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• 26 Mar 2024
• Research & Ideas

How Humans Outshine AI in Adapting to Change

Could artificial intelligence systems eventually perform surgeries or fly planes? First, AI will have to learn to navigate shifting conditions as well as people do. Julian De Freitas and colleagues pit humans against machines in a video game to study AI's current limits and mine insights for the real world.

• 12 Mar 2024

Publish or Perish: What the Research Says About Productivity in Academia

Universities tend to evaluate professors based on their research output, but does that measure reflect the realities of higher ed? A study of 4,300 professors by Kyle Myers, Karim Lakhani, and colleagues probes the time demands, risk appetite, and compensation of faculty.

• 24 Jan 2024

Why Boeing’s Problems with the 737 MAX Began More Than 25 Years Ago

Aggressive cost cutting and rocky leadership changes have eroded the culture at Boeing, a company once admired for its engineering rigor, says Bill George. What will it take to repair the reputational damage wrought by years of crises involving its 737 MAX?

• 19 Sep 2023

What Chandrayaan-3 Says About India's Entrepreneurial Approach to Space

India reached an unexplored part of the moon despite its limited R&D funding compared with NASA and SpaceX. Tarun Khanna discusses the significance of the landing, and the country's advancements in data and digital technology.

• 28 Mar 2023

The FDA’s Speedy Drug Approvals Are Safe: A Win-Win for Patients and Pharma Innovation

Expediting so-called breakthrough therapies has saved millions of dollars in research time without compromising drug safety or efficacy, says research by Ariel Stern, Amitabh Chandra, and colleagues. Could policymakers harness the approach to bring life-saving treatments to the market faster?

• 16 Mar 2023

Why Business Travel Still Matters in a Zoom World

Meeting in person can make all the difference for colleagues from different time zones or cultural backgrounds. A study by Prithwiraj Choudhury traces flight patterns among 5,000 airports around the world to show how business travel propels innovation.

• 13 Apr 2021
• Working Paper Summaries

Population Interference in Panel Experiments

In panel experiments, units are exposed to different interventions over time. This article introduces a unifying framework for studying panel experiments with population interference, in which a treatment assigned to one experimental unit affects another experimental unit's outcome. Findings have implications for fields as diverse as education, economics, and public health.

• 22 Feb 2021

Private and Social Returns to R&D: Drug Development and Demographics

Research and development (R&D) by pharmaceutical firms focuses disproportionately on medical conditions afflicting the elderly. The proportion of R&D spending targeting older age groups is increasing over time. Even though these investments in R&D prolong life expectancy and improve quality of life, they have little effect on measured productivity and output growth.

• 15 Dec 2020

Designing, Not Checking, for Policy Robustness: An Example with Optimal Taxation

The approach used by most economists to check academic research results is flawed for policymaking and evaluation. The authors propose an alternative method for designing economic policy analyses that might be applied to a wide range of economic policies.

• 30 Nov 2020

Short-Termism, Shareholder Payouts, and Investment in the EU

Shareholder-driven “short-termism,” as evidenced by increasing payouts to shareholders, is said to impede long-term investment in EU public firms. But a deep dive into the data reveals a different story.

• 22 Oct 2020

Estimating Causal Effects in the Presence of Partial Interference Using Multivariate Bayesian Structural Time Series Models

A case study of an Italian supermarket introducing a new pricing policy—in which it reduced prices on some brands—offers managers a new approach to reduce uncertainty. The approach is flexible and can be applied to different business problems.

• 06 Oct 2020

Design and Analysis of Switchback Experiments

This paper presents a framework for managers to design and run switchback experiments.

• 28 Sep 2020

What Can Economics Say About Alzheimer's Disease?

This essay discusses the role of market frictions and "missing medicines" in drug innovation and highlights how frameworks and toolkits of economists can help our understanding of the determinants and effects of Alzheimer's disease on health.

• 24 Aug 2020

When Do Experts Listen to Other Experts? The Role of Negative Information in Expert Evaluations for Novel Projects

Evaluators of early-stage scientific proposals tend to systematically focus on the weaknesses of proposed work rather than its strengths, according to evidence from two field experiments.

• 10 Aug 2020

COVID's Surprising Toll on Careers of Women Scientists

Women scientists and those with young children are paying a steep career price in the pandemic, according to new research by Karim Lakhani, Kyle Myers, and colleagues. Open for comment; 0 Comments.

• 02 Aug 2020
• What Do You Think?

Is the 'Experimentation Organization' Becoming the Competitive Gold Standard?

SUMMING UP: Digital experimentation is gaining momentum as an everyday habit in many organizations, especially those in high tech, say James Heskett's readers. Open for comment; 0 Comments.

• 27 Jul 2020

Gender Inequality in Research Productivity During the COVID-19 Pandemic

Analysis of data from the largest open-access repositories for social science in the world finds that female researchers’ productivity significantly dropped relative to that of male researchers as a result of the lockdown in the United States.

• 08 Jul 2020

Inventing the Endless Frontier: The Effects of the World War II Research Effort on Post-War Innovation

Investments made in World War II by the United States Office of Scientific Research and Development powered decades of subsequent innovation and the take-off of regional technology hubs around the country.

• 06 Apr 2020

A General Theory of Identification

Statistical inference teaches us how to learn from data, whereas identification analysis explains what we can learn from it. This paper proposes a simple unifying theory of identification, encouraging practitioners to spend more time thinking about what they can estimate from the data and assumptions before trying to estimate it.

• 23 Mar 2020

The Effects of Hierarchy on Learning and Performance in Business Experimentation

Do senior managers help or hurt business experiments? Analyzing a dataset of more than 6,300 experiments on the A/B/n testing platform Optimizely, this study suggests that involving senior executives in experimentation teams can have surprising consequences.

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

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
• Get Data, Get Help!

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

• Clinical Trials

About Clinical Studies

Research: it's all about patients.

Mayo's mission is about the patient, the patient comes first. So the mission and research here, is to advance how we can best help the patient, how to make sure the patient comes first in care. So in many ways, it's a cycle. It can start with as simple as an idea, worked on in a laboratory, brought to the patient bedside, and if everything goes right, and let's say it's helpful or beneficial, then brought on as a standard approach. And I think that is one of the unique characteristics of Mayo's approach to research, that patient-centeredness. That really helps to put it in its own spotlight.

At Mayo Clinic, the needs of the patient come first. Part of this commitment involves conducting medical research with the goal of helping patients live longer, healthier lives.

