phd management distance learning

Doctoral Programs

Doctoral programs in accounting, financial economics, marketing, operations, and organizations and management.

The Doctoral Program gives students unparalleled expertise in management. Candidates work under Yale SOM's distinguished faculty, learning side by side with diverse and accomplished scholars.

Deadline: December 15

The application deadline is December 15 of the year in which admission is sought.

Specializations

Students focus in one of the core disciplines of management, developing in-depth knowledge and pursuing their own research interests: Accounting , Finance , Marketing , Operations , or Organizations and Management .

Application for admission to the Doctoral Program in Management is made through the Yale Graduate School.

Students take foundational PhD-level courses in their areas of specialization, and then choose from a course list that spans the university, drawing from some of the best academic departments in the world.

The program's small size allows senior faculty to take an active role in preparing each student for the job search.

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Online Doctorate in Management and Organization

Pursue the pinnacle of your career with a phd in organization and management degree online.

Are you a business professional seeking a terminal degree? Do you want to hone your management skills and become equipped for various leadership and teaching roles? If so, Liberty University’s 100% online Doctor of Philosophy (PhD) in Organization and Management may be for you! Our doctorate in management and organization provides an in-depth examination of theoretical and practical business concepts. Whether you enjoy teaching at the college level or leading teams of people within companies, our online program can help you prepare for the next step in your career.

Throughout this doctoral program, you can learn how to conduct effective research in the field of organization and management. If you don’t have a business-related master’s degree, you can still apply to this program and gain a firm foundation in business that can help you pursue roles in consulting, executive leadership, and more. With a median salary of $107,680 for top executives in 2020,* you can be confident that our program can help you explore rewarding career opportunities.

Partner with us and learn how to lead with integrity in your organization!

*Bureau of Labor Statistics, U.S. Department of Labor, at Top Executives, 2020 (viewed online July 7, 2021). Cited projections may not reflect local and/or short-term economic or job conditions and do not guarantee actual job growth.

Why Choose Liberty’s Online Organization and Management Doctorate?

As a pioneer in distance education since 1985, Liberty knows what it takes to create online programs that blend accessibility, affordability, and high-quality academics. Our organization and management doctorate is offered 100% online, and most of the courses are only 8 weeks long. With our flexible format, you can earn your management degree online from the comfort of your home while staying invested in your family, job, and community.

At Liberty, you won’t have to sacrifice quality for flexibility. We are accredited by the Southern Association of Colleges and Schools Commission on Colleges ( SACSCOC ). By choosing Liberty, you’re choosing a top-notch education from a university that is committed to excellence. All of our courses are taught by industry experts, so you can pursue your degree with confidence.

While pursuing the PhD program in management and organization, you’ll be able to personalize your degree by selecting a specialization. That way, you can focus your studies on the topics that align with your interests and goals. Whether you are looking to advance your career or explore new job opportunities in business, our online doctorate in management and organization can help you become a Champion for Christ in your field.

Military Tuition Discount We want to help you find the doctoral degree you want — at a price you’ve earned. As a thank-you for your military service, Liberty University offers eligible current and former service members like you or your spouse multiple pathways to earn a doctoral degree for only  $300/credit hour .  Find out  how you can take advantage of this unique opportunity as you work towards your goal of reaching the pinnacle of your profession — for less.

Specializations for the Online Doctorate in Management and Organization

At Liberty, you can customize your doctorate in management and organization by selecting a specialization in the area that interests you the most! That way, you can further hone your skill set and become equipped to pursue your unique career goals. Check out our specialization options below.

Entrepreneurship

Liberty’s online PhD in Organization and Management – Entrepreneurship can help you gain an advanced understanding of how to start and run your own business — all while strengthening your research and leadership skills. You’ll explore startup and exit strategies, small business management, methods for raising capital for business startups, and more.

Potential Career Options

• Business strategist
• Executive business leader
• Private business consultant
• Risk assessor
• University professor

View the Degree Completion Plan . View the Graduate Business Course Guides (login required) .

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Executive Coaching

Liberty’s online PhD in Organization and Management – Executive Coaching can help you learn how to mentor other business professionals so they can develop their leadership skills and lead their organizations to success. You’ll explore coaching theory and practice, theories and research in industrial and organizational psychology, techniques for leveraging data for decision-making, and more.

• Executive coach
• Leadership development consultant

Liberty’s online PhD in Organization and Management – Leadership can help enhance your leadership skills as you learn how to manage highly effective teams, foster innovation, and navigate ethical dilemmas in business. You’ll explore topics like leadership theory, ethical leadership, and more.

• Executive officer
• Human resources manager
• Management analyst
• Training and development manager

What Will You Learn in Our Doctorate in Management Online?

Throughout this online PhD program in organizational management, you’ll have the opportunity to develop research and leadership skills that can help you pursue top-level roles in industry and academia. Your core organization and management courses offer an in-depth look at risk management, human resource management, strategy formulation, and strategic thinking. You can also learn how to lead your company through change — equipping you to make a difference in private, public, and nonprofit organizations.

At Liberty, we’ve designed our online PhD in organizational management to offer you maximum flexibility. In addition to completing core management courses, you’ll be able to select a specialization in a subject that interests you. From entrepreneurship to executive coaching, you can explore the area of study that best matches your career goals.

Additionally, you will complete a sequence of research courses designed to help prepare you for the dissertation process. Our research courses cover a variety of quantitative, qualitative, and applied research methods. The dissertation provides an opportunity for you to contribute your scholarly work to the current base of knowledge in your field. By conducting original research, you can become better equipped to pursue roles in academia or to implement needed changes in your organization.

Featured Courses

• BMAL 703 – Managing the Contemporary Organization
• BMAL 704 – Leading Organizational Change
• BMAL 714 – Risk Management Process and Practice
• BUSI 770 – Strategy Formulation and Strategic Thinking

Potential Career Opportunities for Graduates of Liberty’s PhD in Organizational Management Program

Highlights of our phd program in management and organization.

• We are recognized by multiple institutions for our academic quality, affordability, and accessibility . Our commitment to excellence also helped us rank in the top 10% of Niche.com’s best online schools in America . Earning your online degree from a nonprofit university with this kind of recognition can help set you apart from others in your field.
• Your success is our success, which is why we are committed to providing quality academics at an affordable tuition rate. While other colleges are increasing their tuition, we have frozen tuition rates for the majority of our undergraduate, graduate, and doctoral programs for the past 9 years — and counting.
• Liberty University is accredited by the Southern Association of Colleges and Schools Commission on Colleges ( SACSCOC ).
• You can earn your PhD in Organization and Management degree online in as little as 3 years.
• Most of our PhD courses are available in an 8-week format.
• As an online student, you can access a wealth of resources through our top-notch library portal.
• Even if you don’t have a business-related master’s degree, our PhD in Organization and Management degree online can help prepare you for a career as a manager, business leader, consultant, or academic.

Online PhD Program in Organizational Management Information

• This program falls under the School of Business .
• View the Graduate Business Course Guides (login required) .
• View the PhD in Organization and Management Handbook for additional program information.

Admission Requirements for the Online Organization and Management Doctorate

A regionally or nationally accredited master’s degree with a 3.0 or above GPA is required for admission in good standing. Please visit our  admission requirements page  for more detailed admissions-related information.

All applicants must submit the following:

• Admission application
• Application fee*
• Official college transcripts
• Proof of English proficiency (for applicants whose native language is other than English)

If you have further questions about admission requirements for this program, please contact [email protected] .

* There is no upfront application fee; however, a deferred $50 application fee will be assessed during Financial Check-In. This fee is waived for qualifying service members, veterans, and military spouses – documentation verifying military status is required.

Transfer Policies

At Liberty, we want to help you make the most of your prior education by allowing you to transfer in previously earned college credit. That’s why you can transfer in up to 50% of your total credits for a master’s or doctoral program!

Some restrictions apply. Please visit our Transfer Policy page for more information.

*Some restrictions may occur for this promotion to apply. This promotion also excludes active faculty and staff, military, Non-Degree Seeking, DGIA, Continuing Education, WSB, and Certificates.

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The Best Online Doctorate in Management Programs

Staff Writers

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Learn about our editorial process .

Updated October 25, 2023

TheBestSchools.org is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site.

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Earning a Doctorate in Management

Doctoral degrees in management prepare students for careers in organizational leadership. Students who wish to pursue a management doctorate can choose from two specific degrees. The doctor of philosophy, or Ph.D., is a research-oriented degree geared toward learners who plan to work in academia. A doctor of management, on the other hand, focuses more on practical competencies and benefits those seeking advanced management roles. Rather than attending courses on-campus, students may prefer to earn a doctorate of management online.

The doctor of management leads to several high-profile career paths. Certain top executive roles, such as chief operating officers , typically hold an advanced degree. The Bureau of Labor Statistics also notes that a Ph.D. may be required for top executives in education management, such as college presidents and school superintendents . Additionally, postsecondary teachers who lead management courses at four-year colleges and universities may need a doctoral degree in their field of instruction. Based on BLS data, top executives and postsecondary teachers each earn median annual salaries at more than twice the national average.

Featured Online Doctorate in Management Degrees

For career information, skip down:

What Else Can I Expect from an Online Doctorate in Management Program?

• Online Doctorate in Management Careers

Accreditation for Online Doctorate in Management Degrees

Paying for your online doctorate in management, the best online doctorate in management degree programs.

We use trusted sources like Peterson's Data and the National Center for Education Statistics to inform the data for these schools. TheBestSchools.org is an advertising-supported site. Featured or trusted partner programs and all school search, finder, or match results are for schools that compensate us. This compensation does not influence our school rankings, resource guides, or other editorially-independent information published on this site. from our partners appear among these rankings and are indicated as such.

#1 The Best Online Doctorate in Management Programs

Creighton University

• Online + Campus

The online DBA program at Creighton University ranks as one of the best offerings in the field. Degree-seekers benefit from the flexible enrollment options at the private university. Doctoral students receive library access, research support, and career services.

Learners take doctoral classes in the DBA program. After passing comprehensive examinations, doctoral candidates choose a dissertation topic and conduct research in their specialty area. With a doctorate in management, professionals often work in academia, research, and leadership roles.

Online doctoral students at the accredited institution can pay for their degree with scholarships, fellowships, and loans. Reach out to the program to learn more about financial aid opportunities, admission requirements, and start dates.

Creighton University at a Glance:

Online Student Enrollment: 3,328

Online Master's Programs: 12

Online Doctoral Programs: 3

Student-to-Faculty Ratio: 11-to-1

Graduate Tuition Rate: $16,128

#2 The Best Online Doctorate in Management Programs

University of Dallas

The online DBA program at the University of Dallas ranks as one of the best degrees in the management field. With its flexible format, the private university makes a doctorate more accessible. Doctoral students conduct research and participate in academic conferences.

During the online management program, learners take doctoral courses to earn their degree. Doctoral candidates pass comprehensive examinations and conduct research for their dissertation. A doctorate in management trains graduates for roles in research, academia, and leadership.

Online doctoral students can pay for their degree with scholarships, fellowships, and other forms of financial aid. Prospective applicants can contact the program to learn more about financial aid opportunities and the admission process.

University of Dallas at a Glance:

Online Student Enrollment: 595

Online Master's Programs: 1

Graduate Tuition Rate: $33,750

#3 The Best Online Doctorate in Management Programs

California Baptist University

• Riverside, CA

The doctor of business administration at California Baptist University appeals to learners seeking an online doctorate. At the private institution, degree-seekers take doctoral classes in a flexible format. Doctoral students participate in career counseling and benefit from research support.

The doctoral program incorporates advanced business management coursework. After passing comprehensive examinations, doctoral candidates complete a dissertation that contributes original research to the field. With a doctorate in management, graduates can pursue academic, research, and industry roles.

Doctoral students who enroll online at the accredited institution often qualify for fellowships, scholarships, and other forms of financial aid. Prospective applicants can contact the program to learn more about financial aid opportunities and the admission process.

California Baptist University at a Glance:

Online Student Enrollment: 6,112

Online Master's Programs: 13

Online Doctoral Programs: 2

Student-to-Faculty Ratio: 16-to-1

Graduate Tuition Rate: $12,830

#4 The Best Online Doctorate in Management Programs

Concordia University Chicago

• River Forest, IL

Doctoral students seeking an online management program benefit from the FBA program at Concordia University Chicago. With its flexible format, the private university helps degree-seekers complete a doctorate. Doctoral students also benefit from research support, career advising, and library access.

The online program incorporates doctoral coursework. After passing comprehensive examinations, doctoral candidates conduct research for their dissertation. Professionals with a doctorate can work in academia, research, and leadership roles.

Doctoral students may qualify for several forms of financial aid at the accredited institution. Prospective applicants can contact the program to learn more about financial aid opportunities and the admission process.

Concordia University Chicago at a Glance:

Online Student Enrollment: 4,222

Online Master's Programs: 48

Online Doctoral Programs: 9

Student-to-Faculty Ratio: 12-to-1

Graduate Tuition Rate: $8,910

#5 The Best Online Doctorate in Management Programs

Wayland Baptist University

• Plainview, TX

The online doctor of management program at Wayland Baptist University ranks among the best in the field. The private university helps degree-seekers complete a doctorate in a flexible format. Doctoral students receive research support, library access, and career advising.

In the online management program, learners complete doctoral-level coursework. After passing comprehensive examinations, doctoral candidates complete a dissertation that contributes original research to the field. Professionals with a management doctorate work in academia, research, and leadership roles.

At the accredited institution, online doctoral students qualify for scholarships, federal loans, and other forms of financial aid. Reach out to the management program to learn more about admission requirements.

Wayland Baptist University at a Glance:

Online Student Enrollment: 2,485

Online Master's Programs: 10

Online Doctoral Programs: 1

Student-to-Faculty Ratio: 6-to-1

Graduate Tuition Rate: $13,104

#6 The Best Online Doctorate in Management Programs

University of Maryland Global Campus

• Adelphi, MD

The online FBA program at the University of Maryland Global Campus ranks among the best in its field. At the public university, degree-seekers engage in online classes to earn a doctorate. Doctoral students also benefit from research support, career advising, and library access.

The online management program incorporates doctoral coursework. After passing comprehensive examinations, doctoral candidates propose a dissertation topic and conduct research in their specialty area. With a doctorate, graduates work in academia, research positions, and leadership roles.

At the accredited institution, online doctoral students qualify for scholarships, federal loans, and other forms of financial aid. Reach out to the program to learn more about the application process and start dates.

University of Maryland Global Campus at a Glance:

Online Student Enrollment: 52,298

Online Master's Programs: 17

Student-to-Faculty Ratio: 19-to-1

Graduate Tuition Rate: $8,640

#7 The Best Online Doctorate in Management Programs

Franklin University

• Columbus, OH

Franklin University offers a doctor of business administration program with an online learning format. Degree-seekers benefit from flexible course options through the private institution. Doctoral students receive support services like career advising and research support.

The doctoral program incorporates advanced business management coursework. After passing comprehensive examinations, doctoral candidates propose a dissertation topic and conduct research in their specialty area. Graduates with a business management doctorate pursue roles in research, academia, and leadership.

Doctoral students attending the accredited institution online qualify for several forms of financial aid. Contact the business management program for more information about doctoral admissions.

Franklin University at a Glance:

Online Student Enrollment: 4,809

Online Master's Programs: 11

Student-to-Faculty Ratio: 13-to-1

Graduate Tuition Rate: $16,080

#8 The Best Online Doctorate in Management Programs

Lincoln Memorial University

• Harrogate, TN

The online DBA program at Lincoln Memorial University ranks among the best in the field. The private university provides flexible enrollment options to meet the needs of diverse degree-seekers. Doctoral students strengthen their research skills and present at academic conferences.

During the online management program, learners take doctoral courses to earn their degree. Doctoral candidates pass comprehensive examinations and conduct research for their dissertation. As the highest degree in management, the doctorate trains graduates for academic, research, and leadership careers.

Online doctoral students at the accredited institution qualify for several forms of financial aid. Contact the program for more information about doctoral admissions.

Lincoln Memorial University at a Glance:

Online Student Enrollment: 501

Student-to-Faculty Ratio: 15-to-1

Graduate Tuition Rate: $18,093

#9 The Best Online Doctorate in Management Programs

Felician University

The doctor of business administration at Felician University appeals to learners seeking an online doctorate. With its flexible format, the private university helps degree-seekers complete a doctorate. Doctoral students participate in career advising and gain research skills.

The doctoral program incorporates advanced business management coursework. After completing classes, doctoral candidates conduct research for their dissertation or doctoral project. With a doctorate in business management, graduates pursue academic, research, and industry roles.

Doctoral students who enroll online at the accredited institution qualify for fellowships, scholarships, and other forms of financial aid. Prospective applicants can contact the program for more about financial aid and admission requirements.

Felician University at a Glance:

Online Student Enrollment: 972

Online Master's Programs: 3

Student-to-Faculty Ratio: 14-to-1

Graduate Tuition Rate: $19,080

#10 The Best Online Doctorate in Management Programs

Saybrook University

• Pasadena, CA

The online DBA program, offered by Saybrook University, ranks as a top program in the field. Thanks to a flexible format, the private institution makes it easier to complete a doctorate. Doctoral students receive research support throughout the program.

During the online program, learners take doctoral courses to earn their degree. After passing comprehensive examinations, doctoral candidates conduct research for their dissertation. Graduates with a doctorate pursue roles in research, academia, and leadership.

At the accredited institution, doctoral students can use fellowships, scholarships, and other forms of financial aid to pay for their degree. Reach out to the program to learn more about the application process and start dates.

Saybrook University at a Glance:

Online Student Enrollment: 688

Online Master's Programs: 4

Online Doctoral Programs: 5

Graduate Tuition Rate: $23,589

All online Ph.D. in management and doctor of management programs differ. Students should carefully research their options to determine which institution and degree paths best meet their educational and professional needs.

Curriculum for an Online Doctorate in Management

• Management Theory: This course explores various strategies for effective organizational leadership, creating the ability to recognize and solve problems, identify oneself as a leader, and manage personnel in the face of ambiguity and adversity. The curriculum provides students with a well-rounded understanding of management theory through exposure to traditional and contemporary writings and approaches.
• Mechanics of Individual and Group Decision Making: Decision-making models and practices are crucial for organizational success. This course introduces students to different approaches toward individual and group decisions and the various ways these approaches affect both organizational leadership and society as a whole.
• Ethics and Leadership in a Global Environment: Many management practitioners work in international business, and cultural intelligence enables these professionals to create a respectful, ethical environment for all employees. This course discusses theories and practices related to ethics, as well as guidelines for ethical decision making and how leaders fit into global business.
• Organization Innovation and Scenario Thinking: Here, students learn through scenario building. This management technique involves creating stories based on analysis of practical situations and other techniques that foster innovation at the organizational level. Students develop future business plans based on predictive analysis and study innovation models and dissemination methods
• Leadership, Influence, and Power: This course focuses on the complex ― and at times, problematic ― relationship between these three factors. Students analyze and research past and present leaders in order to understand character and personality traits often used in conjunction with leadership. They also examine tactics and tools that people use to exert influence over others.

How Long Does It Take to Get an Online Doctorate Degree in Management?

Although the length of an online doctorate in management varies by institution, U.S. News estimates that most consist of up to 60 credits. Students usually complete their doctoral program in four or five years, although some institutions allow up to eight years of study prior to graduation. Cohort-based programs ― student groups that take courses and complete milestones at the same pace ― typically have a set duration, while individual-based programs depend on the student's progress.

Back to School Rankings

Accreditation refers to an evaluation that colleges and universities undergo to demonstrate standards of quality education. Most employers prefer to hire candidates with degrees from accredited institutions. Other colleges and universities also use accreditation status to determine whether a student's previous coursework qualifies for transfer credits. Additionally, students who wish to qualify for federal student aid must attend schools with nationally recognized accreditors.

Colleges and universities may receive one of three types of accreditation . Regional accreditation is normally reserved for academic, not-for-profit, and state universities. On the other hand, for-profit and vocational schools generally receive national accreditation. Students should note the differences between regional and national accreditation, as many regionally accredited institutions will not accept credits from schools that are nationally accredited. Lastly, specialized or programmatic accreditation is granted to specific degree programs or sub-colleges dedicated to certain fields, such as law, medicine, and education.

The Council for Higher Education Accreditation (CHEA) oversees accrediting agencies recognized by 'professional and state licensing authorities' without federal recognition. These include 60 regional, national, and programmatic accrediting organizations , as well as roughly 3,000 degree-granting postsecondary institutions. CHEA reviews accredited institutions every 3-10 years, and evaluates them based on a review process that takes key principles like quality assurance, advocacy, and inclusion into account. The U.S. Department of Education oversees federally recognized accrediting organizations and maintains a list of active and inactive regional, national, and specialized accreditation providers.

Employment Outlook for Doctorate in Management Graduates

Doctorate in management careers.

Because online doctorate in management programs emphasize topics like organizational behavior and ethical leadership, these programs prepare students for various careers in business and education. Although a doctoral degree may not be explicitly required for some of these roles, the BLS notes that doctorate holders in the U.S. earn a median weekly salary of $2,083 ― $420 per week more than master's holders and more than $650 per week than bachelor's degree holders.

• Median Annual Salary: $100,090
• Projected Growth Rate: 3%
• Median Annual Salary: $147,247
• Median Annual Salary: $155,616
• Median Annual Salary: $80,840
• Projected Growth Rate: 8%

Doctorate in Management Salary

The title of chief executive ― which encompasses CEOs, COOs, college presidents, and superintendents ― brings above-average earnings across the country. However, some states offer significantly higher annual salaries than others. According to the Bureau of Labor Statistics, the mean annual wage for chief executives nationwide is $246,440 , but these employees earn at least $279,000 per year in 11 states. The top five states for chief executives in terms of mean annual wage appear in the table below.

Top States for Chief Executives

Sources: BLS

Management Professional Organizations

Joining a professional organization can help connect you to job openings and advanced learning opportunities. Through conferences, career services, and various programs, management professionals can network with others in their field, which can often lead to mentorship and future career references.

• American Management Association : The AMA offers training seminars in two dozen subjects related to management, including communication skills, finance and accounting, project management, and strategic planning. The association is also home to the Women's Leadership Center that helps ensure achievement and job satisfaction among female organizational leaders.
• Executive Leadership Council : Founded in 1986, the ELC trains and empowers African American organizational leaders. The council's C-Suite Academy offers leadership development, networking opportunities, and executive coaching for 'high potential senior executives.' The ELC also offers sponsorship for executives and managers at different professional levels.
• American Society of Association Executives : The ASAE offers the Certified Association Executive credential to executives of nonprofit organizations or association management companies with at least 10 years of paid work experience. The ASAE also offers several leadership development opportunities, including the Diversity Executive Leadership Program and the Women Executives Forum.

A Ph.D. in management online program represents a significant financial investment for most prospective students. Most of these degree programs carry a total cost of $20,000-$51,000, depending on the institution. In order to alleviate these high costs, many students turn to financial aid in the form of loans, grants, and scholarships.

Those earning a doctorate in management can choose from multiple federal student loan options. In order to qualify for any form of federal student aid, candidates must complete and submit a Free Application for Federal Student Aid (FAFSA). This online form uses the applicant's financial history and current income to determine qualifying amount.

In addition to student loans, those seeking a doctorate in management may qualify for grants and scholarships. Unlike loans, most grants and scholarships do not need to be repaid. Students may find these opportunities through their higher education institution, public and private companies, nonprofit foundations, religious groups, and other non-academic organizations.

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PhD in Management

Areas of Specialisation

• Accounting and Control
• Decision Sciences
• Entrepreneurship
• Organisational Behaviour
• Technology and Operations Management

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Your doctoral journey is the start of your exciting career as an innovative researcher and professor of management.

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Roderick SWAAB

Academic director, phd programme professor of organisational behaviour the insead chaired professor of leadership and conflict resolution, your passport to a prestigious academic career.

The INSEAD PhD Programme is designed to amplify your talents by offering the best training, experience and advice to set the foundations for a successful career in business academia. Our curriculum gives you the depth and breadth needed to develop rigorous and relevant research in a truly unique, diverse and global environment. As a doctoral student at INSEAD, you will have the opportunity to start collaborations with globally renowned faculty and thought leaders from our campuses in France, Singapore, and Abu Dhabi as well as from our alliance partners - The Wharton School, Sorbonne University and Tsinghua University. INSEAD’s faculty are deeply committed to your well-being, training and development to help you become an outstanding scholar in your field and place you among the best academic departments worldwide. Our programme provides a generous fellowship that offers health insurance coverage for you and your family and the necessary...

The INSEAD PhD Programme is designed to amplify your talents by offering the best training, experience and advice to set the foundations for a successful career in business academia. Our curriculum gives you the depth and breadth needed to develop rigorous and relevant research in a truly unique, diverse and global environment. As a doctoral student at INSEAD, you will have the opportunity to start collaborations with globally renowned faculty and thought leaders from our campuses in France, Singapore, and Abu Dhabi as well as from our alliance partners - The Wharton School, Sorbonne University and Tsinghua University. INSEAD’s faculty are deeply committed to your well-being, training and development to help you become an outstanding scholar in your field and place you among the best academic departments worldwide. Our programme provides a generous fellowship that offers health insurance coverage for you and your family and the necessary freedom to develop your research interests. Moreover, INSEAD’s close connection to business, government and society offers numerous resources and opportunities to conduct your research in the real world, along with access to our behavioural laboratory facilities in France and Singapore. The world is full of complex problems that we need future generations of scholars to solve. Join the INSEAD PhD Programme on this journey to push the boundaries of your field and make a difference!

Why an INSEAD PhD?

The transformation of your career path starts from here, the only two-continent programme, the most culturally diverse in the world, …and yet the most intimate, close relationships with world experts, a stimulating interdisciplinary environment, full financial support, outstanding career potential.

Our eight choices of specialisation cover the whole breadth of business – and the entire business school curriculum. During the first two years of the programme, there are core courses common to all areas, giving you...

Our eight choices of specialisation cover the whole breadth of business – and the entire business school curriculum. During the first two years of the programme, there are core courses common to all areas, giving you insights from other disciplines and confidence for your future career. As a PhD student, along with these core courses, you will take a set of advanced course requirements applicable to your chosen field of specialisation, and these must be met in order to qualify in the field.

Experience the INSEAD PhD

When you join the INSEAD PhD in Management, you become part of an extraordinary global community. Explore the programme through the experiences of our students, alumni and faculty experts.

Life as a PhD Student

Life as a Business Professor

Intellectual Leadership and Influential Scholarship

You passport to a prestigious academic career

Join us and start living the insead experience.

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Which program is right for you?

Through intellectual rigor and experiential learning, this full-time, two-year MBA program develops leaders who make a difference in the world.

A rigorous, hands-on program that prepares adaptive problem solvers for premier finance careers.

A 12-month program focused on applying the tools of modern data science, optimization and machine learning to solve real-world business problems.

Earn your MBA and SM in engineering with this transformative two-year program.

Combine an international MBA with a deep dive into management science. A special opportunity for partner and affiliate schools only.

A doctoral program that produces outstanding scholars who are leading in their fields of research.

Bring a business perspective to your technical and quantitative expertise with a bachelor’s degree in management, business analytics, or finance.

A joint program for mid-career professionals that integrates engineering and systems thinking. Earn your master’s degree in engineering and management.

An interdisciplinary program that combines engineering, management, and design, leading to a master’s degree in engineering and management.

Executive Programs

A full-time MBA program for mid-career leaders eager to dedicate one year of discovery for a lifetime of impact.

This 20-month MBA program equips experienced executives to enhance their impact on their organizations and the world.

Non-degree programs for senior executives and high-potential managers.

A non-degree, customizable program for mid-career professionals.

PhD Program

Program overview.

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Rigorous, discipline-based research is the hallmark of the MIT Sloan PhD Program. The program is committed to educating scholars who will lead in their fields of research—those with outstanding intellectual skills who will carry forward productive research on the complex organizational, financial, and technological issues that characterize an increasingly competitive and challenging business world.

Start here.

Learn more about the program, how to apply, and find answers to common questions.

Admissions Events

Check out our event schedule, and learn when you can chat with us in person or online.

Start Your Application

Visit this section to find important admissions deadlines, along with a link to our application.

Click here for answers to many of the most frequently asked questions.

PhD studies at MIT Sloan are intense and individual in nature, demanding a great deal of time, initiative, and discipline from every candidate. But the rewards of such rigor are tremendous:  MIT Sloan PhD graduates go on to teach and conduct research at the world's most prestigious universities.

PhD Program curriculum at MIT Sloan is organized under the following three academic areas: Behavior & Policy Sciences; Economics, Finance & Accounting; and Management Science. Our nine research groups correspond with one of the academic areas, as noted below.

MIT Sloan PhD Research Groups

Behavioral & policy sciences.

Economic Sociology

Institute for Work & Employment Research

Organization Studies

Technological Innovation, Entrepreneurship & Strategic Management

Economics, Finance & Accounting

Accounting  

Management Science

Information Technology

System Dynamics  

Those interested in a PhD in Operations Research should visit the Operations Research Center .  

PhD Program Structure

Additional information including coursework and thesis requirements.

MIT Sloan Predoctoral Opportunities

MIT Sloan is eager to provide a diverse group of talented students with early-career exposure to research techniques as well as support in considering research career paths.

Rising Scholars Conference

The fourth annual Rising Scholars Conference on October 25 and 26 gathers diverse PhD students from across the country to present their research.

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The goal of the MIT Sloan PhD Program's admissions process is to select a small number of people who are most likely to successfully complete our rigorous and demanding program and then thrive in academic research careers. The admission selection process is highly competitive; we aim for a class size of nineteen students, admitted from a pool of hundreds of applicants.

