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Science, health, and public trust.

September 8, 2021

Explaining How Research Works

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

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

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

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

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

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

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

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

Below are some additional resources:

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

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Research in or research on

• Thread starter Fenix689
• Start date Feb 8, 2010
• Feb 8, 2010

Hi there, I would be very glad if someone could tell me what is the right preposition used with "research": A)(to do) research in... B)(to do) research on... c)(To do) research about... (is this posible) thanks beforehand.  

Senior Member

A or B are possible. One does research in a particular field of study. He does research in cardiology. One does research on a particular subject. He does research on heart disease.  

• Nov 7, 2018

"Research into" gets over 36 million hits on Google. "They are carrying out/ conducting /doing some fascinating research into/on the language of dolphins ." RESEARCH | meaning in the Cambridge English Dictionary  

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))

21k 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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Research: Where to Begin

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Research isn't something that only scientists and professors do. Any time you use sources to investigate claims or reach new conclusions, you are performing research. Research happens in virtually all fields, so it’s vitally important to know how to conduct research and navigate through source material regardless of your professional or academic role.

Choosing and Narrowing Your Research Topic

Before beginning the process of looking for sources, it’s important to choose a research topic that is specific enough to explore in-depth. If your focus is too broad, it will be difficult to find sources that back up what you’re trying to say.

If your instructor gives you the flexibility to choose your own research topic, you might begin by brainstorming  a list of topics that interest you ( click here to visit an OWL page that can help you get started brainstorming or prewriting ). Once you find something that grabs your attention, the next step is to narrow your topic to a manageable scope. Some ways to narrow your focus are by sub-topic, demographic, or time period.

For example, suppose that you want to research cancer treatments. Cancer treatment is a fairly broad topic, so you would be wise to at least consider narrowing your scope. For example, you could focus on a sub-topic of cancer treatment, such as chemotherapy or radiation therapy. However, these are still broad topics, so you might also narrow your topic to a narrower sub-topic or even examine how these topics relate to a specific demographic or time period. In the end, you might decide to research how radiation therapy for women over fifty has changed in the past twenty years. In sum, having a specific idea of what you want to research helps you find a topic that feels more manageable.

Writing Your Research Question

Writing your research topic as a question helps you focus your topic in a clear and concise way. It ensure that your topic is arguable. While not all research papers have to offer an explicit argument, many do.

For the above example, you might phrase your research question like this: "How has radiation therapy changed in the past twenty years for women over fifty?" Of course, phrasing this topic as a question assumes that the research has, in fact, changed. Reading your sources (or, to begin with, at least summaries and abstracts of those sources) will help you formulate a research question that makes sense.

Knowing What Types of Sources You Need

Depending on the type of research you’re doing, you may need to use different types of sources. Research is usually divided into scholarly and popular, and primary and secondary. For more information on specific details about these types of sources, visit our "Where to Begin" page in our "Evaluating Sources" subsection.  This subsection contains additional pages that explore various kinds of sources (like, e.g., internet sources) in more detail.

Asking Productive Questions

Before you begin your research, you should ask yourself questions that help narrow your search parameters.

What kind of information are you looking for?

Different types of research will require different sources. It’s important to know what kinds of sources your research demands. Ask whether you need facts or opinions, news reports, research studies, statistics and data, personal reflections, archival research, etc. Restricting yourself to only the most relevant kinds of sources will make the research process seem less daunting.

Where do you need to look for your research?

Your research topic will also dictate where you find your sources. This extends beyond simply whether you use the internet or a print source. For example, if you are searching for information on a current event, a well-regarded newspaper like the  New York Times  or  Wall Street Journal  could  be a useful source. If you are searching for statistics on some aspect of the U.S. population, then you might want to start with government documents, such as census reports. While much high-level academic research relies mainly on the sorts of academic journal articles and scholarly books that can be found in university libraries, depending the nature of your research project, you may need to look elsewhere.

