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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

  • disquisition
  • examination
  • exploration
  • inquisition
  • investigation
  • delve (into)
  • inquire (into)
  • investigate
  • look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

  • marketing research
  • market research
  • operations research
  • oppo research

research and development

  • research park
  • translational research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 10 May. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

More from Merriam-Webster on research

Nglish: Translation of research for Spanish Speakers

Britannica English: Translation of research for Arabic Speakers

Britannica.com: Encyclopedia article about research

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research studies definition

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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Department of Health & Human Services

Module 1: Introduction: What is Research?

Module 1

Learning Objectives

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

  • Explain how the scientific method is used to develop new knowledge
  • Describe why it is important to follow a research plan

Text Box: The Scientific Method

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

  • How is a hypothesis like a road map?
  • Who is ultimately responsible for the design and conduct of a research study?
  • How does following the research protocol contribute to informing public health practices?

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What Is Research, and Why Do People Do It?

  • Open Access
  • First Online: 03 December 2022

Cite this chapter

You have full access to this open access chapter

research studies definition

  • 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))

17k Accesses

Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

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. https://doi.org/10.1007/978-3-031-19078-0_1

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  • He has dedicated his life to scientific research.
  • He emphasized that all the people taking part in the research were volunteers .
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  • study What do you plan on studying at university?
  • major US She majored in philosophy at Harvard.
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  • review US We're going to review for the test tomorrow night.
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  • The amount of time and money being spent on researching this disease is pitiful .
  • We are researching the reproduction of elephants .
  • She researched a wide variety of jobs before deciding on law .
  • He researches heart disease .
  • The internet has reduced the amount of time it takes to research these subjects .
  • adjudication
  • interpretable
  • interpretive
  • interpretively
  • investigate
  • reinvestigate
  • reinvestigation
  • risk assessment
  • run over/through something
  • run through something

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Research Method

Home » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

What is Research

Definition:

Research refers to the process of investigating a particular topic or question in order to discover new information , develop new insights, or confirm or refute existing knowledge. It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail.

History of Research

The history of research can be traced back to ancient times when early humans observed and experimented with the natural world around them. Over time, research evolved and became more systematic as people sought to better understand the world and solve problems.

In ancient civilizations such as those in Greece, Egypt, and China, scholars pursued knowledge through observation, experimentation, and the development of theories. They explored various fields, including medicine, astronomy, and mathematics.

During the Middle Ages, research was often conducted by religious scholars who sought to reconcile scientific discoveries with their faith. The Renaissance brought about a renewed interest in science and the scientific method, and the Enlightenment period marked a major shift towards empirical observation and experimentation as the primary means of acquiring knowledge.

The 19th and 20th centuries saw significant advancements in research, with the development of new scientific disciplines and fields such as psychology, sociology, and computer science. Advances in technology and communication also greatly facilitated research efforts.

Today, research is conducted in a wide range of fields and is a critical component of many industries, including healthcare, technology, and academia. The process of research continues to evolve as new methods and technologies emerge, but the fundamental principles of observation, experimentation, and hypothesis testing remain at its core.

Types of Research

Types of Research are as follows:

  • Applied Research : This type of research aims to solve practical problems or answer specific questions, often in a real-world context.
  • Basic Research : This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.
  • Experimental Research : This type of research involves manipulating one or more variables to determine their effects on another variable, while controlling all other variables.
  • Descriptive Research : This type of research aims to describe and measure phenomena or characteristics, without attempting to manipulate or control any variables.
  • Correlational Research: This type of research examines the relationships between two or more variables, without manipulating any variables.
  • Qualitative Research : This type of research focuses on exploring and understanding the meaning and experience of individuals or groups, often through methods such as interviews, focus groups, and observation.
  • Quantitative Research : This type of research uses numerical data and statistical analysis to draw conclusions about phenomena or populations.
  • Action Research: This type of research is often used in education, healthcare, and other fields, and involves collaborating with practitioners or participants to identify and solve problems in real-world settings.
  • Mixed Methods Research : This type of research combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a phenomenon or problem.
  • Case Study Research: This type of research involves in-depth examination of a specific individual, group, or situation, often using multiple data sources.
  • Longitudinal Research: This type of research follows a group of individuals over an extended period of time, often to study changes in behavior, attitudes, or health outcomes.
  • Cross-Sectional Research : This type of research examines a population at a single point in time, often to study differences or similarities among individuals or groups.
  • Survey Research: This type of research uses questionnaires or interviews to gather information from a sample of individuals about their attitudes, beliefs, behaviors, or experiences.
  • Ethnographic Research : This type of research involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices.
  • Historical Research : This type of research investigates events or phenomena from the past using primary sources, such as archival records, newspapers, and diaries.
  • Content Analysis Research : This type of research involves analyzing written, spoken, or visual material to identify patterns, themes, or messages.
  • Participatory Research : This type of research involves collaboration between researchers and participants throughout the research process, often to promote empowerment, social justice, or community development.
  • Comparative Research: This type of research compares two or more groups or phenomena to identify similarities and differences, often across different countries or cultures.
  • Exploratory Research : This type of research is used to gain a preliminary understanding of a topic or phenomenon, often in the absence of prior research or theories.
  • Explanatory Research: This type of research aims to identify the causes or reasons behind a particular phenomenon, often through the testing of theories or hypotheses.
  • Evaluative Research: This type of research assesses the effectiveness or impact of an intervention, program, or policy, often through the use of outcome measures.
  • Simulation Research : This type of research involves creating a model or simulation of a phenomenon or process, often to predict outcomes or test theories.

Data Collection Methods

  • Surveys : Surveys are used to collect data from a sample of individuals using questionnaires or interviews. Surveys can be conducted face-to-face, by phone, mail, email, or online.
  • Experiments : Experiments involve manipulating one or more variables to measure their effects on another variable, while controlling for other factors. Experiments can be conducted in a laboratory or in a natural setting.
  • Case studies : Case studies involve in-depth analysis of a single case, such as an individual, group, organization, or event. Case studies can use a variety of data collection methods, including interviews, observation, and document analysis.
  • Observational research : Observational research involves observing and recording the behavior of individuals or groups in a natural setting. Observational research can be conducted covertly or overtly.
  • Content analysis : Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
  • Ethnography : Ethnography involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices. Ethnographic research can use a range of data collection methods, including observation, interviews, and document analysis.
  • Secondary data analysis : Secondary data analysis involves using existing data from sources such as government agencies, research institutions, or commercial organizations. Secondary data can be used to answer research questions, without collecting new data.
  • Focus groups: Focus groups involve gathering a small group of people together to discuss a topic or issue. The discussions are usually guided by a moderator who asks questions and encourages discussion.
  • Interviews : Interviews involve one-on-one conversations between a researcher and a participant. Interviews can be structured, semi-structured, or unstructured, and can be conducted in person, by phone, or online.
  • Document analysis : Document analysis involves collecting and analyzing written documents, such as reports, memos, and emails. Document analysis can be used to study organizational communication, policy documents, and other forms of written material.

Data Analysis Methods

Data Analysis Methods in Research are as follows:

  • Descriptive statistics : Descriptive statistics involve summarizing and describing the characteristics of a dataset, such as mean, median, mode, standard deviation, and frequency distributions.
  • Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data, using methods such as hypothesis testing, confidence intervals, and regression analysis.
  • Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as text, images, or audio, to identify patterns, themes, or meanings. Qualitative analysis can be used to study subjective experiences, social norms, and cultural practices.
  • Content analysis: Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
  • Grounded theory: Grounded theory involves developing a theory or model based on empirical data, using methods such as constant comparison, memo writing, and theoretical sampling.
  • Discourse analysis : Discourse analysis involves analyzing language use, including the structure, function, and meaning of words and phrases, to understand how language reflects and shapes social relationships and power dynamics.
  • Network analysis: Network analysis involves analyzing the structure and dynamics of social networks, including the relationships between individuals and groups, to understand social processes and outcomes.

Research Methodology

Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or test hypotheses. The main components of research methodology include:

  • Research design : Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods.
  • Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units from the population of interest. The choice of sampling strategy will depend on the research question and the nature of the population being studied.
  • Data collection methods : Data collection methods refer to the techniques used to collect data from study participants or sources, such as surveys, interviews, observations, or secondary data sources.
  • Data analysis methods: Data analysis methods refer to the techniques used to analyze and interpret the data collected in the study, such as descriptive statistics, inferential statistics, qualitative analysis, or content analysis.
  • Ethical considerations: Ethical considerations refer to the principles and guidelines that govern the treatment of human participants or the use of sensitive data in the research study.
  • Validity and reliability : Validity and reliability refer to the extent to which the study measures what it is intended to measure and the degree to which the study produces consistent and accurate results.

Applications of Research

Research has a wide range of applications across various fields and industries. Some of the key applications of research include:

  • Advancing scientific knowledge : Research plays a critical role in advancing our understanding of the world around us. Through research, scientists are able to discover new knowledge, uncover patterns and relationships, and develop new theories and models.
  • Improving healthcare: Research is instrumental in advancing medical knowledge and developing new treatments and therapies. Clinical trials and studies help to identify the effectiveness and safety of new drugs and medical devices, while basic research helps to uncover the underlying causes of diseases and conditions.
  • Enhancing education: Research helps to improve the quality of education by identifying effective teaching methods, developing new educational tools and technologies, and assessing the impact of various educational interventions.
  • Driving innovation: Research is a key driver of innovation, helping to develop new products, services, and technologies. By conducting research, businesses and organizations can identify new market opportunities, gain a competitive advantage, and improve their operations.
  • Informing public policy : Research plays an important role in informing public policy decisions. Policy makers rely on research to develop evidence-based policies that address societal challenges, such as healthcare, education, and environmental issues.
  • Understanding human behavior : Research helps us to better understand human behavior, including social, cognitive, and emotional processes. This understanding can be applied in a variety of settings, such as marketing, organizational management, and public policy.

Importance of Research

Research plays a crucial role in advancing human knowledge and understanding in various fields of study. It is the foundation upon which new discoveries, innovations, and technologies are built. Here are some of the key reasons why research is essential:

  • Advancing knowledge: Research helps to expand our understanding of the world around us, including the natural world, social structures, and human behavior.
  • Problem-solving: Research can help to identify problems, develop solutions, and assess the effectiveness of interventions in various fields, including medicine, engineering, and social sciences.
  • Innovation : Research is the driving force behind the development of new technologies, products, and processes. It helps to identify new possibilities and opportunities for improvement.
  • Evidence-based decision making: Research provides the evidence needed to make informed decisions in various fields, including policy making, business, and healthcare.
  • Education and training : Research provides the foundation for education and training in various fields, helping to prepare individuals for careers and advancing their knowledge.
  • Economic growth: Research can drive economic growth by facilitating the development of new technologies and innovations, creating new markets and job opportunities.

When to use Research

Research is typically used when seeking to answer questions or solve problems that require a systematic approach to gathering and analyzing information. Here are some examples of when research may be appropriate:

  • To explore a new area of knowledge : Research can be used to investigate a new area of knowledge and gain a better understanding of a topic.
  • To identify problems and find solutions: Research can be used to identify problems and develop solutions to address them.
  • To evaluate the effectiveness of programs or interventions : Research can be used to evaluate the effectiveness of programs or interventions in various fields, such as healthcare, education, and social services.
  • To inform policy decisions: Research can be used to provide evidence to inform policy decisions in areas such as economics, politics, and environmental issues.
  • To develop new products or technologies : Research can be used to develop new products or technologies and improve existing ones.
  • To understand human behavior : Research can be used to better understand human behavior and social structures, such as in psychology, sociology, and anthropology.

Characteristics of Research

The following are some of the characteristics of research:

  • Purpose : Research is conducted to address a specific problem or question and to generate new knowledge or insights.
  • Systematic : Research is conducted in a systematic and organized manner, following a set of procedures and guidelines.
  • Empirical : Research is based on evidence and data, rather than personal opinion or intuition.
  • Objective: Research is conducted with an objective and impartial perspective, avoiding biases and personal beliefs.
  • Rigorous : Research involves a rigorous and critical examination of the evidence and data, using reliable and valid methods of data collection and analysis.
  • Logical : Research is based on logical and rational thinking, following a well-defined and logical structure.
  • Generalizable : Research findings are often generalized to broader populations or contexts, based on a representative sample of the population.
  • Replicable : Research is conducted in a way that allows others to replicate the study and obtain similar results.
  • Ethical : Research is conducted in an ethical manner, following established ethical guidelines and principles, to ensure the protection of participants’ rights and well-being.
  • Cumulative : Research builds on previous studies and contributes to the overall body of knowledge in a particular field.

