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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

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

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

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

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

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

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

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

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

Below are some additional resources:

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

Connect with Us

• More Social Media from NIH

2.1 Why is Research Important

Learning objectives.

By the end of this section, you will be able to:

• Explain how scientific research addresses questions about behavior
• Discuss how scientific research guides public policy
• Appreciate how scientific research can be important in making personal decisions

   Scientific research is a critical tool for successfully navigating our complex world. Without it, we would be forced to rely solely on intuition, other people’s authority, and blind luck. While many of us feel confident in our abilities to decipher and interact with the world around us, history is filled with examples of how very wrong we can be when we fail to recognize the need for evidence in supporting claims. At various times in history, we would have been certain that the sun revolved around a flat earth, that the earth’s continents did not move, and that mental illness was caused by possession (figure below). It is through systematic scientific research that we divest ourselves of our preconceived notions and superstitions and gain an objective understanding of ourselves and our world.

Some of our ancestors, across the work and over the centuries, believed that trephination – the practice of making a hole in the skull, as shown here – allowed evil spirits to leave the body, thus curing mental illness and other diseases (credit” “taiproject/Flickr)

   The goal of all scientists is to better understand the world around them. Psychologists focus their attention on understanding behavior, as well as the cognitive (mental) and physiological (body) processes that underlie behavior. In contrast to other methods that people use to understand the behavior of others, such as intuition and personal experience, the hallmark of scientific research is that there is evidence to support a claim. Scientific knowledge is empirical : It is grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing.

We can easily observe the behavior of others around us. For example, if someone is crying, we can observe that behavior. However, the reason for the behavior is more difficult to determine. Is the person crying due to being sad, in pain, or happy? Sometimes, asking about the underlying cognitions is as easy as asking the subject directly: “Why are you crying?” However, there are situations in which an individual is either uncomfortable or unwilling to answer the question honestly, or is incapable of answering. For example, infants would not be able to explain why they are crying. In other situations, it may be hard to identify exactly why you feel the way you do. Think about times when you suddenly feel annoyed after a long day. There may be a specific trigger for your annoyance (a loud noise), or you may be tired, hungry, stressed, or all of the above. Human behavior is often a complicated mix of a variety of factors. In such circumstances, the psychologist must be creative in finding ways to better understand behavior. This chapter explores how scientific knowledge is generated, and how important that knowledge is in forming decisions in our personal lives and in the public domain.

USE OF RESEARCH INFORMATION

   Trying to determine which theories are and are not accepted by the scientific community can be difficult, especially in an area of research as broad as psychology. More than ever before, we have an incredible amount of information at our fingertips, and a simple internet search on any given research topic might result in a number of contradictory studies. In these cases, we are witnessing the scientific community going through the process of coming to an agreement, and it could be quite some time before a consensus emerges. In other cases, rapidly developing technology is improving our ability to measure things, and changing our earlier understanding of how the mind works.

In the meantime, we should strive to think critically about the information we encounter by exercising a degree of healthy skepticism. When someone makes a claim, we should examine the claim from a number of different perspectives: what is the expertise of the person making the claim, what might they gain if the claim is valid, does the claim seem justified given the evidence, and what do other researchers think of the claim? Science is always changing and new evidence is alwaus coming to light, thus this dash of skepticism should be applied to all research you interact with from now on. Yes, that includes the research presented in this textbook.

Evaluation of research findings can have widespread impact. Imagine that you have been elected as the governor of your state. One of your responsibilities is to manage the state budget and determine how to best spend your constituents’ tax dollars. As the new governor, you need to decide whether to continue funding the D.A.R.E. (Drug Abuse Resistance Education) program in public schools (figure below). This program typically involves police officers coming into the classroom to educate students about the dangers of becoming involved with alcohol and other drugs. According to the D.A.R.E. website (www.dare.org), this program has been very popular since its inception in 1983, and it is currently operating in 75% of school districts in the United States and in more than 40 countries worldwide. Sounds like an easy decision, right? However, on closer review, you discover that the vast majority of research into this program consistently suggests that participation has little, if any, effect on whether or not someone uses alcohol or other drugs (Clayton, Cattarello, & Johnstone, 1996; Ennett, Tobler, Ringwalt, & Flewelling, 1994; Lynam et al., 1999; Ringwalt, Ennett, & Holt, 1991). If you are committed to being a good steward of taxpayer money, will you fund this particular program, or will you try to find other programs that research has consistently demonstrated to be effective?

The D.A.R.E. program continues to be popular in schools around the world despite research suggesting that it is ineffective.

It is not just politicians who can benefit from using research in guiding their decisions. We all might look to research from time to time when making decisions in our lives. Imagine you just found out that a close friend has breast cancer or that one of your young relatives has recently been diagnosed with autism. In either case, you want to know which treatment options are most successful with the fewest side effects. How would you find that out? You would probably talk with a doctor or psychologist and personally review the research that has been done on various treatment options—always with a critical eye to ensure that you are as informed as possible.

In the end, research is what makes the difference between facts and opinions. Facts are observable realities, and opinions are personal judgments, conclusions, or attitudes that may or may not be accurate. In the scientific community, facts can be established only using evidence collected through empirical research.

THE PROCESS OF SCIENTIFIC RESEARCH

   Scientific knowledge is advanced through a process known as the scientific method . Basically, ideas (in the form of theories and hypotheses) are tested against the real world (in the form of empirical observations), and those observations lead to more ideas that are tested against the real world, and so on. In this sense, the scientific process is circular. We continually test and revise theories based on new evidence.

Two types of reasoning are used to make decisions within this model: Deductive and inductive. In deductive reasoning, ideas are tested against the empirical world. Think about a detective looking for clues and evidence to test their “hunch” about whodunit. In contrast, in inductive reasoning, empirical observations lead to new ideas. In other words, inductive reasoning involves gathering facts to create or refine a theory, rather than testing the theory by gathering facts (figure below). These processes are inseparable, like inhaling and exhaling, but different research approaches place different emphasis on the deductive and inductive aspects.

Psychological research relies on both inductive and deductive reasoning.

   In the scientific context, deductive reasoning begins with a generalization—one hypothesis—that is then used to reach logical conclusions about the real world. If the hypothesis is correct, then the logical conclusions reached through deductive reasoning should also be correct. A deductive reasoning argument might go something like this: All living things require energy to survive (this would be your hypothesis). Ducks are living things. Therefore, ducks require energy to survive (logical conclusion). In this example, the hypothesis is correct; therefore, the conclusion is correct as well. Sometimes, however, an incorrect hypothesis may lead to a logical but incorrect conclusion. Consider the famous example from Greek philosophy. A philosopher decided that human beings were “featherless bipeds”. Using deductive reasoning, all two-legged creatures without feathers must be human, right? Diogenes the Cynic (named because he was, well, a cynic) burst into the room with a freshly plucked chicken from the market and held it up exclaiming “Behold! I have brought you a man!”

Deductive reasoning starts with a generalization that is tested against real-world observations; however, inductive reasoning moves in the opposite direction. Inductive reasoning uses empirical observations to construct broad generalizations. Unlike deductive reasoning, conclusions drawn from inductive reasoning may or may not be correct, regardless of the observations on which they are based. For example, you might be a biologist attempting to classify animals into groups. You notice that quite a large portion of animals are furry and produce milk for their young (cats, dogs, squirrels, horses, hippos, etc). Therefore, you might conclude that all mammals (the name you have chosen for this grouping) have hair and produce milk. This seems like a pretty great hypothesis that you could test with deductive reasoning. You go out an look at a whole bunch of things and stumble on an exception: The coconut. Coconuts have hair and produce milk, but they don’t “fit” your idea of what a mammal is. So, using inductive reasoning given the new evidence, you adjust your theory again for an other round of data collection. Inductive and deductive reasoning work in tandem to help build and improve scientific theories over time.

We’ve stated that theories and hypotheses are ideas, but what sort of ideas are they, exactly? A theory is a well-developed set of ideas that propose an explanation for observed phenomena. Theories are repeatedly checked against the world, but they tend to be too complex to be tested all at once. Instead, researchers create hypotheses to test specific aspects of a theory.

A hypothesis is a testable prediction about how the world will behave if our theory is correct, and it is often worded as an if-then statement (e.g., if I study all night, I will get a passing grade on the test). The hypothesis is extremely important because it bridges the gap between the realm of ideas and the real world. As specific hypotheses are tested, theories are modified and refined to reflect and incorporate the result of these tests (figure below).

The scientific method of research includes proposing hypotheses, conducting research, and creating or modifying theories based on results.

   To see how this process works, let’s consider a specific theory and a hypothesis that might be generated from that theory. As you’ll learn in a later chapter, the James-Lange theory of emotion asserts that emotional experience relies on the physiological arousal associated with the emotional state. If you walked out of your home and discovered a very aggressive snake waiting on your doorstep, your heart would begin to race and your stomach churn. According to the James-Lange theory, these physiological changes would result in your feeling of fear. A hypothesis that could be derived from this theory might be that a person who is unaware of the physiological arousal that the sight of the snake elicits will not feel fear.

A scientific hypothesis is also falsifiable, or capable of being shown to be incorrect. Recall from the introductory chapter that Sigmund Freud had lots of interesting ideas to explain various human behaviors (figure below). However, a major criticism of Freud’s theories is that many of his ideas are not falsifiable. The essential characteristic of Freud’s building blocks of personality, the id, ego, and superego, is that they are unconscious, and therefore people can’t observe them. Because they cannot be observed or tested in any way, it is impossible to say that they don’t exist, so they cannot be considered scientific theories. Despite this, Freud’s theories are widely taught in introductory psychology texts because of their historical significance for personality psychology and psychotherapy, and these remain the root of all modern forms of therapy.

Many of the specifics of (a) Freud’s theories, such ad (b) his division on the mind into the id, ego, and superego, have fallen out of favor in recent decades because they are not falsifiable (i.e., cannot be verified through scientific investigation).  In broader strokes, his views set the stage for much psychological thinking today, such as the idea that some psychological process occur at the level of the unconscious.

In contrast, the James-Lange theory does generate falsifiable hypotheses, such as the one described above. Some individuals who suffer significant injuries to their spinal columns are unable to feel the bodily changes that often accompany emotional experiences. Therefore, we could test the hypothesis by determining how emotional experiences differ between individuals who have the ability to detect these changes in their physiological arousal and those who do not. In fact, this research has been conducted and while the emotional experiences of people deprived of an awareness of their physiological arousal may be less intense, they still experience emotion (Chwalisz, Diener, & Gallagher, 1988).

Scientific research’s dependence on falsifiability allows for great confidence in the information that it produces. Typically, by the time information is accepted by the scientific community, it has been tested repeatedly.

