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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))

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Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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U.S. Tightens Rules on Risky Virus Research

A long-awaited new policy broadens the type of regulated viruses, bacteria, fungi and toxins, including those that could threaten crops and livestock.

• Share full article

By Carl Zimmer and Benjamin Mueller

The White House has unveiled tighter rules for research on potentially dangerous microbes and toxins, in an effort to stave off laboratory accidents that could unleash a pandemic.

The new policy, published Monday evening, arrives after years of deliberations by an expert panel and a charged public debate over whether Covid arose from an animal market or a laboratory in China.

A number of researchers worried that the government had been too lax about lab safety in the past, with some even calling for the creation of an independent agency to make decisions about risky experiments that could allow viruses, bacteria or fungi to spread quickly between people or become more deadly. But others warned against creating restrictive rules that would stifle valuable research without making people safer.

The debate grew sharper during the pandemic, as politicians raised questions about the origin of Covid. Those who suggested it came from a lab raised concerns about studies that tweaked pathogens to make them more dangerous — sometimes known as “gain of function” research.

The new policy, which applies to research funded by the federal government, strengthens the government’s oversight by replacing a short list of dangerous pathogens with broad categories into which more pathogens might fall. The policy pays attention not only to human pathogens, but also those that could threaten crops and livestock. And it provides more details about the kinds of experiments that would draw the attention of government regulators.

The rules will take effect in a year, giving government agencies and departments time to update their guidance to meet the new requirements.

“It’s a big and important step forward,” said Dr. Tom Inglesby, the director of the Johns Hopkins Center for Health Security and a longtime proponent of stricter safety regulations. “I think this policy is what any reasonable member of the public would expect is in place in terms of oversight of the world’s most transmissible and lethal organisms.”

Still, the policy does not embrace the most aggressive proposals made by lab safety proponents, such as creating an independent regulatory agency. It also makes exemptions for certain types of research, including disease surveillance and vaccine development. And some parts of the policy are recommendations rather than government-enforced requirements.

“It’s a moderate shift in policy, with a number of more significant signals about how the White House expects the issue to be treated moving forward,” said Nicholas Evans, an ethicist at University of Massachusetts Lowell.

Experts have been waiting for the policy for more than a year. Still, some said they were surprised that it came out at such a politically fraught moment . “I wasn’t expecting anything, especially in an election year,” Dr. Evans said. “I’m pleasantly surprised.”

Under the new policy, scientists who want to carry out experiments will need to run their proposals past their universities or research institutions, which will to determine if the work poses a risk. Potentially dangerous proposals will then be reviewed by government agencies. The most scrutiny will go to experiments that could result in the most dangerous outcomes, such as those tweaking pathogens that could start a pandemic.

In a guidance document , the White House provided examples of research that would be expected to come under such scrutiny. In one case, they envisioned scientists trying to understand the evolutionary steps a pathogen needed to transmit more easily between humans. The researchers might try to produce a transmissible strain to study, for example, by repeatedly infecting human cells in petri dishes, allowing the pathogens to evolve more efficient ways to enter the cells.

Scientists who do not follow the new policy could become ineligible for federal funding for their work. Their entire institution may have its support for life science research cut off as well.

One of the weaknesses of existing policies is that they only apply to funding given out by the federal government. But for years , the National Institutes of Health and other government agencies have struggled with stagnant funding, leading some researchers to turn instead to private sources. In recent years, for example, crypto titans have poured money into pandemic prevention research.

The new policy does not give the government direct regulation of privately funded research. But it does say that research institutions that receive any federal money for life-science research should apply a similar oversight to scientists doing research with support from outside the government.

“This effectively limits them, as the N.I.H. does a lot of work everywhere in the world,” Dr. Evans said.

The new policy takes into account the advances in biotechnology that could lead to new risks. When pathogens become extinct, for example, they can be resurrected by recreating their genomes. Research on extinct pathogens will draw the highest levels of scrutiny.

Dr. Evans also noted that the new rules emphasize the risk that lab research can have on plants and animals. In the 20th century, the United States and Russia both carried out extensive research on crop-destroying pathogens such as wheat-killing fungi as part of their biological weapons programs. “It’s significant as a signal the White House is sending,” Dr. Evans said.