Advantages of Research

Research has several advantages, including:

  • Generates new knowledge: Research is conducted to generate new knowledge and understanding of a particular topic or phenomenon, which can be used to inform policy, practice, and decision-making.
  • Provides evidence-based solutions : Research provides evidence-based solutions to problems and issues, which can be used to develop effective interventions and strategies.
  • Improves quality : Research can improve the quality of products, services, and programs by identifying areas for improvement and developing solutions to address them.
  • Enhances credibility : Research enhances the credibility of an organization or individual by providing evidence to support claims and assertions.
  • Enables innovation: Research can lead to innovation by identifying new ideas, approaches, and technologies.
  • Informs decision-making : Research provides information that can inform decision-making, helping individuals and organizations make more informed and effective choices.
  • Facilitates progress: Research can facilitate progress by identifying challenges and opportunities and developing solutions to address them.
  • Enhances understanding: Research can enhance understanding of complex issues and phenomena, helping individuals and organizations navigate challenges and opportunities more effectively.
  • Promotes accountability : Research promotes accountability by providing a basis for evaluating the effectiveness of policies, programs, and interventions.
  • Fosters collaboration: Research can foster collaboration by bringing together individuals and organizations with diverse perspectives and expertise to address complex issues and problems.

Limitations of Research

Some Limitations of Research are as follows:

  • Cost : Research can be expensive, particularly when large-scale studies are required. This can limit the number of studies that can be conducted and the amount of data that can be collected.
  • Time : Research can be time-consuming, particularly when longitudinal studies are required. This can limit the speed at which research findings can be generated and disseminated.
  • Sample size: The size of the sample used in research can limit the generalizability of the findings to larger populations.
  • Bias : Research can be affected by bias, both in the design and implementation of the study, as well as in the analysis and interpretation of the data.
  • Ethics : Research can present ethical challenges, particularly when human or animal subjects are involved. This can limit the types of research that can be conducted and the methods that can be used.
  • Data quality: The quality of the data collected in research can be affected by a range of factors, including the reliability and validity of the measures used, as well as the accuracy of the data entry and analysis.
  • Subjectivity : Research can be subjective, particularly when qualitative methods are used. This can limit the objectivity and reliability of the findings.
  • Accessibility : Research findings may not be accessible to all stakeholders, particularly those who are not part of the academic or research community.
  • Interpretation : Research findings can be open to interpretation, particularly when the data is complex or contradictory. This can limit the ability of researchers to draw firm conclusions.
  • Unforeseen events : Unexpected events, such as changes in the environment or the emergence of new technologies, can limit the relevance and applicability of research findings.

About the author

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Are Cancer Research Studies?

What is cancer research and why is it important.

This infographic describes the four main types of cancer research, the differences between each type, and how they can help drive progress.

Research is the key to progress against cancer and is a complex process involving professionals from many fields. It is also thanks to the participation of people with cancer, cancer survivors, and healthy volunteers that any breakthroughs go on to improve treatment and care for those who need it.

Cancer research studies may lead to discoveries such as new drugs to treat cancer, new therapies to make symptoms less severe, or lifestyle changes to reduce the chances of getting cancer.

Cancer research may also address big picture questions like why cancer is more prevalent in certain populations or how doctors can make existing cancer detection tools more effective in health care settings.

These discoveries can help people with cancer and their caregivers live fuller lives.

Who should join cancer research studies?

When you choose to participate in a research study, you become a partner in scientific discovery. Your generous contribution can make a world of difference for people like you.

As scientists continue to conduct cancer research, anyone can consider joining a research study. The best research includes everyone, and everyone includes you.

Your unique experience with cancer is incredibly valuable and may help current and future generations lead healthier lives.

When more people of all different races, ethnicities, ages, genders, abilities, and backgrounds participate, more people benefit.

It is important for scientists to capture the full genetic diversity of human populations so that the lessons learned are applicable to everyone.

What are the types of cancer research studies?

See below for definitions on the four major types of research and their subtypes:

  • basic research
  • quality of life/supportive care
  • natural history
  • longitudinal
  • population-based
  • epidemiological research
  • translational research

Basic Research

Basic cancer research studies explore the very laws of nature. Scientists learn how cancer cells grow and divide, for example, by growing and testing bacteria , viruses , fungi , animal cells, and human cells in a lab. Scientists also study, for example, the genes that make up tumors in mice and rats in the lab. These experiments help build the foundation for further discovery.

Doctor talking to Hispanic patient

Why Participate in a Clinical Trial?

Get information on how to evaluate a clinical trial and what questions to ask.

Clinical Research

Clinical research involves the study of cancer in people. These cancer research studies are further broken down into two types: clinical trials and observational studies .

  • Treatment trials test how safe and useful a new treatment or way of using existing treatments is for people with cancer. Test treatments may include drugs, approaches to surgery or radiation therapy , or combinations of treatments.
  • Prevention trials are for people who do not have cancer but are at a high risk for developing cancer or for cancer coming back. Prevention clinical trials target lifestyle changes (doing something) or focus on certain nutrients or medicines (adding something).
  • Screening trials test how effective screening tests are for healthy people. The goal of these trials is to discover screening tools or methods that reduce deaths from cancer by finding it earlier.
  • Quality-of-life/supportive care tests aim to help people with cancer, as well as their family and loved ones, cope with side effects like pain, nutrition problems, nausea and vomiting , sleeping problems, and depression . These trials may involve drugs or activities like therapy and exercising.  

Female doctor speaks caringly to Black female patient

Find Observation Studies >

View a studies that are looking for people now.

  • Natural history studies look at certain conditions in people with cancer or people who are at a high risk of developing cancer. Researchers often collect information about a person and their family medical history , as well as blood, saliva, and tumor samples. For example, a biomarker test may be used to get a genetic profile of a person’s cancer tissue. This may reveal how certain tumors change over the course of treatment .
  • Longitudinal studies gather data on people or groups of people over time, often to see the result of a habit, treatment, or change. For example, two groups of people may be identified as those who smoke and those who do not. These two groups are compared over time to see whether one group is more likely to develop cancer than the other group.
  • Population-based studies explore the causes of cancer, cancer trends, and factors that affect cancer care in specific populations. For example, a population-based study may explore the causes of a high cancer rate in a regional Native American population.

Epidemiological Research

Epidemiological research is the study of the patterns, causes, and effects of cancer in a group of people of a certain background. This research encompasses both observational population-based studies but also includes clinical epidemiological studies where the relationship between a population’s risk factors and treatments are tested.

Translational Research

Translational research is when cancer research moves across research disciplines, from basic lab research into clinical settings, and from clinical settings into everyday care. In turn, findings from clinical studies and population-based studies can inform basic cancer research. For example, data from the genetic profile of a tumor during an observational study may help scientists develop a clinical trial to test which drugs to prescribe to cancer patients with specific tumor genes.

Headshot of Dr. Monica Bertagnolli

Monica Bertagnolli, Director, NIH; former director, NCI; cancer survivor

Participation in Cancer Research Matters

I am so happy to have the opportunity to acknowledge the courage and generosity of an estimated 494,018 women who agreed to participate in randomized clinical trials with results reported between 1971 and 2018.

Their contributions showed that mammography can detect cancer at an early stage, that mastectomies and axillary lymph node dissections are not always necessary, that chemotherapy can benefit some people with early estrogen receptor–positive, progesterone receptor–positive, HER2-negative breast cancer but is not needed for all, and that hormonal therapy can prevent disease recurrence.

For just the key studies that produced these results, it took the strength and commitment of almost 500,000 women. I am the direct beneficiary of their contributions, and I am profoundly grateful.

The true number of brave souls contributing to this reduction in breast cancer mortality over the past 30 years? Many millions. These are our heroes.

— From NCI Director’s Remarks by then-NCI Director Monica M. Bertagnolli, M.D., at the American Society of Clinical Oncology Annual Meeting, June 3, 2023

National Center for Science and Engineering Statistics

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Definitions of Research and Development: An Annotated Compilation of Official Sources

Introduction.

This document provides definitions of research and development from U.S. and international sources.

The first section (I) presents statistical definitions of R&D from the Organisation for Economic Co-operation and Development (OECD) Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The next three sections are organized by sectors of the U.S. economy that perform or fund R&D—businesses (II), federal and state governments (III), and academic and nonprofit organizations (IV). Sources for definitions of R&D include the Office of Management and Budget (OMB), federal procurement, tax and accounting guidance, and surveys from the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF). The last section (V) presents R&D definitions from international statistical manuals on the System of National Accounts and globalization.

R&D definitions are provided unedited as they appear in their original sources.

I. OECD—Frascati Manual

Description.

The updated Frascati Manual (7th ed., OECD 2015) provides the definition of research and experimental development (R&D) and of its components: basic research, applied research, and experimental development. To provide guidance on what is and what is not an R&D activity, five criteria are provided requiring the activity to be novel, creative, uncertain in its outcome, systematic, and transferable and/or reproducible.

2.5 Research and experimental development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture and society—and to devise new applications of available knowledge.

2.6 A set of common features identifies R&D activities, even if these are carried out by different performers. R&D activities may be aimed at achieving either specific or general objectives. R&D is always aimed at new findings, based on original concepts (and their interpretation) or hypotheses. It is largely uncertain about its final outcome (or at least about the quantity of time and resources needed to achieve it), it is planned for and budgeted (even when carried out by individuals), and it is aimed at producing results that could be either freely transferred or traded in a marketplace. For an activity to be an R&D activity, it must satisfy five core criteria.

2.7 The activity must be:

  • transferable and/or reproducible.

2.8 All five criteria are to be met, at least in principle, every time an R&D activity is undertaken whether on a continuous or occasional basis. The definition of R&D just given is consistent with the definition of R&D used in the previous editions of the Frascati Manual and covers the same range of activities.

2.9 The term R&D covers three types of activity: basic research, applied research and experimental development. Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view. Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective. Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

Distribution by type of R&D

2.23 A breakdown by type of R&D is recommended for use in all four of the sectors used in this manual [Business enterprise; Higher education; Government; and Private nonprofit].

2.24 There are three types of R&D:

  • basic research
  • applied research
  • experimental development.

Basic research

2.25 Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view.

Applied research

2.29 Applied research is original investigation undertaken in order to acquire new knowledge. It is, however, directed primarily towards a specific, practical aim or objective.

Experimental development

2.32 Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

OECD, Frascati Manual , 7th ed, Chapter 2. The full Frascati Manual is available at http://oe.cd/frascati.

II. U.S. Business Enterprise R&D

A. financial accounting standards board.

Financial Accounting Standards Board (FASB) Accounting Standards Codification (ASC) provides U.S. GAAP (generally accepted accounting principles) for businesses. ASC is organized by “topics” and Topic 730 is devoted to research and development (formerly covered in FASB Statement No. 2 “Accounting for Research and Development Costs”). Material formerly covered in FASB Statement No. 68 “Research and Development Arrangements” also appears under Topic 730. The FASB material below, copyrighted by the Financial Accounting Foundation, 401 Merritt 7, Norwalk, CT 06856, is used with permission.

Topic 730 Research and Development, 730-10-20 Glossary

Research is planned search or critical investigation aimed at discovery of new knowledge with the hope that such knowledge will be useful in developing a new product or service (hereinafter “product”) or a new process or technique (hereinafter “process”) or in bringing about a significant improvement to an existing product or process.

Development is the translation of research findings or other knowledge into a plan or design for a new product or process or for a significant improvement to an existing product or process whether intended for sale or use. It includes the conceptual formulation, design, and testing of product alternatives, construction of prototypes, and operation of pilot plants.

Topic 730 Research and Development, 730-10-55 Implementation Guidance and Illustrations Examples of Activities Typically Included in Research and Development 55-1.

The following activities typically would be considered research and development within the scope of this Topic (unless conducted for others under a contractual arrangement [see NOTES below]):

  • Laboratory research aimed at discovery of new knowledge
  • Searching for applications of new research findings or other knowledge
  • Conceptual formulation and design of possible product or process alternatives
  • Testing in search for or evaluation of product or process alternatives
  • Modification of the formulation or design of a product or process
  • Design, construction, and testing of preproduction prototypes and models
  • Design of tools, jigs, molds, and dies involving new technology
  • Design, construction, and operation of a pilot plant that is not of a scale economically feasible to the entity for commercial production
  • Engineering activity required to advance the design of a product to the point that it meets specific functional and economic requirements and is ready for manufacture
  • Design and development of tools used to facilitate research and development or components of a product or process that are undergoing research and development activities.