What We Seek

• Outstanding intellectual ability
• Excellent academic records
• Previous work in disciplines related to the intended area of concentration
• Strong commitment to a career in research

MIT Sloan PhD Program Admissions Requirements Common Questions

Dates and Deadlines

Admissions for 2024 is closed. The next opportunity to apply will be for 2025 admission. The 2025 application will open in September 2024. 

More information on program requirements and application components

Students in good academic standing in our program receive a funding package that includes tuition, medical insurance, and a fellowship stipend and/or TA/RA salary. We also provide a new laptop computer and a conference travel/research budget.

Funding Information

Throughout the year, we organize events that give you a chance to learn more about the program and determine if a PhD in Management is right for you.

PhD Program Events

May phd program overview.

During this webinar, you will hear from the PhD Program team and have the chance to ask questions about the application and admissions process.

June PhD Program Overview

July phd program overview, august phd program overview.

Complete PhD Admissions Event Calendar

Unlike formulaic approaches to training scholars, the PhD Program at MIT Sloan allows students to choose their own adventure and develop a unique scholarly identity. This can be daunting, but students are given a wide range of support along the way - most notably having access to world class faculty and coursework both at MIT and in the broader academic community around Boston.

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Profiles of our current students

MIT Sloan produces top-notch PhDs in management. Immersed in MIT Sloan's distinctive culture, upcoming graduates are poised to innovate in management research and education.

Academic Job Market

Doctoral candidates on the current academic market

Academic Placements

Graduates of the MIT Sloan PhD Program are researching and teaching at top schools around the world.

view recent placements 

MIT Sloan Experience

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The PhD Program is integral to the research of MIT Sloan's world-class faculty. With a reputation as risk-takers who are unafraid to embrace the unconventional, they are engaged in exciting disciplinary and interdisciplinary research that often includes PhD students as key team members.

Research centers across MIT Sloan and MIT provide a rich setting for collaboration and exploration. In addition to exposure to the faculty, PhD students also learn from one another in a creative, supportive research community.

Throughout MIT Sloan's history, our professors have devised theories and fields of study that have had a profound impact on management theory and practice.

From Douglas McGregor's Theory X/Theory Y distinction to Nobel-recognized breakthroughs in finance by Franco Modigliani and in option pricing by Robert Merton and Myron Scholes, MIT Sloan's faculty have been unmatched innovators.

This legacy of innovative thinking and dedication to research impacts every faculty member and filters down to the students who work beside them.

Faculty Links

• Accounting Faculty
• Economic Sociology Faculty
• Finance Faculty
• Information Technology Faculty
• Institute for Work and Employment Research (IWER) Faculty
• Marketing Faculty
• Organization Studies Faculty
• System Dynamics Faculty
• Technological Innovation, Entrepreneurship, and Strategic Management (TIES) Faculty

Student Research

“MIT Sloan PhD training is a transformative experience. The heart of the process is the student’s transition from being a consumer of knowledge to being a producer of knowledge. This involves learning to ask precise, tractable questions and addressing them with creativity and rigor. Hard work is required, but the reward is the incomparable exhilaration one feels from having solved a puzzle that had bedeviled the sharpest minds in the world!” -Ezra Zuckerman Sivan Alvin J. Siteman (1948) Professor of Entrepreneurship

Sample Dissertation Abstracts - These sample Dissertation Abstracts provide examples of the work that our students have chosen to study while in the MIT Sloan PhD Program.

We believe that our doctoral program is the heart of MIT Sloan's research community and that it develops some of the best management researchers in the world. At our annual Doctoral Research Forum, we celebrate the great research that our doctoral students do, and the research community that supports that development process.

The videos of their presentations below showcase the work of our students and will give you insight into the topics they choose to research in the program.

How Should We Measure the Digital Economy?

2020 PhD Doctoral Research Forum Winner - Avinash Collis

Watch more MIT Sloan PhD Program  Doctoral Forum Videos

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PhD/ MPhil/ MSc Management (Research)

• Visit an Open Day
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• Course details
• Entry Requirements
• Teaching and assessment
• Employability

Our PhD in Management comprises a short taught component followed by a longer research phase. The programme will allow you to broaden and deepen your knowledge of research methods whilst undertaking your own research and developing a set of transferable professional skills.

The PhD programme in Management will facilitate the creation and interpretation of new knowledge by the research student, demonstrated through the thesis. The taught component is designed to ensure that doctoral researchers understand the breath of techniques used in modern social science research.

Doctoral researchers will be capable of analysing a range of data using a range of qualitative and quantitative techniques. They will be able to explain theories underlying different approaches to social science research. Doctoral researchers are expected to participate to the fullest possible extent in the life of the Business School. This means attending seminars organised by the Business School thereby helping expose doctoral researchers to new ideas emanating from outside their own area of specialisation. It also requires actively participating in PhD workshops and conferences organised by the Business School and Graduate School as well as institutions outside the University of Birmingham.

Ultimately all doctoral researchers will have the ability to characterise and solve business and management problems using advanced research tools. They should be able to derive policy implications from their research and communicate these to policy makers, practitioners and other academics in a manner which is comprehensible. They will also be able to peer review others’ research and offer constructive criticism; and to extend the frontiers of the discipline through their own innovative research.

Doctoral researchers may choose to become academics, work in Government, businesses, supranational organisations or in the research arms of major financial institutions. They are expected to achieve a substantial understanding of contemporaneous management and business issues enabling them to take a lead in ongoing debates within society. They will be aware of and understand the function of related institutions at both a national and international level.

Fees 2024 - 2025

• Code 8169 - £4,778 (UK) PhD Full time
• Code 8171 - £2,389 (UK) PhD Part time
• Code 8169 - £23,520 (International) PhD Full time
• Code 698B - £2,389 (UK) Distance Learning 8 Years Part time
• Code 8165 - £4,778 (UK) MPhil Full time
• Code 8168 - £2,389 (UK) MPhil Part time
• Code 8165 - £23,520 (International) MPhil Full time
• Code 8176 - £4,778 (UK) MSc (Research) Full time
• Code 8179 - £2,389 (UK) MSc (Research) Part time
• Code 8176 - £23,520 (International) MSc (Research) Full time

Learn more about fees and funding

Scholarships and studentships

Scholarships may be available. International students can often gain funding through overseas research scholarships, Commonwealth scholarships or their home government.

The Business School and the University provide some scholarships and bursaries for postgraduate research students.For details of these, please contact the Business School's Research Office at [email protected] . For further information contact the School directly or email [email protected] .

How To Apply

• How to apply

To apply for a postgraduate research programme, you will need to submit your application and supporting documents online. We have put together some helpful information on the research programme application process and supporting documents on our how to apply page . Please read this information carefully before completing your application.

Our Standard Requirements

The Business School's entry requirement is a good honours degree (first or upper second class honours) awarded by a recognised University in an appropriate subject, and a merit in a relevant Master’s degree. We usually ask students for an average of 65 in the taught component of their Masters. All international students also need to show that they have adequate knowledge of written and spoken English. Learn more about entry requirement

Writing your Research Proposal

Your research proposal should illustrate your ability to plan an independent research study and the relevance of your topic to the research interests and expertise of Birmingham Business School.You need to demonstrate that you understand the field that you plan to research, identify an interesting and original research question, and develop a tentative plan of study. It's critical that your research proposal is written to the guidelines specified below.

Guidelines for the Research Proposal

International requirements.

Applicants for postgraduate research programmes should hold a Bachelors degree and a Masters degree, with a GPA of 14/20 from a recognised institution to be considered. Applicants with lower grades than this may be considered on an individual basis.

Holders of the Licenciado or an equivalent professional title from a recognised Argentinian university, with a promedio of at least 7.5, may be considered for entry to a postgraduate degree programme. Applicants for PhD degrees will normally have a Maestria or equivalent

Applicants who hold a Masters degree will be considered for admission to PhD study.

Holders of a good four-year Diplomstudium/Magister or a Masters degree from a recognised university with a minimum overall grade of 2.5 will be considered for entry to postgraduate research programmes.

Students with a good 5-year Specialist Diploma or 4-year Bachelor degree from a recognised higher education institution in Azerbaijan, with a minimum GPA of 4/5 or 80% will be considered for entry to postgraduate taught programmes at the University of Birmingham.

For postgraduate research programmes applicants should have a good 5-year Specialist Diploma (completed after 1991), with a minimum grade point average of 4/5 or 80%, from a recognised higher education institution or a Masters or “Magistr Diplomu” or “Kandidat Nauk” from a recognised higher education institution in Azerbaijan.

Applicants for postgraduate research programmes should hold a Bachelors degree and a Masters degree, with a GPA of 3.0/4.0 or 75% from a recognised institution to be considered. Applicants with lower grades than this may be considered on an individual basis.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree, with a CGPA of 3.0-3.3/4.0 or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Students who hold a Masters degree from the University of Botswana with a minimum GPA of 3.0/4.0 or 3.5/5.0 (70%/B/'very good') will be considered for Postgraduate Diplomas and Masters degrees.

Please note 4-year bachelor degrees from the University of Botswana are considered equivalent to a Diploma of Higher Education. 5-year bachelor degrees from the University of Botswana are considered equivalent to a British Bachelor (Ordinary) degree.

Students who have completed a Masters degree from a recognised institution will be considered for PhD study.

A Licenciatura or Bacharelado degree from a recognised Brazilian university:

• A grade of 7.5/10 for entry to programmes with a 2:1 requirement
• A grade of 6.5/10for entry to programmes with a 2:2 requirement

Holders of a good Bachelors degree with honours (4 to 6 years) from a recognised university with a upper second class grade or higher will be considered for entry to taught postgraduate programmes.  Holders of a good Masters degree from a recognised university will be considered for entry to postgraduate research programmes.

Holders of a good post-2001 Masters degree from a recognised university will be considered for entry to postgraduate research programmes.

Students with a minimum average of 14 out of 20 (or 70%) on a 4-year Licence, Bachelor degree or Diplôme d'Etudes Superieures de Commerce (DESC) or Diplôme d'Ingénieur or a Maîtrise will be considered for Postgraduate Diplomas and Masters degrees.

Holders of a bachelor degree with honours from a recognised Canadian university may be considered for entry to a postgraduate degree programme. A GPA of 3.0/4, 7.0/9 or 75% is usually equivalent to a UK 2.1.

Holders of the Licenciado or equivalent Professional Title from a recognised Chilean university will be considered for Postgraduate Diplomas and Masters degrees. Applicants for PhD study will preferably hold a Magister degree or equivalent.

Students with a bachelor’s degree (4 years minimum) may be considered for entry to a postgraduate degree programme. However please note that we will only consider students who meet the entry guidance below.  Please note: for the subject areas below we use the Shanghai Ranking 2022 (full table)  ,  Shanghai Ranking 2023 (full table) , and Shanghai Ranking of Chinese Art Universities 2023 .

需要具备学士学位（4年制）的申请人可申请研究生课程。请根据所申请的课程查看相应的入学要求。 请注意，中国院校名单参考 软科中国大学排名2022（总榜） ，  软科中国大学排名2023（总榜） ，以及 软科中国艺术类高校名单2023 。  

Business School    - MSc programmes (excluding MBA)  

商学院硕士课程（MBA除外）入学要求

School of Computer Science – all MSc programmes 计算机学院硕士课程入学要求

College of Social Sciences – courses listed below 社会科学 学院部分硕士课程入学要求 MA Education  (including all pathways) MSc TESOL Education MSc Public Management MA Global Public Policy MA Social Policy MA Sociology Department of Political Science and International Studies  全部硕士课程 International Development Department  全部硕士课程

  All other programmes (including MBA)   所有其他 硕士课程（包括 MBA）入学要求

Please note:

• Borderline cases: We may consider students with lower average score (within 5%) on a case-by-case basis if you have a relevant degree and very excellent grades in relevant subjects and/or relevant work experience. 如申请人均分低于相应录取要求（5%以内），但具有出色学术背景，优异的专业成绩，以及（或）相关的工作经验，部分课程将有可能单独酌情考虑。
• Please contact the China Recruitment Team for any questions on the above entry requirements. 如果您对录取要求有疑问，请联系伯明翰大学中国办公室   [email protected]

Holders of the Licenciado/Professional Title from a recognised Colombian university will be considered for our Postgraduate Diploma and Masters degrees. Applicants for PhD degrees will normally have a Maestria or equivalent.

Holders of a good bachelor degree with honours (4 to 6 years) from a recognised university with a upper second class grade or higher will be considered for entry to taught postgraduate programmes.  Holders of a good Masters degree from a recognised university will be considered for entry to postgraduate research programmes.

Holders of a good Bacclaureus (Bachelors) from a recognised Croatian Higher Education institution with a minimum overall grade of 4.0 out of 5.0, vrlo dobar ‘very good’, or a Masters degree, will be considered for entry to postgraduate research programmes.

Holders of a Bachelors degree(from the University of the West Indies or the University of Technology) may be considered for entry to a postgraduate degree programme. A Class II Upper Division degree is usually equivalent to a UK 2.1. For further details on particular institutions please refer to the list below.  Applicants for PhD level study will preferably hold a Masters degree or Mphil from the University of the West Indies.

Applicants for postgraduate research programmes should hold a good Bachelors degree from a recognised institution with a minimum overall grade of 6.5 out of 10, or a GPA of 3 out of 4, and will usually be required to have completed a good Masters degree to be considered for entry to postgraduate research programmes. Applicants with lower grades than this may be considered on an individual basis.

Holders of a good Bakalár from a recognised Czech Higher Education institution with a minimum overall grade of 1.5, B, velmi dobre ‘very good’ (post-2004) or 2, velmi dobre ‘good’ (pre-2004), or a good post-2002 Magistr (Masters), will be considered for entry to postgraduate research programmes.

Applicants for postgraduate research programmes should hold a good Bachelors degree from a recognised institution with a minimum overall grade of 7-10 out of 12 (or 8 out of 13) or higher for 2:1 equivalence and will usually be required to have completed a good Masters/ Magisterkonfereus/Magister Artium degree to be considered for entry to postgraduate research programmes. Applicants with lower grades than this may be considered on an individual basis.

Holders of the Licenciado or an equivalent professional title from a recognised Ecuadorian university may be considered for entry to a postgraduate degree programme. Grades of 70% or higher can be considered as UK 2.1 equivalent.  Applicants for PhD level study will preferably hold a Magister/Masterado or equivalent qualification, but holders of the Licenciado with excellent grades can be considered.

Applicants for postgraduate research programmes should hold a Bachelors degree and a Masters degree, with a GPA of 3.0/4.0 or 75% from a recognised institution. Applicants with lower grades than this may be considered on an individual basis.

Holders of a good Bakalaurusekraad from a recognised university with a minimum overall grade of 4/5 or B, or a good one- or two-year Magistrikraad from a recognised university, will be considered for entry to postgraduate research programmes.

Students who hold a Masters degree with very good grades (grade B, 3.5/4 GPA or 85%) will be considered for Postgraduate Diplomas and Masters degrees. 

Holders of a good Kandidaatti / Kandidat (old system), a professional title such as Ekonomi, Diplomi-insinööri, Arkkitehti, Lisensiaatti (in Medicine, Dentistry and Vetinary Medicine), or a Maisteri / Magister (new system), Lisensiaatti / Licenciat, Oikeustieteen Kandidaatti / Juris Kandidat (new system) or Proviisori / Provisor from a recognised Finnish Higher Education institution, with a minimum overall grade of 2/3 or 4/5, will be considered for entry to postgraduate research programmes.

Applicants for postgraduate research programmes should hold a should hold a Bachelors degree and will usually be required to have completed a Masters/Maîtrise with a minimum overall grade of 13 out of 20, or a Magistère / Diplôme d'Etudes Approfondies / Diplôme d'Etudes Supérieures Specialisées / Mastère Specialis, from a recognised French university or Grande École to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Holders of a Magister Artium, a Diplom or an Erstes Staatsexamen from a recognised university with a minimum overall grade of 2.5, or a good two-year Lizentiat / Aufbaustudium / Zweites Staatsexamen or a Masters degree from a recognised university, will be considered for entry to postgraduate research programmes.

Students who hold a Bachelor degree from a recognised institution will be considered for Postgraduate Diplomas and Masters degrees. Most taught Masters programmes require a minimum of an upper second class degree (2.1) with a minimum GPA of at least 3.0/4.0 or 3.5/5.0 Students who have completed a Masters degree from a recognised institution will be considered for PhD study.

Applicants for postgraduate research programmes should hold a good four-year Ptychio (Bachelor degree) with a minimum overall grade of 6.5 out of 10, from a recognised Greek university (AEI), and will usually be required to have completed a good Metaptychiako Diploma Eidikefsis (Masters degree) from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

4-year Licenciado is deemed equivalent to a UK bachelors degree. A score of 75 or higher from Universidad de San Carlos de Guatemala (USAC) can be considered comparable to a UK 2.1, 60 is comparable to a UK 2.2.  Private universities have a higher pass mark, so 80 or higher should be considered comparable to a UK 2.1, 70 is comparable to a UK 2.2

The Hong Kong Bachelor degree is considered comparable to British Bachelor degree standard. Students with bachelor degrees awarded by universities in Hong Kong may be considered for entry to one of our postgraduate degree programmes.

Students with Masters degrees may be considered for PhD study.

Holders of a good Alapfokozat / Alapképzés or Egyetemi Oklevel from a recognised university with a minimum overall grade of 3.5, or a good Mesterfokozat (Masters degree) or Egyetemi Doktor (university doctorate), will be considered for entry to postgraduate research programmes.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree, with a 60% or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Holders of the 4 year Sarjana (S1) from a recognised Indonesian institution will be considered for postgraduate study. Entry requirements vary with a minimum requirement of a GPA of 2.8.

Applicants for postgraduate research programmes should hold a Bachelors degree and a Masters degree, with a score of 14/20 or 70% from a recognised institution to be considered. Applicants with lower grades than this may be considered on an individual basis.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree from a recognised institution, with 100 out of 110 or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Students who hold the Maitrise, Diplome d'Etude Approfondies, Diplome d'Etude Superieures or Diplome d'Etude Superieures Specialisees will be considered for Postgraduate Diplomas and Masters degrees (14-15/20 or Bien from a well ranked institution is considered comparable to a UK 2.1, while a score of 12-13/20 or Assez Bien is considered comparable to a UK 2.2).

Students with a Bachelor degree from a recognised university in Japan will be considered for entry to a postgraduate Masters degree provided they achieve a sufficiently high overall score in their first (Bachelor) degree. A GPA of 3.0/4.0 or a B average from a good Japanese university is usually considered equivalent to a UK 2:1.

Students with a Masters degree from a recognised university in Japan will be considered for PhD study. A high overall grade will be necessary to be considered.

Students who have completed their Specialist Diploma Мамаң дипломы/Диплом специалиста) or "Magistr" (Магистр дипломы/Диплом магистра) degree (completed after 1991) from a recognised higher education institution, with a minimum GPA of 2.67/4.00 for courses requiring a UK lower second and 3.00/4.00 for courses requiring a UK upper second class degree, will be considered for entry to postgraduate Masters degrees and, occasionally, directly for PhD degrees.  Holders of a Bachelor "Bakalavr" degree (Бакалавр дипломы/Диплом бакалавра) from a recognised higher education institution, with a minimum GPA of  2.67/4.00 for courses requiring a UK lower second and 3.00/4.00 for courses requiring a UK upper second class degree, may also be considered for entry to taught postgraduate programmes.

Students who hold a Bachelor degree from a recognised institution will be considered for Postgraduate Diplomas and Masters degrees. Most taught Masters programmes require a minimum of an upper second class degree (2.1) with a minimum GPA of at least 3.0/4.0 or 3.5/50

Holders of a good Postgraduate Diploma (professional programme) from a recognised university or institution of Higher Education, with a minimum overall grade of 7.5 out of 10, or a post-2000 Magistrs, will be considered for entry to postgraduate research programmes.

Applicants for postgraduate research programmes should hold a Bachelors degree and a Masters degree, with a score of 16/20 or 80% from a recognised institution to be considered. Applicants with lower grades than this may be considered on an individual basis.

Holders of a Bachelors degree from a recognised university in Libya will be considered for postgraduate study. Holders of a Bachelors degree will normally be expected to have achieved score of 70% for 2:1 equivalency or 65% for 2:2 equivalency. Alternatively students will require a minimum of 3.0/4.0 or BB to be considered.

Holders of a good pre-2001 Magistras from a recognised university with a minimum overall grade of 8 out of 10, or a good post-2001 Magistras, will be considered for entry to postgraduate research programmes

Holders of a good Bachelors degree from a recognised Luxembourgish Higher Education institution with a minimum overall grade of 16 out of 20, or a Diplôme d'Études Supérieures Spécialisées (comparable to a UK PGDip) or Masters degree from a recognised Luxembourgish Higher Education institution will be considered for entry to postgraduate research programmes.

Students who hold a Masters degree will be considered for Postgraduate Diplomas and Masters degrees (70-74% or A or Marginal Distinction from a well ranked institution is considered comparable to a UK 2.1, while a score of 60-69% or B or Bare Distinction/Credit is considered comparable to a UK 2.2).

Holders of a Bachelors degree from a recognised Malaysian institution (usually achieved with the equivalent of a second class upper or a grade point average minimum of 3.0) will be considered for postgraduate study at Diploma or Masters level.

Holders of a good Bachelors degree from the University of Malta with a minimum grade of 2:1 (Hons), and/or a Masters degree, will be considered for entry to postgraduate research programmes.

Students who hold a Bachelor degree (Honours) from a recognised institution (including the University of Mauritius) will be considered for Postgraduate Diplomas and Masters degrees.  Most taught Masters programmes require a minimum of an upper second class degree (2:1).

Students who hold the Licenciado/Professional Titulo from a recognised Mexican university with a promedio of at least 8 will be considered for Postgraduate Diplomas and Masters degrees.

Students who have completed a Maestria from a recognised institution will be considered for PhD study.

Applicants for postgraduate research programmes should hold a Bachelors degree, licence or Maîtrise and a Masters degree, with a score of 14/20 or 70% from a recognised institution to be considered. Applicants with lower grades than this may be considered on an individual basis.

Students with a good four year honours degree from a recognised university will be considered for postgraduate study at the University of Birmingham. PhD applications will be considered on an individual basis.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree, with 60-74% or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Holders of a good Doctoraal from a recognised Dutch university with a minimum overall grade of 7 out of 10, and/or a good Masters degree, will be considered for entry to postgraduate research programmes.

Students who hold a Bachelor degree (minimum 4 years and/or level 400) from a recognised institution will be considered for Postgraduate Diplomas and Masters degrees.  Most taught Masters programmes require a minimum of an upper second class degree (2.1) with a minimum GPA of at least 3.0/4.0 or 3.5/5.0

Applicants for postgraduate research programmes should hold a good Bachelors degree from a recognised institution with a minimum GPA of B/Very Good or 1.6-2.5 for a 2.1 equivalency, and will usually be required to have completed a good Masters, Mastergrad, Magister. Artium, Sivilingeniør, Candidatus realium or Candidatus philologiae degree to be considered for entry to postgraduate research programmes. Applicants with lower grades than this may be considered on an individual basis.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree, with a CGPA of 3.0/4 or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Holders of a Bachelors degree from a recognised university in the Palestinian Territories will be considered for postgraduate study. Holders of Bachelors degree will normally be expected to have achieved a GPA of 3/4 or 80% for 2:1 equivalency or a GPA of 2.5/4 or 70% for 2:2 equivalency.    

Holders of the Título de Licenciado /Título de (4-6 years) or an equivalent professional title from a recognised Paraguayan university may be considered for entry to a postgraduate degree programme. Grades of 4/5 or higher can be considered as UK 2.1 equivalent.  The Título Intermedio is a 2-3 year degree and is equivalent to a HNC, it is not suitable for postgraduate entry but holders of this award could be considered for second year undergraduate entry or pre-Masters.  Applicants for PhD level study will preferably hold a Título de Maestría / Magister or equivalent qualification, but holders of the Título/Grado de Licenciado/a with excellent grades can be considered.

Holders of the Licenciado, with at least 13/20 may be considered as UK 2.1 equivalent. The Grado de Bachiller is equivalent to an ordinary degree, so grades of 15+/20 are required.  Applicants for PhD level study will preferably hold a Título de Maestría or equivalent qualification.

Holders of a good pre-2001 Magister from a recognised Polish university with a minimum overall grade of 4 out of 5, dobry ‘good’, and/or a good Swiadectwo Ukonczenia Studiów Podyplomowych (Certificate of Postgraduate Study) or post-2001 Magister from a recognised Polish university with a minimum overall grade of 4.5/4+ out of 5, dobry plus 'better than good', will be considered for entry to postgraduate research programmes.

Holders of a good Licenciado from a recognised university, or a Diploma de Estudos Superiores Especializados (DESE) from a recognised Polytechnic Institution, with a minimum overall grade of 16 out of 20, and/or a good Mestrado / Mestre (Masters) from a recognised university, will be considered for entry to postgraduate research programmes.

Applicants for postgraduate research programmes should hold a good Bachelors degree from a recognised Romanian Higher Education institution with a minimum overall grade of 8 out of 10, and will usually be required to have completed a Masters degree/Diploma de Master/Diploma de Studii Academice Postuniversitare (Postgraduate Diploma - Academic Studies) or Diploma de Studii Postuniversitare de Specializare (Postgraduate Diploma - Specialised Studies) to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Holders of a good Диплом Специалиста (Specialist Diploma) or Диплом Магистра (Magistr) degree from recognised universities in Russia (minimum GPA of 4.0) will be considered for entry to taught postgraduate programmes/PhD study.

Students who hold a 4-year Bachelor degree with at least 16/20 or 70% will be considered for Postgraduate Diplomas and Masters degrees.   

Students who hold a Maitrise, Diplome d'Etude Approfondies,Diplome d'Etude Superieures or Diplome d'Etude Superieures Specialisees will be considered for Postgraduate Diplomas and Masters degrees. A score of 14-15/20 or Bien from a well ranked institution is considered comparable to a UK 2.1, while a score of 12-13/20 or Assez Bien is considered comparable to a UK 2.2

Students who hold a Bachelor (Honours) degree from a recognised institution with a minimum GPA of 3.0/4.0 or 3.5/5.0 (or a score of 60-69% or B+) from a well ranked institution will be considered for most our Postgraduate Diplomas and Masters degrees with a 2:1 requirement.

Students holding a good Bachelors Honours degree will be considered for postgraduate study at Diploma or Masters level.

Holders of a good three-year Bakalár or pre-2002 Magister from a recognised Slovakian Higher Education institution with a minimum overall grade of 1.5, B, Vel’mi dobrý ‘very good’, and/or a good Inžinier or a post-2002 Magister from a recognised Slovakian Higher Education institution will be considered for entry to postgraduate research programmes.

Holders of a good Diploma o pridobljeni univerzitetni izobrazbi (Bachelors degree), Diplomant (Professionally oriented first degree), Univerzitetni diplomant (Academically oriented first degree) or Visoko Obrazovanja (until 1999) from a recognised Slovenian Higher Education institution with a minimum overall grade of 8.0 out of 10, and/or a good Diploma specializacija (Postgraduate Diploma) or Magister (Masters) will be considered for entry to postgraduate research programmes.

Students who hold a Bachelor Honours degree (also known as Baccalaureus Honores / Baccalaureus Cum Honoribus) from a recognised institution will be considered for Postgraduate Diplomas and Masters degrees. Most Masters programmes will require a second class upper (70%) or a distinction (75%).

Holders of a Masters degree will be considered for entry to postgraduate research programmes.

Holders of a Bachelor degree from a recognised South Korean institution (usually with the equivalent of a second class upper or a grade point average 3.0/4.0 or 3.2/4.5) will be considered for Masters programmes.

Holders of a good Masters degree from a recognised institution will be considered for PhD study on an individual basis.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree, with 7 out of 10 or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Applicants for postgraduate research programmes should hold a Bachelors degree and will usually be required to have completed a Masters degree, with 60-74% or a CGPA 3.30/4.0 or higher for 2:1 equivalency from a recognised institution to be considered for entry. Applicants with lower grades than this may be considered on an individual basis.

Holders of a good Kandidatexamen (Bachelors degree) or Yrkesexamen (Professional Bachelors degree) from a recognised Swedish Higher Education institution with the majority of subjects with a grade of VG (Val godkänd), and/or a good Magisterexamen (Masters degree), International Masters degree or Licentiatexamen (comparable to a UK Mphil), will be considered for entry to postgraduate research programmes.

Holders of a good "PostGraduate Certificate" or "PostGraduate Diploma" or a Masters degree from a recognised Swiss higher education institution (with a minimum GPA of 5/6 or 8/10 or 2/5 (gut-bien-bene/good) for a 2.1 equivalence) may be considered for entry to postgraduate research programmes.

Applicants for postgraduate research programmes should hold a Bachelors degree and a Masters degree, with a GPA of 3.0/4.0, 3.5/5 or 75% from a recognised institution to be considered. Applicants with lower grades than this may be considered on an individual basis.

Holders of a good Bachelor degree (from 75% to 85% depending upon the university in Taiwan) from a recognised institution will be considered for postgraduate Masters study. Holders of a good Masters degree from a recognised institution will be considered for PhD study.

Students who hold a Bachelor degree from a recognised institution will be considered for Postgraduate Diplomas and Masters degrees.  Most taught Masters programmes require a minimum of an upper second class degree (2.1) Students who have completed a Masters degree from a recognised institution will be considered for PhD study.

Holders of a good Masters degree from a recognised institution will be considered for entry to our postgraduate research programmes.

Holders of a good Masters degree or Mphil from a recognised university will be considered for entry to postgraduate research programmes.

Students with a Bachelors degree from the following universities may be considered for entry to postgraduate programmes:

• Ateneo de Manila University - Quezon City
• De La Salle University - Manila
• University of Santo Tomas
• University of the Philippines - Diliman

Students from all other institutions with a Bachelors and a Masters degree or relevant work experience may be considered for postgraduate programmes.