Marc Lipsitch, an epidemiologist at Harvard and a longtime critic of the government’s policy, gave the new one a grade of A minus. “I think it’s a lot clearer and more specific in many ways than the old guidance,” he said. But he was disappointed that the government will not provide detailed information to the public about the risky research it evaluates. “The transparency is far from transparent,” he said.

Scientists who have warned of the dangers of impeding useful virus research were also largely optimistic about the new rules.

Gigi Gronvall, a biosafety specialist at the Johns Hopkins Bloomberg School of Public Health, said the policy’s success would depend on how federal health officials interpreted it, but applauded the way it recognized the value of research needed during a crisis, such as the current bird flu outbreak .

“I was cautiously optimistic in reading through it,” she said of the policy. “It seems like the orientation is for it to be thoughtfully implemented so it doesn’t have a chilling effect on needed research.”

Anice Lowen, an influenza virologist at Emory University, said the expanded scope of the new policy was “reasonable.” She said, for instance, that the decision not to create an entirely new review body helped to alleviate concerns about how unwieldy the process might become.

Still, she said, ambiguities in the instructions for assessing risks in certain experiments made it difficult to know how different university and health officials would police them.

“I think there will be more reviews carried out, and more research will be slowed down because of it,” she said.

Carl Zimmer covers news about science for The Times and writes the Origins column . More about Carl Zimmer

Benjamin Mueller reports on health and medicine. He was previously a U.K. correspondent in London and a police reporter in New York. More about Benjamin Mueller

Making sense of research: A guide for critiquing a paper

Affiliation.

• 1 School of Nursing, Griffith University, Meadowbrook, Queensland.
• PMID: 16114192
• DOI: 10.5172/conu.14.1.38

Learning how to critique research articles is one of the fundamental skills of scholarship in any discipline. The range, quantity and quality of publications available today via print, electronic and Internet databases means it has become essential to equip students and practitioners with the prerequisites to judge the integrity and usefulness of published research. Finding, understanding and critiquing quality articles can be a difficult process. This article sets out some helpful indicators to assist the novice to make sense of research.

Publication types

• Data Interpretation, Statistical
• Research Design
• Review Literature as Topic

Overview of Critical Reading

How to Critcally Review an Article

•   1  
• |   2  
• |   3  
• |   4  
• |   5  
• |   6  
• |   7  

All Modules

Epi_Tools.XLSX

What is a Scientific Article?

A scientific article is a publication that is based on empirical evidence. It can support a hypothesis with original research, describe existing research or comment on current trends in a specific field. Types of scientific research include:

Original Research

• Descriptive

Methods Papers

Meta-analysis

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Content ©2016. All Rights Reserved. Date last modified: June 8, 2016. Wayne W. LaMorte, MD, PhD, MPH

It flickers, then it tips -- study identifies early warning signals for the end of the African humid period

Tipping points in the climate system can be the result of a slow but linear development. However, they can also be accompanied by a "flickering," with two stable climatic states that alternate before a final transition occurs -- and the climate tips permanently. A study by the research team around Potsdam geoscientist Prof. Dr. Martin H. Trauth confirms this for the end of the African Humid Period and the transition to the pronounced aridity that is typical today.

The researchers analyzed several sediment cores measuring up to 280 meters from the Chew Bahir Basin in southern Ethiopia, which act as a "record" of 620,000 years of East African climate history. The results of the study, which have just been published in "Nature Communications," show that at the end of the African Humid Period, intense dry and wet events alternated regularly over a period of around 1,000 years before a dry climate prevailed around 5,000 years ago. A better understanding of the various tipping points and, above all, their typical early warning signals could prove essential for further climate change research and modeling.

The transition from the African Humid Period (AHP) to dry conditions in North Africa is the clearest example of climate tipping points in recent geological history. They occur when small perturbations trigger a large, non-linear response in the system and shift the climate to a different future state, usually with dramatic consequences for the biosphere. That was also the case in North Africa, where the grasslands, forests, and lakes favored by humans disappeared, causing them to retreat to areas like the mountains, oases, and the Nile Delta. This development is of particular relevance for researchers, not least because it is an impressive example of how quickly and extensively climate change can affect human societies.

