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Goddard institute for space studies, goddard space flight center sciences and exploration directorate earth sciences division, research features, exploring the climates of earth’s future supercontinent with a nasa supercomputer.

By Jarrett Cohen, NASA Center for Climate Simulations — November 30, 2021

Scientists from the NASA Goddard Institute for Space Studies (GISS), the University of Lisbon (Portugal), and Bangor University (United Kingdom) leveraged a NASA supercomputer to explore possible scenarios for Earth supercontinents and climate 200 and 250 million years into the future.

This video depicts all seven continents on Earth converging over 250 million years into one new supercontinent called Aurica. Using a NASA supercomputer, scientists simulated multiple supercontinent climate scenarios and their implications for future habitability. (Credit: Hannah Sophia Davies, University of Lisbon, Instituto Dom Luiz.)

The main goal of Way and Davies' computational study was to see if Earth’s climate would change dramatically from that of today given a vastly different land layout, slightly higher insolation (Sun brightness), and a slower planetary rotation rate (adding 30 minutes to each day). The results appear in the journal Geochemistry, Geophysics, Geosystems .

Impact: These future Earth simulations demonstrate that the locations of continental landmasses and their topographic height are critical to understanding past and future Earth climate as well as climates on hypothetical exoplanetary worlds.

The land layouts focused on two theoretical future supercontinents that form over millions of years as the underlying tectonic plates shift the existing continents:

  • Aurica: all continents combine into a single landmass near the equator 250 million years into the future.
  • Amasia: Antarctica stays put, but the other continents combine well north of the equator 200 million years into the future.

Graphic of six maps showing possible supercontinent configurations

The maps show land (gray) and ocean/lake (white) configurations used for simulations based on the theoretical future supercontinents Aurica (top) and Amasia (bottom). Present-day Earth continental outlines are shown for reference. (Credit: Way et al. (2021)).

Variations on these two supercontinent configurations and the present-day Earth continents (for comparison) served as inputs to 45 simulations with the NASA GISS ROCKE-3D model that ran on the NASA Center for Climate Simulation (NCCS) Discover supercomputer. Each simulation consumed 44 cores for 1 to 3 months, with total output data of 37 terabytes initially stored on Discover’s disk.

From that full simulation set, the nine published simulations explored combinations of the following parameters:

While all the Aurica and Amasia supercontinent cases allow liquid water to exist year-round, Way said that the study team did not expect the Amasia-with-higher-topography case to be so icy and have surface temperatures several degrees Celsius below the other future Earth simulations. These results show the importance of considering topography for similar climate studies of both Earth and hypothetical exoplanetary atmospheres.

Graphic of six maps showing ocean currents and sea surface temperatures for three modeling scenarios

Left: Maps of ocean heat transport for the future supercontinents Aurica (a) and Amasia (b) and the present-day Earth (c) show the limits of the ocean’s ability to keep high latitude areas warm where continents may block access (a, c). Right: These effects are reflected in the corresponding surface temperature maps (d, e, f). (Credit: Way et al. (2021)).

Noting the critical role of supercomputing, Way observed, “we simply could not do this sort of study on any other resource available to us. NCCS support for our climate runs in terms of compilers and libraries is unsurpassed and vital to the success of our present and future endeavors.”

Joining Way and Davies on the study were Joäo C. Duarte, University of Lisbon, and Mattias Green, Bangor University. The research team plans to study two other future supercontinent scenarios using the same methods.

Way, M.J. , H.S. Davies, J.C. Duarte, and J.A.M. Green, 2021: The climates of Earth's next supercontinent: Effects of tectonics, rotation rate, and insolation . Geochem. Geophys. Geosyst. , 22 , no. 8, e2021GC009983, doi:10.1029/2021GC009983.

This article was originally prepared as an NCCS Highlight for the NASA Center for Climate Simulations .

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What Is Research, and Why Do People Do It?

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  • First Online: 03 December 2022

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research features

  • 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.

An image of the book's description with the words like research, science, and inquiry and what the word research meant in the scientific world.

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.

An image represents the observation required in the scientific inquiry including planning and explaining.

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.

An image represents the data explanation as it is not limited and takes numerous non-quantitative forms including an interview, journal entries, etc.

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. ).

An image represents the scientific inquiry definition given by the editors of Britannica and also defines the hypothesis on the basis of the experiments.

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.

An image represents the rationale and the prediction for the scientific inquiry and different types of information provided by the terms.

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.

An image represents the cycle of events that take place before making predictions, developing the rationale, and studying the prediction and rationale multiple times.

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.

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What is Scientific Research and How Can it be Done?

Scientific researches are studies that should be systematically planned before performing them. In this review, classification and description of scientific studies, planning stage randomisation and bias are explained.

Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new information is revealed with respect to diagnosis, treatment and reliability of applications. The purpose of this review is to provide information about the definition, classification and methodology of scientific research.

Before beginning the scientific research, the researcher should determine the subject, do planning and specify the methodology. In the Declaration of Helsinki, it is stated that ‘the primary purpose of medical researches on volunteers is to understand the reasons, development and effects of diseases and develop protective, diagnostic and therapeutic interventions (method, operation and therapies). Even the best proven interventions should be evaluated continuously by investigations with regard to reliability, effectiveness, efficiency, accessibility and quality’ ( 1 ).

The questions, methods of response to questions and difficulties in scientific research may vary, but the design and structure are generally the same ( 2 ).

Classification of Scientific Research

Scientific research can be classified in several ways. Classification can be made according to the data collection techniques based on causality, relationship with time and the medium through which they are applied.

  • Observational
  • Experimental
  • Descriptive
  • Retrospective
  • Prospective
  • Cross-sectional
  • Social descriptive research ( 3 )

Another method is to classify the research according to its descriptive or analytical features. This review is written according to this classification method.

I. Descriptive research

  • Case series
  • Surveillance studies

II. Analytical research

  • Observational studies: cohort, case control and cross- sectional research
  • Interventional research: quasi-experimental and clinical research
  • Case Report: it is the most common type of descriptive study. It is the examination of a single case having a different quality in the society, e.g. conducting general anaesthesia in a pregnant patient with mucopolysaccharidosis.
  • Case Series: it is the description of repetitive cases having common features. For instance; case series involving interscapular pain related to neuraxial labour analgesia. Interestingly, malignant hyperthermia cases are not accepted as case series since they are rarely seen during historical development.
  • Surveillance Studies: these are the results obtained from the databases that follow and record a health problem for a certain time, e.g. the surveillance of cross-infections during anaesthesia in the intensive care unit.

Moreover, some studies may be experimental. After the researcher intervenes, the researcher waits for the result, observes and obtains data. Experimental studies are, more often, in the form of clinical trials or laboratory animal trials ( 2 ).

Analytical observational research can be classified as cohort, case-control and cross-sectional studies.

Firstly, the participants are controlled with regard to the disease under investigation. Patients are excluded from the study. Healthy participants are evaluated with regard to the exposure to the effect. Then, the group (cohort) is followed-up for a sufficient period of time with respect to the occurrence of disease, and the progress of disease is studied. The risk of the healthy participants getting sick is considered an incident. In cohort studies, the risk of disease between the groups exposed and not exposed to the effect is calculated and rated. This rate is called relative risk. Relative risk indicates the strength of exposure to the effect on the disease.

Cohort research may be observational and experimental. The follow-up of patients prospectively is called a prospective cohort study . The results are obtained after the research starts. The researcher’s following-up of cohort subjects from a certain point towards the past is called a retrospective cohort study . Prospective cohort studies are more valuable than retrospective cohort studies: this is because in the former, the researcher observes and records the data. The researcher plans the study before the research and determines what data will be used. On the other hand, in retrospective studies, the research is made on recorded data: no new data can be added.

In fact, retrospective and prospective studies are not observational. They determine the relationship between the date on which the researcher has begun the study and the disease development period. The most critical disadvantage of this type of research is that if the follow-up period is long, participants may leave the study at their own behest or due to physical conditions. Cohort studies that begin after exposure and before disease development are called ambidirectional studies . Public healthcare studies generally fall within this group, e.g. lung cancer development in smokers.

  • Case-Control Studies: these studies are retrospective cohort studies. They examine the cause and effect relationship from the effect to the cause. The detection or determination of data depends on the information recorded in the past. The researcher has no control over the data ( 2 ).

Cross-sectional studies are advantageous since they can be concluded relatively quickly. It may be difficult to obtain a reliable result from such studies for rare diseases ( 2 ).

Cross-sectional studies are characterised by timing. In such studies, the exposure and result are simultaneously evaluated. While cross-sectional studies are restrictedly used in studies involving anaesthesia (since the process of exposure is limited), they can be used in studies conducted in intensive care units.

  • Quasi-Experimental Research: they are conducted in cases in which a quick result is requested and the participants or research areas cannot be randomised, e.g. giving hand-wash training and comparing the frequency of nosocomial infections before and after hand wash.
  • Clinical Research: they are prospective studies carried out with a control group for the purpose of comparing the effect and value of an intervention in a clinical case. Clinical study and research have the same meaning. Drugs, invasive interventions, medical devices and operations, diets, physical therapy and diagnostic tools are relevant in this context ( 6 ).

Clinical studies are conducted by a responsible researcher, generally a physician. In the research team, there may be other healthcare staff besides physicians. Clinical studies may be financed by healthcare institutes, drug companies, academic medical centres, volunteer groups, physicians, healthcare service providers and other individuals. They may be conducted in several places including hospitals, universities, physicians’ offices and community clinics based on the researcher’s requirements. The participants are made aware of the duration of the study before their inclusion. Clinical studies should include the evaluation of recommendations (drug, device and surgical) for the treatment of a disease, syndrome or a comparison of one or more applications; finding different ways for recognition of a disease or case and prevention of their recurrence ( 7 ).

Clinical Research

In this review, clinical research is explained in more detail since it is the most valuable study in scientific research.

Clinical research starts with forming a hypothesis. A hypothesis can be defined as a claim put forward about the value of a population parameter based on sampling. There are two types of hypotheses in statistics.

  • H 0 hypothesis is called a control or null hypothesis. It is the hypothesis put forward in research, which implies that there is no difference between the groups under consideration. If this hypothesis is rejected at the end of the study, it indicates that a difference exists between the two treatments under consideration.
  • H 1 hypothesis is called an alternative hypothesis. It is hypothesised against a null hypothesis, which implies that a difference exists between the groups under consideration. For example, consider the following hypothesis: drug A has an analgesic effect. Control or null hypothesis (H 0 ): there is no difference between drug A and placebo with regard to the analgesic effect. The alternative hypothesis (H 1 ) is applicable if a difference exists between drug A and placebo with regard to the analgesic effect.

The planning phase comes after the determination of a hypothesis. A clinical research plan is called a protocol . In a protocol, the reasons for research, number and qualities of participants, tests to be applied, study duration and what information to be gathered from the participants should be found and conformity criteria should be developed.

The selection of participant groups to be included in the study is important. Inclusion and exclusion criteria of the study for the participants should be determined. Inclusion criteria should be defined in the form of demographic characteristics (age, gender, etc.) of the participant group and the exclusion criteria as the diseases that may influence the study, age ranges, cases involving pregnancy and lactation, continuously used drugs and participants’ cooperation.

The next stage is methodology. Methodology can be grouped under subheadings, namely, the calculation of number of subjects, blinding (masking), randomisation, selection of operation to be applied, use of placebo and criteria for stopping and changing the treatment.