Grading Schemes

1-5 where 1 is the highest 2.1 = 1.75 2.2 = 2.25 

Out of 4.0 where 4 is the highest 2.1 = 3.0 2.2 = 2.5

Letter grades and percentages 2.1 = B / 3.00 / 83% 2.2 = C+ / 2.5 / 77%

Holders of a postdoctoral qualification from a recognised institution will be considered for PhD study.  Students may be considered for PhD study if they have a Masters from one of the above listed universities.

Holders of a Lisans Diplomasi with a minimum grade point average (GPA) of 3.0/4.0 from a recognised university will be considered for postgraduate study at Diploma or Masters level.

Holders of a Yuksek Diplomasi from a recognised university will be considered for PhD study.

Students who hold a Bachelor degree from a recognised institution will be considered for Postgraduate Diplomas and Masters degrees. Most Masters programmes will require a second class upper (2.1) or GPA of 3.5/5.0

Applicants for postgraduate research programmes should hold a good Bachelors degree / Диплом бакалавра (Dyplom Bakalavra), Диплом спеціаліста (Specialist Diploma) or a Dyplom Magistra from a recognised Ukrainian higher education institution with a minimum GPA of 4.0/5.0, 3.5/4, 8/12 or 80% or higher for 2:1 equivalence and will usually be required to have completed a good Masters degree to be considered for entry to postgraduate research programmes. Applicants with lower grades than this may be considered on an individual basis.

The University will consider students who hold an Honours degree from a recognised institution in the USA with a GPA of:

• 2.8 GPA (on a 4.0 scale) for entry to programmes with a 2:2 requirement 
• 3.2 GPA (on a 4.0 scale) for entry to programmes with a 2:1 requirement 

Please note that some subjects which are studied at postgraduate level in the USA, eg. Medicine and Law, are traditionally studied at undergraduate level in the UK.

Holders of the Magistr Diplomi (Master's degree) or Diplomi (Specialist Diploma), awarded by prestigious universities, who have attained high grades in their studies will be considered for postgraduate study.  Holders of the Fanlari Nomzodi (Candidate of Science), where appropriate, will be considered for PhD study.

Holders of the Licenciatura/Título or an equivalent professional title from a recognised Venezuelan university may be considered for entry to a postgraduate degree programme. Scales of 1-5, 1-10 and 1-20 are used, an overall score of 70% or equivalent can be considered equivalent to a UK 2.1.  Applicants for PhD level study will preferably hold a Maestria or equivalent qualification

Holders of a Bachelors degree from a recognised Vietnamese institution (usually achieved with the equivalent of a second class upper or a grade point average minimum GPA of 7.0 and above) will be considered for postgraduate study at Diploma or Masters level.  Holders of a Masters degree (thac si) will be considered for entry to PhD programmes.

Students who hold a Masters degree with a minimum GPA of 3.5/5.0 or a mark of 2.0/2.5 (A) will be considered for Postgraduate Diplomas and Masters degrees.   

Students who hold a good Bachelor Honours degree will be considered for Postgraduate Diplomas and Masters degrees. 

International Students

English requirement - IELTS 7 with no less than 6.5 in any band or equivalent.

The diversity of our research strengths at Birmingham Business School means we cover a wide range of management specialisms and if your application is successful you would join one of the five departments that collectively contribute to the PhD programme in Management.

Business and Labour Economics  - Research interests include labour markets, employment regulation and incentives, international trade, piracy and production and global value chains.

Entrepreneurship & Local Economy  - Research interests include leadership, local and regional economic development and entrepreneurship.

Organisation, Work and Employment  - Research interests include work and employment in contemporary organisations in the public and private sectors, human resource management and employment relations.

Procurement and Operations Management  - Research interests include organisational buying behaviour, supplier relationship management, public sector contracting, project management, small firm operations and high value engineering management.

Strategy and International Business  - Research interests include corporate social responsibility and sustainability, internationalisation and China, knowledge and innovation and also resilience and extreme events.

>Doctoral researchers in Management are registered for a full time 3-year PhD or a part-time 6-year PhD. In the first year of the programme (first two years for those registered part-time) students are required to take 60 credits of core Research Methods modules from the MA Social Research programme. They are also recommended to take Advanced Training Modules from the MA Social Research Programme as appropriate to their research and training needs.

Depending on their needs and accredited prior learning and subject to supervisory approval doctoral researchers can substitute 20 credits of the introductory MA Social research modules for Advanced Training Modules. By the end of their first year doctoral students will have completed an 8,000 word research proposal that they will present at the first annual review.  This forms the basis for supervised research over the remaining two years of the programme and the production of an 80,000 word thesis.

If I gain a postgraduate research degree from Birmingham Business School, what are my career prospects?

Birmingham’s Business graduates are sought after by a wide range of financial, commercial and public sector employers. They can typically offer a wide range of skills including analytical & research, numeracy, communication, team working and political & commercial awareness.

For those entering employment after graduating, traditionally popular areas include banking, accountancy/professional services and financial services. Many of our programmes involve studying towards a professional qualification. Outside of these areas, options include teaching abroad and retail management. Many PhD graduates also go on to forge successful academic careers of their own in teaching and academic research.

What type of career assistance is available to doctoral researchers in Birmingham Business School?

The University of Birmingham has invested heavily in careers and employability support.  The Careers Team have been praised for enhanced developments within their team and for adopting a model of integrated employability and internship support; something that has been rolled out and implemented across all Schools and Colleges at the University.

Doctoral researchers at Birmingham Business School will benefit from this additional investment; the school  now has its own well qualified dedicated Careers Team to support students with employment opportunities, work placements, internships and how to succeed at interview. In addition, a range of career management, personal development and employer events are run each year by the Careers in Business Team to help you make the most of the opportunities available. 

The University also has dedicated careers advisors for International students who run workshops and networking opportunities with potential employers. These are especially popular with International postgraduate researchers.

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• PhD in Business Management
• Project Management

Project Management Doctor of Philosophy in Business Management

Capella's online PhD in Business Management, Project Management prepares you to strategize and lead projects in a wide range of global and complex business environments. Learn current and emerging project management methodologies, contemporary leadership theories and practices, and communications approaches to help you grow as an effective leader. Become a driving force in the field as you develop high-level research skills, culminating in a dissertation that advances project management as a whole.

The mission of Capella University’s PhD in Business Management specialization in Project Management is to promote the development of business-focused knowledge and research skills necessary to effectively lead projects in one’s professional career.

Virtual Residencies

Network with faculty and peers, and gain access to valuable resources to use during your program and beyond, through three doctoral virtual residencies.

Research mentor

Maintain progress through your dissertation; we’ll pair you with an academic mentor who can help you keep your momentum.

Stay on track with your academic goals and dissertation; we’ll help you organize your work into manageable segments.

Use the code WINTER  to waive the $50 application fee.

At a glance

• 11 Core courses
• 12 max transfer credits
• 4 Specialization courses
• 1 Elective course
• 3 Virtual Residencies

Comprehensive exam

Dissertation

• ACBSP-accredited

Reduce your tuition by $20,000

Enroll in a qualified program and apply for a $20K Capella Progress Reward, a scholarship to help fund your doctoral degree. Eligibility rules and exclusions apply. Connect with us for details.

Courses and skills

Explore project management courses.

• This degree requires at least 90 program credits
• You’ll need to complete 11 core courses, four specialization courses, one elective course, three virtual residencies, one comprehensive exam and one dissertation

View all courses

What you'll learn

This program solidifies your ability to initiate, plan, execute, monitor, control, and close projects in a range of global business settings. You’ll learn how to evaluate different approaches to project management, develop your own research, and add critical knowledge to the field.

On successful completion of this program, you should be able to:

• Synthesize multi-disciplinary theories that inform and shape the theory and practice of project management
• Fuse research and practice to contribute new knowledge to the field
• Apply ethical leadership strategies in diverse project management settings
• Exhibit proficiency in communication, research, writing, and critical thinking skills for successful project management
• Evaluate functional and cross-functional management approaches for projects, programs and portfolios ranging from operational to strategic, small to complex, and local to global

Review the Capella career exploration guide  to learn more about this program and professional paths to explore.

Tuition and learning format

How much does the phd in business management cost.

The total cost of your degree will depend on academic performance, transfer credits, scholarships and other factors. See GuidedPath cost information below.

A structured learning format with an active peer community and faculty guidance. We’ll set the schedule, you meet the deadlines.

• Based on the quarter system; 1-2 courses per 10-week quarter
• 1 semester credit = 1.5 quarter credits
• Weekly assignments and courseroom discussions
• Pay for what you take, price varies by courseload or term

$985 per credit,  $5,000  quarterly tuition max,  75  coursework credits, 12 max transfer credits

Learn more about GuidedPath »

Tuition breakdown

Program phases.

$985 Per quarter credit

75 coursework credits; dissertation is additional

Per quarter credit

$2955 Per quarter

Per quarter

Resource kit fee

$175 Per quarter

Coursework phase only; includes eBooks, textbooks, interactive media, software, course packs, articles, test kits, and other instructional materials

Application fee

$50 One-time fee

One-time fee

Tuition and program length are unique to you

Your total tuition and program length depend on a variety of factors:

• The program specialization you choose
• Scholarships and finances
• Prior coursework
• Transfer credits
• Employer and/or military benefits
• Number of quarters spent working on dissertation
• Complexity of your capstone
• Academic performance
• School/work/life balance
• Unexpected life events

About cost scenarios

The cost scenarios below are examples based on general program pricing and 2023–24 Capella tuition rates, and assume the average number of transfer credits a student brings into the program. Pacing information is current as of January 1, 2023. These rates are the same nationwide and may change depending on factors affecting program length and price. You are responsible for paying your own travel costs related to residencies, including plane, hotel, and food expenses.

To discuss whether the specialization you’re interested in has additional factors that may affect program cost and length, contact a Capella enrollment counselor.

Cost scenarios

*Eligibility rules apply. Connect with us for details.

Get the details

Connect with an enrollment counselor to further discuss the cost of the program and explore your eligibility for scholarships and discounts.

Scholarships and savings

Are there scholarships available for doctoral degrees.

Your education is an investment in your future that's within reach. There are  more ways to save  than you might think.

$5,000 quarterly tuition maximum Maximize your courseload – take 6 or more credits per quarter and pay just $5,000.*

$20K toward your doctorate

Apply for a $20K Capella Progress Reward , a scholarship to help fund your doctoral degree. Eligibility rules and exclusions apply. Connect with us for details.

10% military discount

If you’re an active-duty military service member, spouse, dependent, or veteran, you may be eligible for discounts on tuition at Capella.  Get details

*Cost of each residency is included in the $5,000 quarterly tuition maximum; books, resource kit, travel, lodging, meals, and other expenses are not included.

Accredited and recognized

Capella is accredited by the higher learning commission..

Accreditation and recognitions provide assurance that we meet standards for quality of faculty, curriculum, learner services, and fiscal stability.

The PhD in Business Management with a specialization in project management is accredited by the PMI Global Accreditation Center for Project Management Education Programs (GAC).*

See all our  accreditations  and  recognitions .

To meet a PMI-GAC accreditation requirement, student achievement data can be found here.

The GAC Accredited Program seal is a mark of Project Management Institute, Inc.

*The FlexPath option for the project management specializations are not accredited by PMI-GAC.

How to apply

Phd in business management admission requirements.

Applicants must provide the following information for  admission  to Capella programs and specializations:

• A master’s degree from an institution accredited by an agency recognized by the U.S. Department of Education, or from an internationally recognized institution
•  Your official master’s transcripts, with a minimum grade point average of 3.0 or higher on a 4.0 scale
• A valid, government-issued form of photo identification

GRE and GMAT are not required for admission.

International Student Requirements

If you completed your most recent academic coursework, degree, or credential at an institution outside the United States, regardless of your citizenship or where you currently live, you are considered an international applicant.

In addition to the above admission requirements, you will need to submit these materials:

• Minimum score on acceptable test for proof of English proficiency
• Transcript evaluation

Learn more about  international student admissions .

Faculty and support

What support does capella offer online students.

Our programs are designed to meet the unique needs of doctoral students. We’ve structured the experience in manageable pieces that build on one another to help you earn your doctorate. You’ll have support from faculty, staff, and online resources along the way.

Doctoral faculty

Work with faculty members who have years of experience and specialize in their areas of expertise throughout each phase of your program, including literature review and implementation planning.

Enrollment counselors

These experts will set you up for success. They’ll help you find the right degree program and answer all your questions about Capella.

Academic coaches

Through quarterly appointments and as-needed counseling sessions, these specialists introduce you to Capella and help you tailor your program to your personal goals and experiences.

Articles and resources

Expand your perspective on academic and career topics with articles and resources from Capella University.

10 qualities to look for in a career mentor

Finding a career mentor can really help as you develop your professional skills and move up the ladder.

5 Ways a Doctoral Degree May Strengthen Your Career

There’s plenty to think about when considering a doctorate. The ultimate question might be: What does it mean for your career?

What’s it like to be a doctoral student?

What does it take to earn a doctoral degree? Learn more about the experience and explore each step of the journey.

Career exploration

What can you do with a phd in project management degree.

Your education can help you reach your goals, professionally and personally. Here are some of the jobs and employment settings to consider with a PhD in Project Management.

Related job titles to explore*

• Adjunct or part-time project business faculty
• Full-time business faculty
• Dean or associate dean of business program
• Business researcher
• Program manager
• Program director
• Vice president of project management
• Senior project manager
• Director of project management
• Director of program office

Employment settings to explore

• Land-based or online college or university
• Community college
• Corporation
• Manufacturing
• Health care organization
• Defense contractor
• Government—local, state, federal
• Nonprofit organization
• Consulting firm

*These are examples intended to serve as a general guide. Some positions may prefer or even require previous experience, licensure, certifications, and/or other designations along with a degree. Because many factors determine what position an individual may attain, Capella cannot guarantee that a graduate will secure any specific job title, a promotion, salary increase, or other career outcome. We encourage you to research requirements for your job target and career goals.

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Discover more about postgraduate research

PhD Business and Management / Overview

Year of entry: 2024

• View full page
• Bachelor's (Honours) degree at 2:1 or above (or overseas equivalent); and
• Master's degree in a relevant cognate subject - with an overall average of 65% or above (or overseas equivalent)
• Professional qualifications other than a Bachelors Degree and/or relevant and appropriate experience may be taken into account for entry to a PhD programme.

Full entry requirements

Apply online

Please ensure you include all required supporting documents at the time of submission, as incomplete applications may not be considered.

Application Deadlines

The current deadline for consideration in internal funding competitions is 15 March 2024.

If you are applying for or have secured external funding (for example, from an employer or government) or are self-funding, you must submit your application before the below deadline to be considered. You will not be able to apply after this date has passed.

• For September 2024 entry: 30 June 2024

Programme options

Programme overview.

• Join one of the world's most innovative and ambitious doctoral research schools.
• Work alongside a range of specialists conducting cutting-edge research in business, marketing management and strategy.
• Ranked 3rd in the UK for research power 'Business and Management Studies' in the REF2021.

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We will be conducting our Humanities PGR virtual open week in October 2024. Find our about future events and postgraduate research sessions by signing up our email alerts.

For entry in the academic year beginning September 2024, the tuition fees are as follows:

• PhD (full-time) UK students (per annum): £4,786 International, including EU, students (per annum): £21,000
• PhD (part-time) UK students (per annum): £2,393 International, including EU, students (per annum): £10,500

Further information for EU students can be found on our dedicated EU page.

Scholarships/sponsorships

To apply University of Manchester funding, you must indicate in your application the competitions for which you wish to be considered. The current deadline for most internal competitions, including Alliance Manchester Business School studentships is 15 March 2024.

All external funding competitions have a specified deadline for submitting your funding application and a separate (earlier) deadline for submitting the online programme application form, both of which will be stated in the funding competition details below.

For more information about funding, visit our funding page to browse for scholarships, studentships and awards you may be eligible for.

• ESRC North West Social Science Doctoral Training Partnership (NWSSDTP) PhD Studentships - Competition Closed for 2024 Entry
• Alliance Manchester Business School PhD Studentships 2024 Entry - Competition Closed for 2024 Entry
• Commonwealth PhD Scholarships (High Income Countries)
• Humanities Doctoral Academy Humanitarian Scholarship 2024 Entry
• Commonwealth PhD Scholarships (Least Developed Countries and Fragile States)
• RADMA Doctoral Studies Funding 2024 Entry - Competition Closed for 2024 Entry
• PhD Scholarship for Research into Productivity
• President's Doctoral Scholar (PDS) Awards - Competition Closed for 2024 Entry
• Engineering and Physical Sciences Research Council Doctoral Training Partnership Studentship (EPSRC DTP)

Contact details

Programmes in related subject areas.

Use the links below to view lists of programmes in related subject areas.

• Business and Management

Regulated by the Office for Students

The University of Manchester is regulated by the Office for Students (OfS). The OfS aims to help students succeed in Higher Education by ensuring they receive excellent information and guidance, get high quality education that prepares them for the future and by protecting their interests. More information can be found at the OfS website .

You can find regulations and policies relating to student life at The University of Manchester, including our Degree Regulations and Complaints Procedure, on our regulations website .

PhD with Integrated Study in Management

Our PhD with Integrated Study in Management programme challenges students to make the most of their potential. Our objective is that when you graduate as an alumnus of the Business School, you will be able to enjoy international recognition for your achievements.

This programme is currently undergoing the final approval process. We anticipate applications will open in January 2024.

PhD with Integrated Study in Management at a glance

• Study over four years full-time, with an additional 'writing-up' year available to all
• During your first year, undertake 180 of taught courses designed to train you in the theory and methods necessary to conduct high-quality research
• As part of a collaborative academic community, we support students with cross-disciplinary interests to find expertise, support, and supervision across the University of Edinburgh
• We encourage our students to aim higher, presenting papers at prestigious conferences and submit articles for publication
• Access to a wide range of professional and personal development opportunities during your studies
• While we invite students from around the globe to join our community, we don't offer this programme online or by distance learning

Entry requirements

Find out our academic, English language, and application requirements for the PhD in Management.

Programme overview

View the learning outcomes and a typical training course schedule for a first-year student.

Fees and living expenses

When budgeting please consider associated administration fees and expenses as well as our funding support.

Ready to apply?

Review our application checklist and make your application to the PhD in Management.

Professional pathway

This route allows students to maintain their current occupational role while also studying for a PhD with the Business School.

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12 Distance Learning PhD Degrees in Management 2024

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Doctor of Philosophy (Ph.D.) in Petroleum Industry Management

Selinus university of sciences and literature.

The petroleum industry is organized into four major sectors: exploration and production of crude oil and natural gas; transportation; refining and marketing and distribution.

Doctor of Philosophy (Ph.D.) in Social Media Management

In this social media-related curriculum, the student is expected to explore how to strategically create and distribute a brand's content across a variety of different social networks. Social media managers are tasked with representing a company across social channels as the sole voice of the brand.

Doctor of Philosophy in Business and Management Studies

International business school the hague.

• The Hague, Netherlands
• Online Netherlands

Blended, Distance Learning, On-Campus

The International Business School, The Hague (IBSH), offers a Ph.D. program in Business and Management Studies, in cooperation with Sapienza University of Rome, Italy. The Ph.D. program is part-time, and therefore well-suited for professionals that want to get a doctorate in combination with their career.

Doctorate in Management - UCAM, Spain

Exeed college.

• Sharjah, United Arab Emirates
• Online Spain

Full time, Part time

The Doctorate of Management, awarded by UCAM, Spain, is designed for executives who want to pursue research or want to lead their organizations towards achieving higher levels of success.

M.Phil in Applied Business Leadership and Management - UCAM, Spain

The 60-credit Master of Philosophy (M.Phil) program in Business Leadership and Management offered by Universidad Católica San Antonio de Murcia (UCAM) University, Spain, is designed to empower managers to harness the power of applied research. The one-year program enables learners to conduct applied research in organizational behavior and leadership, contemporary research in business analytics, and action research in operational management.

PhD in Management - Online

Asia pacific university of technology & innovation (apu).

• Kuala Lumpur, Malaysia

Why our APU Ph.D. by research program? You will be assigned to a group of highly qualified supervisors; a Wide range of the latest research areas in the fields of management.

Doctor of Philosophy (D.Phil.)

Ous royal academy of economics and technology in switzerland.

• Zürich, Switzerland

The Doctorate of Philosophy (DPhil) is recognized worldwide as a research program. You will choose a specialist area of research, write a thesis based on your research and then complete a defense.

Doctor of Philosophy in Business Administration (PHDBA)

Trident university international.

• Cypress, USA

The PhD in Business Administration curriculum is designed to help you develop advanced research skills. You will demonstrate and apply these skills toward the creation of new knowledge in your PhD. Dissertation.

Doctoral Program in Management Sciences

Nanhua university institute of international and cross-strait affairs.

• Chiayi, Taiwan

To improve the learning effect, provide diversified learning mode, participate in the actual working environment as early as possible, combine the theory with practice, cultivate students to become talents with professional knowledge and practical skills, formulate the Measures for the implementation of Off-Campus Internship of Students of Department of Business Administration, Nanhua University (hereinafter referred to as the Measures) in accordance with the Measures for Implementation of Off-Campus Internship of Students of Nanhua University. ．Visit the website of the program to check the details: http://bmanagement2.nhu.edu.tw/main.php ．Vistit the website of International and Cross-Strait Affairs to check how to apply: http://iica3.nhu.edu.tw/Web/index

Ph.D in Management

Texila american university.

• Lusaka, Zambia

Transform Yourself Into a Business Leader With a Ph.D. in Management From Texila American University.

Ph.D. in Business and Management

The university of zambia ecampus.

At the University of Zambia, our doctorate in business management will have you maintain a laser-focus on highly-relevant skills and knowledge that will make you even more attractive to hiring managers. The doctoral degree in business management will give you the opportunity to make a significant contribution to business practice through an extensive and innovative research-driven program.

Ph.D. in Management

Smc business school.

The PhD in Management is being offered jointly with Universidad Central de Nicaragua. The PhD in Management concentrates on exploring advanced management strategies and corporate governance issues assisting learners as they design, conduct, and apply innovative and practice-oriented research.

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Distance Learning PhD Degrees in Management

The management field often applies to individuals in leadership positions in business. Students seeking a management education can learn to be representatives of their respective organizations. They can also learn to handle administrative and employee-related issues.

Requirements for the PhD program often involve the student having already obtained a Master’s degree. Additionally, a thesis or dissertation primarily consisting of original academic research must be submitted. In some countries, this work may even need to be defended in front of a panel.

Online learning refers to use of electronic media and information and communication technologies (ICT) in education. With online learning one has the flexibility to access their studies at any time and from anywhere they can log on.

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scientific method to involve critical thinking

The Relationship Between Scientific Method & Critical Thinking

What Is the Function of the Hypothesis?

Critical thinking, that is the mind’s ability to analyze claims about the world, is the intellectual basis of the scientific method. The scientific method can be viewed as an extensive, structured mode of critical thinking that involves hypothesis, experimentation and conclusion.

Critical Thinking

Broadly speaking, critical thinking is any analytical thought aimed at determining the validity of a specific claim. It can be as simple as a nine-year-old questioning a parent’s claim that Santa Claus exists, or as complex as physicists questioning the relativity of space and time. Critical thinking is the point when the mind turns in opposition to an accepted truth and begins analyzing its underlying premises. As American philosopher John Dewey said, it is the “active, persistent and careful consideration of a belief or supposed form of knowledge in light of the grounds that support it, and the further conclusions to which it tends.”

Critical thinking initiates the act of hypothesis. In the scientific method, the hypothesis is the initial supposition, or theoretical claim about the world, based on questions and observations. If critical thinking asks the question, then the hypothesis is the best attempt at the time to answer the question using observable phenomenon. For example, an astrophysicist may question existing theories of black holes based on his own observation. He may posit a contrary hypothesis, arguing black holes actually produce white light. It is not a final conclusion, however, as the scientific method requires specific forms of verification.

Experimentation

The scientific method uses formal experimentation to analyze any hypothesis. The rigorous and specific methodology of experimentation is designed to gather unbiased empirical evidence that either supports or contradicts a given claim. Controlled variables are used to provide an objective basis of comparison. For example, researchers studying the effects of a certain drug may provide half the test population with a placebo pill and the other half with the real drug. The effects of the real drug can then be assessed relative to the control group.

In the scientific method, conclusions are drawn only after tested, verifiable evidence supports them. Even then, conclusions are subject to peer review and often retested before general consensus is reached. Thus, what begins as an act of critical thinking becomes, in the scientific method, a complex process of testing the validity of a claim. English philosopher Francis Bacon put it this way: “If a man will begin with certainties, he shall end in doubts; but if he will be content to begin with doubts, he shall end in certainties.”

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• How We Think: John Dewey
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Scott Neuffer is an award-winning journalist and writer who lives in Nevada. He holds a bachelor's degree in English and spent five years as an education and business reporter for Sierra Nevada Media Group. His first collection of short stories, "Scars of the New Order," was published in 2014.

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Science, method and critical thinking

Antoine danchin.

1 School of Biomedical Sciences, Li KaShing Faculty of Medicine, Hong Kong University, Pokfulam Hong Kong, China

Science is founded on a method based on critical thinking. A prerequisite for this is not only a sufficient command of language but also the comprehension of the basic concepts underlying our understanding of reality. This constraint implies an awareness of the fact that the truth of the World is not directly accessible to us, but can only be glimpsed through the construction of models designed to anticipate its behaviour. Because the relationship between models and reality rests on the interpretation of founding postulates and instantiations of their predictions (and is therefore deeply rooted in language and culture), there can be no demarcation between science and non‐science. However, critical thinking is essential to ensure that the link between models and reality is gradually made more adequate to reality, based on what has already been established, thus guaranteeing that science progresses on this basis and excluding any form of relativism.

Science understands that we only can reach the truth of the World via creation of models. The method, based on critical thinking, is embedded in the scientific method, named here the Critical Generative Method.

Before illustrating the key requirements for critical thinking, one point must be made clear from the outset: thinking involves using language, and the depth of thought is directly related to the ‘active’ vocabulary (Magyar,  1942 ) used by the thinker. A recent study of young students in France showed that a significant percentage of the population had a very limited vocabulary. This unfortunate situation is shared by many countries (Fournier & Rakocevic,  2023 ). This omnipresent fact, which precludes any attempt to improve critical thinking in the general population, is very visible in a great many texts published on social networks. This is the more concerning because science uses a vocabulary that lies well beyond that available to most people. For example, a word such as ‘metabolism’ is generally not understood. As a consequence, it is essential to agree on a minimal vocabulary before teaching paths to critical thinking. This may look trivial, but this is an essential prerequisite. Typically, words such as analysis and synthesis must be understood (and the idea of what a ‘concept’ is not widely shared). It must also be remembered that the way the scientific vocabulary kept creating neologisms in the most creative times of science was based on using the Ancient Greek language, and for a good reason: a considerable advantage of that unsaid rule is that this makes scientific objects and concepts prominent for scientists from all over the world, while precluding implicit domination by any country over the others when science is at stake (Iliopoulos et al.,  2019 ). Unfortunately, and this demonstrates how the domination of an ignorant subset of the research community gains ground, this rule is now seldom followed. This also highlights the lack of extensive scientific background of the majority of researchers: the creation of new words now follows the rule of the self‐assertive. Interestingly, the very observation that a neologism in a scientific paper does not follow the traditional rule provides us with a critical way to identify either ignorance of the scientific background of the work or the presence in the text of hidden agendas that have nothing to do with science.

In practice, the initiation of the process of critical thinking ought to begin with a step similar to the ‘due diligence’ required by investors when they study whether they will invest, or not, in a start‐up company. The first expected action should be ‘verify’, ‘verify’, ‘verify’… any statement which is used as a basis for the reasoning that follows. This asks not only for understanding what is said or written (hence the importance of language), but also for checking the origins of the statement, not only by investigating who is involved but also by checking that the historical context is well known.

Of course, nobody has complete knowledge of everything, not even anything in fact, which means that at some point people have to accept that they will base their reasoning on some kind of ‘belief’. This inevitable imperative forces future scientists asking a question about reality to resort to a set of assertions called ‘postulates’ in conventional science, that is, beliefs temporarily accepted without further discussion but understood as such. The way in which postulates are formulated is therefore key to their subsequent role in science. Similarly, the fact that they are temporary is essential to understanding their role. A fundamental feature of critical thinking is to be able to identify these postulates and then remember that they are provisional in nature. When needed this enables anyone to return to the origins of reasoning and then decide whether it is reasonable to retain the postulates or modify or even abandon them.

Here is an example illustrated with the famous greenhouse effect that allows our planet not to be a snowball (Arrhenius,  1896 ). Note that understanding this phenomenon requires a fair amount of basic physics, as well as a trait that is often forgotten: common sense. There is no doubt that carbon dioxide is a greenhouse gas (this is based on well‐established physics, which, nevertheless must be accepted as a postulate by the majority, as they would not be able to demonstrate that). However, a straightforward question arises, which is almost never asked in its proper details. There are many gases in the atmosphere, and the obvious preliminary question should be to ask what they all are, and each of their relative contribution to greenhouse effect. This is partially understood by a fraction of the general public as asking for the contribution of methane, and sometimes N 2 O and ozone. However, this is far from enough, because the gas which contributes the most to the greenhouse effect on our planet is … water vapour (about 60% of the total effect: https://www.acs.org/climatescience/climatesciencenarratives/its‐water‐vapor‐not‐the‐co2.html )! This fact is seldom highlighted. Yet it is extremely important because water is such a strange molecule. Around 300 K water can evolve rapidly to form a liquid, a gas, or a solid (ice). The transitions between these different states (with only the gas having a greenhouse effect, while water droplets in clouds have generally a cooling effect) make that water is unable to directly control the Earth's temperature. Worse, in fact, these phase transitions will amplify the fluctuations around a given temperature, generally in a feedforward way. We know very well the situation in deserts, where the night temperature is very low, with a very high temperature during the day. In fact, this explains why ‘global warming’ (i.e. shifting upwards the average temperature of the planet) is also parallel with an amplification of weather extremes. It is quite remarkable that the role of water, which is well established, does not belong to popular knowledge. Standard ‘due diligence’ would have made this knowledge widely shared.