Through clinical studies, which involve people who volunteer to participate in them, researchers can better understand how to diagnose, treat and prevent diseases or conditions.

Types of clinical studies

• Observational study. A type of study in which people are observed or certain outcomes are measured. No attempt is made by the researcher to affect the outcome — for example, no treatment is given by the researcher.
• Clinical trial (interventional study). During clinical trials, researchers learn if a new test or treatment works and is safe. Treatments studied in clinical trials might be new drugs or new combinations of drugs, new surgical procedures or devices, or new ways to use existing treatments. Find out more about the five phases of non-cancer clinical trials on ClinicalTrials.gov or the National Cancer Institute phases of cancer trials .
• Medical records research. Medical records research involves the use of information collected from medical records. By studying the medical records of large groups of people over long periods of time, researchers can see how diseases progress and which treatments and surgeries work best. Find out more about Minnesota research authorization .

Clinical studies may differ from standard medical care

A health care provider diagnoses and treats existing illnesses or conditions based on current clinical practice guidelines and available, approved treatments.

But researchers are constantly looking for new and better ways to prevent and treat disease. In their laboratories, they explore ideas and test hypotheses through discovery science. Some of these ideas move into formal clinical trials.

During clinical studies, researchers formally and scientifically gather new knowledge and possibly translate these findings into improved patient care.

Before clinical trials begin

This video demonstrates how discovery science works, what happens in the research lab before clinical studies begin, and how a discovery is transformed into a potential therapy ready to be tested in trials with human participants:

How clinical trials work

Trace the clinical trial journey from a discovery research idea to a viable translatable treatment for patients:

See a glossary of terms related to clinical studies, clinical trials and medical research on ClinicalTrials.gov.

Watch a video about clinical studies to help you prepare to participate.

Let's Talk About Clinical Research

Narrator: This presentation is a brief introduction to the terms, purposes, benefits and risks of clinical research.

If you have questions about the content of this program, talk with your health care provider.

What is clinical research?

Clinical research is a process to find new and better ways to understand, detect, control and treat health conditions. The scientific method is used to find answers to difficult health-related questions.

Ways to participate

There are many ways to participate in clinical research at Mayo Clinic. Three common ways are by volunteering to be in a study, by giving permission to have your medical record reviewed for research purposes, and by allowing your blood or tissue samples to be studied.

Types of clinical research

There are many types of clinical research:

• Prevention studies look at ways to stop diseases from occurring or from recurring after successful treatment.
• Screening studies compare detection methods for common conditions.
• Diagnostic studies test methods for early identification of disease in those with symptoms.
• Treatment studies test new combinations of drugs and new approaches to surgery, radiation therapy and complementary medicine.
• The role of inheritance or genetic studies may be independent or part of other research.
• Quality of life studies explore ways to manage symptoms of chronic illness or side effects of treatment.
• Medical records studies review information from large groups of people.

Clinical research volunteers

Participants in clinical research volunteer to take part. Participants may be healthy, at high risk for developing a disease, or already diagnosed with a disease or illness. When a study is offered, individuals may choose whether or not to participate. If they choose to participate, they may leave the study at any time.

Research terms

You will hear many terms describing clinical research. These include research study, experiment, medical research and clinical trial.

Clinical trial

A clinical trial is research to answer specific questions about new therapies or new ways of using known treatments. Clinical trials take place in phases. For a treatment to become standard, it usually goes through two or three clinical trial phases. The early phases look at treatment safety. Later phases continue to look at safety and also determine the effectiveness of the treatment.

Phase I clinical trial

A small number of people participate in a phase I clinical trial. The goals are to determine safe dosages and methods of treatment delivery. This may be the first time the drug or intervention is used with people.

Phase II clinical trial

Phase II clinical trials have more participants. The goals are to evaluate the effectiveness of the treatment and to monitor side effects. Side effects are monitored in all the phases, but this is a special focus of phase II.

Phase III clinical trial

Phase III clinical trials have the largest number of participants and may take place in multiple health care centers. The goal of a phase III clinical trial is to compare the new treatment to the standard treatment. Sometimes the standard treatment is no treatment.

Phase IV clinical trial

A phase IV clinical trial may be conducted after U.S. Food and Drug Administration approval. The goal is to further assess the long-term safety and effectiveness of a therapy. Smaller numbers of participants may be enrolled if the disease is rare. Larger numbers will be enrolled for common diseases, such as diabetes or heart disease.

Clinical research sponsors

Mayo Clinic funds clinical research at facilities in Rochester, Minnesota; Jacksonville, Florida; and Arizona, and in the Mayo Clinic Health System. Clinical research is conducted in partnership with other medical centers throughout the world. Other sponsors of research at Mayo Clinic include the National Institutes of Health, device or pharmaceutical companies, foundations and organizations.

Clinical research at Mayo Clinic

Dr. Hugh Smith, former chair of Mayo Clinic Board of Governors, stated, "Our commitment to research is based on our knowledge that medicine must be constantly moving forward, that we need to continue our efforts to better understand disease and bring the latest medical knowledge to our practice and to our patients."

This fits with the term "translational research," meaning what is learned in the laboratory goes quickly to the patient's bedside and what is learned at the bedside is taken back to the laboratory.

Ethics and safety of clinical research

All clinical research conducted at Mayo Clinic is reviewed and approved by Mayo's Institutional Review Board. Multiple specialized committees and colleagues may also provide review of the research. Federal rules help ensure that clinical research is conducted in a safe and ethical manner.

Institutional review board

An institutional review board (IRB) reviews all clinical research proposals. The goal is to protect the welfare and safety of human subjects. The IRB continues its review as research is conducted.

Consent process

Participants sign a consent form to ensure that they understand key facts about a study. Such facts include that participation is voluntary and they may withdraw at any time. The consent form is an informational document, not a contract.

Study activities

Staff from the study team describe the research activities during the consent process. The research may include X-rays, blood tests, counseling or medications.

Study design

During the consent process, you may hear different phrases related to study design. Randomized means you will be assigned to a group by chance, much like a flip of a coin. In a single-blinded study, participants do not know which treatment they are receiving. In a double-blinded study, neither the participant nor the research team knows which treatment is being administered.

Some studies use an inactive substance called a placebo.

Multisite studies allow individuals from many different locations or health care centers to participate.

Remuneration

If the consent form states remuneration is provided, you will be paid for your time and participation in the study.