Scientists are engaged in explaining and understanding how the world around them works, and they are able to do so by coming up with theories that generate hypotheses that are testable and falsifiable. Theories that stand up to their tests are retained and refined, while those that do not are discarded or modified. IHaving good information generated from research aids in making wise decisions both in public policy and in our personal lives.

Review Questions:

1. Scientific hypotheses are ________ and falsifiable.

a. observable

b. original

c. provable

d. testable

2. ________ are defined as observable realities.

a. behaviors

c. opinions

d. theories

3. Scientific knowledge is ________.

a. intuitive

b. empirical

c. permanent

d. subjective

4. A major criticism of Freud’s early theories involves the fact that his theories ________.

a. were too limited in scope

b. were too outrageous

c. were too broad

d. were not testable

Critical Thinking Questions:

1. In this section, the D.A.R.E. program was described as an incredibly popular program in schools across the United States despite the fact that research consistently suggests that this program is largely ineffective. How might one explain this discrepancy?

2. The scientific method is often described as self-correcting and cyclical. Briefly describe your understanding of the scientific method with regard to these concepts.

Personal Application Questions:

1. Healthcare professionals cite an enormous number of health problems related to obesity, and many people have an understandable desire to attain a healthy weight. There are many diet programs, services, and products on the market to aid those who wish to lose weight. If a close friend was considering purchasing or participating in one of these products, programs, or services, how would you make sure your friend was fully aware of the potential consequences of this decision? What sort of information would you want to review before making such an investment or lifestyle change yourself?

deductive reasoning

falsifiable

hypothesis:  (plural

inductive reasoning

Answers to Exercises

Review Questions: 

1. There is probably tremendous political pressure to appear to be hard on drugs. Therefore, even though D.A.R.E. might be ineffective, it is a well-known program with which voters are familiar.

2. This cyclical, self-correcting process is primarily a function of the empirical nature of science. Theories are generated as explanations of real-world phenomena. From theories, specific hypotheses are developed and tested. As a function of this testing, theories will be revisited and modified or refined to generate new hypotheses that are again tested. This cyclical process ultimately allows for more and more precise (and presumably accurate) information to be collected.

deductive reasoning:  results are predicted based on a general premise

empirical:  grounded in objective, tangible evidence that can be observed time and time again, regardless of who is observing

fact:  objective and verifiable observation, established using evidence collected through empirical research

falsifiable:  able to be disproven by experimental results

hypothesis:  (plural: hypotheses) tentative and testable statement about the relationship between two or more variables

inductive reasoning:  conclusions are drawn from observations

opinion:  personal judgments, conclusions, or attitudes that may or may not be accurate

theory:  well-developed set of ideas that propose an explanation for observed phenomena

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October 2021

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How Research Works

Have you ever wondered what it means to “follow the science?” Sometimes it may seem like what’s true one day changes the next. But when what we know changes, it often means science is working.

Research helps us understand the world through careful testing. Each advance builds on past discoveries. This process can take a long time. But the end result is a better understanding of the world around us.

In general, the scientific process follows many steps. First, scientists start with a question. They look at past research to see what others have learned. Different scientists have diverse skills and training. They each bring their own approaches and ideas. And they design new experiments to test their ideas.

Next, scientists perform their experiments and collect data. Then, they evaluate what their findings might mean. This often leads them to new questions and ideas to test.

The next step is to share their data and ideas with other scientists. Other experts can give new perspectives or point out problems.

It’s natural to want answers. But it’s important not to draw conclusions based on a single study. Scientists start to form conclusions only after looking at many studies over time. Sometimes, even these conclusions change with more evidence. Science is an evolving process. But it’s the best way we have to seek out answers.

NIH has created a one-page guide to explain more about how research works. Find the guide in English or Spanish .

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How Psychological Science is Benefiting the World

• Perspectives on Psychological Science

Technological advances have allowed psychological scientists to measure everything from cognitive impairments to everyday decision-making. Now, the scientists are using their research to inform tools, programs, and interventions that are helping to cultivate a healthier, happier, and more sustainable world.

In a special issue of Perspectives on Psychological Science , a journal of the Association for Psychological Science , more than 25 psychological researchers write about expanding their research beyond academic articles and applying it to the betterment of society and the environment.

“Their work spans ways to make the world a better place by considering individuals, relationships and interactions among people, and broad-scale social and national policies,” June Gruber, the journal’s interim editor and an assistant professor at University of Colorado Boulder, writes with her associate editors in an introduction to the issue .

Gruber and her colleagues highlight the importance of psychological scientists’ involvement in addressing societal challenges, including mental illness, isolation and loneliness, sexual harassment, policies that harm vulnerable refugees, lack of concern for animals, and environmental deterioration. The issue highlights how psychological science has helped disadvantaged youth achieve academic success, improved the efficacy of psychotherapy, helped military officers surmount errors and biases in their decision-making, and fostered peace and reconciliation in ethnic conflicts, among other  impacts.   

Among the contributors to the issue are some of the world’s most eminent scientists.

Albert Bandura, widely described as one of the greatest living psychologists, discusses the use of social cognitive theory to change behaviors and create sustainable social and environmental futures.

Acclaimed psychiatrist Aaron Beck describes how his pioneering work on cognitive behavioral therapy has led to one of the most widely used interventions for increasing individual well-being.

University of Pennsylvania researcher and author Angela Duckworth, known for investigating the science of “grit”, examines her path from school teacher to scientist in helping children and adults persist and succeed in the face of challenges.

Stanford University professor Carol Dweck shares how she took her prominent research on mindsets into educational settings and other real-world environments.

Longtime collaborators Kathy Hirsh-Pasek and Roberta Michnick Golinkoff detail their success at designing public spaces to foster learning.

University of Delaware social psychologist James Jones details his research on diversity, race, and racism and his efforts to expand graduate programs for students of color.

Ervin Staub, a well-known researcher on peace and violence, discusses applying his findings to workshops and educational programs for reconciliation in Rwanda.

And organizational researcher and best-selling author Adam Grant calls attention to the importance of disseminating scientific findings to the general public

Gruber and her colleagues say the special issue is intended to inspire future and current scientists who are hoping to make a positive difference in the world.

The issue is available for free to the public online for a short time.

APS regularly opens certain online articles for discussion on our website. Effective February 2021, you must be a logged-in APS member to post comments. By posting a comment, you agree to our Community Guidelines and the display of your profile information, including your name and affiliation. Any opinions, findings, conclusions, or recommendations present in article comments are those of the writers and do not necessarily reflect the views of APS or the article’s author. For more information, please see our Community Guidelines .

Please login with your APS account to comment.

For a copy of the research article and access to other Perspectives on Psychological Science research findings, please contact: - 202.293.9300

Does Psychology Need More Effective Suspicion Probes?

Suspicion probes are meant to inform researchers about how participants’ beliefs may have influenced the outcome of a study, but it remains unclear what these unverified probes are really measuring or how they are currently being used.

Scientists Discuss How to Study the Psychology of Collectives, Not Just Individuals

In a set of articles appearing in Perspectives on Psychological Science, an international array of scientists discusses how the study of neighborhoods, work units, activist groups, and other collectives can help us better understand and respond to societal changes.

How Science Can Reward Cooperation, Not Just Individual Achievement

Two social scientists propose a different approach to scientific recognition and rewards: shifting the focus away from individual scientists and toward the larger groups in which scientists are embedded.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

• 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))

24k Accesses

Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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What is quality research? A guide to identifying the key features and achieving success

Every researcher worth their salt strives for quality. But in research, what does quality mean?

Simply put, quality research is thorough, accurate, original and relevant. And to achieve this, you need to follow specific standards. You need to make sure your findings are reliable and valid. And when you know they're quality assured, you can share them with absolute confidence.

You’ll be able to draw accurate conclusions from your investigations and contribute to the wider body of knowledge in your field.

Importance of quality research

Quality research helps us better understand complex problems. It enables us to make decisions based on facts and evidence. And it empowers us to solve real-world issues. Without quality research, we can't advance knowledge or identify trends and patterns. We also can’t develop new theories and approaches to solving problems.

With rigorous and transparent research methods, you’ll produce reliable findings that other researchers can replicate. This leads to the development of new theories and interventions. On the other hand, low-quality research can hinder progress by producing unreliable findings that can’t be replicated, wasting resources and impeding advancements in the field.

In all cases, quality control is critical. It ensures that decisions are based on evidence rather than gut feeling or bias.

Standards for quality research

Over the years, researchers, scientists and authors have come to a consensus about the standards used to check the quality of research. Determined through empirical observation, theoretical underpinnings and philosophy of science, these include:

1. Having a well-defined research topic and a clear hypothesis

This is essential to verify that the research is focused and the results are relevant and meaningful. The research topic should be well-scoped and the hypothesis should be clearly stated and falsifiable .

For example, in a quantitative study about the effects of social media on behavior, a well-defined research topic could be, "Does the use of TikTok reduce attention span in American adolescents?"

This is good because:

• The research topic focuses on a particular platform of social media ( TikTok ). And it also focuses on a specific group of people (American adolescents).
• The research question is clear and straightforward, making it easier to design the study and collect relevant data.
• You can test the hypothesis and a research team can evaluate it easily. This can be done through the use of various research methods, such as survey research, experiments or observational studies.
• The hypothesis is focused on a specific outcome (the attention span). Then, this can be measured and compared to control groups or previous research studies.

2. Ensuring transparency

Transparency is crucial when conducting research. You need to be upfront about the methods you used, such as:

• Describing how you recruited the participants.
• How you communicated with them.
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You also need to explain how you analyzed the data, so other researchers can replicate your results if necessary. re-registering your study is a great way to be as transparent in your research as possible. This  involves publicly documenting your study design, methods and analysis plan before conducting the research. This reduces the risk of selective reporting and increases the credibility of your findings.

3. Using appropriate research methods

Depending on the topic, some research methods are better suited than others for collecting data. To use our TikTok example, a quantitative research approach, such as a behavioral test that measures the participants' ability to focus on tasks, might be the most appropriate.

On the other hand, for topics that require a more in-depth understanding of individuals' experiences or perspectives, a qualitative research approach, such as interviews or focus groups, might be more suitable. These methods can provide rich and detailed information that you can’t capture through quantitative data alone.

4. Assessing limitations and the possible impact of systematic bias

When you present your research, it’s important to consider how the limitations of your study could affect the result. This could be systematic bias in the sampling procedure or data analysis, for instance. Let’s say you only study a small sample of participants from one school district. This would limit the generalizability and content validity of your findings.