Another straightforward example of the need to have a clear knowledge of the thought of our predecessors is illustrated in the following. When we see expressions such as ‘paradigm change’, ‘change of paradigm’, ‘paradigm shift’ or ‘shift of paradigm’ (12,424 articles listed in PubMed as of June 26, 2023), we should be aware that the subject of interest of these articles has nothing to do with a paradigm shift, simply because such a change in paradigm is extremely rare, being distributed over centuries, at best (Kuhn,  1962 ). Worse, the use of the word implies that the authors of the works have most probably never read Thomas Kuhn's work, and are merely using a fashionable hearsay. As a consequence, critical thinking should lead authentic scientists to put aside all these works before further developing their investigation (Figure  1 ).

Number of articles identified in the PubMed database with the keywords ‘paradigm change’ or ‘change of paradigm’ or ‘paradigm shift’ or ‘shift of paradigm’. A very low number of articles, generally reporting information consistent with the Kuhnian view of scientific revolutions is published before 1993. Between 1993 and 2000 a looser view of the term paradigm begins to be used in a metaphoric way. Since then the word has become fashionable while losing entirely its original meaning, while carrying over lack of epistemological knowledge. This example of common behaviour illustrates the decadence of contemporary science.

This being understood, we can now explore the general way science proceeds. This has been previously discussed at a conference meant to explain the scientific method to an audience of Chinese philosophers, anthropologists and scientists and held at Sun Yat Sen (Zhong Shan) University in Canton (Guangzhou) in 1991. This discussion is expanded in The Delphic Boat (Danchin,  2002 ). For a variety of reasons, it would be useful to anticipate the future of our world. This raises an unlimited number of questions and the aim of the scientific method is to try and answer those. The way in which questions emerge is a subject in itself. This is not addressed here, but this should also be the subject of critical thinking (Yanai & Lercher,  2019 ).

The basis for scientific investigation accepts that, while the truth of the world exists in itself (‘relativism’ is foreign to scientific knowledge, as science keeps building up its progresses on previous knowledge, even when changing its paradigms), we can only access it through the mediation of a representation. This has been extensively debated at the time, 2500 years ago, when science and philosophy designed the common endeavour meant to generate knowledge (Frank,  1952 ). It was then apparent that we cannot escape this omnipresent limitation of human rationality, as Xenophanes of Colophon explicitly stated at the time [discussed in Popper,  1968 ]. This limitation comes from an inevitable constraint: contrary to what many keep saying, data do not speak . Reality must be interpreted within the frame of a particular representation that critical thinking aims at making visible. A sentence that we all forget to reject, such as ‘results show…’ is meaningless: results are interpreted as meaning this or that.

Accepting this limitation is a difficult attribute of scientific judgement. Yet the quality of thought progresses as the understanding of this constraint becomes more effective: to answer our questions we have to build models of the world, and be satisfied with this perspective. It is through our knowledge of the world's models that we are able to explore and act upon it. We can even become the creators of new behaviours of reality, including new artefacts such as a laser beam, a physics‐based device that is unlikely to exist in the universe except in places where agents with an ability similar to ours would exist. Indeed, to create models is to introduce a distance, a mediation through some kind of symbolic coding (via the construction of a model), between ourselves and the world. It is worth pointing out that this feature highlights how science builds its strength from its very radical weakness, which is to know that it is incapable, in principle, of attaining truth. Furthermore and fortunately, we do not have to begin with a tabula rasa . Science keeps progressing. The ideas and the models we have received from our fathers form the basis of our first representation of the world. The critical question we all face, then, is: how well these models match up with reality? how do they fare in answering our questions?

Many, over time, think they achieve ultimate understanding of reality (or force others to think so) and abide by the knowledge reached at the time, precluding any progress. A few persist in asking questions about what remains enigmatic in the way things behave. Until fairly recently (and this can still be seen in the fashion for ‘organic’ things, or the idea, similar to that of the animating ‘phlogiston’ of the Middle Ages, that things spontaneously organize themselves in certain elusive circumstances usually represented by fancy mathematical models), things were thought to combine four elements: fire, air, water, and earth, in a variety of proportions and combinations. In China, wood, a fifth element that had some link to life was added to the list. Later on, the world was assumed to result from the combination of 10 categories (Danchin,  2009 ). It took time to develop a physic of reality involving space, time, mass, and energy. What this means is still far from fully understood. How, in our times when the successes of the applications of science are so prominent, is it still possible to question the generally accepted knowledge, to progress in the construction of a new representation of reality?

This is where critical thinking comes in. The first step must be to try and simplify the problem, to abstract from the blurred set of inherited ideas a few foundational concepts that will not immediately be called into question, at least as a preliminary stage of investigation. We begin by isolating a phenomenon whose apparent clarity contrasts with its environment. A key point in the process is to be aware of the fact that the links between correlation and causation are not trivial (Altman & Krzywinski,  2015 ). The confusion between both properties results probably in the major anti‐science behaviour that prevents the development of knowledge. In our time, a better understanding of what causality is is essential to understand the present development of Artificial Intelligence (Schölkopf et al.,  2021 ) as this is directly linked to the process of rational decision (Simon,  1996 ).

Subsequently, a set of undisputed rules, phenomenological criteria and postulates is associated with the phenomenon. It constitutes temporarily the founding dogma of the theory, made up of the phenomenon of interest, the postulates, the model and the conditions and results of its application to reality. This epistemological attitude can legitimately be described as ‘dogmatic’ and it remains unchanged for a long time in the progression of scientific knowledge. This is well illustrated by the fact that the word ‘dogma’, a religious word par excellence, is often misused when referring to a scientific theory. Many still refer, for example, to the expression ‘the central dogma of molecular biology’ to describe the rules for rewriting the genetic program from DNA to RNA and then proteins (Crick,  1970 ). Of course, critical thinking understands that this is no dogma, and variations on the theme are omnipresent, as seen for instance in the role of the enzyme reverse transcriptase which allows RNA to be rewritten into a DNA sequence.

Yet, whereas isolating postulates is an important step, it does not permit one to give explanations or predictions. To go further, one must therefore initiate a constructive process. The essential step there will be the constitution of a model (or in weaker instances, a simulation) of the phenomenon (Figure  2 ).

The Critical Generative Method. Science is based on the premises that while we can look for the truth of reality, this is in principle impossible. The only way out is to build up models of reality (‘realistic models’) and find ways to compare their outcome to the behaviour of reality [see an explicit example for genome sequences in Hénaut et al.,  1996 ]. The ultimate model is mathematical model, but this is rarely possible to achieve. Other models are based on simulations, that is, models that mimic the behaviour of reality without trying to propose an explanation of that behaviour. A primitive attempt of this endeavour is illustrated when people use figurines that they manipulate hoping that this will anticipate the behaviour of their environment (e.g. ‘voodoo’). This is also frequent in borderline science (Friedman & Brown,  2018 ).

To this aim, the postulates will be interpreted in the form of entities (concrete or abstract) or of relationships between entities, which will be further manipulated by an independent set of processes. The perfect stage, generally considered as the ultimate one, associates the manipulation of abstract entities, interpreting postulates into axioms and definitions, manipulable according to the rules of logic. In the construction of a model, one assists therefore first to a process of abstraction , which allows one to go from the postulates to the axioms. Quite often, however, one will not be able to axiomatize the postulates. It will only be possible to represent them using analogies involving the founding elements of another phenomenon, better known and considered as analogous. One could also change the scales of a phenomenon (this is the case when one uses mock‐ups as models). In these families of approaches, the model is considered as a simulation. For example, it will be possible to simulate an electromagnetic phenomenon using a hydrodynamic phenomenon [for a general example in physics (Vives & Ricou,  1985 )]. In recent times the simulation is generally performed numerically, using (super)computers [e.g. the mesoscopic scale typical for cells (Huber & McCammon,  2019 )]. While all these approaches have important implications in terms of diagnostic, for example, they are generally purely phenomenological and descriptive. This is understood by critical thinking, despite the general tendency to mistake the mimic for what it represents. Recent artificial intelligence approaches that use ‘neuronal networks’ are not, at least for the time being, models of the brain.

However useful and effective, the simulation of a phenomenon is clearly an admission of failure. A simulation represents behaviour that conforms to reality, but does not explain it. Yet science aims to do more than simply represent a phenomenon; it aims to anticipate what will happen in the near and distant future. To get closer to the truth, we need to understand and explain, that is, reduce the representation to simpler elementary principles (and as few as possible) in order to escape the omnipresent anecdotes that parasitize our vision of the future. In the case of the study of genomes, for example, this will lead us to question their origin and evolution. It will also require us to understand the formal nature of the control processes (of which feedback, e.g. is one) that they encode. As soon as possible, therefore, we would like to translate the postulates that enabled the model's construction into well‐formed statements that will constitute the axioms and definitions of an explanatory model. At a later stage, the axioms and definitions will be linked together to create a demonstration leading to a theorem or, more often than not, a simple conjecture.

When based on mathematics, the model is made up of its axioms and definitions, and the demonstrations and theorems it conveys. It is an entirely autonomous entity, which can only be justified by its own rules. To be valid, it must necessarily be true according to the rules of mathematical logic. So here we have an essential truth criterion, but one that can say nothing about the truth of the phenomenon. A key feature of critical thinking is the understanding that the truth of the model is not the truth of the phenomenon. The amalgam of these two truths, common in magical thinking, often results in the model (identified as a portion of the world) being given a sacred value, and changes the role of the scientist to that of a priest.

Having started from the phenomenon of interest to build the model, we now need to return from the model to the real world. A process symmetrical to that which provided the basis for the model, an instantiation of the conclusions summarized in the theorem, is now required. This can take the form of predictions, observations or experiments, for which at least two types can be broadly identified. These predictions are either existential (the object, process, or relations predicted by the instantiation of the theorem must be discovered), or phenomenological, and therefore subject to verification and deniability. An experimental set‐up will have to be constructed to explore what has been predicted by the instantiations of the model theorems and to support or falsify the predictions. In the case of hypotheses based on genes, for example, this will lead to synthetic biology constructs experiments (Danchin & Huang,  2023 ), where genes are replaced by counterparts, even made of atoms that differ from the canonical ones.

The reaction of reality, either to simple (passive) observation or to the observation of phenomena triggered by the experiments, will validate the model and measure the degree of adequacy between the model and the reality. This follows a constructive path when the model's shortcomings are identified, and when are discovered the predicted new objects that must now be included in further models of reality. This process imposes the falsification of certain instantiated conclusions that have been falsified as a major driving force for the progression of the model in line with reality. This part of the thought process is essential to escape infinite regression in a series of confirmation experiments, one after the other, ad infinitum. Identifying this type of situation, based on the understanding that the behaviour of the model is not reality but an interpretation of reality, is essential to promote critical thinking.

It must also be stressed that, of course, the weight of the proof of the model's adequacy to reality belongs to the authors of the model. It would be both contrary to the simplest rules of logic (the proof of non‐existence is only possible for finite sets), and also totally inefficient, as well as sterile, to produce an unfalsifiable model. This is indeed a critical way to identify the many pretenders who plague science. They are easy to recognize since they identify themselves precisely by the fact that they ask the others: ‘repeat my experiments again and show me that they are wrong!’. Unfortunately, this old conjuring trick is still well spread, especially in a world dominated by mass media looking for scoops, not for truth.

When certain predictions of the model are not verified, critical thinking forces us to study its relationship with reality, and we must proceed in reverse, following the path that led to these inadequate predictions (Figure  2 ). In this reverse process, we go backwards until we reach the postulates on which the model was built, at which point we modify, refine and, if necessary, change them. The explanatory power of the model will increase each time we can reduce the number of postulates on which it is built. This is another way of developing critical thinking skills: the more factors there are underlying an explanation, the less reliable the model. As an example in molecular biology, the selective model used by Monod and coworkers to account for allostery (Monod et al.,  1965 ) used far fewer adjustable parameters than Koshland's induced‐fit model (Koshland,  1959 ).

In real‐life situations, this reverse path is long and difficult to build. The model's resistance to change is quickly organized, if only because, lacking critical thinking, its creators cannot help thinking that, in fact, the model manifests, rather than represents, the truth of the world. It is only natural, then, to think that the lack of predictive power is primarily due not to the model's inadequacy, but to the inappropriate way in which its broad conclusions have been instantiated. This corresponds, in effect, to a stage where formal terms have been interpreted in terms of real behaviour, which involves a great deal of fine‐tuning. Because it is inherently difficult to identify the inadequacy of the model or its links with the phenomenon of interest, it is often the case that a model persists, sometimes for a very long time, despite numerous signs of imperfection.

During this critical process, the very nature of the model is questioned, and its construction, the meaning it represents, is clarified and refined under the constraint of contradictions. The very terms of the instantiations of predictions, or of the abstraction of founding postulates, are made finer and finer. This is why this dogmatic stage plays such an essential role: a model that was too inadequate would have been quickly discarded, and would not have been able to generate and advance knowledge, whereas a succession of improvements leads to an ever finer understanding, and hence better representation of the phenomenon of interest. Then comes a time when the very axioms on which the model is based are called into question, and when the most recent abstractions made from the initial postulates lead to them being called into question. This is of course very rare and difficult, and is the source of those genuine scientific revolutions, those paradigm shifts (to use Thomas Kuhn's word), from which new models are born, develop and die, based on assumptions that differ profoundly from those of their predecessors. This manifests an ultimate, but extremely rare, success of critical thinking.

A final comment. Karl Popper in his Logik der Forschung ( The Logic of Scientific Discovery ) tried to show that there was a demarcation separating science from non‐science (Keuth and Popper,  1934 ). This resulted from the implementation of a refutation process that he named falsification that was sufficient to tell the observer that a model was failing. However, as displayed in Figure  2 , refutation does not work directly on the model of interest, but on the interpretation of its predictions . This means that while science is associated with a method, its implementation in practice is variable, and its borders fuzzy. In fact, trying to match models with reality allows us to progress by producing better adequacy with reality (Putnam,  1991 ). Nevertheless, because the separation between models and reality rests on interpretations (processes rooted in culture and language), establishing an explicit demarcation is impossible. This intrinsic difficulty, which is associated with a property that we could name ‘context associated with a research programme’ (Lakatos,  1976 , 1978 ), shows that the demarcation between science and non‐science is dominated by a particular currency of reality, which we have to consider under the name information , using the word with all its common (and accordingly fuzzy) connotations, and which operates in addition to the standard categories, mass, energy, space and time.

The first attempts to solve contradictions between model predictions and observed phenomena do not immediately discard the model, as Popper would have it. The common practice is for the authors of a model to re‐interpret the instantiation process that has coupled the theorem to reality. Typically: ‘exceptions make the rule’, or ‘this is not exactly what we meant, we need to focus more on this or that feature’, etc. This polishing step is essential, it allows the frontiers of the model and its associated phenomena to be defined as accurately as possible. It marks the moment when technically arid efforts such as defining a proper nomenclature, a database data schema, etc., have a central role. In contrast to the hopes of Popper, who sought for a principle telling us whether a particular creation of knowledge can be named Science, using refutation as principle, there is no ultimate demarcation between science and non‐science. Then comes a time when, despite all efforts to reconcile predictions and phenomena, the inadequacy between the model and reality becomes insoluble. Assuming no mistake in the demonstration (within the model), this contradiction implies that we need to reconsider the axioms and definitions upon which the model has been constructed. This is the time when critical thinking becomes imperative.

AUTHOR CONTRIBUTIONS

Antoine Danchin: Conceptualization (lead); writing – original draft (lead); writing – review and editing (lead).

CONFLICT OF INTEREST STATEMENT

This work belongs to efforts pertaining to epistemological thinking and does not imply any conflict of interest.

ACKNOWLEDGEMENTS

The general outline of the Critical Generative Method presented at Zhong Shan University in Guangzhou, China in 1991, and discussed over the years in the Stanislas Noria seminar ( https://www.normalesup.org/~adanchin/causeries/causeries‐en.html ) has previously been published in Danchin ( 2009 ) and in a variety of texts. Because scientific knowledge results from accumulation of knowledge painstakingly created by the generations that preceded us, the present text purposely makes reference to work which is seldom cited at a moment when scientists become amnesiac and tend to reinvent the wheel.

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A Guide to Using the Scientific Method in Everyday Life

The  scientific method —the process used by scientists to understand the natural world—has the merit of investigating natural phenomena in a rigorous manner. Working from hypotheses, scientists draw conclusions based on empirical data. These data are validated on large-scale numbers and take into consideration the intrinsic variability of the real world. For people unfamiliar with its intrinsic jargon and formalities, science may seem esoteric. And this is a huge problem: science invites criticism because it is not easily understood. So why is it important, then, that every person understand how science is done?

Because the scientific method is, first of all, a matter of logical reasoning and only afterwards, a procedure to be applied in a laboratory.

Individuals without training in logical reasoning are more easily victims of distorted perspectives about themselves and the world. An example is represented by the so-called “ cognitive biases ”—systematic mistakes that individuals make when they try to think rationally, and which lead to erroneous or inaccurate conclusions. People can easily  overestimate the relevance  of their own behaviors and choices. They can  lack the ability to self-estimate the quality of their performances and thoughts . Unconsciously, they could even end up selecting only the arguments  that support their hypothesis or beliefs . This is why the scientific framework should be conceived not only as a mechanism for understanding the natural world, but also as a framework for engaging in logical reasoning and discussion.

A brief history of the scientific method

The scientific method has its roots in the sixteenth and seventeenth centuries. Philosophers Francis Bacon and René Descartes are often credited with formalizing the scientific method because they contrasted the idea that research should be guided by metaphysical pre-conceived concepts of the nature of reality—a position that, at the time,  was highly supported by their colleagues . In essence, Bacon thought that  inductive reasoning based on empirical observation was critical to the formulation of hypotheses  and the  generation of new understanding : general or universal principles describing how nature works are derived only from observations of recurring phenomena and data recorded from them. The inductive method was used, for example, by the scientist Rudolf Virchow to formulate the third principle of the notorious  cell theory , according to which every cell derives from a pre-existing one. The rationale behind this conclusion is that because all observations of cell behavior show that cells are only derived from other cells, this assertion must be always true. 

Inductive reasoning, however, is not immune to mistakes and limitations. Referring back to cell theory, there may be rare occasions in which a cell does not arise from a pre-existing one, even though we haven’t observed it yet—our observations on cell behavior, although numerous, can still benefit from additional observations to either refute or support the conclusion that all cells arise from pre-existing ones. And this is where limited observations can lead to erroneous conclusions reasoned inductively. In another example, if one never has seen a swan that is not white, they might conclude that all swans are white, even when we know that black swans do exist, however rare they may be.  

The universally accepted scientific method, as it is used in science laboratories today, is grounded in  hypothetico-deductive reasoning . Research progresses via iterative empirical testing of formulated, testable hypotheses (formulated through inductive reasoning). A testable hypothesis is one that can be rejected (falsified) by empirical observations, a concept known as the  principle of falsification . Initially, ideas and conjectures are formulated. Experiments are then performed to test them. If the body of evidence fails to reject the hypothesis, the hypothesis stands. It stands however until and unless another (even singular) empirical observation falsifies it. However, just as with inductive reasoning, hypothetico-deductive reasoning is not immune to pitfalls—assumptions built into hypotheses can be shown to be false, thereby nullifying previously unrejected hypotheses. The bottom line is that science does not work to prove anything about the natural world. Instead, it builds hypotheses that explain the natural world and then attempts to find the hole in the reasoning (i.e., it works to disprove things about the natural world).

How do scientists test hypotheses?

Controlled experiments

The word “experiment” can be misleading because it implies a lack of control over the process. Therefore, it is important to understand that science uses controlled experiments in order to test hypotheses and contribute new knowledge. So what exactly is a controlled experiment, then? 

Let us take a practical example. Our starting hypothesis is the following: we have a novel drug that we think inhibits the division of cells, meaning that it prevents one cell from dividing into two cells (recall the description of cell theory above). To test this hypothesis, we could treat some cells with the drug on a plate that contains nutrients and fuel required for their survival and division (a standard cell biology assay). If the drug works as expected, the cells should stop dividing. This type of drug might be useful, for example, in treating cancers because slowing or stopping the division of cells would result in the slowing or stopping of tumor growth.

Although this experiment is relatively easy to do, the mere process of doing science means that several experimental variables (like temperature of the cells or drug, dosage, and so on) could play a major role in the experiment. This could result in a failed experiment when the drug actually does work, or it could give the appearance that the drug is working when it is not. Given that these variables cannot be eliminated, scientists always run control experiments in parallel to the real ones, so that the effects of these other variables can be determined.  Control experiments  are designed so that all variables, with the exception of the one under investigation, are kept constant. In simple terms, the conditions must be identical between the control and the actual experiment.     

Coming back to our example, when a drug is administered it is not pure. Often, it is dissolved in a solvent like water or oil. Therefore, the perfect control to the actual experiment would be to administer pure solvent (without the added drug) at the same time and with the same tools, where all other experimental variables (like temperature, as mentioned above) are the same between the two (Figure 1). Any difference in effect on cell division in the actual experiment here can be attributed to an effect of the drug because the effects of the solvent were controlled.

In order to provide evidence of the quality of a single, specific experiment, it needs to be performed multiple times in the same experimental conditions. We call these multiple experiments “replicates” of the experiment (Figure 2). The more replicates of the same experiment, the more confident the scientist can be about the conclusions of that experiment under the given conditions. However, multiple replicates under the same experimental conditions  are of no help  when scientists aim at acquiring more empirical evidence to support their hypothesis. Instead, they need  independent experiments  (Figure 3), in their own lab and in other labs across the world, to validate their results. 

Often times, especially when a given experiment has been repeated and its outcome is not fully clear, it is better  to find alternative experimental assays  to test the hypothesis. 

Applying the scientific approach to everyday life

So, what can we take from the scientific approach to apply to our everyday lives?

A few weeks ago, I had an agitated conversation with a bunch of friends concerning the following question: What is the definition of intelligence?

Defining “intelligence” is not easy. At the beginning of the conversation, everybody had a different, “personal” conception of intelligence in mind, which – tacitly – implied that the conversation could have taken several different directions. We realized rather soon that someone thought that an intelligent person is whoever is able to adapt faster to new situations; someone else thought that an intelligent person is whoever is able to deal with other people and empathize with them. Personally, I thought that an intelligent person is whoever displays high cognitive skills, especially in abstract reasoning. 

The scientific method has the merit of providing a reference system, with precise protocols and rules to follow. Remember: experiments must be reproducible, which means that an independent scientists in a different laboratory, when provided with the same equipment and protocols, should get comparable results.  Fruitful conversations as well need precise language, a kind of reference vocabulary everybody should agree upon, in order to discuss about the same “content”. This is something we often forget, something that was somehow missing at the opening of the aforementioned conversation: even among friends, we should always agree on premises, and define them in a rigorous manner, so that they are the same for everybody. When speaking about “intelligence”, we must all make sure we understand meaning and context of the vocabulary adopted in the debate (Figure 4, point 1).  This is the first step of “controlling” a conversation.

There is another downside that a discussion well-grounded in a scientific framework would avoid. The mistake is not structuring the debate so that all its elements, except for the one under investigation, are kept constant (Figure 4, point 2). This is particularly true when people aim at making comparisons between groups to support their claim. For example, they may try to define what intelligence is by comparing the  achievements in life of different individuals: “Stephen Hawking is a brilliant example of intelligence because of his great contribution to the physics of black holes”. This statement does not help to define what intelligence is, simply because it compares Stephen Hawking, a famous and exceptional physicist, to any other person, who statistically speaking, knows nothing about physics. Hawking first went to the University of Oxford, then he moved to the University of Cambridge. He was in contact with the most influential physicists on Earth. Other people were not. All of this, of course, does not disprove Hawking’s intelligence; but from a logical and methodological point of view, given the multitude of variables included in this comparison, it cannot prove it. Thus, the sentence “Stephen Hawking is a brilliant example of intelligence because of his great contribution to the physics of black holes” is not a valid argument to describe what intelligence is. If we really intend to approximate a definition of intelligence, Steven Hawking should be compared to other physicists, even better if they were Hawking’s classmates at the time of college, and colleagues afterwards during years of academic research. 

In simple terms, as scientists do in the lab, while debating we should try to compare groups of elements that display identical, or highly similar, features. As previously mentioned, all variables – except for the one under investigation – must be kept constant.

This insightful piece  presents a detailed analysis of how and why science can help to develop critical thinking.

In a nutshell

Here is how to approach a daily conversation in a rigorous, scientific manner:

• First discuss about the reference vocabulary, then discuss about the content of the discussion.  Think about a researcher who is writing down an experimental protocol that will be used by thousands of other scientists in varying continents. If the protocol is rigorously written, all scientists using it should get comparable experimental outcomes. In science this means reproducible knowledge, in daily life this means fruitful conversations in which individuals are on the same page. 
• Adopt “controlled” arguments to support your claims.  When making comparisons between groups, visualize two blank scenarios. As you start to add details to both of them, you have two options. If your aim is to hide a specific detail, the better is to design the two scenarios in a completely different manner—it is to increase the variables. But if your intention is to help the observer to isolate a specific detail, the better is to design identical scenarios, with the exception of the intended detail—it is therefore to keep most of the variables constant. This is precisely how scientists ideate adequate experiments to isolate new pieces of knowledge, and how individuals should orchestrate their thoughts in order to test them and facilitate their comprehension to others.   

Not only the scientific method should offer individuals an elitist way to investigate reality, but also an accessible tool to properly reason and discuss about it.

Edited by Jason Organ, PhD, Indiana University School of Medicine.

Simone is a molecular biologist on the verge of obtaining a doctoral title at the University of Ulm, Germany. He is Vice-Director at Culturico (https://culturico.com/), where his writings span from Literature to Sociology, from Philosophy to Science. His writings recently appeared in Psychology Today, openDemocracy, Splice Today, Merion West, Uncommon Ground and The Society Pages. Follow Simone on Twitter: @simredaelli

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This has to be the best article I have ever read on Scientific Thinking. I am presently writing a treatise on how Scientific thinking can be adopted to entreat all situations.And how, a 4 year old child can be taught to adopt Scientific thinking, so that, the child can look at situations that bothers her and she could try to think about that situation by formulating the right questions. She may not have the tools to find right answers? But, forming questions by using right technique ? May just make her find a way to put her mind to rest even at that level. That is why, 4 year olds are often “eerily: (!)intelligent, I have iften been intimidated and plain embarrassed to see an intelligent and well spoken 4 year old deal with celibrity ! Of course, there are a lot of variables that have to be kept in mind in order to train children in such controlled thinking environment, as the screenplay of little Sheldon shows. Thanking the author with all my heart – #ershadspeak #wearescience #weareallscientists Ershad Khandker

Simone, thank you for this article. I have the idea that I want to apply what I learned in Biology to everyday life. You addressed this issue, and have given some basic steps in using the scientific method.

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1.5: The Scientific Method

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The procedure that scientists use is also a standard form of argument. Its conclusions only give you the likelihood or the probability that something is true (if your theory or hypothesis is confirmed), and not the certainty that it’s true. But when it is done correctly, the conclusions it reaches are very well-grounded in experimental evidence. Here’s a rough outline of how the procedure works.

• Observation: Something is observed in the world which invokes your curiosity.
• Theory: An idea is proposed which could explain why the thing which you observed happened, or why it is what it is. This is the part of the procedure where scientists can get quite creative and imaginative.
• Prediction: A test is planned which could prove or disprove the theory. As part of the plan, the scientist will offer a proposition in this form: “If my theory is true, then the experiment will have [whatever] result.”
• Experiment: The test is performed, and the results are recorded.
• Successful Result: If the prediction you made came true, then the theory devised is strengthened, not proved or made certain. The theory is “verified.” And then we go back and make more predictions and do more and more tests, to see if the theory can get stronger yet.
• Failed Result: If the prediction did not come true, then the theory is falsified, and there are strong reasons to believe the theory is false. Nothing is ever certain (the sun may not actually rise tomorrow, for example, even though we all know it will), but we will assume that we were wrong if observations do not match our theories. When our predictions fail, we go back and devise a new theory to put to the test, and a new prediction to go with it.

Actually, a failed experimental result is really a kind of success, because falsification tells us what doesn’t work. And that frees up the scientist to pursue other, more promising theories. Scientists often test more than one theory at the same time, so that they can eventually arrive at the “last theory standing.” In this way, scientists can use a form of disjunctive syllogism (a deductive argument form) to arrive at definitive conclusions about what theory is the best explanation for the observation. Here’s how that part of the procedure works.

(P1) Either Theory 1 is true, or Theory 2 is true, or Theory 3 is true, or Theory 4 is true. (And so on, for however many theories are being tested.)

(P2) By experimental observation, Theories 1 and 2 and 3 were falsified.

(C) Therefore, Theory 4 is true.

Or, at least, Theory 4 is strengthened to the point where it would be quite absurd to believe anything else. After all, there might be other theories that we haven’t thought of, or tested yet. But until we think of them, and test them, we’re going to go with the best theory we’ve got.

There’s a bit more to scientific method than this. There are paradigms and paradigm shifts, epistemic values, experimental controls and variables, and the various ways that scientists negotiate with each other as they interpret experimental results. There are also a few differences between the experimental methods used by physical scientists (such as chemists), and social scientists (such as anthropologists). Scientific method is the most powerful and successful form of knowing that has been employed. Every advance in engineering, medicine, and technology has been made possible by people applying science to their problems. It is adventurous, curious, rigorously logical, and inspirational – it is even possible to be artistic about scientific discoveries. And the best part about science is that anyone can do it. Science can look difficult because there’s a lot of jargon involved, and a lot of math. But even the most complicated quantum physics and the most far-reaching astronomy follows the same method, in principle, as that primary school project in which you played with magnets or built a model volcano.