Examples of Activities Typically Excluded from Research and Development 55-2.

The following activities typically would not be considered research and development within the scope of this Topic:

  • Engineering follow-through in an early phase of commercial production
  • Quality control during commercial production including routine testing of products
  • Trouble-shooting in connection with break-downs during commercial production
  • Routine, ongoing efforts to refine, enrich, or otherwise improve upon the qualities of an existing product
  • Adaptation of an existing capability to a particular requirement or customer's need as part of a continuing commercial activity
  • Seasonal or other periodic design changes to existing products
  • Routine design of tools, jigs, molds, and dies
  • Activity, including design and construction engineering, related to the construction, relocation, rearrangement, or start-up of facilities or equipment other than the following:
  • Pilot plants (see [h] in the preceding paragraph)
  • Facilities or equipment whose sole use is for a particular R&D project [see NOTES below]
  • Legal work in connection with patent applications or litigation, and the sale or licensing of patents.

Topic 730 covers R&D expense or R&D costs funded by the reporting entity. Accounting for the costs of R&D activities conducted for others under a contractual arrangement is part of accounting for contracts in general (see, for example, Topic 606). See also paragraphs 25-8 to 25-10 under 730-20-25.

See Subtopic 912 under 730 for guidance to government contractors related to identifying R&D activities included in government contracts and the accounting for such activities.

For guidance on R&D arrangements, see Subtopics 730-20 and 810-30. For guidance regarding design and development costs for products to be sold under long-term supply arrangements, see Subtopic 340-10. Topic 850 specifies disclosure requirements for related party transactions.

For guidance on materials, property, plant, and equipment acquired or constructed for R&D projects, see paragraph 25-2 under 730-10-25 and Topic 360. For intangibles and contract services used for R&D, see paragraph 25-2 under 730-10-25 and Topic 720.

For guidance on computer software as a cost of R&D (formerly covered in part in FASB Statement No. 86 “Accounting for the Costs of Computer Software to Be Sold, Leased, or Otherwise Marketed” paragraphs 28–36), see Topic 730, Subtopic 10, especially paragraphs 25-3 and 25-4. Subtopic 350-40 covers general guidance on costs of computer software developed or obtained for internal use and Subtopic 985-20 covers computer software intended to be sold, leased or marketed. In particular, paragraph 985-20-25-1 offers guidance regarding costs incurred to establish the technological feasibility of a computer software product. For guidance related to a funded software-development arrangement, see paragraphs 985-605-25-86 through 25-87.

The accounting for recognized intangible assets acquired by an entity, other than intangibles acquired in a business combination, is specified in Topic 350 (formerly covered in FASB Statement No. 142 “Goodwill and Other Intangible Assets”). R&D assets acquired in a business combination or an acquisition by a not-for-profit entity is covered in Subtopic 805-20.

The material from FASB in this section was compiled in 2016 and is not meant to be an exhaustive summary of U.S. business R&D accounting guidance. For more information and FASB updates, see cited source.

FASB statements and other pronouncements. Available at https://asc.fasb.org and http://www.iasplus.com/en-us/standards/fasb/expenses/asc730 .

B. U.S. Code of Federal Regulations

Section 1.174-2 of the U.S. Code of Federal Regulations ( Title 26, Internal Revenue ) specifies the definition of R&D for tax filing purposes.

1.174-2 Definition of research and development expenditures.

(a) In general.

(1) The term research or experimental expenditures , as used in section 174, means expenditures incurred in connection with the taxpayer’s trade or business which represent research and development costs in the experimental or laboratory sense. The term generally includes all such costs incident to the development or improvement of a product. The term includes the costs of obtaining a patent, such as attorneys’ fees expended in making and perfecting a patent application. Expenditures represent research and development costs in the experimental or laboratory sense if they are for activities intended to discover information that would eliminate uncertainty concerning the development or improvement of a product. Uncertainty exists if the information available to the taxpayer does not establish the capability or method for developing or improving the product or the appropriate design of the product. Whether expenditures qualify as research or experimental expenditures depends on the nature of the activity to which the expenditures relate, not the nature of the product or improvement being developed or the level of technological advancement the product or improvement represents.

(2) For purposes of this section, the term product includes any pilot model, process, formula, invention, technique, patent, or similar property, and includes products to be used by the taxpayer in its trade or business as well as products to be held for sale, lease, or license.

(3) The term research or experimental expenditures does not include expenditures for:

i. The ordinary testing or inspection of materials or products for quality control (quality control testing);

ii. Efficiency surveys;

iii. Management studies;

iv. Consumer surveys;

v.  Advertising or promotions;

vi. The acquisition of another’s patent, model, production or process; or

vii. Research in connection with literary, historical, or similar projects.

26 CFR 1.174-2. Available at https://www.law.cornell.edu/cfr/text/26/1.174-2 .

C. NCSES Surveys on Business R&D

Business enterprise research and development (berd) survey.

  • Annual Business Survey (R&D for Microbusiness module)

The BERD Survey is the primary source of information on R&D performed or funded by businesses within the United States and is successor to the Business R&D and Innovation Survey (BRDIS) and the Survey of Industrial Research and Development. The BERD Survey covers for-profit, nonfarm businesses with ten or more employees. The survey is conducted by the Census Bureau for NCSES. For more information and statistics, see https://www.nsf.gov/statistics/srvyberd/ .

R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge and to devise new applications of available knowledge. This includes (a) activities aimed at acquiring new knowledge or understanding without specific immediate commercial applications or uses (basic research); (b) activities aimed at solving a specific problem or meeting a specific commercial objective (applied research); and (c) systematic work, drawing on research and practical experience and resulting in additional knowledge, which is directed to producing new products or processes or to improving existing products or processes (development). R&D includes both direct costs such as salaries of researchers as well as administrative and overhead costs clearly associated with the company’s R&D.

The term R&D does NOT include expenditures for the following:

  • Costs for routine product testing, quality control, and technical services unless they are an integral part of an R&D project
  • Market research
  • Efficiency surveys or management studies
  • Literary, artistic, or historical projects, such as films, music, or books and other publications
  • Prospecting or exploration for natural resources

The following are examples of activities that typically would be excluded from research and development (in accordance with FASB Statement of Financial Accounting Standards No. 2 “Accounting for Research and Development Costs” https://fasb.org/page/document?pdf=aop_fas2.pdf&title=FAS%202%20(AS%20AMENDED ):

  • Engineering follow-through in an early phase of commercial production.
  • Quality control during commercial production including routine testing of products.
  • Trouble-shooting in connection with break-downs during commercial production.
  • Routine, ongoing efforts to refine, enrich, or otherwise improve upon the qualities of an existing product.
  • Adaptation of an existing capability to a particular requirement or customer's need as part of a continuing commercial activity.
  • Seasonal or other periodic design changes to existing products.
  • Routine design of tools, jigs, molds, and dies.
  • Activity, including design and construction engineering, related to the construction, relocation, rearrangement, or start-up of facilities or equipment other than (1) pilot plants and (2) facilities or equipment whose sole use is for a particular research and development project.

Does R&D include development of software and Internet applications?

Research and development activity in software and Internet applications refers only to activities with an element of uncertainty and that are intended to close knowledge gaps and meet scientific and technological needs…. regardless of the eventual user (internal or external).

R&D activity in software INCLUDES the following:

  • Software development or improvement activities that expand scientific or technological knowledge
  • Construction of new theories and algorithms in the field of computer science

R&D activity in software EXCLUDES the following:

  • Software development that does not depend on a scientific or technological advance, such as the following:
  • supporting or adapting existing systems
  • adding functionality to existing application programs, and
  • routine debugging of existing systems and software
  • Creation of new software based on known methods and applications
  • Conversion or translation of existing software and software languages
  • Adaptation of a product to a specific client, unless knowledge that significantly improved the base program was added in that process

NCSES BERD survey questionnaires. Available at https://www.nsf.gov/statistics/srvyberd/ .

Annual Business Survey (R&D for Microbusinesses module)

The Annual Business Survey (ABS) is the primary source of information on R&D for nonfarm, for-profit businesses operating in the United States with one to nine employees. For businesses with one or more employees, ABS also collects data on innovation, technology, intellectual property, business owner characteristics, and additional content that changes annually. The ABS is conducted by the Census Bureau in partnership with NCSES within NSF.

ABS Microbusinesses module: For businesses with one to nine employees, the survey collects the following information:

  • R&D performance
  • Total and R&D employment
  • Sources of R&D funding
  • Type of R&D work (basic research, applied research, and development)
  • Type of R&D cost (e.g., salaries and fringe benefits)

Research and development (R&D) comprise creative and systematic work undertaken in order to increase the stock of knowledge and to devise new applications of available knowledge.

R&D activity in software EXCLUDES:

Type of R&D

  • Basic research–activities aimed at acquiring new knowledge or understanding without specific immediate commercial applications or uses.
  • Applied research–activities aimed at solving a specific problem or meeting a specific commercial objective.
  • Experimental development–systematic work, drawing on research and practical experience and resulting in additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

NCSES ABS description and questionnaires. Available at https://www.nsf.gov/statistics/srvyabs/ .

III. Federal and State Government R&D

A. office of management and budget circular a-11.

The OMB prescribes budget regulations for federal agencies. Part II of Circular A-11 covers development of the president’s budget and provides guidance on agency submissions to OMB. Section 84 of the circular defines budget authority, outlays, and offsetting receipts for the conduct of R&D, construction and rehabilitation of R&D facilities, and R&D equipment.

Conduct of research and development (R&D): Research and experimental development activities are defined as creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of people, culture, and society—and to devise new applications using available knowledge.

  • Administrative expenses for R&D, such as the operating costs of research facilities and equipment and other overhead costs.
  • Investments in physical assets such as major equipment and facilities that support R&D programs. These investments should generally be reported under physical assets.
  • Routine product testing, quality control, collection of general-purpose statistics, routine monitoring, and evaluation of an operational program (when that program is not R&D). Spending of this type should generally be reported as non-investment activities.
  • Training of scientific and technical personnel should be reported as conduct of education and training. However, if an activity includes a mixture of R&D objectives as well as the education of graduate students, agencies should report under the lowest relevant line item.

Basic research is defined as experimental or theoretical work undertaken primarily to acquire new knowledge of underlying foundations of phenomena and observable facts. Basic research may include activities with broad or general applications in mind, such as the study of how plant genomes change, but should exclude research directed towards a specific application or requirement include, such as the optimization of the genome of a specific crop species.

Applied research is defined as original investigation undertaken in order to acquire new knowledge. Applied research is, however, directed primarily towards a specific practical aim or objective.

Experimental development is defined as creative and systematic work, drawing on knowledge gained from research and practical experience, which is directed at producing new products or processes or improving existing products or processes. Like research, experimental development will result in gaining additional knowledge.

For reporting experimental development activities, include the following:

  • The production of materials, devices, and systems or methods, including the design, construction and testing of experimental prototypes.
  • Technology demonstrations, in cases where a system or component is being demonstrated at scale for the first time, and it is realistic to expect additional refinements to the design (feedback R&D) following the demonstration. However, not all activities that are identified as “technology demonstrations” are R&D.
  • User demonstrations where the cost and benefit of a system are being validated for a specific use case. This includes low-rate initial production activities.
  • Pre-production development, which is defined as non-experimental work on a product or system before it goes into full production, including activities such as tooling, and development of production facilities. For example, exclude activities and programs that are categorized as “Operational Systems Development” in the Department of Defense’s budget activity structure. Activities and programs of this type should generally be reported as investments in other major equipment.

Physical assets are land, structures, equipment, and intellectual property (e.g., software or applications) that have an estimated useful life of two years or more; or commodity inventories. This character class code is used to enter amounts for the purchase, construction, manufacture, rehabilitation, or major improvement of physical assets regardless of whether the assets are owned or operated by the Federal Government, States, municipalities, or private individuals. The cost of the asset includes both its purchase price and all other costs incurred to bring it to a form and location suitable for its use. Within this character class code, agencies are also required to identify spending for R&D facilities and major equipment.