5. Conducting accurate reporting

This is an essential aspect of any research project. You need to be able to clearly communicate the findings and implications of your study . Also, provide citations for any claims made in your report. When you present your work, it’s vital that you describe the variables involved in your study accurately and how you measured them.

Curious to learn more? Read our Data Quality eBook .

How to identify credible research findings

To determine whether a published study is trustworthy, consider the following:

• Peer review: If a study has been peer-reviewed by recognized experts, rest assured that it’s a reliable source of information. Peer review means that other scholars have read and verified the study before publication.
• Researcher's qualifications: If they're an expert in the field, that’s a good sign that you can trust their findings. However, if they aren't, it doesn’t necessarily mean that the study's information is unreliable. It simply means that you should be extra cautious about accepting its conclusions as fact.
• Study design: The design of a study can make or break its reliability. Consider factors like sample size and methodology.
• Funding source: Studies funded by organizations with a vested interest in a particular outcome may be less credible than those funded by independent sources.
• Statistical significance: You've heard the phrase "numbers don't lie," right? That's what statistical significance is all about. It refers to the likelihood that the results of a study occurred by chance. Results that are statistically significant are more credible.

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• Your Health
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• Health Inc.
• Public Health

How Stories Connect And Persuade Us: Unleashing The Brain Power Of Narrative

Elena Renken

When you listen to a story, your brain waves actually start to synchronize with those of the storyteller. And reading a narrative activates brain regions involved in deciphering or imagining a person's motives and perspective, research has found. aywan88/Getty Images hide caption

When you listen to a story, your brain waves actually start to synchronize with those of the storyteller. And reading a narrative activates brain regions involved in deciphering or imagining a person's motives and perspective, research has found.

When you listen to a story, whatever your age, you're transported mentally to another time and place — and who couldn't use that right now?

"We all know this delicious feeling of being swept into a story world," says Liz Neeley , who directs The Story Collider, a nonprofit production company that, in nonpandemic times, stages live events filled with personal stories about science. "You forget about your surroundings," she says, "and you're entirely immersed."

Depending on the story you're reading, watching or listening to, your palms may start to sweat, scientists find. You'll blink faster, and your heart might flutter or skip. Your facial expressions shift, and the muscles above your eyebrows will react to the words — another sign that you're engaged.

A growing body of brain science offers even more insight into what's behind these experiences.

Shots - Health News

Photos: life and work amid the outbreak.

Storytelling Helps Hospital Staff Discover The Person Within The Patient

On functional MRI scans , many different areas of the brain light up when someone is listening to a narrative, Neeley says — not only the networks involved in language processing, but other neural circuits, too. One study of listeners found that the brain networks that process emotions arising from sounds — along with areas involved in movement — were activated, especially during the emotional parts of the story.

As you hear a story unfold, your brain waves actually start to synchronize with those of the storyteller, says Uri Hasson , professor of psychology and neuroscience at Princeton University. When he and his research team recorded the brain activity in two people as one person told a story and the other listened, they found that the greater the listener's comprehension, the more closely the brain wave patterns mirrored those of the storyteller.

Brain regions that do complex information processing seem to be engaged, Hasson explains: It's as though, "I'm trying to make your brain similar to mine in areas that really capture the meaning, the situation, the schema — the context of the world."

Other scientists turned up interesting activity in the parts of the brain engaged in making predictions. When we read, brain networks involved in deciphering — or imagining — another person's motives, and the areas involved in guessing what will happen next are activated, Neeley says. Imagining what drives other people — which feeds into our predictions — helps us see a situation from different perspectives . It can even shift our core beliefs, Neeley says, when we "come back out of the story world into regular life."

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'there's no fruit or veg': u.k. nurse makes emotional plea to panicked shoppers.

Your Brain On Storytelling

Listeners, in turn, may keep thinking about the story and talk to others about it, she says, which reinforces the memory and, over time, can drive a broader change in attitudes.

Different formats of information — lists of facts, say, or charts — may be better suited to different situations, researchers say, but stories wield a particularly strong influence over our attitudes and behavior.

In health care contexts, for example, people are more likely to change their lifestyles when they see a character they identify with making the same change, notes Melanie Green , a communication professor at the University at Buffalo who studies the power of narrative, including in doctor-patient communication. Anecdotes can make health advice personally important to a patient, she finds. When you hear or read about someone you identify with who has taken up meditation , for example, you might be more likely to stick with it yourself.

Stories can alter broader attitudes as well, Green says — like our views on relationships, politics or the environment. Messages that feel like commands — even good advice coming from a friend — aren't always received well. If you feel like you're being pushed into a corner, you're more likely to push back. But if someone tells you a story about the time they, too, had to end a painful relationship, for example, the information will likely come across less like a lecture and more like a personal truth.

Neeley has been taking advantage of these effects to shift perceptions about science and scientists in her work with Story Collider. "We try and take everybody — all different people and perspectives — put them onstage, and hear what a life in science is really like," she says.

Solid information in any form is good, Green says. "But that's not necessarily enough." A vivid, emotional story "can give that extra push to make it feel more real or more important." If you look at the times somebody's beliefs have been changed, she says, it's often because of a story that "hits them in the heart."

This story adapted from an episode of NPR's weekday science podcast Short Wave.

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Speaking, writing and reading are integral to everyday life, where language is the primary tool for expression and communication. Studying how people use language – what words and phrases they unconsciously choose and combine – can help us better understand ourselves and why we behave the way we do.

Linguistics scholars seek to determine what is unique and universal about the language we use, how it is acquired and the ways it changes over time. They consider language as a cultural, social and psychological phenomenon.

“Understanding why and how languages differ tells about the range of what is human,” said Dan Jurafsky , the Jackson Eli Reynolds Professor in Humanities and chair of the Department of Linguistics in the School of Humanities and Sciences at Stanford . “Discovering what’s universal about languages can help us understand the core of our humanity.”

The stories below represent some of the ways linguists have investigated many aspects of language, including its semantics and syntax, phonetics and phonology, and its social, psychological and computational aspects.

Understanding stereotypes

Stanford linguists and psychologists study how language is interpreted by people. Even the slightest differences in language use can correspond with biased beliefs of the speakers, according to research.

One study showed that a relatively harmless sentence, such as “girls are as good as boys at math,” can subtly perpetuate sexist stereotypes. Because of the statement’s grammatical structure, it implies that being good at math is more common or natural for boys than girls, the researchers said.

Language can play a big role in how we and others perceive the world, and linguists work to discover what words and phrases can influence us, unknowingly.

How well-meaning statements can spread stereotypes unintentionally

New Stanford research shows that sentences that frame one gender as the standard for the other can unintentionally perpetuate biases.

Algorithms reveal changes in stereotypes

New Stanford research shows that, over the past century, linguistic changes in gender and ethnic stereotypes correlated with major social movements and demographic changes in the U.S. Census data.

Exploring what an interruption is in conversation

Stanford doctoral candidate Katherine Hilton found that people perceive interruptions in conversation differently, and those perceptions differ depending on the listener’s own conversational style as well as gender.

Cops speak less respectfully to black community members

Professors Jennifer Eberhardt and Dan Jurafsky, along with other Stanford researchers, detected racial disparities in police officers’ speech after analyzing more than 100 hours of body camera footage from Oakland Police.

How other languages inform our own

People speak roughly 7,000 languages worldwide. Although there is a lot in common among languages, each one is unique, both in its structure and in the way it reflects the culture of the people who speak it.

Jurafsky said it’s important to study languages other than our own and how they develop over time because it can help scholars understand what lies at the foundation of humans’ unique way of communicating with one another.

“All this research can help us discover what it means to be human,” Jurafsky said.

Stanford PhD student documents indigenous language of Papua New Guinea

Fifth-year PhD student Kate Lindsey recently returned to the United States after a year of documenting an obscure language indigenous to the South Pacific nation.

Students explore Esperanto across Europe

In a research project spanning eight countries, two Stanford students search for Esperanto, a constructed language, against the backdrop of European populism.

Chris Manning: How computers are learning to understand language​

A computer scientist discusses the evolution of computational linguistics and where it’s headed next.

Stanford research explores novel perspectives on the evolution of Spanish

Using digital tools and literature to explore the evolution of the Spanish language, Stanford researcher Cuauhtémoc García-García reveals a new historical perspective on linguistic changes in Latin America and Spain.

Language as a lens into behavior

Linguists analyze how certain speech patterns correspond to particular behaviors, including how language can impact people’s buying decisions or influence their social media use.

For example, in one research paper, a group of Stanford researchers examined the differences in how Republicans and Democrats express themselves online to better understand how a polarization of beliefs can occur on social media.

“We live in a very polarized time,” Jurafsky said. “Understanding what different groups of people say and why is the first step in determining how we can help bring people together.”

Analyzing the tweets of Republicans and Democrats

New research by Dora Demszky and colleagues examined how Republicans and Democrats express themselves online in an attempt to understand how polarization of beliefs occurs on social media.

Examining bilingual behavior of children at Texas preschool

A Stanford senior studied a group of bilingual children at a Spanish immersion preschool in Texas to understand how they distinguished between their two languages.

Predicting sales of online products from advertising language

Stanford linguist Dan Jurafsky and colleagues have found that products in Japan sell better if their advertising includes polite language and words that invoke cultural traditions or authority.

Language can help the elderly cope with the challenges of aging, says Stanford professor

By examining conversations of elderly Japanese women, linguist Yoshiko Matsumoto uncovers language techniques that help people move past traumatic events and regain a sense of normalcy.

A report from GHTC and Policy Cures Research examines how US federal funding for global health R&D is delivering impact both abroad and at home.

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5 ways science is transforming global health and saving lives

Science expands our understanding, makes the impossible possible, and helps us build the future we want for all people. science drives the work of ghtc, so we wanted to take a step back to reflect on five ways science is transforming global health..

Science expands our understanding, makes the impossible possible, and helps us build the future we want for all people. Science drives the work of our Global Health Technologies Coalition, so we wanted to take a step back to reflect on five ways science is transforming global health:

1. Science is generating treatments, cures, and vaccines to tackle the world’s most devastating diseases. 

From a vaccine that has put us at the brink of eradicating polio to antiretroviral treatments that have dramatically extended the lives of people living with HIV and AIDS, science has generated new health technologies that have driven tremendous progress in global health. Thanks to investments in science and research, 82 new vaccines, drugs, diagnostics, and other lifesaving global health tools have been developed and introduced since 2000. These tools include a new meningitis A vaccine —which has already saved 378,000 lives and prevented 673,000 new infections since 2010—and new child-friendly malaria drugs that have helped cut childhood malaria deaths by 65 percent since 2000. Science has also fueled a robust pipeline of over 670 global health technologies now in development poised to further build upon these gains.

2. Science is helping us understand the unique needs of users and communities so we can design the right tools for impact.