Doing the Scientific Method Yourself

We do the scientific method every day all the time when we learn or predict things. What will happen if you don’t text/call/message your significant other for longer than you normally do? Test it and find out! What will happen if you put 5 packets of ketchup on a hot dog? Find out! Pick some variables, make a prediction of what will happen when you change the variables, and then observe the results when you make those changes. Were you correct? What have you learned? What experiment would you like to do to test your new understandings? Follow the method mentioned in this chapter and see what you can learn.

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Scientific Method

Science is an enormously successful human enterprise. The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories. How these are carried out in detail can vary greatly, but characteristics like these have been looked to as a way of demarcating scientific activity from non-science, where only enterprises which employ some canonical form of scientific method or methods should be considered science (see also the entry on science and pseudo-science ). Others have questioned whether there is anything like a fixed toolkit of methods which is common across science and only science. Some reject privileging one view of method as part of rejecting broader views about the nature of science, such as naturalism (Dupré 2004); some reject any restriction in principle (pluralism).

Scientific method should be distinguished from the aims and products of science, such as knowledge, predictions, or control. Methods are the means by which those goals are achieved. Scientific method should also be distinguished from meta-methodology, which includes the values and justifications behind a particular characterization of scientific method (i.e., a methodology) — values such as objectivity, reproducibility, simplicity, or past successes. Methodological rules are proposed to govern method and it is a meta-methodological question whether methods obeying those rules satisfy given values. Finally, method is distinct, to some degree, from the detailed and contextual practices through which methods are implemented. The latter might range over: specific laboratory techniques; mathematical formalisms or other specialized languages used in descriptions and reasoning; technological or other material means; ways of communicating and sharing results, whether with other scientists or with the public at large; or the conventions, habits, enforced customs, and institutional controls over how and what science is carried out.

While it is important to recognize these distinctions, their boundaries are fuzzy. Hence, accounts of method cannot be entirely divorced from their methodological and meta-methodological motivations or justifications, Moreover, each aspect plays a crucial role in identifying methods. Disputes about method have therefore played out at the detail, rule, and meta-rule levels. Changes in beliefs about the certainty or fallibility of scientific knowledge, for instance (which is a meta-methodological consideration of what we can hope for methods to deliver), have meant different emphases on deductive and inductive reasoning, or on the relative importance attached to reasoning over observation (i.e., differences over particular methods.) Beliefs about the role of science in society will affect the place one gives to values in scientific method.

The issue which has shaped debates over scientific method the most in the last half century is the question of how pluralist do we need to be about method? Unificationists continue to hold out for one method essential to science; nihilism is a form of radical pluralism, which considers the effectiveness of any methodological prescription to be so context sensitive as to render it not explanatory on its own. Some middle degree of pluralism regarding the methods embodied in scientific practice seems appropriate. But the details of scientific practice vary with time and place, from institution to institution, across scientists and their subjects of investigation. How significant are the variations for understanding science and its success? How much can method be abstracted from practice? This entry describes some of the attempts to characterize scientific method or methods, as well as arguments for a more context-sensitive approach to methods embedded in actual scientific practices.

1. Overview and organizing themes

2. historical review: aristotle to mill, 3.1 logical constructionism and operationalism, 3.2. h-d as a logic of confirmation, 3.3. popper and falsificationism, 3.4 meta-methodology and the end of method, 4. statistical methods for hypothesis testing, 5.1 creative and exploratory practices.

• 5.2 Computer methods and the ‘new ways’ of doing science

6.1 “The scientific method” in science education and as seen by scientists

6.2 privileged methods and ‘gold standards’, 6.3 scientific method in the court room, 6.4 deviating practices, 7. conclusion, other internet resources, related entries.

This entry could have been given the title Scientific Methods and gone on to fill volumes, or it could have been extremely short, consisting of a brief summary rejection of the idea that there is any such thing as a unique Scientific Method at all. Both unhappy prospects are due to the fact that scientific activity varies so much across disciplines, times, places, and scientists that any account which manages to unify it all will either consist of overwhelming descriptive detail, or trivial generalizations.

The choice of scope for the present entry is more optimistic, taking a cue from the recent movement in philosophy of science toward a greater attention to practice: to what scientists actually do. This “turn to practice” can be seen as the latest form of studies of methods in science, insofar as it represents an attempt at understanding scientific activity, but through accounts that are neither meant to be universal and unified, nor singular and narrowly descriptive. To some extent, different scientists at different times and places can be said to be using the same method even though, in practice, the details are different.

Whether the context in which methods are carried out is relevant, or to what extent, will depend largely on what one takes the aims of science to be and what one’s own aims are. For most of the history of scientific methodology the assumption has been that the most important output of science is knowledge and so the aim of methodology should be to discover those methods by which scientific knowledge is generated.

Science was seen to embody the most successful form of reasoning (but which form?) to the most certain knowledge claims (but how certain?) on the basis of systematically collected evidence (but what counts as evidence, and should the evidence of the senses take precedence, or rational insight?) Section 2 surveys some of the history, pointing to two major themes. One theme is seeking the right balance between observation and reasoning (and the attendant forms of reasoning which employ them); the other is how certain scientific knowledge is or can be.

Section 3 turns to 20 th century debates on scientific method. In the second half of the 20 th century the epistemic privilege of science faced several challenges and many philosophers of science abandoned the reconstruction of the logic of scientific method. Views changed significantly regarding which functions of science ought to be captured and why. For some, the success of science was better identified with social or cultural features. Historical and sociological turns in the philosophy of science were made, with a demand that greater attention be paid to the non-epistemic aspects of science, such as sociological, institutional, material, and political factors. Even outside of those movements there was an increased specialization in the philosophy of science, with more and more focus on specific fields within science. The combined upshot was very few philosophers arguing any longer for a grand unified methodology of science. Sections 3 and 4 surveys the main positions on scientific method in 20 th century philosophy of science, focusing on where they differ in their preference for confirmation or falsification or for waiving the idea of a special scientific method altogether.

In recent decades, attention has primarily been paid to scientific activities traditionally falling under the rubric of method, such as experimental design and general laboratory practice, the use of statistics, the construction and use of models and diagrams, interdisciplinary collaboration, and science communication. Sections 4–6 attempt to construct a map of the current domains of the study of methods in science.

As these sections illustrate, the question of method is still central to the discourse about science. Scientific method remains a topic for education, for science policy, and for scientists. It arises in the public domain where the demarcation or status of science is at issue. Some philosophers have recently returned, therefore, to the question of what it is that makes science a unique cultural product. This entry will close with some of these recent attempts at discerning and encapsulating the activities by which scientific knowledge is achieved.

Attempting a history of scientific method compounds the vast scope of the topic. This section briefly surveys the background to modern methodological debates. What can be called the classical view goes back to antiquity, and represents a point of departure for later divergences. [ 1 ]

We begin with a point made by Laudan (1968) in his historical survey of scientific method:

Perhaps the most serious inhibition to the emergence of the history of theories of scientific method as a respectable area of study has been the tendency to conflate it with the general history of epistemology, thereby assuming that the narrative categories and classificatory pigeon-holes applied to the latter are also basic to the former. (1968: 5)

To see knowledge about the natural world as falling under knowledge more generally is an understandable conflation. Histories of theories of method would naturally employ the same narrative categories and classificatory pigeon holes. An important theme of the history of epistemology, for example, is the unification of knowledge, a theme reflected in the question of the unification of method in science. Those who have identified differences in kinds of knowledge have often likewise identified different methods for achieving that kind of knowledge (see the entry on the unity of science ).

Different views on what is known, how it is known, and what can be known are connected. Plato distinguished the realms of things into the visible and the intelligible ( The Republic , 510a, in Cooper 1997). Only the latter, the Forms, could be objects of knowledge. The intelligible truths could be known with the certainty of geometry and deductive reasoning. What could be observed of the material world, however, was by definition imperfect and deceptive, not ideal. The Platonic way of knowledge therefore emphasized reasoning as a method, downplaying the importance of observation. Aristotle disagreed, locating the Forms in the natural world as the fundamental principles to be discovered through the inquiry into nature ( Metaphysics Z , in Barnes 1984).

Aristotle is recognized as giving the earliest systematic treatise on the nature of scientific inquiry in the western tradition, one which embraced observation and reasoning about the natural world. In the Prior and Posterior Analytics , Aristotle reflects first on the aims and then the methods of inquiry into nature. A number of features can be found which are still considered by most to be essential to science. For Aristotle, empiricism, careful observation (but passive observation, not controlled experiment), is the starting point. The aim is not merely recording of facts, though. For Aristotle, science ( epistêmê ) is a body of properly arranged knowledge or learning—the empirical facts, but also their ordering and display are of crucial importance. The aims of discovery, ordering, and display of facts partly determine the methods required of successful scientific inquiry. Also determinant is the nature of the knowledge being sought, and the explanatory causes proper to that kind of knowledge (see the discussion of the four causes in the entry on Aristotle on causality ).

In addition to careful observation, then, scientific method requires a logic as a system of reasoning for properly arranging, but also inferring beyond, what is known by observation. Methods of reasoning may include induction, prediction, or analogy, among others. Aristotle’s system (along with his catalogue of fallacious reasoning) was collected under the title the Organon . This title would be echoed in later works on scientific reasoning, such as Novum Organon by Francis Bacon, and Novum Organon Restorum by William Whewell (see below). In Aristotle’s Organon reasoning is divided primarily into two forms, a rough division which persists into modern times. The division, known most commonly today as deductive versus inductive method, appears in other eras and methodologies as analysis/​synthesis, non-ampliative/​ampliative, or even confirmation/​verification. The basic idea is there are two “directions” to proceed in our methods of inquiry: one away from what is observed, to the more fundamental, general, and encompassing principles; the other, from the fundamental and general to instances or implications of principles.

The basic aim and method of inquiry identified here can be seen as a theme running throughout the next two millennia of reflection on the correct way to seek after knowledge: carefully observe nature and then seek rules or principles which explain or predict its operation. The Aristotelian corpus provided the framework for a commentary tradition on scientific method independent of science itself (cosmos versus physics.) During the medieval period, figures such as Albertus Magnus (1206–1280), Thomas Aquinas (1225–1274), Robert Grosseteste (1175–1253), Roger Bacon (1214/1220–1292), William of Ockham (1287–1347), Andreas Vesalius (1514–1546), Giacomo Zabarella (1533–1589) all worked to clarify the kind of knowledge obtainable by observation and induction, the source of justification of induction, and best rules for its application. [ 2 ] Many of their contributions we now think of as essential to science (see also Laudan 1968). As Aristotle and Plato had employed a framework of reasoning either “to the forms” or “away from the forms”, medieval thinkers employed directions away from the phenomena or back to the phenomena. In analysis, a phenomena was examined to discover its basic explanatory principles; in synthesis, explanations of a phenomena were constructed from first principles.

During the Scientific Revolution these various strands of argument, experiment, and reason were forged into a dominant epistemic authority. The 16 th –18 th centuries were a period of not only dramatic advance in knowledge about the operation of the natural world—advances in mechanical, medical, biological, political, economic explanations—but also of self-awareness of the revolutionary changes taking place, and intense reflection on the source and legitimation of the method by which the advances were made. The struggle to establish the new authority included methodological moves. The Book of Nature, according to the metaphor of Galileo Galilei (1564–1642) or Francis Bacon (1561–1626), was written in the language of mathematics, of geometry and number. This motivated an emphasis on mathematical description and mechanical explanation as important aspects of scientific method. Through figures such as Henry More and Ralph Cudworth, a neo-Platonic emphasis on the importance of metaphysical reflection on nature behind appearances, particularly regarding the spiritual as a complement to the purely mechanical, remained an important methodological thread of the Scientific Revolution (see the entries on Cambridge platonists ; Boyle ; Henry More ; Galileo ).

In Novum Organum (1620), Bacon was critical of the Aristotelian method for leaping from particulars to universals too quickly. The syllogistic form of reasoning readily mixed those two types of propositions. Bacon aimed at the invention of new arts, principles, and directions. His method would be grounded in methodical collection of observations, coupled with correction of our senses (and particularly, directions for the avoidance of the Idols, as he called them, kinds of systematic errors to which naïve observers are prone.) The community of scientists could then climb, by a careful, gradual and unbroken ascent, to reliable general claims.

Bacon’s method has been criticized as impractical and too inflexible for the practicing scientist. Whewell would later criticize Bacon in his System of Logic for paying too little attention to the practices of scientists. It is hard to find convincing examples of Bacon’s method being put in to practice in the history of science, but there are a few who have been held up as real examples of 16 th century scientific, inductive method, even if not in the rigid Baconian mold: figures such as Robert Boyle (1627–1691) and William Harvey (1578–1657) (see the entry on Bacon ).

It is to Isaac Newton (1642–1727), however, that historians of science and methodologists have paid greatest attention. Given the enormous success of his Principia Mathematica and Opticks , this is understandable. The study of Newton’s method has had two main thrusts: the implicit method of the experiments and reasoning presented in the Opticks, and the explicit methodological rules given as the Rules for Philosophising (the Regulae) in Book III of the Principia . [ 3 ] Newton’s law of gravitation, the linchpin of his new cosmology, broke with explanatory conventions of natural philosophy, first for apparently proposing action at a distance, but more generally for not providing “true”, physical causes. The argument for his System of the World ( Principia , Book III) was based on phenomena, not reasoned first principles. This was viewed (mainly on the continent) as insufficient for proper natural philosophy. The Regulae counter this objection, re-defining the aims of natural philosophy by re-defining the method natural philosophers should follow. (See the entry on Newton’s philosophy .)

To his list of methodological prescriptions should be added Newton’s famous phrase “ hypotheses non fingo ” (commonly translated as “I frame no hypotheses”.) The scientist was not to invent systems but infer explanations from observations, as Bacon had advocated. This would come to be known as inductivism. In the century after Newton, significant clarifications of the Newtonian method were made. Colin Maclaurin (1698–1746), for instance, reconstructed the essential structure of the method as having complementary analysis and synthesis phases, one proceeding away from the phenomena in generalization, the other from the general propositions to derive explanations of new phenomena. Denis Diderot (1713–1784) and editors of the Encyclopédie did much to consolidate and popularize Newtonianism, as did Francesco Algarotti (1721–1764). The emphasis was often the same, as much on the character of the scientist as on their process, a character which is still commonly assumed. The scientist is humble in the face of nature, not beholden to dogma, obeys only his eyes, and follows the truth wherever it leads. It was certainly Voltaire (1694–1778) and du Chatelet (1706–1749) who were most influential in propagating the latter vision of the scientist and their craft, with Newton as hero. Scientific method became a revolutionary force of the Enlightenment. (See also the entries on Newton , Leibniz , Descartes , Boyle , Hume , enlightenment , as well as Shank 2008 for a historical overview.)

Not all 18 th century reflections on scientific method were so celebratory. Famous also are George Berkeley’s (1685–1753) attack on the mathematics of the new science, as well as the over-emphasis of Newtonians on observation; and David Hume’s (1711–1776) undermining of the warrant offered for scientific claims by inductive justification (see the entries on: George Berkeley ; David Hume ; Hume’s Newtonianism and Anti-Newtonianism ). Hume’s problem of induction motivated Immanuel Kant (1724–1804) to seek new foundations for empirical method, though as an epistemic reconstruction, not as any set of practical guidelines for scientists. Both Hume and Kant influenced the methodological reflections of the next century, such as the debate between Mill and Whewell over the certainty of inductive inferences in science.

The debate between John Stuart Mill (1806–1873) and William Whewell (1794–1866) has become the canonical methodological debate of the 19 th century. Although often characterized as a debate between inductivism and hypothetico-deductivism, the role of the two methods on each side is actually more complex. On the hypothetico-deductive account, scientists work to come up with hypotheses from which true observational consequences can be deduced—hence, hypothetico-deductive. Because Whewell emphasizes both hypotheses and deduction in his account of method, he can be seen as a convenient foil to the inductivism of Mill. However, equally if not more important to Whewell’s portrayal of scientific method is what he calls the “fundamental antithesis”. Knowledge is a product of the objective (what we see in the world around us) and subjective (the contributions of our mind to how we perceive and understand what we experience, which he called the Fundamental Ideas). Both elements are essential according to Whewell, and he was therefore critical of Kant for too much focus on the subjective, and John Locke (1632–1704) and Mill for too much focus on the senses. Whewell’s fundamental ideas can be discipline relative. An idea can be fundamental even if it is necessary for knowledge only within a given scientific discipline (e.g., chemical affinity for chemistry). This distinguishes fundamental ideas from the forms and categories of intuition of Kant. (See the entry on Whewell .)

Clarifying fundamental ideas would therefore be an essential part of scientific method and scientific progress. Whewell called this process “Discoverer’s Induction”. It was induction, following Bacon or Newton, but Whewell sought to revive Bacon’s account by emphasising the role of ideas in the clear and careful formulation of inductive hypotheses. Whewell’s induction is not merely the collecting of objective facts. The subjective plays a role through what Whewell calls the Colligation of Facts, a creative act of the scientist, the invention of a theory. A theory is then confirmed by testing, where more facts are brought under the theory, called the Consilience of Inductions. Whewell felt that this was the method by which the true laws of nature could be discovered: clarification of fundamental concepts, clever invention of explanations, and careful testing. Mill, in his critique of Whewell, and others who have cast Whewell as a fore-runner of the hypothetico-deductivist view, seem to have under-estimated the importance of this discovery phase in Whewell’s understanding of method (Snyder 1997a,b, 1999). Down-playing the discovery phase would come to characterize methodology of the early 20 th century (see section 3 ).

Mill, in his System of Logic , put forward a narrower view of induction as the essence of scientific method. For Mill, induction is the search first for regularities among events. Among those regularities, some will continue to hold for further observations, eventually gaining the status of laws. One can also look for regularities among the laws discovered in a domain, i.e., for a law of laws. Which “law law” will hold is time and discipline dependent and open to revision. One example is the Law of Universal Causation, and Mill put forward specific methods for identifying causes—now commonly known as Mill’s methods. These five methods look for circumstances which are common among the phenomena of interest, those which are absent when the phenomena are, or those for which both vary together. Mill’s methods are still seen as capturing basic intuitions about experimental methods for finding the relevant explanatory factors ( System of Logic (1843), see Mill entry). The methods advocated by Whewell and Mill, in the end, look similar. Both involve inductive generalization to covering laws. They differ dramatically, however, with respect to the necessity of the knowledge arrived at; that is, at the meta-methodological level (see the entries on Whewell and Mill entries).

3. Logic of method and critical responses

The quantum and relativistic revolutions in physics in the early 20 th century had a profound effect on methodology. Conceptual foundations of both theories were taken to show the defeasibility of even the most seemingly secure intuitions about space, time and bodies. Certainty of knowledge about the natural world was therefore recognized as unattainable. Instead a renewed empiricism was sought which rendered science fallible but still rationally justifiable.

Analyses of the reasoning of scientists emerged, according to which the aspects of scientific method which were of primary importance were the means of testing and confirming of theories. A distinction in methodology was made between the contexts of discovery and justification. The distinction could be used as a wedge between the particularities of where and how theories or hypotheses are arrived at, on the one hand, and the underlying reasoning scientists use (whether or not they are aware of it) when assessing theories and judging their adequacy on the basis of the available evidence. By and large, for most of the 20 th century, philosophy of science focused on the second context, although philosophers differed on whether to focus on confirmation or refutation as well as on the many details of how confirmation or refutation could or could not be brought about. By the mid-20 th century these attempts at defining the method of justification and the context distinction itself came under pressure. During the same period, philosophy of science developed rapidly, and from section 4 this entry will therefore shift from a primarily historical treatment of the scientific method towards a primarily thematic one.

Advances in logic and probability held out promise of the possibility of elaborate reconstructions of scientific theories and empirical method, the best example being Rudolf Carnap’s The Logical Structure of the World (1928). Carnap attempted to show that a scientific theory could be reconstructed as a formal axiomatic system—that is, a logic. That system could refer to the world because some of its basic sentences could be interpreted as observations or operations which one could perform to test them. The rest of the theoretical system, including sentences using theoretical or unobservable terms (like electron or force) would then either be meaningful because they could be reduced to observations, or they had purely logical meanings (called analytic, like mathematical identities). This has been referred to as the verifiability criterion of meaning. According to the criterion, any statement not either analytic or verifiable was strictly meaningless. Although the view was endorsed by Carnap in 1928, he would later come to see it as too restrictive (Carnap 1956). Another familiar version of this idea is operationalism of Percy William Bridgman. In The Logic of Modern Physics (1927) Bridgman asserted that every physical concept could be defined in terms of the operations one would perform to verify the application of that concept. Making good on the operationalisation of a concept even as simple as length, however, can easily become enormously complex (for measuring very small lengths, for instance) or impractical (measuring large distances like light years.)

Carl Hempel’s (1950, 1951) criticisms of the verifiability criterion of meaning had enormous influence. He pointed out that universal generalizations, such as most scientific laws, were not strictly meaningful on the criterion. Verifiability and operationalism both seemed too restrictive to capture standard scientific aims and practice. The tenuous connection between these reconstructions and actual scientific practice was criticized in another way. In both approaches, scientific methods are instead recast in methodological roles. Measurements, for example, were looked to as ways of giving meanings to terms. The aim of the philosopher of science was not to understand the methods per se , but to use them to reconstruct theories, their meanings, and their relation to the world. When scientists perform these operations, however, they will not report that they are doing them to give meaning to terms in a formal axiomatic system. This disconnect between methodology and the details of actual scientific practice would seem to violate the empiricism the Logical Positivists and Bridgman were committed to. The view that methodology should correspond to practice (to some extent) has been called historicism, or intuitionism. We turn to these criticisms and responses in section 3.4 . [ 4 ]

Positivism also had to contend with the recognition that a purely inductivist approach, along the lines of Bacon-Newton-Mill, was untenable. There was no pure observation, for starters. All observation was theory laden. Theory is required to make any observation, therefore not all theory can be derived from observation alone. (See the entry on theory and observation in science .) Even granting an observational basis, Hume had already pointed out that one could not deductively justify inductive conclusions without begging the question by presuming the success of the inductive method. Likewise, positivist attempts at analyzing how a generalization can be confirmed by observations of its instances were subject to a number of criticisms. Goodman (1965) and Hempel (1965) both point to paradoxes inherent in standard accounts of confirmation. Recent attempts at explaining how observations can serve to confirm a scientific theory are discussed in section 4 below.

The standard starting point for a non-inductive analysis of the logic of confirmation is known as the Hypothetico-Deductive (H-D) method. In its simplest form, a sentence of a theory which expresses some hypothesis is confirmed by its true consequences. As noted in section 2 , this method had been advanced by Whewell in the 19 th century, as well as Nicod (1924) and others in the 20 th century. Often, Hempel’s (1966) description of the H-D method, illustrated by the case of Semmelweiss’ inferential procedures in establishing the cause of childbed fever, has been presented as a key account of H-D as well as a foil for criticism of the H-D account of confirmation (see, for example, Lipton’s (2004) discussion of inference to the best explanation; also the entry on confirmation ). Hempel described Semmelsweiss’ procedure as examining various hypotheses explaining the cause of childbed fever. Some hypotheses conflicted with observable facts and could be rejected as false immediately. Others needed to be tested experimentally by deducing which observable events should follow if the hypothesis were true (what Hempel called the test implications of the hypothesis), then conducting an experiment and observing whether or not the test implications occurred. If the experiment showed the test implication to be false, the hypothesis could be rejected. If the experiment showed the test implications to be true, however, this did not prove the hypothesis true. The confirmation of a test implication does not verify a hypothesis, though Hempel did allow that “it provides at least some support, some corroboration or confirmation for it” (Hempel 1966: 8). The degree of this support then depends on the quantity, variety and precision of the supporting evidence.

Another approach that took off from the difficulties with inductive inference was Karl Popper’s critical rationalism or falsificationism (Popper 1959, 1963). Falsification is deductive and similar to H-D in that it involves scientists deducing observational consequences from the hypothesis under test. For Popper, however, the important point was not the degree of confirmation that successful prediction offered to a hypothesis. The crucial thing was the logical asymmetry between confirmation, based on inductive inference, and falsification, which can be based on a deductive inference. (This simple opposition was later questioned, by Lakatos, among others. See the entry on historicist theories of scientific rationality. )

Popper stressed that, regardless of the amount of confirming evidence, we can never be certain that a hypothesis is true without committing the fallacy of affirming the consequent. Instead, Popper introduced the notion of corroboration as a measure for how well a theory or hypothesis has survived previous testing—but without implying that this is also a measure for the probability that it is true.

Popper was also motivated by his doubts about the scientific status of theories like the Marxist theory of history or psycho-analysis, and so wanted to demarcate between science and pseudo-science. Popper saw this as an importantly different distinction than demarcating science from metaphysics. The latter demarcation was the primary concern of many logical empiricists. Popper used the idea of falsification to draw a line instead between pseudo and proper science. Science was science because its method involved subjecting theories to rigorous tests which offered a high probability of failing and thus refuting the theory.

A commitment to the risk of failure was important. Avoiding falsification could be done all too easily. If a consequence of a theory is inconsistent with observations, an exception can be added by introducing auxiliary hypotheses designed explicitly to save the theory, so-called ad hoc modifications. This Popper saw done in pseudo-science where ad hoc theories appeared capable of explaining anything in their field of application. In contrast, science is risky. If observations showed the predictions from a theory to be wrong, the theory would be refuted. Hence, scientific hypotheses must be falsifiable. Not only must there exist some possible observation statement which could falsify the hypothesis or theory, were it observed, (Popper called these the hypothesis’ potential falsifiers) it is crucial to the Popperian scientific method that such falsifications be sincerely attempted on a regular basis.

The more potential falsifiers of a hypothesis, the more falsifiable it would be, and the more the hypothesis claimed. Conversely, hypotheses without falsifiers claimed very little or nothing at all. Originally, Popper thought that this meant the introduction of ad hoc hypotheses only to save a theory should not be countenanced as good scientific method. These would undermine the falsifiabililty of a theory. However, Popper later came to recognize that the introduction of modifications (immunizations, he called them) was often an important part of scientific development. Responding to surprising or apparently falsifying observations often generated important new scientific insights. Popper’s own example was the observed motion of Uranus which originally did not agree with Newtonian predictions. The ad hoc hypothesis of an outer planet explained the disagreement and led to further falsifiable predictions. Popper sought to reconcile the view by blurring the distinction between falsifiable and not falsifiable, and speaking instead of degrees of testability (Popper 1985: 41f.).

From the 1960s on, sustained meta-methodological criticism emerged that drove philosophical focus away from scientific method. A brief look at those criticisms follows, with recommendations for further reading at the end of the entry.

Thomas Kuhn’s The Structure of Scientific Revolutions (1962) begins with a well-known shot across the bow for philosophers of science:

History, if viewed as a repository for more than anecdote or chronology, could produce a decisive transformation in the image of science by which we are now possessed. (1962: 1)

The image Kuhn thought needed transforming was the a-historical, rational reconstruction sought by many of the Logical Positivists, though Carnap and other positivists were actually quite sympathetic to Kuhn’s views. (See the entry on the Vienna Circle .) Kuhn shares with other of his contemporaries, such as Feyerabend and Lakatos, a commitment to a more empirical approach to philosophy of science. Namely, the history of science provides important data, and necessary checks, for philosophy of science, including any theory of scientific method.

The history of science reveals, according to Kuhn, that scientific development occurs in alternating phases. During normal science, the members of the scientific community adhere to the paradigm in place. Their commitment to the paradigm means a commitment to the puzzles to be solved and the acceptable ways of solving them. Confidence in the paradigm remains so long as steady progress is made in solving the shared puzzles. Method in this normal phase operates within a disciplinary matrix (Kuhn’s later concept of a paradigm) which includes standards for problem solving, and defines the range of problems to which the method should be applied. An important part of a disciplinary matrix is the set of values which provide the norms and aims for scientific method. The main values that Kuhn identifies are prediction, problem solving, simplicity, consistency, and plausibility.

An important by-product of normal science is the accumulation of puzzles which cannot be solved with resources of the current paradigm. Once accumulation of these anomalies has reached some critical mass, it can trigger a communal shift to a new paradigm and a new phase of normal science. Importantly, the values that provide the norms and aims for scientific method may have transformed in the meantime. Method may therefore be relative to discipline, time or place

Feyerabend also identified the aims of science as progress, but argued that any methodological prescription would only stifle that progress (Feyerabend 1988). His arguments are grounded in re-examining accepted “myths” about the history of science. Heroes of science, like Galileo, are shown to be just as reliant on rhetoric and persuasion as they are on reason and demonstration. Others, like Aristotle, are shown to be far more reasonable and far-reaching in their outlooks then they are given credit for. As a consequence, the only rule that could provide what he took to be sufficient freedom was the vacuous “anything goes”. More generally, even the methodological restriction that science is the best way to pursue knowledge, and to increase knowledge, is too restrictive. Feyerabend suggested instead that science might, in fact, be a threat to a free society, because it and its myth had become so dominant (Feyerabend 1978).

