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Later this year, the FDA plans to decide whether MDMA can be used to treat PTSD Eva Almqvist/Getty Images hide caption

Misconduct claims may derail MDMA psychedelic treatment for PTSD

June 3, 2024 • People with post-traumatic stress disorder (PTSD) may soon have a new treatment option: MDMA, the chemical found in ecstasy. In August, the Food and Drug Administration plans to decide whether MDMA-assisted therapy for PTSD will be approved for market based on years of research. But serious allegations of research misconduct may derail the approval timeline.

Former President Donald Trump holds a press conference following the verdict in his hush-money trial at Trump Tower on May 31 in New York City. Spencer Platt/Getty Images hide caption

Trump repeats claims — without evidence — that his trial was rigged

May 31, 2024 • Former President Donald Trump reiterated many of claims — without evidence — that his criminal trial was rigged, a day after a New York jury found him guilty of 34 counts of falsifying business records.

Researchers have detected microplastics in human testicles. Volodymyr Zakharov/Getty Images hide caption

Shots - Health News

Plastic junk researchers find tiny particles in men's testicles.

May 22, 2024 • The new study has scientists concerned that microplastics may be contributing to reproductive health issues.

Harlan Gough holds a recently collected tiger beetle on a tether. Lawrence Reeves hide caption

To escape hungry bats, these flying beetles create an ultrasound 'illusion'

May 22, 2024 • A study of tiger beetles has found a possible explanation for why they produce ultrasound noises right before an echolocating bat swoops in for the kill.

A sea otter in Monterey Bay with a rock anvil on its belly and a scallop in its forepaws. Jessica Fujii hide caption

When sea otters lose their favorite foods, they can use tools to go after new ones

May 20, 2024 • Some otters rely on tools to bust open hard-shelled prey items like snails, and a new study suggests this tool use is helping them to survive as their favorite, easier-to-eat foods disappear.

Lauren Hill, a graduate student at Cal State LA, holds a bird at the bird banding site at Bear Divide in the San Gabriel Mountains. Grace Widyatmadja/NPR hide caption

On this unassuming trail near LA, bird watchers see something spectacular

May 13, 2024 • At Bear Divide, just outside Los Angeles, you can see a rare spectacle of nature. This is one of the only places in the western United States where you can see bird migration during daylight hours.

The inside of a cell is a complicated orchestration of interactions between molecules. Keith Chambers/Science Photo Library hide caption

AI gets scientists one step closer to mapping the organized chaos in our cells

May 13, 2024 • As artificial intelligence seeps into some realms of society, it rushes into others. One area it's making a big difference is protein science — as in the "building blocks of life," proteins! Producer Berly McCoy talks to host Emily Kwong about the newest advance in protein science: AlphaFold3, an AI program from Google DeepMind. Plus, they talk about the wider field of AI protein science and why researchers hope it will solve a range of problems, from disease to the climate.

NASA's Solar Dynamics Observatory captured this image of a strong solar flare on May 8, 2024. The Wednesday solar flares kicked off the geomagnetic storm happening this weekend. NASA/SDO hide caption

NOAA Issues First Severe Geomagnetic Storm Watch Since 2005

May 10, 2024 • Scientists at the National Oceanic and Atmospheric Administration observed a cluster of sunspots on the surface of the sun this week. With them came solar flares that kicked off a severe geomagnetic storm. That storm is expected to last throughout the weekend as at least five coronal mass ejections — chunks of the sun — are flung out into space, towards Earth! NOAA uses a five point scale to rate these storms, and this weekend's storm is a G4. It's expected to produce auroras as far south as Alabama. To contextualize this storm, we are looking back at the largest solar storm on record: the Carrington Event.

Esther Nesbitt lost two of her children to drug overdoses, and her grandchildren are among more than 320,000 who lost parents in the overdose epidemic. Andrew Lichtenstein/Corbis via Getty Images hide caption

In a decade of drug overdoses, more than 320,000 American children lost a parent

May 8, 2024 • New research documents how many children lost a parent to an opioid or other overdose in the period from 2011 to 2021. Bereaved children face elevated risks to their physical and emotional health.

This illustration depicts a washed-up Ichthyotitan severnensis carcass on the beach. Sergey Krasovskiy hide caption

Largest-ever marine reptile found with help from an 11-year-old girl

May 6, 2024 • A father and daughter discovered fossil remnants of a giant ichthyosaur that scientists say may have been the largest-known marine reptile to ever swim the seas.

A survey shows that doctors have trouble taking full vacations from their high-stress jobs. Even when they do, they often still do work on their time off. Wolfgang Kaehler/LightRocket via Getty Images hide caption

Perspective

When pto stands for 'pretend time off': doctors struggle to take real breaks.

May 4, 2024 • What's a typical vacation activity for doctors? Work. A new study finds that most physicians do work on a typical day off. In this essay, a family doctor considers why that is and why it matters.

Weliton Menário Costa (center) holds a laptop while surrounded by dancers for his music video, "Kangaroo Time." From left: Faux Née Phish (Caitlin Winter), Holly Hazlewood, and Marina de Andrade. Nic Vevers/ANU hide caption

'Dance Your Ph.D.' winner on science, art, and embracing his identity

May 4, 2024 • Weliton Menário Costa's award-winning music video showcases his research on kangaroo personality and behavior — and offers a celebration of human diversity, too.

Researchers in a rainforest in Indonesia spotted an injury on the face of a male orangutan they named Rakus. They were stunned to watch him treat his wound with a medicinal plant. Armas/Suaq Project hide caption

Orangutan in the wild applied medicinal plant to heal its own injury, biologists say

May 3, 2024 • It is "the first known case of active wound treatment in a wild animal with a medical plant," biologist Isabelle Laumer told NPR. She says the orangutan, called Rakus, is now thriving.

The federal government says it has taken steps toward developing a vaccine to protect against bird flu should it become a threat to humans. skodonnell/Getty Images hide caption

Launching an effective bird flu vaccine quickly could be tough, scientists warn

May 3, 2024 • Federal health officials say the U.S. has the building blocks to make a vaccine to protect humans from bird flu, if needed. But experts warn we're nowhere near prepared for another pandemic.

A Nazca booby in the Galápagos Islands incubates eggs with its webbed feet. Wolfgang Kaehler/LightRocket via Getty Images hide caption

The Science of Siblings

For birds, siblinghood can be a matter of life or death.

May 1, 2024 • Some birds kill their siblings soon after hatching. Other birds spend their whole lives with their siblings and will even risk their lives to help each other.

Planet Money

How do you counter misinformation critical thinking is step one.

April 30, 2024 • An economic perspective on misinformation

This image shows a brain "assembloid" consisting of two connected brain "organoids." Scientists studying these structures have restored impaired brain cells in Timothy syndrome patients. Pasca lab, Stanford University hide caption

Scientists restore brain cells impaired by a rare genetic disorder

April 30, 2024 • A therapy that restores brain cells impaired by a rare genetic disorder may offer a strategy for treating conditions like autism, epilepsy, and schizophrenia.

Katie Krimitsos is among the majority of American women who have trouble getting healthy sleep, according to a new Gallup survey. Krimitsos launched a podcast called Sleep Meditation for Women to offer some help. Natalie Champa Jennings/Natalie Jennings, courtesy of Katie Krimitsos hide caption

Helping women get better sleep by calming the relentless 'to-do lists' in their heads

April 26, 2024 • A recent survey found that Americans' sleep patterns have been getting worse. Adult women under 50 are among the most sleep-deprived demographics.

Bird flu is spreading through U.S. dairy cattle. Scientists say the risk to people is minimal, but open questions remain, including how widespread the outbreak is and how the virus is spreading. DOUGLAS MAGNO/AFP via Getty Images hide caption

As bird flu spreads in cows, here are 4 big questions scientists are trying to answer

April 26, 2024 • Health officials say there's very little risk to humans from the bird flu outbreak among dairy cattle, but there's still much they don't know. Here are four questions scientists are trying to answer.

A coyote at the Fort Worth Zoo is photographed in the hours leading up to the April 8 total solar eclipse. The Hartstone-Rose Research Lab, NC State hide caption

Animals get stressed during eclipses. But not for the reason you think

April 25, 2024 • After studying various species earlier this month, some scientists now say they understand the origin of animal behavior during solar eclipses.

Dr. Jeffrey Stern, assistant professor in the Department of Surgery at NYU Grossman School of Medicine, and Dr. Robert Montgomery, director of the NYU Langone Transplant Institute, prepare the gene-edited pig kidney with thymus for transplantation. Joe Carrotta for NYU Langone Health hide caption

A woman with failing kidneys receives genetically modified pig organs

April 24, 2024 • Surgeons transplanted a kidney and thymus gland from a gene-edited pig into a 54-year-old woman in an attempt to extend her life. It's the latest experimental use of animal organs in humans.

Drug companies often do one-on-one outreach to doctors. A new study finds these meetings with drug reps lead to more prescriptions for cancer patients, but not longer survival. Chris Hondros/Getty Images hide caption

Oncologists' meetings with drug reps don't help cancer patients live longer

April 22, 2024 • Drug company reps commonly visit doctors to talk about new medications. A team of economists wanted to know if that helps patients live longer. They found that for cancer patients, the answer is no.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 25, 2024 11:09 AM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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Home » Scientific Research – Types, Purpose and Guide

Scientific Research – Types, Purpose and Guide

Table of Contents

Scientific Research

Definition:

Scientific research is the systematic and empirical investigation of phenomena, theories, or hypotheses, using various methods and techniques in order to acquire new knowledge or to validate existing knowledge.

It involves the collection, analysis, interpretation, and presentation of data, as well as the formulation and testing of hypotheses. Scientific research can be conducted in various fields, such as natural sciences, social sciences, and engineering, and may involve experiments, observations, surveys, or other forms of data collection. The goal of scientific research is to advance knowledge, improve understanding, and contribute to the development of solutions to practical problems.

Types of Scientific Research

There are different types of scientific research, which can be classified based on their purpose, method, and application. In this response, we will discuss the four main types of scientific research.

Descriptive Research

Descriptive research aims to describe or document a particular phenomenon or situation, without altering it in any way. This type of research is usually done through observation, surveys, or case studies. Descriptive research is useful in generating ideas, understanding complex phenomena, and providing a foundation for future research. However, it does not provide explanations or causal relationships between variables.

Exploratory Research

Exploratory research aims to explore a new area of inquiry or develop initial ideas for future research. This type of research is usually conducted through observation, interviews, or focus groups. Exploratory research is useful in generating hypotheses, identifying research questions, and determining the feasibility of a larger study. However, it does not provide conclusive evidence or establish cause-and-effect relationships.

Experimental Research

Experimental research aims to test cause-and-effect relationships between variables by manipulating one variable and observing the effects on another variable. This type of research involves the use of an experimental group, which receives a treatment, and a control group, which does not receive the treatment. Experimental research is useful in establishing causal relationships, replicating results, and controlling extraneous variables. However, it may not be feasible or ethical to manipulate certain variables in some contexts.

Correlational Research

Correlational research aims to examine the relationship between two or more variables without manipulating them. This type of research involves the use of statistical techniques to determine the strength and direction of the relationship between variables. Correlational research is useful in identifying patterns, predicting outcomes, and testing theories. However, it does not establish causation or control for confounding variables.

Scientific Research Methods

Scientific research methods are used in scientific research to investigate phenomena, acquire knowledge, and answer questions using empirical evidence. Here are some commonly used scientific research methods:

Observational Studies

This method involves observing and recording phenomena as they occur in their natural setting. It can be done through direct observation or by using tools such as cameras, microscopes, or sensors.

Experimental Studies

This method involves manipulating one or more variables to determine the effect on the outcome. This type of study is often used to establish cause-and-effect relationships.

Survey Research

This method involves collecting data from a large number of people by asking them a set of standardized questions. Surveys can be conducted in person, over the phone, or online.

Case Studies

This method involves in-depth analysis of a single individual, group, or organization. Case studies are often used to gain insights into complex or unusual phenomena.

Meta-analysis

This method involves combining data from multiple studies to arrive at a more reliable conclusion. This technique can be used to identify patterns and trends across a large number of studies.

Qualitative Research

This method involves collecting and analyzing non-numerical data, such as interviews, focus groups, or observations. This type of research is often used to explore complex phenomena and to gain an understanding of people’s experiences and perspectives.

Quantitative Research

This method involves collecting and analyzing numerical data using statistical techniques. This type of research is often used to test hypotheses and to establish cause-and-effect relationships.

Longitudinal Studies

This method involves following a group of individuals over a period of time to observe changes and to identify patterns and trends. This type of study can be used to investigate the long-term effects of a particular intervention or exposure.

Data Analysis Methods

There are many different data analysis methods used in scientific research, and the choice of method depends on the type of data being collected and the research question. Here are some commonly used data analysis methods:

• Descriptive statistics: This involves using summary statistics such as mean, median, mode, standard deviation, and range to describe the basic features of the data.
• Inferential statistics: This involves using statistical tests to make inferences about a population based on a sample of data. Examples of inferential statistics include t-tests, ANOVA, and regression analysis.
• Qualitative analysis: This involves analyzing non-numerical data such as interviews, focus groups, and observations. Qualitative analysis may involve identifying themes, patterns, or categories in the data.
• Content analysis: This involves analyzing the content of written or visual materials such as articles, speeches, or images. Content analysis may involve identifying themes, patterns, or categories in the content.
• Data mining: This involves using automated methods to analyze large datasets to identify patterns, trends, or relationships in the data.
• Machine learning: This involves using algorithms to analyze data and make predictions or classifications based on the patterns identified in the data.

Application of Scientific Research

Scientific research has numerous applications in many fields, including:

• Medicine and healthcare: Scientific research is used to develop new drugs, medical treatments, and vaccines. It is also used to understand the causes and risk factors of diseases, as well as to develop new diagnostic tools and medical devices.
• Agriculture : Scientific research is used to develop new crop varieties, to improve crop yields, and to develop more sustainable farming practices.
• Technology and engineering : Scientific research is used to develop new technologies and engineering solutions, such as renewable energy systems, new materials, and advanced manufacturing techniques.
• Environmental science : Scientific research is used to understand the impacts of human activity on the environment and to develop solutions for mitigating those impacts. It is also used to monitor and manage natural resources, such as water and air quality.
• Education : Scientific research is used to develop new teaching methods and educational materials, as well as to understand how people learn and develop.
• Business and economics: Scientific research is used to understand consumer behavior, to develop new products and services, and to analyze economic trends and policies.
• Social sciences : Scientific research is used to understand human behavior, attitudes, and social dynamics. It is also used to develop interventions to improve social welfare and to inform public policy.