I. Calculation of the Number of Subjects

The entire source from which the data are obtained is called a universe or population . A small group selected from a certain universe based on certain rules and which is accepted to highly represent the universe from which it is selected is called a sample and the characteristics of the population from which the data are collected are called variables. If data is collected from the entire population, such an instance is called a parameter . Conducting a study on the sample rather than the entire population is easier and less costly. Many factors influence the determination of the sample size. Firstly, the type of variable should be determined. Variables are classified as categorical (qualitative, non-numerical) or numerical (quantitative). Individuals in categorical variables are classified according to their characteristics. Categorical variables are indicated as nominal and ordinal (ordered). In nominal variables, the application of a category depends on the researcher’s preference. For instance, a female participant can be considered first and then the male participant, or vice versa. An ordinal (ordered) variable is ordered from small to large or vice versa (e.g. ordering obese patients based on their weights-from the lightest to the heaviest or vice versa). A categorical variable may have more than one characteristic: such variables are called binary or dichotomous (e.g. a participant may be both female and obese).

If the variable has numerical (quantitative) characteristics and these characteristics cannot be categorised, then it is called a numerical variable. Numerical variables are either discrete or continuous. For example, the number of operations with spinal anaesthesia represents a discrete variable. The haemoglobin value or height represents a continuous variable.

Statistical analyses that need to be employed depend on the type of variable. The determination of variables is necessary for selecting the statistical method as well as software in SPSS. While categorical variables are presented as numbers and percentages, numerical variables are represented using measures such as mean and standard deviation. It may be necessary to use mean in categorising some cases such as the following: even though the variable is categorical (qualitative, non-numerical) when Visual Analogue Scale (VAS) is used (since a numerical value is obtained), it is classified as a numerical variable: such variables are averaged.

Clinical research is carried out on the sample and generalised to the population. Accordingly, the number of samples should be correctly determined. Different sample size formulas are used on the basis of the statistical method to be used. When the sample size increases, error probability decreases. The sample size is calculated based on the primary hypothesis. The determination of a sample size before beginning the research specifies the power of the study. Power analysis enables the acquisition of realistic results in the research, and it is used for comparing two or more clinical research methods.

Because of the difference in the formulas used in calculating power analysis and number of samples for clinical research, it facilitates the use of computer programs for making calculations.

It is necessary to know certain parameters in order to calculate the number of samples by power analysis.

  • Type-I (α) and type-II (β) error levels
  • Difference between groups (d-difference) and effect size (ES)
  • Distribution ratio of groups
  • Direction of research hypothesis (H1)

a. Type-I (α) and Type-II (β) Error (β) Levels

Two types of errors can be made while accepting or rejecting H 0 hypothesis in a hypothesis test. Type-I error (α) level is the probability of finding a difference at the end of the research when there is no difference between the two applications. In other words, it is the rejection of the hypothesis when H 0 is actually correct and it is known as α error or p value. For instance, when the size is determined, type-I error level is accepted as 0.05 or 0.01.

Another error that can be made during a hypothesis test is a type-II error. It is the acceptance of a wrongly hypothesised H 0 hypothesis. In fact, it is the probability of failing to find a difference when there is a difference between the two applications. The power of a test is the ability of that test to find a difference that actually exists. Therefore, it is related to the type-II error level.

Since the type-II error risk is expressed as β, the power of the test is defined as 1–β. When a type-II error is 0.20, the power of the test is 0.80. Type-I (α) and type-II (β) errors can be intentional. The reason to intentionally make such an error is the necessity to look at the events from the opposite perspective.

b. Difference between Groups and ES

ES is defined as the state in which statistical difference also has clinically significance: ES≥0.5 is desirable. The difference between groups is the absolute difference between the groups compared in clinical research.

c. Allocation Ratio of Groups

The allocation ratio of groups is effective in determining the number of samples. If the number of samples is desired to be determined at the lowest level, the rate should be kept as 1/1.

d. Direction of Hypothesis (H1)

The direction of hypothesis in clinical research may be one-sided or two-sided. While one-sided hypotheses hypothesis test differences in the direction of size, two-sided hypotheses hypothesis test differences without direction. The power of the test in two-sided hypotheses is lower than one-sided hypotheses.

After these four variables are determined, they are entered in the appropriate computer program and the number of samples is calculated. Statistical packaged software programs such as Statistica, NCSS and G-Power may be used for power analysis and calculating the number of samples. When the samples size is calculated, if there is a decrease in α, difference between groups, ES and number of samples, then the standard deviation increases and power decreases. The power in two-sided hypothesis is lower. It is ethically appropriate to consider the determination of sample size, particularly in animal experiments, at the beginning of the study. The phase of the study is also important in the determination of number of subjects to be included in drug studies. Usually, phase-I studies are used to determine the safety profile of a drug or product, and they are generally conducted on a few healthy volunteers. If no unacceptable toxicity is detected during phase-I studies, phase-II studies may be carried out. Phase-II studies are proof-of-concept studies conducted on a larger number (100–500) of volunteer patients. When the effectiveness of the drug or product is evident in phase-II studies, phase-III studies can be initiated. These are randomised, double-blinded, placebo or standard treatment-controlled studies. Volunteer patients are periodically followed-up with respect to the effectiveness and side effects of the drug. It can generally last 1–4 years and is valuable during licensing and releasing the drug to the general market. Then, phase-IV studies begin in which long-term safety is investigated (indication, dose, mode of application, safety, effectiveness, etc.) on thousands of volunteer patients.

II. Blinding (Masking) and Randomisation Methods

When the methodology of clinical research is prepared, precautions should be taken to prevent taking sides. For this reason, techniques such as randomisation and blinding (masking) are used. Comparative studies are the most ideal ones in clinical research.

Blinding Method

A case in which the treatments applied to participants of clinical research should be kept unknown is called the blinding method . If the participant does not know what it receives, it is called a single-blind study; if even the researcher does not know, it is called a double-blind study. When there is a probability of knowing which drug is given in the order of application, when uninformed staff administers the drug, it is called in-house blinding. In case the study drug is known in its pharmaceutical form, a double-dummy blinding test is conducted. Intravenous drug is given to one group and a placebo tablet is given to the comparison group; then, the placebo tablet is given to the group that received the intravenous drug and intravenous drug in addition to placebo tablet is given to the comparison group. In this manner, each group receives both the intravenous and tablet forms of the drug. In case a third party interested in the study is involved and it also does not know about the drug (along with the statistician), it is called third-party blinding.

Randomisation Method

The selection of patients for the study groups should be random. Randomisation methods are used for such selection, which prevent conscious or unconscious manipulations in the selection of patients ( 8 ).

No factor pertaining to the patient should provide preference of one treatment to the other during randomisation. This characteristic is the most important difference separating randomised clinical studies from prospective and synchronous studies with experimental groups. Randomisation strengthens the study design and enables the determination of reliable scientific knowledge ( 2 ).

The easiest method is simple randomisation, e.g. determination of the type of anaesthesia to be administered to a patient by tossing a coin. In this method, when the number of samples is kept high, a balanced distribution is created. When the number of samples is low, there will be an imbalance between the groups. In this case, stratification and blocking have to be added to randomisation. Stratification is the classification of patients one or more times according to prognostic features determined by the researcher and blocking is the selection of a certain number of patients for each stratification process. The number of stratification processes should be determined at the beginning of the study.

As the number of stratification processes increases, performing the study and balancing the groups become difficult. For this reason, stratification characteristics and limitations should be effectively determined at the beginning of the study. It is not mandatory for the stratifications to have equal intervals. Despite all the precautions, an imbalance might occur between the groups before beginning the research. In such circumstances, post-stratification or restandardisation may be conducted according to the prognostic factors.

The main characteristic of applying blinding (masking) and randomisation is the prevention of bias. Therefore, it is worthwhile to comprehensively examine bias at this stage.

Bias and Chicanery

While conducting clinical research, errors can be introduced voluntarily or involuntarily at a number of stages, such as design, population selection, calculating the number of samples, non-compliance with study protocol, data entry and selection of statistical method. Bias is taking sides of individuals in line with their own decisions, views and ideological preferences ( 9 ). In order for an error to lead to bias, it has to be a systematic error. Systematic errors in controlled studies generally cause the results of one group to move in a different direction as compared to the other. It has to be understood that scientific research is generally prone to errors. However, random errors (or, in other words, ‘the luck factor’-in which bias is unintended-do not lead to bias ( 10 ).

Another issue, which is different from bias, is chicanery. It is defined as voluntarily changing the interventions, results and data of patients in an unethical manner or copying data from other studies. Comparatively, bias may not be done consciously.

In case unexpected results or outliers are found while the study is analysed, if possible, such data should be re-included into the study since the complete exclusion of data from a study endangers its reliability. In such a case, evaluation needs to be made with and without outliers. It is insignificant if no difference is found. However, if there is a difference, the results with outliers are re-evaluated. If there is no error, then the outlier is included in the study (as the outlier may be a result). It should be noted that re-evaluation of data in anaesthesiology is not possible.

Statistical evaluation methods should be determined at the design stage so as not to encounter unexpected results in clinical research. The data should be evaluated before the end of the study and without entering into details in research that are time-consuming and involve several samples. This is called an interim analysis . The date of interim analysis should be determined at the beginning of the study. The purpose of making interim analysis is to prevent unnecessary cost and effort since it may be necessary to conclude the research after the interim analysis, e.g. studies in which there is no possibility to validate the hypothesis at the end or the occurrence of different side effects of the drug to be used. The accuracy of the hypothesis and number of samples are compared. Statistical significance levels in interim analysis are very important. If the data level is significant, the hypothesis is validated even if the result turns out to be insignificant after the date of the analysis.

Another important point to be considered is the necessity to conclude the participants’ treatment within the period specified in the study protocol. When the result of the study is achieved earlier and unexpected situations develop, the treatment is concluded earlier. Moreover, the participant may quit the study at its own behest, may die or unpredictable situations (e.g. pregnancy) may develop. The participant can also quit the study whenever it wants, even if the study has not ended ( 7 ).

In case the results of a study are contrary to already known or expected results, the expected quality level of the study suggesting the contradiction may be higher than the studies supporting what is known in that subject. This type of bias is called confirmation bias. The presence of well-known mechanisms and logical inference from them may create problems in the evaluation of data. This is called plausibility bias.

Another type of bias is expectation bias. If a result different from the known results has been achieved and it is against the editor’s will, it can be challenged. Bias may be introduced during the publication of studies, such as publishing only positive results, selection of study results in a way to support a view or prevention of their publication. Some editors may only publish research that extols only the positive results or results that they desire.

Bias may be introduced for advertisement or economic reasons. Economic pressure may be applied on the editor, particularly in the cases of studies involving drugs and new medical devices. This is called commercial bias.

In recent years, before beginning a study, it has been recommended to record it on the Web site for the purpose of facilitating systematic interpretation and analysis in scientific research, informing other researchers, preventing bias, provision of writing in a standard format, enhancing contribution of research results to the general literature and enabling early intervention of an institution for support. This Web site is a service of the US National Institutes of Health.