3. Science is helping us predict, detect, and track emerging health risks so we can be better prepared to confront tomorrow's challenges.

From using weather patterns to forecast the risk of insect-borne disease outbreaks, to employing genomics and evolutionary theory to predict how bacteria will become resistant to antibiotics, to advancing new hybrid systems that combine crowdsourced data with traditional disease surveillance, science is helping us better predict, detect, and track infectious disease outbreaks and other emerging health challenges. Early detection can make the difference between an outbreak becoming an epidemic and is critical to mounting an effective response.

4. Science is helping us understand what works and what doesn't so we can better target interventions and design health programs for maximum impact. 

How often does an insecticide-treated bed net need to be replaced , and how many tears can it sustain before its stops working? In an era of limited resources, can we predict which technologies and interventions are likely to save the most lives in a country if brought to scale? How do we get people to change their handwashing habits to reduce diarrheal disease and childhood deaths? These are the questions big and small that scientists, data analysts, and other health researchers are working to answer in labs, offices, and program sites across the United States and world. The answers they get are helping us better target health solutions and refine health programming to more save lives and more dollars.

5. Science is putting information and data at our fingertips to help us fight global diseases and health challenges in new and unusual ways.

The revolution in mobile technology, digital health, and big data is transforming our approach to fighting global diseases and health challenges. Health care workers are using mobile devices to track immunization coverage door-to-door and monitor vaccine supplies to prevent stockouts, doctors are using SMS to remind patients to take their tuberculosis drugs and treatment adherence, and health ministries are deploying new data visualization toolsto turn a mountain of data into accessible and actionable information to guide decision on to best deploy and target resources.

Science creates a foundation upon which improvements in global health are built. It unlocks discoveries and fuels innovation, informs policies and programs, breaks down barriers, and ultimately advances better, healthier lives for all people. At this moment in time, it is more vital than ever that we build a convincing case of the benefits that flow from science and the importance of strong investment in science and research.

In global health, science matters because #scienceserves and science saves.

About the authors

Jamie bay nishi ghtc.

Jamie served as GHTC Executive Director for seven years until the end of 2023, leading the coalition’s policy and advocacy portfolio, as well as managing its engagement with GHTC members and other stakeholders and partners in government, the private sector, and civil society. She has over 12 years of experience in business development, project management, stakeholder engagement, and strategic partnership building.

Marissa Chmiola GHTC

Marissa manages the development and implementation of the coalition’s communications activities, overseeing GHTC’s digital presence, media outreach, events, publications, and internal communication practices. She also manages GHTC's monitoring, evaluation, and adaptive learning and donor reporting... read more about this author

Other Breakthroughs Blog posts you may like:

Research roundup: progress in fighting ntds and tb, polio eradication setbacks, and the crisis surrounding..., global health r&d gamified.

Kellogg School of Management at Northwestern University

Organizations Strategy Feb 12, 2024

Organizations are complex. complexity science can help us understand them., you can’t study the behavior of a flock by looking at individual birds. it’s time to bring that holistic approach to the social sciences, too..

Benjamin F. Jones

Dashun Wang

Jesus Escudero

Scientists, trying to make sense of a complicated world, have historically tried to simplify things. For instance, running an experiment generally involves holding a lot of variables constant, and then tweaking one or two at a time to see what happens.

But over time, as technology has improved, we have gotten better at collecting and analyzing data. A lot better. In response, a new approach to making sense of complicated phenomena—a new scientific discipline, really—has started to take off. That discipline is called complexity science.

When faced with a complex problem—like how the oceans will respond to climate change, or how the neurons in our brain communicate with one another, or how traffic might best be routed through a quickly growing city—complexity science encourages a holistic view. Rather than breaking the problem into tiny parts and studying each of them separately, you can analyze the system as a whole.

And if this all sounds a little abstract, like something businesses don’t need to think about at all, think again—because markets and organizations are complex systems, too.

In 2023, Kellogg launched the Ryan Institute on Complexity , cofounded by three Kellogg professors with very different academic backgrounds: Brian Uzzi (a sociologist by training), Dashun Wang (a “reformed physicist”), and Ben Jones (an economist).

Kellogg Insight recently recorded a podcast, airing February 19, with Uzzi, Wang, and Jones. On it, we discuss why complexity science’s time has come, and why you should be paying attention. Here are some highlights from that conversation, edited for length and clarity.

On why it’s so important to analyze the entirety of a system

Dashun WANG: The canonical example is to think about a flock of birds. If you look at the flock of birds, you realize they go together almost like an organism, but there is no middle manager in that flock. There is no CEO. But as a whole, they exhibit a kind of property that no individual bird can. Individual birds actually can achieve very little; as a flock they have a competitive advantage. Then they can go after food sources that individual birds couldn’t.

On what counts as a system

Brian UZZI: In complex systems, the boundary of the system is always an issue because it’s somewhat subjective as to where you want to define it. So if you’re looking at a rock and roll band, is it the five people in the group? Is it the group plus the producer on the album plus the record label that markets it? You can open and close the boundaries and see different things depending on where you begin and end.

WANG: Or another way I look at is: everything’s a system, it’s just at a different scale. A team is a system of different minds working together. Every mind of every brain is a system that’s billions of neurons.

On the importance of bringing an interdisciplinary perspective to studying systems

Ben JONES: One thing that unifies us is trying to understand where big breakthroughs in technology, innovation, and science come from. I’m going to come at that with a certain kind of economic frame. Brian’s going to come at that with more of a sociology frame, which is going to emphasize more network orientation, more social context. And Dashun might come at it with models that are from physics but are analogous to human behavior. And so when we work together, we are able to open up our conceptual orientations and come at a question in a more novel way, because we can listen to each other and find new and broader ways of thinking.

On what a “systems approach” can tell us about one topic businesses are likely to care a lot about: innovation

JONES: Is an R&D team like a string quartet or is it more like the copilots in the cockpit of a plane?

If you had four pilots, two really good and two sketchy, and two planes, would you want to put the sketchy pilots together or would you want to split them up between the good pilots? You would want to put a good pilot in each plane. You would not want to put the sketchy pilots together because you want to make sure that each plane has at least one good pilot, right? Because effectively the quality of the plane flying is going to depend on the best person in the cockpit. So you don’t want to have only bad people in the cockpit.

But let’s say I’m trying to build string quartets. Do I want to spread my sketchy musicians around? I do not. Because one sketchy member on the quartet is going to ruin the whole sound: if someone’s playing off time or off tune, it’s going to ruin the whole effect. So what you’d actually like to do is isolate problem cases, and you want your best people working together.

Okay, now go to an R&D team in an organization. You’ve got a couple of people who have really great ideas. Do you split them up? Or do you want to put them together? So we studied this, looking at large-scale data and watching what happens as people move around from different teams. You see how they perform in different contexts.

It turns out that R&D teams look a lot like string quartets, not like pilots. So, in other words, you actually don’t want to spread your best people around. You’d rather have them work together. And this gets back to people being specialized. In a modern R&D situation, people are bringing specialized skills into teams, and if you have a weak link in one of the specialists, they can cause the whole project to be defeated.

On why the time for complexity science is now

WANG: If we boil it down to two major factors, it’s data and tools. I think on the one hand there is this large amount of data that captures many aspects of business and a range of human behavior: this data has now become available in a way that it was hard to imagine 10 years ago or 20 years ago. So just the unprecedented level of access that we have to data.

But at the same time, I think what is often overlooked is the availability of tools that can help us make sense of this data. And these tools include complexity science and networks as a tool, as well as large language models or AI as a tool. You can then borrow these kinds of tools to ask very fundamental social-science questions that otherwise we couldn’t.

On their goals for the Ryan Institute on Complexity

WANG: Complexity science has won the Nobel Prize in physics, but in terms of applying it to business and social science, we’re just at the beginning. We’re really just scratching the surface.

Traditionally, social-science research has been individual faculty members working in their offices. But one of the things we’re hoping to do with the Ryan Institute is to bring the kind of lab model that exists in the hard sciences into the social sciences. Hopefully this will enable faculty members at Kellogg, as well as at the university, to more readily establish new collaborations, scale faculty’s time, work together on bigger problems, and attack those problems faster.

JONES: Today, we have so much more extent knowledge, technical knowledge, scientific knowledge about how to do things. And so you just can’t be an expert as easily as you were in the past. The Wright brothers were two brothers who produced the first airplane, and they’re both considered leading aeronauts of their time. They’re conceiving of the plane, they’re designing it, they’re building it, they’re even flying it. And that’s just two people, and they’re able to make this work. But a modern airframe, like an Airbus or a Boeing 787, just the jet engines are 30 different PhD-level disciplines. And that’s just the jet engines!

UZZI: As scientists and people trained to solve problems see a smaller piece of the world, they’re also equipped to only solve problems involving a smaller piece of the world. But the problems have gotten bigger and more complex and global in nature. And the only way to make it work is you’ve got to put people back together in a network. The Ryan Institute is an incredible opportunity to be part of trying to define that future.

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Animal studies in psychology

• Animal Research

Undergraduates sometimes ask what the value of animal research is in psychology. The study of nonhuman animals has actually played a huge role in psychology, and it continues to do so today. If you’ve taken an introductory psychology class, then you have probably read about seminal psychological research that was done with animals: Skinner’s rats, Pavlov’s dogs, Harlow’s monkeys. Unfortunately, many introductory textbooks don’t give the full picture of animal research. Studies are often described without specifying that they were animal studies. When human studies are presented, there is rarely discussion of the basic animal research that enabled those studies to be done. Finally, information regarding the ethical and regulatory environments in which animal research is conducted is covered in a superficial manner or omitted altogether. These are important issues that deserve better understanding and broader discussion.

Why Nonhuman Animals are Studied in Psychology

Part of the justification for why nonhuman animals are studied in psychology has to do with the fact of evolution. Humans share common ancestry with the species most commonly studied in psychology: mice, rats, monkeys. To be sure, each species has its own specializations that enable it to fit into its unique ecological niche; but common ancestry results in structural (e.g., brain) and functional (e.g., memory) processes that are remarkably similar between humans and nonhumans. In addition, we can better understand fundamental processes because of the precise control enabled by animal research (e.g., living environments, experimental conditions, etc.). We can also ask and answer certain questions that would be difficult or impossible to do with humans. For example, we know what the connections are between the amygdala and other brain regions, but how does activity in the amygdala affect brain functioning? Using a new technique, it is now possible to temporarily inactivate the amygdala in a monkey and see how other brain areas (including those that are not directly connected to the amygdala) change their activity (Grayson et al., 2016). A study such as this not only helps us better understand how the brain works, but it also has enormous potential for developing treatments for people who have abnormal patterns of brain activity, such as those with epilepsy or Parkinson’s disease. Ten years from now, students may very well read in their textbooks about a “new treatment” to help people with Parkinson’s disease. Will this monkey study, which enabled such a discovery to be made, be described? Probably not, in much the same way that nonhuman research that permitted a significant human study to be conducted is rarely described in today’s textbooks.