Some studies may involve additional cost. To address costs in a study, carefully review the consent form and discuss questions with the research team and your insurance company. Medicare may cover routine care costs that are part of clinical trials. Medicaid programs in some states may also provide routine care cost coverage, as well.

When considering participation in a research study, carefully look at the benefits and risks. Benefits may include earlier access to new clinical approaches and regular attention from a research team. Research participation often helps others in the future.

Risks/inconveniences

Risks may include side effects. The research treatment may be no better than the standard treatment. More visits, if required in the study, may be inconvenient.

Weigh your risks and benefits

Consider your situation as you weigh the risks and benefits of participation prior to enrolling and during the study. You may stop participation in the study at any time.

Ask questions

Stay informed while participating in research:

• Write down questions you want answered.
• If you do not understand, say so.
• If you have concerns, speak up.

Website resources are available. The first website lists clinical research at Mayo Clinic. The second website, provided by the National Institutes of Health, lists studies occurring in the United States and throughout the world.

Additional information about clinical research may be found at the Mayo Clinic Barbara Woodward Lips Patient Education Center and the Stephen and Barbara Slaggie Family Cancer Education Center.

Clinical studies questions

• Phone: 800-664-4542 (toll-free)
• Contact form

Cancer-related clinical studies questions

• Phone: 855-776-0015 (toll-free)

International patient clinical studies questions

• Phone: 507-284-8884
• Email: [email protected]

Clinical Studies in Depth

Learning all you can about clinical studies helps you prepare to participate.

• Institutional Review Board

The Institutional Review Board protects the rights, privacy, and welfare of participants in research programs conducted by Mayo Clinic and its associated faculty, professional staff, and students.

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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The 10 Most Significant Education Studies of 2021

From reframing our notion of “good” schools to mining the magic of expert teachers, here’s a curated list of must-read research from 2021.

It was a year of unprecedented hardship for teachers and school leaders. We pored through hundreds of studies to see if we could follow the trail of exactly what happened: The research revealed a complex portrait of a grueling year during which persistent issues of burnout and mental and physical health impacted millions of educators. Meanwhile, many of the old debates continued: Does paper beat digital? Is project-based learning as effective as direct instruction? How do you define what a “good” school is?

Other studies grabbed our attention, and in a few cases, made headlines. Researchers from the University of Chicago and Columbia University turned artificial intelligence loose on some 1,130 award-winning children’s books in search of invisible patterns of bias. (Spoiler alert: They found some.) Another study revealed why many parents are reluctant to support social and emotional learning in schools—and provided hints about how educators can flip the script.

1. What Parents Fear About SEL (and How to Change Their Minds)

When researchers at the Fordham Institute asked parents to rank phrases associated with social and emotional learning , nothing seemed to add up. The term “social-emotional learning” was very unpopular; parents wanted to steer their kids clear of it. But when the researchers added a simple clause, forming a new phrase—”social-emotional & academic learning”—the program shot all the way up to No. 2 in the rankings.

What gives?

Parents were picking up subtle cues in the list of SEL-related terms that irked or worried them, the researchers suggest. Phrases like “soft skills” and “growth mindset” felt “nebulous” and devoid of academic content. For some, the language felt suspiciously like “code for liberal indoctrination.”

But the study suggests that parents might need the simplest of reassurances to break through the political noise. Removing the jargon, focusing on productive phrases like “life skills,” and relentlessly connecting SEL to academic progress puts parents at ease—and seems to save social and emotional learning in the process.

2. The Secret Management Techniques of Expert Teachers

In the hands of experienced teachers, classroom management can seem almost invisible: Subtle techniques are quietly at work behind the scenes, with students falling into orderly routines and engaging in rigorous academic tasks almost as if by magic. 

That’s no accident, according to new research . While outbursts are inevitable in school settings, expert teachers seed their classrooms with proactive, relationship-building strategies that often prevent misbehavior before it erupts. They also approach discipline more holistically than their less-experienced counterparts, consistently reframing misbehavior in the broader context of how lessons can be more engaging, or how clearly they communicate expectations.

Focusing on the underlying dynamics of classroom behavior—and not on surface-level disruptions—means that expert teachers often look the other way at all the right times, too. Rather than rise to the bait of a minor breach in etiquette, a common mistake of new teachers, they tend to play the long game, asking questions about the origins of misbehavior, deftly navigating the terrain between discipline and student autonomy, and opting to confront misconduct privately when possible.

3. The Surprising Power of Pretesting

Asking students to take a practice test before they’ve even encountered the material may seem like a waste of time—after all, they’d just be guessing.

But new research concludes that the approach, called pretesting, is actually more effective than other typical study strategies. Surprisingly, pretesting even beat out taking practice tests after learning the material, a proven strategy endorsed by cognitive scientists and educators alike. In the study, students who took a practice test before learning the material outperformed their peers who studied more traditionally by 49 percent on a follow-up test, while outperforming students who took practice tests after studying the material by 27 percent.

The researchers hypothesize that the “generation of errors” was a key to the strategy’s success, spurring student curiosity and priming them to “search for the correct answers” when they finally explored the new material—and adding grist to a 2018 study that found that making educated guesses helped students connect background knowledge to new material.

Learning is more durable when students do the hard work of correcting misconceptions, the research suggests, reminding us yet again that being wrong is an important milestone on the road to being right.

4. Confronting an Old Myth About Immigrant Students

Immigrant students are sometimes portrayed as a costly expense to the education system, but new research is systematically dismantling that myth.

In a 2021 study , researchers analyzed over 1.3 million academic and birth records for students in Florida communities, and concluded that the presence of immigrant students actually has “a positive effect on the academic achievement of U.S.-born students,” raising test scores as the size of the immigrant school population increases. The benefits were especially powerful for low-income students.

While immigrants initially “face challenges in assimilation that may require additional school resources,” the researchers concluded, hard work and resilience may allow them to excel and thus “positively affect exposed U.S.-born students’ attitudes and behavior.” But according to teacher Larry Ferlazzo, the improvements might stem from the fact that having English language learners in classes improves pedagogy , pushing teachers to consider “issues like prior knowledge, scaffolding, and maximizing accessibility.”

5. A Fuller Picture of What a ‘Good’ School Is

It’s time to rethink our definition of what a “good school” is, researchers assert in a study published in late 2020.⁣ That’s because typical measures of school quality like test scores often provide an incomplete and misleading picture, the researchers found.