For reporting construction of R&D facilities, include the following:

  • Construction of facilities that are necessary for the execution of an R&D program. This may include land, major fixed equipment, and supporting infrastructure such as a sewer line, or housing at a remote location. Many laboratory buildings will include a mixture of R&D facilities and office space. The fraction of the building directly related to the conduct R&D may be calculated based on the percentage of the square footage.
  • Construction of other facilities, such as office space (which should be reported in the other construction and rehabilitation category on line 1313 or 1314).
  • Major movable R&D equipment.

For reporting Major equipment R&D (lines 1321 and 1322), include the following:

  • Acquisition, design, or production of major movable equipment, such as mass spectrometers, research vessels, DNA sequencers, and other movable major instruments for use in R&D activities.
  • Programs of $1 million or more that are devoted to the purchase or construction of R&D major equipment (see section 84.3(a)).
  • Minor equipment purchases, such as personal computers, standard microscopes, and simple spectrometers.

OMB Circular A-11. Available at https://www.whitehouse.gov/omb/circulars/.

B. Federal Acquisitions Regulations

The Federal Acquisitions Regulations (FAR) were established to codify uniform policies for the acquisition of supplies and services by executive agencies. Basic research is defined in FAR Part 2–Definitions of Words and Terms, subpart 2.101 “Definitions.” Applied research and development are defined in FAR Part 35–Research and Development Contracting, subpart 35.001 “Definitions.” Full text of FAR Parts is available at https://www.acquisition.gov/?q=browsefar.

Basic research means that research directed toward increasing knowledge in science. The primary aim of basic research is a fuller knowledge or understanding of the subject under study, rather than any practical application of that knowledge.

Applied research means the effort that (a) normally follows basic research, but may not be severable from the related basic research; (b) attempts to determine and exploit the potential of scientific discoveries or improvements in technology materials, processes, methods, devices, or techniques; and (c) attempts to advance the state of the art. When being used by contractors in cost principle applications, this term does not include efforts whose principal aim is the design, development, or testing of specific items or services to be considered for sale; these efforts are within the definition of "development," given below.

Development, as used in this part, means the systematic use of scientific and technical knowledge in the design, development, testing, or evaluation of a potential new product or service (or of an improvement in an existing product or service) to meet specific performance requirements or objectives. It includes the functions of design engineering, prototyping, and engineering testing; it excludes subcontracted technical effort that is for the sole purpose of developing an additional source for an existing product.

The Federal Acquisitions Regulations (FAR). Available at https://www.acquisition.gov/?q=browsefar.

C. Department of Defense Research, Development, Test, and Evaluation Budget Activities

The Research, Development, Test, and Evaluation (RDT&E) budget activities are broad categories reflecting different types of DOD science and technology activities. These definitions guide internal budget documents and submissions of data to other government agencies. The following is drawn from DOD’s Financial Management Regulation (DOD 7000.14-R), Volume 2B, Chapter 5 (Research, Development and Evaluation Appropriations). (As a historical artifact from previous DOD budget authority terminology, funds for RDT&E budget activity categories 1 through 7 are sometimes referred to as 6.1 through 6.7.) The full text of Chapter 5 is available at http://comptroller.defense.gov/FMR/vol2b_chapters.aspx .

Budget Activity 1, Basic Research. Basic research is systematic study directed toward greater knowledge or understanding of the fundamental aspects of phenomena and of observable facts without specific applications towards processes or products in mind. It includes all scientific study and experimentation directed toward increasing fundamental knowledge and understanding in those fields of the physical, engineering, environmental, and life sciences related to long-term national security needs. It is farsighted high payoff research that provides the basis for technological progress. Basic research may lead to: (a) subsequent applied research and advanced technology developments in Defense-related technologies, and (b) new and improved military functional capabilities in areas such as communications, detection, tracking, surveillance, propulsion, mobility, guidance and control, navigation, energy conversion, materials and structures, and personnel support. Program elements in this category involve pre-Milestone A efforts.

Budget Activity 2, Applied Research. Applied research is systematic study to understand the means to meet a recognized and specific need. It is a systematic expansion and application of knowledge to develop useful materials, devices, and systems or methods. It may be oriented, ultimately, toward the design, development, and improvement of prototypes and new processes to meet general mission area requirements. Applied research may translate promising basic research into solutions for broadly defined military needs, short of system development. This type of effort may vary from systematic mission-directed research beyond that in Budget Activity 1 to sophisticated breadboard hardware, study, programming and planning efforts that establish the initial feasibility and practicality of proposed solutions to technological challenges. It includes studies, investigations, and non-system specific technology efforts. The dominant characteristic is that applied research is directed toward general military needs with a view toward developing and evaluating the feasibility and practicality of proposed solutions and determining their parameters. Applied Research precedes system specific technology investigations or development. Program control of the Applied Research program element is normally exercised by general level of effort. Program elements in this category involve pre-Milestone B efforts, also known as Concept and Technology Development phase tasks, such as concept exploration efforts and paper studies of alternative concepts for meeting a mission need.

Budget Activity 3, Advanced Technology Development (ATD). This budget activity includes development of subsystems and components and efforts to integrate subsystems and components into system prototypes for field experiments and/or tests in a simulated environment. Budget Activity 3 includes concept and technology demonstrations of components and subsystems or system models. The models may be form, fit, and function prototypes or scaled models that serve the same demonstration purpose. The results of this type of effort are proof of technological feasibility and assessment of subsystem and component operability and producibility rather than the development of hardware for service use. Projects in this category have a direct relevance to identified military needs. Advanced Technology Development demonstrates the general military utility or cost reduction potential of technology when applied to different types of military equipment or techniques. Program elements in this category involve pre-Milestone B efforts, such as system concept demonstration, joint and Service-specific experiments or Technology Demonstrations and generally have Technology Readiness Levels of 4, 5, or 6. (For further discussion on Technology Readiness Levels, see the Assistant Secretary of Defense for Research and Engineering’s Technology Readiness Assessment (TRA) Guidance.) Projects in this category do not necessarily lead to subsequent development or procurement phases, but should have the goal of moving out of Science and Technology (S&T) and into the acquisition process within the Future Years Defense Program (FYDP). Upon successful completion of projects that have military utility, the technology should be available for transition.

Budget Activity 4, Advanced Component Development and Prototypes (ACD&P). Efforts necessary to evaluate integrated technologies, representative modes or prototype systems in a high fidelity and realistic operating environment are funded in this budget activity. The ACD&P phase includes system specific efforts that help expedite technology transition from the laboratory to operational use. Emphasis is on proving component and subsystem maturity prior to integration in major and complex systems and may involve risk reduction initiatives. Program elements in this category involve efforts prior to Milestone B and are referred to as advanced component development activities and include technology demonstrations. Completion of Technology Readiness Levels 6 and 7 should be achieved for major programs. Program control is exercised at the program and project level. A logical progression of program phases and development and/or production funding must be evident in the FYDP.

Budget Activity 5, System Development and Demonstration (SDD). SDD programs have passed Milestone B approval and are conducting engineering and manufacturing development tasks aimed at meeting validated requirements prior to full-rate production. This budget activity is characterized by major line item projects and program control is exercised by review of individual programs and projects. Prototype performance is near or at planned operational system levels. Characteristics of this budget activity involve mature system development, integration and demonstration to support Milestone C decisions, and conducting live fire test and evaluation and initial operational test and evaluation of production representative articles. A logical progression of program phases and development and production funding must be evident in the FYDP consistent with the Department’s full funding policy.

Budget Activity 6, RDT&E Management Support. This budget activity includes management and support for research, development, test and evaluation efforts and funds to sustain and/or modernize the installations or operations required for general research, development, test and evaluation. Test ranges, military construction, maintenance support of laboratories, operation and maintenance of test aircraft and ships, and studies and analyses in support of the RDT&E program are funded in this budget activity. Costs of laboratory personnel, either in-house or contractor operated, would be assigned to appropriate projects or as a line item in the Basic Research, Applied Research, or ATD program areas, as appropriate. Military construction costs directly related to major development programs are included in this budget activity.

Budget Activity 7, Operational System Development. This budget activity includes development efforts to upgrade systems that have been fielded or have received approval for full rate production and anticipate production funding in the current or subsequent fiscal year. All items are major line item projects that appear as RDT&E Costs of Weapon System Elements in other programs. Program control is exercised by review of individual projects. Programs in this category involve systems that have received approval for Low Rate Initial Production (LRIP). A logical progression of program phases and development and production funding must be evident in the FYDP, consistent with the Department’s full funding policy.

DOD, Financial Management Regulation (DOD 7000.14-R), Volume 2B, Chapter 5. Available at http://comptroller.defense.gov/FMR/vol2b_chapters.aspx .

D. NCSES Surveys on Federal R&D Funding

Survey of federal funds for research and development, survey of federal science and engineering support to universities, colleges, and nonprofit institutions, ffrdc research and development survey.

The Survey of Federal Funds for Research and Development is the primary source of information about federal funding for R&D in the United States. The survey is an annual census completed by the federal agencies that conduct R&D programs. For general information about this survey, please see https://www.nsf.gov/statistics/srvyfedfunds/.

R&D: Research and experimental development (R&D) activities are defined as creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of people, culture, and society—and to devise new applications using available knowledge.

For reporting R&D activities, include the following:

  • Investments in physical assets such as major equipment and facilities that support R&D programs. These investments should generally be reported under R&D Plant (see Tables 1, 1B, 2, 9, and 13 in the 2020 survey questionnaire available at https://www.nsf.gov/statistics/srvyfedfunds/#qs ).
  • Routine product testing, quality control, collection of general-purpose statistics, routine monitoring, and evaluation of an operational program (when that program is not R&D).
  • Training of scientific and technical personnel should be reported as conduct of education and training.

RDT&E (for DOD only): The Department of Defense’s Research, Development, Test, and Evaluation (RDT&E) can be both (1) activities for the development of a new system, or to expand the performance of fielded systems, and (2) an appropriation. The RDT&E budget activities are broad categories reflecting different types of RDT&E efforts, which include Basic Research (BA 1); Applied Research (BA 2); Advanced Technology Development (ATD) (BA 3); Major Systems Development, which includes Advanced Component Development and Prototypes (ACD&P) (BA 4), System Development and Demonstration (SDD) (BA 5), and RDT&E Management Support (BA 6); and Operational Systems Development (BA 7). The definitions of these categories are established by Department of Defense Instruction 5000.02, “Operation of the Defense Acquisition System.” For more information, see Budget Activities 1 through 7 in the DOD Financial Management Regulation (FMR), Volume 2B, Chapter 5, pages 5-4, 5-5, and 5-6 at http://comptroller.defense.gov/Portals/45/documents/fmr/Volume_02b.pdf .

R&D plant: R&D plant is defined as spending on both R&D facilities and major equipment as defined in Office of Management and Budget (OMB) Circular A-11 Section 84 (Schedule C) and includes physical assets, such as land, structures, equipment, and intellectual property (e.g., software or applications) that have an estimated useful life of two years or more. Reporting for R&D plant includes the purchase, construction, manufacture, rehabilitation, or major improvement of physical assets regardless of whether the assets are owned or operated by the Federal Government, States, municipalities, or private individuals. The cost of the asset includes both its purchase price and all other costs incurred to bring it to a form and location suitable for use.

For reporting construction of R&D facilities and major moveable R&D equipment, include the following:

  • Construction of facilities that are necessary for the execution of an R&D program. This may include land, major fixed equipment, and supporting infrastructure such as a sewer line, or housing at a remote location. Many laboratory buildings will include a mixture of R&D facilities and office space. The fraction of the building that is considered to be R&D may be calculated based on the percentage of square footage that is used for R&D.
  • Acquisition, design, or production of major moveable equipment, such as mass spectrometers, research vessels, DNA sequencers, and other moveable major instrumentation for use in R&D activities.
  • Programs of $1 million or more that are devoted to the purchase or construction of R&D major equipment.

Exclude the following:

  • Construction of other non-R&D facilities
  • Minor equipment purchases, such as personal computers, standard microscopes, and simple spectrometers (report these costs under total R&D, not R&D Plant)

Obligations for foreign R&D plant are limited to federal funds for facilities that are located abroad and used in support of foreign R&D.

Type of R&D: Type of R&D has three components for non-DOD respondents: basic research, applied research, and development.

Basic research: Basic research is defined as experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts. Basic research may include activities with broad or general applications in mind, such as the study of how plant genomes change, but should exclude research directed towards a specific application or requirement, such as the optimization of the genome of a specific crop species. Basic research represents Department of Defense Budget Activity 1.

