissertation if there are no deviations from the normal specifications that would prevent proper dissemination and utilization of the dissertation.  If the published material does not correspond to these standards, it will be necessary for the student to reformat that portion of the dissertation.

6. Multiple authorship has implications with respect to copyright and public release of the material.  Be sure to discuss copyright clearance and embargo options with your co-authors and your advisor well in advance of preparing your thesis for submission.

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How to tackle the PhD dissertation

Finding time to write can be a challenge for graduate students who often juggle multiple roles and responsibilities. Mabel Ho provides some tips to make the process less daunting

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Is it time to turn off turnitin, use ai to get your students thinking critically, taming anxiety around public speaking, emotions and learning: what role do emotions play in how and why students learn.

Writing helps you share your work with the wider community. Your scholarship is important and you are making a valuable contribution to the field. While it might be intimidating to face a blank screen, remember, your first draft is not your final draft! The difficult part is getting something on the page to begin with. 

As the adage goes, a good dissertation is a done dissertation, and the goal is for you to find balance in your writing and establish the steps you can take to make the process smoother. Here are some practical strategies for tackling the PhD dissertation.

Write daily

This is a time to have honest conversations with yourself about your writing and work habits. Do you tackle the most challenging work in the morning? Or do you usually start with emails? Knowing your work routine will help you set parameters for the writing process, which includes various elements, from brainstorming ideas to setting outlines and editing. Once you are aware of your energy and focus levels, you’ll be ready to dedicate those times to writing.

While it might be tempting to block a substantial chunk of time to write and assume anything shorter is not useful, that is not the case. Writing daily, whether it’s a paragraph or several pages, keeps you in conversation with your writing practice. If you schedule two hours to write, remember to take a break during that time and reset. You can try:

• The Pomodoro Technique: a time management technique that breaks down your work into intervals
• Taking breaks: go outside for a walk or have a snack so you can come back to your writing rejuvenated
• Focus apps: it is easy to get distracted by devices and lose direction. Here are some app suggestions: Focus Bear (no free version); Forest (free version available); Cold Turkey website blocker (free version available) and Serene (no free version). 

This is a valuable opportunity to hone your time management and task prioritisation skills. Find out what works for you and put systems in place to support your practice. 

• Resources on academic writing for higher education professionals
• Stretch your work further by ‘triple writing’
• What is your academic writing temperament?

Create a community

While writing can be an isolating endeavour, there are ways to start forming a community (in-person or virtual) to help you set goals and stay accountable. There might be someone in your cohort who is also at the writing stage with whom you can set up a weekly check-in. Alternatively, explore your university’s resources and centres because there may be units and departments on campus that offer helpful opportunities, such as a writing week or retreat. Taking advantage of these opportunities helps combat isolation, foster accountability and grow networks. They can even lead to collaborations further down the line.

• Check in with your advisers and mentors. Reach out to your networks to find out about other people’s writing processes and additional resources.
• Don’t be afraid to share your work. Writing requires constant revisions and edits and finding people who you trust with feedback will help you grow as a writer. Plus, you can also read their work and help them with their editing process.
• Your community does not have to be just about writing!  If you enjoy going on hikes or trying new coffee shops, make that part of your weekly habit.  Sharing your work in different environments will help clarify your thoughts and ideas.

Address the why

The PhD dissertation writing process is often lengthy and it is sometimes easy to forget why you started. In these moments, it can be helpful to think back to what got you excited about your research and scholarship in the first place. Remember it is not just the work but also the people who propelled you forward. One idea is to start writing your “acknowledgements” section. Here are questions to get you started:

• Do you want to dedicate your work to someone? 
• What ideas sparked your interest in this journey? 
• Who cheered you on? 

This practice can help build momentum, as well as serve as a good reminder to carve out time to spend with your community. 

You got this!

Writing is a process. Give yourself grace, as you might not feel motivated all the time. Be consistent in your approach and reward yourself along the way. There is no single strategy when it comes to writing or maintaining motivation, so experiment and find out what works for you. 

Suggested readings

• Thriving as a Graduate Writer by Rachel Cayley (2023)
• Destination Dissertation by Sonja K. Foss and William Waters (2015)
• The PhD Writing Handbook by Desmond Thomas (2016).

Mabel Ho is director of professional development and student engagement at Dalhousie University.

If you would like advice and insight from academics and university staff delivered direct to your inbox each week,  sign up for the Campus newsletter .

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Home » Dissertation – Format, Example and Template

Dissertation – Format, Example and Template

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Dissertation

Definition:

Dissertation is a lengthy and detailed academic document that presents the results of original research on a specific topic or question. It is usually required as a final project for a doctoral degree or a master’s degree.

Dissertation Meaning in Research

In Research , a dissertation refers to a substantial research project that students undertake in order to obtain an advanced degree such as a Ph.D. or a Master’s degree.

Dissertation typically involves the exploration of a particular research question or topic in-depth, and it requires students to conduct original research, analyze data, and present their findings in a scholarly manner. It is often the culmination of years of study and represents a significant contribution to the academic field.

Types of Dissertation

Types of Dissertation are as follows:

Empirical Dissertation

An empirical dissertation is a research study that uses primary data collected through surveys, experiments, or observations. It typically follows a quantitative research approach and uses statistical methods to analyze the data.

Non-Empirical Dissertation

A non-empirical dissertation is based on secondary sources, such as books, articles, and online resources. It typically follows a qualitative research approach and uses methods such as content analysis or discourse analysis.

Narrative Dissertation

A narrative dissertation is a personal account of the researcher’s experience or journey. It typically follows a qualitative research approach and uses methods such as interviews, focus groups, or ethnography.

Systematic Literature Review

A systematic literature review is a comprehensive analysis of existing research on a specific topic. It typically follows a qualitative research approach and uses methods such as meta-analysis or thematic analysis.

Case Study Dissertation

A case study dissertation is an in-depth analysis of a specific individual, group, or organization. It typically follows a qualitative research approach and uses methods such as interviews, observations, or document analysis.

Mixed-Methods Dissertation

A mixed-methods dissertation combines both quantitative and qualitative research approaches to gather and analyze data. It typically uses methods such as surveys, interviews, and focus groups, as well as statistical analysis.