The last stage in the methodology of clinical studies is the selection of intervention to be conducted. Placebo use assumes an important place in interventions. In Latin, placebo means ‘I will be fine’. In medical literature, it refers to substances that are not curative, do not have active ingredients and have various pharmaceutical forms. Although placebos do not have active drug characteristic, they have shown effective analgesic characteristics, particularly in algology applications; further, its use prevents bias in comparative studies. If a placebo has a positive impact on a participant, it is called the placebo effect ; on the contrary, if it has a negative impact, it is called the nocebo effect . Another type of therapy that can be used in clinical research is sham application. Although a researcher does not cure the patient, the researcher may compare those who receive therapy and undergo sham. It has been seen that sham therapies also exhibit a placebo effect. In particular, sham therapies are used in acupuncture applications ( 11 ). While placebo is a substance, sham is a type of clinical application.

Ethically, the patient has to receive appropriate therapy. For this reason, if its use prevents effective treatment, it causes great problem with regard to patient health and legalities.

Before medical research is conducted with human subjects, predictable risks, drawbacks and benefits must be evaluated for individuals or groups participating in the study. Precautions must be taken for reducing the risk to a minimum level. The risks during the study should be followed, evaluated and recorded by the researcher ( 1 ).

After the methodology for a clinical study is determined, dealing with the ‘Ethics Committee’ forms the next stage. The purpose of the ethics committee is to protect the rights, safety and well-being of volunteers taking part in the clinical research, considering the scientific method and concerns of society. The ethics committee examines the studies presented in time, comprehensively and independently, with regard to ethics and science; in line with the Declaration of Helsinki and following national and international standards concerning ‘Good Clinical Practice’. The method to be followed in the formation of the ethics committee should be developed without any kind of prejudice and to examine the applications with regard to ethics and science within the framework of the ethics committee, Regulation on Clinical Trials and Good Clinical Practice ( ). The necessary documents to be presented to the ethics committee are research protocol, volunteer consent form, budget contract, Declaration of Helsinki, curriculum vitae of researchers, similar or explanatory literature samples, supporting institution approval certificate and patient follow-up form.

Only one sister/brother, mother, father, son/daughter and wife/husband can take charge in the same ethics committee. A rector, vice rector, dean, deputy dean, provincial healthcare director and chief physician cannot be members of the ethics committee.

Members of the ethics committee can work as researchers or coordinators in clinical research. However, during research meetings in which members of the ethics committee are researchers or coordinators, they must leave the session and they cannot sign-off on decisions. If the number of members in the ethics committee for a particular research is so high that it is impossible to take a decision, the clinical research is presented to another ethics committee in the same province. If there is no ethics committee in the same province, an ethics committee in the closest settlement is found.

Thereafter, researchers need to inform the participants using an informed consent form. This form should explain the content of clinical study, potential benefits of the study, alternatives and risks (if any). It should be easy, comprehensible, conforming to spelling rules and written in plain language understandable by the participant.

This form assists the participants in taking a decision regarding participation in the study. It should aim to protect the participants. The participant should be included in the study only after it signs the informed consent form; the participant can quit the study whenever required, even when the study has not ended ( 7 ).

Peer-review: Externally peer-reviewed.

Author Contributions: Concept - C.Ö.Ç., A.D.; Design - C.Ö.Ç.; Supervision - A.D.; Resource - C.Ö.Ç., A.D.; Materials - C.Ö.Ç., A.D.; Analysis and/or Interpretation - C.Ö.Ç., A.D.; Literature Search - C.Ö.Ç.; Writing Manuscript - C.Ö.Ç.; Critical Review - A.D.; Other - C.Ö.Ç., A.D.

Conflict of Interest: No conflict of interest was declared by the authors.

Financial Disclosure: The authors declared that this study has received no financial support.

Qualitative Research: Characteristics, Design, Methods & Examples

Lauren McCall

MSc Health Psychology Graduate

MSc, Health Psychology, University of Nottingham

Lauren obtained an MSc in Health Psychology from The University of Nottingham with a distinction classification.

Learn about our Editorial Process

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

Qualitative research is a type of research methodology that focuses on gathering and analyzing non-numerical data to gain a deeper understanding of human behavior, experiences, and perspectives.

It aims to explore the “why” and “how” of a phenomenon rather than the “what,” “where,” and “when” typically addressed by quantitative research.

Unlike quantitative research, which focuses on gathering and analyzing numerical data for statistical analysis, qualitative research involves researchers interpreting data to identify themes, patterns, and meanings.

Qualitative research can be used to:

  • Gain deep contextual understandings of the subjective social reality of individuals
  • To answer questions about experience and meaning from the participant’s perspective
  • To design hypotheses, theory must be researched using qualitative methods to determine what is important before research can begin. 

Examples of qualitative research questions include: 

  • How does stress influence young adults’ behavior?
  • What factors influence students’ school attendance rates in developed countries?
  • How do adults interpret binge drinking in the UK?
  • What are the psychological impacts of cervical cancer screening in women?
  • How can mental health lessons be integrated into the school curriculum? 


Naturalistic setting.

Individuals are studied in their natural setting to gain a deeper understanding of how people experience the world. This enables the researcher to understand a phenomenon close to how participants experience it. 

Naturalistic settings provide valuable contextual information to help researchers better understand and interpret the data they collect.

The environment, social interactions, and cultural factors can all influence behavior and experiences, and these elements are more easily observed in real-world settings.

Reality is socially constructed

Qualitative research aims to understand how participants make meaning of their experiences – individually or in social contexts. It assumes there is no objective reality and that the social world is interpreted (Yilmaz, 2013). 

The primacy of subject matter 

The primary aim of qualitative research is to understand the perspectives, experiences, and beliefs of individuals who have experienced the phenomenon selected for research rather than the average experiences of groups of people (Minichiello, 1990).

An in-depth understanding is attained since qualitative techniques allow participants to freely disclose their experiences, thoughts, and feelings without constraint (Tenny et al., 2022). 

Variables are complex, interwoven, and difficult to measure

Factors such as experiences, behaviors, and attitudes are complex and interwoven, so they cannot be reduced to isolated variables , making them difficult to measure quantitatively.

However, a qualitative approach enables participants to describe what, why, or how they were thinking/ feeling during a phenomenon being studied (Yilmaz, 2013). 

Emic (insider’s point of view)

The phenomenon being studied is centered on the participants’ point of view (Minichiello, 1990).

Emic is used to describe how participants interact, communicate, and behave in the research setting (Scarduzio, 2017).

Interpretive analysis

In qualitative research, interpretive analysis is crucial in making sense of the collected data.

This process involves examining the raw data, such as interview transcripts, field notes, or documents, and identifying the underlying themes, patterns, and meanings that emerge from the participants’ experiences and perspectives.

Collecting Qualitative Data

There are four main research design methods used to collect qualitative data: observations, interviews,  focus groups, and ethnography.


This method involves watching and recording phenomena as they occur in nature. Observation can be divided into two types: participant and non-participant observation.

In participant observation, the researcher actively participates in the situation/events being observed.

In non-participant observation, the researcher is not an active part of the observation and tries not to influence the behaviors they are observing (Busetto et al., 2020). 

Observations can be covert (participants are unaware that a researcher is observing them) or overt (participants are aware of the researcher’s presence and know they are being observed).

However, awareness of an observer’s presence may influence participants’ behavior. 

Interviews give researchers a window into the world of a participant by seeking their account of an event, situation, or phenomenon. They are usually conducted on a one-to-one basis and can be distinguished according to the level at which they are structured (Punch, 2013). 

Structured interviews involve predetermined questions and sequences to ensure replicability and comparability. However, they are unable to explore emerging issues.

Informal interviews consist of spontaneous, casual conversations which are closer to the truth of a phenomenon. However, information is gathered using quick notes made by the researcher and is therefore subject to recall bias. 

Semi-structured interviews have a flexible structure, phrasing, and placement so emerging issues can be explored (Denny & Weckesser, 2022).

The use of probing questions and clarification can lead to a detailed understanding, but semi-structured interviews can be time-consuming and subject to interviewer bias. 

Focus groups 

Similar to interviews, focus groups elicit a rich and detailed account of an experience. However, focus groups are more dynamic since participants with shared characteristics construct this account together (Denny & Weckesser, 2022).

A shared narrative is built between participants to capture a group experience shaped by a shared context. 

The researcher takes on the role of a moderator, who will establish ground rules and guide the discussion by following a topic guide to focus the group discussions.

Typically, focus groups have 4-10 participants as a discussion can be difficult to facilitate with more than this, and this number allows everyone the time to speak.


Ethnography is a methodology used to study a group of people’s behaviors and social interactions in their environment (Reeves et al., 2008).

Data are collected using methods such as observations, field notes, or structured/ unstructured interviews.

The aim of ethnography is to provide detailed, holistic insights into people’s behavior and perspectives within their natural setting. In order to achieve this, researchers immerse themselves in a community or organization. 

Due to the flexibility and real-world focus of ethnography, researchers are able to gather an in-depth, nuanced understanding of people’s experiences, knowledge and perspectives that are influenced by culture and society.

In order to develop a representative picture of a particular culture/ context, researchers must conduct extensive field work. 

This can be time-consuming as researchers may need to immerse themselves into a community/ culture for a few days, or possibly a few years.

Qualitative Data Analysis Methods

Different methods can be used for analyzing qualitative data. The researcher chooses based on the objectives of their study. 

The researcher plays a key role in the interpretation of data, making decisions about the coding, theming, decontextualizing, and recontextualizing of data (Starks & Trinidad, 2007). 

Grounded theory

Grounded theory is a qualitative method specifically designed to inductively generate theory from data. It was developed by Glaser and Strauss in 1967 (Glaser & Strauss, 2017).

 This methodology aims to develop theories (rather than test hypotheses) that explain a social process, action, or interaction (Petty et al., 2012). To inform the developing theory, data collection and analysis run simultaneously. 

There are three key types of coding used in grounded theory: initial (open), intermediate (axial), and advanced (selective) coding. 

Throughout the analysis, memos should be created to document methodological and theoretical ideas about the data. Data should be collected and analyzed until data saturation is reached and a theory is developed. 

Content analysis

Content analysis was first used in the early twentieth century to analyze textual materials such as newspapers and political speeches.

Content analysis is a research method used to identify and analyze the presence and patterns of themes, concepts, or words in data (Vaismoradi et al., 2013). 

This research method can be used to analyze data in different formats, which can be written, oral, or visual. 

The goal of content analysis is to develop themes that capture the underlying meanings of data (Schreier, 2012). 

Qualitative content analysis can be used to validate existing theories, support the development of new models and theories, and provide in-depth descriptions of particular settings or experiences.

The following six steps provide a guideline for how to conduct qualitative content analysis.
  • Define a Research Question : To start content analysis, a clear research question should be developed.
  • Identify and Collect Data : Establish the inclusion criteria for your data. Find the relevant sources to analyze.
  • Define the Unit or Theme of Analysis : Categorize the content into themes. Themes can be a word, phrase, or sentence.
  • Develop Rules for Coding your Data : Define a set of coding rules to ensure that all data are coded consistently.
  • Code the Data : Follow the coding rules to categorize data into themes.
  • Analyze the Results and Draw Conclusions : Examine the data to identify patterns and draw conclusions in relation to your research question.

Discourse analysis

Discourse analysis is a research method used to study written/ spoken language in relation to its social context (Wood & Kroger, 2000).

In discourse analysis, the researcher interprets details of language materials and the context in which it is situated.

Discourse analysis aims to understand the functions of language (how language is used in real life) and how meaning is conveyed by language in different contexts. Researchers use discourse analysis to investigate social groups and how language is used to achieve specific communication goals.