Weighing Harm and Benefit

Researchers who study nonhumans recognize that their studies may involve certain harms that can range from the relatively minor (e.g., drawing a blood sample) to the more serious (e.g., neurosurgery). The research community tries to mitigate some of the harms by insuring, for example, that the animals’ psychological well-being is optimized; in fact, there is a large body of psychological research that focuses on animal welfare and identifying best practices to house and care for animals in captivity. Still, some harms will remain, and ethically, one must weigh those harms against the potential benefits (for humans and for the animals themselves) to be obtained from the research. Equally important is the consideration of the potential harms to humans of not doing the research. For example, without any animal research, effective treatments for human conditions like Alzheimer’s disease may very well be found, but it would certainly take decades longer to find them, and in the meantime, millions and millions of additional people would suffer.

Regulations for Animal Research

Finally, it’s important to note that animal research in the United States is very tightly regulated by a series of federal and state laws, policies and regulations, dating back to the landmark Animal Welfare Act from 1966. Oversight and inspection of facilities is provided by the U.S. Dept. of Agriculture, and, at the local level by Institutional Animal Care and Use Committees (IACUCs). Even procedures as simple as drawing a blood sample or testing an animal on a cognitive task must be approved by the local IACUC before the work can begin. Part of that approval process requires the scientist to identify whether there might be less invasive ways to do the same thing. In addition, the scientist must justify the numbers of animals that they use, insuring they are using the smallest number possible.

Animal research continues to play a vital role in psychology, enabling discoveries of basic psychological and physiological processes that are important for living healthy lives. You can learn more about some of this research, as well as the ethical and regulatory issues that are involved, by consulting online resources such as Speaking of Research . 

Grayson D.S., Bliss-Moreau E., Machado C.J., Bennett J., Shen K., Grant K.A., Fair D.A., Amaral, D.G. The rhesus monkey connectome predicts disrupted functional networks resulting from pharmacogenetic inactivation of the amygdala. Neuron . 2016 Jul 20;91(2):453-66. 

About the author

• Open access
• Published: 13 September 2024

Is this really Empowerment? Enhancing our understanding of empowerment in patient and public involvement within clinical research

• Imke Schilling 1 , 2 &
• Ansgar Gerhardus 1 , 2  

BMC Medical Research Methodology volume  24 , Article number:  205 ( 2024 ) Cite this article

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There has been a growing push to involve patients in clinical research, shifting from conducting research on, about, or for them to conducting it with them. Two arguments advocate for this approach, known as Patient and Public Involvement (PPI): to improve research quality, appropriateness, relevance, and credibility by including patients’ diverse perspectives, and to use PPI to empower patients and democratize research for more equity in research and healthcare. However, while empowerment is a core objective, it is often not clear what is meant by empowerment in the context of PPI in clinical research. This vacancy can lead to insecurities for both patients and researchers and a disconnect between the rhetoric of empowerment in PPI and the reality of its practice in clinical trials. Thus, clarifying the understanding of empowerment within PPI in clinical research is essential to ensure that involvement does not become tokenistic and depletes patients’ capacity to advocate for their rights and needs.

We explored the historical roots of empowerment, primarily emerging from mid-20th century social movements like feminism and civil rights and reflected the conceptual roots of empowerment from diverse fields to better understand the (potential) role of empowerment in PPI in clinical research including its possibilities and limitations.

Common themes of empowerment in PPI and other fields are participation, challenging power structures, valuing diverse perspectives, and promoting collaboration. On the other hand, themes such as contextual differences in the empowerment objectives, the relationship between empowerment and scientific demands, research expertise, and power asymmetries mark a clear distinction from empowerment in other fields.

PPI offers potential for patient empowerment in clinical trials, even when its primary goal may be research quality. Elements like participation, sharing opinions, and active engagement can contribute to patient empowerment. Nonetheless, some expectations tied to empowerment might not be met within the constraints of clinical research. To empower patients, stakeholders must be explicit about what empowerment means in their research, engage in transparent communication about its realistic scope, and continuously reflect on how empowerment can be fostered and sustained within the research process.

Peer Review reports

Background and problem statemen

Introduction.

There has been a growing demand from patients, researchers, research sponsors, and scientific journals to shift clinical studies from being exclusively conducted on, about, or for patients to involving patients themselves or members of the public [ 1 , 2 ]. Two primary lines of reasoning underlie active patient and public involvement (PPI):

By integrating patients’ diverse perspectives into research, the aim is to enhance the quality, appropriateness, relevance, and credibility of the research [ 3 , 4 ].

Additionally, there are normative arguments supporting PPI that revolve around moral, ethical, and rights-based considerations, primarily linked to empowering patients or the public [ 5 ]. In essence, the idea is that patients should have a say in research that directly concerns them [ 3 , 6 ]. This notion aligns with the principle of “nothing about us, without us,” which has guided movements in various contexts, including the disability rights movement [ 7 ] and Indigenous contexts [ 8 ].

By empowering patients and upholding their right to participate in research, PPI seeks to diminish social inequalities. In doing so, it aims to democratize the research process, making it more accountable and transparent to the broader population [ 2 , 3 , 4 , 5 , 9 ]. This democratization is particularly significant for marginalized groups whose perspectives are often overlooked [ 1 , 5 ].

While patient empowerment is a core objective of PPI [ 4 ], it is seldom explicitly defined within the context of PPI. Based on the etymology, the root of the term implies that ‘empowerment’ concerns matters of ‘power’. The Oxford English Dictionary offers three distinct meanings of the verb “empower” [ 10 ]. One involves granting someone legal or formal authority, another focuses on bestowing power over something, and the third pertains to strengthening an individual by providing greater control, specific attributes, or enhanced abilities. Empowerment can denote either a process or a state of being respectively an outcome.

A narrative review by Gradinger et al. revealed that in the context of public involvement, normative values are frequently referenced without clear definitions, resulting in significant variations in the understanding of empowerment [ 4 ]. While there is a general need to clarify the conceptualization of PPI to align with its intended goals [ 11 ], the emancipatory aspect of PPI remains underexplored compared to other approaches [ 12 ]. Without a precise meaning and operationalization of the term ‘empowerment’, the normative claim of PPI becomes difficult to realize and its implementation virtually impossible to assess. The lack of a shared understanding of empowerment within PPI not only fosters misinterpretation and arbitrariness in PPI practices but may also inadvertently undermine patient empowerment. From the perspective of a patient, ambiguous roles, a sense of inability to contribute, insufficient recognition of one’s contributions, or inadequate information about the benefits of involvement could potentially be rather disempowering than empowering [ 13 ]. There is a risk that the involvement may become tokenistic, and patients’ voices might be silenced when they are merely involved for show, as a formality, without genuine influence on the research. Additionally, this involvement may deplete patients’ resources and capacity to advocate for their rights and needs in potentially more effective ways [ 14 , 15 ].

In a previous study, we discovered that within the same project, patients and researchers assign varying degrees of importance to patient empowerment. While patients engaged in a patient board for a clinical trial endorsed the idea of empowerment through research participation, only one out of five researchers explicitly addressed patient empowerment as a rationale for conducting PPI [ 16 ]. Furthermore, the experiences of patients and researchers with the patient board indicated that patient empowerment is often overlooked in the implementation of PPI. Other forms of collaboration, such as open dialogues on an equal footing and providing training to enhance patients’ confidence and skills, might have proven more effective in empowering patients [ 17 ]. These findings align with those of Ives et al. [ 3 ], who also noted a potential mismatch between the stated goals of PPI and its practical execution. Ives et al. argue that the nature and conduct of PPI can vary significantly depending on who initiates it and for what purpose. For instance, if researchers involve patients primarily to enhance the quality of their research projects, the focus might be on outcome-oriented, pragmatic consultation, potentially sidelining the goal of patient empowerment. Patients may be relegated to an informational role rather than active partners in the research process. Based on these insights, we assume that empowerment does not naturally evolve from PPI and is not an automatic byproduct of it.

Aim, research interest and approach

Considering the above, it seems necessary to clarify the term empowerment within PPI in clinical research. Despite the absence of a precise understanding of empowerment in the context of PPI, the term “empowerment” has been in use across various domains for over half a century, including social work, education, corporate settings, psychology, and healthcare [ 18 ]. Therefore, this article aims to contribute to the understanding of empowerment in PPI by reflecting on the history and tradition of the term and concept of empowerment in other fields. Building on this, we aim to reflect on what lies behind the term empowerment in the context of PPI in clinical research and try to explain the disconnect between the rhetoric of empowerment in PPI and the reality of its practice in clinical trials. We have been guided by the following questions and have structured the article accordingly:

How has the concept of empowerment evolved historically?

How has empowerment been conceptualized in other fields?

To what extent does the concept of empowerment of patients through or for PPI in clinical research align with conceptual approaches to empowerment in other fields?

The article provides researchers who organize PPI with orientation on the relationship between empowerment and PPI. It offers perspectives on the possibilities and limits of empowerment in this context and invites further reflection on the topic from both researchers and patients involved in PPI.

For consistency, the term ‘patient’ is exclusively used in this article to refer to individuals who have had specific health-related experiences. However, we acknowledge that other terms, such as ‘service users’, may be more suitable and better reflect the active role that PPI strives for. This article is centered around PPI in clinical research and does not encompass reflections on PPI in other contexts, such as healthcare.

Historical development of the term empowerment

The term “to empower” has been documented since the mid-17th century, with older forms such as ‘impover,’ ‘empour,’ and ‘empowre’ [ 10 ]. In the mid-17th century, William Penn, founder of the Quaker colony of Pennsylvania, utilized the term in a religious and early democratic context. Penn’s theology of individual empowerment was based on the belief in the intrinsic dignity of all individuals, the presence of a part of God within each person (referred to as the “inward light” or “inner spirit”), and the assertion of the right to freedom of conscience. Penn’s ideas influenced the formulation of a groundbreaking constitution for Pennsylvania, serving as a model for subsequent democratic constitutions [ 19 ].

History of empowerment in the social movements

The term “empowerment,” intertwined with democracy since its inception, has evolved over time, primarily shaped by mid-20th-century social movements.