How to Write a Dissertation

Here are some general steps to help guide you through the process of writing a dissertation:

• Choose a topic : Select a topic that you are passionate about and that is relevant to your field of study. It should be specific enough to allow for in-depth research but broad enough to be interesting and engaging.
• Conduct research : Conduct thorough research on your chosen topic, utilizing a variety of sources, including books, academic journals, and online databases. Take detailed notes and organize your information in a way that makes sense to you.
• Create an outline : Develop an outline that will serve as a roadmap for your dissertation. The outline should include the introduction, literature review, methodology, results, discussion, and conclusion.
• Write the introduction: The introduction should provide a brief overview of your topic, the research questions, and the significance of the study. It should also include a clear thesis statement that states your main argument.
• Write the literature review: The literature review should provide a comprehensive analysis of existing research on your topic. It should identify gaps in the research and explain how your study will fill those gaps.
• Write the methodology: The methodology section should explain the research methods you used to collect and analyze data. It should also include a discussion of any limitations or weaknesses in your approach.
• Write the results: The results section should present the findings of your research in a clear and organized manner. Use charts, graphs, and tables to help illustrate your data.
• Write the discussion: The discussion section should interpret your results and explain their significance. It should also address any limitations of the study and suggest areas for future research.
• Write the conclusion: The conclusion should summarize your main findings and restate your thesis statement. It should also provide recommendations for future research.
• Edit and revise: Once you have completed a draft of your dissertation, review it carefully to ensure that it is well-organized, clear, and free of errors. Make any necessary revisions and edits before submitting it to your advisor for review.

Dissertation Format

The format of a dissertation may vary depending on the institution and field of study, but generally, it follows a similar structure:

• Title Page: This includes the title of the dissertation, the author’s name, and the date of submission.
• Abstract : A brief summary of the dissertation’s purpose, methods, and findings.
• Table of Contents: A list of the main sections and subsections of the dissertation, along with their page numbers.
• Introduction : A statement of the problem or research question, a brief overview of the literature, and an explanation of the significance of the study.
• Literature Review : A comprehensive review of the literature relevant to the research question or problem.
• Methodology : A description of the methods used to conduct the research, including data collection and analysis procedures.
• Results : A presentation of the findings of the research, including tables, charts, and graphs.
• Discussion : A discussion of the implications of the findings, their significance in the context of the literature, and limitations of the study.
• Conclusion : A summary of the main points of the study and their implications for future research.
• References : A list of all sources cited in the dissertation.
• Appendices : Additional materials that support the research, such as data tables, charts, or transcripts.

Dissertation Outline

Dissertation Outline is as follows:

Title Page:

• Title of dissertation
• Author name
• Institutional affiliation
• Date of submission
• Brief summary of the dissertation’s research problem, objectives, methods, findings, and implications
• Usually around 250-300 words

Table of Contents:

• List of chapters and sections in the dissertation, with page numbers for each

I. Introduction

• Background and context of the research
• Research problem and objectives
• Significance of the research

II. Literature Review

• Overview of existing literature on the research topic
• Identification of gaps in the literature
• Theoretical framework and concepts

III. Methodology

• Research design and methods used
• Data collection and analysis techniques
• Ethical considerations

IV. Results

• Presentation and analysis of data collected
• Findings and outcomes of the research
• Interpretation of the results

V. Discussion

• Discussion of the results in relation to the research problem and objectives
• Evaluation of the research outcomes and implications
• Suggestions for future research

VI. Conclusion

• Summary of the research findings and outcomes
• Implications for the research topic and field
• Limitations and recommendations for future research

VII. References

• List of sources cited in the dissertation

VIII. Appendices

• Additional materials that support the research, such as tables, figures, or questionnaires.

Example of Dissertation

Here is an example Dissertation for students:

Title : Exploring the Effects of Mindfulness Meditation on Academic Achievement and Well-being among College Students

This dissertation aims to investigate the impact of mindfulness meditation on the academic achievement and well-being of college students. Mindfulness meditation has gained popularity as a technique for reducing stress and enhancing mental health, but its effects on academic performance have not been extensively studied. Using a randomized controlled trial design, the study will compare the academic performance and well-being of college students who practice mindfulness meditation with those who do not. The study will also examine the moderating role of personality traits and demographic factors on the effects of mindfulness meditation.

Chapter Outline:

Chapter 1: Introduction

• Background and rationale for the study
• Research questions and objectives
• Significance of the study
• Overview of the dissertation structure

Chapter 2: Literature Review

• Definition and conceptualization of mindfulness meditation
• Theoretical framework of mindfulness meditation
• Empirical research on mindfulness meditation and academic achievement
• Empirical research on mindfulness meditation and well-being
• The role of personality and demographic factors in the effects of mindfulness meditation

Chapter 3: Methodology

• Research design and hypothesis
• Participants and sampling method
• Intervention and procedure
• Measures and instruments
• Data analysis method

Chapter 4: Results

• Descriptive statistics and data screening
• Analysis of main effects
• Analysis of moderating effects
• Post-hoc analyses and sensitivity tests

Chapter 5: Discussion

• Summary of findings
• Implications for theory and practice
• Limitations and directions for future research
• Conclusion and contribution to the literature

Chapter 6: Conclusion

• Recap of the research questions and objectives
• Summary of the key findings
• Contribution to the literature and practice
• Implications for policy and practice
• Final thoughts and recommendations.

References :

List of all the sources cited in the dissertation

Appendices :

Additional materials such as the survey questionnaire, interview guide, and consent forms.

Note : This is just an example and the structure of a dissertation may vary depending on the specific requirements and guidelines provided by the institution or the supervisor.

How Long is a Dissertation

The length of a dissertation can vary depending on the field of study, the level of degree being pursued, and the specific requirements of the institution. Generally, a dissertation for a doctoral degree can range from 80,000 to 100,000 words, while a dissertation for a master’s degree may be shorter, typically ranging from 20,000 to 50,000 words. However, it is important to note that these are general guidelines and the actual length of a dissertation can vary widely depending on the specific requirements of the program and the research topic being studied. It is always best to consult with your academic advisor or the guidelines provided by your institution for more specific information on dissertation length.

Applications of Dissertation

Here are some applications of a dissertation:

• Advancing the Field: Dissertations often include new research or a new perspective on existing research, which can help to advance the field. The results of a dissertation can be used by other researchers to build upon or challenge existing knowledge, leading to further advancements in the field.
• Career Advancement: Completing a dissertation demonstrates a high level of expertise in a particular field, which can lead to career advancement opportunities. For example, having a PhD can open doors to higher-paying jobs in academia, research institutions, or the private sector.
• Publishing Opportunities: Dissertations can be published as books or journal articles, which can help to increase the visibility and credibility of the author’s research.
• Personal Growth: The process of writing a dissertation involves a significant amount of research, analysis, and critical thinking. This can help students to develop important skills, such as time management, problem-solving, and communication, which can be valuable in both their personal and professional lives.
• Policy Implications: The findings of a dissertation can have policy implications, particularly in fields such as public health, education, and social sciences. Policymakers can use the research to inform decision-making and improve outcomes for the population.