Different methods of discourse analysis can be used depending on the aims and objectives of a study. However, the following steps provide a guideline on how to conduct discourse analysis.
  • Define the Research Question : Develop a relevant research question to frame the analysis.
  • Gather Data and Establish the Context : Collect research materials (e.g., interview transcripts, documents). Gather factual details and review the literature to construct a theory about the social and historical context of your study.
  • Analyze the Content : Closely examine various components of the text, such as the vocabulary, sentences, paragraphs, and structure of the text. Identify patterns relevant to the research question to create codes, then group these into themes.
  • Review the Results : Reflect on the findings to examine the function of the language, and the meaning and context of the discourse. 

Thematic analysis

Thematic analysis is a method used to identify, interpret, and report patterns in data, such as commonalities or contrasts. 

Although the origin of thematic analysis can be traced back to the early twentieth century, understanding and clarity of thematic analysis is attributed to Braun and Clarke (2006).

Thematic analysis aims to develop themes (patterns of meaning) across a dataset to address a research question. 

In thematic analysis, qualitative data is gathered using techniques such as interviews, focus groups, and questionnaires. Audio recordings are transcribed. The dataset is then explored and interpreted by a researcher to identify patterns. 

This occurs through the rigorous process of data familiarisation, coding, theme development, and revision. These identified patterns provide a summary of the dataset and can be used to address a research question.

Themes are developed by exploring the implicit and explicit meanings within the data. Two different approaches are used to generate themes: inductive and deductive. 

An inductive approach allows themes to emerge from the data. In contrast, a deductive approach uses existing theories or knowledge to apply preconceived ideas to the data.

Phases of Thematic Analysis

Braun and Clarke (2006) provide a guide of the six phases of thematic analysis. These phases can be applied flexibly to fit research questions and data. 

Template analysis

Template analysis refers to a specific method of thematic analysis which uses hierarchical coding (Brooks et al., 2014).

Template analysis is used to analyze textual data, for example, interview transcripts or open-ended responses on a written questionnaire.

To conduct template analysis, a coding template must be developed (usually from a subset of the data) and subsequently revised and refined. This template represents the themes identified by researchers as important in the dataset. 

Codes are ordered hierarchically within the template, with the highest-level codes demonstrating overarching themes in the data and lower-level codes representing constituent themes with a narrower focus.

A guideline for the main procedural steps for conducting template analysis is outlined below.
  • Familiarization with the Data : Read (and reread) the dataset in full. Engage, reflect, and take notes on data that may be relevant to the research question.
  • Preliminary Coding : Identify initial codes using guidance from the a priori codes, identified before the analysis as likely to be beneficial and relevant to the analysis.
  • Organize Themes : Organize themes into meaningful clusters. Consider the relationships between the themes both within and between clusters.
  • Produce an Initial Template : Develop an initial template. This may be based on a subset of the data.
  • Apply and Develop the Template : Apply the initial template to further data and make any necessary modifications. Refinements of the template may include adding themes, removing themes, or changing the scope/title of themes. 
  • Finalize Template : Finalize the template, then apply it to the entire dataset. 

Frame analysis

Frame analysis is a comparative form of thematic analysis which systematically analyzes data using a matrix output.

Ritchie and Spencer (1994) developed this set of techniques to analyze qualitative data in applied policy research. Frame analysis aims to generate theory from data.

Frame analysis encourages researchers to organize and manage their data using summarization.

This results in a flexible and unique matrix output, in which individual participants (or cases) are represented by rows and themes are represented by columns. 

Each intersecting cell is used to summarize findings relating to the corresponding participant and theme.

Frame analysis has five distinct phases which are interrelated, forming a methodical and rigorous framework.
  • Familiarization with the Data : Familiarize yourself with all the transcripts. Immerse yourself in the details of each transcript and start to note recurring themes.
  • Develop a Theoretical Framework : Identify recurrent/ important themes and add them to a chart. Provide a framework/ structure for the analysis.
  • Indexing : Apply the framework systematically to the entire study data.
  • Summarize Data in Analytical Framework : Reduce the data into brief summaries of participants’ accounts.
  • Mapping and Interpretation : Compare themes and subthemes and check against the original transcripts. Group the data into categories and provide an explanation for them.

Preventing Bias in Qualitative Research

To evaluate qualitative studies, the CASP (Critical Appraisal Skills Programme) checklist for qualitative studies can be used to ensure all aspects of a study have been considered (CASP, 2018).

The quality of research can be enhanced and assessed using criteria such as checklists, reflexivity, co-coding, and member-checking. 


Relying on only one researcher to interpret rich and complex data may risk key insights and alternative viewpoints being missed. Therefore, coding is often performed by multiple researchers.

A common strategy must be defined at the beginning of the coding process  (Busetto et al., 2020). This includes establishing a useful coding list and finding a common definition of individual codes.

Transcripts are initially coded independently by researchers and then compared and consolidated to minimize error or bias and to bring confirmation of findings. 

Member checking

Member checking (or respondent validation) involves checking back with participants to see if the research resonates with their experiences (Russell & Gregory, 2003).

Data can be returned to participants after data collection or when results are first available. For example, participants may be provided with their interview transcript and asked to verify whether this is a complete and accurate representation of their views.

Participants may then clarify or elaborate on their responses to ensure they align with their views (Shenton, 2004).

This feedback becomes part of data collection and ensures accurate descriptions/ interpretations of phenomena (Mays & Pope, 2000). 

Reflexivity in qualitative research

Reflexivity typically involves examining your own judgments, practices, and belief systems during data collection and analysis. It aims to identify any personal beliefs which may affect the research. 

Reflexivity is essential in qualitative research to ensure methodological transparency and complete reporting. This enables readers to understand how the interaction between the researcher and participant shapes the data.

Depending on the research question and population being researched, factors that need to be considered include the experience of the researcher, how the contact was established and maintained, age, gender, and ethnicity.

These details are important because, in qualitative research, the researcher is a dynamic part of the research process and actively influences the outcome of the research (Boeije, 2014). 

Reflexivity Example

Who you are and your characteristics influence how you collect and analyze data. Here is an example of a reflexivity statement for research on smoking. I am a 30-year-old white female from a middle-class background. I live in the southwest of England and have been educated to master’s level. I have been involved in two research projects on oral health. I have never smoked, but I have witnessed how smoking can cause ill health from my volunteering in a smoking cessation clinic. My research aspirations are to help to develop interventions to help smokers quit.

Establishing Trustworthiness in Qualitative Research

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability.

Credibility in Qualitative Research

Credibility refers to how accurately the results represent the reality and viewpoints of the participants.

To establish credibility in research, participants’ views and the researcher’s representation of their views need to align (Tobin & Begley, 2004).

To increase the credibility of findings, researchers may use data source triangulation, investigator triangulation, peer debriefing, or member checking (Lincoln & Guba, 1985). 

Transferability in Qualitative Research

Transferability refers to how generalizable the findings are: whether the findings may be applied to another context, setting, or group (Tobin & Begley, 2004).

Transferability can be enhanced by giving thorough and in-depth descriptions of the research setting, sample, and methods (Nowell et al., 2017). 

Dependability in Qualitative Research

Dependability is the extent to which the study could be replicated under similar conditions and the findings would be consistent.

Researchers can establish dependability using methods such as audit trails so readers can see the research process is logical and traceable (Koch, 1994).

Confirmability in Qualitative Research

Confirmability is concerned with establishing that there is a clear link between the researcher’s interpretations/ findings and the data.

Researchers can achieve confirmability by demonstrating how conclusions and interpretations were arrived at (Nowell et al., 2017).

This enables readers to understand the reasoning behind the decisions made. 

Audit Trails in Qualitative Research

An audit trail provides evidence of the decisions made by the researcher regarding theory, research design, and data collection, as well as the steps they have chosen to manage, analyze, and report data. 

The researcher must provide a clear rationale to demonstrate how conclusions were reached in their study.

A clear description of the research path must be provided to enable readers to trace through the researcher’s logic (Halpren, 1983).

Researchers should maintain records of the raw data, field notes, transcripts, and a reflective journal in order to provide a clear audit trail. 

Discovery of unexpected data

Open-ended questions in qualitative research mean the researcher can probe an interview topic and enable the participant to elaborate on responses in an unrestricted manner.

This allows unexpected data to emerge, which can lead to further research into that topic. 

The exploratory nature of qualitative research helps generate hypotheses that can be tested quantitatively (Busetto et al., 2020).


Data collection and analysis can be modified and adapted to take the research in a different direction if new ideas or patterns emerge in the data.

This enables researchers to investigate new opportunities while firmly maintaining their research goals. 

Naturalistic settings

The behaviors of participants are recorded in real-world settings. Studies that use real-world settings have high ecological validity since participants behave more authentically. 


Time-consuming .

Qualitative research results in large amounts of data which often need to be transcribed and analyzed manually.

Even when software is used, transcription can be inaccurate, and using software for analysis can result in many codes which need to be condensed into themes. 


The researcher has an integral role in collecting and interpreting qualitative data. Therefore, the conclusions reached are from their perspective and experience.

Consequently, interpretations of data from another researcher may vary greatly. 

Limited generalizability

The aim of qualitative research is to provide a detailed, contextualized understanding of an aspect of the human experience from a relatively small sample size.

Despite rigorous analysis procedures, conclusions drawn cannot be generalized to the wider population since data may be biased or unrepresentative.

Therefore, results are only applicable to a small group of the population. 

Extraneous variables

Qualitative research is often conducted in real-world settings. This may cause results to be unreliable since extraneous variables may affect the data, for example:

  • Situational variables : different environmental conditions may influence participants’ behavior in a study. The random variation in factors (such as noise or lighting) may be difficult to control in real-world settings.
  • Participant characteristics : this includes any characteristics that may influence how a participant answers/ behaves in a study. This may include a participant’s mood, gender, age, ethnicity, sexual identity, IQ, etc.
  • Experimenter effect : experimenter effect refers to how a researcher’s unintentional influence can change the outcome of a study. This occurs when (i) their interactions with participants unintentionally change participants’ behaviors or (ii) due to errors in observation, interpretation, or analysis. 

What sample size should qualitative research be?

The sample size for qualitative studies has been recommended to include a minimum of 12 participants to reach data saturation (Braun, 2013).

Are surveys qualitative or quantitative?

Surveys can be used to gather information from a sample qualitatively or quantitatively. Qualitative surveys use open-ended questions to gather detailed information from a large sample using free text responses.

The use of open-ended questions allows for unrestricted responses where participants use their own words, enabling the collection of more in-depth information than closed-ended questions.

In contrast, quantitative surveys consist of closed-ended questions with multiple-choice answer options. Quantitative surveys are ideal to gather a statistical representation of a population.

What are the ethical considerations of qualitative research?

Before conducting a study, you must think about any risks that could occur and take steps to prevent them. Participant Protection : Researchers must protect participants from physical and mental harm. This means you must not embarrass, frighten, offend, or harm participants. Transparency : Researchers are obligated to clearly communicate how they will collect, store, analyze, use, and share the data. Confidentiality : You need to consider how to maintain the confidentiality and anonymity of participants’ data.

What is triangulation in qualitative research?

Triangulation refers to the use of several approaches in a study to comprehensively understand phenomena. This method helps to increase the validity and credibility of research findings. 

Types of triangulation include method triangulation (using multiple methods to gather data); investigator triangulation (multiple researchers for collecting/ analyzing data), theory triangulation (comparing several theoretical perspectives to explain a phenomenon), and data source triangulation (using data from various times, locations, and people; Carter et al., 2014).