How much information do you need?

Different research projects require different numbers of sources. For example, if you need to address both sides of a controversial issue, you may need to find more sources than if you were pursuing a non-controversial topic. Be sure to speak with your instructor if you are unclear on how many sources you will be expected to use.

How timely does your research need to be?

Depending on your research topic, the timeliness of your source may or may not matter. For example, if you are looking into recent changes in a specific scientific field, you would want the most up-to-date research. However, if you were researching the War of 1812, you might benefit from finding primary sources written during that time period.

Research shows our bodies go through rapid changes in our 40s and our 60s

For many people, reaching their mid-40s may bring unpleasant signs the body isn’t working as well as it once did. Injuries seem to happen more frequently. Muscles may feel weaker.

A new study, published Wednesday in Nature Aging , shows what may be causing the physical decline. Researchers have found that molecules and microorganisms both inside and outside our bodies are going through dramatic changes, first at about age 44 and then again when we hit 60. Those alterations may be causing significant differences in cardiovascular health and immune function.

The findings come from Stanford scientists who analyzed blood and other biological samples of 108 volunteers ages 25 to 75, who continued to donate samples for several years. 

“While it’s obvious that you’re aging throughout your entire life, there are two big periods where things really shift,” said the study’s senior author, Michael Snyder, a professor of genetics and director of the Center for Genomics and Personalized Medicine at Stanford Medicine. For example, “there’s a big shift in the metabolism of lipids when people are in their 40s and in the metabolism of carbohydrates when people are in their 60s.”

Lipids are fatty substances, including LDL, HDL and triglycerides, that perform a host of functions in the body, but they can be harmful if they build up in the blood.

The scientists tracked many kinds of molecules in the samples, including RNA and proteins, as well as the participants’ microbiomes.

The metabolic changes the researchers discovered indicate not that people in their 40s are burning calories more slowly but rather that the body is breaking food down differently. The scientists aren’t sure exactly what impact those changes have on health.

Previous research showed that resting energy use, or metabolic rate , didn’t change from ages 20 to 60. The new study’s findings don't contradict that.

The changes in metabolism affect how the body reacts to alcohol or caffeine, although the health consequences aren’t yet clear. In the case of caffeine, it may result in higher sensitivity. 

It’s also not known yet whether the shifts could be linked to lifestyle or behavioral factors. For example, the changes in alcohol metabolism might be because people are drinking more in their mid-40s, Snyder said.

For now, Snyder suggests people in their 40s keep a close eye on their lipids, especially LDL cholesterol.

“If they start going up, people might want to think about taking statins if that’s what their doctor recommends,” he said. Moreover, “knowing there’s a shift in the molecules that affect muscles and skin, you might want to warm up more before exercising so you don’t hurt yourself.”

Until we know better what those changes mean, the best way to deal with them would be to eat healthy foods and to exercise regularly, Snyder said.

Dr. Josef Coresh, founding director of the Optimal Aging Institute at the NYU Grossman School of Medicine, compared the new findings to the invention of the microscope.

“The beauty of this type of paper is the level of detail we can see in molecular changes,” said Coresh, a professor of medicine at the school. “But it will take time to sort out what individual changes mean and how we can tailor medications to those changes. We do know that the origins of many diseases happen in midlife when people are in their 40s, though the disease may occur decades later.”

The new study “is an important step forward,” said Dr. Lori Zeltser, a professor of pathology and cell biology at the Columbia University Vagelos College of Physicians and Surgeons. While we don’t know what the consequences of those metabolic changes are yet, “right now, we have to acknowledge that we metabolize food differently in our 40s, and that is something really new.”

The shifts the researchers found might help explain numerous age-related health changes, such as muscle loss, because “your body is breaking down food differently,” Zeltser said.