Climate researchers have identified two main types of tipping points: With the first type, processes slow at an increasing rate and the climate has a hard time recovering from disturbances until a transition occurs. The second type is characterized by a flickering between stable humid and dry climates that occurs shortly before the transition. "The two types of tipping points differ with regard to the early warning signals that can be used to recognize them," explains Martin Trauth. "Researching and better understanding them is important if we want to be able to predict possible future climate tipping points caused by humans. While the slowdown seen in the first type of tipping point leads to a decrease in variability, autocorrelation, and skewness, the flickering in the second type leads to the exact opposite -- and, in some cases, to the impending tipping point not being recognized."

In the large-scale project, led by Martin Trauth together with colleagues from the Universities of Cologne, Aberystwyth, and Addis Ababa and funded, among others, by the German Research Foundation (DFG), researchers are analyzing lake sediments obtained by means of scientific deep drilling in the Chew Bahir Basin, a former freshwater lake in eastern Africa. "For the current study, six shorter (9 to 17 meters) and two long (292 meters) drill cores were evaluated, which can be used to reconstruct the past 620,000 years of climate history in the region," explains Dr. Verena Förster-Indenhuck from the University of Cologne.

"At the end of the AHP, we observed at least 14 dry events in the short cores from Chew Bahir, each of which lasted 20-80 years and recurred at intervals of 160±40 years," says Trauth. "Later in the transitional phase, starting in 6,000 BC, seven wet events occurred in addition to the dry events, which were of a similar duration and frequency. These high-frequency, extreme wet-dry events represent a pronounced 'climate flickering' that can be simulated in climate models and can also be observed in earlier climate transitions in the environmental records from Chew Bahir. This indicates that transitions with flickering are characteristic of this region."

The fact that very similar transitions can also be found in the older sections of the sediment cores also supports this. In particular, the changeover from humid to dry climate around 379,000 years ago looks like a perfect copy of the transition at the end of the African Humid Period. "This is interesting because this transition was natural, so to speak, as it occurred at a time when human influence on the environment was negligible," says co-author Prof. Stefanie Kaboth-Bahr of Freie Universität Berlin. Thus, there are many arguments against human activity leading to an accelerated end of the AHP, as proposed by American colleagues. Conversely, people in the region where undoubtedly affected by the climate tipping: The traces of settlement in the Nile valley at the end of the African Humid Period attract millions of tourists to the region every year.

• Environmental Awareness
• Global Warming
• Early Climate
• Origin of Life
• Anthropology
• Global warming controversy
• Consensus of scientists regarding global warming
• Climate model
• Climate engineering
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• IPCC Report on Climate Change - 2007

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Materials provided by University of Potsdam . Note: Content may be edited for style and length.

Journal Reference :

• Martin H. Trauth, Asfawossen Asrat, Markus L. Fischer, Peter O. Hopcroft, Verena Foerster, Stefanie Kaboth-Bahr, Karin Kindermann, Henry F. Lamb, Norbert Marwan, Mark A. Maslin, Frank Schaebitz, Paul J. Valdes. Early warning signals of the termination of the African Humid Period(s) . Nature Communications , 2024; 15 (1) DOI: 10.1038/s41467-024-47921-1

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

Rhitu Chatterjee

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Shots - Health News

Rare and mysterious vomiting illness linked to heavy marijuana use.

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

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

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

• drug policy
• legal marijuana
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Scientific Papers

Scientific papers are for sharing your own original research work with other scientists or for reviewing the research conducted by others. As such, they are critical to the evolution of modern science, in which the work of one scientist builds upon that of others. To reach their goal, papers must aim to inform, not impress. They must be highly readable — that is, clear, accurate, and concise. They are more likely to be cited by other scientists if they are helpful rather than cryptic or self-centered.

Scientific papers typically have two audiences: first, the referees, who help the journal editor decide whether a paper is suitable for publication; and second, the journal readers themselves, who may be more or less knowledgeable about the topic addressed in the paper. To be accepted by referees and cited by readers, papers must do more than simply present a chronological account of the research work. Rather, they must convince their audience that the research presented is important, valid, and relevant to other scientists in the same field. To this end, they must emphasize both the motivation for the work and the outcome of it, and they must include just enough evidence to establish the validity of this outcome.

Papers that report experimental work are often structured chronologically in five sections: first, Introduction ; then Materials and Methods , Results , and Discussion (together, these three sections make up the paper's body); and finally, Conclusion .