How to Conduct Scientific Research

Conducting scientific research involves several steps, including:

• Identify a research question: Start by identifying a question or problem that you want to investigate. This question should be clear, specific, and relevant to your field of study.
• Conduct a literature review: Before starting your research, conduct a thorough review of existing research in your field. This will help you identify gaps in knowledge and develop hypotheses or research questions.
• Develop a research plan: Once you have a research question, develop a plan for how you will collect and analyze data to answer that question. This plan should include a detailed methodology, a timeline, and a budget.
• Collect data: Depending on your research question and methodology, you may collect data through surveys, experiments, observations, or other methods.
• Analyze data: Once you have collected your data, analyze it using appropriate statistical or qualitative methods. This will help you draw conclusions about your research question.
• Interpret results: Based on your analysis, interpret your results and draw conclusions about your research question. Discuss any limitations or implications of your findings.
• Communicate results: Finally, communicate your findings to others in your field through presentations, publications, or other means.

Purpose of Scientific Research

The purpose of scientific research is to systematically investigate phenomena, acquire new knowledge, and advance our understanding of the world around us. Scientific research has several key goals, including:

• Exploring the unknown: Scientific research is often driven by curiosity and the desire to explore uncharted territory. Scientists investigate phenomena that are not well understood, in order to discover new insights and develop new theories.
• Testing hypotheses: Scientific research involves developing hypotheses or research questions, and then testing them through observation and experimentation. This allows scientists to evaluate the validity of their ideas and refine their understanding of the phenomena they are studying.
• Solving problems: Scientific research is often motivated by the desire to solve practical problems or address real-world challenges. For example, researchers may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Advancing knowledge: Scientific research is a collective effort to advance our understanding of the world around us. By building on existing knowledge and developing new insights, scientists contribute to a growing body of knowledge that can be used to inform decision-making, solve problems, and improve our lives.

Examples of Scientific Research

Here are some examples of scientific research that are currently ongoing or have recently been completed:

• Clinical trials for new treatments: Scientific research in the medical field often involves clinical trials to test new treatments for diseases and conditions. For example, clinical trials may be conducted to evaluate the safety and efficacy of new drugs or medical devices.
• Genomics research: Scientists are conducting research to better understand the human genome and its role in health and disease. This includes research on genetic mutations that can cause diseases such as cancer, as well as the development of personalized medicine based on an individual’s genetic makeup.
• Climate change: Scientific research is being conducted to understand the causes and impacts of climate change, as well as to develop solutions for mitigating its effects. This includes research on renewable energy technologies, carbon capture and storage, and sustainable land use practices.
• Neuroscience : Scientists are conducting research to understand the workings of the brain and the nervous system, with the goal of developing new treatments for neurological disorders such as Alzheimer’s disease and Parkinson’s disease.
• Artificial intelligence: Researchers are working to develop new algorithms and technologies to improve the capabilities of artificial intelligence systems. This includes research on machine learning, computer vision, and natural language processing.
• Space exploration: Scientific research is being conducted to explore the cosmos and learn more about the origins of the universe. This includes research on exoplanets, black holes, and the search for extraterrestrial life.

When to use Scientific Research

Some specific situations where scientific research may be particularly useful include:

• Solving problems: Scientific research can be used to investigate practical problems or address real-world challenges. For example, scientists may investigate the causes of a disease in order to develop new treatments, or explore ways to make renewable energy more affordable and accessible.
• Decision-making: Scientific research can provide evidence-based information to inform decision-making. For example, policymakers may use scientific research to evaluate the effectiveness of different policy options or to make decisions about public health and safety.
• Innovation : Scientific research can be used to develop new technologies, products, and processes. For example, research on materials science can lead to the development of new materials with unique properties that can be used in a range of applications.
• Knowledge creation : Scientific research is an important way of generating new knowledge and advancing our understanding of the world around us. This can lead to new theories, insights, and discoveries that can benefit society.

Advantages of Scientific Research

There are many advantages of scientific research, including:

• Improved understanding : Scientific research allows us to gain a deeper understanding of the world around us, from the smallest subatomic particles to the largest celestial bodies.
• Evidence-based decision making: Scientific research provides evidence-based information that can inform decision-making in many fields, from public policy to medicine.
• Technological advancements: Scientific research drives technological advancements in fields such as medicine, engineering, and materials science. These advancements can improve quality of life, increase efficiency, and reduce costs.
• New discoveries: Scientific research can lead to new discoveries and breakthroughs that can advance our knowledge in many fields. These discoveries can lead to new theories, technologies, and products.
• Economic benefits : Scientific research can stimulate economic growth by creating new industries and jobs, and by generating new technologies and products.
• Improved health outcomes: Scientific research can lead to the development of new medical treatments and technologies that can improve health outcomes and quality of life for people around the world.
• Increased innovation: Scientific research encourages innovation by promoting collaboration, creativity, and curiosity. This can lead to new and unexpected discoveries that can benefit society.

Limitations of Scientific Research

Scientific research has some limitations that researchers should be aware of. These limitations can include:

• Research design limitations : The design of a research study can impact the reliability and validity of the results. Poorly designed studies can lead to inaccurate or inconclusive results. Researchers must carefully consider the study design to ensure that it is appropriate for the research question and the population being studied.
• Sample size limitations: The size of the sample being studied can impact the generalizability of the results. Small sample sizes may not be representative of the larger population, and may lead to incorrect conclusions.
• Time and resource limitations: Scientific research can be costly and time-consuming. Researchers may not have the resources necessary to conduct a large-scale study, or may not have sufficient time to complete a study with appropriate controls and analysis.
• Ethical limitations : Certain types of research may raise ethical concerns, such as studies involving human or animal subjects. Ethical concerns may limit the scope of the research that can be conducted, or require additional protocols and procedures to ensure the safety and well-being of participants.
• Limitations of technology: Technology may limit the types of research that can be conducted, or the accuracy of the data collected. For example, certain types of research may require advanced technology that is not yet available, or may be limited by the accuracy of current measurement tools.
• Limitations of existing knowledge: Existing knowledge may limit the types of research that can be conducted. For example, if there is limited knowledge in a particular field, it may be difficult to design a study that can provide meaningful results.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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TODAY'S HOURS:

Research Process

• Select a Topic
• Find Background Info
• Focus Topic
• List Keywords
• Search for Sources
• Evaluate & Integrate Sources
• Cite and Track Sources

What is Scientific Research?

Research study design, natural vs. social science, qualitative vs. quantitative research, more information on qualitative research in the social sciences, acknowledgements.

Thank you to Julie Miller, reference intern, for helping to create this page.

Some people use the term research loosely, for example:

• People will say they are researching different online websites to find the best place to buy a new appliance or locate a lawn care service.
• TV news may talk about conducting research when they conduct a viewer poll on current event topic such as an upcoming election.
• Undergraduate students working on a term paper or project may say they are researching the internet to find information.
• Private sector companies may say they are conducting research to find a solution for a supply chain holdup.

However, none of the above is considered “scientific research” unless:

• The research contributes to a body of science by providing new information through ethical study design or
• The research follows the scientific method, an iterative process of observation and inquiry.

The Scientific Method

• Make an observation: notice a phenomenon in your life or in society or find a gap in the already published literature.
• Ask a question about what you have observed.
• Hypothesize about a potential answer or explanation.
• Make predictions if our hypothesis is correct.
• Design an experiment or study that will test your prediction.
• Test the prediction by conducting an experiment or study; report the outcomes of your study.
• Iterate! Was your prediction correct? Was the outcome unexpected? Did it lead to new observations?

The scientific method is not separate from the Research Process as described in the rest of this guide, in fact the Research Process is directly related to the observation stage of the scientific method. Understanding what other scientists and researchers have already studied will help you focus your area of study and build on their knowledge.

Designing your experiment or study is important for both natural and social scientists. Sage Research Methods (SRM) has an excellent "Project Planner" that guides you through the basic stages of research design. SRM also has excellent explanations of qualitative and quantitative research methods for the social sciences.

For the natural sciences, Springer Nature Experiments and Protocol Exchange have guidance on quantitative research methods.

Books, journals, reference books, videos, podcasts, data-sets, and case studies on social science research methods.

Sage Research Methods includes over 2,000 books, reference books, journal articles, videos, datasets, and case studies on all aspects of social science research methodology. Browse the methods map or the list of methods to identify a social science method to pursue further. Includes a project planning tool and the "Which Stats Test" tool to identify the best statistical method for your project. Includes the notable "little green book" series (Quantitative Applications in the Social Sciences) and the "little blue book" series (Qualitative Research Methods).

Platform connecting researchers with protocols and methods.

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Qualitative research is primarily exploratory. It is used to gain an understanding of underlying reasons, opinions, and motivations. Qualitative research is also used to uncover trends in thought and opinions and to dive deeper into a problem by studying an individual or a group.

Qualitative methods usually use unstructured or semi-structured techniques. The sample size is typically smaller than in quantitative research.

Example: interviews and focus groups.

Quantitative research is characterized by the gathering of data with the aim of testing a hypothesis. The data generated are numerical, or, if not numerical, can be transformed into useable statistics.

Quantitative data collection methods are more structured than qualitative data collection methods and sample sizes are usually larger.

Example: survey

Note: The above descriptions of qualitative and quantitative research are mainly for research in the Social Sciences, rather than for Natural Sciences as most natural sciences rely on quantitative methods for their experiments.

Qualitative research is approaching the world in its natural setting and in a way that reveals the particularities rather than doing studies in a controlled setting. It aims to understand, describe, and sometimes explain social phenomena in a number of different ways:

• Experiences of individuals or groups
• Interactions and communications
• Documents (texts, images, film, or sounds, and digital documents)
• Experiences or interactions

Qualitative researchers seek to understand how people conceptualize the world around them, what they are doing, how they are doing it or what is happening to them in terms that are significant and that offer meaningful learnings.

Qualitative researchers develop and refine concepts (or hypotheses, if they are used) in the process of research and of collecting data. Cases (its history and complexity) are an important context for understanding the issue that is studied. A major part of qualitative research is based on text and writing – from field notes and transcripts to descriptions and interpretations and finally to the presentation of the findings and of the research as a whole.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

Part of the book series: Research in Mathematics Education ((RME))

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Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Ethics in scientific research: a lens into its importance, history, and future

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Introduction

Ethics are a guiding principle that shapes the conduct of researchers. It influences both the process of discovery and the implications and applications of scientific findings 1 . Ethical considerations in research include, but are not limited to, the management of data, the responsible use of resources, respect for human rights, the treatment of human and animal subjects, social responsibility, honesty, integrity, and the dissemination of research findings 1 . At its core, ethics in scientific research aims to ensure that the pursuit of knowledge does not come at the expense of societal or individual well-being. It fosters an environment where scientific inquiry can thrive responsibly 1 .

The need to understand and uphold ethics in scientific research is pertinent in today’s scientific community. First, the rapid advancement of technology and science raises ethical questions in fields like biotechnology, biomedical science, genetics, and artificial intelligence. These advancements raise questions about privacy, consent, and the potential long-term impacts on society and its environment 2 . Furthermore, the rise in public perception and scrutiny of scientific practices, fueled by a more informed and connected populace, demands greater transparency and ethical accountability from researchers and institutions.

This commentary seeks to bring to light the need and benefits associated with ethical adherence. The central theme of this paper highlights how upholding ethics in scientific research is a cornerstone for progress. It buttresses the fact that ethics in scientific research is vital for maintaining the trust of the public, ensuring the safety of participants, and legitimizing scientific findings.

Historical perspective

Ethics in research is significantly shaped by past experiences where a lack of ethical consideration led to negative consequences. One of the most striking examples of ethical misconduct is the Tuskegee Syphilis Study 3 conducted between 1932 and 1972 by the U.S. Public Health Service. In this study, African American men in Alabama were used as subjects to study the natural progression of untreated syphilis. They were not informed of their condition and were denied effective treatment, even after penicillin became available as a cure in the 1940s 3 .

From an ethical lens today, this is a gross violation of informed consent and an exploitation of a vulnerable population. The public outcry following the revelation of the study’s details led to the establishment of the National Commission for the Protection of Human Subjects of Biomedical and Behavioural Research 4 . This commission eventually produced the Belmont Report in 1979 4 , setting forth principles such as respect for persons, beneficence, and justice, which now underpin ethical research practices 4 .

Another example that significantly impacted ethical regulations was the thalidomide tragedy of the late 1950s and early 1960s 5 . Thalidomide was marketed as a safe sedative for pregnant women to combat morning sickness in Europe. Thalidomide resulted in the birth of approximately ten thousand children with severe deformities due to its teratogenic effects 5 , which were not sufficiently researched prior to the drug’s release. This incident underscored the critical need for comprehensive clinical testing and highlighted the ethical imperative of understanding and communicating potential risks, particularly for vulnerable groups such as pregnant women. In response, drug testing regulations became more rigorous, and the importance of informed consent, especially in clinical trials, was emphasized.

The Stanford Prison Experiment of 1971, led by psychologist Philip Zimbardo is another prime example of ethical oversight leading to harmful consequences 6 . The experiment, which aimed to study the psychological effects of perceived power, resulted in emotional trauma for participants. Underestimating potential psychological harm with no adequate systems to safeguard human participants from harm was a breach of ethics in psychological studies 6 . This case highlighted the necessity for ethical guidelines that prioritize the mental and emotional welfare of participants, especially in psychological research. It led to stricter review processes and the establishment of guidelines to prevent psychological harm in research studies. It influenced the American Psychological Association and other bodies to refine their ethical guidelines, ensuring the protection of participants’ mental and emotional well-being.

Impact on current ethical standards

These historical, ethical oversights have been instrumental in shaping the current landscape of ethical standards in scientific research. The Tuskegee Syphilis Study led to the Belmont Report in 1979, which laid out key ethical principles such as respect for persons, beneficence, and justice. It also prompted the establishment of Institutional Review Boards (IRBs) to oversee research involving human subjects. The thalidomide tragedy catalyzed stricter drug testing regulations and informed consent requirements for clinical trials. The Stanford Prison Experiment influenced the American Psychological Association to refine its ethical guidelines, placing greater emphasis on the welfare and rights of participants.

These historical episodes of ethical oversights have been pivotal in forging the comprehensive ethical frameworks that govern scientific research today. They serve as stark reminders of the potential consequences of ethical neglect and the perpetual need to prioritize the welfare and rights of participants in any research endeavor.

One may ponder on the reason behind the Tuskegee Syphilis Study, where African American men with syphilis were deliberately left untreated. What led scientists to prioritize research outcomes over human well-being? At the time, racial prejudices, lack of understanding of ethical principles in human research, and regulatory oversight made such studies pass. Similarly, the administration of thalidomide to pregnant women initially intended as an antiemetic to alleviate morning sickness, resulted in unforeseen and catastrophic birth defects. This tragedy highlights a critical lapse in the pre-marketing evaluation of drugs’ safety.

Furthermore, the Stanford prison experiment, designed to study the psychological effects of perceived power, spiraled into an ethical nightmare as participants suffered emotional trauma. This begs the question on how these researchers initially justified their methods. From today’s lens of ethics, the studies conducted were a complete breach of misconduct, and I wonder if there were any standards that guided primitive research in science.

Current ethical standards and guidelines in research

Informed consent.

This mandates that participants are fully informed about the nature of the research, including its objectives, procedures, potential risks, and benefits 7 , 8 . They must be given the opportunity to ask questions and must voluntarily agree to participate without coercion 7 , 8 . This ensures respect for individual autonomy and decision-making.