The study looked at over 150,000 ninth-grade students who attended Chicago public schools and concluded that emphasizing the social and emotional dimensions of learning—relationship-building, a sense of belonging, and resilience, for example—improves high school graduation and college matriculation rates for both high- and low-income students, beating out schools that focus primarily on improving test scores.⁣

“Schools that promote socio-emotional development actually have a really big positive impact on kids,” said lead researcher C. Kirabo Jackson in an interview with Edutopia . “And these impacts are particularly large for vulnerable student populations who don’t tend to do very well in the education system.”

The findings reinforce the importance of a holistic approach to measuring student progress, and are a reminder that schools—and teachers—can influence students in ways that are difficult to measure, and may only materialize well into the future.⁣

6. Teaching Is Learning

One of the best ways to learn a concept is to teach it to someone else. But do you actually have to step into the shoes of a teacher, or does the mere expectation of teaching do the trick?

In a 2021 study , researchers split students into two groups and gave them each a science passage about the Doppler effect—a phenomenon associated with sound and light waves that explains the gradual change in tone and pitch as a car races off into the distance, for example. One group studied the text as preparation for a test; the other was told that they’d be teaching the material to another student.

The researchers never carried out the second half of the activity—students read the passages but never taught the lesson. All of the participants were then tested on their factual recall of the Doppler effect, and their ability to draw deeper conclusions from the reading.

The upshot? Students who prepared to teach outperformed their counterparts in both duration and depth of learning, scoring 9 percent higher on factual recall a week after the lessons concluded, and 24 percent higher on their ability to make inferences. The research suggests that asking students to prepare to teach something—or encouraging them to think “could I teach this to someone else?”—can significantly alter their learning trajectories.

7. A Disturbing Strain of Bias in Kids’ Books

Some of the most popular and well-regarded children’s books—Caldecott and Newbery honorees among them—persistently depict Black, Asian, and Hispanic characters with lighter skin, according to new research .

Using artificial intelligence, researchers combed through 1,130 children’s books written in the last century, comparing two sets of diverse children’s books—one a collection of popular books that garnered major literary awards, the other favored by identity-based awards. The software analyzed data on skin tone, race, age, and gender.

Among the findings: While more characters with darker skin color begin to appear over time, the most popular books—those most frequently checked out of libraries and lining classroom bookshelves—continue to depict people of color in lighter skin tones. More insidiously, when adult characters are “moral or upstanding,” their skin color tends to appear lighter, the study’s lead author, Anjali Aduki,  told The 74 , with some books converting “Martin Luther King Jr.’s chocolate complexion to a light brown or beige.” Female characters, meanwhile, are often seen but not heard.

Cultural representations are a reflection of our values, the researchers conclude: “Inequality in representation, therefore, constitutes an explicit statement of inequality of value.”

8. The Never-Ending ‘Paper Versus Digital’ War

The argument goes like this: Digital screens turn reading into a cold and impersonal task; they’re good for information foraging, and not much more. “Real” books, meanwhile, have a heft and “tactility”  that make them intimate, enchanting—and irreplaceable.

But researchers have often found weak or equivocal evidence for the superiority of reading on paper. While a recent study concluded that paper books yielded better comprehension than e-books when many of the digital tools had been removed, the effect sizes were small. A 2021 meta-analysis further muddies the water: When digital and paper books are “mostly similar,” kids comprehend the print version more readily—but when enhancements like motion and sound “target the story content,” e-books generally have the edge.

Nostalgia is a force that every new technology must eventually confront. There’s plenty of evidence that writing with pen and paper encodes learning more deeply than typing. But new digital book formats come preloaded with powerful tools that allow readers to annotate, look up words, answer embedded questions, and share their thinking with other readers.

We may not be ready to admit it, but these are precisely the kinds of activities that drive deeper engagement, enhance comprehension, and leave us with a lasting memory of what we’ve read. The future of e-reading, despite the naysayers, remains promising.

9. New Research Makes a Powerful Case for PBL

Many classrooms today still look like they did 100 years ago, when students were preparing for factory jobs. But the world’s moved on: Modern careers demand a more sophisticated set of skills—collaboration, advanced problem-solving, and creativity, for example—and those can be difficult to teach in classrooms that rarely give students the time and space to develop those competencies.

Project-based learning (PBL) would seem like an ideal solution. But critics say PBL places too much responsibility on novice learners, ignoring the evidence about the effectiveness of direct instruction and ultimately undermining subject fluency. Advocates counter that student-centered learning and direct instruction can and should coexist in classrooms.

Now two new large-scale studies —encompassing over 6,000 students in 114 diverse schools across the nation—provide evidence that a well-structured, project-based approach boosts learning for a wide range of students.

In the studies, which were funded by Lucas Education Research, a sister division of Edutopia , elementary and high school students engaged in challenging projects that had them designing water systems for local farms, or creating toys using simple household objects to learn about gravity, friction, and force. Subsequent testing revealed notable learning gains—well above those experienced by students in traditional classrooms—and those gains seemed to raise all boats, persisting across socioeconomic class, race, and reading levels.

10. Tracking a Tumultuous Year for Teachers

The Covid-19 pandemic cast a long shadow over the lives of educators in 2021, according to a year’s worth of research.

The average teacher’s workload suddenly “spiked last spring,” wrote the Center for Reinventing Public Education in its January 2021 report, and then—in defiance of the laws of motion—simply never let up. By the fall, a RAND study recorded an astonishing shift in work habits: 24 percent of teachers reported that they were working 56 hours or more per week, compared to 5 percent pre-pandemic.

The vaccine was the promised land, but when it arrived nothing seemed to change. In an April 2021 survey  conducted four months after the first vaccine was administered in New York City, 92 percent of teachers said their jobs were more stressful than prior to the pandemic, up from 81 percent in an earlier survey.

It wasn’t just the length of the work days; a close look at the research reveals that the school system’s failure to adjust expectations was ruinous. It seemed to start with the obligations of hybrid teaching, which surfaced in Edutopia ’s coverage of overseas school reopenings. In June 2020, well before many U.S. schools reopened, we reported that hybrid teaching was an emerging problem internationally, and warned that if the “model is to work well for any period of time,” schools must “recognize and seek to reduce the workload for teachers.” Almost eight months later, a 2021 RAND study identified hybrid teaching as a primary source of teacher stress in the U.S., easily outpacing factors like the health of a high-risk loved one.