• The Introduction section clarifies the motivation for the work presented and prepares readers for the structure of the paper.
• The Materials and Methods section provides sufficient detail for other scientists to reproduce the experiments presented in the paper. In some journals, this information is placed in an appendix, because it is not what most readers want to know first.
• The Results and Discussion sections present and discuss the research results, respectively. They are often usefully combined into one section, however, because readers can seldom make sense of results alone without accompanying interpretation — they need to be told what the results mean.
• The Conclusion section presents the outcome of the work by interpreting the findings at a higher level of abstraction than the Discussion and by relating these findings to the motivation stated in the Introduction .

(Papers reporting something other than experiments, such as a new method or technology, typically have different sections in their body, but they include the same Introduction and Conclusion sections as described above.)

Although the above structure reflects the progression of most research projects, effective papers typically break the chronology in at least three ways to present their content in the order in which the audience will most likely want to read it. First and foremost, they summarize the motivation for, and the outcome of, the work in an abstract, located before the Introduction . In a sense, they reveal the beginning and end of the story — briefly — before providing the full story. Second, they move the more detailed, less important parts of the body to the end of the paper in one or more appendices so that these parts do not stand in the readers' way. Finally, they structure the content in the body in theorem-proof fashion, stating first what readers must remember (for example, as the first sentence of a paragraph) and then presenting evidence to support this statement.

The introduction

• First, provide some context to orient those readers who are less familiar with your topic and to establish the importance of your work.
• Second, state the need for your work, as an opposition between what the scientific community currently has and what it wants.
• Third, indicate what you have done in an effort to address the need (this is the task).
• Finally, preview the remainder of the paper to mentally prepare readers for its structure, in the object of the document.

Context and need

At the beginning of the Introduction section, the context and need work together as a funnel: They start broad and progressively narrow down to the issue addressed in the paper. To spark interest among your audience — referees and journal readers alike — provide a compelling motivation for the work presented in your paper: The fact that a phenomenon has never been studied before is not, in and of itself, a reason to study that phenomenon.

Write the context in a way that appeals to a broad range of readers and leads into the need. Do not include context for the sake of including context: Rather, provide only what will help readers better understand the need and, especially, its importance. Consider anchoring the context in time, using phrases such as recently , in the past 10 years , or since the early 1990s . You may also want to anchor your context in space (either geographically or within a given research field).

Convey the need for the work as an opposition between actual and desired situations. Start by stating the actual situation (what we have) as a direct continuation of the context. If you feel you must explain recent achievements in much detail — say, in more than one or two paragraphs — consider moving the details to a section titled State of the art (or something similar) after the Introduction , but do provide a brief idea of the actual situation in the Introduction . Next, state the desired situation (what we want). Emphasize the contrast between the actual and desired situations with such words as but , however, or unfortunately .

One elegant way to express the desired part of the need is to combine it with the task in a single sentence. This sentence expresses first the objective, then the action undertaken to reach this objective, thus creating a strong and elegant connection between need and task. Here are three examples of such a combination:

To confirm this assumption , we studied the effects of a range of inhibitors of connexin channels . . . on . . .

To assess whether such multiple-coil sensors perform better than single-signal ones , we tested two of them — the DuoPXK and the GEMM3 — in a field where . . . To form a better view of the global distribution and infectiousness of this pathogen , we examined 1645 postmetamorphic and adult amphibians collected from 27 countries between 1984 and 2006 for the presence of . . .

Task and object

An Introduction is usually clearer and more logical when it separates what the authors have done (the task) from what the paper itself attempts or covers (the object of the document). In other words, the task clarifies your contribution as a scientist, whereas the object of the document prepares readers for the structure of the paper, thus allowing focused or selective reading.

For the task,

• use whoever did the work (normally, you and your colleagues) as the subject of the sentence: we or perhaps the authors;
• use a verb expressing a research action: measured , calculated , etc.;
• set that verb in the past tense.

The three examples below are well-formed tasks.

To confirm this assumption, we studied the effects of a range of inhibitors of connexin channels, such as the connexin mimetic peptides Gap26 and Gap27 and anti-peptide antibodies, on calcium signaling in cardiac cells and HeLa cells expressing connexins.

During controlled experiments, we investigated the influence of the HMP boundary conditions on liver flows.

To tackle this problem, we developed a new software verification technique called oblivious hashing, which calculates the hash values based on the actual execution of the program.

The list below provides examples of verbs that express research actions:

For the object of the document,

• use the document itself as the subject of the sentence: this paper , this letter , etc.;
• use a verb expressing a communication action: presents , summarizes , etc.;
• set the verb in the present tense.