Confidentiality and privacy

Confidentiality is pivotal in research involving human subjects. Participants’ personal information must be protected from unauthorized access or disclosure 7 , 8 . Researchers are obliged to take measures to preserve the anonymity and privacy of participants, which fosters trust and encourages participation in research 7 , 8 .

Non-maleficence and beneficence

These principles revolve around the obligation to avoid harm (non-maleficence) and to maximize possible benefits while minimizing potential harm (beneficence) 7 , 8 . Researchers must ensure that their studies do not pose undue risks to participants and that any potential risks are outweighed by the benefits.

Justice in research ethics refers to the fair selection and treatment of research participants 8 . It ensures that the benefits and burdens of research are distributed equitably among different groups in society, preventing the exploitation of vulnerable populations 8 .

The role of Institutional Review Boards (IRB)

Institutional Review Boards play critical roles in upholding ethical standards in research. An IRB is a committee established by an institution conducting research to review, approve, and monitor research involving human subjects 7 , 8 . Their primary role is to ensure that the rights and welfare of participants are protected.

Review and approval

Before a study commences, the IRB reviews the research proposal to ensure it adheres to ethical guidelines. This includes evaluating the risks and benefits, the process of obtaining informed consent, and measures for maintaining confidentiality 7 , 8 .

Monitoring and compliance

IRB also monitors ongoing research projects to ensure compliance with ethical standards. They may require periodic reports and can conduct audits to ensure ongoing adherence to ethical principles 7 , 8 .

Handling ethical violations

In cases where ethical standards are breached, IRB has the authority to impose sanctions, which can range from requiring modifications to the study to completely halting the research project 7 , 8 .

Other agencies and boards enforcing standards

Beyond IRB, there are other regulatory bodies and agencies at national and international levels that enforce ethical standards in research. These include:

The Office for Human Research Protections (OHRP) in the United States, which oversees compliance with the Federal Policy for the Protection of Human Subjects.

The World Health Organization (WHO) , which provides international ethical guidelines for biomedical research.

The International Committee of Medical Journal Editors (ICMJE) , which sets ethical standards for the publication of biomedical research.

These organizations, along with IRB, form a comprehensive network that ensures the ethical conduct of scientific research. They safeguard the integrity of research using the reflections and lesson learnt from the past.

Benefits of ethical research

Credible and reliable outcomes, why is credibility so crucial in research, and how do ethical practices contribute to it.

Ethical practices such as rigorous peer review, transparent methodology, and adherence to established protocols ensure that research findings are reliable and valid 9 . When studies are conducted ethically, they are less likely to be marred by biases, fabrications, or errors that could compromise credibility. For instance, ethical standards demand accurate data reporting and full disclosure of any potential conflicts of interest 9 , which directly contribute to the integrity and trustworthiness of research findings.

How do ethical practices lead to socially beneficial outcomes?

Ethical research practices often align with broader societal values and needs, leading to outcomes that are not only scientifically significant but also socially beneficial. By respecting principles like justice and beneficence, researchers ensure that their work with human subjects contributes positively to society 7 , 8 . For example, ethical guidelines in medical research emphasize the need to balance scientific advancement with patient welfare, ensuring that new treatments are both effective and safe. This balance is crucial in addressing pressing societal health concerns while safeguarding individual rights and well-being.

Trust between the public and the scientific community

The relationship between the public and the scientific community is heavily reliant on trust, which is fostered through consistent ethical conduct in research. When the public perceives that researchers are committed to ethical standards, it reinforces their confidence in the scientific process and its outcomes. Ethical research practices demonstrate a respect for societal norms and values, reinforcing the perception that science serves the public good.

Case studies

Case study 1: the development and approval of covid-19 vaccines.

The development and approval of COVID-19 vaccines within a short time is a testament to how adherence to ethical research practices can achieve credible and beneficial outcomes. Strict adherence to ethical guidelines, even in the face of a global emergency, ensured that the vaccines were developed swiftly. However, safety standards were compromised to some extent as no animal trials were done before humans. The vaccine development was not transparent to the public, and this fuelled the anti-vaccination crowd in some regions. Ethical compliance, including rigorous testing and transparent reporting, should expedite scientific innovation while maintaining public trust.

Case study 2: The CRISPR babies

What ethical concerns were raised by the creation of the crispr babies, and what were the consequences.

The creation of the first genetically edited babies using CRISPR technology in China raised significant ethical concerns 10 . The lack of transparency, inadequate consent process, and potential risks to the children can be likened to ethical misconduct in genetic engineering research. This case resulted in widespread condemnation from the scientific community and the public, as well as international regulatory frameworks and guidelines for genetic editing research 10 .

Recommendation and conclusion

Continuous education and training.

The scientific community should prioritize ongoing education and training in ethics for researchers at all levels, ensuring awareness and understanding of ethical standards and their importance.

Enhanced dialogue and collaboration

Encourage multidisciplinary collaborations and dialogues between scientists, ethicists, policymakers, and the public to address emerging ethical challenges and develop adaptive guidelines.

Fostering a culture of ethical responsibility

Institutions and researchers should cultivate an environment where ethical considerations are integral to the research process, encouraging transparency, accountability, and social responsibility.

Global standards and cooperation

Work toward establishing and harmonizing international ethical standards and regulatory frameworks, particularly in areas like genetic engineering and AI, where the implications of research are global.

Ethics approval

Ethics approval was not required for this editorial.

Informed consent was not required for this editorial

Sources of funding

No funding was received for this research.

Author contribution

G.D.M. wrote this paper.

Conflicts of interest disclosure

The authors declare no conflicts of interest.

Research registration unique identifying number (UIN)

Goshen David Miteu.

Data availability statement

Provenance and peer review.

Not commissioned, externally peer-reviewed.

Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.

Published online 21 March 2024

December 21, 2015

The Most Popular Science Studies of the Year

The attention-grabbing academic papers of 2015 include research on sexist video games and Homo erectus

By Jennifer Hackett

Issouf Sanogo Getty Images

The 2015 science research that set the Internet abuzz included a super antibiotic, plastics pollution in the ocean , climate change , and species extinction , according to Altmetric , a start-up that analyzes online activity surrounding academic papers.

Research never rests: every year thousands of scientific articles are published across dozens of journals and disciplines. Some studies capture the media’s attention and get coverage in numerous news stories; others speak to a more niche audience and take off in passionate social media discussions. For the second year Altmetric has compiled a list of the top 100 academic articles of the year . They studied the attention garnered by scientific articles from November 2014 up until November 16, 2015, examining how papers fared in news coverage and social media outlets such as Twitter, Facebook, and the popular Chinese microblogging site Sina Weibo. They also looked to see if studies were referenced by Wikipedia and policy papers outlining plans of actions written by analysts and think tanks.

To be clear, theirs is not a list of the most important studies of 2015. “It’s not about quality, necessarily, or even always about impact,” says Stacy Konkiel, an outreach manager at Altmetric. “We’re just looking at attention.” That explains how the twentieth overall story achieved its rank. On the surface, it was a paleontology paper. But it wasn’t the new horned dinosaur that interested the public: It was a marriage proposal tucked away in the paper’s footnotes.

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This year’s list includes studies from 34 different journals—both traditional and open access, the latter of which is steadily gaining ground and tends to get more of a boost from social media. Forty-two of the top 100 studies came from an open access journal.

More than half of the hottest studies were health-related, with environment-related studies trending right behind. Some of these studies might have gotten a boost from the attention on climate change due to the COP21 talks in Paris. These papers were particularly successful with traditional news coverage, with some having more than 100 news articles written about them.

Health and environment are typically hot topics, but there were surprises too: On the list was a story that gained quite a bit of traction (the eighth most popular article ) despite the fact it didn’t belong to either of the popular categories and only had two news articles written about it. The study, which compared the time effectiveness of major document preparation systems (word processors, such as Microsoft Word and the science-beloved LaTeX,) used by researchers to create their manuscripts saw 1,000 more tweets than any of the other top ten articles. “That is very surprising to me, personally, because it’s such a niche audience,” Konkiel says. “It obviously captured the interest of scientists who are very active on social media.”

The most-tweeted about article, a psychology study investigating whether or not sexist video games imparted sexist attitudes or mindsets onto the people who played them, was also an outlier. The study shared the most via Facebook, on the other hand, was about Homo erectus using shells as tools. Neither, clearly, involved health or environment. “We’re definitely seeing social media amplify studies that the mainstream media wouldn’t pick up,” Konkiel says. “As long as there’s an active community on social media, we see stories that otherwise might be niche get a lot of attention as measured by our score.”

“It’s not just about how many citations you’ve got or the impact factor of the journal you’ve published in, it’s who’s they’re sharing it with, and if they’re incorporating it into their day to day lives,” Konkiel says.

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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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Erica McNamee

It’s not just rising air and water temperatures influencing the decades-long decline of Arctic sea ice. Clouds, aerosols, even the bumps and dips on the ice itself can play a role. To explore how these factors interact and impact sea ice melting, NASA is flying two aircraft equipped with scientific instruments over the Arctic Ocean north of Greenland this summer. The first flights of the field campaign, called ARCSIX (Arctic Radiation Cloud Aerosol Surface Interaction Experiment), successfully began taking measurements on May 28.

“The ARCSIX mission aims to measure the evolution of the sea ice pack over the course of an entire summer,” said Patrick Taylor, deputy science lead with the campaign from NASA’s Langley Research Center in Hampton, Virginia. “There are many different factors that influence the sea ice. We’re measuring them to determine which were most important to melting ice this summer.”

On a completely clear day over smooth sea ice, most sunlight would reflect back into the atmosphere, which is one way that sea ice cools the planet. But when the ice has ridges or darker melt ponds — or is dotted with pollutants — it can change the equation, increasing the amount of ice melt. In the atmosphere, cloudy conditions and drifting aerosols also impact the rate of melt.

“An important goal of ARCSIX is to better understand the surface radiation budget — the energy interacting with the ice and the atmosphere,” said Rachel Tilling, a campaign scientist from NASA’s Goddard Space Flight Center in Greenbelt, Maryland.

About 75 scientists, instrument operators, and flight crew are participating in ARCSIX’s two segments based out of Pituffik Space Base in northwest Greenland. The first three-week deployment, in May and June of this year, is timed to document the start of the ice melt season. The second deployment will occur in July and August to monitor late summer conditions and the start of the freeze-up period.

“Scientists from three key disciplines came together for ARCSIX: sea ice surface researchers, aerosol researchers, and cloud researchers,” Tilling said. “Each of us has been working to understand the radiation budget in our specific area, but we’ve brought all three areas together for this campaign.”

Two aircraft will fly over the Arctic during each deployment. NASA’s P-3 Orion aircraft from the agency’s Wallops Flight Facility in Virginia, will fly below the clouds at times to document the surface properties of the ice and the amount of energy radiating off it. The team will also fly the aircraft through the clouds to sample aerosol particles, cloud optical properties, chemistry, and other atmospheric components.

A Gulfstream III aircraft, managed by NASA Langley, will fly higher in the atmosphere to observe properties of the tops of the clouds, take profiles of the atmosphere above the ice, and add a perspective similar to that of orbiting satellites.

The teams will also compare airborne data with satellite data. Satellite instruments like the Multi-angle Imaging Spectroradiometer and the Moderate Resolution Imaging Spectroradiometer will provide additional information about clouds and aerosol particles, while the Ice, Cloud, and land Elevation Satellite 2 will provide insights into the ice topography below both satellites and aircraft.

The aircraft will fly coordinated routes to take measurements of the atmosphere above ice in three-dimensional space, said Sebastian Schmidt, the mission’s science lead with the University of Colorado Boulder.

“The area off the northern coast of Greenland can be considered the last bastion of multi-year sea ice, as the Arctic transitions to a seasonally ice-free ocean,” Schmidt said. “By observing here, we will gain insight into cloud-aerosol-sea ice-interaction processes of the ‘old’ and ‘new’ Arctic — all while improving satellite-based remote sensing by comparing what we’re seeing with the airborne and satellite instruments.” 

By Kate Ramsayer

NASA’s Goddard Space Flight Center, Greenbelt, Md.

Related Terms

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The real dolphin tale: They’re smart, sometimes vicious and highly sexed

The longest-running wild dolphin research study paints a fuller picture of the marine mammals; they’re not “humans in wet suits.”

SARASOTA, Fla. — The research vessel Martha Jane glided slowly across the teal waters of Sarasota Bay on Florida’s Gulf Coast under a cloudless sky tailor-made for tourists on a recent day. “There’s 2094!” one of the scientists on the boat called out. “She’s still with us!”

The bottlenose dolphin known to researchers as 2094 had poked her dorsal fin out of the water for only a few seconds, but that was enough to identify her as a young female that had been the focus of a dramatic rescue from a fishing line a year ago.

No. 2094 is one of thousands of dolphins registered in the Sarasota Dolphin Research Program ’s database, each individual identified by the nicks and notches on their dorsal — or back — fins.

The world’s longest-running study of a wild dolphin population, the Sarasota effort has sighted and recorded more than 5,750 dolphins and made the shallow waters of Sarasota Bay a living laboratory for 53 years.

Among the program’s key findings: The individual dolphins here live in specific “neighborhoods” generation after generation, forming a mosaic of adjacent communities along Florida’s west coast. Many males forge buddy pairs for protection and stay together for life. And hetero- and same-sex interactions are used to establish and maintain social bonds over dolphin life spans that can stretch well past the age of 60.

Not ‘humans in wet suits’

In 1970, when the Sarasota Dolphin Research Program launched, dolphins were the subject of numerous romantic myths, including that they were intelligent and kind — animals that could be friends and even movie stars.

People viewed them as “humans in wet suits,” said Randy Wells, the director of the program, which is administered by the Brookfield Zoo Chicago.

People viewed them as “humans in wet suits.” — Randy Wells, director of the Sarasota Dolphin Research Program

But research has shown that, while they are highly intelligent, they have sensory systems very different from those of humans and a complex and unique means of communication. Listening stations the program installed around Sarasota Bay have recorded thousands of hours of dolphin vocalizations, and the team’s work with collaborators has shown that each dolphin has its own whistle, used for life like a name.

People also once believed that dolphins liked being near humans and benefited from food handouts. But the researchers have found that interactions with people can have dire consequences — including raising risks of the marine mammals ingesting inappropriate food, being exposed to spinning boat propellers and becoming entangled in fishing gear.

When the program started, no one knew whether dolphins generally ranged widely or stayed local — key information for wildlife managers. Using radio tracking devices and other tools, the researchers found that the roughly 170 dolphins that live in Sarasota Bay are organized in a definable range that is their home for life.

Generation after generation also stay in the same area and raise families. One 67-year-old female has given birth in a particular neighborhood at least 12 times, the program says. Before the study began, scientists had no idea bottlenose dolphins could live into their 60s in the wild.

A dolphin’s day

A day in the life of a Sarasota Bay dolphin is one of constant motion in which they feed on a variety of fish, travel, socialize with others and, finally, rest. Program scientists have observed the dolphins moving fluidly in and out of groups, depending on whom they encounter.