New and ever-increasing demands for tech solutions put teachers on a knife’s edge. In several important 2021 studies, researchers concluded that teachers were being pushed to adopt new technology without the “resources and equipment necessary for its correct didactic use.” Consequently, they were spending more than 20 hours a week adapting lessons for online use, and experiencing an unprecedented erosion of the boundaries between their work and home lives, leading to an unsustainable “always on” mentality. When it seemed like nothing more could be piled on—when all of the lights were blinking red—the federal government restarted standardized testing .

Change will be hard; many of the pathologies that exist in the system now predate the pandemic. But creating strict school policies that separate work from rest, eliminating the adoption of new tech tools without proper supports, distributing surveys regularly to gauge teacher well-being, and above all listening to educators to identify and confront emerging problems might be a good place to start, if the research can be believed.

What Is Research, and Why Do People Do It?

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• First Online: 03 December 2022

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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.

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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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• 01 May 2024

Why it’s essential to study sex and gender, even as tensions rise

You have full access to this article via your institution.

In 2023, students protested against a new policy in Texas, where parents would be notified if their child asks to be identified as transgender. Credit: Brett Coomer/Houston Chronicle/Getty

This week, Nature is launching a collection of opinion articles on sex and gender in research. Further articles will be published in the coming months. The series will highlight the necessity and challenges of studying a topic that is both hugely under-researched and, increasingly, the focus of arguments worldwide — many of which are neither healthy nor constructive.

Some scientists have been warned off studying sex differences by colleagues. Others, who are already working on sex or gender-related topics, are hesitant to publish their views. Such a climate of fear and reticence serves no one. To find a way forward we need more knowledge, not less.

Collection: Sex and gender in science

Nearly 20 researchers from diverse fields, including neuroscience, psychology, immunology and cancer, have contributed to the series, which provides a snapshot of where scholars studying sex and gender are aligned — and where they are not. In time, we hope this collection will help to shape research, and provide a reference point for moderating often-intemperate debates.

In practice, people use sex and gender to mean different things. But researchers studying animals typically use sex to refer to male and female individuals , as defined by various anatomical and other biological features. In studies involving humans, participants are generally asked to identify their own sex and/or gender category. Here, gender usually encompasses social and environmental factors , including gender roles, expectations and identity.

For as long as scientific inquiry has existed, people have mainly studied men or male animals. Even as recently as 2009, only 26% of studies using animals included both female and male individuals, according to a review of 10 fields in the biological sciences 1 . This bias has had serious consequences. Between 1997 and 2000, for instance, eight prescription drugs were removed from the US market, because clinical testing had not revealed women’s greater risk of developing health problems after taking the drugs.

Male–female comparisons are powerful in biomedical research — don’t abandon them

The tide, however, is turning. Many journals, including those in the Nature Portfolio , and funders, such as the US National Institutes of Health, have developed guidelines and mandates to encourage scientists to consider sex and, where appropriate, gender in their work.

These efforts are reaping benefits 2 . Studies, for example, are showing that a person’s sex and/or gender can influence their risk of disease and chances of survival when it comes to many common causes of death — including cardiovascular conditions and cancer.

Despite this, many researchers remain unconvinced that the inclusion of sex and gender information is important in their field. Others, who are already doing so, have told Nature that they’re afraid of how their work is perceived and of how it could be misunderstood, or misused.

Podcast: Sex and gender discussions don't need to be toxic

Because researchers who are exploring the effects of sex and gender come from many disciplines, there will be disagreements. An often-raised and valid concern, for example, is that when researchers compare responses between female and male animals, or between men and women, they exclude those whose sex and/or gender doesn’t fall into a binary categorization scheme. Another is that variability between individuals of the same sex could be more important than that between sexes.

Sometimes sense does seem to get lost in the debates. That the term sex refers to a lot of interacting factors, which are not fully understood, does not invalidate its usefulness as a concept 3 . That some people misinterpret and misuse findings concerning differences between sexes, particularly in relation to the human brain , should not mean denying that any differences exist.

Tempering the debate

Many of the questions being raised, however, are important to ask, especially given concerns about how best to investigate biological differences between groups of humans , and the continued — and, in some regions, worsening — marginalization of people whose sex and/or gender identity doesn’t fall into narrowly defined norms. Often, such questions and concerns can be addressed through research. For example, studies might find that variability between individuals of the same sex in diet, or body weight, say, are more important predictors of how likely they are to develop anaemia than whether they are male or female.

We need more-nuanced approaches to exploring sex and gender in research

The problem, then is not the discussions alone: science exists to examine and interrogate disagreements. Rather, the problem is that debates — and work on sex and gender, in general — are being used to polarize opinions about gender identity. As Arthur Arnold, a biologist at the University of California, Los Angeles, and his colleagues describe in their Comment article , last September, legislation banning gender-affirming medical care for people under 18 years old was introduced in Texas on the basis of claims that everyone belongs to one of two gender groups, and that this reality is settled by science. It isn’t. Scientists are reluctant to study sex and gender, not just because of concerns about the complexity and costs of the research, but also because of current tensions.

But it is crucial that scholars do not refrain from considering the effects of sex and gender if such analyses are relevant to their field. Improved knowledge will help to resolve concerns and allow a scholarly consensus to be reached, where possible. Where disagreements persist, our hope is that Nature ’s collection of opinion articles will equip researchers with the tools needed to help them persuade others that going back to assuming that male individuals represent everyone is no longer an option.

Nature 629 , 7-8 (2024)

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Study Shows How Higher Education Supports Asian American, Native Hawaiian, and Pacific Islander Students Through Culturally Relevant Courses, Programs, and Research

Analysis of minority-serving institutions on the East and West Coasts demonstrates layered processes to build students’ capacities

The model minority myth paints a picture of Asian Americans as a monolithic group with unparalleled success in academics. A new NYU study unpacks this myth, exploring the needs of Asian American, Native Hawaiian, and Pacific Islander students and how higher education institutions support these populations.

In 2007, Congress established a federal designation for higher education institutions that enroll at least 10 percent of undergraduate Asian American, Native Hawaiian, and Pacific Islander (AA&NHPI) students, and who enroll a significant proportion of students from low socioeconomic backgrounds. This designation as an Asian American and Native American Pacific Islander Serving Institution (AANAPISI) was among one of the newest categories of minority-serving institutions that receive federal funding to advance educational equity and support for ethnic and racial minorities.