Why is qualitative research important?

Qualitative research allows researchers to describe and explain the social world. The exploratory nature of qualitative research helps to generate hypotheses that can then be tested quantitatively.

In qualitative research, participants are able to express their thoughts, experiences, and feelings without constraint.

Additionally, researchers are able to follow up on participants’ answers in real-time, generating valuable discussion around a topic. This enables researchers to gain a nuanced understanding of phenomena which is difficult to attain using quantitative methods.

What is coding data in qualitative research?

Coding data is a qualitative data analysis strategy in which a section of text is assigned with a label that describes its content.

These labels may be words or phrases which represent important (and recurring) patterns in the data.

This process enables researchers to identify related content across the dataset. Codes can then be used to group similar types of data to generate themes.

What is the difference between qualitative and quantitative research?

Qualitative research involves the collection and analysis of non-numerical data in order to understand experiences and meanings from the participant’s perspective.

This can provide rich, in-depth insights on complicated phenomena. Qualitative data may be collected using interviews, focus groups, or observations.

In contrast, quantitative research involves the collection and analysis of numerical data to measure the frequency, magnitude, or relationships of variables. This can provide objective and reliable evidence that can be generalized to the wider population.

Quantitative data may be collected using closed-ended questionnaires or experiments.

What is trustworthiness in qualitative research?

Trustworthiness is a concept used to assess the quality and rigor of qualitative research. Four criteria are used to assess a study’s trustworthiness: credibility, transferability, dependability, and confirmability. 

Credibility refers to how accurately the results represent the reality and viewpoints of the participants. Transferability refers to whether the findings may be applied to another context, setting, or group.

Dependability is the extent to which the findings are consistent and reliable. Confirmability refers to the objectivity of findings (not influenced by the bias or assumptions of researchers).

What is data saturation in qualitative research?

Data saturation is a methodological principle used to guide the sample size of a qualitative research study.

Data saturation is proposed as a necessary methodological component in qualitative research (Saunders et al., 2018) as it is a vital criterion for discontinuing data collection and/or analysis. 

The intention of data saturation is to find “no new data, no new themes, no new coding, and ability to replicate the study” (Guest et al., 2006). Therefore, enough data has been gathered to make conclusions.

Why is sampling in qualitative research important?

In quantitative research, large sample sizes are used to provide statistically significant quantitative estimates.

This is because quantitative research aims to provide generalizable conclusions that represent populations.

However, the aim of sampling in qualitative research is to gather data that will help the researcher understand the depth, complexity, variation, or context of a phenomenon. The small sample sizes in qualitative studies support the depth of case-oriented analysis.

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Shapson-Coe, A. et al. Science 384 , eadk4858 (2024).

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Home Market Research

What is Research: Definition, Methods, Types & Examples

What is Research

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

Content Index

What is Research?

What are the characteristics of research.

  • Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

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

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

Research is conducted with a purpose to:

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

What is the purpose of research?

There are three main purposes:

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

LEARN ABOUT: Descriptive Analysis

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

LEARN ABOUT: Best Data Collection Tools

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

Here is a comparative analysis chart for a better understanding:

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

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

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

Types of research methods and Examples

what is research

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

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

Types of qualitative methods include:

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

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

Types of quantitative methods include:

  • Survey research
  • Descriptive research
  • Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

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

It is essential to ensure that your data is:

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

Gather insights

What is a research - tips

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

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



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Research: Definition, Characteristics, Goals, Approaches

research definition

Research is an original and systematic investigation undertaken to increase existing knowledge and understanding of the unknown to establish facts and principles.

Let’s understand research:

What is Research?

Research is a voyage of discovery of new knowledge. It comprises creating ideas and generating new knowledge that leads to new and improved insights and the development of new materials, devices, products, and processes.

It should have the potential to produce sufficiently relevant results to increase and synthesize existing knowledge or correct and integrate previous knowledge.

Good reflective research produces theories and hypotheses and benefits any intellectual attempt to analyze facts and phenomena.

Where did the word Research Come from?

The word ‘research’ perhaps originates from the old French word “recerchier” which meant to ‘ search again.’ It implicitly assumes that the earlier search was not exhaustive and complete; hence, a repeated search is called for.

In practice, ‘research’ refers to a scientific process of generating an unexplored horizon of knowledge, aiming at discovering or establishing facts, solving a problem, and reaching a decision. Keeping the above points in view, we arrive at the following definition of research:

Research Definition

Research is a scientific approach to answering a research question, solving a research problem, or generating new knowledge through a systematic and orderly collection, organization, and analysis of data to make research findings useful in decision-making.

When do we call research scientific? Any research endeavor is said to be scientific if

  • It is based on empirical and measurable evidence subject to specific principles of reasoning;
  • It consists of systematic observations, measurement, and experimentation;
  • It relies on the application of scientific methods and harnessing of curiosity;
  • It provides scientific information and theories for the explanation of nature;
  • It makes practical applications possible, and
  • It ensures adequate analysis of data employing rigorous statistical techniques.

The chief characteristic that distinguishes the scientific method from other methods of acquiring knowledge is that scientists seek to let reality speak for itself, supporting a theory when a theory’s predictions are confirmed and challenging a theory when its predictions prove false.

Scientific research has multidimensional functions, characteristics, and objectives.

Keeping these issues in view, we assert that research in any field or discipline:

  • Attempts to solve a research problem;
  • Involves gathering new data from primary or first-hand sources or using existing data for a new purpose;
  • is based upon observable experiences or empirical evidence;
  • Demands accurate observation and description;
  • Employs carefully designed procedures and rigorous analysis;
  • attempts to find an objective, unbiased solution to the problem and takes great pains to validate the methods employed;
  • is a deliberate and unhurried activity that is directional but often refines the problem or questions as the research progresses.

Characteristics of Research

Keeping this in mind that research in any field of inquiry is undertaken to provide information to support decision-making in its respective area, we summarize some desirable characteristics of research:

  • The research should focus on priority problems.
  • The research should be systematic. It emphasizes that a researcher should employ a structured procedure.
  • The research should be logical. Without manipulating ideas logically, the scientific researcher cannot make much progress in any investigation.
  • The research should be reductive. This means that one researcher’s findings should be made available to other researchers to prevent them from repeating the same research.
  • The research should be replicable. This asserts that there should be scope to confirm previous research findings in a new environment and different settings with a new group of subjects or at a different point in time.
  • The research should be generative. This is one of the valuable characteristics of research because answering one question leads to generating many other new questions.
  • The research should be action-oriented. In other words, it should be aimed at solving to implement its findings.
  • The research should follow an integrated multidisciplinary approach, i.e., research approaches from more than one discipline are needed.
  • The research should be participatory, involving all parties concerned (from policymakers down to community members) at all stages of the study.
  • The research must be relatively simple, timely, and time-bound, employing a comparatively simple design.
  • The research must be as much cost-effective as possible.
  • The research results should be presented in formats most useful for administrators, decision-makers, business managers, or community members.

3 Basic Operations of Research

Scientific research in any field of inquiry involves three basic operations:

  • Data collection;
  • Data analysis;
  • Report writing .

3 basic operations of research

  • Data collection refers to observing, measuring, and recording data or information.
  • Data analysis, on the other hand, refers to arranging and organizing the collected data so that we may be able to find out what their significance is and generalize about them.
  • Report writing is the ultimate step of the study . Its purpose is to convey the information contained in it to the readers or audience.

If you note down, for example, the reading habit of newspapers of a group of residents in a community, that would be your data collection.

If you then divide these residents into three categories, ‘regular,’ ‘occasional,’ and ‘never,’ you have performed a simple data analysis. Your findings may now be presented in a report form.

A reader of your report knows what percentage of the community people never read any newspaper and so on.

Here are some examples that demonstrate what research is:

  • A farmer is planting two varieties of jute side by side to compare yields;
  • A sociologist examines the causes and consequences of divorce;
  • An economist is looking at the interdependence of inflation and foreign direct investment;
  • A physician is experimenting with the effects of multiple uses of disposable insulin syringes in a hospital;
  • A business enterprise is examining the effects of advertisement of their products on the volume of sales;
  • An economist is doing a cost-benefit analysis of reducing the sales tax on essential commodities;
  • The Bangladesh Bank is closely observing and monitoring the performance of nationalized and private banks;
  • Based on some prior information, Bank Management plans to open new counters for female customers.
  • Supermarket Management is assessing the satisfaction level of the customers with their products.

The above examples are all researching whether the instrument is an electronic microscope, hospital records, a microcomputer, a questionnaire, or a checklist.

Research Motivation – What makes one motivated to do research?

A person may be motivated to undertake research activities because

  • He might have genuine interest and curiosity in the existing body of knowledge and understanding of the problem;
  • He is looking for answers to questions that have remained unanswered so far and trying to unfold the truth;
  • The existing tools and techniques are accessible to him, and others may need modification and change to suit the current needs.

One might research ensuring.

  • Better livelihood;
  • Better career development;
  • Higher position, prestige, and dignity in society;
  • Academic achievement leading to higher degrees;
  • Self-gratification.

At the individual level, the results of the research are used by many:

  • A villager is drinking water from an arsenic-free tube well;
  • A rural woman is giving more green vegetables to her child than before;
  • A cigarette smoker is actively considering quitting smoking;
  • An old man is jogging for cardiovascular fitness;
  • A sociologist is using newly suggested tools and techniques in poverty measurement.

The above activities are all outcomes of the research.

All involved in the above processes will benefit from the research results. There is hardly any action in everyday life that does not depend upon previous research.

Research in any field of inquiry provides us with the knowledge and skills to solve problems and meet the challenges of a fast-paced decision-making environment.

9 Qualities of Research

Good research generates dependable data. It is conducted by professionals and can be used reliably for decision-making. It is thus of crucial importance that research should be made acceptable to the audience for which research should possess some desirable qualities in terms of.

9 qualities of research are;

Purpose clearly defined

Research process detailed, research design planner, ethical issues considered, limitations revealed, adequate analysis ensured, findings unambiguously presented, conclusions and recommendations justified..

We enumerate below a few qualities that good research should possess.

Good research must have its purposes clearly and unambiguously defined.

The problem involved or the decision to be made should be sharply delineated as clearly as possible to demonstrate the credibility of the research.

The research procedures should be described in sufficient detail to permit other researchers to repeat the research later.

Failure to do so makes it difficult or impossible to estimate the validity and reliability of the results. This weakens the confidence of the readers.

Any recommendations from such research justifiably get little attention from the policymakers and implementation.

The procedural design of the research should be carefully planned to yield results that are as objective as possible.

In doing so, care must be taken so that the sample’s representativeness is ensured, relevant literature has been thoroughly searched, experimental controls, whenever necessary, have been followed, and the personal bias in selecting and recording data has been minimized.

A research design should always safeguard against causing mental and physical harm not only to the participants but also those who belong to their organizations.

Careful consideration must also be given to research situations when there is a possibility for exploitation, invasion of privacy, and loss of dignity of all those involved in the study.

The researcher should report with complete honesty and frankness any flaws in procedural design; he followed and provided estimates of their effects on the findings.

This enhances the readers’ confidence and makes the report acceptable to the audience. One can legitimately question the value of research where no limitations are reported.

Adequate analysis reveals the significance of the data and helps the researcher to check the reliability and validity of his estimates.

Data should, therefore, be analyzed with proper statistical rigor to assist the researcher in reaching firm conclusions.