When to Write a Dissertation

Here are some situations where writing a dissertation may be necessary:

• Pursuing a Doctoral Degree: Writing a dissertation is usually a requirement for earning a doctoral degree, so if you are interested in pursuing a doctorate, you will likely need to write a dissertation.
• Conducting Original Research : Dissertations require students to conduct original research on a specific topic. If you are interested in conducting original research on a topic, writing a dissertation may be the best way to do so.
• Advancing Your Career: Some professions, such as academia and research, may require individuals to have a doctoral degree. Writing a dissertation can help you advance your career by demonstrating your expertise in a particular area.
• Contributing to Knowledge: Dissertations are often based on original research that can contribute to the knowledge base of a field. If you are passionate about advancing knowledge in a particular area, writing a dissertation can help you achieve that goal.
• Meeting Academic Requirements : If you are a graduate student, writing a dissertation may be a requirement for completing your program. Be sure to check with your academic advisor to determine if this is the case for you.

Purpose of Dissertation

some common purposes of a dissertation include:

• To contribute to the knowledge in a particular field : A dissertation is often the culmination of years of research and study, and it should make a significant contribution to the existing body of knowledge in a particular field.
• To demonstrate mastery of a subject: A dissertation requires extensive research, analysis, and writing, and completing one demonstrates a student’s mastery of their subject area.
• To develop critical thinking and research skills : A dissertation requires students to think critically about their research question, analyze data, and draw conclusions based on evidence. These skills are valuable not only in academia but also in many professional fields.
• To demonstrate academic integrity: A dissertation must be conducted and written in accordance with rigorous academic standards, including ethical considerations such as obtaining informed consent, protecting the privacy of participants, and avoiding plagiarism.
• To prepare for an academic career: Completing a dissertation is often a requirement for obtaining a PhD and pursuing a career in academia. It can demonstrate to potential employers that the student has the necessary skills and experience to conduct original research and make meaningful contributions to their field.
• To develop writing and communication skills: A dissertation requires a significant amount of writing and communication skills to convey complex ideas and research findings in a clear and concise manner. This skill set can be valuable in various professional fields.
• To demonstrate independence and initiative: A dissertation requires students to work independently and take initiative in developing their research question, designing their study, collecting and analyzing data, and drawing conclusions. This demonstrates to potential employers or academic institutions that the student is capable of independent research and taking initiative in their work.
• To contribute to policy or practice: Some dissertations may have a practical application, such as informing policy decisions or improving practices in a particular field. These dissertations can have a significant impact on society, and their findings may be used to improve the lives of individuals or communities.
• To pursue personal interests: Some students may choose to pursue a dissertation topic that aligns with their personal interests or passions, providing them with the opportunity to delve deeper into a topic that they find personally meaningful.

Advantage of Dissertation

Some advantages of writing a dissertation include:

• Developing research and analytical skills: The process of writing a dissertation involves conducting extensive research, analyzing data, and presenting findings in a clear and coherent manner. This process can help students develop important research and analytical skills that can be useful in their future careers.
• Demonstrating expertise in a subject: Writing a dissertation allows students to demonstrate their expertise in a particular subject area. It can help establish their credibility as a knowledgeable and competent professional in their field.
• Contributing to the academic community: A well-written dissertation can contribute new knowledge to the academic community and potentially inform future research in the field.
• Improving writing and communication skills : Writing a dissertation requires students to write and present their research in a clear and concise manner. This can help improve their writing and communication skills, which are essential for success in many professions.
• Increasing job opportunities: Completing a dissertation can increase job opportunities in certain fields, particularly in academia and research-based positions.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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This question is about what a researcher does and researcher .

What is the role of a researcher?

The role of a researcher is to use the scientific method and research process to better understand the world around us. Researchers typically work for academic institutions or businesses. Researchers gather data during the project life cycle, analyze the data, and publish the findings to aid new research, enrich scholarly literature, and improve decision-making.

Researcher Duties and Responsibilities:

Work with a team of other researchers and committees to plan research objectives and test parameters

Identify research methods, variables, data collection techniques, and analysis methods

Monitor the project to make sure it follows the requirements and standards

Interpret the data, produce reports discussing research findings and provide recommendations at the end of the project

Determine areas of research to increase knowledge in a particular field

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The academic researcher role: enhancing expectations and improved performance

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This article distinguishes between six tasks related to the academic researcher role: (1) networking; (2) collaboration; (3) managing research; (4) doing research; (5) publishing research; and (6) evaluation of research. Data drawn from surveys of academic staff, conducted in Norwegian universities over three decades, provide evidence that the researcher role has become more demanding with respect to all sub-roles, and that academic staff have responded to increasing external and internal demands by enhancing their role performance.

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Measurement and analysis of change in research scholars’ knowledge and attitudes toward statistics after PhD coursework

• Mariyamma Philip 1  

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Knowledge of statistics is highly important for research scholars, as they are expected to submit a thesis based on original research as part of a PhD program. As statistics play a major role in the analysis and interpretation of scientific data, intensive training at the beginning of a PhD programme is essential. PhD coursework is mandatory in universities and higher education institutes in India. This study aimed to compare the scores of knowledge in statistics and attitudes towards statistics among the research scholars of an institute of medical higher education in South India at different time points of their PhD (i.e., before, soon after and 2–3 years after the coursework) to determine whether intensive training programs such as PhD coursework can change their knowledge or attitudes toward statistics.

One hundred and thirty research scholars who had completed PhD coursework in the last three years were invited by e-mail to be part of the study. Knowledge and attitudes toward statistics before and soon after the coursework were already assessed as part of the coursework module. Knowledge and attitudes towards statistics 2–3 years after the coursework were assessed using Google forms. Participation was voluntary, and informed consent was also sought.

Knowledge and attitude scores improved significantly subsequent to the coursework (i.e., soon after, percentage of change: 77%, 43% respectively). However, there was significant reduction in knowledge and attitude scores 2–3 years after coursework compared to the scores soon after coursework; knowledge and attitude scores have decreased by 10%, 37% respectively.

The study concluded that the coursework program was beneficial for improving research scholars’ knowledge and attitudes toward statistics. A refresher program 2–3 years after the coursework would greatly benefit the research scholars. Statistics educators must be empathetic to understanding scholars’ anxiety and attitudes toward statistics and its influence on learning outcomes.