The three examples below are suitable objects of the document for the three tasks shown above, respectively.

This paper clarifies the role of CxHc on calcium oscillations in neonatal cardiac myocytes and calcium transients induced by ATP in HL-cells originated from cardiac atrium and in HeLa cells expressing connexin 43 or 26. This paper presents the flow effects induced by increasing the hepatic-artery pressure and by obstructing the vena cava inferior. This paper discusses the theory behind oblivious hashing and shows how this approach can be applied for local software tamper resistance and remote code authentication.

The list below provides examples of verbs that express communication actions:

Even the most logical structure is of little use if readers do not see and understand it as they progress through a paper. Thus, as you organize the body of your paper into sections and perhaps subsections, remember to prepare your readers for the structure ahead at all levels. You already do so for the overall structure of the body (the sections) in the object of the document at the end of the Introduction . You can similarly prepare your readers for an upcoming division into subsections by introducing a global paragraph between the heading of a section and the heading of its first subsection. This paragraph can contain any information relating to the section as a whole rather than particular subsections, but it should at least announce the subsections, whether explicitly or implicitly. An explicit preview would be phrased much like the object of the document: "This section first . . . , then . . . , and finally . . . "

Although papers can be organized into sections in many ways, those reporting experimental work typically include Materials and Methods , Results , and Discussion in their body. In any case, the paragraphs in these sections should begin with a topic sentence to prepare readers for their contents, allow selective reading, and — ideally — get a message across.

Materials and methods

Results and discussion.

When reporting and discussing your results, do not force your readers to go through everything you went through in chronological order. Instead, state the message of each paragraph upfront: Convey in the first sentence what you want readers to remember from the paragraph as a whole. Focus on what happened, not on the fact that you observed it. Then develop your message in the remainder of the paragraph, including only that information you think you need to convince your audience.

The conclusion

At the end of your Conclusion , consider including perspectives — that is, an idea of what could or should still be done in relation to the issue addressed in the paper. If you include perspectives, clarify whether you are referring to firm plans for yourself and your colleagues ("In the coming months, we will . . . ") or to an invitation to readers ("One remaining question is . . . ").

If your paper includes a well-structured Introduction and an effective abstract, you need not repeat any of the Introduction in the Conclusion . In particular, do not restate what you have done or what the paper does. Instead, focus on what you have found and, especially, on what your findings mean. Do not be afraid to write a short Conclusion section: If you can conclude in just a few sentences given the rich discussion in the body of the paper, then do so. (In other words, resist the temptation to repeat material from the Introduction just to make the Conclusio n longer under the false belief that a longer Conclusion will seem more impressive.)

The abstract

Typically, readers are primarily interested in the information presented in a paper's Introduction and Conclusion sections. Primarily, they want to know the motivation for the work presented and the outcome of this work. Then (and only then) the most specialized among them might want to know the details of the work. Thus, an effective abstract focuses on motivation and outcome; in doing so, it parallels the paper's Introduction and Conclusion .

Accordingly, you can think of an abstract as having two distinct parts — motivation and outcome — even if it is typeset as a single paragraph. For the first part, follow the same structure as the Introduction section of the paper: State the context, the need, the task, and the object of the document. For the second part, mention your findings (the what ) and, especially, your conclusion (the so what — that is, the interpretation of your findings); if appropriate, end with perspectives, as in the Conclusion section of your paper.

Although the structure of the abstract parallels the Introduction and Conclusion sections, it differs from these sections in the audience it addresses. The abstract is read by many different readers, from the most specialized to the least specialized among the target audience. In a sense, it should be the least specialized part of the paper. Any scientist reading it should be able to understand why the work was carried out and why it is important (context and need), what the authors did (task) and what the paper reports about this work (object of the document), what the authors found (findings), what these findings mean (the conclusion), and possibly what the next steps are (perspectives). In contrast, the full paper is typically read by specialists only; its Introduction and Conclusion are more detailed (that is, longer and more specialized) than the abstract.

An effective abstract stands on its own — it can be understood fully even when made available without the full paper. To this end, avoid referring to figures or the bibliography in the abstract. Also, introduce any acronyms the first time you use them in the abstract (if needed), and do so again in the full paper (see Mechanics: Using abbreviations ).