Nurseries made up of mothers and their youngest calves will swim together for a while, and independent juveniles join up with each other to practice skills needed later in life. During these activities, the dolphins are seeking prey while also keeping an eye out for predatory sharks and boat traffic as well as other disruptive human activities.

Sarasota Bay dolphins dine on a wide variety of fish, the data shows. They use their superb hearing to target prey fish such as toadfish and sea trout, which produce sounds.

Wells said that over the years, the team consistently documented pairs of the same males surfacing together, in a sort of buddy system that begins around the age of 10 and can last a lifetime. The pairs — which are unusual among mammals — protect the animals from predators when they’re resting. And during mating, one dolphin often stands guard while the other spends time with a female. When temporarily separated, the dolphins sometimes call to each other, apparently to maintain contact.

Bottlenose dolphins are very active sexually, Wells says. Both hetero- and homosexual interactions are used to create social bonds, he says, not just for procreation.

The greatest threats

The Sarasota Bay study animals are urban dolphins, living among a burgeoning human population and nearly constant exposure to boat traffic.

“Dolphins can be big, mean jerks.” — Gretchen Lovewell, program manager of Mote Marine Laboratory’s Stranding Investigations Program

Fifty thousand boats are registered in the dolphins’ home range within the bay, and boats pass within 100 yards of a dolphin an average of every six minutes during the day. Program staff were among the first to document the threats of death and serious injury to the dolphins caused by interactions with recreational fishing.

“Interaction with fisheries is the most common cause of death,” said Gretchen Lovewell, program manager of Mote Marine Laboratory’s Stranding Investigations Program , based in Sarasota. Lovewell works closely with Wells’s team to help fill in the dolphins’ life story, studying the animals’ skeletons to determine cause of death — and how they lived.

The bones sometimes reflect a darker side of dolphin behavior, one that belies the smiling caricature perpetuated by sympathetic images. The animals have powerful tails and beaks and use them against each other during conflicts. With males reaching more than nine feet in length and weighing as much as 660 pounds, such conflicts can be lethal.

Some of the bones of calves that Lovewell has examined show signs of being bashed by adult dolphins — deep teeth marks, broken bones and bruising around the babies’ jaws where adults apparently rammed them.

“Dolphins can be big, mean jerks,” Lovewell says.

Besides tangling with recreational fishing, the dolphins increasingly grapple with other threats. After recent severe outbreaks of a harmful algal bloom known as red tide, the dolphins altered their ranging and social patterns, interacting with anglers and boaters more often, with sometimes fatal results.

Dolphin encounters with sharks also rose, probably because red tide’s lethal effects on the fish that sharks normally consume caused them to prey on dolphins instead. However, researchers have documented more healed shark bite marks on paired males than single males, leading scientists to believe wounded paired dolphins survive attacks more often.

Climate change and blubber

Climate change has scientists concerned for the dolphins’ future. The animals’ blubber thickness and lipid content go up and down in response to seasonal temperature changes, the program team has found. “With climate change, rising water temperatures in areas where they live come close to the dolphins’ body temperature, and there’s a limit to how much blubber they can shed to adapt,” Wells said.

In some ways, dolphins can serve as canaries in a global ocean coal mine.

“Understanding dolphin health, behavior and biology helps us conserve dolphins in the wild and better protect their populations,” said Michael Adkesson, president and CEO of the Brookfield Zoo Chicago, which oversees animal conservation projects around the world, including the Sarasota program. “It also provides valuable information on the overall health of the oceans and marine landscapes that impact countless other species, including humans.”

Techniques developed by the team in Sarasota Bay have been used to help other scientists unravel the structure of dolphin populations and conserve them across the country and around the world, including endangered bottlenose dolphins in Greece and Mekong River dolphins in Cambodia.

Small franciscana dolphins that were dying in local fishermen’s nets in two Argentina bays were tracked in collaboration with Argentine scientists using the program’s satellite-linked transmitters, determining that the animals’ range closely matched the fishing zone. The findings have been used by the fishermen and the Argentine government to help protect the dolphins.

Data gathered by the program over the years has contributed to National Oceanic and Atmospheric Administration management plans for the species and has guided officials’ handling of environmental disasters such as the 2010 Deepwater Horizon oil spill.

The Sarasota-based method of temporarily restraining wild dolphins for health assessments was central to understanding the impact of the spill in Louisiana’s Barataria Bay, which was heavily oiled by the spill. The dolphins were found to have significant levels of adrenal toxicity and lung disease, among other disorders related to petroleum hydrocarbon exposure and toxicity.

“The techniques and long-term data coming from Sarasota served as the baseline for the data obtained in Barataria Bay,” said Michael Moore, senior scientist at the Woods Hole Oceanographic Institution in Massachusetts.

“Teams and tools developed by the Sarasota Dolphin Research Program were deployed in the spill area and led to a whole new understanding of how these disasters impact marine mammals,” Moore added. “None of this would have happened without the tools Randy Wells and his team developed.”

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Proof-of-concept study pioneers new brain imaging technique through a transparent skull implant

by Keck School of Medicine of USC

In the first study of its kind, researchers from the Keck School of Medicine of USC and the California Institute of Technology (Caltech) designed and implanted a transparent window in the skull of a patient, then used functional ultrasound imaging (fUSI) to collect high-resolution brain imaging data through the window.

Their preliminary findings , published in Science Translational Medicine , suggest that this sensitive, non-invasive approach could open new avenues for patient monitoring and clinical research , as well as broader studies of how the brain functions.

"This is the first time anyone had applied functional ultrasound imaging through a skull replacement in an awake, behaving human performing a task," said Charles Liu, MD, Ph.D., a professor of clinical neurological surgery, urology and surgery at the Keck School of Medicine and director of the USC Neurorestoration Center.

"The ability to extract this type of information noninvasively through a window is pretty significant, particularly since many of the patients who require skull repair have or will develop neurological disabilities. In addition, 'windows' can be surgically implanted in patients with intact skulls if functional information can help with diagnosis and treatment."

The research participant, 39-year-old Jared Hager, sustained a traumatic brain injury (TBI) from a skateboarding accident in 2019. During emergency surgery , half of Hager's skull was removed to relieve pressure on his brain, leaving part of his brain covered only with skin and connective tissue. Because of the pandemic, he had to wait more than two years to have his skull restored with a prosthesis.

During that time, Hager volunteered for earlier research conducted by Liu, Jonathan Russin, MD, associate surgical director of the USC Neurorestoration Center, and another Caltech team on a new type of brain imaging called fPACT.

The experimental technique had been done on soft tissue, but could only be tested on the brain in patients like Hager who were missing a part of their skull. When the time came for implanting the prosthesis, Hager again volunteered to team up with Liu and his colleagues, who designed a custom skull implant to study the utility of fUSI—which cannot be done through the skull or a traditional implant—while repairing Hager's injury.

Before the reconstructive surgery, the research team tested and optimized fUSI parameters for brain imaging, using both a phantom (a scientific device designed to test medical imaging equipment) and animal models. They then collected fUSI data from Hager while he completed several tasks, both before his surgery and after the clear implant was installed, finding that the window offered an effective way to measure brain activity.

Functional brain imaging, which collects data on brain activity by measuring changes in blood flow or electrical impulses, can offer key insights about how the brain works, both in healthy people and those with neurological conditions.

But current methods, such as functional magnetic resonance imaging (fMRI) and intracranial electroencephalography (EEG) leave many questions unanswered. Challenges include low resolution, a lack of portability or the need for invasive brain surgery. fUSI may eventually offer a sensitive and precise alternative.

"If we can extract functional information through a patient's skull implant, that could allow us to provide treatment more safely and proactively," including to TBI patients who suffer from epilepsy, dementia, or psychiatric problems, Liu said.

A new frontier for brain imaging

As a foundation for the present study, Liu has collaborated for years with Mikhail Shapiro, Ph.D. and Richard Andersen, Ph.D., of Caltech, to develop specialized ultrasound sequences that can measure brain function, as well as to optimize brain-computer interface technology, which transcribes signals from the brain to operate an external device.

With these pieces in place, Liu and his colleagues tested several transparent skull implants on rats, finding that a thin window made from polymethyl methacrylate (PMMA)—which resembles plexiglass—yielded the clearest imaging results. They then collaborated with a neurotechnology company, Longeviti Neuro Solutions, to build a custom implant for Hager.

Before surgery, the researchers collected fUSI data while Hager did two activities: solving a "connect-the-dots" puzzle on a computer monitor and playing melodies on his guitar. After the implant was installed, they collected data on the same tasks, then compared the results to determine whether fUSI could provide accurate and useful imaging data.

"The fidelity of course decreased, but importantly, our research showed that it's still high enough to be useful," Liu said. "And unlike other brain-computer interface platforms, which require electrodes to be implanted in the brain, this has far less barriers to adoption."

fUSI may offer finer resolution than fMRI and unlike intracranial EEG, it does not require electrodes to be implanted inside the brain. It is also less expensive than those methods and could provide some clinical advantages for patients over non-transparent skull implants, said Russin, who is also an associate professor of neurological surgery at the Keck School of Medicine and director of cerebrovascular surgery at Keck Hospital of USC.

"One of the big problems when we do these surgeries is that a blood clot can form underneath the implant, but having a clear window gives us an easy way to monitor that," he said.

Refining functional ultrasound technology

In addition to better monitoring of patients, the new technique could offer population-level insights about TBI and other neurological conditions. It could also allow scientists to collect data on the healthy brain and learn more about how it controls cognitive, sensory, motor and autonomic functions.

"What our findings show is that we can extract useful functional information with this method," Liu said. "The next step is: What specific functional information do we want, and what can we use it for?"

Until the new technologies undergo clinical trials, fUSI and the clear implant are experimental. In the meantime, the research team is working to improve their fUSI protocols to further enhance image resolution . Future research should also build on this early proof-of-concept study by testing more participants to better establish the link between fUSI data and specific brain functions, the researchers said.

"Jared is an amazing guy," said Liu, who is continuing to collaborate with the study participant on refining new technologies, including laser spectroscopy, which measures blood flow in the brain. "His contributions have really helped us explore new frontiers that we hope can ultimately help many other patients."

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Study links sleep apnea treatment and happier, healthier relationships

Couples demonstrated more satisfaction and less conflict with sleep apnea treatment.

A new study to be presented at the SLEEP 2024 annual meeting demonstrates that when individuals with obstructive sleep apnea use their positive airway pressure machine more regularly, it benefits their relationship with their partner.

Results show that greater adherence to PAP therapy was associated with higher levels of relationship satisfaction and lower levels of relationship conflict. Higher sleep efficiency among patients also was associated with higher levels of relationship satisfaction as reported by both the patient and their partner.

"Recognizing that sleep and sleep disorders have an impact on the quality of a relationship could be a powerful motivator for those affected with sleep apnea to adhere to treatment," said lead author Wendy Troxel, who is a senior behavioral scientist with RAND and licensed clinical psychologist and adjunct professor at the University of Utah, where the study was conducted. "We developed a couples-based treatment called 'We-PAP' in recognition of the fact that couples' sleep is a shared experience and to help patients and partners overcome challenges to adhering PAP together."

According to the American Academy of Sleep Medicine, nearly 30 million adults in the U.S. have obstructive sleep apnea, a chronic disease that involves the repeated collapse of the upper airway during sleep. Snoring is one of the most recognizable symptoms of sleep apnea and is often a nuisance to bed partners. A common treatment for sleep apnea is PAP therapy, which uses mild levels of air pressure, provided through a mask, to keep the throat open during sleep.

The study involved 36 couples comprising patients initiating PAP treatment for sleep apnea and their partners. Objective PAP therapy adherence data were recorded over three months. Sleep duration and efficiency were estimated using actigraphy. Relationship satisfaction and conflict were self-reported.

Troxel noted that it is essential to consider the importance of sleep when evaluating relationship status.

"No one is at their best when they aren't sleeping," Troxel said. "In an age where we see couples going through 'sleep divorces,' and roughly 50% of marriages end in actual divorce, recognizing how healthy sleep can contribute to healthy relationships is imperative."

This study was supported by a grant from the National Institutes of Health. The research abstract was published recently in an online supplement of the journal Sleep and will be presented Wednesday, June 5, during SLEEP 2024 in Houston. SLEEP is the annual meeting of the Associated Professional Sleep Societies, a joint venture of the AASM and the Sleep Research Society.

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Journal Reference :

• Wendy Troxel, Brian Baucom, Stevie Shock, Kelly Baron. 0569 Breathing Easy Together: How Positive Airway Pressure Adherence Benefits Both Patients and Partners . SLEEP , 2024; 47 (Supplement_1): A243 DOI: 10.1093/sleep/zsae067.0569

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• Published: 17 October 2023

The impact of founder personalities on startup success

• Paul X. McCarthy 1 , 2 ,
• Xian Gong 3 ,
• Fabian Braesemann 4 , 5 ,
• Fabian Stephany 4 , 5 ,
• Marian-Andrei Rizoiu 3 &
• Margaret L. Kern 6  

Scientific Reports volume  13 , Article number:  17200 ( 2023 ) Cite this article

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An Author Correction to this article was published on 07 May 2024

This article has been updated

Startup companies solve many of today’s most challenging problems, such as the decarbonisation of the economy or the development of novel life-saving vaccines. Startups are a vital source of innovation, yet the most innovative are also the least likely to survive. The probability of success of startups has been shown to relate to several firm-level factors such as industry, location and the economy of the day. Still, attention has increasingly considered internal factors relating to the firm’s founding team, including their previous experiences and failures, their centrality in a global network of other founders and investors, as well as the team’s size. The effects of founders’ personalities on the success of new ventures are, however, mainly unknown. Here, we show that founder personality traits are a significant feature of a firm’s ultimate success. We draw upon detailed data about the success of a large-scale global sample of startups (n = 21,187). We find that the Big Five personality traits of startup founders across 30 dimensions significantly differ from that of the population at large. Key personality facets that distinguish successful entrepreneurs include a preference for variety, novelty and starting new things (openness to adventure), like being the centre of attention (lower levels of modesty) and being exuberant (higher activity levels). We do not find one ’Founder-type’ personality; instead, six different personality types appear. Our results also demonstrate the benefits of larger, personality-diverse teams in startups, which show an increased likelihood of success. The findings emphasise the role of the diversity of personality types as a novel dimension of team diversity that influences performance and success.

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Introduction.

The success of startups is vital to economic growth and renewal, with a small number of young, high-growth firms creating a disproportionately large share of all new jobs 1 , 2 . Startups create jobs and drive economic growth, and they are also an essential vehicle for solving some of society’s most pressing challenges.