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A PhD degree is a research degree, and research scholars submit a thesis based on original research in their chosen field. Doctor of Philosophy (PhD) degrees are awarded in a wide range of academic disciplines, and the PhD students are usually referred as research scholars. A comprehensive understanding of statistics allows research scholars to add rigour to their research. This approach helps them evaluate the current practices and draw informed conclusions from studies that were undertaken to generate their own hypotheses and to design, analyse and interpret complex clinical decisions. Therefore, intensive training at the beginning of the PhD journey is essential, as intensive training in research methodology and statistics in the early stages of research helps scholars design and plan their studies efficiently.

The University Grants Commission of India has taken various initiatives to introduce academic reforms to higher education institutions in India and mandated in 2009 that coursework be treated as a prerequisite for PhD preparation and that a minimum of four credits be assigned to one or more courses on research methodology, which could cover areas such as quantitative methods, computer applications, and research ethics. UGC also clearly states that all candidates admitted to PhD programmes shall be required to complete the prescribed coursework during the initial two semesters [ 1 ]. National Institute of Mental Health and Neurosciences (NIMHANS) at Bangalore, a tertiary care hospital and medical higher education institute in South India, that trains students in higher education in clinical fields, also introduced coursework in the PhD program for research scholars from various backgrounds, such as basic, behavioral and neurosciences, as per the UGC mandate. Research scholars undertake coursework programs soon after admission, which consist of several modules that include research methodology and statistical software training, among others.

Most scholars approach a course in statistics with the prejudice that statistics is uninteresting, demanding, complex or involve much mathematics and, most importantly, it is not relevant to their career goals. They approach statistics with considerable apprehension and negative attitudes, probably because of their inability to grasp the relevance of the application of the methods in their fields of study. This could be resolved by providing sufficient and relevant examples of the application of statistical techniques from various fields of medical research and by providing hands-on experience to learn how these techniques are applied and interpreted on real data. Hence, research methodology and statistical methods and the application of statistical methods using software have been given much importance and are taught as two modules, named Research Methodology and Statistics and Statistical Software Training, at this institute of medical higher education that trains research scholars in fields as diverse as basic, behavioural and neurosciences. Approximately 50% of the coursework curriculum focused on these two modules. Research scholars were thus given an opportunity to understand the theoretical aspects of the research methodology and statistical methods. They were also given hands-on training on statistical software to analyse the data using these methods and to interpret the findings. The coursework program was designed in this specific manner, as this intensive training would enable the research scholars to design their research studies more effectively and analyse their data in a better manner.

It is important to study attitudes toward statistics because attitudes are known to impact the learning process. Also, most importantly, these scholars are expected to utilize the skills in statistics and research methods to design research projects or guide postgraduate students and research scholars in the near future. Several authors have assessed attitudes toward statistics among various students and examined how attitudes affect academic achievement, how attitudes are correlated with knowledge in statistics and how attitudes change after a training program. There are studies on attitudes toward statistics among graduate [ 2 , 3 , 4 ] and postgraduate [ 5 ] medical students, politics, sociology, ( 6 – 7 ) psychology [ 8 , 9 , 10 ], social work [ 11 ], and management students [ 12 ]. However, there is a dearth of related literature on research scholars, and there are only two studies on the attitudes of research scholars. In their study of doctoral students in education-related fields, Cook & Catanzaro (2022) investigated the factors that contribute to statistics anxiety and attitudes toward statistics and how anxiety, attitudes and plans for future research use are connected among doctoral students [ 13 ]. Another study by Sohrabi et al. (2018) on research scholars assessed the change in knowledge and attitude towards teaching and educational design of basic science PhD students at a Medical University after a two-day workshop on empowerment and familiarity with the teaching and learning principles [ 14 ]. There were no studies that assessed changes in the attitudes or knowledge of research scholars across the PhD training period or after intensive training programmes such as PhD coursework. Even though PhD coursework has been established in institutes of higher education in India for more than a decade, there are no published research on the effectiveness of coursework from Indian universities or institutes of higher education.

This study aimed to determine the effectiveness of PhD coursework and whether intensive training programs such as PhD coursework can influence the knowledge and attitudes toward statistics of research scholars. Additionally, it would be interesting to know if the acquired knowledge could be retained longer, especially 2–3 years after the coursework, the crucial time of PhD data analysis. Hence, this study compares the scores of knowledge in statistics and attitude toward statistics of the research scholars at different time points of their PhD training, i.e., before, soon after and 2–3 years after the coursework.

Participants

This is an observational study of single group with repeated assessments. The institute offers a three-month coursework program consisting of seven modules, the first module is ethics; the fifth is research methodology and statistics; and the last is neurosciences. The study was conducted in January 2020. All research scholars of the institute who had completed PhD coursework in the last three years were considered for this study ( n  = 130). Knowledge and attitudes toward statistics before and soon after the coursework module were assessed as part of the coursework program. They were collected on the first and last day of the program respectively. The author who was also the coordinator of the research methodology and statistics module of the coursework have obtained the necessary permission to use the data for this study. The scholars invited to be part of the study by e-mail. Knowledge and attitude towards statistics 2–3 years after the coursework were assessed online using Google forms. They were also administered a semi structured questionnaire to elicit details about the usefulness of coursework. Participation was voluntary, and consent was also sought online. The confidentiality of the data was assured. Data were not collected from research scholars of Biostatistics or from research scholars who had more than a decade of experience or who had been working in the institute as faculty, assuming that their scores could be higher and could bias the findings. This non funded study was reviewed and approved by the Institute Ethics Committee.

Instruments

Knowledge in Statistics was assessed by a questionnaire prepared by the author and was used as part of the coursework evaluation. The survey included 25 questions that assessed the knowledge of statistics on areas such as descriptive statistics, sampling methods, study design, parametric and nonparametric tests and multivariate analyses. Right answers were assigned a score of 1, and wrong answers were assigned a score of 0. Total scores ranged from 0 to 25. Statistics attitudes were assessed by the Survey of Attitudes toward Statistics (SATS) scale. The SATS is a 36-item scale that measures 6 domains of attitudes towards statistics. The possible range of scores for each item is between 1 and 7. The total score was calculated by dividing the summed score by the number of items. Higher scores indicate more positive attitudes. The SAT-36 is a copyrighted scale, and researchers are allowed to use it only with prior permission. ( 15 – 16 ) The author obtained permission for use in the coursework evaluation and this study. A semi structured questionnaire was also used to elicit details about the usefulness of coursework.