An even more fundamental kind of criticism was offered by several sociologists of science from the 1970s onwards who rejected the methodology of providing philosophical accounts for the rational development of science and sociological accounts of the irrational mistakes. Instead, they adhered to a symmetry thesis on which any causal explanation of how scientific knowledge is established needs to be symmetrical in explaining truth and falsity, rationality and irrationality, success and mistakes, by the same causal factors (see, e.g., Barnes and Bloor 1982, Bloor 1991). Movements in the Sociology of Science, like the Strong Programme, or in the social dimensions and causes of knowledge more generally led to extended and close examination of detailed case studies in contemporary science and its history. (See the entries on the social dimensions of scientific knowledge and social epistemology .) Well-known examinations by Latour and Woolgar (1979/1986), Knorr-Cetina (1981), Pickering (1984), Shapin and Schaffer (1985) seem to bear out that it was social ideologies (on a macro-scale) or individual interactions and circumstances (on a micro-scale) which were the primary causal factors in determining which beliefs gained the status of scientific knowledge. As they saw it therefore, explanatory appeals to scientific method were not empirically grounded.

A late, and largely unexpected, criticism of scientific method came from within science itself. Beginning in the early 2000s, a number of scientists attempting to replicate the results of published experiments could not do so. There may be close conceptual connection between reproducibility and method. For example, if reproducibility means that the same scientific methods ought to produce the same result, and all scientific results ought to be reproducible, then whatever it takes to reproduce a scientific result ought to be called scientific method. Space limits us to the observation that, insofar as reproducibility is a desired outcome of proper scientific method, it is not strictly a part of scientific method. (See the entry on reproducibility of scientific results .)

By the close of the 20 th century the search for the scientific method was flagging. Nola and Sankey (2000b) could introduce their volume on method by remarking that “For some, the whole idea of a theory of scientific method is yester-year’s debate …”.

Despite the many difficulties that philosophers encountered in trying to providing a clear methodology of conformation (or refutation), still important progress has been made on understanding how observation can provide evidence for a given theory. Work in statistics has been crucial for understanding how theories can be tested empirically, and in recent decades a huge literature has developed that attempts to recast confirmation in Bayesian terms. Here these developments can be covered only briefly, and we refer to the entry on confirmation for further details and references.

Statistics has come to play an increasingly important role in the methodology of the experimental sciences from the 19 th century onwards. At that time, statistics and probability theory took on a methodological role as an analysis of inductive inference, and attempts to ground the rationality of induction in the axioms of probability theory have continued throughout the 20 th century and in to the present. Developments in the theory of statistics itself, meanwhile, have had a direct and immense influence on the experimental method, including methods for measuring the uncertainty of observations such as the Method of Least Squares developed by Legendre and Gauss in the early 19 th century, criteria for the rejection of outliers proposed by Peirce by the mid-19 th century, and the significance tests developed by Gosset (a.k.a. “Student”), Fisher, Neyman & Pearson and others in the 1920s and 1930s (see, e.g., Swijtink 1987 for a brief historical overview; and also the entry on C.S. Peirce ).

These developments within statistics then in turn led to a reflective discussion among both statisticians and philosophers of science on how to perceive the process of hypothesis testing: whether it was a rigorous statistical inference that could provide a numerical expression of the degree of confidence in the tested hypothesis, or if it should be seen as a decision between different courses of actions that also involved a value component. This led to a major controversy among Fisher on the one side and Neyman and Pearson on the other (see especially Fisher 1955, Neyman 1956 and Pearson 1955, and for analyses of the controversy, e.g., Howie 2002, Marks 2000, Lenhard 2006). On Fisher’s view, hypothesis testing was a methodology for when to accept or reject a statistical hypothesis, namely that a hypothesis should be rejected by evidence if this evidence would be unlikely relative to other possible outcomes, given the hypothesis were true. In contrast, on Neyman and Pearson’s view, the consequence of error also had to play a role when deciding between hypotheses. Introducing the distinction between the error of rejecting a true hypothesis (type I error) and accepting a false hypothesis (type II error), they argued that it depends on the consequences of the error to decide whether it is more important to avoid rejecting a true hypothesis or accepting a false one. Hence, Fisher aimed for a theory of inductive inference that enabled a numerical expression of confidence in a hypothesis. To him, the important point was the search for truth, not utility. In contrast, the Neyman-Pearson approach provided a strategy of inductive behaviour for deciding between different courses of action. Here, the important point was not whether a hypothesis was true, but whether one should act as if it was.

Similar discussions are found in the philosophical literature. On the one side, Churchman (1948) and Rudner (1953) argued that because scientific hypotheses can never be completely verified, a complete analysis of the methods of scientific inference includes ethical judgments in which the scientists must decide whether the evidence is sufficiently strong or that the probability is sufficiently high to warrant the acceptance of the hypothesis, which again will depend on the importance of making a mistake in accepting or rejecting the hypothesis. Others, such as Jeffrey (1956) and Levi (1960) disagreed and instead defended a value-neutral view of science on which scientists should bracket their attitudes, preferences, temperament, and values when assessing the correctness of their inferences. For more details on this value-free ideal in the philosophy of science and its historical development, see Douglas (2009) and Howard (2003). For a broad set of case studies examining the role of values in science, see e.g. Elliott & Richards 2017.

In recent decades, philosophical discussions of the evaluation of probabilistic hypotheses by statistical inference have largely focused on Bayesianism that understands probability as a measure of a person’s degree of belief in an event, given the available information, and frequentism that instead understands probability as a long-run frequency of a repeatable event. Hence, for Bayesians probabilities refer to a state of knowledge, whereas for frequentists probabilities refer to frequencies of events (see, e.g., Sober 2008, chapter 1 for a detailed introduction to Bayesianism and frequentism as well as to likelihoodism). Bayesianism aims at providing a quantifiable, algorithmic representation of belief revision, where belief revision is a function of prior beliefs (i.e., background knowledge) and incoming evidence. Bayesianism employs a rule based on Bayes’ theorem, a theorem of the probability calculus which relates conditional probabilities. The probability that a particular hypothesis is true is interpreted as a degree of belief, or credence, of the scientist. There will also be a probability and a degree of belief that a hypothesis will be true conditional on a piece of evidence (an observation, say) being true. Bayesianism proscribes that it is rational for the scientist to update their belief in the hypothesis to that conditional probability should it turn out that the evidence is, in fact, observed (see, e.g., Sprenger & Hartmann 2019 for a comprehensive treatment of Bayesian philosophy of science). Originating in the work of Neyman and Person, frequentism aims at providing the tools for reducing long-run error rates, such as the error-statistical approach developed by Mayo (1996) that focuses on how experimenters can avoid both type I and type II errors by building up a repertoire of procedures that detect errors if and only if they are present. Both Bayesianism and frequentism have developed over time, they are interpreted in different ways by its various proponents, and their relations to previous criticism to attempts at defining scientific method are seen differently by proponents and critics. The literature, surveys, reviews and criticism in this area are vast and the reader is referred to the entries on Bayesian epistemology and confirmation .

5. Method in Practice

Attention to scientific practice, as we have seen, is not itself new. However, the turn to practice in the philosophy of science of late can be seen as a correction to the pessimism with respect to method in philosophy of science in later parts of the 20 th century, and as an attempted reconciliation between sociological and rationalist explanations of scientific knowledge. Much of this work sees method as detailed and context specific problem-solving procedures, and methodological analyses to be at the same time descriptive, critical and advisory (see Nickles 1987 for an exposition of this view). The following section contains a survey of some of the practice focuses. In this section we turn fully to topics rather than chronology.

A problem with the distinction between the contexts of discovery and justification that figured so prominently in philosophy of science in the first half of the 20 th century (see section 2 ) is that no such distinction can be clearly seen in scientific activity (see Arabatzis 2006). Thus, in recent decades, it has been recognized that study of conceptual innovation and change should not be confined to psychology and sociology of science, but are also important aspects of scientific practice which philosophy of science should address (see also the entry on scientific discovery ). Looking for the practices that drive conceptual innovation has led philosophers to examine both the reasoning practices of scientists and the wide realm of experimental practices that are not directed narrowly at testing hypotheses, that is, exploratory experimentation.

Examining the reasoning practices of historical and contemporary scientists, Nersessian (2008) has argued that new scientific concepts are constructed as solutions to specific problems by systematic reasoning, and that of analogy, visual representation and thought-experimentation are among the important reasoning practices employed. These ubiquitous forms of reasoning are reliable—but also fallible—methods of conceptual development and change. On her account, model-based reasoning consists of cycles of construction, simulation, evaluation and adaption of models that serve as interim interpretations of the target problem to be solved. Often, this process will lead to modifications or extensions, and a new cycle of simulation and evaluation. However, Nersessian also emphasizes that

creative model-based reasoning cannot be applied as a simple recipe, is not always productive of solutions, and even its most exemplary usages can lead to incorrect solutions. (Nersessian 2008: 11)

Thus, while on the one hand she agrees with many previous philosophers that there is no logic of discovery, discoveries can derive from reasoned processes, such that a large and integral part of scientific practice is

the creation of concepts through which to comprehend, structure, and communicate about physical phenomena …. (Nersessian 1987: 11)

Similarly, work on heuristics for discovery and theory construction by scholars such as Darden (1991) and Bechtel & Richardson (1993) present science as problem solving and investigate scientific problem solving as a special case of problem-solving in general. Drawing largely on cases from the biological sciences, much of their focus has been on reasoning strategies for the generation, evaluation, and revision of mechanistic explanations of complex systems.

Addressing another aspect of the context distinction, namely the traditional view that the primary role of experiments is to test theoretical hypotheses according to the H-D model, other philosophers of science have argued for additional roles that experiments can play. The notion of exploratory experimentation was introduced to describe experiments driven by the desire to obtain empirical regularities and to develop concepts and classifications in which these regularities can be described (Steinle 1997, 2002; Burian 1997; Waters 2007)). However the difference between theory driven experimentation and exploratory experimentation should not be seen as a sharp distinction. Theory driven experiments are not always directed at testing hypothesis, but may also be directed at various kinds of fact-gathering, such as determining numerical parameters. Vice versa , exploratory experiments are usually informed by theory in various ways and are therefore not theory-free. Instead, in exploratory experiments phenomena are investigated without first limiting the possible outcomes of the experiment on the basis of extant theory about the phenomena.

The development of high throughput instrumentation in molecular biology and neighbouring fields has given rise to a special type of exploratory experimentation that collects and analyses very large amounts of data, and these new ‘omics’ disciplines are often said to represent a break with the ideal of hypothesis-driven science (Burian 2007; Elliott 2007; Waters 2007; O’Malley 2007) and instead described as data-driven research (Leonelli 2012; Strasser 2012) or as a special kind of “convenience experimentation” in which many experiments are done simply because they are extraordinarily convenient to perform (Krohs 2012).

5.2 Computer methods and ‘new ways’ of doing science

The field of omics just described is possible because of the ability of computers to process, in a reasonable amount of time, the huge quantities of data required. Computers allow for more elaborate experimentation (higher speed, better filtering, more variables, sophisticated coordination and control), but also, through modelling and simulations, might constitute a form of experimentation themselves. Here, too, we can pose a version of the general question of method versus practice: does the practice of using computers fundamentally change scientific method, or merely provide a more efficient means of implementing standard methods?

Because computers can be used to automate measurements, quantifications, calculations, and statistical analyses where, for practical reasons, these operations cannot be otherwise carried out, many of the steps involved in reaching a conclusion on the basis of an experiment are now made inside a “black box”, without the direct involvement or awareness of a human. This has epistemological implications, regarding what we can know, and how we can know it. To have confidence in the results, computer methods are therefore subjected to tests of verification and validation.

The distinction between verification and validation is easiest to characterize in the case of computer simulations. In a typical computer simulation scenario computers are used to numerically integrate differential equations for which no analytic solution is available. The equations are part of the model the scientist uses to represent a phenomenon or system under investigation. Verifying a computer simulation means checking that the equations of the model are being correctly approximated. Validating a simulation means checking that the equations of the model are adequate for the inferences one wants to make on the basis of that model.

A number of issues related to computer simulations have been raised. The identification of validity and verification as the testing methods has been criticized. Oreskes et al. (1994) raise concerns that “validiation”, because it suggests deductive inference, might lead to over-confidence in the results of simulations. The distinction itself is probably too clean, since actual practice in the testing of simulations mixes and moves back and forth between the two (Weissart 1997; Parker 2008a; Winsberg 2010). Computer simulations do seem to have a non-inductive character, given that the principles by which they operate are built in by the programmers, and any results of the simulation follow from those in-built principles in such a way that those results could, in principle, be deduced from the program code and its inputs. The status of simulations as experiments has therefore been examined (Kaufmann and Smarr 1993; Humphreys 1995; Hughes 1999; Norton and Suppe 2001). This literature considers the epistemology of these experiments: what we can learn by simulation, and also the kinds of justifications which can be given in applying that knowledge to the “real” world. (Mayo 1996; Parker 2008b). As pointed out, part of the advantage of computer simulation derives from the fact that huge numbers of calculations can be carried out without requiring direct observation by the experimenter/​simulator. At the same time, many of these calculations are approximations to the calculations which would be performed first-hand in an ideal situation. Both factors introduce uncertainties into the inferences drawn from what is observed in the simulation.

For many of the reasons described above, computer simulations do not seem to belong clearly to either the experimental or theoretical domain. Rather, they seem to crucially involve aspects of both. This has led some authors, such as Fox Keller (2003: 200) to argue that we ought to consider computer simulation a “qualitatively different way of doing science”. The literature in general tends to follow Kaufmann and Smarr (1993) in referring to computer simulation as a “third way” for scientific methodology (theoretical reasoning and experimental practice are the first two ways.). It should also be noted that the debates around these issues have tended to focus on the form of computer simulation typical in the physical sciences, where models are based on dynamical equations. Other forms of simulation might not have the same problems, or have problems of their own (see the entry on computer simulations in science ).

In recent years, the rapid development of machine learning techniques has prompted some scholars to suggest that the scientific method has become “obsolete” (Anderson 2008, Carrol and Goodstein 2009). This has resulted in an intense debate on the relative merit of data-driven and hypothesis-driven research (for samples, see e.g. Mazzocchi 2015 or Succi and Coveney 2018). For a detailed treatment of this topic, we refer to the entry scientific research and big data .

6. Discourse on scientific method

Despite philosophical disagreements, the idea of the scientific method still figures prominently in contemporary discourse on many different topics, both within science and in society at large. Often, reference to scientific method is used in ways that convey either the legend of a single, universal method characteristic of all science, or grants to a particular method or set of methods privilege as a special ‘gold standard’, often with reference to particular philosophers to vindicate the claims. Discourse on scientific method also typically arises when there is a need to distinguish between science and other activities, or for justifying the special status conveyed to science. In these areas, the philosophical attempts at identifying a set of methods characteristic for scientific endeavors are closely related to the philosophy of science’s classical problem of demarcation (see the entry on science and pseudo-science ) and to the philosophical analysis of the social dimension of scientific knowledge and the role of science in democratic society.

One of the settings in which the legend of a single, universal scientific method has been particularly strong is science education (see, e.g., Bauer 1992; McComas 1996; Wivagg & Allchin 2002). [ 5 ] Often, ‘the scientific method’ is presented in textbooks and educational web pages as a fixed four or five step procedure starting from observations and description of a phenomenon and progressing over formulation of a hypothesis which explains the phenomenon, designing and conducting experiments to test the hypothesis, analyzing the results, and ending with drawing a conclusion. Such references to a universal scientific method can be found in educational material at all levels of science education (Blachowicz 2009), and numerous studies have shown that the idea of a general and universal scientific method often form part of both students’ and teachers’ conception of science (see, e.g., Aikenhead 1987; Osborne et al. 2003). In response, it has been argued that science education need to focus more on teaching about the nature of science, although views have differed on whether this is best done through student-led investigations, contemporary cases, or historical cases (Allchin, Andersen & Nielsen 2014)

Although occasionally phrased with reference to the H-D method, important historical roots of the legend in science education of a single, universal scientific method are the American philosopher and psychologist Dewey’s account of inquiry in How We Think (1910) and the British mathematician Karl Pearson’s account of science in Grammar of Science (1892). On Dewey’s account, inquiry is divided into the five steps of

(i) a felt difficulty, (ii) its location and definition, (iii) suggestion of a possible solution, (iv) development by reasoning of the bearing of the suggestions, (v) further observation and experiment leading to its acceptance or rejection. (Dewey 1910: 72)

Similarly, on Pearson’s account, scientific investigations start with measurement of data and observation of their correction and sequence from which scientific laws can be discovered with the aid of creative imagination. These laws have to be subject to criticism, and their final acceptance will have equal validity for “all normally constituted minds”. Both Dewey’s and Pearson’s accounts should be seen as generalized abstractions of inquiry and not restricted to the realm of science—although both Dewey and Pearson referred to their respective accounts as ‘the scientific method’.

Occasionally, scientists make sweeping statements about a simple and distinct scientific method, as exemplified by Feynman’s simplified version of a conjectures and refutations method presented, for example, in the last of his 1964 Cornell Messenger lectures. [ 6 ] However, just as often scientists have come to the same conclusion as recent philosophy of science that there is not any unique, easily described scientific method. For example, the physicist and Nobel Laureate Weinberg described in the paper “The Methods of Science … And Those By Which We Live” (1995) how

The fact that the standards of scientific success shift with time does not only make the philosophy of science difficult; it also raises problems for the public understanding of science. We do not have a fixed scientific method to rally around and defend. (1995: 8)

Interview studies with scientists on their conception of method shows that scientists often find it hard to figure out whether available evidence confirms their hypothesis, and that there are no direct translations between general ideas about method and specific strategies to guide how research is conducted (Schickore & Hangel 2019, Hangel & Schickore 2017)

Reference to the scientific method has also often been used to argue for the scientific nature or special status of a particular activity. Philosophical positions that argue for a simple and unique scientific method as a criterion of demarcation, such as Popperian falsification, have often attracted practitioners who felt that they had a need to defend their domain of practice. For example, references to conjectures and refutation as the scientific method are abundant in much of the literature on complementary and alternative medicine (CAM)—alongside the competing position that CAM, as an alternative to conventional biomedicine, needs to develop its own methodology different from that of science.

Also within mainstream science, reference to the scientific method is used in arguments regarding the internal hierarchy of disciplines and domains. A frequently seen argument is that research based on the H-D method is superior to research based on induction from observations because in deductive inferences the conclusion follows necessarily from the premises. (See, e.g., Parascandola 1998 for an analysis of how this argument has been made to downgrade epidemiology compared to the laboratory sciences.) Similarly, based on an examination of the practices of major funding institutions such as the National Institutes of Health (NIH), the National Science Foundation (NSF) and the Biomedical Sciences Research Practices (BBSRC) in the UK, O’Malley et al. (2009) have argued that funding agencies seem to have a tendency to adhere to the view that the primary activity of science is to test hypotheses, while descriptive and exploratory research is seen as merely preparatory activities that are valuable only insofar as they fuel hypothesis-driven research.

In some areas of science, scholarly publications are structured in a way that may convey the impression of a neat and linear process of inquiry from stating a question, devising the methods by which to answer it, collecting the data, to drawing a conclusion from the analysis of data. For example, the codified format of publications in most biomedical journals known as the IMRAD format (Introduction, Method, Results, Analysis, Discussion) is explicitly described by the journal editors as “not an arbitrary publication format but rather a direct reflection of the process of scientific discovery” (see the so-called “Vancouver Recommendations”, ICMJE 2013: 11). However, scientific publications do not in general reflect the process by which the reported scientific results were produced. For example, under the provocative title “Is the scientific paper a fraud?”, Medawar argued that scientific papers generally misrepresent how the results have been produced (Medawar 1963/1996). Similar views have been advanced by philosophers, historians and sociologists of science (Gilbert 1976; Holmes 1987; Knorr-Cetina 1981; Schickore 2008; Suppe 1998) who have argued that scientists’ experimental practices are messy and often do not follow any recognizable pattern. Publications of research results, they argue, are retrospective reconstructions of these activities that often do not preserve the temporal order or the logic of these activities, but are instead often constructed in order to screen off potential criticism (see Schickore 2008 for a review of this work).

Philosophical positions on the scientific method have also made it into the court room, especially in the US where judges have drawn on philosophy of science in deciding when to confer special status to scientific expert testimony. A key case is Daubert vs Merrell Dow Pharmaceuticals (92–102, 509 U.S. 579, 1993). In this case, the Supreme Court argued in its 1993 ruling that trial judges must ensure that expert testimony is reliable, and that in doing this the court must look at the expert’s methodology to determine whether the proffered evidence is actually scientific knowledge. Further, referring to works of Popper and Hempel the court stated that

ordinarily, a key question to be answered in determining whether a theory or technique is scientific knowledge … is whether it can be (and has been) tested. (Justice Blackmun, Daubert v. Merrell Dow Pharmaceuticals; see Other Internet Resources for a link to the opinion)

But as argued by Haack (2005a,b, 2010) and by Foster & Hubner (1999), by equating the question of whether a piece of testimony is reliable with the question whether it is scientific as indicated by a special methodology, the court was producing an inconsistent mixture of Popper’s and Hempel’s philosophies, and this has later led to considerable confusion in subsequent case rulings that drew on the Daubert case (see Haack 2010 for a detailed exposition).

The difficulties around identifying the methods of science are also reflected in the difficulties of identifying scientific misconduct in the form of improper application of the method or methods of science. One of the first and most influential attempts at defining misconduct in science was the US definition from 1989 that defined misconduct as

fabrication, falsification, plagiarism, or other practices that seriously deviate from those that are commonly accepted within the scientific community . (Code of Federal Regulations, part 50, subpart A., August 8, 1989, italics added)

However, the “other practices that seriously deviate” clause was heavily criticized because it could be used to suppress creative or novel science. For example, the National Academy of Science stated in their report Responsible Science (1992) that it

wishes to discourage the possibility that a misconduct complaint could be lodged against scientists based solely on their use of novel or unorthodox research methods. (NAS: 27)

This clause was therefore later removed from the definition. For an entry into the key philosophical literature on conduct in science, see Shamoo & Resnick (2009).

The question of the source of the success of science has been at the core of philosophy since the beginning of modern science. If viewed as a matter of epistemology more generally, scientific method is a part of the entire history of philosophy. Over that time, science and whatever methods its practitioners may employ have changed dramatically. Today, many philosophers have taken up the banners of pluralism or of practice to focus on what are, in effect, fine-grained and contextually limited examinations of scientific method. Others hope to shift perspectives in order to provide a renewed general account of what characterizes the activity we call science.

One such perspective has been offered recently by Hoyningen-Huene (2008, 2013), who argues from the history of philosophy of science that after three lengthy phases of characterizing science by its method, we are now in a phase where the belief in the existence of a positive scientific method has eroded and what has been left to characterize science is only its fallibility. First was a phase from Plato and Aristotle up until the 17 th century where the specificity of scientific knowledge was seen in its absolute certainty established by proof from evident axioms; next was a phase up to the mid-19 th century in which the means to establish the certainty of scientific knowledge had been generalized to include inductive procedures as well. In the third phase, which lasted until the last decades of the 20 th century, it was recognized that empirical knowledge was fallible, but it was still granted a special status due to its distinctive mode of production. But now in the fourth phase, according to Hoyningen-Huene, historical and philosophical studies have shown how “scientific methods with the characteristics as posited in the second and third phase do not exist” (2008: 168) and there is no longer any consensus among philosophers and historians of science about the nature of science. For Hoyningen-Huene, this is too negative a stance, and he therefore urges the question about the nature of science anew. His own answer to this question is that “scientific knowledge differs from other kinds of knowledge, especially everyday knowledge, primarily by being more systematic” (Hoyningen-Huene 2013: 14). Systematicity can have several different dimensions: among them are more systematic descriptions, explanations, predictions, defense of knowledge claims, epistemic connectedness, ideal of completeness, knowledge generation, representation of knowledge and critical discourse. Hence, what characterizes science is the greater care in excluding possible alternative explanations, the more detailed elaboration with respect to data on which predictions are based, the greater care in detecting and eliminating sources of error, the more articulate connections to other pieces of knowledge, etc. On this position, what characterizes science is not that the methods employed are unique to science, but that the methods are more carefully employed.

Another, similar approach has been offered by Haack (2003). She sets off, similar to Hoyningen-Huene, from a dissatisfaction with the recent clash between what she calls Old Deferentialism and New Cynicism. The Old Deferentialist position is that science progressed inductively by accumulating true theories confirmed by empirical evidence or deductively by testing conjectures against basic statements; while the New Cynics position is that science has no epistemic authority and no uniquely rational method and is merely just politics. Haack insists that contrary to the views of the New Cynics, there are objective epistemic standards, and there is something epistemologically special about science, even though the Old Deferentialists pictured this in a wrong way. Instead, she offers a new Critical Commonsensist account on which standards of good, strong, supportive evidence and well-conducted, honest, thorough and imaginative inquiry are not exclusive to the sciences, but the standards by which we judge all inquirers. In this sense, science does not differ in kind from other kinds of inquiry, but it may differ in the degree to which it requires broad and detailed background knowledge and a familiarity with a technical vocabulary that only specialists may possess.

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Scientific Method Steps in Psychology Research

Steps, Uses, and Key Terms

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Verywell / Theresa Chiechi

How do researchers investigate psychological phenomena? They utilize a process known as the scientific method to study different aspects of how people think and behave.

When conducting research, the scientific method steps to follow are:

• Observe what you want to investigate
• Ask a research question and make predictions
• Test the hypothesis and collect data
• Examine the results and draw conclusions
• Report and share the results 

This process not only allows scientists to investigate and understand different psychological phenomena but also provides researchers and others a way to share and discuss the results of their studies.

Generally, there are five main steps in the scientific method, although some may break down this process into six or seven steps. An additional step in the process can also include developing new research questions based on your findings.

What Is the Scientific Method?

What is the scientific method and how is it used in psychology?

The scientific method consists of five steps. It is essentially a step-by-step process that researchers can follow to determine if there is some type of relationship between two or more variables.

By knowing the steps of the scientific method, you can better understand the process researchers go through to arrive at conclusions about human behavior.

Scientific Method Steps

While research studies can vary, these are the basic steps that psychologists and scientists use when investigating human behavior.

The following are the scientific method steps:

Step 1. Make an Observation

Before a researcher can begin, they must choose a topic to study. Once an area of interest has been chosen, the researchers must then conduct a thorough review of the existing literature on the subject. This review will provide valuable information about what has already been learned about the topic and what questions remain to be answered.

A literature review might involve looking at a considerable amount of written material from both books and academic journals dating back decades.

The relevant information collected by the researcher will be presented in the introduction section of the final published study results. This background material will also help the researcher with the first major step in conducting a psychology study: formulating a hypothesis.

Step 2. Ask a Question

Once a researcher has observed something and gained some background information on the topic, the next step is to ask a question. The researcher will form a hypothesis, which is an educated guess about the relationship between two or more variables

For example, a researcher might ask a question about the relationship between sleep and academic performance: Do students who get more sleep perform better on tests at school?

In order to formulate a good hypothesis, it is important to think about different questions you might have about a particular topic.

You should also consider how you could investigate the causes. Falsifiability is an important part of any valid hypothesis. In other words, if a hypothesis was false, there needs to be a way for scientists to demonstrate that it is false.

Step 3. Test Your Hypothesis and Collect Data

Once you have a solid hypothesis, the next step of the scientific method is to put this hunch to the test by collecting data. The exact methods used to investigate a hypothesis depend on exactly what is being studied. There are two basic forms of research that a psychologist might utilize: descriptive research or experimental research.

Descriptive research is typically used when it would be difficult or even impossible to manipulate the variables in question. Examples of descriptive research include case studies, naturalistic observation , and correlation studies. Phone surveys that are often used by marketers are one example of descriptive research.

Correlational studies are quite common in psychology research. While they do not allow researchers to determine cause-and-effect, they do make it possible to spot relationships between different variables and to measure the strength of those relationships. 

Experimental research is used to explore cause-and-effect relationships between two or more variables. This type of research involves systematically manipulating an independent variable and then measuring the effect that it has on a defined dependent variable .

One of the major advantages of this method is that it allows researchers to actually determine if changes in one variable actually cause changes in another.

While psychology experiments are often quite complex, a simple experiment is fairly basic but does allow researchers to determine cause-and-effect relationships between variables. Most simple experiments use a control group (those who do not receive the treatment) and an experimental group (those who do receive the treatment).

Step 4. Examine the Results and Draw Conclusions

Once a researcher has designed the study and collected the data, it is time to examine this information and draw conclusions about what has been found.  Using statistics , researchers can summarize the data, analyze the results, and draw conclusions based on this evidence.

So how does a researcher decide what the results of a study mean? Not only can statistical analysis support (or refute) the researcher’s hypothesis; it can also be used to determine if the findings are statistically significant.

When results are said to be statistically significant, it means that it is unlikely that these results are due to chance.

Based on these observations, researchers must then determine what the results mean. In some cases, an experiment will support a hypothesis, but in other cases, it will fail to support the hypothesis.

So what happens if the results of a psychology experiment do not support the researcher's hypothesis? Does this mean that the study was worthless?

Just because the findings fail to support the hypothesis does not mean that the research is not useful or informative. In fact, such research plays an important role in helping scientists develop new questions and hypotheses to explore in the future.

After conclusions have been drawn, the next step is to share the results with the rest of the scientific community. This is an important part of the process because it contributes to the overall knowledge base and can help other scientists find new research avenues to explore.

Step 5. Report the Results

The final step in a psychology study is to report the findings. This is often done by writing up a description of the study and publishing the article in an academic or professional journal. The results of psychological studies can be seen in peer-reviewed journals such as  Psychological Bulletin , the  Journal of Social Psychology ,  Developmental Psychology , and many others.

The structure of a journal article follows a specified format that has been outlined by the  American Psychological Association (APA) . In these articles, researchers:

• Provide a brief history and background on previous research
• Present their hypothesis
• Identify who participated in the study and how they were selected
• Provide operational definitions for each variable
• Describe the measures and procedures that were used to collect data
• Explain how the information collected was analyzed
• Discuss what the results mean

Why is such a detailed record of a psychological study so important? By clearly explaining the steps and procedures used throughout the study, other researchers can then replicate the results. The editorial process employed by academic and professional journals ensures that each article that is submitted undergoes a thorough peer review, which helps ensure that the study is scientifically sound.