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AgriLife Today

Texas A&M AgriLife's digital magazine and newsroom

Dara Wald honored as Andrew Carnegie Fellow

Texas a&m college of agriculture and life sciences professor among 2024 honorees.

May 7, 2024 - by Susan Himes

Dara Wald, Ph.D., has been named to the 2024 class of the Andrew Carnegie Fellows Program by the Carnegie Corporation of New York .

Wald is a tenured Texas A&M  College of Agriculture and Life Sciences  associate professor in the Department of Agricultural Leadership, Education and Communications , Bryan-College Station. She is also a research fellow at the Institute for Science, Technology and Public Policy .

“This is a momentous honor for Dr. Wald,” said Texas A&M University provost and executive vice president Alan Sams, Ph.D. “The Carnegie Fellowship is one of the most prestigious fellowships given to scholars in the humanities and social sciences, and we are very proud of her work. Her research clearly demonstrates a relevant and trailblazing area of study with profound real-world implications. It is a remarkable and well-deserved honor.”

Wald was one of 28 distinguished scholars selected from over 360 nominations for the honor. According to the Carnegie Corporation, each winner will receive up to $200,000 for research that seeks to understand how and why society has become polarized and how it can strengthen the forces of cohesion to fortify democracy.

Wald was sitting at her office desk on a Friday when she got the email.

“I was completely shocked,” she said. “For academics, this time of the year is such an intense period. The semester is wrapping up, and everybody’s tired. To get this news, particularly at this time of year, is thrilling. I am very excited about the possibilities this creates for my research program.”

Wald’s work demonstrates the value and need for interdisciplinary scholarship to address polarized perspectives of agricultural and environmental issues.

“The importance of Dr. Wald’s work cannot be overstated,” said Jeffrey W. Savell, Ph.D., vice chancellor and dean for Agriculture and Life Sciences. “Her ongoing research engages in public conversations throughout agricultural, life and environmental sciences, opening key dialogue about public perceptions of complex science issues.”

Award will further Wald’s research

The Carnegie Fellowship award will allow Wald to dig deeper into questions she and her team have been asking as part of her National Science Foundation funded Faculty Early Career Development Program , CAREER, award on public perceptions of science and place-based conservation. Wald’s research highlights critical areas such as trust, credibility and collaboration in natural resource contexts.

“Dr. Wald’s work asks vital questions about how scientists can build trust and emphasize their commitment to the public good,” said Matt Baker, Ph.D., head of the Department of Agricultural Leadership, Education and Communications, Bryan-College Station. “These questions are critical for our understanding of public perceptions of science and policy.”

Wald’s research aims to maximize the benefits of scientific discovery for people and the environment. She does this by examining the causes and consequences of conflict and collaboration over natural resources and identifying effective communication strategies. The research also examines how public perceptions of science shape attitudes toward environmental and agricultural policies and programs.

“My research focuses on water, wildlife, land and energy,” she said. “These are fundamental things we all need for life. Science can help us protect our communities from floods, improve agricultural production and guard us from emerging health issues.”

Wald said if a community is not getting the information, however, or the messages do not reach or appeal to that community, then that science stays in an ivory tower.

“In general, people pay more attention to information from a credible source they have a relationship with,” she said. “But credibility means different things to different people. Understanding what credibility means to different audiences will help bridge the divide.”

In addition to diving deeper into her current research, gathered from interviews with landowners, land managers and producers in rural areas, Wald said the award will enable her to expand this work to look at polarization about scientific credibility and develop a book proposal about those research results.

“There’s a need to understand ways to find common ground and move together as a force,” she said.

Addressing political polarization

The Andrew Carnegie Fellows Program was established in 2015 to support humanities and social sciences research. As of last year, it had 243 fellowships, totaling an investment of $48 million. Last year marked the start of a new program phase with the exclusive theme of political polarization in the U.S.

“The fragility of American democracy has been exposed in recent years to a degree that is quite frightening,” said Dame Louise Richardson, Carnegie Corporation of New York president. “The driving force appears to be the increasing polarization of American politics and, by extension, American society. We would like to understand this polarization, what caused it, what perpetuates it, and above all, how it might be mitigated, or even reversed, by strengthening the forces of cohesion in our society.”

Through the research of the Andrew Carnegie fellows, the corporation seeks to raise awareness of political polarization in the philanthropic sector, guide public policy and help inform the foundation’s grantmaking in democracy, education, and international peace and security.

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