Linda Carroll is a regular health contributor to NBC News. She is coauthor of "The Concussion Crisis: Anatomy of a Silent Epidemic" and "Out of the Clouds: The Unlikely Horseman and the Unwanted Colt Who Conquered the Sport of Kings." 

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Older Adults Do Not Benefit From Moderate Drinking, Large Study Finds

Virtually any amount increased the risk for cancer, and there were no heart benefits, the researchers reported.

By Roni Caryn Rabin

Even light drinking was associated with an increase in cancer deaths among older adults in Britain, researchers reported on Monday in a large study. But the risk was accentuated primarily in those who had existing health problems or who lived in low-income areas.

The study, which tracked 135,103 adults aged 60 and older for 12 years, also punctures the long-held belief that light or moderate alcohol consumption is good for the heart.

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• He has dedicated his life to scientific research.
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• It took months of painstaking research to write the book .
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• major US She majored in philosophy at Harvard.
• cram She's cramming for her history exam.
• revise UK I'm revising for tomorrow's test.
• review US We're going to review for the test tomorrow night.
• research Scientists are researching possible new treatments for cancer.
• The amount of time and money being spent on researching this disease is pitiful .
• We are researching the reproduction of elephants .
• She researched a wide variety of jobs before deciding on law .
• He researches heart disease .
• The internet has reduced the amount of time it takes to research these subjects .
• adjudication
• have the measure of someone/something idiom
• interpretable
• interpretive
• reinspection
• reinterpret
• reinterpretation
• reinvestigate
• reinvestigation

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Teenagers' motivation dips in high school—research shows supportive teachers can really help

by Andrew J. Martin and Rebecca J. Collie, The Conversation

Adolescence is often characterized as a time of " storm and stress ".

Young people are dealing with physical and cognitive changes and, as they move from childhood, can become increasingly distant from the adults in their lives.

In academic terms, this time of major hormonal change is also accompanied by a well-known dip in students ' motivation and engagement at school. This often coincides with students' going to high school .

How can schools better help young people at this time? In a new four-year study we looked at the role of teaching support. We were especially interested to know if teachers' influence on students' motivation and engagement grows or fades across the adolescent years.

Our study involved 7,769 Year 6 New South Wales government school students who were tracked annually into Year 9. The students were part of the NSW Department of Education's annual "Tell Them From Me" student survey .

Students were asked questions about the teaching support they received, as well as questions about their motivation and engagement. They were given a 0–4 point rating scale (strongly disagree to strongly agree).

There were three categories of teaching support:

emotional support : did teachers support and care for students?

instrumental support : did teachers have clear expectations for students and did they make learning content seem relevant?

management support : were there clear rules and routines for the class?

Motivation was measured through students' academic aspirations about the future and how much they valued school (or saw it as important). Engagement was assessed via students' perseverance, efforts with homework, making school friends and whether they had any behavior issues.

In our analysis we also accounted for students' backgrounds, such as gender, socioeconomic status and prior academic achievement.

Our findings

Our findings confirm there is a decline in students' motivation and engagement from Year 6 to Year 9 (around 18% in total). This is consistent with the known dip in early- to mid-adolescence.

But we also found in each of these four years, teaching support overall (and each of the three teaching support categories) was significantly associated with students' motivation and engagement.

That is, more teaching support was linked to greater student aspirations, valuing school, perseverance, homework effort, connections with school friends and less misconduct at school.

Of particular note, we found the link between teaching support and students' motivation and engagement strengthened each year. For example, teaching support was more strongly linked to students' motivation and engagement in Year 9 than it was in Year 8. Taken together, between Year 6 and Year 9, there was a 40% increase in the role of teaching support in students' motivation and engagement.

What this means

This is an empowering finding for teachers because adolescence is typically seen as a time when the influence of adults declines. Our results show students remain within their teacher's orbit as they move further into adolescence.

What can we do?