When statistical methods have been employed, the probability of error should be estimated, and criteria of statistical significance applied.

The presentation of the results should be comprehensive, easily understood by the readers, and organized so that the readers can readily locate the critical and central findings.

Proper research always specifies the conditions under which the research conclusions seem valid.

Therefore, it is important that any conclusions drawn and recommendations made should be solely based on the findings of the study.

No inferences or generalizations should be made beyond the data. If this were not followed, the objectivity of the research would tend to decrease, resulting in confidence in the findings.

The researcher’s experiences were reflected.

The research report should contain information about the qualifications of the researchers .

If the researcher is experienced, has a good reputation in research, and is a person of integrity, his report is likely to be highly valued. The policymakers feel confident in implementing the recommendations made in such reports.

4 Goals of Research

goals of research

The primary goal or purpose of research in any field of inquiry; is to add to what is known about the phenomenon under investigation by applying scientific methods. Though each research has its own specific goals, we may enumerate the following 4 broad goals of scientific research:

Exploration and Explorative Research

Description and descriptive research, causal explanation and causal research, prediction and predictive research.

The link between the 4 goals of research and the questions raised in reaching these goals.

Let’s try to understand the 4 goals of the research.

Exploration is finding out about some previously unexamined phenomenon. In other words, an explorative study structures and identifies new problems.

The explorative study aims to gain familiarity with a phenomenon or gain new insights into it.

Exploration is particularly useful when researchers lack a clear idea of the problems they meet during their study.

Through exploration, researchers attempt to

  • Develop concepts more clearly;
  • Establish priorities among several alternatives;
  • Develop operational definitions of variables;
  • Formulate research hypotheses and sharpen research objectives;
  • Improve the methodology and modify (if needed) the research design .

Exploration is achieved through what we call exploratory research.

The end of an explorative study comes when the researchers are convinced that they have established the major dimensions of the research task.

Many research activities consist of gathering information on some topic of interest. The description refers to these data-based information-gathering activities. Descriptive studies portray precisely the characteristics of a particular individual, situation, or group.

Here, we attempt to describe situations and events through studies, which we refer to as descriptive research.

Such research is undertaken when much is known about the problem under investigation.

Descriptive studies try to discover answers to the questions of who, what, when, where, and sometimes how.

Such research studies may involve the collection of data and the creation of distribution of the number of times the researcher observes a single event or characteristic, known as a research variable.

A descriptive study may also involve the interaction of two or more variables and attempts to observe if there is any relationship between the variables under investigation .

Research that examines such a relationship is sometimes called a correlational study. It is correlational because it attempts to relate (i.e., co-relate) two or more variables.

A descriptive study may be feasible to answer the questions of the following types:

  • What are the characteristics of the people who are involved in city crime? Are they young? Middle-aged? Poor? Muslim? Educated?
  • Who are the potential buyers of the new product? Men or women? Urban people or rural people?
  • Are rural women more likely to marry earlier than their urban counterparts?
  • Does previous experience help an employee to get a higher initial salary?

Although the data description in descriptive research is factual, accurate, and systematic, the research cannot describe what caused a situation.

Thus, descriptive research cannot be used to create a causal relationship where one variable affects another.

In other words, descriptive research can be said to have a low requirement for internal validity. In sum, descriptive research deals with everything that can be counted and studied.

But there are always restrictions on that. All research must impact the lives of the people around us.

For example, finding the most frequent disease that affects the people of a community falls under descriptive research.

But the research readers will have the hunch to know why this has happened and what to do to prevent that disease so that more people will live healthy lives.

It dictates that we need a causal explanation of the situation under reference and a causal study vis-a-vis causal research .

Explanation reveals why and how something happens.

An explanatory study goes beyond description and attempts to establish a cause-and-effect relationship between variables. It explains the reason for the phenomenon that the descriptive study observed.

Thus, if a researcher finds that communities with larger family sizes have higher child deaths or that smoking correlates with lung cancer, he is performing a descriptive study.

If he explains why it is so and tries to establish a cause-and-effect relationship, he is performing explanatory or causal research . The researcher uses theories or at-least hypotheses to account for the factors that caused a certain phenomenon.

Look at the following examples that fit causal studies:

  • Why are people involved in crime? Can we explain this as a consequence of the present job market crisis or lack of parental care?
  • Will the buyers be motivated to purchase the new product in a new container ? Can an attractive advertisement motivate them to buy a new product?
  • Why has the share market shown the steepest-ever fall in stock prices? Is it because of the IMF’s warnings and prescriptions on the commercial banks’ exposure to the stock market or because of an abundant increase in the supply of new shares?

Prediction seeks to answer when and in what situations will occur if we can provide a plausible explanation for the event in question.

However, the precise nature of the relationship between explanation and prediction has been a subject of debate.

One view is that explanation and prediction are the same phenomena, except that prediction precedes the event while the explanation takes place after the event has occurred.

Another view is that explanation and prediction are fundamentally different processes.

We need not be concerned with this debate here but can simply state that in addition to being able to explain an event after it has occurred, we would also be able to predict when it will occur.

Research Approaches

4 research approaches

There are two main approaches to doing research.

The first is the basic approach, which mostly pertains to academic research. Many people view this as pure research or fundamental research.

The research implemented through the second approach is variously known as applied research, action research, operations research, or contract research.

Also, the third category of research, evaluative research, is important in many applications. All these approaches have different purposes influencing the nature of the respective research.

Lastly, precautions in research are required for thorough research.

So, 4 research approaches are;

  • Basic Research .
  • Applied Research .
  • Evaluative Research .
  • Precautions in Research.

Areas of Research

The most important fields or areas of research, among others, are;

  • Social Research .
  • Health Research .
  • Population Research .
  • Business Research .
  • Marketing Research .
  • Agricultural Research .
  • Biomedical Research.
  • Clinical Research .
  • Outcomes Research.
  • Internet Research.
  • Archival Research.
  • Empirical Research.
  • Legal Research .
  • Education Research .
  • Engineering Research .
  • Historical Research.

Check out our article describing all 16 areas of research .

Precautions in Research

Whether a researcher is doing applied or basic research or research of any other form, he or she must take necessary precautions to ensure that the research he or she is doing is relevant, timely, efficient, accurate, and ethical .

The research is considered relevant if it anticipates the kinds of information that decision-makers, scientists, or policymakers will require.

Timely research is completed in time to influence decisions.

  • Research is efficient when it is of the best quality for the minimum expenditure and the study is appropriate to the research context.
  • Research is considered accurate or valid when the interpretation can account for both consistencies and inconsistencies in the data.
  • Research is ethical when it can promote trust, exercise care, ensure standards, and protect the rights of the participants in the research process.

What is the definition of research?

What are the characteristics of good research, what are the three basic operations involved in scientific research, what are the four broad goals of scientific research, what distinguishes the scientific method from other methods of acquiring knowledge, what is the origin of the word ‘research’, how is “research methodology” defined, how does research methodology ensure the appropriateness of a research method.

After discussing the research definition and knowing the characteristics, goals, and approaches, it’s time to delve into the research fundamentals. For a comprehensive understanding, refer to our detailed research and methodology concepts guide .

Research should be relevant, timely, efficient, accurate, and ethical. It should anticipate the information required by decision-makers, be completed in time to influence decisions, be of the best quality for the minimum expenditure, and protect the rights of participants in the research process.

The two main approaches to research are the basic approach, often viewed as pure or fundamental research, and the applied approach, which includes action research, operations research, and contract research.

30 Accounting Research Paper Topics and Ideas for Writing


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Station science 101 | research in microgravity: higher, faster, longer.

NASA astronaut Megan McArthur services donor cells inside the Kibo laboratory module’s Life Science Glovebox for the Celestial Immunity study.

Melissa L. Gaskill

Microgravity, a unique orbit, crewed laboratory, twenty years and counting, adding subjects adds time.

The International Space Station provides unique features that enable innovative research, including microgravity, exposure to space, a unique orbit, and hands-on operation by crew members.

The space station provides consistent, long-term access to microgravity. Eliminating the effects of Earth’s gravity on experiments is a game-changer across many disciplines, including research on living things and physical and chemical processes. For example, without gravity hot air does not rise, so flames become spherical and behave differently. Removing the forces of surface tension and capillary movement allows scientists to examine fluid behavior more closely.

A spherical orange flame surrounds the round tip of the ignition tube, pointed toward the right of the image, and streams to the left. There are brighter spots on the rounded end of the flame.

The speed, pattern, and altitude of the space station’s orbit provide unique advantages. Traveling at 17,500 miles per hour, it circles the planet every 90 minutes, passing over a majority of Earth’s landmass and population centers in daylight and darkness. Its 250-mile-high altitude is low enough for detailed observation of features, atmospheric phenomena, and natural disasters from different angles and with varying lighting conditions. At the same time, the station is high enough to study how space radiation affects material durability and how organisms adapt and examine phenomena such as neutron stars and blackholes. The spacecraft also places observing instruments outside Earth’s atmosphere and magnetic field, which can interfere with observations from the ground.

The Kibo module is a large silver horizontal cylinder on the right, with a shorter upright cylinder on its top. Extending from the module’s left end is a platform with multiple attachments, including large white rectangular instruments in front and on the end, a large upright tower with a shiny box on top, and several satellite disks. A remote manipulator arm extends from the module to one of the instruments on top of the platform. The cloud-covered Earth is visible in the background.

Other satellites in orbit contain scientific experiments and conduct Earth observations, but the space station also has crew members aboard to manage and maintain scientific activities. Human operators can respond to and assess events in real time, swap out experiment samples, troubleshoot, and observe results first-hand. Crew members also pack experiment samples and send them back to the ground for detailed analysis.

Vande Hei is on the left side of the image, wearing a black short-sleeved t-shirt, glasses, and a headlamp. He has his left hand on the base of a large microscope with a sample plate visible under the large lens on the top. The walls around him are covered with cables, hoses, switches, storage boxes, and lighted screens.

Thanks to the space station’s longevity, experiments can continue for months or even years. Scientists can design follow-up studies based on previous results, and every expedition offers the chance to expand the number of subjects for human research.

One area of long-term human research is on changes in vision, first observed when astronauts began spending months at a time in space. Scientists wondered whether fluids shifting from the lower to the upper body in microgravity caused increased pressure inside the head that changed eye shape. The Fluid Shifts investigation began in 2015 and continued to measure the extent of fluid shifts in multiple astronauts through 2020. 1

Whether the original study is long or short, it can take years for research to go from the lab into practical applications. Many steps are involved, some of them lengthy. First, researchers must come up with a question and a possible answer, or hypothesis. For example, Fluid Shifts questioned what was causing vision changes and a possible answer was increased fluid pressure in the head. Scientists must then design an experiment to test the hypothesis, determining what data to collect and how to do so.

astronaut Nick Hague collecting intraocular pressure measurements

Getting research onto the space station in the first place takes time, too. NASA reviews proposals for scientific merit and relevance to the agency’s goals. Selected investigations are assigned to a mission, typically months in the future. NASA works with investigators to meet their science requirements, obtain approvals, schedule crew training, develop flight procedures, launch hardware and supplies, and collect any preflight data needed. Once the study launches, in-flight data collection begins. When scientists complete their data collection, they need time to analyze the data and determine what it means. This may take a year or more.

Scientists then write a paper about the results – which can take many months – and submit it to a scientific journal. Journals send the paper to other experts in the same field, a process known as peer review. According to one analysis, this review takes an average of 100 days. 2 The editors may request additional analysis and revisions based on this review before publishing.