As a poignant example, six centuries ago, the German city of Mainz was abuzz as the birthplace of the world’s first moveable-type press created by Johannes Gutenberg. However, in the early part of this century, it faced several economic challenges, including rising unemployment and a significant and growing municipal debt. Then in 2008, two Turkish immigrants formed the company BioNTech in Mainz with another university research colleague. Together they pioneered new mRNA-based technologies. In 2020, BioNTech partnered with US pharmaceutical giant Pfizer to create one of only a handful of vaccines worldwide for Covid-19, saving an estimated six million lives 3 . The economic benefit to Europe and, in particular, the German city where the vaccine was developed has been significant, with windfall tax receipts to the government clearing Mainz’s €1.3bn debt and enabling tax rates to be reduced, attracting other businesses to the region as well as inspiring a whole new generation of startups 4 .

While stories such as the success of BioNTech are often retold and remembered, their success is the exception rather than the rule. The overwhelming majority of startups ultimately fail. One study of 775 startups in Canada that successfully attracted external investment found only 35% were still operating seven years later 5 .

But what determines the success of these ‘lucky few’? When assessing the success factors of startups, especially in the early-stage unproven phase, venture capitalists and other investors offer valuable insights. Three different schools of thought characterise their perspectives: first, supply-side or product investors : those who prioritise investing in firms they consider to have novel and superior products and services, investing in companies with intellectual property such as patents and trademarks. Secondly, demand-side or market-based investors : those who prioritise investing in areas of highest market interest, such as in hot areas of technology like quantum computing or recurrent or emerging large-scale social and economic challenges such as the decarbonisation of the economy. Thirdly, talent investors : those who prioritise the foundation team above the startup’s initial products or what industry or problem it is looking to address.

Investors who adopt the third perspective and prioritise talent often recognise that a good team can overcome many challenges in the lead-up to product-market fit. And while the initial products of a startup may or may not work a successful and well-functioning team has the potential to pivot to new markets and new products, even if the initial ones prove untenable. Not surprisingly, an industry ‘autopsy’ into 101 tech startup failures found 23% were due to not having the right team—the number three cause of failure ahead of running out of cash or not having a product that meets the market need 6 .

Accordingly, early entrepreneurship research was focused on the personality of founders, but the focus shifted away in the mid-1980s onwards towards more environmental factors such as venture capital financing 7 , 8 , 9 , networks 10 , location 11 and due to a range of issues and challenges identified with the early entrepreneurship personality research 12 , 13 . At the turn of the 21st century, some scholars began exploring ways to combine context and personality and reconcile entrepreneurs’ individual traits with features of their environment. In her influential work ’The Sociology of Entrepreneurship’, Patricia H. Thornton 14 discusses two perspectives on entrepreneurship: the supply-side perspective (personality theory) and the demand-side perspective (environmental approach). The supply-side perspective focuses on the individual traits of entrepreneurs. In contrast, the demand-side perspective focuses on the context in which entrepreneurship occurs, with factors such as finance, industry and geography each playing their part. In the past two decades, there has been a revival of interest and research that explores how entrepreneurs’ personality relates to the success of their ventures. This new and growing body of research includes several reviews and meta-studies, which show that personality traits play an important role in both career success and entrepreneurship 15 , 16 , 17 , 18 , 19 , that there is heterogeneity in definitions and samples used in research on entrepreneurship 16 , 18 , and that founder personality plays an important role in overall startup outcomes 17 , 19 .

Motivated by the pivotal role of the personality of founders on startup success outlined in these recent contributions, we investigate two main research questions:

Which personality features characterise founders?

Do their personalities, particularly the diversity of personality types in founder teams, play a role in startup success?

We aim to understand whether certain founder personalities and their combinations relate to startup success, defined as whether their company has been acquired, acquired another company or listed on a public stock exchange. For the quantitative analysis, we draw on a previously published methodology 20 , which matches people to their ‘ideal’ jobs based on social media-inferred personality traits.

We find that personality traits matter for startup success. In addition to firm-level factors of location, industry and company age, we show that founders’ specific Big Five personality traits, such as adventurousness and openness, are significantly more widespread among successful startups. As we find that companies with multi-founder teams are more likely to succeed, we cluster founders in six different and distinct personality groups to underline the relevance of the complementarity in personality traits among founder teams. Startups with diverse and specific combinations of founder types (e. g., an adventurous ‘Leader’, a conscientious ‘Accomplisher’, and an extroverted ‘Developer’) have significantly higher odds of success.

We organise the rest of this paper as follows. In the Section " Results ", we introduce the data used and the methods applied to relate founders’ psychological traits with their startups’ success. We introduce the natural language processing method to derive individual and team personality characteristics and the clustering technique to identify personality groups. Then, we present the result for multi-variate regression analysis that allows us to relate firm success with external and personality features. Subsequently, the Section " Discussion " mentions limitations and opportunities for future research in this domain. In the Section " Methods ", we describe the data, the variables in use, and the clustering in greater detail. Robustness checks and additional analyses can be found in the Supplementary Information.

Our analysis relies on two datasets. We infer individual personality facets via a previously published methodology 20 from Twitter user profiles. Here, we restrict our analysis to founders with a Crunchbase profile. Crunchbase is the world’s largest directory on startups. It provides information about more than one million companies, primarily focused on funding and investors. A company’s public Crunchbase profile can be considered a digital business card of an early-stage venture. As such, the founding teams tend to provide information about themselves, including their educational background or a link to their Twitter account.

We infer the personality profiles of the founding teams of early-stage ventures from their publicly available Twitter profiles, using the methodology described by Kern et al. 20 . Then, we correlate this information to data from Crunchbase to determine whether particular combinations of personality traits correspond to the success of early-stage ventures. The final dataset used in the success prediction model contains n = 21,187 startup companies (for more details on the data see the Methods section and SI section  A.5 ).

Revisions of Crunchbase as a data source for investigations on a firm and industry level confirm the platform to be a useful and valuable source of data for startups research, as comparisons with other sources at micro-level, e.g., VentureXpert or PwC, also suggest that the platform’s coverage is very comprehensive, especially for start-ups located in the United States 21 . Moreover, aggregate statistics on funding rounds by country and year are quite similar to those produced with other established sources, going to validate the use of Crunchbase as a reliable source in terms of coverage of funded ventures. For instance, Crunchbase covers about the same number of investment rounds in the analogous sectors as collected by the National Venture Capital Association 22 . However, we acknowledge that the data source might suffer from registration latency (a certain delay between the foundation of the company and its actual registration on Crunchbase) and success bias in company status (the likeliness that failed companies decide to delete their profile from the database).

The definition of startup success

The success of startups is uncertain, dependent on many factors and can be measured in various ways. Due to the likelihood of failure in startups, some large-scale studies have looked at which features predict startup survival rates 23 , and others focus on fundraising from external investors at various stages 24 . Success for startups can be measured in multiple ways, such as the amount of external investment attracted, the number of new products shipped or the annual growth in revenue. But sometimes external investments are misguided, revenue growth can be short-lived, and new products may fail to find traction.

Success in a startup is typically staged and can appear in different forms and times. For example, a startup may be seen to be successful when it finds a clear solution to a widely recognised problem, such as developing a successful vaccine. On the other hand, it could be achieving some measure of commercial success, such as rapidly accelerating sales or becoming profitable or at least cash positive. Or it could be reaching an exit for foundation investors via a trade sale, acquisition or listing of its shares for sale on a public stock exchange via an Initial Public Offering (IPO).

For our study, we focused on the startup’s extrinsic success rather than the founders’ intrinsic success per se, as its more visible, objective and measurable. A frequently considered measure of success is the attraction of external investment by venture capitalists 25 . However, this is not in and of itself a good measure of clear, incontrovertible success, particularly for early-stage ventures. This is because it reflects investors’ expectations of a startup’s success potential rather than actual business success. Similarly, we considered other measures like revenue growth 26 , liquidity events 27 , 28 , 29 , profitability 30 and social impact 31 , all of which have benefits as they capture incremental success, but each also comes with operational measurement challenges.

Therefore, we apply the success definition initially introduced by Bonaventura et al. 32 , namely that a startup is acquired, acquires another company or has an initial public offering (IPO). We consider any of these major capital liquidation events as a clear threshold signal that the company has matured from an early-stage venture to becoming or is on its way to becoming a mature company with clear and often significant business growth prospects. Together these three major liquidity events capture the primary forms of exit for external investors (an acquisition or trade sale and an IPO). For companies with a longer autonomous growth runway, acquiring another company marks a similar milestone of scale, maturity and capability.

Using multifactor analysis and a binary classification prediction model of startup success, we looked at many variables together and their relative influence on the probability of the success of startups. We looked at seven categories of factors through three lenses of firm-level factors: (1) location, (2) industry, (3) age of the startup; founder-level factors: (4) number of founders, (5) gender of founders, (6) personality characteristics of founders and; lastly team-level factors: (7) founder-team personality combinations. The model performance and relative impacts on the probability of startup success of each of these categories of founders are illustrated in more detail in section  A.6 of the Supplementary Information (in particular Extended Data Fig.  19 and Extended Data Fig.  20 ). In total, we considered over three hundred variables (n = 323) and their relative significant associations with success.

The personality of founders

Besides product-market, industry, and firm-level factors (see SI section  A.1 ), research suggests that the personalities of founders play a crucial role in startup success 19 . Therefore, we examine the personality characteristics of individual startup founders and teams of founders in relationship to their firm’s success by applying the success definition used by Bonaventura et al. 32 .

Employing established methods 33 , 34 , 35 , we inferred the personality traits across 30 dimensions (Big Five facets) of a large global sample of startup founders. The startup founders cohort was created from a subset of founders from the global startup industry directory Crunchbase, who are also active on the social media platform Twitter.

To measure the personality of the founders, we used the Big Five, a popular model of personality which includes five core traits: Openness to Experience, Conscientiousness, Extraversion, Agreeableness, and Emotional stability. Each of these traits can be further broken down into thirty distinct facets. Studies have found that the Big Five predict meaningful life outcomes, such as physical and mental health, longevity, social relationships, health-related behaviours, antisocial behaviour, and social contribution, at levels on par with intelligence and socioeconomic status 36 Using machine learning to infer personality traits by analysing the use of language and activity on social media has been shown to be more accurate than predictions of coworkers, friends and family and similar in accuracy to the judgement of spouses 37 . Further, as other research has shown, we assume that personality traits remain stable in adulthood even through significant life events 38 , 39 , 40 . Personality traits have been shown to emerge continuously from those already evident in adolescence 41 and are not significantly influenced by external life events such as becoming divorced or unemployed 42 . This suggests that the direction of any measurable effect goes from founder personalities to startup success and not vice versa.

As a first investigation to what extent personality traits might relate to entrepreneurship, we use the personality characteristics of individuals to predict whether they were an entrepreneur or an employee. We trained and tested a machine-learning random forest classifier to distinguish and classify entrepreneurs from employees and vice-versa using inferred personality vectors alone. As a result, we found we could correctly predict entrepreneurs with 77% accuracy and employees with 88% accuracy (Fig.  1 A). Thus, based on personality information alone, we correctly predict all unseen new samples with 82.5% accuracy (See SI section  A.2 for more details on this analysis, the classification modelling and prediction accuracy).

We explored in greater detail which personality features are most prominent among entrepreneurs. We found that the subdomain or facet of Adventurousness within the Big Five Domain of Openness was significant and had the largest effect size. The facet of Modesty within the Big Five Domain of Agreeableness and Activity Level within the Big Five Domain of Extraversion was the subsequent most considerable effect (Fig.  1 B). Adventurousness in the Big Five framework is defined as the preference for variety, novelty and starting new things—which are consistent with the role of a startup founder whose role, especially in the early life of the company, is to explore things that do not scale easily 43 and is about developing and testing new products, services and business models with the market.

Once we derived and tested the Big Five personality features for each entrepreneur in our data set, we examined whether there is evidence indicating that startup founders naturally cluster according to their personality features using a Hopkins test (see Extended Data Figure  6 ). We discovered clear clustering tendencies in the data compared with other renowned reference data sets known to have clusters. Then, once we established the founder data clusters, we used agglomerative hierarchical clustering. This ‘bottom-up’ clustering technique initially treats each observation as an individual cluster. Then it merges them to create a hierarchy of possible cluster schemes with differing numbers of groups (See Extended Data Fig.  7 ). And lastly, we identified the optimum number of clusters based on the outcome of four different clustering performance measurements: Davies-Bouldin Index, Silhouette coefficients, Calinski-Harabas Index and Dunn Index (see Extended Data Figure  8 ). We find that the optimum number of clusters of startup founders based on their personality features is six (labelled #0 through to #5), as shown in Fig.  1 C.

To better understand the context of different founder types, we positioned each of the six types of founders within an occupation-personality matrix established from previous research 44 . This research showed that ‘each job has its own personality’ using a substantial sample of employees across various jobs. Utilising the methodology employed in this study, we assigned labels to the cluster names #0 to #5, which correspond to the identified occupation tribes that best describe the personality facets represented by the clusters (see Extended Data Fig.  9 for an overview of these tribes, as identified by McCarthy et al. 44 ).

Utilising this approach, we identify three ’purebred’ clusters: #0, #2 and #5, whose members are dominated by a single tribe (larger than 60% of all individuals in each cluster are characterised by one tribe). Thus, these clusters represent and share personality attributes of these previously identified occupation-personality tribes 44 , which have the following known distinctive personality attributes (see also Table  1 ):

Accomplishers (#0) —Organised & outgoing. confident, down-to-earth, content, accommodating, mild-tempered & self-assured.

Leaders (#2) —Adventurous, persistent, dispassionate, assertive, self-controlled, calm under pressure, philosophical, excitement-seeking & confident.

Fighters (#5) —Spontaneous and impulsive, tough, sceptical, and uncompromising.

We labelled these clusters with the tribe names, acknowledging that labels are somewhat arbitrary, based on our best interpretation of the data (See SI section  A.3 for more details).

For the remaining three clusters #1, #3 and #4, we can see they are ‘hybrids’, meaning that the founders within them come from a mix of different tribes, with no one tribe representing more than 50% of the members of that cluster. However, the tribes with the largest share were noted as #1 Experts/Engineers, #3 Fighters, and #4 Operators.

To label these three hybrid clusters, we examined the closest occupations to the median personality features of each cluster. We selected a name that reflected the common themes of these occupations, namely:

Experts/Engineers (#1) as the closest roles included Materials Engineers and Chemical Engineers. This is consistent with this cluster’s personality footprint, which is highest in openness in the facets of imagination and intellect.

Developers (#3) as the closest roles include Application Developers and related technology roles such as Business Systems Analysts and Product Managers.

Operators (#4) as the closest roles include service, maintenance and operations functions, including Bicycle Mechanic, Mechanic and Service Manager. This is also consistent with one of the key personality traits of high conscientiousness in the facet of orderliness and high agreeableness in the facet of humility for founders in this cluster.

Founder-Level Factors of Startup Success. ( A ), Successful entrepreneurs differ from successful employees. They can be accurately distinguished using a classifier with personality information alone. ( B ), Successful entrepreneurs have different Big Five facet distributions, especially on adventurousness, modesty and activity level. ( C ), Founders come in six different types: Fighters, Operators, Accomplishers, Leaders, Engineers and Developers (FOALED) ( D ), Each founder Personality-Type has its distinct facet.