Previous research suggests ideas for how teachers can provide emotional support , instructional support , and management support to students, including:

• spending time getting to know students
• respecting students' individuality
• listening to students' perspectives
• providing emotional encouragement when needed
• ensuring content and tasks are interesting and meaningful to students
• explaining how schoolwork is useful for other schoolwork, or things outside school (for example, world events or paid work)
• having clear, consistent, and logical expectations about classroom behavior
• encouraging student input as classroom rules are developed.

There are also further practical ideas in a NSW Department of Education guide that accompanies our study.

Provided by The Conversation

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Nasa funds research projects advancing stem career development.

Jessica Taveau

Nasa headquarters.

NASA has awarded $6 million to 20 teams from emerging research institutions across the United States supporting projects that offer career development opportunities for science, technology, engineering, and mathematics (STEM) students.

This is the third round of seed funding awarded through the agency’s MOSAICS (Mentoring and Opportunities in STEM with Academic Institutions for Community Success) program, formerly the Science Mission Directorate Bridge Program. The program seeks to expand access to NASA research opportunities in the science and engineering disciplines, as well as to NASA’s workforce.

“The STEM workforce continues to grow, and today’s students, studying at a variety of higher-education institutions — community colleges, primarily undergraduate institutions, and minority-serving institutions — are the STEM workforce of tomorrow, who will work to solve some of our biggest challenges at home while answering some of our biggest questions about our universe,” said Padi Boyd, director of MOSAICS at NASA Headquarters in Washington. “Exposing today’s students to the incredibly inspiring and cutting-edge discoveries made through NASA’s space science people and resources ensures that these students get the training they need to persist in STEM careers, while fostering enduring collaborations between NASA researchers and faculty at a wide range of institutions.”

NASA’s Science Mission Directorate MOSAICS program funds research projects building relationships between college faculty and researchers at the agency while providing mentorship and training for students in STEM disciplines. The projects support teams at academic institutions that historically have not been part of the agency’s research enterprise — including Hispanic-serving institutions, historically Black colleges and universities, Asian American and Native American Pacific Islander-serving institutions, and primarily undergraduate institutions.

The program previously awarded seed funding to 11 teams in February and 13 teams in April. This third cohort brings the total number of projects funded to 44 teams at 36 academic institutions in 21 U.S. states and territories, including Washington and Puerto Rico, in collaboration with seven NASA centers. A new opportunity to apply for seed funding is now open until March 28, 2025.

The following projects were selected as the third cohort to receive seed funding:

“Bridging Fundamental Ice Chemistry Studies and Ocean World Explorations” Principal investigator: Chris Arumainayagam, Wellesley College, Massachusetts NASA center: NASA’s Jet Propulsion Laboratory (JPL), Southern California

“Planetary Analog Field Science Experiences for Undergraduates: Advancing Fundamental Research and Testing Field Instrument Operations” Principal investigator: Alice Baldridge, Saint Mary’s College of California NASA center: NASA’s Goddard Space Flight Center, Greenbelt, Maryland

“Building an FSU-JPL Partnership to Advance Science Productivity Through Applications of Deep Learning” Principal investigator: Sambit Bhattacharya, Fayetteville State University, North Carolina NASA center: NASA JPL

“CSTAT: Establishing Center for Safe and Trustworthy Autonomous Technologies” Principal investigator: Moitrayee Chatterjee, New Jersey City University NASA center: NASA Goddard

“Development of Biomechanics Simulation Tool for Muscle Mechanics in Reduced Gravity to Enhance Astronaut Mission Readiness” Principal investigator: Ji Chen, University of the District of Columbia NASA center: NASA’s Johnson Space Center, Houston

“NASA Next Level” Principal investigator: Teresa Ciardi, Santa Clarita Community College District, California NASA center: NASA JPL

“Controlled Assembly of Amphiphilic Janus Particles in Polymer Matrix for Novel 3D Printing Applications in Space ” Principal investigator: Ubaldo Cordova-Figueroa, Recinto Universitario Mayaguez NASA center: NASA’s Glenn Research Center, Cleveland