Aspects of research on the space station can add more time to the process. Generally, the more test subjects, the better – from 100 to 1,000 subjects for statistically significant results for clinical research. But the space station typically only houses about six people at a time.

Lighting Effects shows how the need for more subjects adds time to a study. This investigation examined whether adjusting the intensity and color of lighting inside the station could help improve crew circadian rhythms, sleep, and cognitive performance. To collect data from enough crew members, the study ran from 2016 until 2020.

Other lengthy studies about how humans adapt to life in space include research on loss of heart muscle and a suite of long-term studies on nutrition, including producing fresh food in space.

Watkins, smiling at the camera, wears a gray sweatshirt, black pants, and black gloves. She floats in space, holding on to a wall with her right hand and gripping an ice-covered black box about the size of a box of tissues. There is a laptop Velcroed to her leg and a lot of cables over her head.

For physical science studies, investigators can send batches of samples to the space station and collect data more quickly, but results can create a need for additional research. Burning and Suppression of Solids ( BASS ) examined the characteristics of a wide variety of fuel samples from 2011 to 2013, and BASS-II continued that work through 2017. The Saffire series of fire safety demonstrations began in 2016 and wrapped up in 2024. Researchers have answered many burning (pun intended) questions, but still have much to learn about preventing, detecting, and extinguishing fires in space.

From left to right, this image has a band of black from top to bottom, a scattering of bright white specks of smoldering cotton like snowflakes, a band of orange flame, a small region of black where the cotton is beginning to char, and a wide band of unburned composite material that appears green because the sample is illuminated with green LED lights.

The timeline for scientific results can run long, especially in microgravity. But those results can be well worth the wait.

Melissa Gaskill International Space Station Research Communications Team Johnson Space Center

Search this database of scientific experiments to learn more about those mentioned above.

1 Macias BR, Liu JHK, Grande-Gutierrez N, Hargens AR. Intraocular and intracranial pressures during head-down tilt with lower body negative pressure. Aerosp Med Hum Perform. 2015; 86(1):3–7.;jsessionid=31bonpcj2e8tj.x-ic-live-01

2 Powell K. Does it take too long to publish research? Nature 530, pages148–151 (2016).

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Purdue researchers experiment with harvesting static electricity as an energy source

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WEST LAFAYETTE, Ind. — Researchers in Purdue’s College of Engineering have developed an apparatus to generate and measure gas breakdown during contact electrification — the process responsible for static electricity. Their findings, published in Nature Communications , offer insight into the potential and limitations of this type of electricity when harvested as an energy source for a variety of devices, including e-textiles, wearables and smart packaging.

The apparatus, which is housed in an acrylic vacuum chamber, can isolate the potential materials used to make these devices and test their charge transfer capabilities without the use of an external voltage source. This creates the potential to improve the performance of these devices, as the apparatus measures the voltage generated between different pairs of materials.

The apparatus was designed and 3D printed by postdoctoral researcher Hongcheng Tao and associate professor of mechanical engineering James Gibert using additive manufacturing equipment at Ray W. Herrick Laboratories at Purdue University. Gibert has spent much of his career developing novel ways to generate electricity in unique situations. He has experimented with carboard boxes that power their own internal sensors, multifunction composite materials, and a tractor-trailer that harvests electricity from its own vibrations .

Tao and Gibert currently study the mechanisms behind triboelectric devices, which convert mechanical energy into electricity by rubbing two surfaces together. The surfaces can be either conductors or insulators, and they must be capable of reaching a high enough voltage to generate a sufficient electric charge without being damaged.

Triboelectric devices are still relatively in their infancy, with a number of researchers looking to further explore their potential. According to Gibert, there’s still a lot to learn about the physics behind triboelectrics.

“Typically, before you design one of these devices, you have to understand the application, including materials that are suitable for use and the power ranges required,” Gibert said. “So, our job is to investigate what can be achieved and what pairs of materials will give you the best voltage and subsequent current flow through a device.”

Quantifying that electric current between these materials can be difficult because, unlike similar apparatuses that use electrodes to demonstrate charge transfer, there is no external source generating a consistent, known voltage. So, Tao and Gibert had to find a workaround with their device that allowed them to calculate the voltage that is generated as the two materials make contact.

Their test apparatus consists of a small loading frame with motors that press and rub two sample materials together. The vacuum chamber removes all the air surrounding the device, and a single gas, such as nitrogen, is pumped into the chamber. As the samples make contact, they build up opposing charges. Then, when the samples are separated, the gas in the space between them breaks down and generates tiny sparks.

Tao and Gibert’s device measures that gas breakdown, or charge transfer, using a sensor that detects the Coulomb force — the attraction between the two charged objects. This measurement allows them to calculate the corresponding surface charge density that creates the attraction. From there, they can measure the charge transfer between the two samples.

The principle that governs this phenomenon, Paschen’s law, has been experimentally validated, but only between conductors. Tao and Gibert built their test apparatus to conduct the same experiment but with insulators.

“We have been assuming that Paschen’s law is valid when modeling gas breakdown in contact electrification, but there actually have not been sufficient experimental results to prove this,” Tao said. “So, we 3D printed every component to reconstruct an apparatus that was first implemented 30 years ago. All of the parts are flexible and customizable so we can ensure that all the samples align and the entire surface area is charged by contact.”

So far, Tao and Gibert have tested two combinations of materials: silicone-acrylic and copper-nylon, with the silicone-acrylic showing particular promise in generating a sufficient charge. When they perform these tests, they vary the pressure when the two samples make contact and the gap between them once they are separated. As a result of this process, they discovered some interesting phenomena.

“We found some regions where Paschen’s law didn’t hold,” Gibert said. “There are some regions where you have these discharges that can’t be explained by Paschen’s law. That’s an interesting finding that we’re hoping to look at in the future.”

So, while contributing to the development of triboelectric devices is the main objective for this research, Tao and Gibert are also excited about the basic science behind it all.

“The design of the device is largely an engineering challenge, but what’s really exciting is how the outcomes contribute to fundamental scientific knowledge. That’s the aspect we like most about this project,” Tao said. “I think it is very encouraging that a team of two people from an engineering department can still contribute to fundamental science research with a comparatively limited budget.”

This research was funded by an award from the National Science Foundation Division of Civil, Mechanical and Manufacturing Innovation (CMMI). CMMI funds innovative research that advances technologies related to manufacturing, materials and infrastructure.

About Purdue University Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and locations, including nearly 50,000 in person on the West Lafayette campus. Committed to affordability and accessibility, Purdue’s main campus has frozen tuition 13 years in a row. See how Purdue never stops in the persistent pursuit of the next giant leap — including its first comprehensive urban campus in Indianapolis, the new Mitchell E. Daniels, Jr. School of Business, and Purdue Computes — at .

Writer/Media contact: Lindsey Macdonald, [email protected]

Sources: James Gibert, [email protected]

Hongcheng Tao, [email protected]


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Research: What Companies Don’t Know About How Workers Use AI

  • Jeremie Brecheisen

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Three Gallup studies shed light on when and why AI is being used at work — and how employees and customers really feel about it.

Leaders who are exploring how AI might fit into their business operations must not only navigate a vast and ever-changing landscape of tools, but they must also facilitate a significant cultural shift within their organizations. But research shows that leaders do not fully understand their employees’ use of, and readiness for, AI. In addition, a significant number of Americans do not trust business’ use of AI. This article offers three recommendations for leaders to find the right balance of control and trust around AI, including measuring how their employees currently use AI, cultivating trust by empowering managers, and adopting a purpose-led AI strategy that is driven by the company’s purpose instead of a rules-heavy strategy that is driven by fear.

If you’re a leader who wants to shift your workforce toward using AI, you need to do more than manage the implementation of new technologies. You need to initiate a profound cultural shift. At the heart of this cultural shift is trust. Whether the use case for AI is brief and experimental or sweeping and significant, a level of trust must exist between leaders and employees for the initiative to have any hope of success.

  • Jeremie Brecheisen is a partner and managing director of The Gallup CHRO Roundtable.

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Can pink noise enhance sleep and memory? Early research drives a color noise buzz

White noise is frequently used to mask background sounds and it now has competition. There’s a growing buzz around pink, brown and green noise and their theoretical effects on sleep and concentration. (AP Video: Laura Bargfeld; production: Shelby Lum)

CAPTION CORRECTS TITLE Dr. Roneil Malkani shows an example of pink noise being used to enhance slow brainwaves during deep sleep at the Center for Circadian & Sleep Medicine at Northwestern Medicine in Chicago on May 16, 2024. Pink noise has a frequency profile “very similar to the distribution of brain wave frequencies we see in slow-wave sleep because these are large, slow waves,” said Malkani, associate professor of neurology at Northwestern University Feinberg School of Medicine. (AP Photo/Laura Bargfeld)

CAPTION CORRECTS TITLE Dr. Roneil Malkani shows an example of pink noise being used to enhance slow brainwaves during deep sleep at the Center for Circadian & Sleep Medicine at Northwestern Medicine in Chicago on May 16, 2024. Pink noise has a frequency profile “very similar to the distribution of brain wave frequencies we see in slow-wave sleep because these are large, slow waves,” said Malkani, associate professor of neurology at Northwestern University Feinberg School of Medicine. (AP Photo/Laura Bargfeld)

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CAPTION CORRECTS TITLE Dr. Roneil Malkani points to a recording of pink noise being played at brief intervals to enhance slow brain waves during deep sleep at the Center for Circadian & Sleep Medicine at Northwestern Medicine in Chicago on May 16, 2024. Pink noise has a frequency profile “very similar to the distribution of brain wave frequencies we see in slow-wave sleep because these are large, slow waves,” said Malkani, associate professor of neurology at Northwestern University Feinberg School of Medicine. (AP Photo/Laura Bargfeld)

CAPTION CORRECTS TITLE Dr. Roneil Malkani demonstrates the set up for a sleep study at the Center for Circadian & Sleep Medicine at Northwestern Medicine in Chicago on May 16, 2024. Pink noise has a frequency profile “very similar to the distribution of brain wave frequencies we see in slow-wave sleep because these are large, slow waves,” said Malkani, associate professor of neurology at Northwestern University Feinberg School of Medicine. (AP Photo/Laura Bargfeld)

You may have heard of white noise used to mask background sounds. Now, it has colorful competition.

There’s a growing buzz around pink noise, brown noise, green noise — a rainbow of soothing sounds — and their theoretical effects on sleep, concentration and the relaxation response.

The science is new with only a few small studies behind it, but that hasn’t stopped thousands of people from listening to hours of these noises on YouTube and on meditation apps that provide a palette of color noises with paid subscriptions.


To understand pink noise, start with white, the most familiar of the color noises.

White noise is similar to static on a radio or TV. Sound engineers define it as having equal volume across all the frequencies audible to the human ear. It gets its name from white light, which contains all the visible color wavelengths.

But the high frequencies of white noise can sound harsh. Pink noise turns down the volume on those higher frequencies, so it sounds lower in pitch and more like the natural sound of rain or the ocean.

Brown noise sounds even lower in pitch, giving it a pleasing, soothing rumble.

FILE - A woman meditates on the beach in Miami Beach, Fla., on April 28, 2010. Research shows a daily meditation practice can reduce anxiety, improve overall health and increase social connections, among other benefits. (AP Photo/Lynne Sladky, File)

Pink and brown, like white, have standard definitions to audio experts. Other color noises are more recent creations with very flexible definitions.