Together, these six different types of startup founders (Fig.  1 C) represent a framework we call the FOALED model of founder types—an acronym of Fighters, Operators, Accomplishers, Leaders, Engineers and D evelopers.

Each founder’s personality type has its distinct facet footprint (for more details, see Extended Data Figure  10 in SI section  A.3 ). Also, we observe a central core of correlated features that are high for all types of entrepreneurs, including intellect, adventurousness and activity level (Fig.  1 D).To test the robustness of the clustering of the personality facets, we compare the mean scores of the individual facets per cluster with a 20-fold resampling of the data and find that the clusters are, overall, largely robust against resampling (see Extended Data Figure  11 in SI section  A.3 for more details).

We also find that the clusters accord with the distribution of founders’ roles in their startups. For example, Accomplishers are often Chief Executive Officers, Chief Financial Officers, or Chief Operating Officers, while Fighters tend to be Chief Technical Officers, Chief Product Officers, or Chief Commercial Officers (see Extended Data Fig.  12 in SI section  A.4 for more details).

The ensemble theory of success

While founders’ individual personality traits, such as Adventurousness or Openness, show to be related to their firms’ success, we also hypothesise that the combination, or ensemble, of personality characteristics of a founding team impacts the chances of success. The logic behind this reasoning is complementarity, which is proposed by contemporary research on the functional roles of founder teams. Examples of these clear functional roles have evolved in established industries such as film and television, construction, and advertising 45 . When we subsequently explored the combinations of personality types among founders and their relationship to the probability of startup success, adjusted for a range of other factors in a multi-factorial analysis, we found significantly increased chances of success for mixed foundation teams:

Initially, we find that firms with multiple founders are more likely to succeed, as illustrated in Fig.  2 A, which shows firms with three or more founders are more than twice as likely to succeed than solo-founded startups. This finding is consistent with investors’ advice to founders and previous studies 46 . We also noted that some personality types of founders increase the probability of success more than others, as shown in SI section  A.6 (Extended Data Figures  16 and 17 ). Also, we note that gender differences play out in the distribution of personality facets: successful female founders and successful male founders show facet scores that are more similar to each other than are non-successful female founders to non-successful male founders (see Extended Data Figure  18 ).

The Ensemble Theory of Team-Level Factors of Startup Success. ( A ) Having a larger founder team elevates the chances of success. This can be due to multiple reasons, e.g., a more extensive network or knowledge base but also personality diversity. ( B ) We show that joint personality combinations of founders are significantly related to higher chances of success. This is because it takes more than one founder to cover all beneficial personality traits that ‘breed’ success. ( C ) In our multifactor model, we show that firms with diverse and specific combinations of types of founders have significantly higher odds of success.

Access to more extensive networks and capital could explain the benefits of having more founders. Still, as we find here, it also offers a greater diversity of combined personalities, naturally providing a broader range of maximum traits. So, for example, one founder may be more open and adventurous, and another could be highly agreeable and trustworthy, thus, potentially complementing each other’s particular strengths associated with startup success.

The benefits of larger and more personality-diverse foundation teams can be seen in the apparent differences between successful and unsuccessful firms based on their combined Big Five personality team footprints, as illustrated in Fig.  2 B. Here, maximum values for each Big Five trait of a startup’s co-founders are mapped; stratified by successful and non-successful companies. Founder teams of successful startups tend to score higher on Openness, Conscientiousness, Extraversion, and Agreeableness.

When examining the combinations of founders with different personality types, we find that some ensembles of personalities were significantly correlated with greater chances of startup success—while controlling for other variables in the model—as shown in Fig.  2 C (for more details on the modelling, the predictive performance and the coefficient estimates of the final model, see Extended Data Figures  19 , 20 , and 21 in SI section  A.6 ).

Three combinations of trio-founder companies were more than twice as likely to succeed than other combinations, namely teams with (1) a Leader and two Developers , (2) an Operator and two Developers , and (3) an Expert/Engineer , Leader and Developer . To illustrate the potential mechanisms on how personality traits might influence the success of startups, we provide some examples of well-known, successful startup founders and their characteristic personality traits in Extended Data Figure  22 .

Startups are one of the key mechanisms for brilliant ideas to become solutions to some of the world’s most challenging economic and social problems. Examples include the Google search algorithm, disability technology startup Fingerwork’s touchscreen technology that became the basis of the Apple iPhone, or the Biontech mRNA technology that powered Pfizer’s COVID-19 vaccine.

We have shown that founders’ personalities and the combination of personalities in the founding team of a startup have a material and significant impact on its likelihood of success. We have also shown that successful startup founders’ personality traits are significantly different from those of successful employees—so much so that a simple predictor can be trained to distinguish between employees and entrepreneurs with more than 80% accuracy using personality trait data alone.

Just as occupation-personality maps derived from data can provide career guidance tools, so too can data on successful entrepreneurs’ personality traits help people decide whether becoming a founder may be a good choice for them.

We have learnt through this research that there is not one type of ideal ’entrepreneurial’ personality but six different types. Many successful startups have multiple co-founders with a combination of these different personality types.

To a large extent, founding a startup is a team sport; therefore, diversity and complementarity of personalities matter in the foundation team. It has an outsized impact on the company’s likelihood of success. While all startups are high risk, the risk becomes lower with more founders, particularly if they have distinct personality traits.

Our work demonstrates the benefits of personality diversity among the founding team of startups. Greater awareness of this novel form of diversity may help create more resilient startups capable of more significant innovation and impact.

The data-driven research approach presented here comes with certain methodological limitations. The principal data sources of this study—Crunchbase and Twitter—are extensive and comprehensive, but there are characterised by some known and likely sample biases.

Crunchbase is the principal public chronicle of venture capital funding. So, there is some likely sample bias toward: (1) Startup companies that are funded externally: self-funded or bootstrapped companies are less likely to be represented in Crunchbase; (2) technology companies, as that is Crunchbase’s roots; (3) multi-founder companies; (4) male founders: while the representation of female founders is now double that of the mid-2000s, women still represent less than 25% of the sample; (5) companies that succeed: companies that fail, especially those that fail early, are likely to be less represented in the data.

Samples were also limited to those founders who are active on Twitter, which adds additional selection biases. For example, Twitter users typically are younger, more educated and have a higher median income 47 . Another limitation of our approach is the potentially biased presentation of a person’s digital identity on social media, which is the basis for identifying personality traits. For example, recent research suggests that the language and emotional tone used by entrepreneurs in social media can be affected by events such as business failure 48 , which might complicate the personality trait inference.

In addition to sampling biases within the data, there are also significant historical biases in startup culture. For many aspects of the entrepreneurship ecosystem, women, for example, are at a disadvantage 49 . Male-founded companies have historically dominated most startup ecosystems worldwide, representing the majority of founders and the overwhelming majority of venture capital investors. As a result, startups with women have historically attracted significantly fewer funds 50 , in part due to the male bias among venture investors, although this is now changing, albeit slowly 51 .

The research presented here provides quantitative evidence for the relevance of personality types and the diversity of personalities in startups. At the same time, it brings up other questions on how personality traits are related to other factors associated with success, such as:

Will the recent growing focus on promoting and investing in female founders change the nature, composition and dynamics of startups and their personalities leading to a more diverse personality landscape in startups?

Will the growth of startups outside of the United States change what success looks like to investors and hence the role of different personality traits and their association to diverse success metrics?

Many of today’s most renowned entrepreneurs are either Baby Boomers (such as Gates, Branson, Bloomberg) or Generation Xers (such as Benioff, Cannon-Brookes, Musk). However, as we can see, personality is both a predictor and driver of success in entrepreneurship. Will generation-wide differences in personality and outlook affect startups and their success?

Moreover, the findings shown here have natural extensions and applications beyond startups, such as for new projects within large established companies. While not technically startups, many large enterprises and industries such as construction, engineering and the film industry rely on forming new project-based, cross-functional teams that are often new ventures and share many characteristics of startups.

There is also potential for extending this research in other settings in government, NGOs, and within the research community. In scientific research, for example, team diversity in terms of age, ethnicity and gender has been shown to be predictive of impact, and personality diversity may be another critical dimension 52 .

Another extension of the study could investigate the development of the language used by startup founders on social media over time. Such an extension could investigate whether the language (and inferred psychological characteristics) change as the entrepreneurs’ ventures go through major business events such as foundation, funding, or exit.

Overall, this study demonstrates, first, that startup founders have significantly different personalities than employees. Secondly, besides firm-level factors, which are known to influence firm success, we show that a range of founder-level factors, notably the character traits of its founders, significantly impact a startup’s likelihood of success. Lastly, we looked at team-level factors. We discovered in a multifactor analysis that personality-diverse teams have the most considerable impact on the probability of a startup’s success, underlining the importance of personality diversity as a relevant factor of team performance and success.

Data sources

Entrepreneurs dataset.

Data about the founders of startups were collected from Crunchbase (Table  2 ), an open reference platform for business information about private and public companies, primarily early-stage startups. It is one of the largest and most comprehensive data sets of its kind and has been used in over 100 peer-reviewed research articles about economic and managerial research.

Crunchbase contains data on over two million companies - mainly startup companies and the companies who partner with them, acquire them and invest in them, as well as profiles on well over one million individuals active in the entrepreneurial ecosystem worldwide from over 200 countries and spans. Crunchbase started in the technology startup space, and it now covers all sectors, specifically focusing on entrepreneurship, investment and high-growth companies.

While Crunchbase contains data on over one million individuals in the entrepreneurial ecosystem, some are not entrepreneurs or startup founders but play other roles, such as investors, lawyers or executives at companies that acquire startups. To create a subset of only entrepreneurs, we selected a subset of 32,732 who self-identify as founders and co-founders (by job title) and who are also publicly active on the social media platform Twitter. We also removed those who also are venture capitalists to distinguish between investors and founders.

We selected founders active on Twitter to be able to use natural language processing to infer their Big Five personality features using an open-vocabulary approach shown to be accurate in the previous research by analysing users’ unstructured text, such as Twitter posts in our case. For this project, as with previous research 20 , we employed a commercial service, IBM Watson Personality Insight, to infer personality facets. This service provides raw scores and percentile scores of Big Five Domains (Openness, Conscientiousness, Extraversion, Agreeableness and Emotional Stability) and the corresponding 30 subdomains or facets. In addition, the public content of Twitter posts was collected, and there are 32,732 profiles that each had enough Twitter posts (more than 150 words) to get relatively accurate personality scores (less than 12.7% Average Mean Absolute Error).

The entrepreneurs’ dataset is analysed in combination with other data about the companies they founded to explore questions about the nature and patterns of personality traits of entrepreneurs and the relationships between these patterns and company success.

For the multifactor analysis, we further filtered the data in several preparatory steps for the success prediction modelling (for more details, see SI section  A.5 ). In particular, we removed data points with missing values (Extended Data Fig.  13 ) and kept only companies in the data that were founded from 1990 onward to ensure consistency with previous research 32 (see Extended Data Fig.  14 ). After cleaning, filtering and pre-processing the data, we ended up with data from 25,214 founders who founded 21,187 startup companies to be used in the multifactor analysis. Of those, 3442 startups in the data were successful, 2362 in the first seven years after they were founded (see Extended Data Figure  15 for more details).

Entrepreneurs and employees dataset

To investigate whether startup founders show personality traits that are similar or different from the population at large (i. e. the entrepreneurs vs employees sub-analysis shown in Fig.  1 A and B), we filtered the entrepreneurs’ data further: we reduced the sample to those founders of companies, which attracted more than US$100k in investment to create a reference set of successful entrepreneurs (n \(=\) 4400).

To create a control group of employees who are not also entrepreneurs or very unlikely to be of have been entrepreneurs, we leveraged the fact that while some occupational titles like CEO, CTO and Public Speaker are commonly shared by founders and co-founders, some others such as Cashier , Zoologist and Detective very rarely co-occur seem to be founders or co-founders. To illustrate, many company founders also adopt regular occupation titles such as CEO or CTO. Many founders will be Founder and CEO or Co-founder and CTO. While founders are often CEOs or CTOs, the reverse is not necessarily true, as many CEOs are professional executives that were not involved in the establishment or ownership of the firm.

Using data from LinkedIn, we created an Entrepreneurial Occupation Index (EOI) based on the ratio of entrepreneurs for each of the 624 occupations used in a previous study of occupation-personality fit 44 . It was calculated based on the percentage of all people working in the occupation from LinkedIn compared to those who shared the title Founder or Co-founder (See SI section  A.2 for more details). A reference set of employees (n=6685) was then selected across the 112 different occupations with the lowest propensity for entrepreneurship (less than 0.5% EOI) from a large corpus of Twitter users with known occupations, which is also drawn from the previous occupational-personality fit study 44 .

These two data sets were used to test whether it may be possible to distinguish successful entrepreneurs from successful employees based on the different patterns of personality traits alone.

Hierarchical clustering

We applied several clustering techniques and tests to the personality vectors of the entrepreneurs’ data set to determine if there are natural clusters and, if so, how many are the optimum number.

Firstly, to determine if there is a natural typology to founder personalities, we applied the Hopkins statistic—a statistical test we used to answer whether the entrepreneurs’ dataset contains inherent clusters. It measures the clustering tendency based on the ratio of the sum of distances of real points within a sample of the entrepreneurs’ dataset to their nearest neighbours and the sum of distances of randomly selected artificial points from a simulated uniform distribution to their nearest neighbours in the real entrepreneurs’ dataset. The ratio measures the difference between the entrepreneurs’ data distribution and the simulated uniform distribution, which tests the randomness of the data. The range of Hopkins statistics is from 0 to 1. The scores are close to 0, 0.5 and 1, respectively, indicating whether the dataset is uniformly distributed, randomly distributed or highly clustered.

To cluster the founders by personality facets, we used Agglomerative Hierarchical Clustering (AHC)—a bottom-up approach that treats an individual data point as a singleton cluster and then iteratively merges pairs of clusters until all data points are included in the single big collection. Ward’s linkage method is used to choose the pair of groups for minimising the increase in the within-cluster variance after combining. AHC was widely applied to clustering analysis since a tree hierarchy output is more informative and interpretable than K-means. Dendrograms were used to visualise the hierarchy to provide the perspective of the optimal number of clusters. The heights of the dendrogram represent the distance between groups, with lower heights representing more similar groups of observations. A horizontal line through the dendrogram was drawn to distinguish the number of significantly different clusters with higher heights. However, as it is not possible to determine the optimum number of clusters from the dendrogram, we applied other clustering performance metrics to analyse the optimal number of groups.

A range of Clustering performance metrics were used to help determine the optimal number of clusters in the dataset after an apparent clustering tendency was confirmed. The following metrics were implemented to evaluate the differences between within-cluster and between-cluster distances comprehensively: Dunn Index, Calinski-Harabasz Index, Davies-Bouldin Index and Silhouette Index. The Dunn Index measures the ratio of the minimum inter-cluster separation and the maximum intra-cluster diameter. At the same time, the Calinski-Harabasz Index improves the measurement of the Dunn Index by calculating the ratio of the average sum of squared dispersion of inter-cluster and intra-cluster. The Davies-Bouldin Index simplifies the process by treating each cluster individually. It compares the sum of the average distance among intra-cluster data points to the cluster centre of two separate groups with the distance between their centre points. Finally, the Silhouette Index is the overall average of the silhouette coefficients for each sample. The coefficient measures the similarity of the data point to its cluster compared with the other groups. Higher scores of the Dunn, Calinski-Harabasz and Silhouette Index and a lower score of the Davies-Bouldin Index indicate better clustering configuration.