“Development of a Non-Invasive Sweat Biosensor for Traumatic Brain Injury Compatible With In-Space Manufacturing to Monitor the Health of Astronauts” Principal investigator: Lisandro Cunci, University of Puerto Rico, Rio Pedras NASA center: NASA’s Ames Research Center, Silicon Valley, California

“Examining Climate Impacts of Cirrus Clouds Through Past, Present, and Future NASA Airborne Campaigns” Principal investigator: Minghui Diao, San Jose State University Research Foundation, California NASA center: NASA Ames

“CSUN-JPL Collaboration to Study Ocean Fronts Using Big Data and Open Science Structures in Coastal North America” Principal investigator: Mario Giraldo, California State University, Northridge NASA center: NASA JPL

“Accelerating Electric Propulsion Development for Planetary Science Missions With Optical Plasma Diagnostics” Principal investigator: Nathaniel Hicks, University of Alaska, Anchorage NASA center: NASA JPL

“Advancing Students Through Research Opportunities in Los Angeles (ASTRO-LA)” Principal investigator: Margaret Lazzarini, California State University, Los Angeles NASA center: NASA JPL

“Bridging Toward a More Inclusive Learning Environment Through Gamma-ray Burst Studies With Machine Learning and Citizen Science” Principal investigator: Amy Lien, University of Tampa, Florida NASA center: NASA Goddard

“Hampton University STEM Experience With NASA Langley Research Center: Polarimetry for Aerosol Characterization” Principal investigator: Robert Loughman, Hampton University, Virginia NASA center: NASA’s Langley Research Center, Hampton, Virginia

“Aerocapture Analysis and Development for Uranus and Neptune Planetary Missions” Principal investigator: Ping Lu, San Diego State University NASA center: NASA Langley

“Pathways from Undergraduate Research to the Habitable Worlds Observatory” Principal investigator: Ben Ovryn, New York Institute of Technology NASA center: NASA Goddard

“Point-Diffraction Interferometer for Digital Holography” Principal investigator: James Scire, New York Institute of Technology NASA center: NASA Goddard

“From Sunbeams to Career Dreams: Illuminating Pathways for NMSU Students in Solar-Terrestrial Physics in Partnership With NASA GSFC” Principal investigator: Juie Shetye, New Mexico State University NASA center: NASA Goddard

“CONNECT-SBG: Collaborative Nexus for Networking, Education, and Career Training in Surface Biology and Geology” Principal investigator: Gabriela Shirkey, Chapman University, California NASA center: NASA JPL

“Multiplexed Phytohormone and Nitrate Sensors for Real-Time Analysis of Plant Responses to Pathogenic Stress in Spaceflight-Like Conditions” Principal investigator: Shawana Tabassum, University of Texas, Tyler NASA center: NASA’s Kennedy Space Center, Florida

Learn more about the MOSAICS program at:

https://science.nasa.gov/researchers/smd-bridge-program

Alise Fisher Headquarters, Washington 202-358-2546 [email protected]

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Harris Energizes Democrats in Transformed Presidential Race

1. the presidential matchup: harris, trump, kennedy, table of contents.

• Other findings: Both Harris and Trump are viewed more favorably than a few months ago
• Voting preferences among demographic groups
• How have voters shifted their preferences since July?
• Harris’ supporters back her more strongly than Biden’s did last month
• Large gap in motivation to vote emerges between the candidates’ younger supporters
• Harris and Trump have gained ground with their own coalitions
• Share of ‘double negatives’ drops significantly with change in presidential candidates
• Views of Biden have changed little since his withdrawal from the 2024 presidential race
• Acknowledgments
• The American Trends Panel survey methodology

Nationally, Vice President Kamala Harris and former President Donald Trump are essentially tied among registered voters in the current snapshot of the presidential race: 46% prefer Harris, 45% prefer Trump and 7% prefer Robert F. Kennedy Jr.