Statistical analysis

Descriptive statistics such as mean, standard deviation, number and percentages were used to describe the socio-demographic data. General Linear Model Repeated Measures of Analysis of variance was used to compare knowledge and attitude scores across assessments. Categorical data from the semi structured questionnaire are presented as percentages. All the statistical tests were two-tailed, and a p value < 0.05 was set a priori as the threshold for statistical significance. IBM SPSS (28.0) was used to analyse the data.

One hundred and thirty research scholars who had completed coursework (CW) in the last 2–3 years were considered for the study. These scholars were sent Google forms to assess their knowledge and attitudes 2–3 years after coursework. 81 scholars responded (62%), and 4 scholars did not consent to participate in the study. The data of 77 scholars were merged with the data obtained during the coursework program (before and soon after CW). Socio-demographic characteristics of the scholars are presented in Table  1 .

The age of the respondents ranged from 23 to 36 years, with an average of 28.7 years (3.01), and the majority of the respondents were females (65%). Years of experience (i.e., after masters) before joining a PhD programme ranged from 0.5 to 9 years, and half of them had less than three years of experience before joining the PhD programme (median-3). More than half of those who responded were research scholars from the behavioural sciences (55%), while approximately 30% were from the basic sciences (29%).

General Linear Model Repeated Measures of Analysis of variance was used to compare the knowledge and attitude scores of scholars before, soon after and 2–3 after the coursework (will now be referred as “later the CW”), and the results are presented below (Table  2 ; Fig.  1 ).

Comparison of knowledge and attitude scores across the assessments. Later the CW – 2–3 years after the coursework

The scores for knowledge and attitude differed significantly across time. Scores of knowledge and attitude increased soon after the coursework; the percentage of change was 77% and 43% respectively. However, significant reductions in knowledge and attitude scores were observed 2–3 years after the coursework compared to scores soon after the coursework. The reduction was higher for attitude scores; knowledge and attitude scores have decreased by 10% and 37% respectively. The change in scores across assessments is evident from the graph, and clearly the effect size is higher for attitude than knowledge.

The scores of knowledge or attitude before the coursework did not significantly differ with respect to gender or age or were not correlated with years of experience. Hence, they were not considered as covariates in the above analysis.

A semi structured questionnaire with open ended questions was also administered to elicit in-depth information about the usefulness of the coursework programme, in which they were also asked to self- rate their knowledge. The data were mostly categorical or narratives. Research scholars’ self-rated knowledge scores (on a scale of 0–10) also showed similar changes; knowledge improved significantly and was retained even after the training (Fig.  2 ).

Self-rated knowledge scores of research scholars over time. Later the CW – 2–3 years after the coursework

The response to the question “ How has coursework changed your attitude toward statistics?”, is presented in Fig.  3 . The responses were Yes, positively, Yes - Negatively, No change – still apprehensive, No change – still appreciate, No change – still hate statistics. The majority of the scholars (70%) reported a positive change in their attitude toward statistics. Moreover, none of the scholars reported negative changes. Approximately 9% of the scholars reported that they were still apprehensive about statistics or hate statistics after the coursework.

How has coursework changed your attitude toward statistics?

Those scholars who reported that they were apprehensive about statistics or hate statistics noted the complexity of the subject, lack of clarity, improper instructions and fear of mathematics as major reasons for their attitude. Some responses are listed below.

“The statistical concepts were not taught in an understandable manner from the UG level” , “I am weak in mathematical concepts. The equations and formulae in statistics scare me”. “Lack of knowledge about the importance of statistics and fear of mathematical equations”. “The preconceived notion that Statistics is difficult to learn” . “In most of the places, it is not taught properly and conceptual clarity is not focused on, and because of this an avoidance builds up, which might be a reason for the negative attitude”.

Majority of the scholars (92%) felt that coursework has helped them in their PhD, and they were happy to recommend it for other research scholars (97%). The responses of the scholars to the question “ How was coursework helpful in your PhD journey ?”, are listed below.

“Course work gave a fair idea on various things related to research as well as statistics” . “Creating the best design while planning methodology, which is learnt form course work, will increase efficiency in completing the thesis, thereby making it faster”. “Course work give better idea of how to proceed in many areas like literature search, referencing, choosing statistical methods, and learning about research procedures”. “Course work gave a good idea of research methodology, biostatistics and ethics. This would help in writing a better protocol and a better thesis”. “It helps us to plan our research well and to formulate, collect and plan for analysis”. “It makes people to plan their statistical analysis well in advance” .

This study evaluated the effectiveness of the existing coursework programme in an institution of higher medical education, and investigated whether the coursework programme benefits research scholars by improving their knowledge of statistics and attitudes towards statistics. The study concluded that the coursework program was beneficial for improving scholars’ knowledge about statistics and attitudes toward statistics.

Unlike other studies that have assessed attitudes toward statistics, the study participants in this study were research scholars. Research scholars need extensive training in statistics, as they need to apply statistical tests and use statistical reasoning in their research thesis, and in their profession to design research projects or their future student dissertations. Notably, no studies have assessed the attitudes or knowledge of research scholars in statistics either across the PhD training period or after intensive statistics training programs. However, the findings of this study are consistent with the findings of a study that compared the knowledge and attitudes toward teaching and education design of PhD students after a two-day educational course and instructional design workshop [ 14 ].

Statistics educators need not only impart knowledge but they should also motivate the learners to appreciate the role of statistics and to continue to learn the quantitative skills that is needed in their professional lives. Therefore, the role of learners’ attitudes toward statistics requires special attention. Since PhD coursework is possibly a major contributor to creating a statistically literate research community, scholars’ attitudes toward statistics need to be considered important and given special attention. Passionate and engaging statistics educators who have adequate experience in illustrating relatable examples could help scholars feel less anxious and build competence and better attitudes toward statistics. Statistics educators should be aware of scholars’ anxiety, fears and attitudes toward statistics and about its influence on learning outcomes and further interest in the subject.

Strengths and limitations

Analysis of changes in knowledge and attitudes scores across various time points of PhD training is the major strength of the study. Additionally, this study evaluates the effectiveness of intensive statistical courses for research scholars in terms of changes in knowledge and attitudes. This study has its own limitations: the data were collected through online platforms, and the nonresponse rate was about 38%. Ability in mathematics or prior learning experience in statistics, interest in the subject, statistics anxiety or performance in coursework were not assessed; hence, their influence could not be studied. The reliability and validity of the knowledge questionnaire have not been established at the time of this study. However, author who had prepared the questionnaire had ensured questions from different areas of statistics that were covered during the coursework, it has also been used as part of the coursework evaluation. Despite these limitations, this study highlights the changes in attitudes and knowledge following an intensive training program. Future research could investigate the roles of age, sex, mathematical ability, achievement or performance outcomes and statistics anxiety.