White noise and pink noise may provide small benefits for people with attention-deficit/hyperactivity disorder, according to a recent review of limited ADHD studies . In theory, it wakes up the brain, said ADHD researcher and co-author Joel Nigg of Oregon Health & Science University in Portland.

“The noise provides stimulation to the brain without providing information, and so it doesn’t distract,” Nigg said.

White noise has been used to treat ringing or buzzing in the ear, called tinnitus.

Scientists at Northwestern University are studying how short pulses of pink noise can enhance the slow brain waves of deep sleep. In small studies, these pink-noise pulses have shown promise in improving memory and the relaxation response.

Pink noise has a frequency profile “very similar to the distribution of brain wave frequencies we see in slow-wave sleep because these are large, slow waves,” said Dr. Roneil Malkani, associate professor of neurology at Northwestern University Feinberg School of Medicine.

If Northwestern’s research pans out, it could lead to a medical device to improve sleep or memory through personalized pulses of pink noise. But many scientific questions remain unanswered, Malkani said. “There’s still a lot of work we have to do.”


If color noises feel calming and help you drown out distractions, it makes sense to use them. Keep them at a quiet level, of course, to prevent hearing loss and take “plenty of breaks for the ears to rest,” Nigg said.

The Associated Press Health and Science Department receives support from the Howard Hughes Medical Institute’s Science and Educational Media Group. The AP is solely responsible for all content.

research features

Items used to test blood glucose levels on a picnic table

Diabetes, heart problems and kidney disease are closely linked

Uc expert discusses prevention in new york times article.

headshot of Tim Tedeschi

Heart disease, diabetes and kidney disease, among the most common chronic illnesses in the United States, are all closely connected.

People with diabetes are twice as likely to have heart disease or a stroke and are at risk of developing kidney disease, while heart disease is more likely for those with kidney problems as the heart works harder to pump blood to the kidneys.

The New York Times reports people should pay attention to shared risk factors for these illnesses, including excess body fat, uncontrolled blood sugar, high blood pressure and high cholesterol.

The University of Cincinnati's Estrelita Dixon, MD, commented in the New York Times article on the importance of prevention.

Preventive measures can include adding more fiber, fruit and vegetables to your diet to regulate blood sugar and lower blood pressure and increasing muscle mass through strength training to help with insulin resistance. Just moving in general can be beneficial, and experts recommend aiming for 150 minutes of exercise each week, but Dixon noted gradual steps can still make a difference.

“Don’t think in terms of all or nothing,” said Dixon, division chief and associate professor in the Department of Internal Medicine in UC's College of Medicine.

R ead the New York Times story. (Note: Subscription may be required to access full article.)

Featured image at top of diabetes testing supplies. Photo/David Moruzzi/Unsplash.

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Digital articles are a fantastic way to provide a brief overview of a paper or study. Our editorial team will help to distil these materials down into a 1000-word article, complete with references, illustrations, DOI number and Search Engine Optimisation which will help guide internet users to it.

These articles are great to use on personal blogs, as communications documents, to introduce work to audiences who may not have the required skill set or time to read a peer-reviewed paper and as handouts at conferences.

Social media dissemination and reporting is not included but can be added on for an extra £380.

  • Researcher Blog

research features

Our Researcher Blog is currently undergoing a refresh. We are not accepting submissions at this time.

We understand that not every researcher will have a budget for our services, but we are still dedicated to providing a platform for outreach and dissemination for the research community.

From our many discussions with academics we have realised that many of them don’t have the skillset, time or desire to run and maintain their blog, but equally need a platform they can use to discuss their work.

As a result, we have created the Researcher Blog – a place where researchers can upload their own articles to our website, these webpages can be distributed as they want and there is absolutely no charge involved.

We believe there should be no barriers to conducting effective science communication and by doing this we believe we have removed as many as possible.

For this content, we do ask that the article is written to be based on published materials which are referenced within the article.

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For commercial pricing enquiries, please feel free to contact us .

Sample Impact Analysis

research features

How is your money spent on the article

Please note that the below breakdown is based on a standard offering and assumes the average size of publication, publishing 30 articles per month. It is possible that the exact breakdown may vary slightly from project to project and the below is intended as a guide only.

research features


  1. Qualitative Research: Definition, Types, Methods and Examples (2022)

    research features

  2. The Research Process

    research features

  3. essential characteristics of research-requirements of a good research

    research features

  4. The Ultimate Guide on Academic Sources for Research Papers

    research features

  5. What is Research

    research features

  6. What is Research Design in Qualitative Research

    research features


  1. Sociology- social research features and its importance


  3. Delv AI

  4. Research: Meaning I Purpose I Nature I Types I Scientific Research/process/paradigm/ethical concern

  5. Metho 4: Good Research Qualities / Research Process / Research Methods Vs Research Methodology

  6. Testis Cancer Roundtable


  1. Is a request to publish an article in Research Features a scam?

    Research Features is a UK-based magazine that offers to rewrite and promote scientific research for a fee. Users share their experiences and opinions on whether it is a legitimate or predatory service.

  2. Research Features Magazine

    Research Features is a digital magazine that showcases research stories from various fields and disciplines. You can browse and view the latest editions for free, without entering any personal details.

  3. Research Features

    Research Features distils academic work into engaging content, truly connecting science with society. Subscribe to their newsletter and get access to their latest publications on various topics, such as carcinisation, business groups, and image restoration.

  4. Research Features Magazine

    Research Features is a digital magazine that bridges the gap between researchers and readers, and delivers knowledge in a clear and visual format. It offers free access to government funded research, simplifies complex topics, and engages in social media campaigns to achieve broad visibility and impact.

  5. Research Features

    The team behind Research Features have one simple goal - to make complex, basic and applied research beautifully accessible to huge global audiences. Our mission is to break down the barriers ...

  6. Research Features

    Research Features is a company that publishes and shares scientific research in various fields and formats. It aims to make complex science accessible to non-specialists and to celebrate the pioneers driving the latest discoveries and innovations.

  7. ResearchFeatures

    Research Features is a magazine that covers complex science topics and showcases the stories and achievements of researchers. Read about diabetes, cognition, neuroscience, cancer, prostate, and more.

  8. Research Features

    The team behind Research Features Magazine have one simple goal - To make complex basic and applied research beautifully accessible to huge global audiences. Our mission is to break down the barriers between complex scientific text and literature, and those non-specialists who wish to find out more.<br><br>We will never hide our content behind restrictive pay-walls or subscriptions. We will ...

  9. NASA GISS: Research Features: Exploring the Climates of Earth's Future

    Research Features Exploring the Climates of Earth's Future Supercontinent with a NASA Supercomputer. By Jarrett Cohen, NASA Center for Climate Simulations — November 30, 2021 Scientists from the NASA Goddard Institute for Space Studies (GISS), the University of Lisbon (Portugal), and Bangor University (United Kingdom) leveraged a NASA supercomputer to explore possible scenarios for Earth ...

  10. What Is Research, and Why Do People Do It?

    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 ...

  11. Research Features

    Research Features, Stonehouse, Gloucestershire. 78,517 likes · 200 talking about this · 50 were here. Research Features - Making complex science beautifully accessible.

  12. What Is Qualitative Research?

    Learn what qualitative research is, how it differs from quantitative research, and what methods and approaches are used to collect and analyze non-numerical data. Find out the advantages and disadvantages of qualitative research and see examples of research questions and data.

  13. What is Scientific Research and How Can it be Done?

    Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new ...

  14. Characteristics of Qualitative Research

    Qualitative research is a method of inquiry used in various disciplines, including social sciences, education, and health, to explore and understand human behavior, experiences, and social phenomena. It focuses on collecting non-numerical data, such as words, images, or objects, to gain in-depth insights into people's thoughts, feelings, motivations, and perspectives.

  15. Research Features Magazine

    Explore the latest discoveries in neuroscience, genetics and obesity from the researchers featured in Research Features Magazine - 100. Learn how they use cutting-edge techniques and tools to advance our understanding of the brain, cancer and health.

  16. What is Research? Definition, Types, Methods and Process

    Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. Learn more about types of research, processes, and methods with best practices. ... These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress ...

  17. Planning Qualitative Research: Design and Decision Making for New

    While many books and articles guide various qualitative research methods and analyses, there is currently no concise resource that explains and differentiates among the most common qualitative approaches. We believe novice qualitative researchers, students planning the design of a qualitative study or taking an introductory qualitative research course, and faculty teaching such courses can ...

  18. Cubic millimetre of brain mapped in spectacular detail

    The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. It ...

  19. What is Research

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

  20. Research: Definition, Characteristics, Goals, Approaches

    Characteristics of Research. Keeping this in mind that research in any field of inquiry is undertaken to provide information to support decision-making in its respective area, we summarize some desirable characteristics of research: The research should focus on priority problems. The research should be systematic.

  21. Research in Microgravity: Higher, Faster, Longer

    The International Space Station provides unique features that enable innovative research, including microgravity, exposure to space, a unique orbit, and hands-on operation by crew members. Microgravity. The space station provides consistent, long-term access to microgravity. Eliminating the effects of Earth's gravity on experiments is a game ...

  22. Purdue researchers experiment with harvesting static electricity as an

    Purdue University is a public research institution demonstrating excellence at scale. Ranked among top 10 public universities and with two colleges in the top four in the United States, Purdue discovers and disseminates knowledge with a quality and at a scale second to none. More than 105,000 students study at Purdue across modalities and ...

  23. Our Business Model

    Research Features is a publisher that provides free and accessible publications on the latest scientific research from around the world. It works with researchers to create articles that are understandable by a lay audience, and distributes them via digital and print channels.

  24. Research: What Companies Don't Know About How Workers Use AI

    Three Gallup studies shed light on when and why AI is being used at work — and how employees and customers really feel about it. Leaders who are exploring how AI might fit into their business ...

  25. Global Flash Droughts Characteristics: Onset, Duration, and Extent at

    These characteristics significantly impact various aspects of the hydrological cycle, influencing root zone soil moisture, subsurface recharge, and flow patterns. The variability in watershed attributes contributes to the intricate interactions affecting FD propagation, duration, and their implications for the overall water balance (Konapala ...

  26. Science Communications

    Karger offers various outreach services to help academic institutions communicate their research to non-specialist audiences. Learn about their podcasts, animations, websites, infographics, articles and more.

  27. Can pink noise enhance sleep and memory? Early research drives a color

    Scientists at Northwestern University are studying how short pulses of pink noise can enhance the slow brain waves of deep sleep. In small studies, these pink-noise pulses have shown promise in improving memory and the relaxation response. Pink noise has a frequency profile "very similar to the distribution of brain wave frequencies we see in ...

  28. FAQ

    Research Features is a free online platform that showcases the stories behind scientific research and the people who do it. Learn how they work with researchers, charge contributors, and use social media to reach a wider audience.

  29. Diabetes, heart problems and kidney disease are closely linked

    Dermatology Times features UC research of nail squamous cell carcinoma treatment June 9, 2022. Dermatology Times featured recent research by University of Cincinnati researchers that showed a low recurrence rate of nail squamous cell carcinoma when treated with surgical approaches other than amputation.

  30. Services

    Research Features offers various services to help researchers communicate their work to a wider audience, such as feature articles, digital articles, podcasts and videos. Compare the features, prices and benefits of each service and contact them for more information.