Once published, the study becomes another piece of the existing puzzle of our knowledge base on that topic.

Before you begin exploring the scientific method steps, here's a review of some key terms and definitions that you should be familiar with:

• Falsifiable : The variables can be measured so that if a hypothesis is false, it can be proven false
• Hypothesis : An educated guess about the possible relationship between two or more variables
• Variable : A factor or element that can change in observable and measurable ways
• Operational definition : A full description of exactly how variables are defined, how they will be manipulated, and how they will be measured

Uses for the Scientific Method

The  goals of psychological studies  are to describe, explain, predict and perhaps influence mental processes or behaviors. In order to do this, psychologists utilize the scientific method to conduct psychological research. The scientific method is a set of principles and procedures that are used by researchers to develop questions, collect data, and reach conclusions.

Goals of Scientific Research in Psychology

Researchers seek not only to describe behaviors and explain why these behaviors occur; they also strive to create research that can be used to predict and even change human behavior.

Psychologists and other social scientists regularly propose explanations for human behavior. On a more informal level, people make judgments about the intentions, motivations , and actions of others on a daily basis.

While the everyday judgments we make about human behavior are subjective and anecdotal, researchers use the scientific method to study psychology in an objective and systematic way. The results of these studies are often reported in popular media, which leads many to wonder just how or why researchers arrived at the conclusions they did.

Examples of the Scientific Method

Now that you're familiar with the scientific method steps, it's useful to see how each step could work with a real-life example.

Say, for instance, that researchers set out to discover what the relationship is between psychotherapy and anxiety .

• Step 1. Make an observation : The researchers choose to focus their study on adults ages 25 to 40 with generalized anxiety disorder.
• Step 2. Ask a question : The question they want to answer in their study is: Do weekly psychotherapy sessions reduce symptoms in adults ages 25 to 40 with generalized anxiety disorder?
• Step 3. Test your hypothesis : Researchers collect data on participants' anxiety symptoms . They work with therapists to create a consistent program that all participants undergo. Group 1 may attend therapy once per week, whereas group 2 does not attend therapy.
• Step 4. Examine the results : Participants record their symptoms and any changes over a period of three months. After this period, people in group 1 report significant improvements in their anxiety symptoms, whereas those in group 2 report no significant changes.
• Step 5. Report the results : Researchers write a report that includes their hypothesis, information on participants, variables, procedure, and conclusions drawn from the study. In this case, they say that "Weekly therapy sessions are shown to reduce anxiety symptoms in adults ages 25 to 40."

Of course, there are many details that go into planning and executing a study such as this. But this general outline gives you an idea of how an idea is formulated and tested, and how researchers arrive at results using the scientific method.

Erol A. How to conduct scientific research ? Noro Psikiyatr Ars . 2017;54(2):97-98. doi:10.5152/npa.2017.0120102

University of Minnesota. Psychologists use the scientific method to guide their research .

Shaughnessy, JJ, Zechmeister, EB, & Zechmeister, JS. Research Methods In Psychology . New York: McGraw Hill Education; 2015.

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

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An empirical analysis of the relationship between nature of science and critical thinking through science definitions and thinking skills

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Critical thinking (CRT) skills transversally pervade education and nature of science (NOS) knowledge is a key component of science literacy. Some science education researchers advocate that CRT skills and NOS knowledge have a mutual impact and relationship. However, few research studies have undertaken the empirical confirmation of this relationship and most fail to match the two terms of the relationship adequately. This paper aims to test the relationship by applying correlation, regression and ANOVA procedures to the students’ answers to two tests that measure thinking skills and science definitions. The results partly confirm the hypothesised relationship, which displays some complex features: on the one hand, the relationship is positive and significant for the NOS variables that express adequate ideas about science. However, it is non-significant when the NOS variables depict misinformed ideas about science. Furthermore, the comparison of the two student cohorts reveals that two years of science instruction do not seem to contribute to advancing students’ NOS conceptions. Finally, some interpretations and consequences of these results for scientific literacy, teaching NOS (paying attention both to informed and misinformed ideas), for connecting NOS with general epistemic knowledge, and assessing CRT skills are discussed.

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Philosophical Inquiry and Critical Thinking in Primary and Secondary Science Education

Scientific Thinking and Critical Thinking in Science Education

Antonio García-Carmona

Using Socioscientific Issues to Promote the Critical Thinking Skills of Year 10 Science Students in Diverse School Contexts

Avoid common mistakes on your manuscript.

Introduction

Among other objectives, school science education perennially aims to improve scientific literacy for all, which involves being useful and functional for making adequate and sound personal and social daily life decisions. An essential component of scientific literacy is the knowledge “about” science, that is, knowledge about how science works and validates its knowledge and intervenes in the world (along with technology). This study focuses on the knowledge about science, which is often referred to in the literature as nature of science (NOS), scientific practice, ideas about science, etc., in turn, related to a continuous innovative teaching tradition (Vesterinen et al., 2014 ; Khishfe, 2012 ; Lederman, 2007 ; Matthews, 2012 ; McComas, 1996 ; Olson, 2018 ; among others).

On the other hand, some international reports and experts state that critical thinking (CRT) skills are key and transversal competencies for all educational levels, subjects and jobs in the 21st century. For instance, the European Union ( 2014 ) proposes seven key competencies that require developing a set of transversal skills, namely CRT, creativity, initiative, problem-solving, risk assessment, decision-making, communication and constructive management of emotions. In the same vein, the National Research Council ( 2012 ) proposes the transferable knowledge and skills for life and work, which explicitly details the following skills: argumentation, problem-solving, decision-making, analysis, interpretation, creativity, and others. In short, these and many other proposals converge in pointing out that teaching students to think and educating in CRT skills is an innovative and significant challenge for 21st century education and, of course, for science education. The CRT construct has been widely developed within psychological research. Yet, the field is complex, and terminologically bewildering (i.e., higher-order skills, cognitive skills, thinking skills, CRT, and other terms are used interchangeably), and some controversies are still unresolved. For instance, scholars do not agree on a common definition of CRT, and the most appropriate set of skills and dispositions to depict CRT is also disputed. As the differences among scholars still persist, the term CRT will be adopted hereafter to generally describe the variety of higher-order thinking skills that are usually associated in the CRT literature.

Further, some science education research currently suggests connections between NOS and CRT, arguing that CRT skills and NOS knowledge are related. Some claim that thinking skills are key to learning NOS (Erduran & Kaya, 2018 ; Ford & Yore, 2014 ; García-Mila & Andersen, 2008 ; Simonneaux, 2014 ), and specifically, that argumentation skills may enhance NOS understanding (Khishfe et al., 2017 ). In contrast, as argumentation skills are a key competence for the construction and validation of scientific knowledge, other studies claim that NOS knowledge (i.e., understanding the differences between data and claims) is also key to learning CRT skills such as argumentation (Allchin & Zemplén, 2020 ; Greene et al., 2016 ; Settlage & Southerland, 2020 ). Both directions of this intuitive relationship between CRT skills and NOS are fruitful ways to enhance scientific literacy and general learning. Hence, this study aims to empirically explore the NOS-CRT relationship, as the prior literature is somewhat mystifying and its contributions are limited, as will be shown below.

Theoretical contextualization

This study copes with two different, vast and rich realms of research, namely NOS and CRT, and their theoretical frameworks: the interdisciplinary context of philosophy, sociology, and history of science and science education for NOS; and psychology and general education for CRT skills. Both frameworks are summarized below to meet the journal space limitations.

Under the NOS label, science education has developed a fertile and vast realm of “knowledge about scientific knowledge and knowing”, which is obviously a particular case of human thinking, and probably the most developed to date. NOS represents the meta-cognitive, multifaceted and dynamic knowledge about what science is and how science works as a social way of knowing and explaining the natural world (knowledge construction and validation). This knowledge has been interdisciplinarily elaborated from history, philosophy, sociology of science and technology, and other disciplines. Scholars raised many and varied NOS issues (Matthews, 2012 ), which are relevant to scientific research and widely surpass the reduced consensus view (Lederman, 2007 ). Despite NOS complexity, it has been systematized across two broad dimensions: epistemological and social (Erduran & Dagher, 2014 ; Manassero-Mass & Vazquez-Alonso, 2019 ). The epistemological dimension refers to the principles and values underlying knowledge construction and validation, which are often described as the scientific method, empirical basis, observation, data and inference, tentativeness, theory and law, creativity, subjectivity, demarcation, and many others. The social dimension refers to the social construction of scientific knowledge and its social impact. It often deals with the scientific community and institutions, social influences, and general science-technology-society interactions (peer evaluation, communication, gender, innovation, development, funding, technology, psychology, etc.).

From its beginning, NOS research agrees that students (and teachers) hold inadequate and misinformed beliefs on NOS issues across different educational levels and contexts. Further, researchers agree that effective NOS teaching requires explicit and reflective methods to overcome the many learning barriers (Bennássarr et al., 2010 ; García et al., 2011 ; Cofré et al., 2019 ; Deng et al., 2011 ). These barriers relate to the basic processes of gathering (observation) and elaborating (analysis) data, decision-making in science, and specifically, the inability to differentiate facts and explanations and adequately coordinate evidence, justifications, arguments and conclusions; the lack of elementary meta-cognitive and self-regulation skills (i.e., the quick jump to conclusions as self-evident); the introduction of personal opinions, inferences, and reinterpretations and the dismissal of the counter-arguments or evidence that may contradict personal ideas (García-Mila & Andersen, 2008 ; McDonald & McRobbie, 2012 ).

As these barriers point directly to the general abilities involved in thinking (observation, analysis, answering questions, solving problems, decision-making and the like), researchers attribute those difficulties to the lack of the cognitive skills involved in the adequate management of the barriers, whose higher-order cognitive nature corresponds to many CRT skills (Kolstø, 2001 ; Zeidler et al., 2002 ). Thus, the solutions to overcome the barriers imply mastering the CRT skills, and, consequently, achieving successful NOS learning (Ford & Yore, 2014 ; McDonald & McRobbie, 2012 ; Simonneaux, 2014 ). Erduran and Kaya ( 2018 ) argue that the perennial aim of developing students’ and teachers’ NOS epistemic insights still remains a challenge for science education, despite decades of NOS research, due to the many aspects involved. They conclude that NOS knowledge critically demands higher-order cognitive skills. The paragraphs below elaborate on these higher-order cognitive skills or CRT skills.

Critical thinking

As previously stated, the CRT field shows many differences in scholarly knowledge on the conceptualization and composition of CRT. Ennis’ ( 1996 ) simple definition of CRT as reasonable reflective thinking focused on deciding what to believe or do is likely the most celebrated definition among many others. A Delphi panel of experts defined CRT as an intentional and self-regulated judgment, which results in interpretation, analysis, evaluation and inference, as well as the explanation of the evidentiary, conceptual, methodological, criterial or contextual considerations on which that judgment is based (American Psychological Association 1990 ).

However, the varied set of skills associated with CRT is controversial (Fisher, 2009 ). For instance, Ennis ( 2019 ) developed an extensive conception of CRT through a broad set of dispositions and abilities. Similarly, Madison ( 2004 ) proposed an extensive and comprehensive list of skills (Table 1 ).

The development of CRT tests has contributed to clarifying the relevance of the many CRT skills, as the test’s functionality requires concentrating on a few skills. For instance, Halpern’s ( 2010 ) questionnaire assesses, through everyday situations, problem-solving, verbal reasoning, probability and uncertainty, hypothesis-testing, argument analysis and decision-making. Watson and Glaser’s ( 2002 ) instrument assesses deduction, recognition of assumptions, interpretation, inference, and evaluation of arguments. The California Critical Thinking Skills Test assesses analysis, evaluation, inference, deduction and induction (Facione et al., 1998 ). It is also worth mentioning that most CRT tests target adults, although the Cornell Critical Thinking Tests (Ennis & Millman, 2005 ) were developed for a variety of young people and address several CRT skills (X test, induction, deduction, credibility, and identification of assumptions; Class Test, classical logical reasoning from premises to conclusion, etc.). The large number of CRT skills led scholars to perform efforts of synthesis and refinement that are summarized through some exemplary proposals (Table 1 ).

The CRT psychological framework presented above places the complex set of skills within the high-level cognitive constructs whose practice involves a self-directed, self-disciplined, self-supervised, and self-corrective way of thinking that presupposes conscious mastery of skills and conformity with rigorous quality standards. In addition to skills, CRT also involves effective communication and attitudinal commitment to intellectual standards to overcome the natural tendencies to fallacy and bias (self-centeredness and socio-centrism).

Science education and thinking skills

CRT skills mirror the scientific reasoning skills of scientific practice, and vice versa, based on their similar contents. This intuitive resemblance may launch expectations of their mutual relationship. Science education research has increased attention to CRT skills as promotors of meaningful learning, especially when involving NOS and understanding of socio-scientific issues (Vieira et al., 2011 ; Torres & Solbes, 2016 ; Vázquez-Alonso & Manassero-Mas, 2018 ; Yacoubian & Khishfe, 2018 , among others). Furthermore, Yacoubian ( 2015 ) elaborated several reasons to consider CRT a fundamental pillar for NOS learning.

Some authors stress the convergence between science and CRT based on the word critical , as thinking and science are both critical. Critical approaches have always been considered consubstantial to science (and likely a key factor of its success), as their range spreads from specific critical social issues (i.e., scientific controversies, social acceptance of scientific knowledge, social coping with a virus pandemic) to the socially organized scepticism of science (i.e., peer evaluation, scientific communication). The latter is considered a universal value of scientific practice to guarantee the validity of knowledge (Merton, 1968 ; Osborne, 2014 ). In the context of CRT research, the term critical involves normative ways to ensure the quality of good thinking, such as open-minded abilities and a disposition for relentless scrutiny of ideas, criteria for evaluating the goodness of thinking, adherence to the norms, standards of excellence, and avoidance of errors and fallacies (traits of poor thinking). These obviously also apply to scientific knowledge through peer evaluation practice, which represents a superlative form of good normative thinking (Bailin, 2002 ; Paul & Elder, 2008 ).

Another important feature of the convergence of CRT and science is the broad set of common skills sharing the same semantic content in both fields, despite that their names may seem different. Induction, deduction, abduction, and, in general, all kinds of argumentation skills, as well as problem-solving and decision-making, exemplify key tools of scientific practice to validate and defend ideas and develop controversies, discussions, and debates. Concurrently, they, too, are CRT skills (Sprod, 2014 ; Vieira et al., 2011 ; Yacoubian & Kishfe, 2018 ). In addition, Santos’ ( 2017 ) review suggests the following tentative list of skills: observation, exploration, research, problem-solving, decision-making, information-gathering, critical questions, reliable knowledge-building, evaluation, rigorous checks, acceptance and rejection of hypotheses, clarification of meanings, and true conclusions. Beyond skill names and focusing on their semantic content, (Manassero-Mas & Vázquez-Alonso, 2020a ) developed a deeper analysis of the skills usually attributed to scientific thinking and critical thinking, concluding that their constituent skills are deeply intertwined and much more coincident than different. This suggests that scientific and critical thinking may be considered equivalent concepts across the many shared skills they put into practice. However, equivalence does not mean identity, as important differences may still exist. For instance, the evaluation and judgment of ideas involved in organized scientific skepticism (i.e., peer evaluation) are much more demanding and deeper in scientific practice than in daily life thinking realms.

In sum, research on the CRT and NOS constructs is plural, as they draw from two different fields and traditions, general education and cognitive psychology, and science education, respectively. However, CRT and NOS share many skills, processes, and thinking strategies, as they both pursue the same general goal, namely, to establish the true value of knowledge claims. These shared features provide further reasons to investigate the possible relationships between NOS and CRT skills.

Research involving nature of science and thinking skills

The research involving both constructs is heterogeneous, as the operationalisations and methods are quite varied, given the pluralized nature of NOS and thinking. For example, Yang and Tsai ( 2012 ) reviewed 37 empirical studies on the relationship between personal epistemologies and science learning, concluding that research was heterogeneous along different NOS orientations: applications of Kuhn’s ( 2012 ) evolutionary epistemic categories, use of general epistemic knowledge categories, studies on epistemological beliefs about science (empiricism, tentativeness, etc.), and applications of other epistemic frameworks. The studies dealing with the epistemological beliefs about science were a minority. Another example of heterogeneity comes from Koray and Köksal’s ( 2009 ) study about the effect of laboratory instruction versus traditional teaching on creativity and logical thinking in prospective primary school teachers, where the laboratory group showed a significant effect in comparison to the traditional group. However, the NOS contents involved in laboratory instruction are still unclear. Dowd et al. ( 2018 ) examined the relationship between written scientific reasoning and eight specific CRT skills, finding that only three aspects of reasoning were significantly related to one skill (inference) and negatively to argument.

A series of studies suggest implicit relationships between NOS and thinking skills. Yang and Tsai ( 2010 ) interviewed sixth-graders to examine two uncertain science-related issues, finding that children who developed more complex (multiplistic) NOS knowledge displayed better reflective thinking and coordination of theory and evidence. Dogan et al. ( 2020 ) compared the impact of two epistemic-based methodologies (problem-based and history of science) on the creativity skills of prospective primary school teachers, finding that the problem-solving approach was more effective in increasing students’ creative thinking. Khishfe ( 2012 ) and Khishfe et al. ( 2017 ) found no differences in decision-making and argumentation in socio-scientific issues regarding NOS knowledge, but more participants in the treatment groups referred their post-decision-making factors to NOS than the other groups. Other studies found relationships between NOS understanding and variables that do not match CRT skills precisely. For instance, Bogdan ( 2020 ) found that inference and tentativeness relate to attitudes toward the role of science in social progress, but creativity does not, and the same applies to the acceptance of the evolution theory (Cofré et al., 2017 ; Sinatra et al., 2003 ).

Another set of studies comes from science education research on argumentation, which is based on the rationale that argumentation is a key scientific skill for validating knowledge in scientific practice. Thus, reasoning skills should be related to NOS understanding. Students who viewed science as dynamic and changeable were likely to develop more complex arguments (Stathopoulou & Vosnidou, 2007 ). In a floatation experience, Zeineddin and Abd-El-Khalick ( 2010 ) found that the stronger the epistemic commitments, the greater the quality of the scientific reasoning produced by the individuals. Accordingly, the term epistemic cognition of scientific argumentation has been coined, although specific research on argumentation and epistemic cognition is still relatively scarce (He et al., 2020 ).

Weinstock’s ( 2006 ) review suggested that people’s argumentation skills develop in proportion to their epistemic development, which Noroozi ( 2016 ) also confirmed. Further, Mason and Scirica ( 2006 ) studied the contribution of general epistemological comprehension to argumentation skills in two readings, finding that participants at the highest level of epistemic comprehension (evaluative) generated better quality arguments than participants at the previous multiplistic stage (Kuhn, 2012 ). In addition, the review of Rapanta et al. ( 2013 ) on argumentative competence proposed a three-dimensional hierarchical framework, where the highest level is epistemological (the ability to evaluate the relevance, sufficiency, and acceptability of arguments). Again, Henderson et al. ( 2018 ) discussed the key challenges of argumentation research and pointed to students’ shifting epistemologies about what might count as a claim or evidence or what might make an argument persuasive or convincing, as well as developing valid and reliable assessments of argumentation. On the contrary, Yang et al. ( 2019 ) found no significant associations between general epistemic knowledge and the performance of scientific reasoning in a controversial case with undergraduates.

From science education, González‐Howard and McNeill ( 2020 ) analysed middle-school classroom interactions in critique argumentation when an epistemic agency is incorporated, indicating that the development of students’ epistemic agency shows multiple and conflating approaches to address the tensions inherent to critiquing practices and to fostering equitable learning environments. This idea is further developed in the special section on epistemic tools of Science Education (2020), which highlights the continual need to accommodate and adapt the epistemic tools and agencies of scientific practices within classrooms while taking into account teaching, engineering, sustainability, equity and justice (González‐Howard & McNeill, 2020 ; Settlage & Southerland, 2020 ).

Finally, some of the above-mentioned research used a noteworthy concept of epistemic knowledge (EK) as “knowledge about knowledge and knowing” (Hofer & Pintrich, 1997 ), which has been developed in mainstream general education research and involves some meta-cognitions about human knowledge that research has largely connected to general learning and CRT skills (Greene et al., 2016 ). Obviously, EK and NOS knowledge share many common aspects (epistemic), suggesting a considerable overlap between them. However, it is noteworthy that NOS research is oriented toward CRT skills impacting NOS learning, while EK research orientates toward EK impacting CRT skills and general learning.

Regarding the Likert formats for research tools, test makers are concerned about the control of response biases that cause a lack of true reflection on the statement content and may damage the fidelity of data and correlations. Respondents’ tendency to agree with statements (acquiescence bias) is widespread. Further, neutrality bias and polarity bias reflect respondents’ propensity to choose fixed score points of the scale, either the midpoints (neutrality) or the extreme scores (polarity), either extreme high scores (positive bias) or extreme low scores (negative bias). To mitigate biases, experts recommend avoiding the exclusive use of positively worded statements within the instruments and combining positive and reversed items. This recommendation has been implemented here using three categories for NOS phrases that operationalize positive, intermediate and reversed statements (Vázquezr et al., 2006 ; Kreitchmann et al., 2019 ; Suárez-Alvarez et al., 2018 ; Vergara & Balluerka, 2000 ). However, the use of varied styles for phrases harms the instrument’s reliability and validity, and reliability is underestimated (Suárez-Alvarez et al., 2018 ).

All in all, the theoretical framework is twofold: CRT and NOS research. The above-mentioned research shares the hypothesis that the relationship between NOS and CRT skills matters. However, it displays a broad heterogeneity of research methods, variables, instruments and mixed results on the NOS-CRT relationship that do not allow a common methodological standpoint. Further, mainstream research focuses on college students and argumentation skills. In this regard, this study aims to empirically research the NOS-CRT relationship by applying standardized assessment tools for both constructs. This promotes comparability among researchers and provides quick diagnostic tools for teachers. Secondly, this study addresses younger students, which involves the creation of NOS and CRT tools adapted to young participants, for which some test validity and reliability data are provided. The research questions within this framework are: Do NOS knowledge and CRT skills correlate? What are the traits and limits conditions of this relationship, if any?

Materials and methods

The data gathering took place in Spain in the year 2018. At this time, the enacted school curriculum missed the international standards and specific curriculum proposals about CRT and NOS issues, so NOS issues could be implicitly related to some curricular contents about scientific research. Despite this lack of curricular emphasis, the principals of the participant Spanish schools expressed interest in diagnosing students’ thinking skills and NOS knowledge and agreed with the authors on the specific CRT and NOS-skills to be tested. As the Spanish school curriculum does not emphasize CRT and NOS issues, the students are expected to be equally trained, and this context conditioned the design of tentative tests through simple contents and an open-ended format, as they are cheap and easy to administer and interpret.

Participants

The participant schools (17) included some public (4) and state-funded private schools (13) that spread across mixed socio-cultural contexts and large, medium, and small Spanish townships. The participant students were tested in their natural school classes (29) of the two target grades. The valid convenience samples are two cohorts of students, each representing students of 6 th grade of Primary Education (PE6) ( n  = 434; 54.8% girls and 45.2% boys; mean age 11.3 years) and 8th grade of Secondary Compulsory Education (SCE8) ( n  = 347; 48.5% girls and 51.5% boys; mean age 13.3 years). In Spain, 6 th grade is the last year of the primary stage (11–12-year-old students), and the 8 th grade is the second year of the lower secondary compulsory stage (13–14-year-old students).

Instruments

Two assessment tools were tailored by researchers (a CRT skill test and a NOS scenario) to operationalise CRT and NOS to empirically check their relationships. As the Spanish school curriculum lacks CRT standards, the specific thinking skills that represent the CRT construct were agreed upon between principals and researchers. The design of the tool to assess NOS knowledge took into account that NOS was not explicitly taught in Spanish schools. Both tools were designed to match the schools’ interests and the students’ developmental level; the latter particularly led to choosing a simple NOS issue (definition of science) to match the primary students’ capabilities better.

Thinking challenge tests

Two CRT thinking skill test were developed for the two participant cohorts (PE6 and SCE8). The design aligns with the tradition of most CRT standardised tests that concentrate assessment on a few selected thinking skills (i.e., Ennis & Millman, 2005 ; Halpern, 2010 ). The test for the 6th-graders (PE6) assesses five skills: prediction, comparison and contrast, classification, problem-solving and logical reasoning. The test for the 8th-graders (SCE8) assesses causal explanation, decision-making, parts-all relationships, sequence and logical reasoning.

As most CRT tests are designed for adults, many tests and item pools were reviewed to select suitable items for younger students. The selection criteria were the fit of the items’ cognitive demand with students’ age, the addressed skill and the motivational challenge for students. Moreover, items must be readable, understandable, adequate, and interesting for the participant students. Then, two 45-item and 38-item tests were agreed on and piloted. Their results are described elsewhere (Manassero-Mas & Vázquez-Alonso, 2020b ). The items were examined by the authors according to their reliability, correlation and factor analysis to eliminate unfair items. Again, the former criteria were used to add new items to conform the two new 35-item Thinking Challenge Tests (TCT) to assess the CRT skills of this study.

The items of the first two skills were drawn from the Cornell (Nicoma) test, which evaluates four CRT skills through the information provided by a fictional story about some explorers of the Nicoma planet and asks questions about the story. Some items from prediction and comparison skills were drawn for the 6th-grade TCT (PE6), and some items from causal explanation and decision-making skills were drawn for the 8th-grade TCT (SCE8). The two TCT include three additional items on logical reasoning that were selected from the 78-item Class-Reasoning Cornell Test (Ennis & Millman, 2005 ). One item was also drawn from the 25-situation Halpern CRT test (Halpern, 2010 ) for the problem-solving skill of the PE6 test. The authors adapted the remaining figurative items (Table 2 ) to enhance students’ challenge, understanding, and motivation and make the TCT free of school knowledge (Appendix).

Overall, the TCT items pose authentic culture-free challenges, as their contents and cognitive demands are not related to or anchored in any prior school curricular knowledge, especially language and mathematics. Therefore, the TCT are intended to assess culture-free thinking skills.

The item formats involve multiple-choice and Likert scales with appropriate ranges and rubrics that facilitate quick and objective scoring and the elaboration of increasing adjustment between items’ cognitive demand and their corresponding skill, thereby leading to further revision based on validity and reliability improvement. This format also allows setting standardised baselines for hypothesis-testing through comparisons of research, educational programs, and teaching methodologies.

Nature of science assessment

A scenario on science definitions is used to assess the participants’ NOS understanding because this simple issue may better fit the lack of explicit NOS teaching and the developmental stage of the young students, especially the youngest 6th-graders. The scenario provides nine phrases that convey an epistemic, plural and varied range of science definitions, and respondents rate their agreement-disagreement with the phrases on a 9-point Likert scale (1 =  strongly disagree , 9 =  strongly agree ) to allow better nuancing of their NOS beliefs and avoid psychometric objections to the scale intervals. The scenario is drawn from the “Views on Science-Technology-Society” (VOSTS) pool that Aikenhead and Ryan ( 1992 ) developed empirically by synthesizing many students’ interviews and open answers into some scenarios, written in simple, understandable, and non-technical language. They consider that VOSTS items have intrinsic validity due to their empirical development, as the scenario phrases come from students, not from researchers or a particular philosophy, thus avoiding the immaculate perception bias and ensuring students’ understanding. Lederman et al. ( 1998 ) also consider VOSTS a valid and reliable tool for investigating NOS conceptions. Manassero et al. ( 2003 ) adapted the scenarios into the Spanish language and contexts, and developed a multiple-rating assessment rubric, based on the phrase scaling achieved through expert judges’ consensus. The rubric assigns indices whose empirical reliability has been presented elsewhere (Vázquezr et al., 2006 ; Bennássar et al., 2010 ).

The students completed the two tests through digital devices led by their teachers within their natural school classroom groups during 2018–19. To enhance students’ effort and motivation, the applications were infused into curricular learning activities, where students were encouraged to ask about problems and difficulties. During applications students did not ask questions to teacher that may reflect some difficulty to understand the tests. The database was processed with SPSS 25 and Factor program (Baglin, 2014 ) for exploratory and confirmatory factor analysis through polychoric correlations and Robust Unweighted Least Squares (RULS) method that lessen conditions on the score distribution of variables. Effect size statistics use a cut-off point ( d  = 0.30) to discriminate relevant differences.

There was no time limit for students to complete the tests, and the applications took between 25 and 50 min. Correct answers score one point, incorrect answers zero points, and no random corrections were applied. The skill scores were computed by adding the scores of the items that belong to each skill, which are independent. The addition of the five skill scores makes up a test score (thinking total) that estimates students’ global CRT competence and is dependent on the skill scores (Table 2 ).

The different types of validity maintain a reciprocal influence and represent the various parts of a whole, so they are not mutually independent. The Thinking Challenge tests’ validity relies on the quality of the CRT pools and tests examined by the authors, their agreement to choose the items that best matched the criteria, and the reviewed pilot results (Manassero-Mas & Vázquez-Alonso, 2020b ). The Factor program computes several reliability statistics (Cronbach alpha, EAP, Omega, etc.).