The study concluded that a rigorous and intensive training program such as PhD coursework was beneficial for improving knowledge about statistics and attitudes toward statistics. However, the significant reduction in attitude and knowledge scores after 2–3 years of coursework indicates that a refresher program might be helpful for research scholars as they approach the analysis stage of their thesis. Statistics educators must develop innovative methods to teach research scholars from nonstatistical backgrounds. They also must be empathetic to understanding scholars’ anxiety, fears and attitudes toward statistics and to understand its influence on learning outcomes and further interest in the subject.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

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Acknowledgements

The author would like to thank the participants of the study and peers and experts who examined the content of the questionnaire for their time and effort.

This research did not receive any grants from funding agencies in the public, commercial, or not-for-profit sectors.

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This study used data already collected data (before and soon after coursework). The data pertaining to knowledge and attitude towards statistics 2–3 years after coursework were collected from research scholars through the online survey platform Google forms. The participants were invited to participate in the survey through e-mail. The study was explained in detail, and participation in the study was completely voluntary. Informed consent was obtained online in the form of a statement of consent. The confidentiality of the data was assured, even though identifiable personal information was not collected. This non-funded study was reviewed and approved by NIMHANS Institute Ethics Committee (No. NIMHANS/21st IEC (BS&NS Div.)

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7 Biggest Reveals From Netflix’s ‘Hack Your Health: The Secrets of Your Gut’

A new documentary aims to teach us about our gut microbiome and get us talking about poop.

The gut microbiome is having a moment.

If you aren’t convinced, you probably haven’t watched the new Netflix documentary Hack Your Health: The Secrets of Your Gut , which provides a quirky and engaging look into the intricate world of our digestive system and the trillions of bacteria and other microorganisms that inhabit it.

Featuring celebrity gut specialist Giulia Enders, MD , the film explores the profound impact of gut health on brain function, mental health, and overall well-being.

Making lifestyle changes and medical choices that benefit your gut microbiome “can not only be very empowering, but life-changing,” says Justin Sonnenburg, PhD , a professor of microbiology at Stanford University in California, who was featured in the documentary.

Keep reading for seven top lessons from the movie.

1.The Gut Deserves Respect

We feel a lot of guilt and shame when it comes to our gut, Dr. Enders says in the film. “It’s completely crazy when you think about it, because this is the organ that keeps you alive,” she says.

Our gut affects our whole body — even our brain. “The gut really is the second brain, and from an evolutionary perspective, our brains have never existed without signals coming from the gut,” says John Cryan, PhD , a professor of anatomy and neuroscience at University College Cork in Ireland, in the film.

2. Gut Bacteria Play a Key Role in Immune Function and Overall Health

Interesting fact: The body can’t digest food on its own, according to Enders. “We need microbes in order to do that properly,” she says in the documentary.

The tiniest virus works with small bacteria, which works with a much larger yeast, which works with a super big human — and this is what we call the microbiome, she explains. The largest number of these organisms are found in the small and large intestines, but they also exist throughout the body.

Although most people think of bacteria as harmful, 99 percent don’t affect our health at all — and some can even help us, Enders explains.

Approximately 70 percent of our immune system lives in our gut. Bacteria train the immune system to respond to organisms that might have a negative impact on health, and help humans produce disease-fighting chemicals that we can’t make on our own.

3. The Gut Microbiome Plays a Key Role in Managing Weight

Microbes occupy most of our bodies — 70 to 90 percent of all cells in the human body are bacterial, and 99 percent of the unique genes in our bodies are bacterial, according to the film.

The bacteria communicate with our brains and other organs, and even shape our hormones that can make us feel hungry or full.

Although we often believe that our genes determine our health, now we know that the microbiome may be very central to developing obesity , depression , or allergies, and it can even affect stress levels, says Enders in the film. “We don’t know how big is the part that it plays in these entities. For some people it might be really relevant, and for others it might be smaller,” she says.

4. Everyone Has a Unique Collection of ‘Microbial Memories’

Each person’s microbiome is shaped by their individual choices and life experiences. The places you travel, the food you eat, whether you have pets, how much you exercise, your stress level, what your childhood was like, whether you experienced adversity or not — all those and much more play a role.

Enders describes it a “collection of microbial memories,” and each is unique.

5. Each Person’s Microbiome Can Have a Different Response to the Same Food

Because the microbiome plays a key role in digestion and none are the same, two people can eat the exact same food and have very different responses, the film explains.

The documentary illustrates this by sharing the stories of four different people with unique food issues. As they take a bite of an apple (except for one woman who can’t eat apples), we’re told that each person will digest the apple in a different way, including the kinds of nutrients and amount of calories they extract.

Ultimately, an individual’s response to a meal — such as how much it elevates their blood sugar level — is connected to weight loss and gain, and that is influenced by the microbiome.

This may explain why one person has obesity and the other person does not even if they follow the same eating pattern.

6. Let’s Talk About Poop More — Really

Did poop hire a PR team to help fund this movie? Doubtful, but the documentary does a great job of highlighting poop’s under-recognized role in health. In the film, four test subjects participate in a gut study that collects fecal samples to examine the types and levels of gut bacteria present.

“Being open about the gut and poop, and sharing gut-associated issues, will let people communicate about their shared stories, and explore different ways to get healthy,” says  Aashish Jha, PhD , an assistant professor of biology and researcher at NYU Abu Dhabi in the United Arab Emirates, who was featured in the documentary.

“Sharing ideas may lead to innovative ways to restore overall health via the gut,” he says.

7. Building a Healthy Gut Doesn’t Mean Counting Calories or Avoiding ‘Bad’ Foods

There’s no sugar coating it: Eating more whole foods and fewer highly processed ones is associated with a healthier and more diverse microbiome. But having a healthy relationship with food and eating a balanced diet is the goal — not deprivation, according to the film.

Case in point: When a person in the documentary with food issues that severely restrict her diet is finally able to eat potato chips and carbonara, it’s viewed as a victory.

A diet rich in fruits and vegetables is generally considered ideal for the gut, says Annie Gupta, PhD , co-director of the Goodman-Luskin Microbiome Center at UCLA in Los Angeles, in the film. She recommends tracking the number of fruit and vegetable servings you consume every week. “Between 20 and 30 is usually considered good,” she recommends.

Editorial Sources and Fact-Checking

Everyday Health follows strict sourcing guidelines to ensure the accuracy of its content, outlined in our editorial policy . We use only trustworthy sources, including peer-reviewed studies, board-certified medical experts, patients with lived experience, and information from top institutions.