Classification modelling

Classification algorithms.

To obtain a comprehensive and robust conclusion in the analysis predicting whether a given set of personality traits corresponds to an entrepreneur or an employee, we explored the following classifiers: Naïve Bayes, Elastic Net regularisation, Support Vector Machine, Random Forest, Gradient Boosting and Stacked Ensemble. The Naïve Bayes classifier is a probabilistic algorithm based on Bayes’ theorem with assumptions of independent features and equiprobable classes. Compared with other more complex classifiers, it saves computing time for large datasets and performs better if the assumptions hold. However, in the real world, those assumptions are generally violated. Elastic Net regularisation combines the penalties of Lasso and Ridge to regularise the Logistic classifier. It eliminates the limitation of multicollinearity in the Lasso method and improves the limitation of feature selection in the Ridge method. Even though Elastic Net is as simple as the Naïve Bayes classifier, it is more time-consuming. The Support Vector Machine (SVM) aims to find the ideal line or hyperplane to separate successful entrepreneurs and employees in this study. The dividing line can be non-linear based on a non-linear kernel, such as the Radial Basis Function Kernel. Therefore, it performs well on high-dimensional data while the ’right’ kernel selection needs to be tuned. Random Forest (RF) and Gradient Boosting Trees (GBT) are ensembles of decision trees. All trees are trained independently and simultaneously in RF, while a new tree is trained each time and corrected by previously trained trees in GBT. RF is a more robust and straightforward model since it does not have many hyperparameters to tune. GBT optimises the objective function and learns a more accurate model since there is a successive learning and correction process. Stacked Ensemble combines all existing classifiers through a Logistic Regression. Better than bagging with only variance reduction and boosting with only bias reduction, the ensemble leverages the benefit of model diversity with both lower variance and bias. All the above classification algorithms distinguish successful entrepreneurs and employees based on the personality matrix.

Evaluation metrics

A range of evaluation metrics comprehensively explains the performance of a classification prediction. The most straightforward metric is accuracy, which measures the overall portion of correct predictions. It will mislead the performance of an imbalanced dataset. The F1 score is better than accuracy by combining precision and recall and considering the False Negatives and False Positives. Specificity measures the proportion of detecting the true negative rate that correctly identifies employees, while Positive Predictive Value (PPV) calculates the probability of accurately predicting successful entrepreneurs. Area Under the Receiver Operating Characteristic Curve (AUROC) determines the capability of the algorithm to distinguish between successful entrepreneurs and employees. A higher value means the classifier performs better on separating the classes.

Feature importance

To further understand and interpret the classifier, it is critical to identify variables with significant predictive power on the target. Feature importance of tree-based models measures Gini importance scores for all predictors, which evaluate the overall impact of the model after cutting off the specific feature. The measurements consider all interactions among features. However, it does not provide insights into the directions of impacts since the importance only indicates the ability to distinguish different classes.

Statistical analysis

T-test, Cohen’s D and two-sample Kolmogorov-Smirnov test are introduced to explore how the mean values and distributions of personality facets between entrepreneurs and employees differ. The T-test is applied to determine whether the mean of personality facets of two group samples are significantly different from one another or not. The facets with significant differences detected by the hypothesis testing are critical to separate the two groups. Cohen’s d is to measure the effect size of the results of the previous t-test, which is the ratio of the mean difference to the pooled standard deviation. A larger Cohen’s d score indicates that the mean difference is greater than the variability of the whole sample. Moreover, it is interesting to check whether the two groups’ personality facets’ probability distributions are from the same distribution through the two-sample Kolmogorov-Smirnov test. There is no assumption about the distributions, but the test is sensitive to deviations near the centre rather than the tail.

Privacy and ethics

The focus of this research is to provide high-level insights about groups of startups, founders and types of founder teams rather than on specific individuals or companies. While we used unit record data from the publicly available data of company profiles from Crunchbase , we removed all identifiers from the underlying data on individual companies and founders and generated aggregate results, which formed the basis for our analysis and conclusions.

Data availability

A dataset which includes only aggregated statistics about the success of startups and the factors that influence is released as part of this research. Underlying data for all figures and the code to reproduce them are available on GitHub: https://github.com/Braesemann/FounderPersonalities . Please contact Fabian Braesemann ( [email protected] ) in case you have any further questions.

Change history

07 may 2024.

A Correction to this paper has been published: https://doi.org/10.1038/s41598-024-61082-7

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Acknowledgements

We thank Gary Brewer from BuiltWith ; Leni Mayo from Influx , Rachel Slattery from TeamSlatts and Daniel Petre from AirTree Ventures for their ongoing generosity and insights about startups, founders and venture investments. We also thank Tim Li from Crunchbase for advice and liaison regarding data on startups and Richard Slatter for advice and referrals in Twitter .

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All authors designed research; All authors analysed data and undertook investigation; F.B. and F.S. led multi-factor analysis; P.M., X.G. and M.A.R. led the founder/employee prediction; M.L.K. led personality insights; X.G. collected and tabulated the data; X.G., F.B., and F.S. created figures; X.G. created final art, and all authors wrote the paper.

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Correspondence to Fabian Braesemann .

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McCarthy, P.X., Gong, X., Braesemann, F. et al. The impact of founder personalities on startup success. Sci Rep 13 , 17200 (2023). https://doi.org/10.1038/s41598-023-41980-y

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• Published: 03 June 2024

Offering extended use of the contraceptive implant via an implementation science framework: a qualitative study of clinicians’ perceived barriers and facilitators

• Nicole Rigler 1 ,
• Gennifer Kully 2 , 3 ,
• Marisa C. Hildebrand 2 ,
• Sarah Averbach 2 , 3 &
• Sheila K. Mody 2  

BMC Health Services Research volume  24 , Article number:  697 ( 2024 ) Cite this article

Metrics details

The etonogestrel contraceptive implant is currently approved by the United States Food and Drug Administration (FDA) for the prevention of pregnancy up to 3 years. However, studies that suggest efficacy up to 5 years. There is little information on the prevalence of extended use and the factors that influence clinicians in offering extended use. We investigated clinician perspectives on the barriers and facilitators to offering extended use of the contraceptive implant.

Using the Consolidated Framework for Implementation Research (CFIR), we conducted semi-structured qualitative interviews. Participants were recruited from a nationwide survey study of reproductive health clinicians on their knowledge and perspective of extended use of the contraceptive implant. To optimize the diversity of perspectives, we purposefully sampled participants from this study. We used content analysis and consensual qualitative research methods to inform our coding and data analysis. Themes arose deductively and inductively.

We interviewed 20 clinicians including advance practice clinicians, family medicine physicians, obstetrician/gynecologist and complex family planning sub-specialists. Themes regarding barriers and facilitators to extended use of the contraceptive implant emerged. Barriers included the FDA approval for 3 years and clinician concern about liability in the context of off-label use of the contraceptive implant. Educational materials and a champion of extended use were facilitators.

Conclusions

There is opportunity to expand access to extended use of the contraceptive implant by developing educational materials for clinicians and patients, identifying a champion of extended use, and providing information on extended use prior to replacement appointments at 3 years.

Peer Review reports

The etonogestrel contraceptive implant is currently approved by the U.S. Food and Drug Administration (FDA) for 3 years of continuous use for the prevention of pregnancy [ 1 ]. However, there is evidence to support its use for up to 5 years while maintaining a low risk of pregnancy [ 2 , 3 , 4 ]. The off-label use of the contraceptive implant past its FDA-approved duration and up to 5 years is known as extended use. Importantly, the FDA supports off-label use of marketed drugs and medical devices so long as there is strong relevant published evidence [ 5 ]. Off-label use such as extended use of the contraceptive implant is common with many other reproductive devices and medications, including misoprostol for labor induction, the copper intrauterine device (IUD) for emergency contraception, and, prior to its recent FDA-approval for extended use, the 52 mg levonorgestrel (LNG) IUD for pregnancy prevention. The 52 mg LNG IUD was previously FDA-approved for 5 years, however strong published evidence demonstrated longer efficacy up to 8 years, leading clinicians to counsel on extended use and eventually contributing to updated federal guidelines [ 6 , 7 ].

Though there are clinicians who counsel patients on extended use of the contraceptive implant, many patients still undergo implant replacement after only 3 years of use [ 8 , 9 ]. Continuation rates of the contraceptive implant after 1 and 2 years of use is estimated to be at 81.7% and 68.7%, with the most common reason for early discontinuation prior to 3 years being changes to bleeding pattern [ 10 , 11 , 12 , 13 ]. Ali et al. report the most common reasons that patients decided to stop implant use in years 4 and 5: unspecified personal reasons, desired fertility, bleeding problems, and other medical reasons [ 4 ]. Additionally, a recent nationwide, web-based survey amongst a diverse group of reproductive health clinicians investigated the barriers and facilitators regarding extended use of the contraceptive implant up to 5 years [ 14 ]. The most common barriers found in the study were provider concerns about pregnancy risk and the current FDA approval for only 3 years of use. The key facilitators included strong published evidence supporting extended use and patient and clinician education on extended use. Other than these studies, the patient and clinician factors that facilitate and hinder widespread implementation of extended use of the contraceptive implant have not been explored.

Increasing implementation of extended use of the contraceptive implant across practice settings may decrease unnecessary procedures, devices, healthcare visits, and could improve access to, and satisfaction with, the contraceptive implant. Long-acting reversible contraceptive (LARC) methods such as the contraceptive implant and LNG IUD have significantly higher continuation and approval rates and are more efficacious at preventing pregnancy than non-LARC methods such as oral contraceptive pills and depot medroxyprogesterone acetate injection [ 12 , 15 , [ 16 ]. Given the continued high rates of unintended pregnancies in the United States and the consequential increase in healthcare costs and poor outcomes secondary to pregnancy complications, efficacious pregnancy prevention is an important public health objective and cost-saving measure [ 17 ].

Using a qualitative approach guided by an implementation science framework, the Consolidated Framework for Implementation Research (CFIR), [ 18 ] we sought to explore clinician perspectives on extended use of the contraceptive implant up to 5 years as well as the perceived barriers and facilitators for clinicians to offer extended use.

We conducted semi-structured interviews with 20 clinicians including obstetrics and gynecology generalists, family medicine physicians, complex family planning sub-specialists, and advanced practice clinicians. We recruited interview participants from a nationwide, web-based survey that assessed the prevalence of extended use of the contraceptive implant [ 17 ]. This study recruited respondents through email listservs for the Fellowship in Complex Family Planning, the Ryan Residency Training in Family Planning Program, women’s health nurse practitioners, and family medicine physicians, as well as private social media groups for obstetrician-gynecologists. The total reach of the survey was unknown, however, the study had a survey completion rate of 66.6% ( n  = 300/450). Of the 300 completed surveys, 290 respondents indicated their interest in being interviewed (96.7%).

Among the survey respondents, we invited 24 clinicians to participate in interviews, yielding an 83.3% response rate. We selectively recruited interview participants to enrich our sample, specifically focusing on clinician type, practice setting, and region of practice within the United States (U.S.). We also selected interview participants based on whether they always, sometimes, or never counsel on extended use to investigate a broad range of perspectives. For this study, offering extended use is defined as counseling on use past the current FDA-approved duration of 3 years and up to 5 years of use. Offering extended use can occur at any clinical encounter, including insertion appointments, replacement and removal appointments at or before 3 years, and general reproductive health appointments. Clinicians who always offer extended use were defined as those who counsel on extended use to patients who are considering or currently have the contraceptive implant. Clinicians who sometimes offer extended use were defined as those who counsel on extended use, but only to particular patients based on patient-specific factors such as body mass index or insurance coverage. Clinicians who never offer extended use were defined as those who never counsel on use of the contraceptive implant past 3 years of use.

The interview guide was created utilizing an implementation science framework that identifies factors for effectively enacting interventions [ 18 ]. The Consolidated Framework for Implementation Research (CFIR) is organized into 5 major domains: characteristics of the intervention, individual characteristics, inner setting, outer setting, and the process of implementation. The first domain, intervention characteristics, relates to the inherent qualities of the intervention, such as pharmacologic properties and side effects of the contraceptive implant when used up to 5 years. Individual characteristics relates to the roles and characteristics of individual patients and clinicians interacting with the intervention, such as educational background and type of insurance coverage. The inner setting domain assesses the internal setting in which an intervention will be implemented (i.e., clinic type, culture, and policies). The broader context in which an intervention will be implemented, including national policies and social norms is evaluated within the outer setting domain. Finally, the process of implementation domain explores the activities and strategies used to implement the intervention, such as educational materials or clinician and staff trainings on extended use.

We designed the interview guide around these specific domains with questions that aimed to identify targeted strategies to support successful implementation. The complete interview guide is in Appendix A . The interview guide was designed with input from clinicians who regularly prescribe contraception, including extended use of the contraceptive implant, as well as CFIR and implementation science experts. The Human Research Protection Program at our institution approved the study.

A single research team member conducted semi-structured interviews via secure video conference between July and August 2021. Interview participants provided informed consent. All participants were asked a full set of open-ended questions based on the interview guide, with focused follow-up questions to further investigate potential themes or to clarify points. All interviews were audio recorded, then transcribed. For data analysis, we used a content analysis approach to identify concepts and patterns within the dataset [ 19 ]. Themes arose deductively and inductively, with deductive themes identified from the CFIR domains and inductive themes arising from interview insights. Consensual qualitative research methods informed both our data analysis and coding process [ 20 ]. Three authors were involved in the thematic coding of the transcripts. Initially, 5 transcripts were independently coded then checked for inter-coder reliability. Any disagreements were discussed, and a consensus was achieved. The remaining transcripts were then coded by one of the three authors. Once all interviews were coded, major themes and representative quotes were identified. The research team utilized ATLAS.ti for analysis [ 21 ].

Between July and August 2021, we interviewed 20 clinicians from a variety of clinical settings, regions, and women’s health professions, achieving the intended diversity of perspectives (Table  1 ). Among participants, 7 (35.0%) always, 8 (40.0%) sometimes, 5 (25.0%) never offer extended use of the contraceptive implant (Table 2 ).

Characteristics of the intervention

We found that changes to bleeding pattern in or after the third year of use was a barrier to clinicians offering extended use of the contraceptive implant. The participants in this study noted that perceived increases in the irregularity or frequency of a patient’s bleeding makes extended use of the implant difficult for patients to accept. One clinician noticed that some patients correlate changes in their bleeding pattern with a perceived decrease in the efficacy of their implant:

"People who do start noticing changes in bleeding pattern […] [and] associating that with, ‘Oh, my implant is wearing out or becoming expired. I need to get this changed out."