In a two-site case study, Mike Hoa Nguyen , assistant professor of education at NYU Steinhardt, collected data from interviews, internal and public university documents, and observations of activities, courses, and meetings to determine the process in which AANAPISI programs expand students’ capacities through culturally relevant coursework, mentorship, research, and civic engagement. His findings are published in The Review of Higher Education .

“AANAPISIs demonstrate a federal commitment to supporting the unique educational needs of AA&NHPI students, which are too often obscured by the model minority myth,” said Nguyen. “This myth dangerously asserts that Asian American students, and Native Hawaiian and Pacific Islander students by association, are universally successful and unparalleled in their academic achievements. AANAPISIs play a major role in addressing this problem, and in doing so, provide critical resources to uplift the students they serve. This study documents the process in which these colleges and universities engage in this important work.”

Nguyen's study centered on a large, public community college on the West Coast and a large, urban, regional public university on the East Coast. Nguyen’s findings related to the experiences of students in these programs.

He uncovered a five-tiered process that the two institutions use to build opportunities for learning, practice, and engagement:

AA&NHPI Focused Coursework At both institutions, courses focused on these populations are offered through the institutions’ Asian American Studies programs, where students are exposed to concepts connected to their racial and ethnic identities. One student shared her experience with a course, Asian Women in the United States, “Through my experience with that class I learned…for the first time, issues that affected my community. Specifically, me as an Asian American woman, specifically Vietnamese American…”

Teaching and Mentoring Students who had previously taken AA&NHPI coursework provided tutoring and mentoring to support new students with classwork, programs, books, and scholarship applications.  According to one mentor, “Cambodian Americans fall through the cracks, we’re just not in higher ed…It’s not a supportive space for us…[the AANAPISI faculty] understand…from their own community work, from being on campus, and [from] teaching for so long that…when they find students who fit these demographics it makes sense for them to mentor them.”

Advanced AA&NHPI Focused Coursework After serving as mentors, students often take more advanced courses focused on theoretical, historical, and contemporary issues regarding the AA&NHPI experience to continue their academics while gaining tools to make larger contributions toward their communities. 

Academic and Research Development Students who complete advanced coursework are provided opportunities to engage in academic projects and research with faculty and staff, presenting research at conferences or publishing in peer-reviewed journals. 

Professional and Community Experience The final step in the process offers opportunities for students to engage in community-based projects, internships, and employment with partner organizations, government offices, or other schools. A student shared that his research experience led to the creation of a Vietnamese American organizing and training program. “[Researchers] found out that Vietnamese Americans in [the neighborhood] don't participate in civics or politics…they basically feel disenfranchised, like their vote doesn’t matter…So, the research showed that there needs to be an organization to help push and provide opportunities to talk about politics in a Vietnamese American progressive context…”

“AANAPISIs are the backbone for AA&NHPI students in higher education. These institutions account for six percent of all colleges and universities, yet enroll over 40 percent of all AA&NHPI undergraduates,” said Nguyen. “This study offers new understandings of the critical role that AANAPISIs play to expand educational opportunity and enrich learning experiences—which can be adopted beyond AANAPISIs and for other students—as well as inform the work of policymakers as they seek new solutions to refine and regulate the administration of minority-serving institutions.”

Funding for this study was provided by the UCLA Institute of American Cultures and the UCLA Asian American Studies Center. 

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New Study Bolsters Idea of Athletic Differences Between Men and Trans Women

Research financed by the International Olympic Committee introduced new data to the unsettled and fractious debate about bans on transgender athletes.

By Jeré Longman

A new study financed by the International Olympic Committee found that transgender female athletes showed greater handgrip strength — an indicator of overall muscle strength — but lower jumping ability, lung function and relative cardiovascular fitness compared with women whose gender was assigned female at birth.

That data, which also compared trans women with men, contradicted a broad claim often made by proponents of rules that bar transgender women from competing in women’s sports. It also led the study’s authors to caution against a rush to expand such policies, which already bar transgender athletes from a handful of Olympic sports.

The study’s most important finding, according to one of its authors, Yannis Pitsiladis, a member of the I.O.C.’s medical and scientific commission, was that, given physiological differences, “Trans women are not biological men.”

Alternately praised and criticized, the study added an intriguing data set to an unsettled and often politicized debate that may only grow louder with the Paris Olympics and a U.S. presidential election approaching.

The authors cautioned against the presumption of immutable and disproportionate advantages for transgender female athletes who compete in women’s sports, and they advised against “precautionary bans and sport eligibility exclusions” that were not based on sport-specific research.

Outright bans, though, continue to proliferate. Twenty-five U.S. states now have laws or regulations barring transgender athletes from competing in girls and women’s sports, according to the Movement Advancement Project , a nonprofit that focuses on gay, lesbian, bisexual and transgender parity. And the National Association of Intercollegiate Athletics , the governing body for smaller colleges, this month barred transgender athletes from competing in women’s sports unless their sex was assigned female at birth and they had not undergone hormone therapy.

Two of the most visible sports at this summer’s Paris Games — swimming and track and field — along with cycling have effectively barred transgender female athletes who went through puberty as males. Rugby has instituted a total ban on trans female athletes, citing safety concerns, and those permitted to participate in other sports often face stricter requirements in suppressing their levels of testosterone.

The International Olympic Committee has left eligibility rules for transgender female athletes up to the global federations that govern individual sports. And while the Olympic committee provided financing for the study — as it does on a variety of topics through a research fund — Olympic officials had no input or influence on the results, Dr. Pitsiladis said.

In general, the argument for the bans has been that profound advantages gained from testosterone-fueled male puberty — broader shoulders, bigger hands, longer torsos, and greater muscle mass, strength, bone density and heart and lung capacity — give transgender female athletes an inequitable and largely irreversible competitive edge.

The new laboratory-based, peer-reviewed and I.O.C.-funded study at the University of Brighton, published this month in the British Journal of Sports Medicine , tested 19 cisgender men (those whose gender identity matches the sex they were assigned at birth) and 12 trans men, along with 23 trans women and 21 cisgender women.

All of the participants played competitive sports or underwent physical training at least three times a week. And all of the trans female athletes had undergone at least a year of treatment suppressing their testosterone levels and taking estrogen supplementation, the researchers said. None of the participants were athletes competing at the national or international level.

The study found that transgender female participants showed greater handgrip strength than cisgender female participants but lower lung function and relative VO2 max, the amount of oxygen used when exercising. Transgender female athletes also scored below cisgender women and men on a jumping test that measured lower-body power.