• Banskota S et al. Serotonin in the Gut: Blessing or a Curse. Biochimie . June 2019.

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Dissertation Colloquium for Bharati Shah Chakraborty in the Doctoral Program for Educational Leadership for Social Justice

You are cordially invited to:.

The Utilization of On-Campus Counseling Services by Single Mother Students in Higher Education: A Grounded Theory Research on  Wednesday, May 8, at  3 p.m. – 4:30 p.m.

The Institute for Women’s Policy Studies (2017, September) reported that only eight percent of single mothers enrolled in higher education completed their studies and graduated with a degree in six years. This may be due to a lack of social support and not using the counseling services for their own well-being. Studies show that behavioral problems may affect college students’ academic success and retention (Svanum & Zody, 2001). Studies demonstrate the challenges and success of single mothers in completing their bachelor’s degree and found that single mothers’ dropout rate is higher in comparison with other students or that they take longer to graduate (Fluellen, 2016; Vyskocil, 2018). Given challenging enrollment and completion rates, additional research is needed to ascertain what single mother students in higher education were reporting on the use of on-campus counseling services. 

Employing a qualitative research methodology, interviews were conducted with 12 participants who met the eligibility criteria for this study. Using constructive grounded theory research design, findings reveal that the use of on-campus counseling helped the single mothers in higher education with better mental health, physical health and decision making. They reported they became better parents to their children as they have learnt healthy coping skills and parenting skills from their counselors. The study has implications for higher education practitioners and for students in similar circumstances.

Digital twins: The art of the possible in product development and beyond

Industrial companies around the world rely on digital tools to turn ideas into physical products for their customers. These tools have become increasingly more powerful, flexible, and sophisticated since the 1960s and 1970s, when computers first began replacing drawing boards in design offices. Today, product life-cycle management (PLM) has become engineers’ first language: PLM systems help companies to capture, codify, process, and communicate product knowledge across their organizations.

About the authors

This article is a collaborative effort by Mickael Brossard , Sebastien Chaigne, Jacomo Corbo, Bernhard Mühlreiter , and Jan Paul Stein, representing views from McKinsey’s Operations Practice.

Yet as engineering tools have become more capable, the demands placed upon them have also increased. Product functions are increasingly delivered through a combination of hardware and software. Sensors and communications capabilities allow products to offer more features and to respond more effectively to changing operating conditions and user requirements. Advanced, adaptable user interfaces have simplified the operation of complex and sophisticated machines.

Evolving business models are also blurring the boundaries between design and use. Customers expect the performance and functionality of products to improve during their life cycle, enabled by over-the-air software updates or the ability to unlock new features as needed. Many products operate as part of an ecosystem of related products and services. Increasingly, customers are not buying products outright, but paying for the capabilities they provide on a per-use or subscription basis.

The birth of the digital twin

These changing requirements have triggered a transformation in digital product representation and the creation of a new tool: the digital twin. Digital twins combine and build upon existing digital engineering tools, incorporating additional data sources, adding advanced simulation and analytics capabilities, and establishing links to live data generated during the product’s manufacture and use. A conventional PLM system uses one digital model to represent each variant of a product. A digital twin, by contrast, may have one model for each individual product, which is continually updated using data collected during the product’s life cycle.

The digital-twin approach can be applied to products, manufacturing processes, or even entire value chains. In this article, we will focus on their application to products, specifically to product design.

Digital twins offer multiple potential benefits for product-based companies and users. They can aid design optimization, reduce costs and time to market, and accelerate the organization’s response to new customer needs. Digital twins can also be a critical enabler of new revenue streams, such as remote maintenance and support offerings and “as a service” business models.

Based on the experience of companies that have already adopted the approach, we estimate that digital-twin technologies can drive a revenue increase of up to 10 percent, accelerate time to market by as much as 50 percent, and improve product quality by up to 25 percent. Digital-twin technology  is becoming a significant industry. Current estimates indicate that the market for digital twins in Europe alone will be around €7 billion by 2025, with an annual growth rate of 30 to 45 percent. 1 Infinium; MarketsandMarkets; MarkNTel Advisors; Meticulous Market Research; Mordor Intelligence; SBIS; Technavio, last accessed April 2020.

Digital twins in practice

Companies in many different industries are already capturing real value by applying digital twins to product development , manufacturing, and through-life support (exhibit).

An automotive OEM, for example, has used the digital-twin approach to create a concept configurator for early phase development . The start of the development process is especially challenging for complex products because the various stakeholder groups, such as sales, engineering, and finance, may have different or even contradictory product requirements. The OEM now balances these trade-offs using a digital concept configurator that allows for simultaneous evaluation of customer requirements, technical concepts, and product costs. When a technical concept within a system or subsystem of the product is changed, the implications for meeting customer requirements or product cost targets become immediately transparent.

Would you like to learn more about our work in Product Digital Twins ?

Using the configurator within cross-functional development teams has helped the OEM to reallocate 5 to 15 percent of a new vehicle’s material costs to the attributes that drive the most customer value. Applying the approach to select customer-facing components has allowed the company to optimize costs and customer value simultaneously, improving the contribution margin of those parts by 5 to 10 percent. As a further benefit, the configurator helped the team reduce the time taken to reach agreement on changes by 20 percent, thus accelerating time to market.

Digital twins are even being used to replicate systems in complex mission scenarios. Using this approach, one aerospace and defense player has cut the time required to develop advanced products by 30 to 40 percent. The digital twin also aids discussion with customers during the development process, helping the company validate and improve its designs.

In the consumer electronics sector, a company is using product digital twins to boost quality and supply chain resilience . It stores detailed information on the content of its products, including the exact source of individual components. In the event of quality issues during production or early failures in the field, the company can trace problems back to specific supplier facilities, then take appropriate action to prevent reoccurrence of the issue. An automotive supplier uses the same approach to trace quality deviations in its production through to the upstream supply chain, and in the process has reduced scrap by 20 percent.

Digital twins are increasingly being used to improve future product generations . An electric-vehicle (EV) manufacturer, for example, uses live data from more than 80 sensors to track energy consumption under different driving regimes and in varying weather conditions. Analysis of that data allows it to upgrade its vehicle control software, with some updates introduced into new vehicles and others delivered over the air to existing customers.

Developers of autonomous-driving systems , meanwhile, are increasingly developing their technology in virtual environments. The training and validation of algorithms in a simulated environment is safer and cheaper than real-world tests. Moreover, the ability to run numerous simulations in parallel has accelerated the testing process by more than 10,000 times. Incorporating sensor data from real-world vehicles into these tests helps companies improve the veracity of their simulations and identify blind spots in the virtual test database.