Civil Rights Movement and Black Empowerment

The civil rights movement in the 1950s and 1960s among the Black minority in the U.S. significantly influenced the idea and implementation of empowerment. Acts of civil disobedience exposed racial inequalities [ 20 ], and multiplier programs aimed to provide education and raise consciousness among the Black community [ 18 , 20 ]. Grounded in the belief in individuals’ abilities to control their lives, the movement sought to integrate the Black minority as equals with equal social rights into the democratic society. Freeing the Black minority community from oppression through collective self-organization resulted in a “new sense of somebodiness” (Martin Luther King as cited in Simon [ 19 ]).

Feminist movement

Another driver of the empowerment discourse was the second wave of the feminist movement in the 1960s and 1970s, which addressed women’s opportunities and rights for societal equality [ 21 ]. Through expanded education, improved labor conditions, economic independence, far-reaching changes in the possibilities for self-determined birth control and a developed awareness of personal (bodily) autonomy, women’s life plans became more individualized [ 22 ]. Within the movement, women found a protective framework to navigate their evolving opportunities and resulting responsibilities. It provided a social reference structure, creating spaces for self-clarification, collective articulation of devaluation, and deconstruction of internalized beliefs. This support allowed women to envision, develop, and test new life possibilities and identities, thereby fostering self-confidence [ 18 ].

Self-Help movement

A third root of the modern empowerment concept is the self-help movement, which gained importance in the 1970s in the USA and other developed countries, especially within health-related contexts [ 7 , 18 ]. As self-organized networks, self-help aimed to establish social support, explore coping strategies, and reclaim autonomy and empowerment resources. Self-help served as a counter-program to perceived disempowering state care [ 7 ], emphasizing the perspective of individuals as ‘experts on their own account’, introducing self-organized services, creating (a sense of) community and thus producing emotional ‘services’, empowering critical consumers, and representing peoples’ interests to influence socio-political decisions [ 18 ]. Key features of self-help networks included the involvement of members with a common problem, minimal professional helper involvement, emphasis on immaterial support, and goals of self- and social change achieved through equal cooperation and mutual help. Self-help groups provided critical support in niches not covered by professional care services [ 18 ].

Community action programs and community psychology

In the U.S., community-based programs aimed at empowering individuals and building networks to address social segregation [ 23 ]. These programs furnished resources and support to enable individuals and communities to take charge of their lives and implement positive changes in their community. Political initiatives, like the Equal Opportunities Act of 1965, sought to reduce inequalities and poverty, promoting “maximum feasible participation” [ 18 ]. Empowerment was considered a means of encouraging self-sufficiency and reducing dependence on government support.

In the 1970s, community action programs became linked to community psychology, viewing individuals as part of communities and collaborating to identify strengths, resources, and needs. Strategies formulated aimed to empower and promote social justice while reducing social inequalities.

The tradition of empowerment in social movements encompasses both individual self-determination and collective action against structural constraints. The primary concerns were not only about self-empowerment but also about advocating for structural changes through mass mobilization and collective efforts. In these contexts, empowerment was often pursued through independently organized groups that fostered community solidarity and collective identity. Unlike prevalent deficit-based approaches, which tend to focus on individuals’ lacks and weaknesses, empowerment in social movements nurtures and strengthens individuals’ skills and capabilities while also addressing and dismantling oppressive structures.

Today: use in various contexts

Since its emergence in mid-20th-century social movements and subsequent development in community psychology, the concept of empowerment has found application across diverse domains [ 18 , 24 ]:

Social work, encompassing individual support and collective actions.

Educational programs, such as literacy campaigns and increased pupil participation opportunities.

Development aid, representing a shift from external, top-down approaches to fostering local community capacity for participatory development and poverty reduction in developing countries.

Corporate contexts, where empowerment principles are integrated into management strategies.

Healthcare, where applications include shared decision-making and broader patient involvement.

Contemporary movements, such as racial empowerment in the “Black Lives Matter” movement and the Indigenization.

Concepts of empowerment

In this section, we explore the foundational concepts and theoretical underpinnings of empowerment.

Key concepts related to empowerment

Social scientist Barbara Bryant Solomon pioneered the conceptual foundation of empowerment in her 1976 book, “Black Empowerment: Social Work in Oppressed Communities.” Originating as a resource for students and social workers assisting Black minority clients, Solomon’s empowerment concept is based on research into the mechanisms of power and powerlessness. According to her, “empowerment refers to the reduction of an overriding sense of powerlessness to direct one’s life in the direction of meaningful personal satisfaction” [ 25 ]. At the core of this concept is the experience of powerlessness, arising from membership in a minority group subject to negative assumptions and discrimination from the majority society and its institutions [ 26 , 27 ].

While previous authors had emphasized the need to consider stigma as a factor that permeates the social situation of Black people, Solomon added that the unequal distribution of power and the experience of (structural) discrimination could affect the psyche and the negative attributions could find their way into self-perception. Thus, powerlessness of an individual means “the inability to manage emotions, knowledge, skills or material resources in a way that makes possible effective performance of valued social roles so as to receive personal gratification” [ 26 ].

At the community level, powerlessness is described as the inability to utilize resources for collective goals [ 26 ]. In short, stigma affects powerlessness, hindering access to the resources necessary for overcoming negative self-perceptions and social challenges [ 27 ]. Introducing empowerment as a method, Solomon suggested that professionals could employ it to address the powerlessness experienced by stigmatized individuals or groups. Empowerment, in her view, enables individuals to recognize their competence, perceive available opportunities for control, and ultimately enhance their self-worth and dignity [ 25 ]. In summary, Solomon’s empowerment approach is based on the belief that individuals and families have strengths and abilities and that they can be supported to use their resources more effectively for their own benefit. Solomon saw empowerment as both a process and a goal for social work in Black communities, and stated that the success of empowerment is “directly related to the degree to which the service delivery system itself is an obstacle course or an opportunity system” [ 26 ].

In 1981, community psychologist Julian Rappaport advocated for empowerment as a superior approach to paternalistic public health policies and rights-based advocacy in social work [ 28 ]. Acknowledging the diverse nature of social problems, Rappaport urged professionals to reconsider their roles in relation to clients, aligning with Solomon’s view that empowerment enhances individuals’ control over their lives.

Rappaport emphasized viewing individuals not solely as children in need or rights-bearing citizens but as complete human beings with both rights and needs. He argued that even those seemingly incompetent and in need require “[…] more rather than less control over their own lives, and fostering more control does not necessarily mean ignoring them“ [ 28 ]. Increased control is believed to positively influence psychological well-being.

Empowerment, according to Rappaport, relies on the belief that people possess or can acquire competencies, with inadequate functioning attributed to social structures or the lack of resources that prevent people from using these competencies. He advocated for competency development in real-life settings and positioned those providing help as collaborative teammates who take into account social structures and living conditions, and not as authoritative experts [ 28 ].

Furthermore, Rappaport stressed the need for diverse solutions to divergent problems, rejecting a one-size-fits-all approach in social policy. He championed a bottom-up, participatory social policy that recognizes the context-specific and varied nature of empowerment in each situation [ 28 ].

Brazilian educator and social reformer Paulo Freire expanded the concept of empowerment through his work with marginalized communities in Brazil [ 29 ]. Central to his ideas is the development of ‘critical consciousness’ through dialogic education [ 30 ]. Freire contended that oppressed individuals often lack awareness of the social and political factors sustaining their subjugation. Critical consciousness involves recognizing oppressive systems and understanding the socio-economic and political contexts fostering inequality, along with realizing one’s potential for transformation. Freire regarded the critical consciousness experience as the key to gaining strength, with education playing a fundamental role to conscientization. Freire’s dialogic teaching method, emphasizing two-way learning between teachers and students, fosters critical thinking, self-reflection, and active participation, empowers students to question and reshape their reality. Working in partnership assigns the teacher the role of a facilitator and underscores the central importance of the consumer or marginalized individuals in the process of change [ 19 , 30 ]. Complementing this, Freire’s pedagogy of questioning encourages students to critically assess the influences shaping their lives. The emphasis is not on remembering details, but on cultivating analytical skills and the capability to challenge prevailing beliefs.

Beyond individual liberation, Freire argued that true empowerment encompasses collective action and social transformation. He underscored the importance of solidarity and creating dialogic spaces for individuals to collaboratively address common experiences of oppression and work towards societal progress [ 30 ].

In summary, Freire sought to empower individuals and communities by promoting critical consciousness, dialogue, and collective action to challenge oppressive systems and foster a more inclusive and equitable society. While he placed responsibility on the oppressed for seeking their own empowerment, caution was advised to prevent reinforcing a sense of helplessness [ 29 ].

Common principles

While there is no universally agreed upon definition or concept of empowerment, some common principles can be identified, then, from what we have reviewed: Empowerment comes from a variety of sources, refers to processes and outcomes, involves both personal and collective dimensions, is based on participation, assumes that each individual has strength and capacities upon which they can build, challenges power structures with a focus on marginalized groups and the systematic inequalities they face, and must be obtained by the individuals themselves, but can be supported by third parties, e.g. professionals, who facilitate the process of empowerment in collaboration with individuals or communities [ 19 , 26 , 28 , 29 , 30 , 31 ].

As the most basic definition of empowerment, Herringer outlines: “Developmental processes over time in which individuals acquire the skills necessary to live a life that meets their own standards of ‘better’” [ 32 , translated by IS]. These processes of gaining more power or autonomy can be individual and collective [ 32 ].

Controversies

At the same time there exist some controversies around empowerment. Herringer continues his definition with the thought: “[.] what exactly constitutes a “more livable” existence is open to conflicting interpretations and ideological frameworks” [ 32 ]. Other controversies surrounding the concept of empowerment are:

Instrumentalization , tokenism and depoliticization : the concern that empowerment programs or initiatives may be implemented for instrumental, tokenistic purposes or to create the illusion of progress [ 27 ]. In such cases, empowerment becomes an empty concept without substantial impact. The adoption of empowerment concepts by the powerful (e.g. institutions or entities that hold significant structural and decision-making authority) can lead to a depoliticization of empowerment programs, as the transformative potential of such initiatives may be diminished or neutralized when circumscribed by institutional capture. This co-option of empowerment by those in power can result in a form of engagement that maintains existing power dynamics rather than challenging them.

Lack of clarity and measurement : empowerment is so diverse and open-ended that it is difficult to define in a way that its outcomes can be measured [ 24 ]. Clarity is needed regarding which aspects of empowerment are targeted. Without evaluating empowerment attempts, it is challenging to learn from experience.

Empowerment in the context of PPI

The concept of empowerment has deep roots in various social movements that sought to challenge systemic inequalities and give voice to marginalized groups. To analyze how these conceptual approaches to empowerment from social movements relate to the empowerment of patients in PPI within clinical research, we will first provide an overview of the historical development of PPI in research, followed by a recall of the relevance of empowerment in the context of PPI. We will then analyze and critically address (a) the similarities of approaches to empower patients or the public in PPI as compared with other fields, and by that get an impression how PPI in clinical research can empower patients, and (b) the distinctions and limitations of empowerment in this context, both practically and conceptually.