The study acknowledged some limitations, including its small sample size and the fact that the athletes were not followed over the long term as they transitioned. And, as previous research has indicated, it found that transgender female athletes did retain at least one advantage over cisgender female athletes — a measurement of handgrip strength .

But it is a combination of factors, not a single parameter, that determines athletic performance, said Dr. Pitsiladis, a professor of sport and exercise science.

Athletes who grow taller and heavier after going through puberty as males must “carry this big skeleton with a smaller engine” after transitioning, he said. He cited volleyball as an example, saying that, for transgender female athletes, “the jumping and blocking will not be to the same height as they were doing before. And they may find that, overall, their performance is less good.”

But Michael J. Joyner, a doctor at the Mayo Clinic who studies the physiology of male and female athletes, said that, based on his research and the research of others, science supports the bans in elite sports, where events can be decided by the smallest of margins.

“We know testosterone is performance enhancing,” Dr. Joyner said. “And we know testosterone has residual effects.” Additionally, he added, declines in performance by trans women after taking drugs to suppress their testosterone levels do not fully reduce the typical differences in athletic performance between men and women.

Supporters of transgender athletes, and some scientists who disagree with bans, have accused governing bodies and lawmakers of enacting solutions for a problem that doesn’t exist. There are few elite trans female athletes, they have noted. And there has been limited scientific study of presumed unalterable advantages in strength, power and aerobic capacity gained by experiencing puberty as a male.

For those who have competed in the Olympics, results have varied widely. At the 2021 Tokyo Games, Quinn , a soccer player who is trans nonbinary and was assigned female at birth, helped Canada’s team win a gold medal. But Laurel Hubbard , a transgender weight lifter from New Zealand, failed to complete a lift in her event.

“The idea that trans women are going to take over women’s sports is ludicrous,” said Joanna Harper, a leading researcher of trans athletes and a postdoctoral scholar at Oregon Health & Science University.

Dr. Harper, who is transgender, said it was important for sports to consider physiological differences between transgender women and cisgender women and that she supported certain restrictions, such as requiring the suppression of testosterone levels. But she called blanket bans “unnecessary and unjustified” and said she welcomed the I.O.C.-funded study.

“This fear that trans women aren’t really women, that they’re men who are invading women’s sports, and that trans women will carry all of their male athleticism, their athletic capabilities, into women’s sports — neither of those things are true,” Dr. Harper said.

Sebastian Coe, the president of World Athletics, which governs global track and field, acknowledged that the science remains unresolved. But the organization decided to bar transgender female athletes from international track and field, he said, because “I’m not going to take a risk on this.”

“We think this is in the best interest of preserving the female category,” Mr. Coe said.

In at least two prominent cases, the fight over transgender bans has moved to the courts. The former University of Pennsylvania swimmer Lia Thomas is challenging a ban imposed by World Aquatics, swimming’s global governing body, after she won the 500-yard freestyle race at the 2022 N.C.A.A. championships. That victory made Thomas, who had been among the best men’s swimmers in the Ivy League, the first known trans athlete to win a women’s championship event in college sports’ top division.

Thomas did not dominate all of her races, though, finishing tied for fifth in a second race and eighth in a third. Her winning time in the 500 was more than nine seconds slower than the N.C.A.A. record. Her case, filed at the Swiss-based Court of Arbitration for Sport, is not expected to be resolved before the Paris Olympics begin in July.

Meanwhile, more than a dozen current and former U.S. college athletes, including at least one who competed against Thomas, sued the N.C.A.A. last month . They claimed that, by letting Thomas participate in the national championships, the organization had violated their rights under Title IX, the law that prohibits sex discrimination at institutions that receive federal funding. (Title IX has also been relied upon to argue in favor of transgender female athletes.)

Outsports , a website that reports on L.G.B.T.Q. issues, hailed the I.O.C.-funded study as a “landmark” that concluded that “blanket sports bans are a mistake.” But some scientists and athletes called the study deeply flawed in an article in The Telegraph , which labeled the suggestion that transgender women are at a disadvantage in sports a “new low” for the I.O.C.

So heated is the debate that Dr. Pitsiladis said he and his research team have received threats. That, he warned, could lead other scientists to shy away from pursuing research on the topic.

“Why would any scientist do this if you’re going to get totally slammed and character-assassinated?” he said. “This is no longer a science matter. Unfortunately, it’s become a political matter.”

Jeré Longman covers international sports, focusing on competitive, social, cultural and political issues around the world. More about Jeré Longman

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Many see China as increasingly influential and consider limiting its power a top priority

Table of contents.

• Unfavorable views of China prevail
• China’s role in the world
• China’s territorial disputes
• Americans lack confidence in Xi Jinping
• Americans increasingly see China as an enemy
• Limiting China’s power and influence
• China’s economic influence on the U.S.
• Acknowledgments
• The American Trends Panel survey methodology

Pew Research Center conducted this study to understand Americans’ opinions of China, its role in the world and its impact on the U.S. economy. For this analysis, we surveyed 3,600 U.S. adults from April 1 to April 7, 2024. Everyone who took part in this survey is a member of the Center’s American Trends Panel (ATP), an online survey panel that is recruited through national, random sampling of residential addresses. This way nearly all U.S. adults have a chance of selection. The survey is weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis, along with responses, and its methodology .

For the fifth year in a row, about eight-in-ten Americans report an unfavorable view of China, according to a new Pew Research Center survey. Today, 81% of U.S. adults see the country unfavorably, including 43% who hold a very unfavorable opinion. Chinese President Xi Jinping receives similarly negative ratings.

Still, many Americans agree that China’s influence in the world has been getting stronger in recent years (71%). This sense is accompanied by concern about how China interacts with other nations: 61% of Americans are at least somewhat concerned about China’s territorial disputes with neighboring countries. (For more U.S. views of China’s role in the world, go to Chapter 1 .)

When it comes to China’s relationship with the United States, few see China as a partner (6%) and most Americans instead label it a competitor (50%) or an enemy (42%) of the U.S. They are likewise critical of China’s impact on the U.S. economy, describing its influence as large and negative. Roughly half of Americans think limiting China’s power and influence should be a top U.S. foreign policy priority, and another 42% think this should be given some priority. (For more assessments of China’s relationship with the U.S., go to Chapter 2 .)