The mainstreaming of additive manufacturing

A company in the renewable-energy sector is using a digital twin to automate, accelerate, and improve the engineering of hydroelectric turbines . Using the machine learning system to evaluate the likely performance of the new designs allowed it to rate more than a million different designs in seconds rather than the hours required for conventional computational flow dynamics (CFD) analysis. The winning geometry delivers the maximum theoretical performance, significantly higher than what is achievable by conventional optimization methods. Moreover, by using machine learning, the overall end-to-end design cycle time was cut in half compared with the conventional approach.

Digital twins in three dimensions

Digital twins can take many different forms. Organizations that want to take advantage of digital-twin technologies must select an appropriate form that will enhance its technical and business objectives. The design of a digital twin can vary across three dimensions (exhibit).

The first dimension encompasses the value chain steps that the digital twin will cover. An engineering twin covers value chain steps similar to those covered by conventional PLM systems, ranging from product definition to detailed engineering. A production twin replicates a product throughout the manufacturing process, incorporating data such as the components, materials, and process parameters used, as well as the results of tests and quality checks. A service twin incorporates data collected from the product in use, such as operating modes, performance, diagnostic information, and maintenance history. The most sophisticated digital twins span multiple parts of the value chain, allowing in-service data to optimize manufacturing processes or future design iterations.

The second dimension is the scope of the digital twin. A product may consist of several major systems, multiple subsystems, and hundreds or thousands of hardware and software components. Some digital twins cover only one or several components, for example, those that simulate the flow of liquids through a pipe. Others cover a full product, for example, those that simulate a car’s crash characteristics. Given the limitations of computing power, generally, the narrower the scope of a digital twin, the more precise its virtual replica will be. In contrast, full-product digital twins often need to abstract or simplify certain product behaviors to remain manageable.

The final dimension of a digital twin is its degree of sophistication . The simplest digital twins consist of various sources of data relating to a product, often from sources that have few or no links with one another. The second level of sophistication uses traditional simulation tools to perform analyses of design performance and integrate the various sources through a PLM system or similar platform.

At the third level of sophistication, a digital twin will use predictive or prescriptive analytics, as well as machine learning technology to run automated simulation refinements and yield new insights. This allows design and manufacturing teams to make informed decisions based upon direct results and performances.

At the last level of sophistication, digital twins use predictions of component failure rates or performance variations to react to changing environments and manipulate the real-world counterpart in a closed-loop setup. This approach might be used in a condition monitoring system, for example, where sensor data and simulations are combined to make inferences and predictions about the state and behavior of a specific product, and might allow a machine to compensate for wear or variations in operating conditions by adjusting parameters in real time.

Companies in other sectors are also starting to use digital twins to derive deeper insights into customer behaviors and preferences . For example, white-goods manufacturers can use data from in-service products to identify the most and least used features. That can inform future product development decisions, such as deleting rarely used features or revising the user interface to make the features more accessible.

The adoption of digital twins is currently gaining momentum across industries, as companies aim to reap the benefits of various types of digital twins. Given the many different shapes and forms of digital twins (see sidebar, “Digital twins in three dimensions”), and the different starting points of each organization, a clear strategy is needed to help prioritize where to focus digital-twin development and what steps to take to capture the most value.

How to start and succeed on your digital-twin journey

Embarking on a digital-twin journey can look daunting at first sight, especially since the breadth and depth of use cases can span the entire corporate landscape, including product portfolio choices, business model design, R&D, manufacturing, and through-life support.

This versatility can also be a strength, however, as it allows companies to start small and expand the scope, sophistication, and value-chain coverage of their digital-twin projects over time. The experience of companies that have applied digital twins in their own product operations leads to a few simple rules that can greatly increase your odds of success.

Define your aspirations

Be aware of digital-twin best practices. Do your homework and seek out perspectives on best practices and future trends in digital-twin technology. Assess and prioritize the elements of your vision. Evaluate the potential of digital-twin-related opportunities and prioritize them into an implementation road map.

Be clear about the business case. Quantify the value offered by different digital-twin opportunities and determine the minimum level of model sophistication required to generate that value. Successful projects focus on short development times and rapid ROI.

Test the waters by prototyping select use cases. Run a series of hackathons (possibly supported by digital-twin specialists) to assess your capabilities’ baseline, develop solution prototypes, refine, and adjust the initial concepts. This step calibrates the approach and prevents you from losing time and resources by attempting an impossible plan. It is part of a broader value assurance move aimed at bringing the entire project to a successful conclusion.

Know your strengths

Perform a maturity assessment. Understand your current digital product development capabilities along six main dimensions: development methodologies, PLM governance, data strategy, business processes, system complexity, and collaboration. Understanding the areas where you are most advanced and where you are lagging behind will help prioritize areas of investment for a balanced implementation of a digital twin and its use cases.

Access to appropriate talent and capabilities can make or break a digital-twin initiative. Many organizations need to develop additional expertise in areas such as advanced simulation and modeling or data analytics for user experience design.

Plan a step-by-step, agile implementation

Invest several months in developing a minimum viable product (MVP). Incubate a cross-functional, agile team dedicated to bringing priority use cases to life and building digital capabilities in the process. The MVP is now the must-do approach to maximize value gains from the start rather than waiting until the program is finalized before experiencing the first benefits.

Perform an MVP retrospective to pivot or persevere. Derive lessons from the first MVP phase to confirm your digital-twin aspirations or pivot them based on the findings (for example, the validity of use cases, complexity of implementation, and maturity of the organization). This is the second value assurance move that enables you to further calibrate the implementation plan and revise the scope to avoid generating sunk costs.

Scale up the digital-twin initiative and accelerate ROI. Optimize and standardize implementation based on insights from the MVP phase. Define an (internal or external) recruiting and capability-building strategy. Build an operating model to enable rapid scaling of successful approaches. The most advanced organizations typically consider digital-twin technologies a core strategic capability.

By following these simple best practices, you will be able to reap the benefits of digital twins in a scalable, progressive way. Are you ready?

Mickael Brossard is a partner in McKinsey’s Paris office, where Sebastien Chaigne is an associate partner; Jacomo Corbo is a partner in the London office; Bernhard Mühlreiter is a partner in the Vienna office; and Jan Paul Stein is an associate partner in the Munich office.

The authors wish to thank Roberto Argolini, Elia Berteletti, Kimberly Borden, Akshay Desai, Hannes Erntell, Alessandro Faure Ragani, Anna Herlt, Mark Huntington, Mithun Kamat, Michele Manzo, and Alessandro Mattozzi for their contributions to this article.

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