Following Biden’s exit from the race, Trump’s support among voters has remained largely steady (44% backed him in July against Biden, while 45% back him against Harris today). However, Harris’ support is 6 percentage points higher than Biden’s was in July . In addition to holding on to the support of those who backed Biden in July, Harris’ bump has largely come from those who had previously said they supported or leaned toward Kennedy.

Harris performs best among the same demographic groups as Biden. But this coalition of voters is now much more likely to say they strongly support her: In July, 43% of Biden’s supporters characterized their support as strong – today, 62% of Harris’ do.

Overall, many of the same voting patterns that were evident in the Biden-Trump matchup from July continue to be seen today. Harris fares better than Trump among younger voters, Black voters, Asian voters and voters with college degrees. By comparison, the former president does better among older voters, White voters and voters without a college degree.

But Harris performs better than Biden across many of these groups – making the race tighter than it was just a few weeks ago.

• In July, women’s presidential preferences were split: 40% backed Biden, 40% preferred Trump and 17% favored Kennedy. With Harris at the top of the ticket, 49% of women voters now support her, while 42% favor Trump and 7% back Kennedy.
• Among men, Trump draws a similar level of support as he did in the race against Biden (49% today, compared with 48% in July). But the share of men who now say they support Harris has grown (to 44% today, up from 38% last month). As a result, Trump’s 10-point lead among men has narrowed to a 5-point lead today.

Race and ethnicity

Harris has gained substantial ground over Biden’s position in July among Black, Hispanic and Asian voters. Most of this movement is attributable to declining shares of support for Kennedy. Trump performs similarly among these groups as he did in July.

• 77% of Black voters support or lean toward Harris. This compares with 64% of Black voters who said they backed Biden a few weeks ago. Trump’s support is unchanged (13% then vs. 13% today). And while 21% of Black voters supported Kennedy in July, this has dropped to 7% in the latest survey.
• Hispanic voters now favor Harris over Trump by a 17-point margin (52% to 35%). In July, Biden and Trump were tied among Hispanic voters with 36% each.
• By about two-to-one, Asian voters support Harris (62%) over Trump (28%). Trump’s support among this group is essentially unchanged since July, but the share of Asian voters backing Harris is 15 points higher than the share who backed Biden in July.
• On balance, White voters continue to back Trump (52% Trump, 41% Harris), though that margin is somewhat narrower than it was in the July matchup against Biden (50% Trump, 36% Biden).

While the age patterns present in the Harris-Trump matchup remain broadly the same as those in the Biden-Trump matchup in July, Harris performs better across age groups than Biden did last month. That improvement is somewhat more pronounced among voters under 50 than among older voters.

• Today, 57% of voters under 30 say they support Harris, while 29% support Trump and 12% prefer Kennedy. In July, 48% of these voters said they backed Biden. Trump’s support among this group is essentially unchanged. And 12% now back Kennedy, down from 22% in July.
• Voters ages 30 to 49 are now about evenly split (45% Harris, 43% Trump). This is a shift from a narrow Trump lead among this group in July.
• Voters ages 50 and older continue to tilt toward Trump (50% Trump vs. 44% Harris).

With Harris now at the top of the Democratic ticket, the race has become tighter.

Much of this is the result of shifting preferences among registered voters who, in July, said they favored Kennedy over Trump or Biden.

Among the same group of voters surveyed in July and early August, 97% of those who backed Biden a few weeks ago say they support or lean toward Harris today. Similarly, Trump holds on to 95% of those who supported him a few weeks ago.

But there has been far more movement among voters who previously expressed support for Kennedy. While Kennedy holds on to 39% of those who backed him in July, the majority of these supporters now prefer one of the two major party candidates: By about two-to-one, those voters are more likely to have moved to Harris (39%) than Trump (20%). This pattern is evident across most voting subgroups.