Applied research: Applied research is defined as original investigation undertaken in order to acquire new knowledge. Applied research is, however, directed primarily towards a specific practical aim or objective. Applied research represents Department of Defense Budget Activity 2.

Experimental development: Experimental development is defined as creative and systematic work, drawing on knowledge gained from research and practical experience, which is directed at producing new products or processes or improving existing products or processes. Like research, experimental development will result in gaining additional knowledge.

  • The production of materials, devices, and systems or methods, including the design, construction, and testing of experimental prototypes.

For DOD Agencies, development itself is divided into three categories: advanced technology development, major systems development, and operational systems development.

  • Advanced technology development: This category is used for activities in DOD’s Budget Activity 3. For more information, see Budget Activity 3 on pages 5-4 and 5-5 of the DOD Financial Management Regulation (FMR), Volume 2B, Chapter 5, at http://comptroller.defense.gov/Portals/45/documents/fmr/Volume_02b.pdf.
  • Major systems development: This category is used for activities in DOD’s Budget Activities 4 through 6. For more information, see Budget Activities 4 through 6 on page 5-5 of the DOD Financial Management Regulation (FMR), Volume 2B, Chapter 5 at http://comptroller.defense.gov/Portals/45/documents/fmr/Volume_02b.pdf .
  • NOTE: As of the FY 2016 data collection, major systems development no longer includes Budget Activity 7.
  • Operational systems development: This category is used for activities in DOD’s Budget Activity 7. For more information, see Budget Activity 7 on page 5–6 of the DOD Financial Management Regulation (FMR), Volume 2B, Chapter 5 at http://comptroller.defense.gov/Portals/45/documents/fmr/Volume_02b.pdf.

NCSES, Survey of Federal Funds for R&D forms, available at https://www.nsf.gov/statistics/srvyfedfunds/#qs .

This NCSES survey is congressionally mandated and is the only source of comprehensive data on federal science and engineering funding to individual academic and nonprofit institutions. For general information see https://www.nsf.gov/statistics/srvyfedsupport/ .

Research and development (R&D) activities are defined as creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of people, culture, and society—and to devise new applications using available knowledge.

  • Investments in physical assets such as major equipment and facilities that support R&D programs. These investments should generally be reported under physical assets, discussed under R&D plant.

Advanced technology development (DOD only) is one of the two categories the Department of Defense uses for development (the “D” in R&D). The category advanced technology development is used for the activities in DOD’s Budget Activity 3, Advanced Technology Development (ATD). For more information, see Budget Activity 3 on pages 5-4 to 5-5 of the DOD Financial Management Regulation (FMR), Volume 2B, Chapter 5, at http://comptroller.defense.gov/portals/45/documents/fmr/current/02b/02b_05.pdf.

Major systems development (DOD only) is the second of the two categories the Department of Defense uses for development. The category major systems development is used for activities in DOD’s Budget Activities 4 through 6. For more information, see Budget Activities 4 through 6 (Advanced Component Development and Prototypes [ACD&P], System Development and Demonstration [SDD], and RDT&E Management Support) on page 5-5 of the DOD Financial Management Regulation (FMR), Volume 2B, Chapter 5 at http://comptroller.defense.gov/portals/45/documents/fmr/current/02b/02b_05.pdf.

NOTE: As of FY 2016 data collection, major systems development no longer includes Budget Activity 7.

R&D plant is defined as R&D facilities, intellectual property (e.g., software or applications); major fixed equipment, such as reactors, wind tunnels, and particle accelerators; and major moveable equipment, such as mass spectrometers, research vessels, DNA sequencers, and other major moveable instruments for use in R&D activities. Amounts include acquisition of, construction of, major repairs to, or alterations in structures, works, equipment, facilities, or land for use in R&D activities at federal or nonfederal installations. Excluded from the R&D plant category are costs of expendable or movable equipment (e.g., simple spectrometers, standard microscopes), personal computers, and office furniture and equipment. Also excluded are the costs of predesign studies (e.g., those undertaken before commitment to a specific facility).

These excluded costs are reported under “total conduct of research and development.”

If the R&D facilities are a larger facility devoted to other purposes as well, the funds should be distributed among the categories of support involved as appropriate. In general, another category that would be involved is facilities and equipment for instruction in S&E.

NCSES, Survey of Federal Science and Engineering Support to Universities, Colleges, and Nonprofit Institutions, available at https://www.nsf.gov/statistics/srvyfedsupport/#qs .

The FFRDC Research and Development Survey is the primary source of information on separately budgeted R&D expenditures at federally funded research and development centers (FFRDCs) in the United States. Conducted annually for university-administered FFRDCs since FY 1953 and all FFRDCs since FY 2001, the survey collects information on R&D expenditures by source of funds and types of research and expenses. The survey is an annual census of the full population of eligible FFRDCs. See https://www.nsf.gov/statistics/srvyffrdc/ for more on this survey https://www.nsf.gov/statistics/ffrdclist/ for the Master List of FFRDCs maintained by NCSES.

Research and Development (R&D)

R&D is creative and systematic work undertaken in order to increase the stock of knowledge— including knowledge of humankind, culture, and society—and to devise new applications of available knowledge. R&D covers three activities defined below—basic research, applied research, and experimental development.

  • Basic research is experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view.
  • Applied research is original investigation undertaken in order to acquire new knowledge. It is directed primarily towards a specific, practical aim or objective.
  • Experimental development is systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

NCSES, FFRDC R&D Survey forms, available at https://www.nsf.gov/statistics/srvyffrdc/ .

E. State Government R&D

Survey of state government r&d.

This NCSES survey is the only source for comprehensive, uniform statistics regarding the extent of R&D activity performed and funded by departments and agencies in each of the nation’s 50 state governments, the government of the District of Columbia, and the government of Puerto Rico. For general information, see https://www.nsf.gov/statistics/srvystaterd/.

R&D comprise creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture, and society—and to devise new applications of available knowledge.

  • R&D is aimed at new findings (novel)
  • It has not been done before
  • It may produce findings that could be published in academic journals
  • It includes ideas that could be patented
  • R&D focuses on original concepts or ideas (creative)
  • Increases our knowledge of the subject
  • Helps create new products or applications
  • R&D outcomes are uncertain (because it’s never been done before)
  • Solutions are not always obvious or expected
  • Uncertain about, cost, time, or ability to achieve results
  • R&D is planned and budgeted (systematic)
  • Projects processes and outcomes are documented
  • Projects are planned and managed
  • R&D results in solutions that others may find useful (transferable)
  • Findings can be generalized to other situations and locations
  • Findings are reproducible

What is NOT R&D?

  • Construction and acquisition of land and facilities used primarily for R&D (reported separately in this survey)
  • Fixed equipment used primarily for R&D (reported separately in this survey)
  • Program planning and evaluation
  • Business development services for new companies
  • Commercialization (includes promoting/producing the products/services from R&D projects)
  • Economic/policy/feasibility studies
  • General patient services
  • Information systems
  • Management studies
  • Marketing of products/services
  • Market research or analysis
  • Routine data collection/dissemination
  • Routine monitoring/testing
  • Strategic planning
  • Technology transfer

NCSES, Survey of State Government R&D forms. Available at https://www.nsf.gov/statistics/srvystaterd/#qs .

IV. U.S. Higher Education R&D and R&D by Nonprofit Organizations

A. guidance from the office of management and budget.

OMB issued the Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards, Title 2 Part 200 of the Code of Federal Regulations (CFR) in December 2013. This guidance supersedes and streamlines requirements from the following OMB Circulars: A-21, A-50, A-87, A-89, A-102, A-110, A-122, and A-133. The full text of 2 CFR Part 200 is available at http://www.ecfr.gov/cgi-bin/text-idx?ID=68fca03721b9c921be5236306ae7a5fa&tpl=/ecfrbrowse/Title02/2chapterII.tpl .

Previous definitions for R&D reporting relevant to educational institutions, hospitals and nonprofit organizations, state and local governments, and nonprofit organizations were addressed in OMB Circulars A-21, A-110, and A-133. Although these circulars are still available ( https://obamawhitehouse.archives.gov/omb/circulars_default ) they are, with limited exceptions, no longer applied to assistance awards issued after the implementation date of 26 December 2014.

Research and Development (R&D) means all research activities, both basic and applied, and all development activities that are performed by non-federal entities. The term research also includes activities involving the training of individuals in research techniques where such activities utilize the same facilities as other research and development activities and where such activities are not included in the instruction function.

“Research” is defined as a systematic study directed toward fuller scientific knowledge or understanding of the subject studied. “Development” is the systematic use of knowledge and understanding gained from research directed toward the production of useful materials, devices, systems, or methods, including design and development of prototypes and processes.

2 CFR 200.87. Available at http://www.ecfr.gov/cgi-bin/text-idx?tpl=/ecfrbrowse/Title02/2cfr200_main_02.tpl .

B. Higher Education R&D

Higher education research and development (herd) survey.

This NCSES survey is the primary source of information on R&D expenditures at U.S. colleges and universities and is the successor to the Survey of Research and Development Expenditures at Universities and Colleges. The HERD Survey collects information on R&D expenditures by field of research and source of funds and also gathers information on types of research and expenses and headcounts of R&D personnel. The survey is an annual census of institutions that expended at least $150,000 in separately budgeted R&D in the fiscal year. For general information about this survey, please see https://www.nsf.gov/statistics/srvyherd/.

R&D is creative and systematic work undertaken in order to increase the stock of knowledge—including knowledge of humankind, culture, and society—and to devise new applications of available knowledge. R&D covers three activities defined below—basic research, applied research, and experimental development.

NCSES, Higher Education Research and Development Survey forms. Available at https://www.nsf.gov/statistics/srvyherd/.

C. R&D by Nonprofit Organizations

Nonprofit research activities survey.

The Nonprofit Research Activities (NPRA) Survey measures research and experimental development (R&D) performance and funding at U.S. 501(c) nonprofit organizations. It is currently collected as a separate module of the ABS data collection.

  • Type of R&D work (basic research, applied research, and experimental development)
  • R&D field

For the purposes of this survey, research includes research and experimental development. Research and experimental development comprise creative and systematic work to

  • Increase the stock of knowledge, including knowledge of humankind, culture, and society OR
  • Devise new applications of available knowledge, including materials, products, devices, processes, systems, or services

Research activities must be

  • Novel: projects that advance current knowledge or create new knowledge
  • Creative: projects focused on original concepts and hypotheses
  • Uncertain: project outcomes are unable to be completely determined at the outset
  • Systematic: projects are planned and budgeted
  • Transferable/Reproducible: project methodology and results are transferable/reproducible to other situations and locations

May meet the criteria for research

  • Laboratory or animal studies
  • Clinical trials
  • Prototype development
  • Outcomes research
  • Development/measurement of new methods to deliver/measure social service outcomes
  • Policy research
  • Humanities research
  • Research traineeships
  • Other experimental studies

Most likely do not meet the criteria for research

  • Internal program monitoring or evaluation
  • Public service grants or outreach programs
  • Education or training programs
  • Quality control testing
  • Management studies/efficiency surveys
  • Feasibility studies, unless included as part of an overall research project

Type of R&D Work

  • Basic research: Experimental or theoretical work undertaken primarily to acquire new knowledge of the underlying foundations of phenomena and observable facts, without any particular application or use in view.
  • Applied research: Original investigation undertaken in order to acquire new knowledge. It is directed primarily towards a specific, practical aim or objective.
  • Experimental development: Systematic work, drawing on knowledge gained from research and practical experience and producing additional knowledge, which is directed to producing new products or processes or to improving existing products or processes.

NCSES ABS Nonprofit Module questionnaire. Available at https://www.nsf.gov/statistics/srvynpra/ .

V. R&D in National Accounts and Globalization Manuals

A. r&d in the system of national accounts (sna).

The System of National Accounts, 2008 (2008 SNA) is a statistical framework that provides a comprehensive set of macroeconomic accounts for policy and research purposes. The 2008 SNA recognized R&D as investment or produced asset in an economy (SNA 6.230, 10.98). R&D is defined in paragraph 10.103 (Chapter 10: The capital account, Section B: Gross capital formation).

10.103 Intellectual property products include the results of research and development (R&D). Research and [experimental] development consists of the value of expenditures on creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of man, culture and society, and use of this stock of knowledge to devise new applications.

United Nations (UN) Statistical Division—2008 System of National Accounts.