-Complex Family Planning Specialist, Southwest, Academic Setting, sometimes offers extended use

The same clinician discussed that more research on bleeding patterns in the extended use period and potential treatments for implant-associated irregularities could be a facilitator of extended use:

"For bleeding, I think it would be awesome if there is a research study, looking at use of OCPs [oral contraceptive pills] to manage bleeding near the end of the use of an implant or near that three-year mark,, […] So that we could give people… Honestly, either a natural history or a, ‘Here’s how you can manage that if you do want to keep using your implant longer.’"

- Complex Family Planning Specialist, Southwest, Academic Setting, sometimes offers extended use

Information on the bleeding pattern in years 4 and 5 of use and how clinicians can address irregular bleeding during implant use may increase acceptability of extended use.

Individual characteristics

We found that insurance impacts whether a clinician offers extended use:

"I do sometimes have patients saying, ‘I might be changing jobs or I’m going to be turning 27 or whatever.’ And so insurance is a barrier and so they’re like, ‘I want the new one while I still have this insurance.’"

- Family Medicine Physician, Midwest, Community Setting, sometimes offers extended use

Many participants agreed with this concept and stated that acceptability of extended use depends on a patient’s perception of their future insurance status. Clinicians observed that if a patient believes they will have coverage for a replacement or removal in the future, they are more likely to pursue extended use of their implant. Conversely, one clinician discussed how lack of current insurance coverage could be a facilitator of extended use:

"So, I would generally offer extended use to people that didn’t have insurance and would have to self-pay. I would like go through the data with them so they wouldn’t have to pay like $1,000 to get a new implant because it could work another year, or people that were concerned about changing side effects at that time."

- Obstetrician-Gynecologist, Southwest, Academic Setting, sometimes offers extended use

Overall, clinicians perceived that patients’ concerns about current and future insurance coverage may affect acceptance of extended use.

Inner setting

This study found that having a champion of extended use at a clinician’s home or affiliate institution was a facilitator of extended use. Most clinicians in the study stated that it is or would be helpful to have someone who worked with them clinically that was knowledgeable on the data about extended use. When asked which factor would promote extended use of the implant the most, this clinician stated:

"…having a champion who is really ready to present the evidence, because the evidence can be there, but people don’t have time to read it. If it’s not brought to them, they’re not really going to know about it."

- Obstetrician-Gynecologist, West Coast, Community Setting, does not offer extended use

Potential champions identified were physicians, nurses, medical directors, or other clinicians in leadership positions, but participants generally believed that the position should be held by someone who is passionate about contraception, highly familiar with the specific setting, and knowledgeable about the clinical studies on extended use.

A barrier noted by a few participants was the effect of discordant counseling by different clinicians, sometimes within the same clinic, on acceptability of extended use:

"I mean, I guess like getting everyone on the same page, like in your practice can be a barrier. Especially in the practice I’ve been at, which like I said was in a state that was very litigious, so people weren’t always willing to like go outside guidelines that were… So getting your whole group on the same page so patients get like a more consistent message."

- Obstetrician-Gynecologist, Southwest, Academic Setting, sometimes offers extended use.

Participants discussed that it is important for clinician teams to relay a cohesive message to patients, especially in settings where patients may see multiple clinicians for their contraceptive care.

Outer setting

Lack of FDA approval for extended use was identified as barrier by many clinicians, and some clinicians counseled patients only on the FDA-approved duration of the contraceptive implant:

"So, generally in our practice we don’t really talk about extended use. We say this is FDA approved for three years."

- Advanced Practice Clinician, Southeast, Community Setting, sometimes offers extended use.

Even clinicians who do offer extended use of the implant noted that off-label use can be confusing to patients, making it difficult to counsel on extended use:

"So I have patients all the time, who’ll say, ‘Well, what do you mean I can keep X, Y or Z in for an extra year?’ And I’ll say, ‘We have big studies that tell us that this is an okay thing to do.’ But that just feels weird. People don’t necessarily understand the role of the FDA or sort of how it works. And so it’s something like extended use just might be a really such a foreign concept. Right? It’s so far outside. But I think that there are also, there are lay outlets that cover this stuff. So it’s not that it’s impossible to access. It’s just that the patient has to be interested just like the provider has to be interested."

- Complex Family Planning Specialist, East Coast, Academic Setting, sometimes offers extended use.

Clinicians also observed that certain clinics must follow official guidelines without the flexibility to offer extended use, regardless of a clinician’s perspective or willingness to counsel on extended use. Interestingly, patient confusion as well as mistrust of the healthcare system may impact patient acceptability of extended use in the context of a three-year FDA-approved duration:

"The other thing is the FDA approval because the box says three years, but then like I tell people, you can take it out in five years. And then they don’t believe… Like who is right. Is it my doctor who’s getting in front of me right or the box, right?"

- Family Medicine Physician, West Coast, Community Setting, always offers extended use.

This clinician noted that a disconnect between a clinician’s counseling and prescription information may lead patients to be confused about the recommendation for extended use.

Another barrier mentioned by a few participants was provider concern about liability in the event of an unintended pregnancy. Participants discussed fear of both legal and interpersonal repercussions of unintended pregnancy after counseling on off-label use of a contraceptive device:

"Even though there’s a slim chance that a patient would get pregnant on Nexplanon [the contraceptive implant], I feel like if we were to say, ‘Yeah, you can use it beyond the four years,’ and they come up and they get pregnant, they’re that 1% chance that gets pregnant, I feel like there could be a little bit of blame laid on us if we were to tell them that they’re able to it beyond the three years when the label doesn’t say that yet."

- Advanced Practice Clinician, Southeast, Private Practice, does not offer extended use.

Some participants felt that they would “have no ground to stand on” in the event of a lawsuit (OBGYN Physician, Midwest, Private Practice), making them concerned about the possibility of increased liability in counseling on off-label use without FDA approval.

Interestingly, multiple clinicians also discussed abortion restrictions in the United States as influencing patients in their decision to pursue extended use or not:

"In the past four years [2017–2021] have also had a lot of patients express concern about the administration. And so wanting to kind of be as current as they can be with their devices and so potentially exchanging them sooner than they need."

- Complex Family Planning Specialist, West Coast, Academic Setting, always offers extended use.

Clinicians observed that patients are noticing and reacting to abortion restrictions when making their contraceptive decisions, which may impact the widespread implementation of extended use.

Process of implementation

Many clinicians reported that a barrier to implementing extended use was patient preference for removal when they are already in clinic for a scheduled removal or replacement procedure, regardless of being counseled on extended use at that time:

“’Oh, I’m already here. I’m approved. Let’s just go ahead and get it done.’ So there’s probably not a whole lot you can do about that either, once they’re already in the clinic, and have their mind set on it.”

- Obstetrician-Gynecologist, Southeast, Academic Setting, does not offer extended use.

Many participants in this study noted that patients have made logistical arrangements prior to their appointments including paid time off, childcare, or prior authorization. It can be difficult for clinicians to offer extended use within this context, therefore counseling is better done prior to a patient coming in for a replacement appointment.

A perceived facilitator of extended use that was mentioned often was clear, concise clinician educational services or materials that illustrates existing data on efficacy and risks. Clinicians believed that this education could be in the form of continued medical education, targeted trainings, or written summaries of relevant studies, data, and recommendations. One consistency across interviews was that education on extended use must be integrated into regular practice and be easily understood by busy clinicians:

"I think that when we get a pamphlet or a brochure or a one page, something that just has everything condensed so it’s a really quick, oh, okay, this is something that we can be offering patients. And these are the reasons why it would be a benefit to them, and these are the patients that maybe would fall out of not offering this to. I think because of how busy we are, that’s the best way for us to make change."

- Advanced Practice Clinician, Southwest, Academic Setting, does not offer extended use.

Participants reported that these resources should be widely distributed beyond the complex family planning and obstetrician-gynecology community to increase accessibility to extended use.

Another potential facilitator identified was effective patient educational materials such as flyers that state the 5-year efficacy of the contraceptive implant, though producing these might require FDA approval. Participants in this study report that patients rely on clinicians to provide information on the efficacy and duration of their contraceptive implant. However, it is difficult for patients to accept extended use when there are inconsistencies across multiple sources of information:

"I mean, if online, there was information where it said you can keep it in for three to five years and they’re able to back that up. You know, people like to do their own research. I think that would be helpful, versus it says everywhere three, three, three, three, three, and then you’re the only person telling them something different, then it’s a little more tricky."

- Obstetrician-Gynecologist, West Coast, Community Setting, does not offer extended use.

Overall, participants in this study expressed that it would be helpful to have easily understood information for clinicians and patients that explained the evidence for extended use.

Our results demonstrate that there is an opportunity to increase widespread implementation of extended use through multiple interventions. Clinicians reported that patients prefer to have their implants replaced when they are already in clinic for the procedure. Therefore, intervening prior to replacement appointments at 3 years in the form of telemedicine visits or notifications from scheduling staff may make extended use of the contraceptive implant more acceptable to patients. Further, clinician and patient education on extended use that is easily understood and widely disseminated would likely increase use of the contraceptive implant up to 5 years.

The implementation of extended use of the contraceptive implant up to 5 years likely decreases healthcare costs secondary to fewer procedures and unintended pregnancies, and expands reproductive choices for patients seeking contraception. It has been found that clinicians who offer extended use state that most of their patients accept extended use when it is offered [ 14 ]. However, the reasons why a patient may or may not accept extended use are unclear, but may include changes in bleeding and concerns about use past the FDA-approved duration. Research on bleeding patterns in the extended use period may facilitate counseling and give patients a better expectation of possible changes they may see in years 4 and 5. Additionally, research on the patient perspective and acceptability of using the contraceptive implant past its FDA-approved timeframe is needed.

This study focused on clinicians and their perspectives on extended use. However, it is important to note that patients may be fully informed about extended use and choose to replace their implant at or before 3 years of duration. All discussions regarding contraception, including extended use of the implant, should always occur within a patient-centered and shared decision-making model. Widespread offering of extended use may allow for more patients to make fully informed decisions about the duration and use of their contraceptive devices, therefore expanding reproductive choice and agency in addition to potentially sparing patients from unnecessary procedures and extra healthcare costs.

Interestingly, although there are data to reflect high implant efficacy in years 4 and 5, [ 2 , 3 , 4 ] some participants in this study believe there is increased liability in counseling on off-label use without FDA approval. Importantly, off-label use is common among reproductive clinicians and is protected by the FDA if there is strong published evidence supporting off label use [ 5 ]. Additionally, the Society of Family Planning supports extended use of the contraceptive implant up to 5 years [ 22 ]. The FDA requires implant training for clinicians before they can insert or remove the implant. This training includes the FDA product labeling indicating the maximum duration of use for pregnancy prevention as three years [ 1 ]. It is possible that clinician training and product labels that advertise a 3-year duration dissuade clinicians from offering extended use of the contraceptive implant due to concerns about legal repercussions in the event of an unintended pregnancy with extended use. Therefore, organization- or systems-level guidelines, policy changes, and trainings in support of extended use may allow clinicians to feel comfortable offering off-label use of the implant. Additionally, FDA approval of the contraceptive implant to 5 years would likely greatly facilitate implementation of extended use.

Changing the FDA label to reflect extended use can be expensive, and contraceptive companies may not be incentivized to change the label. However, increasing the FDA approval of the contraceptive implant would allow for companies to have a longer-acting contraceptive device that is more directly comparable to other LARC devices such as the 52 mg LNG IUD that can be used for up to 8 years. If FDA approval for 5 years of use were to occur, it is not known if the barriers described in this study would continue to apply. However, it is likely that the facilitators of extended use from this study would support implementation of extended use irrespective of the federally approved duration.

One strength of the study is the national sample and the diversity of clinician types and settings. There is also representation of clinicians who consistently offer extended use and those who do not offer extended use. Another strength of this study is that it was designed utilizing a framework focusing on implementation, thus yielding results that can be used to create effective interventions.

Limitations of this study include the small sample size and selection bias from recruiting from a prior study that utilized listservs and social media. Additionally, we recruited from a population that was specifically interested in family planning and identified mostly as Caucasian and female. Because of this, our results may not be generalizable to the national population of clinicians who offer contraceptive implant services. However, our direct selection of participants who only sometimes or do not offer extended use allowed us to hear diverse perspectives regardless of prior knowledge or interest in extended use. Another limitation is that we did not ask advanced practice clinicians what their specific training was (i.e., nurse practitioner or physician’s assistant). As the training for advanced practice clinicians can vary greatly, our results may not be generalizable to all advanced practice clinicians.

In conclusion, this study describes the barriers and facilitators to widespread implementation of extended use of the contraceptive implant. These results offer new perspectives and potential strategies to increase widespread implementation of extended use of the contraceptive implant up to 5 years of use. Based on our findings, there is opportunity to expand access to extended use by developing educational materials for clinicians and patients, identifying a champion of extended use, and counseling on extended use prior to removal appointments at 3 years. Of note, these results should be viewed in the context of recent policy access issues regarding reproductive health and used to support patient-centered contraceptive choices, regardless of a patient’s decision to extend use of their contraceptive implant up to 5 years. It is important that clinicians and patients utilize shared decision making when discussing extended use of the contraceptive implant.

Availability of data and materials

The datasets generated and/or analyzed during the current study are not publicly available due to being stored in a private, HIPAA-compliant database, but are available from the corresponding author on reasonable request.

Abbreviations

Consolidated Framework for Implementation Research

Food and Drug Administration

CoIntrauterine device

• Long-acting reversible contraception

Levonorgestrel

Obstetrician-Gynecologist

United States

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Acknowledgements

We thank the participants in this study.

This study was funded by Organon (Study #201908). The funder had no role in the study design, analysis, or interpretation of findings.

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Nicole Rigler

Division of Complex Family Planning, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Diego, 9300 Campus Point Dr. MC 7433, La Jolla, San Diego, CA, USA

Gennifer Kully, Marisa C. Hildebrand, Sarah Averbach & Sheila K. Mody

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SM is the principal investigator and lead data analysis, including qualitative coding, and dissemination of findings. She was also involved in study design and participant recruitment. NR was the primary interviewer and was involved in study design, recruitment, data management, data analysis, and dissemination of findings. GK and MH were involved with study design, recruitment, coordination of the study, IRB documentation, data analysis, and dissemination of findings. SA was involved with study design and dissemination of findings. All authors read and approved the final draft of the manuscript.

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Correspondence to Sheila K. Mody .

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S.M. is a consultant for Bayer and Merck. She has grant funding from Organon and receives authorship royalties from UpToDate. S.A. has served as a consultant for Bayer on immediate postpartum IUD use. The remaining authors report no conflict of interest.

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Rigler, N., Kully, G., Hildebrand, M.C. et al. Offering extended use of the contraceptive implant via an implementation science framework: a qualitative study of clinicians’ perceived barriers and facilitators. BMC Health Serv Res 24 , 697 (2024). https://doi.org/10.1186/s12913-024-10991-4

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Received : 19 December 2023

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DOI : https://doi.org/10.1186/s12913-024-10991-4

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