In July, Trump’s voters were far more likely than Biden’s voters to characterize their support for their candidate as “strong” (63% vs. 43%). But that gap is no longer present in the Harris-Trump matchup.

Today, 62% of Harris voters say they strongly support her, while about a third (32%) say they moderately support her. Trump’s voters are just about as likely to say they strongly back him today as they were in July (64% today, 63% then).

Kennedy’s voters make up a smaller share of voters today than a month ago – and just 18% of his voters say they strongly support him, similar to the 15% who said the same in July.

Across demographic groups, strong support for Harris is higher than it was for Biden

Among women voters who supported Biden in July, 45% said they did so strongly. That has grown to 65% today among women voters who support Harris.

Increased intensity of support is similar among men voters who back the Democratic candidate: In July, 42% of men voters who supported Biden said they did so strongly. This has since grown to 59% of Harris’ voters who are men.

Across racial and ethnic groups, Harris’ supporters are more likely than Biden’s were to say they back their candidates strongly.

Among White voters, 43% who supported Biden in July did so strongly. Today, Harris’ strong support among White voters sits at 64%.

A near identical share of Harris’ Black supporters (65%) characterize their support for her as strong today. This is up from the 52% of Biden’s Black supporters who strongly backed him in July. Among Harris’ Hispanic supporters, 56% support her strongly, while 45% of Asian Harris voters feel the same. Strong support for Harris among these voters is also higher than it was for Biden in July.

Across all age groups, Harris’ strength of support is higher than Biden’s was. But the shift from Biden is less pronounced among older Democratic supporters than among younger groups.

Still, older Harris voters are more likely than younger Harris voters to describe their support as strong. For instance, 51% of Harris’ voters under 50 say they strongly support her, while 71% of Harris supporters ages 50 and older characterize their support as strong.

Today, about seven-in-ten of both Trump supporters (72%) and Harris supporters (70%) say they are extremely motivated to vote.

Motivation to vote is higher in both the Democratic and Republican coalitions than it was in July .

These shifts have occurred across groups but are more pronounced among younger voters.

Today, half of voters under 30 say they are extremely motivated to vote, up 16 points since July. Motivation is up 11 points among voters ages 30 to 49 and 50 to 64, and up 6 points among those ages 65 and older.

Among the youngest voters, the increased motivation to vote is nearly all driven by shifts among Democratic supporters.

• In July, 38% of 18- to 29-year-old Trump voters said they were extremely motivated to vote. Today, a similar share of his voters (42%) report that level of motivation.
• But 18- to 29-year-old Harris supporters are far more likely to say they are extremely motivated to vote than Biden’s supporters in this age group were about a month ago. Today, 61% of Harris’ voters under 30 say this. In July, 42% of voters under 30 who supported Biden said they were extremely motivated to vote.

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Research: How IT Can Solve Common Problems in DEI Initiatives

• Monideepa Tarafdar
• Marta Stelmaszak

Lessons from three organizations that successfully leveraged IT to drive structural change.

The authors’ research found that three persistent problems plague DEI initiatives: They do not connect to operational or strategic goals and objectives; they do not include the rank-and-file; and they are often implemented through periodic efforts like annual diversity training that aren’t integrated into day-to-day work processes. Organizations can overcome these problems by using IT in three ways.

Diversity, equity, and inclusion (DEI) programs are under attack. Confronted by high costs, mixed outcomes , unclear organizational benefits , and a political and regulatory backlash , organizations are rolling back their initiatives. Google and Meta, for example, recently reduced investment in their DEI programs and let go of DEI staff.

• Monideepa Tarafdar is Charles J. Dockendorff Endowed Professor at the Isenberg School of Management at the University of Massachusetts Amherst.
• Marta Stelmaszak is an assistant professor of information systems at the Isenberg School of Management at the University of Massachusetts Amherst.

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