B. Measuring R&D in global economic activities

Guidance for official statistics on trade, investment, and international production—called global value chains (GVCs) in recent economics and policy research literature—explicitly cover R&D and related intangible assets under the heading of “intellectual property products” (IPP). (In addition to R&D, IPPs include software and databases, entertainment, literary or artistic originals, and the results from mineral exploration.) The information below briefly covers selected international statistical manuals.

  • OECD Handbook on Deriving Capital Measures of Intellectual Property Products, 2010

This handbook uses the SNA 2008 R&D definition (10.103) and describes domestic R&D output for purposes of national and international economic accounts in terms of three components consistent with both the SNA and Frascati: own account R&D (R&D conducted and used internally regardless of funding source); custom R&D (R&D conducted for, and funded by, another unit); and speculative or non-customized R&D.

  • Balance of Payments and International Investment Position Manual, 6th ed., 2009 (BPM6)

The manual covers accounting and statistical standards to compile the balance of payments (BOP), a statement that summarizes economic transactions—including R&D and other IPP— between residents and nonresidents (BPM6 2.2(b)). BPM6 incorporated R&D as an intellectual property product within the balance of payments (see BPM6 Table 10.4 and related text).

  • OECD Benchmark Definition of Foreign Direct Investment (FDI), 4th ed., 2008

This guidance describes definitions and measurement procedures for FDI flows and stocks consistent with the Balance of Payments and International Investment Position Manual. It also covers definitions of activities of multinational enterprises (MNEs) (AMNE for short) including sales, value added, employment, R&D, and international trade. For related definitions, see Statistics on the Activities of Multinational Enterprises, Chapter 12 in U.S. International Economic Accounts: Concepts & Methods, U.S. Bureau of Economic Analysis, 2014.

  • Manual on Statistics of International Trade in Services (MSITS), 2010

This manual covers statistics on international supply of services, including R&D services as defined in MSITS paragraph 3.234.

3.234. Research and development services covers those services that are associated with basic research, applied research and experimental development of new products and processes and covers activities in the physical sciences, the social sciences and the humanities.

  • Guide to Measuring Global Production, 2015

This manual further elaborates on measurement issues from GVCs and related global manufacturing arrangements and transactions, including exchanges of R&D and other intangibles or intellectual property products. See especially chapter 4 (Ownership of intellectual property products inside global production).

OCED, Frascati Manual , 7th ed, “Measurement of R&D Globalisation,” chapter 11. Available at http://oe.cd/frascati .

International Monetary Fund (IMF). 2009. Balance of Payments and International Investment Position Manual , 6th ed. (BPM6). Washington, D.C. Available at https://www.imf.org/external/pubs/ft/bop/2007/pdf/BPM6.pdf.

Organisation for Economic Cooperation and Development (OECD). 2015. Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development , 7th ed. Paris, France. Available at http://oe.cd/frascati and https://www.oecd.org/publications/frascati-manual-2015-9789264239012-en.htm .

Organisation for Economic Cooperation and Development (OECD). 2010. Handbook on Deriving Capital Measures of Intellectual Property Products (IPP Handbook). Paris, France. Available at http://www.oecd.org/std/na/44312350.pdf .

Organisation for Economic Cooperation and Development (OECD). 2008. OECD Benchmark Definition of Foreign Direct Investment, 4th ed. Paris, France. Available at https://www.oecd.org/daf/inv/investmentstatisticsandanalysis/40193734.pdf.

United Nations Economic Commission for Europe, Organisation for Economic Cooperation and Development (UNECE/OECD). 2015. Guide to Measuring Global Production . Geneva, Switzerland. Available at http://www.unece.org/info/media/news/statistics/2016/unece-provides-practical-guidance-on- measuring-global-production/doc.html .

European Commission, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations, and World Bank. 2009. System of National Accounts 2008 (SNA). New York, NY. Available at http://unstats.un.org/unsd/nationalaccount/sna2008.asp .

United Nations, Eurostat, International Monetary Fund, Organisation for Economic Co-operation and Development, United Nations Conference on Trade and Development, World Tourism Organization, World Trade Organization 2011. Manual on Statistics of International Trade in Services 2010 (MSITS). Geneva, Switzerland. Available at http://unstats.un.org/unsd/tradeserv/TFSITS/manual.htm .

Report Authors

Francisco Moris Senior Analyst Research and Development Statistics Program, NCSES Tel: (703) 292-4678 E-mail: [email protected]

Christopher Pece Survey Manager Research and Development Statistics Program, NCSES Tel: (703) 292-7788 E-mail: [email protected]

National Center for Science and Engineering Statistics Directorate for Social, Behavioral and Economic Sciences National Science Foundation 2415 Eisenhower Avenue, Suite W14200 Alexandria, VA 22314 Tel: (703) 292-8780 FIRS: (800) 877-8339 TDD: (800) 281-8749 E-mail: [email protected]

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Roles Conceptualization, Data curation, Resources, Software, Writing – review & editing

Roles Funding acquisition, Project administration, Supervision, Writing – review & editing

Affiliation Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Miyagi, Japan

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Project administration, Resources, Software, Supervision, Writing – review & editing

Roles Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Department of International and Community Oral Health, Tohoku University Graduate School of Dentistry, Sendai, Miyagi, Japan, Department of Preventive Medicine, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan, Division of Statistics and Data Science, Liaison Center for Innovative Dentistry, Tohoku University Graduate School of Dentistry, Sendai, Miyagi, Japan

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  • Yudai Tamada, 
  • Taro Kusama, 
  • Sachiko Ono, 
  • Megumi Maeda, 
  • Fumiko Murata, 
  • Ken Osaka, 
  • Haruhisa Fukuda, 
  • Kenji Takeuchi

PLOS

  • Published: May 7, 2024
  • https://doi.org/10.1371/journal.pone.0299849
  • Reader Comments

Fig 1

Secondary healthcare data use has been increasing in the dental research field. The validity of the number of remaining teeth assessed from Japanese dental claims data has been reported in several studies, but has not been tested in the general population in Japan.

To evaluate the validity of the number of remaining teeth assessed from Japanese dental claims data and assess its predictability against subsequent health deterioration.

We used the claims data of residents of a municipality that implemented oral health screening programs. Using the number of teeth in the screening records as the reference standard, we assessed the validity of the claims-based number of teeth by calculating the mean differences. In addition, we assessed the association between the claims-based number of teeth and pneumococcal disease (PD) or Alzheimer’s disease (AD) in adults aged ≥65 years using Cox proportional hazards analyses.

Of the 10,154 participants, the mean number of teeth assessed from the claims data was 20.9, that in the screening records was 20.5, and their mean difference was 0.5. During the 3-year follow-up, PD or AD onset was observed in 10.4% (3,212/30,838) and 5.3% (1,589/30,207) of participants, respectively. Compared with participants with ≥20 teeth, those with 1–9 teeth had a 1.29 (95% confidence interval [CI]: 1.17–1.43) or 1.19 (95% CI: 1.04–1.36) times higher risk of developing PD or AD, respectively.

High validity of the claims-based number of teeth was observed. In addition, the claims-based number of teeth was associated with the risk of PD and AD.

Citation: Tamada Y, Kusama T, Ono S, Maeda M, Murata F, Osaka K, et al. (2024) Validity of claims-based definition of number of remaining teeth in Japan: Results from the Longevity Improvement and Fair Evidence Study. PLoS ONE 19(5): e0299849. https://doi.org/10.1371/journal.pone.0299849

Editor: Chung-Ta Chang, Far Eastern Memorial Hospital, TAIWAN

Received: December 13, 2023; Accepted: February 18, 2024; Published: May 7, 2024

Copyright: © 2024 Tamada et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The data used in this study were acquired under agreements between Kyushu University and the participating municipality, which stipulate that the data can only be used by authorized research institutions and cannot be shared with third parties. However, research institutions that have entered into agreements with the authorized research group in Kyushu University may access the data. For inquiries about the datasets used in this study, please contact the principal investigator of the Longevity Improvement and Fair Evidence Study, Dr. Haruhisa Fukuda (email: [email protected] ), upon reasonable request. Alternatively, please contact the Joint Research Department of Kyushu University (email: [email protected] ) or the LIFE Study Secretariat (inquiry form: https://life.hcam.med.kyushu-u.ac.jp/contact/ ) regarding data access. The datasets we used in this study and analytical codes are stored in the multiple storage devices hold by the first author, corresponding author, and LIFE study office to ensure long-term data storage and availability.

Funding: This study was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI in the form of a Grant-in-Aid for Scientific to KT [22H03299, 23K18370], the Ministry of Health, Labour and Welfare, Japan in the form of a Health Labour Sciences Research Grant to KO [23FA1022], and the the Japan Science and Technology Agency in the form of a JST FOREST Program Grant to HF [JPMJFR205J].

Competing interests: The authors have declared that no competing interests exist.

Secondary use of healthcare data (e.g., administrative claims databases) has been increasing in the field of dental research [ 1 , 2 ]. Such health data are often large and population-representative, and the results of their analyses are expected to provide information on therapeutic effectiveness and safety in real-world settings. However, because such data were initially collected for non-research purposes, it is possible that they do not always precisely reflect patients’ health conditions. For instance, claims data are collected for reimbursement purposes; hence, if patients have a record of a disease, it is sometimes difficult to differentiate whether the disease was registered because the patient had the disease or because the healthcare providers wanted to provide disease-related procedures (e.g., the patients who required a chest scan might be recorded as having lung cancer, in extreme cases). Therefore, validation studies are required to minimize misclassification and enhance the reliability of secondary health data analyses [ 3 ].

In Japan, various oral health promotion policies, such as the 8020 Campaign [ 4 ] and Health Japan 21 [ 5 ], have been implemented to maintain citizens’ oral health and prevent tooth loss. From a policymaking perspective, it is important to evaluate the effectiveness of such policies by assessing whether people retained their teeth after policy implementation. In this process, the number of remaining teeth needs to be assessed accurately and in a timely manner; however, self-reported questionnaires or dental examinations, which take time and are costly, are often used. Although there is potential for the number of teeth to be efficiently assessed using claims data, the use of claims-based assessment is limited, perhaps because its validity has not been sufficiently established. Several studies have reported the validity of the number of remaining teeth in dental claims data [ 6 , 7 ]; however, these studies were limited to patients in one hospital [ 6 ] or to assessments at the population level [ 7 ], rather than at the individual level. Therefore, it remains unclear whether the number of remaining teeth in dental claims data is valid for the general population if assessed using individual-level data.

Tooth loss is one of the most prevalent conditions affecting millions of people worldwide [ 8 ] and is associated with a higher risk of mortality [ 9 ] and systemic diseases [ 10 – 14 ]. Although various systemic diseases have been reported to be associated with oral diseases, a recent umbrella review indicated that there is a greater amount of strong evidence for an association between tooth loss and neurodegenerative diseases, such as Alzheimer’s disease (AD), than other diseases [ 15 ]. Another review concluded that periodontitis, the main cause of tooth loss [ 16 ], is a strong risk factor for pneumonia [ 17 ]. Given that pneumococcal pneumonia, a lung infection caused by Streptococcus pneumoniae , is the most common type of pneumonia [ 18 ], tooth loss is also considered to be associated with pneumococcal disease (PD). However, no study has investigated the association between the number of teeth and PD or AD using Japanese claims data.

In this context, the primary purpose of this study was to examine the validity of the claims-based number of remaining teeth assessed using Japanese dental claims data from community-dwelling adults using oral health screening records as reference standards. As the secondary purpose, this study aimed to assess whether the claims-based number of teeth predicts the subsequent risk of PD and AD in adults aged ≥65 years, using the Japanese claims data.

Study population and setting

This study used data from the Longevity Improvement & Fair Evidence (LIFE) Study [ 19 ], a longitudinal multi-region community-based database project that aims to provide evidence for extending healthy life expectancy and reducing health disparities. We used data from a municipality with a residential population of approximately 700,000 that provided oral health screening data and healthcare claims data for two insurance systems (i.e., the National Health Insurance and the Latter-Stage Older Persons Health Care System). The municipality has implemented two annual oral health-screening programs (i.e., periodontal disease screening and oral healthcare screening). Periodontal disease screening was conducted for adults aged 20–70 years in five-year increments from June to November, and oral healthcare screening was conducted for those aged ≥65 years throughout the year. Both screenings were conducted in designated dental clinics as individual screenings, and the dentists determined the number of remaining teeth. National Health Insurance is a public insurance scheme for those not employed; enrollees include self-employed or part-time workers, retired workers, and their dependents. In addition, the Latter-Stage Older Persons Health Care System is a public insurance scheme for those aged ≥75 years; the enrollees cover all the residents aged ≥75 years in the municipality. The health insurance system in Japan has been described in detail elsewhere [ 20 ]. In the LIFE Study, a unique research ID was assigned to each resident by the data managers, and each data point was linked at the individual level.