Evolution of PPI in research

Patient advocacy movements, gaining momentum in the mid-20th century, played a pivotal role in pushing for increased patient involvement in research [ 33 ]. These movements, which often emerged from broader social and civil rights movements, laid the foundation for what we now recognize as PPI.

For instance, the HIV-AIDS activism of the 1980s, heavily influenced by the gay civil rights movement, led to significant changes in health research by challenging the prevalent research expertise and bringing in “a ´patient perspective` to bear on institutions of health research” [ 34 ].

In the 1970s, Rose Kushner, a breast cancer patient and writer, exemplified this movement by assessing research proposals for the US National Cancer Institute, marking a notable instance of patient influence [ 33 ]. Her efforts reflected a broader movement towards giving patients a voice in research, a theme that is echoed in many PPI initiatives. The 1980s collaboration between patient organizations and the Association for Maternity Services, endorsing a randomized controlled trial on chorionic villus sampling, is another example where patient involvement began to influence research decisions directly. The 1997 international breast cancer advocacy conference organized by the US National Breast Cancer Association (NBCC) and supported by patient organizations from several countries marked a pivotal shift towards PPI, fostering dialogue on patient experiences and challenges. The conference demonstrated the NBCC’s belief that breast cancer patients should be consulted when making policies and decisions regarding research funding, and was instrumental in establishing an international advocacy movement [ 35 ].

The connection between PPI and social movements became more explicit with the establishment of organizations like INVOLVE in 1996, funded by the British government as part of their aim to create a patient-oriented healthcare system, the Canadian Institutes for Health Research in 2000, and the Patient-Centered Outcomes Research Institute (PCORI) in the United States in 2010. These organizations, drawing inspiration from social movements, emphasize the importance of involving patients and the public throughout the research process, thereby continuing the advocacy for marginalized voices in health research [ 36 , 37 , 38 ].

Globally, there is a trend toward formalized PPI approaches. Research funders, regulatory bodies, and institutions recognize the importance of involving patients and the public throughout the research process, from prioritization to dissemination [ 1 , 2 ]. At current there is still a lot of development and movement in the process.

Relevance of empowerment in PPI

As discussed, there are two arguments advocating for PPI use in research, that Ives et al. summarize [ 3 ]: (1) to improve research quality, appropriateness, relevance, and credibility (PPI as a means to an end) and (2) to use PPI to empower patients and democratize research along with its consequential impact on health(care) (PPI as an end in itself). However, empowerment through PPI should not be seen as an isolated goal, and Ives et al. phrasing as “an end in itself” might be misleading and be better put as “an end beyond narrowly instrumental goals”. PPI is a strategy that allows patients to actively shape research, thereby ensuring that the research directly addresses the practical problems they face – an argument rooted in the social movements.

PPI is essential in transforming the relationship between patients and institutions, challenging traditional power dynamics [ 34 ]. Its role is dual-faceted: it improves the quality and relevance of research while simultaneously fostering a more participatory and inclusive approach to healthcare. This dual function makes PPI a powerful tool for achieving both immediate research goals and broader societal change.

However, depending on the reasons and initiators of PPI, PPI practices can vary greatly. According to Ives et al. [ 3 ], different aims of PPI can result in distinct forms of involvement, as illustrated in Table  1 . While Ives et al. [ 3 ] seem to indicate two opposite ends of the spectrum, these “ideals” do not always play out and there are numerous intermediate forms of involvement that can exist. However, this example illustrates that the potential for empowerment in PPI, as well as its manifestations, can vary greatly depending on the approach taken.

Today PPI spans a broad range, from sporadic consultations, to ongoing collaboration between patients and researchers, and even (still rare examples of) research led by patients with support from researchers [ 39 ].

Similarity of empowerment in PPI in clinical research to earlier concepts

In the following sections we analyze and critically address the similarities and limits of empowerment in PPI in clinical research as compared with earlier concepts. Similarities of empowerment in PPI in clinical research to earlier concepts seem to be in a focus on participation, challenging power structures, valuing diverse knowledge and perspectives, and supporting collaboration.

Emphasis on participation

Active participation in decision-making processes that influence the lives of individuals and communities is a fundamental aspect of empowerment concepts across various fields [ 24 ]. In research-based PPI, facilitating the ability of patients and members of the public to have a voice, participate in decision-making processes, and contribute to research aligns with the core principles of empowerment.

Challenging power structures

Empowerment theories from different disciplines aim to reduce powerlessness and increase the power of marginalized individuals [ 25 , 28 , 29 ]. The objective of challenging power structures aligns with the concept of empowerment in PPI in research. Involving patients in planning, conducting, and communicating clinical research on a regular basis constitutes a significant shift in the power dynamics of the research landscape. Individual patients may be engaged on a one-time basis, but the collective voice of patients and the public becomes significant and co-determines research. Long-term patient involvement may be achieved through the integration of patient advisory boards in research institutions [ 40 ]. The inclusion of patient perspectives has become an expected practice, influencing power dynamics within the clinical research domain.

Recognition of diverse knowledge and perspectives

Empowerment in various fields recognizes the worth of diverse knowledge and perspectives [ 26 , 28 , 32 ]. By incorporating them, empowerment aims to challenge the conventional power structures that have systemically marginalized some voices and sustained inequality. Moreover, involving individuals with varied experiences offers exceptional insights and understandings that enhance dialogues and contribute to more thorough resolutions [ 28 ]. Similarly, patient experiential knowledge and unique insights are recognized as crucial in PPI for shaping research and complementing the specialist knowledge of clinical researchers [ 4 , 13 ]. According to the Montreal Model, patients’ experiences with illnesses, which they must manage for the rest of their lives if chronically, offer a rich source of knowledge essential for decision-making [ 41 ]. This experiential knowledge includes patients’ insights into their health issues, the trajectory of their care, and the impacts on their personal lives and those of their loved ones [ 41 ]. The involvement of patients strengthens the focus of clinical research on patients’ needs, ultimately enhancing its quality, adequacy, relevance, and credibility [ 3 , 4 ].

Collaborative relationship

Empowerment approaches typically foster collaborative relationships among various stakeholders [ 19 , 28 ]. In social work, these relationships arise between the practitioner and the client and are characterized, analogous to the idea of an alliance, by a “shared sense of urgency” (regarding the client’s problems), a “conjoint commitment to problem solving in as democratic a manner as possible”, and a “shared emphasis [.] on [the] common humanity” in the relationship [ 19 ]. Depending on the PPI approach, the concept of collaborative relationships among various stakeholders can also apply to empowering patients in research. Three involvement approaches in PPI are distinguished [ 6 ]: (1) The consultation approach achieves the lowest level of engagement and collaborative relationships, wherein patients provide advice to researchers but are not involved in decision-making. (2) Patients are partners in the research process in the collaboration approach, with their involvement in decision making and shared responsibility for the research. (3) Patients in user-led research take full responsibility for individual aspects or the whole research, with support from researchers [ 6 ]. User-led research can only be implemented to a limited extent in clinical studies, as it is subject to ethical and legal framework conditions.

To strengthen the principles of social movements in PPI, a collective approach to research, as proposed by MacDonald’s theory of civic patienthood, could provide valuable insights [ 34 ]. This theory views patients as civic actors who seek collective solutions to collective problems, shifting the understanding of patients from merely clinical subjects to engaged participants in shaping research and healthcare outcomes. This approach needs robust institutions, resources, and socialization processes to support patients’ involvement. It is particularly critical in ensuring that PPI remains genuinely democratic and is not co-opted by more powerful interests [ 34 ].

Distinctions of empowerment in PPI in clinical research to earlier concepts

While we found the heritage of social movements to inform the ethos of PPI in the principles of participation, giving people a say in decisions that affect their lives, confronting power structures—albeit on a smaller scale—, and collaborative relationships, we also found distinctions of empowerment in PPI in clinical research to earlier concepts. These seem to be in the areas of context and focus, scientific demands and ethics, expertise in research methods, and power dynamics.

Context and focus

While the goals of empowerment in other fields and PPI share similarities, there are differences in the context and focus. In social movements, empowerment refers to the process through which marginalized individuals and communities obtain power, active participation, and the ability to challenge oppressive systems [ 18 , 29 ]. These movements often aim to effect systemic changes and combat inequalities, drawing upon collective action, raising awareness, and advocacy to achieve their goals [ 29 , 30 ]. In contrast, the context of empowerment in PPI is more specific to the research process. Here, empowerment is about providing patients and the public with a voice in decision-making within that process [ 4 ]. While the influence of social movements is undeniable, the primary objective is not necessarily to address systemic inequalities on a broad scale but to enhance the quality and relevance of research by incorporating diverse perspectives. In PPI, people are empowered or given a voice “to influence research outcomes that will (or may) have a direct impact on their health status“ [ 6 ]. Though not the main objective, this involvement of diverse perspectives in research may nonetheless potentially contribute to a reduction in inequalities [ 42 , 43 ].

However, the practical implementation of PPI often faces challenges that may undermine its empowering potential. Researchers, under pressure to demonstrate measurable impact, tend to focus the conduct of involvement on substantive values such as effectiveness, quality, and validity – outcomes that are more easily quantified and aligned with traditional research goals [ 4 , 14 ]. This focus may lead to the marginalization of crucial but less easily measured normative values like empowerment, rights and accountability and process values such as partnership or respect. The demand for measurable outcomes and recommendations for the conduct of PPI that lead to rather structured and controlled PPI mechanisms shape PPI practices in ways that may suppress rather than amplify the voices of patients [ 14 ]. A more reflexive and dialogic approach to evaluating PPI might better capture its ethical and formative dimensions, ensuring that public involvement in research remains a tool for true empowerment rather than an instrument of containment [ 14 ].

Scientific demands and ethics

Empowerment in clinical research must balance patient empowerment with scientific demands and the integrity of research findings. Empowerment approaches in other fields may concentrate on personal growth and social change. However, in clinical research there is a need to find ways that respect both the methodological and ethical requirements of research and the interests of PPI. This aspect, which is specific to this context, distinguishes it from empowerment in other fields and may restrict the potential for empowerment in clinical research as well as put specific demands on the conduct of research [ 17 , 44 ]. As a result, the level of patient co-determination may be limited. For example, for methodological reasons randomization might be preferable, even if alternative methods are perceived as more appropriate by the patients involved for understandable reasons. Additionally, patients may lack a full understanding of these restrictions, causing them to suggest ideas that do not comply with the logic of scientific protocols. This encounter with limitations during interactions with scientists can potentially diminish their level of empowerment.