According to the Center survey, which was conducted April 1-7, 2024, among 3,600 U.S. adults, Republicans are more wary of China than Democrats are.

Republicans and Republican-leaning independents are about twice as likely as Democrats and Democratic leaners to hold a very unfavorable view of China and to consider China an enemy of the U.S. They are also more likely to say that China has recently become more influential.

Republicans also have wider ideological differences within their party, and conservative Republicans stand out on many measures :

• Conservative Republicans are 25 percentage points more likely than moderate and liberal Republicans to express a very unfavorable view of China (68% vs. 43%). There is no difference between liberal Democrats and moderate and conservative Democrats on this question.
• Conservative Republicans are also 31 points more likely than moderate and liberal Republicans to see China as an enemy of the U.S. No ideological difference is present among Democrats.
• While 83% of conservative Republicans say China’s influence in the world has been getting stronger in recent years, 68% of moderate and liberal Republicans say the same. The latter is similar to the shares of moderate and conservative Democrats (67%) and liberal Democrats (69%) who hold this view.

Older Americans are generally more critical of China. A 61% majority of adults ages 65 and older have a very unfavorable view of China, compared with 27% of adults under 30. Adults ages 65 and older are also more than twice as likely as those ages 18 to 29 to see China as an enemy of the U.S. For their part, younger adults are more likely than older ones to label China as a competitor and as a partner.

Older Americans also perceive more growth in China’s international influence. Roughly three-quarters of adults ages 65 and older say China’s influence has been getting stronger in recent years, while about two-thirds of adults under 30 say the same.

Americans with a sour view of the U.S. economy have more critical opinions of China. Those who say the current U.S. economic situation is bad are more likely to hold an unfavorable opinion of China and to say China has a great deal or fair amount of negative influence on the U.S. economy. They are also more likely to see China as an enemy when compared with those who see the economy positively.

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Scientists welcome new rules on marijuana, but research will still face obstacles

Rhitu Chatterjee

For decades, researchers in the U.S. had to use only marijuana grown at a facility located in Oxford, Mississippi. A few other approved growers have been added in recent years. Brad Horrigan/Hartford Courant/Tribune News Service via Getty Images hide caption

For decades, researchers in the U.S. had to use only marijuana grown at a facility located in Oxford, Mississippi. A few other approved growers have been added in recent years.

As the Biden administration moves to reclassify marijuana as a less dangerous drug, scientists say the change will lift some of the restrictions on studying the drug.

But the change won't lift all restrictions, they say, neither will it decrease potential risks of the drug or help users better understand what those risks are.

Marijuana is currently classified as a Schedule I controlled substance , which is defined as a substance with no accepted medical use and a high potential for abuse. The Biden administration proposed this week to classify cannabis as a Schedule III controlled substance, a category that acknowledges it has some medical benefits.

The current Schedule I status imposes many regulations and restrictions on scientists' ability to study weed, even as state laws have made it increasingly available to the public.

"Cannabis as a Schedule I substance is associated with a number of very, very restrictive regulations," says neuroscientist Staci Gruber at McLean Hospital and Harvard Medical School. "You have very stringent requirements, for example, for storage and security and reporting all of these things."

These requirements are set by the Food and Drug Administration, the Drug Enforcement Administration, the Institutional Review Board and local authorities, she says. Scientists interested in studying the drug also have to register with the DEA and get a state and federal license to conduct research on the drug.

"It's a burdensome process and it is certainly a process that has prevented a number of young and rather invested researchers from pursuing [this kind of work]," says Gruber.

Reclassifying the drug as Schedule III puts it in the same category as ketamine and Tylenol with codeine. Substances in this category have accepted medical use in the United States, have less potential for abuse than in higher categories and abuse could lead to low to moderate levels of dependence on the drug.

This reclassification is "a very, very big paradigm shift," says Gruber. "I think that has a big trickle down effect in terms of the perspectives and the attitudes with regard to the actual sort of differences between studying Schedule III versus Schedule I substances."

Gruber welcomes the change, particularly for what it will mean for younger colleagues. "For researchers who are looking to get into the game, it will be easier. You don't have to have a Schedule I license," she says. "That's a big deal."

The rescheduling of cannabis will also "translate to more research on the benefits and risks of cannabis for the treatment of medical conditions," writes Dr. Andrew Monte in an email. He is associate director of Rocky Mountain Poison and Drug Safety and an emergency physician and toxicologist at the University of Colorado School of Medicine.

"This will also help improve the quality of the research since more researchers will be able to contribute," he adds.

Senate Democrats hold a press conference on Wednesday pitching new, less strict marijuana laws. From left are Senators Cory Booker of N.J., Majority Leader Chuck Schumer of N.Y., and Ron Wyden of Oregon. Tom Williams/CQ-Roll Call, Inc via Getty Imag hide caption

Senate Democrats hold a press conference on Wednesday pitching new, less strict marijuana laws. From left are Senators Cory Booker of N.J., Majority Leader Chuck Schumer of N.Y., and Ron Wyden of Oregon.

But the change in classification won't significantly expand the number of sources for the drug for researchers, says Gruber. For 50 years, researchers were allowed to use cannabis from only one source – a facility at the University of Mississippi. Then, in 2021, the DEA started to add a few more companies to that list of approved sources for medical and scientific research.

While she expects more sources to be added in time, she and many of the researchers she knows have yet to benefit from the recently added sources, as most have limited products available.

"And what we haven't seen is any ability for researchers –cannabis researchers, clinical researchers – to have the ability to study products that our patients and our recreational consumers or adult consumers are actually using," she adds. "That remains impossible."

Shots - Health News

Rare and mysterious vomiting illness linked to heavy marijuana use.

There is very little known information about what is in cannabis products on the market today. Some studies show that the level of THC, the main intoxicant in marijuana, being sold to consumers today is significantly higher than what was available decades ago, and high THC levels are known to pose more health risks.

And Monte cautions that the reclassification itself doesn't mean that cannabis has no health risks. Monte and his colleagues have been documenting some of those risks in Colorado by studying people who show up in the emergency room after consuming cannabis. Intoxication and cyclical vomiting ( cannabinoid hyperemesis syndrome ) and alarming psychiatric symptoms such as psychosis are among the top problems bringing some marijuana users to the hospital.

Research on cannabis has been lacking surveillance of these kinds of impacts for decades, he says. And rescheduling the drug will not fill that "gaping hole in risk surveillance," he writes.

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