Nature of science scenario

The nine phrases describe different science definitions, and students rated each one on a 1–9 agreement scale. According to the experts’ current views on NOS, a panel of qualified judges reached a 2/3-consensus to categorize each phrase within a 3-level scheme (Adequate, Plausible, Naive), which has been widely used in NOS assessment (Khishfe, 2012 ; Liang et al., 2008 ; Rubba et al., 1996 ). The scheme means the phrases express informed (Adequate), partially informed (Plausible), or uninformed (Naive) NOS knowledge (see Appendix). According to this scheme, an evaluation rubric transforms the students’ direct ratings (1–9) into an index [− 1 to + 1], which is proportionally higher when the person agrees with an Adequate phrase, partially agrees with a Plausible phrase, or disagrees with a Naive phrase. All the rubric indices balance positive and negative scores, which are symmetrical for Adequate and Naïve phrases, but plausible indices are somewhat loaded toward agreement, as higher agreement would be expected. The index unifies the NOS measurements to make them homogeneous (positive indices mean informed conceptions), invariant (measurement independent of scenario/phrase/category), and standardised (all measures within the same interval [− 1, + 1]). The index proportionally values the adjustment of students’ NOS knowledge to the current views of science: the higher (or lower) the index, the better (or worse) informed is their NOS knowledge (Vázquezr et al., 2006 ).

Three category variables (Adequate, Plausible, and Naïve) are computed by averaging their phrase indices, which are mutually independent. The average of the three category variables computes a global NOS index representing the student’s overall NOS knowledge (Global). The use of three categories aligns with test makers’ recommendations to avoid using only positively worded phrases in order to elude the acquiescence bias, which harms reliability and validity (Suárez-Alvarez et al., 2018 ).

The links between thinking skills and NOS are empirically explored through correlational methods and one-way ANOVA procedures of the variables of the Thinking Challenge test and science definitions.

The results include the descriptive statistics of the target variables, twelve thinking variables (five skills plus thinking total for each group) and four variables of the science definitions (adequate, plausible, naive, and global), the analysis of the correlations, a linear regression analysis among these variables, and a comparison of thinking skills between NOS categorical groups through a one-way ANOVA.

Descriptive statistics

Most mean thinking variables scores fell near the midpoint of the scale range. Four skills (classification, problem-solving, causal explanation and sequence) scored above the midpoints of their ranges, whereas two variables (logical reasoning and decision) scored slightly below their midpoints. Overall, these results indicate the medium difficulty of the tests for the students, neither easy nor difficult, which means the CRT tests can be acceptable to assess young students’ thinking skills (Table 3 ).

The EAP reliability indices of classification, problem-solving, sequence, parts (mainly figurative items) and thinking scales were excellent, good for the remaining scales, but poor for logical reasoning. Low reliability indicates a need for item revision and limited applicability (i.e., inappropriate for individual diagnosis), but is insufficient to reject the test in research purposes (U.S. Department of Labor, 1999 ). As test reliability critically depends on the number of items, increasing the length of logical reasoning over its three current items will improve its reliability.

The descriptive results for the direct scores of the NOS variables (Table 4 ) showed a biased pattern toward agreement (average phrases between 4.9 and 7.4), which suggests some acquiescence bias in spite of presenting varied phrases. The average indices obtained positive scores for the adequate category, slightly negative ones for the naïve category, and close-to-zero for the plausible phrases (the effect size of the differences concerning a zero score was low). The overall weighted average index for the whole sample (global variable) was close-to-zero and slightly positive, meaning that the students’ overall epistemic conception of science definition was not significantly informed. The overall average index of Adequate phrases obtained the highest positive score for both samples of students, which means that most students agreed with the Adequate phrases (expressing informed beliefs about science). In contrast, the Naïve overall average index obtained the lowest negative mean score, indicating that the students agreed instead of disagreeing with phrases expressing uninformed views about science. The Plausible variable (phrases expressing partially informed beliefs, neither adequate nor naive) obtained a close-to-zero average score, meaning that the students’ beliefs about these variables were far from informed. Overall, the students presented slightly informed views on Adequate phrases, close-to-zero average indices scores (not informed views) for Plausible phrases and slightly uninformed views on Naive statements.

Polychoric correlations among NOS direct scores computed through Factor attained good scores on all NOS items, indicating a unidimensional structure (but Phrase I). The exploratory factor analysis (EFA) applied to phrase scores displayed a dominant eigenvalue, whose general factor had acceptable loadings for all phrases (only phrase I had low loading). The unidimensional model obtained fair statistics in the confirmatory factor analysis. These results suggest one general factor underlying students’ scores and justify a global score representing the variance of all the NOS phrases. The expected a posteriori (EAP) reliability scores for the entire NOS scale were good (Table 4 ).

The comparison of NOS scores between primary and secondary grades highlights that the four NOS variable scores on science definitions were significantly equal for both cohorts of students, despite the two years separation. So, the educational impact of the two-year period on NOS seems almost null, given the close-to-zero differences in science definitions. This result could be expected, as NOS is not explicitly planned in Spanish science curricula and is not usually taught in the classroom.

Both cohorts answered the same anchoring CRT item (see Appendix), whose correct answer rate (27% primary; 33% secondary) suggests a slight improvement in CRT skills that sharply contrasts with the former NOS comparison. Summing up, despite that CRT and NOS have not been taught to Spanish students, developmental learning may increase CRT skills but not improve NOS knowledge. This reinforces the claim for explicit and reflective teaching of NOS, as implicit developmental maturation alone seems ineffective.

Correlations between nature of science and thinking skills

The empirical analysis of the hypothesised relationships between thinking skills and NOS epistemic variables (Adequate, Plausible, Naive) was performed through correlational methods (Pearson’s bivariate correlation coefficients and linear regression analysis) and one-way analysis of variance.

The Pearson correlation coefficients revealed a pattern of the relationships between NOS and thinking skills (Table 5 ): all thinking skills positively correlated with the Adequate variable, and most were significant, except for prediction and logical reasoning in EP6, which were non-significant. However, the correlations with the Naive and Plausible variables were overall non-significant. However, there were some exceptions: first, the Plausible/problem-solving correlation in EP6 was significant (and negative); second, the correlations between Naïve and logical reasoning (positive in EP6) and also between decision-making, logical reasoning and the thinking total score (negative in SCE8) were significant.

Thus, the noteworthy pattern for the NOS-CRT relationship showed that the Adequate variable positively correlated with all the thinking variables and was mostly statistically significant (83%); the highest positive correlations corresponded to problem-solving (EP6), sequence and parts-all (ES8), and the thinking total skills for both groups ( p  < 0.01). This pattern means that students with higher (lower) thinking skill scores expressed higher (lower) agreement with Adequate phrases.

The correlation pattern between thinking skills and the Plausible and Naive variables was mainly non-significant (75%). Only two correlations were significant in the EP6 group; the Plausible-problem-solving correlation was negative (higher scorers on problem-solving did not recognize the intermediate value of Plausible science definitions), whereas the Naïve-logical reasoning correlation was positive (higher scorers on logical reasoning tended to disagree with Naive science definitions). Three Naïve correlations were significant and negative in the secondary group (SCE8): parts-all, logical reasoning skills and thinking total.

Overall, the positive and significant correlation pattern of the Adequate variable was stronger than the mainly non-significant and somewhat negative Naive and Plausible correlation pattern.

Linear regression analysis between nature of science and thinking skills

Regression analysis (RA) compares the power of a set of variables to predict a dependent variable and the common variance. Two linear regression analyses were carried out to test the mutual contribution of the CRT and NOS variables. The first RA uses the NOS variables (Adequate, Plausible, Naive and Global) as the dependent variables, and the five independent thinking skills as predictors (Table 6 ). The second RA (Table 7 ) reversed the roles of the variables, thus establishing the thinking skills as the dependent variables and the three independent NOS variables (Adequate, Plausible and Naive) as the predictors. Collinearity tests were negative for all RAs through tolerance, variance inflation factor and condition index statistics.

The first RA (Table 6 ) showed that the NOS Adequate variable achieved the highest proportion of common variance with thinking skill predictors at both educational levels (4.2% in PE6 and 9.2% in SCE8), whereas the other two NOS variables achieved much lower levels of explained variance. In PE6, the most significant predictor skill of NOS was problem-solving, whereas the other predictor skills did not reach statistical significance in any case. In SCE8, the most significant predictors were three skills (sequencing, reasoning, and parts-all), whereas the remaining skills did not reach statistical significance (the predictors of the Plausible variable were negative).

The second RA (Table 7 ) showed that the Adequate variable achieved the greatest predictive power, as most thinking skills displayed statistically significant standardised beta coefficients at the two educational levels, while Plausible and Naïve variables had a much lower predictive power, and Plausible standardised coefficients were non-significant for any skill predictor. The common variance displayed a similar amount to the first analysis; the thinking total variable displayed the largest variance at both educational levels (4.8% PE6; 9.6% SCE8), and the problem-solving skills at PE6 (5.3%) and parts-all at SCE8 (7.1%).

In summary, the Adequate variable and the classification and problem-solving skills (PE6) and sequencing and parts-all skills (SCE8) were the variables that presented the largest standardised coefficients and statistical significance regarding the research question raised in this study about the positive relationship between NOS and thinking skills.

Analysis of variance between nature of science and thinking skills

Further exploration of the NOS-skills relationship was conducted through one-way between-groups analysis of variance. According to performance on the Adequate, Plausible and Naive variables, the participants were allocated to four percentile groups (low group: 0–25%; medium–low: 25–50%; medium–high: 50–75%; high: 75–100%), which made up the independent variable of the ANOVA for testing the differences in thinking skills (dependent variable) among these four groups.

The Adequate groups yielded a statistically significant main effect for the thinking total in primary [ F (3, 429) = 7.745, p  = 0.000] and secondary education [ F (3, 343) = 2.607, p  = 0.052]. The effect size of the differences in the thinking total scores between the high and low groups was large for the primary ( d  = 0.69) and secondary ( d  = 0.86) cohorts. Furthermore, comparison, classification, and problem-solving skills also replicated this pattern of large differences between high-low groups that supports the NOS/CRT positive relationship. However, prediction ( p  = 0.069) and logical reasoning ( p  = 0.504) did not display differences among the Adequate groups.

Post-hoc comparisons (Scheffé test) showed that the low group achieved significantly lower scores than the other three Adequate groups. The Adequate low group scores on thinking total, comparison, classification, and problem-solving skills were significantly lower than the scores of the other three groups, whereas the differences among the Adequate groups on prediction and logical reasoning scores were non-significant.

The main effect of the Plausible groups on the thinking total variable did not reach statistical significance for the primary F (3, 430) = 1.805, p = 0.145] and secondary groups [ F (3, 343) = 2.607, p  = 0.052]. The effect size was small ( d  = − 0.31 primary; d  = − 0.32 secondary) and negative (the thinking total mean score of the low group was higher than that of the high group). Post-hoc comparisons (Scheffé test) confirmed the trend, as they did not yield significant differences among the Plausible groups, although the mean score of the Plausible high group was lower than the other three groups. Exceptionally, problem-solving skill (primary) displayed a statistically significant difference between the Plausible high group (the lowest mean score) and the remaining three groups.

The main effect of Naive groups on the thinking total variable did not reach statistical significance [ F (3, 430) = 1.075, p  = 0.367 primary; F (3, 343) = 1.642, p  = 0.179 secondary] and the effect size of the differences was small ( d  = 0.32 primary; d  = − 0.31 secondary). The opposite direction of the differences in primary (positive) and secondary education (negative) is noteworthy, as it means that the highest mean score corresponded to the Naive high group in primary (positive) or the Naive low group in secondary (negative). Post-hoc comparisons (Scheffé test) showed that there were no significant differences among the Naive groups. However, the league table of groups across the Naive groups revealed differences between primary and secondary cohorts. Overall, the primary Naive groups followed the pattern of the Adequate variable (the low group displayed the lowest score), whereas the secondary Naive groups followed the pattern of the Plausible variable (the high group tended to display the lowest score).

The empirical findings of this study quantify through correlations some significant and positive relationships between thinking skills and NOS beliefs about science definitions, as the main answer to the research question. However, the analysis shows a complex pattern of the relationship, which depends on the kind of the NOS variable under consideration: the NOS Adequate variable, which represents phrases expressing informed views on science, is positively and significantly related to most thinking skills, whereas the uninformed Naive and intermediate Plausible variables show a lower predictive power of thinking skills. Summing up, the positive significant CRT-NOS relationship is not displayed by all NOS variables, as it is limited to those NOS variables that express an Adequate view of science, while the other NOS variables do not significantly correlate with CRT skills.

The implications of this study for research are twofold. On the one hand, the variables of this study specifically operationalise the two constructs under investigation, namely, CRT skills and NOS knowledge, which has been a challenge throughout their mixed operationalisation in the reviewed research. On the other hand, via Pearson correlations and regression analysis, this study quantifies the amount of the common variance between specific CRT skills and specific NOS knowledge, which is significant in many cases. Both contributions improve the features of previous studies, as most of them investigated the relationship from varied methodological frameworks: some reported group comparison, fewer analysed correlations, and most of the latter used a diversity of variables, which often did not match either CRT skills or NOS variables. For instance, Vieira et al. ( 2011 ) correlated thinking skills with science literacy (not NOS) and reported Pearson correlations that were lower than the correlations obtained herein, even though they used a smaller sample, which favours higher correlations.

The findings reveal the complexity of the NOS-CRT relationship, which limits the positive and relevant relationship to the NOS Adequate variables about science definitions, but not to the Plausible or Naive conceptualizations, which mainly display non-significant and somewhat negative correlations. The positive relationship between thinking and Adequate science definitions is a remarkable finding, which empirically supports the hypothesis that better thinking skills involve better NOS knowledge and confirms the concomitant intuitions and claims of some studies about the importance of thinking skills for learning NOS epistemic topics (Erduran & Kaya, 2018 ; Ford & Yore, 2014 ; Simonneaux, 2014 ; Torres & Solbes, 2016 ; Yacoubian, 2015 ). The findings also contribute to establishing the limit of the significant relationship, which applies when the NOS is conveyed by informed statements (Adequate phrases) and does not apply for non-adequate NOS statements, which are a minority in the face of most NOS literature, which conveys informed statements on NOS (Cofré et al., 2019 ).

The implications of the collateral finding on the lack of differences in science definitions between primary and secondary cohorts deserve further comments. Obviously, the finding confirms that two educational years have a scarce impact on improving Spanish students’ understanding of science definitions; that is, NOS teaching seems ineffective and stagnated, probably due to poor curriculum development and the lack of teacher training and educational resources. Besides, the students’ higher performance on adequate phrases than on plausible and naïve phrases also suggests that Spanish students may achieve some mild knowledge about the informed traits of science because they are implicitly displayed in teaching, textbooks and media. However, plausible and naïve knowledge is not usually available from those sources, as it requires explicit and reflective teaching, which Spanish students usually lack. Both findings suggest the need for further attention to misinformed NOS knowledge to invigorate explicit and reflective NOS teaching (Cofré et al., 2019 ; McDonald & McRobbie, 2012 ).

The unexpected non-significant/negative relationships between thinking and Plausible and Naive variables may need some elaboration due to the complexity of students’ NOS conceptions. For instance, Bennássar et al. ( 2010 ) described the students’ inconsistent agreements when rating opposite statements. Bogdan ( 2020 ) found that epistemic conceptions of science creativity did not relate to attitudes to science, and Khishfe ( 2012 ) reported complex relationships between epistemic aspects of science and decision-making about genetically modified organisms or the acceptance of the evolution theory (Cofré et al., 2017 ; Sinatra, et al., 2003 ). Thus, a tentative interpretation of those paradoxical relationships is elaborated.

Higher-thinking-skill students might develop better quality reflections that elicit more confident and higher scores on NOS phrases than lower-thinking-skill students. The latter tend toward less confident and low-quality reflection, which may elicit intermediate, less polarized scores. On average, this differential pattern explains the complex pattern of relationships between CRT and NOS variables. For the Adequate phrases (where the rubric assigns the best indices to the highest scores), higher-thinking students will achieve higher NOS indices than lower-thinking students, explaining the observed positive CRT-NOS correlations in the Adequate variables and the ANOVA results. On the other hand, when Naive and, especially, Plausible phrases are involved (which obtain their highest indices at low and intermediate scores, respectively), the differential response pattern would lead the lower-thinking students to achieve higher NOS indices than the higher-thinking students, thus shifting to the observed non-significant or negative correlations for Naive and Plausible phrases. In short, unconfident/confident and lower/higher quality reflection on NOS knowledge of the lower-/higher-thinking students would explain the shift from the positive and significant relationship of CRT-Adequate phrases to the non-significant correlations of Plausible and Naive phrases. This interpretation agrees with the striking finding of O’Brien et al. ( 2021 ) about a similar unexpected higher adherence to pseudoscientific claims in students with higher trust in science, which the authors attributed to the acritical acceptation of any scientific contents. Similarly, mastery of CRT skills is a desirable learning outcome, but it may make master students vulnerable to positive polarization in science definitions. However, further research is needed to confirm the non-significant correlations and the interpretation of the differential response pattern.

As the previous reference suggests, the findings about the complex CRT-NOS relationship connect with some pending controversies about NOS teaching, namely, the marginalized attention paid to misinformed ideas or myths about science, in favour of the informed ideas, which reveal implicit and non-reflective NOS teaching, as obviously misinformed ideas contribute to triggering more reflection than informed ideas (Acevedo et al., 2007 ; McComas, 1996 ). The effect of this under-exposure is students’ under-training about misinformed NOS ideas, which may act as obstacles to authentic NOS epistemic learning, explaining the differences presented herein. The remedy to this situation and the unconfident bias may lie in devoting more time and explicit attention to uninformed or incomplete NOS claims through reflective teaching.

This study is determined and limited by the contextual conditions of its correlational methodology. First, the research question implied measurements of thinking skills and NOS knowledge; second, the young participants (12–14-year-olds) required measurement tools appropriate to this age; third, the thinking skill tests had to match the thinking skills demanded by the participant school; fourth, the selected NOS tool was conditioned by the students’ age and the lack of appropriate NOS assessment tools. Thus, further suggestions to overcome these limitations are focused on expanding empirical support for the NOS-CRT relationship. On the one hand, some new NOS issues, such as additional epistemological and social aspects of science, should be explored to extend the representativeness of NOS knowledge. Similar reflections apply to including new skills to expand the scope of the CRT tool. Furthermore, the number of items of the logical reasoning scale should be increased to improve its reliability. Overall, the perennial debate between open-ended and closed formats is also noteworthy for future research, as quantitative methods could be complemented with qualitative methods (such as students’ interviews and the like).

Finally, the main educational implication of this study is that students may need to master some competence in CRT skills to learn NOS knowledge or general epistemic knowledge. Conversely, mastery of CRT skills may foster learning NOS knowledge. Although this study focuses on epistemic NOS knowledge drawn from science education, educational research has parallelly elaborated the epistemic knowledge (EK) construct for general education (Hofer & Pintrich, 1997 ), which opens further prospective research developments for NOS comprehension and CRT skills. On the one hand, the study of the NOS-EK relationship may shed light on convergent epistemic teaching and learning, both in science and in general education. On the other hand, the importance of CRT skills for NOS, and vice versa, may help coordinate teaching NOS-EK issues (Erduran & Kaya, 2018 ; Ford & Yore, 2014 ; McDonald & McRobbie, 2012 ; Simonneaux, 2014 ). This joint prospective of NOS-EK elaboration may also provide new answers to two aspects: the mutual connections between CRT skills and NOS-EK issues and the EK assessment tools that may also contribute to advancing the evaluation of CRT skills and NOS.

Data availability

The Spanish State Research Agency and the University of the Balearic Islands hold the property of all data and materials of this study, which may be available under reasonable request to them.

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Manassero-Mas, M.A., Vázquez-Alonso, Á. An empirical analysis of the relationship between nature of science and critical thinking through science definitions and thinking skills. SN Soc Sci 2 , 270 (2022). https://doi.org/10.1007/s43545-022-00546-x

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What’s the Difference Between Critical Thinking and Scientific Thinking?

Thinking deeply about things is a defining feature of what it means to be human, but, surprising as it may seem, there isn’t just one way to ‘think’ about something; instead, humans have been developing organized and varied schools of thought for thousands of years.

Discussions about morality, religion, and the meaning of life often drive knowledge-seeking inquiry, leading people to wonder what the difference is between critical thinking and Scientific Thinking.

Critical thinkers prioritize objectivity to analyze a problem, deduce logical solutions, and examine what the ramifications of those solutions are.

While scientific thinking often relies heavily on critical thinking, scientific inquiry is more dedicated to acquiring knowledge rather than mere abstraction.

There are a lot of nuances between critical thinking and scientific thinking, and most of us probably utilize these skills in our everyday lives. The rest of this article will thoroughly define the two terms and relate how they are similar and different.

What Is Critical Thinking?

Critical thinking is a mindset ― a lens, if you will, through which one may view the world. Critical thinkers rely on a lot of introspection, constantly self-evaluating how they came to a conclusion, and what that conclusion naturally entails.

A critical thinker may discern what they already know about a subject, what that information suggests, why that information is relevant, and how that information could be linked to further lines of inquiry. Critical thinking is, therefore, simply the ability to think clearly and logically.

Systematic reasoning is prized over gut instinct, and determining relevance is crucial to parsing out useful data from extraneous information.

Naturally, the ability to think critically is highly prized in an academic setting, and most educators seek to enable their students to think critically.

What is the link between the styles and motivations of these two Romantic era poets? How can your current understanding of algebra be applied to geometry? How does our understanding of this historical figure influence our understanding of social life at the time?

So much information can be interlinked to develop our understanding of the world, and critical thinking is the basis for using objectivity to not only establish likely outcomes to a scenario, but also inquire on the repercussions of that outcome and reflect on the process by which one came to that conclusion.

What Is Scientific Thinking?

The objective of scientific thinking is the acquisition of knowledge. The more we know, the more we can hope to know.

Scientific thinking begins by imagining what the outcome of a problem may be, observing the situation, and then making notes and changing the initial hypothesis.

The commonly used scientific method is as follows:

• Define the purpose of the experiment
• Formulate a hypothesis
• Study the phenomenon and collect data
• Draw results

As you might imagine, this process can be repeated ad infinitum. So, you draw a conclusion that’s scientifically verifiable? Great! Now you can take that conclusion and use it as a basis for a new experiment. Of course, the scientific method has limits.

It’s hard to apply the scientific method when it comes to morality or religious beliefs. A revelation of a prophet cannot be empirically verified.

We can’t go inside said prophet’s mind and see exactly what neurons were firing to recreate the conditions under which the vision was made, and even if we could, the nature of such a revelation is spiritual and immaterial.

It’s impossible to influence the supernatural in the material world, and as such, creating a test that relies on changing something to see the outcome is impossible. Where scientific thinking does excel is in the fields of math and, well, science.

Physics is known as the perfect science because the forces that comprise our world are well understood and don’t tend to exhibit anomalies, making the empirically verified scientific method perfect for improving our understanding of the natural world.

How Are Critical Thinking and Scientific Thinking Similar and Different?

Both critical and scientific thinking rely on the use of empirical, objective evidence. Thinking scientifically or critically relies on using the data available and following it to its likely conclusion.

Scientific thinking can be seen as a stricter, more regulated version of critical thinking. It takes the tenets of critically thinking and narrows the focus.

Both fields of study eschew personal bias and gut instinct as both unreliable and unhelpful.

The main difference between the two, however, is the goal of each discipline.

While both prioritize learning and using data to make hypotheses, critical thinking is prone to much more abstraction and self-reflection.

With little variation in the scientific method, there’s not really any need to reflect on how those conclusions were drawn or if those conclusions are a result of any kind of bias. It’s just not useful information.

For a critical thinker, however, self-reflection is key to identifying inconsistencies and refining one’s argument.

Both scientific thinking and critical thinking tend to draw links between concepts, evaluating how they are related and what knowledge may be gleaned from that connection.

While critical thinking can be applied to most concepts, even those of morality and anthropology, scientific thinking is often problem oriented. If a problem exists, scientific inquiry attempts to gain the necessary information to solve it, overcoming obstacles along the way.

Both critical thinkers and scientific thinkers may very well end up at the same conclusion― they will just draw those conclusions differently. Critical thinkers are concerned with logic, order, and rational thinking.

Establishing already-understood information, applying that information to a query, and then establishing a defensible argument on the accuracy and relevance of the conclusion is the trademark of a critical thinker. Scientific thinkers, on the other hand, work towards solving knowledge almost exclusively through the acquisition of knowledge through the scientific method.

Scientific thinkers develop a hypothesis, test it, and then rinse and repeat until the phenomenon is understood. As such, scientific thinkers are obsessed with why questions. Why does this phenomenon happen?

Why does matter behave like this? In the end, both schools are thought have a lot of interesting ideas guiding them, and most of us probably use them throughout our daily lives.

https://www.vwaust.com/resource/what-is-scientific-thinking/

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https://psycnet.apa.org/record/2010-22950-019

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Scientific reasoning, then, may be interpreted as the subset of critical-thinking skills (cognitive and metacognitive processes and dispositions) that 1) are involved in making meaning of information in scientific domains and 2) support the epistemological commitment to scientific methodology and paradigm(s).

Critical thinking initiates the act of hypothesis. In the scientific method, the hypothesis is the initial supposition, or theoretical claim about the world, based on questions and observations. If critical thinking asks the question, then the hypothesis is the best attempt at the time to answer the question using observable phenomenon.

The method, based on critical thinking, is embedded in the scientific method, named here the Critical Generative Method. Before illustrating the key requirements for critical thinking, one point must be made clear from the outset: thinking involves using language, and the depth of thought is directly related to the 'active' vocabulary ...

Because the scientific method is, first of all, a matter of logical reasoning and only afterwards, a procedure to be applied in a laboratory. ... This insightful piece presents a detailed analysis of how and why science can help to develop critical thinking. Figure 4. A framework for applying the scientific approach to everyday conversations ...

Critical thinking is a widely accepted educational goal. Its definition is contested, but the competing definitions can be understood as differing conceptions of the same basic concept: careful thinking directed to a goal. Conceptions differ with respect to the scope of such thinking, the type of goal, the criteria and norms for thinking ...

From the turn of the 20th century, he and others working in the overlapping fields of psychology, philosophy, and educational theory sought to rigorously apply the scientific method to understand and define the process of thinking. They conceived critical thinking to be related to the scientific method but more open, flexible, and self ...

Both the scientific method and critical thinking are applications of logic and related forms of rationality that date to the Ancient Greeks. The full spectrum of critical/rational thinking includes logic, informal logic, and systemic or analytic thinking. This common core is shared by the natural sciences and other domains of inquiry share, and ...

studies, authors advocate adopting critical thinking as the course framework (Pukkila, 2004) and developing explicit examples of how critical thinking relates to the scientific method (Miri et al., 2007). In these examples, the important connection between writ-ing and critical thinking is highlighted by the fact that each

Scientific thinking and critical thinking are two intellectual processes that are considered keys in the basic and comprehensive education of citizens. For this reason, their development is also contemplated as among the main objectives of science education. However, in the literature about the two types of thinking in the context of science education, there are quite frequent allusions to one ...

This page titled 1.5: The Scientific Method is shared under a CC BY-NC-SA license and was authored, remixed, and/or curated by Noah Levin ( NGE Far Press) . The procedure that scientists use is also a standard form of argument. Its conclusions only give you the likelihood or the probability that something is true (if your theory or hypothesis ...

The scientific method helps you avoid making mistakes when thinking of a situation. However, there are many ways to approach critical thinking; feel free to dig deeper into some of them: A philosophical-historical approach from Greek philosophers or the Enlightenment (university studies, philosophy, etc.). A popular approach (YouTube channels ...

Scientific Thinking is More Than "the Scientific Method" Students in many science classrooms are presented with the scientific method as the fundamental plan scientists use to gain their understandings. Scientists throughout history have come to their conclusions in a variety of ways, not always following such a specific method.

The Scientific Method: Critical Thinking at its Best. Experiments are a great way to incorporate higher level thinking into the science classroom. When you ask a science teacher to describe the experiments he or she conducts in class, you will get a variety of responses. While some teachers like experiments with simple demonstrations of science ...

Modern use and critical thought. The term "scientific method" came into popular use in the twentieth century; Dewey's 1910 book, ... Science applied to complex systems can involve elements such as transdisciplinarity, systems theory, ... Scientific Thinking and a scientific method Archived 2018-01-01 at the Wayback Machine by Steven D. Schafersman.

scientific method is to try and answer those. The way in which questions emerge is a subject in itself. This is not addressed here, but this should also be the subject of critical thinking (Yanai & Lercher, 2019). The basis for scientific investigation accepts that, while the truth of the world exists in itself ('relativism'

Critical thinking is a reflective pattern based on focused reasoning to determine what should be believed and carried out [ 7 ]. In addition, it is a comprehensive introduction for better reasoning [ 8 ]. Critical thinking is one of the mandatory skills to deal with various situations, from school to community.

The study of scientific method is the attempt to discern the activities by which that success is achieved. Among the activities often identified as characteristic of science are systematic observation and experimentation, inductive and deductive reasoning, and the formation and testing of hypotheses and theories.

The scientific method is critical to the development of scientific theories, which explain empirical (experiential) laws in a scientifically rational manner. In a typical application of the scientific method, a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the ...

When conducting research, the scientific method steps to follow are: Observe what you want to investigate. Ask a research question and make predictions. Test the hypothesis and collect data. Examine the results and draw conclusions. Report and share the results. This process not only allows scientists to investigate and understand different ...

The Scientific Method. The scientific method is a systematic approach to investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. It involves the following steps: ... Scientific and critical thinking also involve being open-minded, questioning assumptions, and seeking out new information. How does ...

The scientific method is a systematic way of conducting experiments or studies so that you can explore the world around you and answer questions using reason and evidence. It's a step-by-step ...

Critical thinking (CRT) skills transversally pervade education and nature of science (NOS) knowledge is a key component of science literacy. Some science education researchers advocate that CRT skills and NOS knowledge have a mutual impact and relationship. However, few research studies have undertaken the empirical confirmation of this relationship and most fail to match the two terms of the ...

Both critical and scientific thinking rely on the use of empirical, objective evidence. Thinking scientifically or critically relies on using the data available and following it to its likely conclusion. Scientific thinking can be seen as a stricter, more regulated version of critical thinking. It takes the tenets of critically thinking and ...