In addition to methodological hurdles, PPI must address ethical considerations in the pursuit of empowerment. Although it is generally assumed that patient involvement does not necessitate an ethics vote, it is nonetheless crucial to discuss with potentially involved parties regarding matters such as safeguarding their privacy and potential conflict of interests, and to furnish them with comprehensive information about the involvement’s goals and methodology [ 45 ]. The framing of the involvement, and therefore the empowerment, in this manner distinguishes it from empowerment in other fields.

Expertise in research methods

Empowering patients in research requires providing objective support and resources to enhance their comprehension of research methods and ethics [ 17 ]. Usually, patients need assistance in navigating the complexities of research processes and methodologies [ 17 , 46 ], which distinguishes empowerment in PPI from other fields. However, learning is a common aspect in any kind of empowerment. For instance, Freire’s theory of critical consciousness highlights education’s role in empowering marginalized individuals [ 30 ]. His approach centers on learners directing their own education by posing questions and emphasizes skill development over knowledge acquisition with a focus on increasing critical awareness of their circumstances.

The disparity in PPI may stem from individuals, who desire and deserve empowerment, not being the ones to decide what to learn, but from the fact that this choice is often made for them and is very factual. In terms of preparation for PPI, the learning is mostly unidirectional, whereby the researchers instruct the patients on research fundamentals [ 47 ]. However, there is a mismatch between the perception of training needs between researchers and PPI contributors (i.e. patients), both in terms of training for PPI contributors and researchers. Dudley et al. [ 47 ] found that this discrepancy leads to gaps in the support and training provided. That said, the characterization of unidirectional learning does not apply universally. For example, some PPI initiatives have employed more interactive and participatory training methods, allowing patients to engage more actively in shaping their learning experience [ 48 ].

Providing PPI support and training enables patients to acquire the necessary knowledge and skills to work alongside researchers on an equal basis, and to furnish patients with the confidence they need to challenge researchers opinions when needed [ 49 ]. Importantly, expertise in clinical research methods is not only a means of achieving empowerment but also a crucial component of enhancing the quality and relevance of research. By developing expertise, patients can contribute more meaningfully to the research process, ensuring that their perspectives and experiences are integrated in ways that improve research outcomes.

To strengthen empowerment in PPI and reduce vulnerability to co-optation by more powerful forces with different problem-solving interests, it is critical that participants have a clear understanding of the power they seek to build [ 34 ]. MacDonald’s theory of civic patienthood illustrates that socialization is central to helping patients understand their agency, role, and limitations as civic actors in PPI [ 34 ]. The design of this process can significantly impact how power and empowerment are navigated within PPI.

Power dynamics

Self-determination of the client is an essential aspect of empowerment practice in social work, and it is commonly believed that empowerment cannot be imposed upon anyone else [ 29 ]. In this regard, professionals are responsible for providing support and facilitation and it is crucial to minimize power differentials between all parties involved in order to foster relationships based on equality and partnership [ 29 ].

In research-based PPI, addressing power asymmetries between researchers and patients is critical. Researchers typically operate with institutions that have structures and established norms, facing constraints and pressures imposed by their institutions which can influence the extent of shared-decision making and the balance of power. Often, researchers have the final say in decisions [ 6 ]. These dynamics of institutional power can lead to challenges in achieving equal partnerships.

To navigate these constraints effectively, it is crucial to understand the extent to which patients are involved in the research process, how their roles are negotiated with researchers, and the level of their involvement in decision-making. Researchers must balance their own institutional limitations and the robustness of the research with the need to foster patient empowerment. This process can be challenging and at times frustrating. Promoting patient empowerment in clinical research impacts organizational processes, cultures and public relationships, requiring frameworks that recognize, address and integrate patient perspectives into research activities [ 49 ].

Discussion and implications

The goal of this article is to contribute to the understanding of empowerment in PPI in clinical research by analyzing the history and development of the concept of empowerment in earlier fields. We presented an overview of the history of empowerment in the social movements of the 20th century and outlined key concepts of empowerment from Solomon, Rappaport, and Freire. Based on this, we suggested common principles of empowerment concepts. We then presented an overview of the historical development of PPI in research, that is strongly connected to the social movements’ heritage, and reflected on the relevance of empowerment in PPI. Finally, we assessed in how far empowerment in PPI mirrors the previously developed common principles of empowerment, and analyzed similarities and distinctions.

We found the heritage of social movements to inform the ethos of PPI, as principles such as promoting participation, providing people with a say in decisions that may affect their lives, appreciating diverse knowledge, fostering respectful collaborations, and confronting power structures (even at a smaller, less existential scale) are deeply embedded in PPI practices. However, we also observed considerable distinctions in contexts and objectives: Social movement-based empowerment aimed to effect systemic changes and combat inequalities. Empowerment movements typically arose from significant inequalities and were often initiated by the oppressed. While these movements laid the groundwork for later involvement in research, the empowerment objectives in PPI are more specific to the research context. Today, the involvement process is predominantly initiated by researchers seeking to incorporate patients to increase the quality and relevance of their trials.

In the practical implementation of PPI in clinical research, empowerment may often play only a minor role, irrespective of claims made to the contrary. PPI may offer ample opportunities for fostering patient empowerment, even if the primary goal is to involve patients for the enhancement of research quality or for meeting certain requirements. Nevertheless, even in trials explicitly designed to promote patient empowerment, the level of empowerment may not satisfy each individual involved. We found that these constraints are often related to researchers’ need to adhere to institutional requirements, the duration of PPI involvement, and power imbalances in relation to researchers.

Still, we feel that tentative recommendations are warranted for facilitating empowerment in clinical trials:

Throughout the planning, execution, and dissemination of the study, close collaboration between patients and researchers is crucial. The relationship between patients and researchers should be marked by respect and mutual appreciation [ 4 ]. Both parties should value all perspectives and prioritize inclusivity in decision-making processes. MacDonalds’ model of civic patienthood offers valuable insights for strengthening patients’ voice and the power dynamics in PPI [ 34 ].

As defined by Salomon, the success of empowerment depends on “the extent to which the service delivery system functions as either an obstacle course or an opportunity system” [ 26 ]. In the case of PPI, the study and patient involvement should be designed in such a way that patients fully understand the process and its realistic limitations. It is essential to make the research accessible and transparent, with clear communication about what it can and cannot promise. Acknowledging the limitations of clinical research as a vehicle for empowerment respects patients’ capacity to understand these limitations and helps manage their expectations, fostering a more honest and trustful relationship between researchers and patients [ 16 , 17 ].

Prior to and throughout their collaboration, patients and researchers should engage in discussions about their shared objectives, expectations, and experiences [ 16 ]. These should include notions of empowerment and empowerment should be an aspect that guides the involvement.

To promote collaborative equality, patients may participate in training sessions prior to or at the beginning of their involvement. These sessions should offer a comprehensive understanding of clinical research and enhance their perspective as patients, empowering them to challenge researchers when necessary [ 3 ]. In the spirit of peer support and collective action [ 30 ], patients themselves may offer these training sessions for the benefit of their fellow patients, thereby reducing power imbalances in the learning environment.

Researchers ought to engage in training sessions for PPI [ 50 ], including instructions on how to foster empowerment.

Patients collaborating with researchers should be accompanied and supported as needed by a person who feels responsible and plays a role similar to that of a social worker in other contexts [ 29 ]. Despite time constraints in PPI, there should be opportunities for patients to share and analyze experiences, provide mutual support, and collaborate during the course of the clinical trial [ 18 , 30 ].

At the end of the participation, there should be a closing session where, among other things, the participation is reflected upon and its added value is highlighted [ 17 ]. This includes not only aspects that have changed the quality of the study, but also, for example, changes and developments at the personal level of patients and researchers. Patients who wish to continue their involvement should have opportunities to do so.

This list presents several ways for promoting empowerment within the context of PPI. It is not conclusive but rather intended to be extended and elaborated upon in further examinations of the subject. However, defining empowerment is a complex undertaking, and one may select different criteria or aspects that may lead to alternative approaches to promoting it.

Conclusions

The primary objective of clinical research is not to empower patients but to generate scientific knowledge that can improve healthcare outcomes. However, with the increasing call for involving patients in research, the concept of empowerment has become an associated goal. Our investigation sought to unpack what empowerment might mean within the context of PPI in clinical research.

Given the absence of a consensus on what empowerment in this context entails, we turned to the history and foundational concepts of empowerment from various social movements to illuminate its potential meanings and implications. We found both similarities and differences between empowerment in PPI and earlier empowerment concepts. While PPI reflects principles such as participation, challenging power structures, and valuing diverse perspectives, the empowerment it offers is often constrained by the specific context of clinical research.

Some limitations to empowerment in PPI are intrinsic to the research context itself, such as the need to adhere to rigorous scientific standards. However, other limitations are less evident and may, in fact, undermine the empowerment of patients. These include institutional power dynamics, limited opportunities for genuine decision-making, and inadequate support for patients to navigate the complexities of research processes.

To address these challenges, it is crucial for those involved in PPI to be explicit about what they mean by empowerment and to consider whether and how it is valued in their research endeavors. Transparency regarding both external and internal limitations is essential. This includes an explicit exchange between researchers and patients about the realistic scope and potential of patients’ involvement, as well as ongoing reflection and dialogue about how empowerment can be fostered and sustained within the research process. By doing so, PPI can move closer to fulfilling its promise of genuinely empowering patients, rather than merely using the term as a rhetorical tool.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

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Imke Schilling was awarded a post-doctoral scholarship by the University of Bremen.

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Schilling, I., Gerhardus, A. Is this really Empowerment? Enhancing our understanding of empowerment in patient and public involvement within clinical research. BMC Med Res Methodol 24 , 205 (2024). https://doi.org/10.1186/s12874-024-02323-1

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The proportion of people surviving brain tumours has remained low since the 1970s.* Although there have been some important advances , there is still a pressing need to find new ways to tackle this hard-to-treat disease.  

That’s why, for the past 10 years, we’ve highlighted brain tumours as one of our cancers of unmet need and made researching them a strategic priority. In 2018, we launched two specialised Brain Tumour Centres of Excellence . Already, they’ve made great advances by bringing together world-leading research communities and equipping them with innovative tools and technologies.  

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Since their launch, our Brain Tumour Centres of Excellence have made great progress. But, we still have further to go.  

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* Ten-year age-standardised net survival for brain cancer  in men has increased from 5% during 1971-1972 to a predicted survival of 13% during 2010-2011 in England and Wales. In women, ten-year survival has increased from 6% to 14% over the same time period.  

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