Two series of analyses were performed. First, we examined the validity of the claims-based number of remaining teeth, using information on the number of teeth, based on oral health screening records as the reference standard (validation analysis). The number of teeth in the screening records was used as the reference standard because it was expected to be more reliable than our algorithm-based estimation as it was determined by dentists following an instruction manual [ 21 ]. We extracted 28,539 screening records from the database that were collected in the fiscal years 2018 or 2019 (April 2018–March 2020). We excluded the following screening records: records that were not identifiable to a particular resident (n = 17), those of people who undertook either screening (periodontal disease screening or oral healthcare screening) ≥2 times in a year or duplicated records (n = 214), those of people who undertook both screenings in a year (n = 14), those recorded as having ≥32 teeth (n = 10), and those of people not in the eligible age range for the screenings (n = 465). Of the 27,819 eligible records, 10,154 could be linked with claims-based data on the number of remaining teeth ( Fig 1 ). In the main analysis, the participants’ claims-based number of teeth was assessed using their 12 months of dental claims data prior to the screening month. In the sensitivity analysis, we defined the claims-based number of teeth using the participants’ 12 months of dental claims data after the screening month. Second, we examined the associations between the claims-based number of teeth and the risks of PD or AD, which are previously reported consequences of poor oral health (prediction analysis) [ 10 – 14 ]. Using dental claims between April 2016 and March 2017, we assessed the number of teeth of eligible participants, aged ≥65 years as of March 2017, who had continuous health insurance enrollment during the assessment period. After excluding participants diagnosed with PD or AD during the number-of-teeth assessment period, we followed the participants from April 1, 2017, to March 31, 2020. The design diagram [ 22 ] is shown in S1 Fig .

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https://doi.org/10.1371/journal.pone.0299849.g001

Claims-based assessment of number of remaining teeth

Although direct information on the number of remaining teeth is not included in the Japanese dental claims data, monthly data on the number of treated teeth per treatment are available using the listed dental formulas. The dental formula is a six-digit code, with the first four digits identifying the teeth (e.g., the maxillary central incisor), the next digit indicating the status of the teeth (e.g., remaining or missing), and the last digit assigned to the treated part of the teeth (e.g., mesial or distal). Considering that all remaining teeth would be assessed in basic/comprehensive periodontal examinations, we extracted the dental claims for the months when the procedures were conducted by identifying them with the corresponding procedure codes (304000410, 304000510, 304000610, 304000710, 304000810, and 304000910). After excluding the dental formulas of missing teeth identified by the fifth digit, we counted the number of dental formulas per treatment. Finally, the maximum number of dental formulas in the assessment period (12 months) was defined as the participants’ claims-based number of remaining teeth. In addition, in the sensitivity analysis, we defined the number in the latest claims data in the assessment period as the claims-based number of remaining teeth.

PD and AD assessments

In this study, according to definitions used in previous studies [ 23 , 24 ], the onset of PD was identified using the original Japanese diagnostic codes in the medical claims data ( S1 Table ). As the claims included only monthly data on diseases, we defined the onset dates in two ways, based on the types of patient care: (i) for outpatients, the 15th of the month when they were diagnosed with PD for the first time during follow-up, and (ii) for inpatients, the admission date for in-hospital PD treatments for the first time during follow-up. The onset of AD was identified using the International Classification of Diseases, 10th Revision (ICD-10) code (G30) [ 25 , 26 ], and the onset dates were defined in the same manner as those for PD. Additionally, we conducted a sensitivity analysis to identify the onset of AD using a combination of the corresponding ICD-10 code and prescriptions for AD medications (donepezil, rivastigmine, galantamine, and memantine). In the analysis, the onset date was defined as the first prescription date of AD medication during follow-up.

Statistical analysis

In the validation analysis, we generated a Bland–Altman plot and heatmap and calculated the intraclass correlation coefficient (ICC) between the claims-based number of teeth and the number of teeth in the screening records. In addition, we calculated ICCs based on the number of teeth groups and age groups. For sensitivity analyses, we generated a Bland–Altman plot using the claims-based number of teeth defined by the participants’ 12 months of dental claims data after the screening month. In addition, we generated heatmaps using the subsample stratified by age group (20–39/40–64/65–74/≥75 years) to assess the consistency of validity of the claims-based number of teeth across life stages. The age-group classification was employed to approximately capture the participants’ life stages as young (20–39 years), middle-aged (40–64 years), early older (65–74 years), and late older adults (≥75 years).

In the prediction analysis, log-rank tests were used to compare the cumulative incidence curves by the claims-based number of teeth categories (1–9/10–19/≥20 teeth), at baseline. In addition, Cox proportional hazards models were used to estimate the hazard ratios (HRs) for the onset of PD/AD during the 3-year follow-up period, according to the claims-based number of teeth categories, after adjusting for sex, age group, hypertension (ICD-10 code: I10), and diabetes (ICD-10 codes: E10–E14). Their 95% confidence intervals (CIs) were obtained by bootstrapping with 1,000 replications [ 27 ]. The proportional hazard assumption was verified using Schoenfeld residuals. Follow-up was censored at the onset of PD/AD, disenrollment from health insurance, or end of follow-up (March 31, 2020), whichever occurred first. In the additional analysis, we used a composite variable defined by combining the claims-based number of teeth and denture use status (1–9 teeth not using dentures/1–9 teeth using dentures/10–19 teeth not using dentures/10–19 teeth using dentures/≥20 teeth) as the exposure instead of the claims-based number of teeth. Denture use status was assessed by the routinely reimbursed denture management fee (procedure codes: 308002510, 308002610, 308002710, or 308004210) appearing in the claims-based number of teeth assessment period.

All analyses were conducted using Stata (version 17.0; Stata Corp., College Station, TX, USA). This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Data analysis was conducted from September 27, 2022, to June 27, 2023.

Ethical considerations

This study was approved by the Kyushu University Institutional Review Board for Clinical Research (approval number: 22114–02) and the Ethics Committee of Tohoku University Graduate School of Dentistry (approval number: 23835). Data usage approval was obtained from the municipality’s Personal Information Protection Review Board. Informed consent was waived by the ethics committee since this study was based on previously collected medical records data. All the information that we had access were fully anonymized and could not be used to identify individual participants during or after data collection.

Validity of claims-based number of remaining teeth

Table 1 presents the descriptive statistics of the analytical sample in the validation analysis according to the oral health screening programs. Among the 10,154 participants, 1,999 (age: 59.0±12.8 years [mean±standard deviation (SD)]) underwent periodontal disease screening and 8,155 (age: 77.1±6.0 years [mean±SD]) underwent oral healthcare screening. Of the participants from the periodontal disease screening group, the mean number of teeth assessed from the claims data was 24.6 (SD: 4.9) and that in the screening records was 24.7 (SD: 5.2). However, among those from the oral healthcare screening group, the mean number of teeth assessed from the claims data was 20.1 (SD: 7.2) and that in the screening records was 19.5 (SD: 7.7).

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https://doi.org/10.1371/journal.pone.0299849.t001

Fig 2A is the Bland–Altman plot of the number of teeth between the claims-based assessment and the screening records. Overall, the number of teeth assessed from the claims data was slightly higher than that in the screening records and their mean differences were 0.5 (SD: 2.1) in all participants, 2.2 (SD: 4.3) in those with 1–9 teeth, 0.7 (SD: 1.8) in those with 10–19 teeth, and 0.0 (SD: 1.1) in those with ≥20 teeth. In addition, the mean difference between the number of teeth in the screening records and that defined using the participants’ dental claims data after the screening month was 0.2 (SD: 2.1) ( S2 Fig ). Fig 2B shows the heatmap of the number of teeth assessed from the claims data and that in the oral health screening records. The claims-based number of teeth was highly correlated with the number of teeth in the screening records (ICC: 0.96). In the stratified analysis based on age group, a higher correlation was observed in the older population (ICC: 20–39 years, 0.58; 40–64 years, 0.97; 65–74 years, 0.98; ≥75 years, 0.97) ( S2 Table and S3 Fig ). In addition, in the stratified analysis based on number of teeth group in screening records, a higher correlation was observed in the participants with a higher number of teeth (ICC: ≤9 teeth, 0.44; 10–19 teeth, 0.89; ≥20 teeth, 0.96) ( S1 Table ). Furthermore, similar results were observed in the sensitivity analysis that defined the claims-based number of teeth using the latest claims data in the 12 months before the screening month ( S4 Fig ). The mean differences between the claims-based assessment and the screening records were 0.3 (SD: 1.8), in all participants, 1.7 (SD: 3.6) in those with 1–9 teeth, 0.5 (SD: 1.7) in those with 10–19 teeth, and -0.1 (SD: 1.1) in those with ≥20 teeth.

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(A) Bland-Altman plot of claims-based number of teeth and number of teeth in screening records. (B) Heatmap of claims-based number of teeth and number of teeth in screening records.

https://doi.org/10.1371/journal.pone.0299849.g002

Association between claims-based number of teeth and PD/AD

S3 Table shows the descriptive statistics of the analytical sample used in the prediction analysis for PD, according to the number of teeth. Of the 30,838 participants (age: 76.4±7.3 years [mean±SD]), 14.1% had 1–9 teeth, 27.9% had 10–19 teeth, and 58.0% had ≥20 teeth. By the end of the follow-up period (mean: 2.7 years), PD onset was observed in 3,212 (10.4%) participants, especially those with a lower number of teeth, who were more likely to develop PD (log-rank test: P <0.001, Fig 3A ). Descriptive statistics of the analytical sample and the cumulative incidence curves in the prediction analysis for AD are presented in S4 Table and Fig 3B . During the follow-up period (mean: 2.8 years), 1,589 (5.3%) participants developed AD; AD onset was more likely to be observed in participants with a lower number of teeth (log-rank test: P <0.001).

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https://doi.org/10.1371/journal.pone.0299849.g003

Table 2 shows the results of the Cox proportional hazards analyses of the associations between the claims-based number of teeth and PD/AD onset during follow-up. Even after adjusting for sex, age group, hypertension, and diabetes, fewer teeth remained associated with a higher risk of developing PD/AD. Compared with the participants with ≥20 teeth, those with 1–9 teeth had a 1.29 (95% CI: 1.17–1.42) times higher risk of PD and a 1.19 (95% CI: 1.04–1.35) times higher risk of AD. The results of the sensitivity analysis that integrated AD medications into the definition of AD, were similar to those of the primary analysis ( S5 Table and S5 Fig ). For instance, the participants with 1–9 teeth had a 1.08 (95% CI: 0.92–1.26) times higher risk of AD than those with ≥20 teeth. In the additional analysis that considered denture use status, participants using dentures had a lower risk of PD/AD than those not using dentures ( S6 Table ). For instance, the participants with 1–9 teeth using dentures had a 1.24 (95% CI: 1.12–1.38) times higher risk of PD and a 1.14 (95% CI: 0.98–1.32) times higher risk of AD than those with ≥20 teeth, while those with 1–9 teeth not using dentures had a 1.41 (95% CI: 1.21–1.66) times higher risk of PD and a 1.35 (95% CI: 1.08–1.70) times higher risk of AD.

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https://doi.org/10.1371/journal.pone.0299849.t002

In this study of community-dwelling adults in Japan, the claims-based number of remaining teeth was broadly consistent with that in the screening records. The difference in the number of teeth between claims-based assessment and screening records was slightly larger for participants with fewer teeth. In addition, we observed an inverse association between the claims-based number of teeth and risk of PD/AD onset among older adults, indicating that individuals with fewer teeth had a higher risk of PD or AD onset during the three-year follow-up period.

Our validation results for the claims-based number of remaining teeth were generally comparable with those previously reported [ 6 , 7 ]. In a study of academic hospital patients, the mean difference was 1.0 teeth when the number of teeth assessed from the dental claims data was compared with that from radiographs [ 6 ], and this result was consistent with ours. In contrast, an ecological study based on claims-based assessment using a national database and summary results of a national survey in Japan [ 7 ] reported larger differences in the mean number of teeth among the older population. This difference to our findings may be partially explained by the tendency of the number of teeth in claims-based assessments to differ from the actual number of teeth in individuals with fewer teeth, such as older adults. We believe that our findings provide additional evidence that the claims-based number of teeth can be used as a parameter to determine the actual oral health status of community-dwelling populations.

The observed number of teeth in the claims-based assessment was slightly higher than that in the screening records, especially when participants had fewer teeth. A possible explanation is that we defined the claims-based number of teeth as the maximum number of candidates assessed using participants’ 12 months of claims data prior to the screening month. Hence, it is possible that if the participants had lost their teeth during the relevant 12-month period, the number of teeth in the screening records would have been fewer than in the claims-based assessment. This explanation is supported by our sensitivity analysis, which defined the claims-based number of teeth using participants’ claims data after the screening month. We found smaller differences in the number of teeth between the claims-based assessment and screening records than in the primary analysis.

Several possible mechanisms may underlie the association between a lower number of teeth and a higher risk of PD/AD. First, periodontal disease, which is the main cause of tooth loss [ 16 ], may induce systemic inflammation. Although this study did not include an evaluation of inflammation due to periodontal disease, the existing literature has demonstrated that chronic exposure to periodontal inflammation results in tooth loss [ 28 , 29 ]. Hence, people with fewer teeth may have been continuously exposed to increased inflammatory mediators due to periodontal disease, at least until before losing their teeth, which is considered to contribute to the development of AD [ 30 , 31 ]. Second, masticatory and swallowing dysfunctions caused by tooth loss [ 32 , 33 ] may lead to subsequent functional decline and health behavioral changes that could be associated with diseases. For instance, swallowing difficulties increase the risk of aspiration of microbes from oral biofilms [ 34 ] and may contribute to the onset of PD. Active mastication stimulates cerebral blood flow and increases cerebral arousal [ 35 ]; hence, masticatory dysfunction can contribute to the onset of AD through a decline in brain function. Moreover, poor oral function is thought to decrease vegetable and fruit intake [ 36 ], and consequently increase the risk of cognitive impairment [ 37 ].

Our validation results for the claims-based number of teeth may have several implications. For research purposes, the claims-based number of teeth could be used as a covariate in a statistical model when conducting clinical epidemiology studies in which the number of teeth may be regarded as a confounding factor. In addition, the number of teeth is considered to be influenced by social and lifestyle-related factors throughout life [ 16 , 38 ]. For instance, previous studies have indicated that social factors in childhood, such as adverse childhood experiences [ 39 , 40 ], are associated with fewer remaining teeth, later in life. Given that the Japanese claims data do not include sufficient information on social background, the claims-based number of teeth may be used as a proxy that partially reflects the participants’ socioeconomic status and health conditions. For health policy evaluation purposes, the claims-based number of teeth can be used to assess the success of oral health promotion policies. Since claims data are already collected for reimbursement, municipal governments may be able to assess the oral health status of their residents by evaluating the number of teeth through claims-based assessments without costly primary data collection, such as dental examinations or surveys.

This study had several limitations. First, oral examinations in the screening programs were not performed by calibrated examiners; thus, the number of teeth in the screening records may have differed between examiners. However, considering that the examinations were performed following an instruction manual [ 21 ], misclassification may not have occurred often, which may not have significantly affected the results. Second, our analytical sample included only a limited number of younger participants, particularly those with fewer teeth. Therefore, our validation results should be interpreted with caution, especially when generalizing them to younger populations. Third, our analytical sample relied on a selective population of individuals who underwent periodontal disease screening or oral healthcare screening. However, there is little possibility that the input of dental formulas into the dental claims data was systemically different between people who underwent the screenings and those who did not. Therefore, we believe that our findings can be generalized. Fourth, we observed that the claims-based number of teeth was associated with PD/AD; however, there may be unadjusted potential confounders, such as education and income levels. Although there may be many potential confounders, we only adjusted for sex, age group, hypertension, and diabetes to assess whether the number of remaining teeth predicts subsequent PD/AD onset, rather than exploring their causality [ 41 ]. Hence, our results need to be interpreted with caution, as there may be no causal relationship. Fifth, if participants with cognitive impairment fail to maintain good oral hygiene and consequently lose their teeth, there may be a possibility of reverse causation in the association between the claims-based number of teeth and AD. However, by excluding participants who were diagnosed with AD during the 12 months before starting the follow-up from the analytical sample ( S1 Fig ), we can partially address the possibility of reverse causation.

In conclusion, our study demonstrated the validity of the claims-based number of remaining teeth in a community-dwelling population. In addition, a lower number of teeth were associated with a higher risk of PD/AD among older adults. Our findings may facilitate the use of dental claims data for research and health policy evaluation.

Supporting information

S1 fig. design diagram of this study..

https://doi.org/10.1371/journal.pone.0299849.s001

S2 Fig. Bland-Altman plot of claims-based number of teeth and number of teeth in screening records using different definition of claims-based number of teeth.

https://doi.org/10.1371/journal.pone.0299849.s002

S3 Fig. Heatmaps of claims-based number of teeth and number of teeth in screening records among participants aged (A) 20–39 years, (B) 40–64 years, (C) 65–74 years, or (D) ≥75 years.

https://doi.org/10.1371/journal.pone.0299849.s003

(A) Bland-Altman plot of claims-based number of teeth (latest one in assessment period) and number of teeth in screening records. (B) Heatmap of claims-based number of teeth (latest one in assessment period) and number of teeth in screening records.

https://doi.org/10.1371/journal.pone.0299849.s004

S5 Fig. Cumulative incidence curves of Alzheimer’s disease according to the claims-based number of remaining teeth using Alzheimer’s disease definition that integrates Alzheimer’s disease medications.

https://doi.org/10.1371/journal.pone.0299849.s005

S1 Table. Definition of Japanese original diagnosis codes for pneumococcal disease.

https://doi.org/10.1371/journal.pone.0299849.s006

S2 Table. Correlation between claims-based number of teeth and number of teeth in screening records by age group and number of teeth group in screening records.

https://doi.org/10.1371/journal.pone.0299849.s007

S3 Table. Characteristics of analytical sample in prediction analysis for pneumococcal disease according to the claims-based number of remaining teeth.

https://doi.org/10.1371/journal.pone.0299849.s008

S4 Table. Characteristics of analytical sample in prediction analysis for Alzheimer’s disease according to the claims-based number of remaining teeth.

https://doi.org/10.1371/journal.pone.0299849.s009

S5 Table. Association between the claims-based number of remaining teeth and the onset of Alzheimer’s disease during follow-up using Alzheimer’s disease definition that integrates Alzheimer’s disease medications.

https://doi.org/10.1371/journal.pone.0299849.s010

S6 Table. Association between the dental status and the onsets of pneumococcal disease and Alzheimer’s disease during the follow-up.

https://doi.org/10.1371/journal.pone.0299849.s011

Acknowledgments

We are grateful to the other investigators, staff, and participants of the LIFE Study for their valuable contributions. We would like to thank Editage ( www.editage.jp ) for English language editing. YT was supported by Nagoya University/MEXT CIBoG WISE Program.

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Based on Ainsworth BE, et al. 2011 compendium of physical activities: A second update of codes and MET values. Medicine & Science in Sports & Exercise. 2011;43:1575.

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  • Physical Activity Guidelines for Americans. 2nd ed. U.S. Department of Health and Human Services. https://health.gov/paguidelines/second-edition. Accessed March 13, 2024.
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  • Published: 06 May 2024

Venus water loss is dominated by HCO + dissociative recombination

  • M. S. Chaffin   ORCID: orcid.org/0000-0002-1939-4797 1   na1 ,
  • E. M. Cangi 1   na1 ,
  • B. S. Gregory 1 ,
  • R. V. Yelle 2 ,
  • J. Deighan   ORCID: orcid.org/0000-0003-3667-902X 1 ,
  • R. D. Elliott 1 &
  • H. Gröller 2  

Nature volume  629 ,  pages 307–310 ( 2024 ) Cite this article

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  • Atmospheric chemistry
  • Inner planets

Despite its Earth-like size and source material 1 , 2 , Venus is extremely dry 3 , 4 , indicating near-total water loss to space by means of hydrogen outflow from an ancient, steam-dominated atmosphere 5 , 6 . Such hydrodynamic escape likely removed most of an initial Earth-like 3-km global equivalent layer (GEL) of water but cannot deplete the atmosphere to the observed 3-cm GEL because it shuts down below about 10–100 m GEL 5 , 7 . To complete Venus water loss, and to produce the observed bulk atmospheric enrichment in deuterium of about 120 times Earth 8 , 9 , nonthermal H escape mechanisms still operating today are required 10 , 11 . Early studies identified these as resonant charge exchange 12 , 13 , 14 , hot oxygen impact 15 , 16 and ion outflow 17 , 18 , establishing a consensus view of H escape 10 , 19 that has since received only minimal updates 20 . Here we show that this consensus omits the most important present-day H loss process, HCO + dissociative recombination. This process nearly doubles the Venus H escape rate and, consequently, doubles the amount of present-day volcanic water outgassing and/or impactor infall required to maintain a steady-state atmospheric water abundance. These higher loss rates resolve long-standing difficulties in simultaneously explaining the measured abundance and isotope ratio of Venusian water 21 , 22 and would enable faster desiccation in the wake of speculative late ocean scenarios 23 . Design limitations prevented past Venus missions from measuring both HCO + and the escaping hydrogen produced by its recombination; future spacecraft measurements are imperative.

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Oxygen production from dissociation of Europa’s water-ice surface

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Isotopic fractionation of water and its photolytic products in the atmosphere of Mars

research studies definition

Dry late accretion inferred from Venus’s coupled atmosphere and internal evolution

Data availability.

Tables containing all reactions used in the model, including their adopted rate coefficients and computed column rates, are provided in a supplementary PDF file accessible on the journal website. These rates are also accessible in the archived code repository listed below, which also includes our adopted photo cross-sections and all other source data used in our model. Model densities for all species, computed rates for reactions shown in Fig. 2 , assumed temperature and escape probabilities and computed photo rates are provided in Excel format in the online version of the paper; this file also includes data for our illustrative water-inventory timelines.  Source data are provided with this paper.

Code availability

All model code is available at github.com/emcangi/VenusPhotochemistry . The version of the model used to prepare the manuscript is archived on Zenodo at https://doi.org/10.5281/zenodo.10460004 .

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Acknowledgements

M.S.C., E.M.C., B.S.G. and R.D.E. were supported by NASA Solar System Workings grant 80NSSC19K0164 and Planetary Science Early Career Award grant 80NSSC20K1081. E.M.C. was also supported by NASA FINESST award 80NSSC22K1326. M.S.C. and E.M.C. thank M. Landis for helpful discussions about water delivery.

Author information

These authors contributed equally: M. S. Chaffin, E. M. Cangi

Authors and Affiliations

Laboratory for Atmospheric and Space Physics, University of Colorado Boulder, Boulder, CO, USA

M. S. Chaffin, E. M. Cangi, B. S. Gregory, J. Deighan & R. D. Elliott

Lunar and Planetary Laboratory, University of Arizona, Tucson, AZ, USA

R. V. Yelle & H. Gröller

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Contributions

M.S.C. oversaw the study, performed final model calculations and the photochemical equilibrium calculation and wrote the initial text of the paper. E.M.C. developed the H-bearing and D-bearing photochemical model and nonthermal escape calculation originally used at Mars with a reaction network provided by R.V.Y. and performed initial model calculations for Venus. B.S.G. developed and ran the Monte Carlo model to generate escape probability curves. R.D.E. initially developed the Monte Carlo escape model with support from J.D. and H.G. H.G. performed pilot studies of HCO + -driven loss in the Mars atmosphere. All authors contributed to the interpretation and presentation of model results.

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Correspondence to M. S. Chaffin .

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Extended data figures and tables

Extended data fig. 1 model densities for all species..

The six panels function only to separate species for clarity.

Extended Data Fig. 2 Key photochemical model inputs.

a , Temperature profiles for neutrals, ions and electrons adapted from the inputs in ref.  28 . b , Adopted eddy diffusion profile and molecular diffusion coefficients for H and O atoms.

Extended Data Fig. 3 Implications of HCO + -driven loss for Venus ocean scenarios.

a , Escaping H production rates for the two most important processes in our model. b , Schematic water loss history of Venus.

Supplementary information

Supplementary information.

This file contains Supplementary Methods and Supplementary Tables. Merged PDF containing tables of reactions used in the model, assumed reaction rate coefficients and computed equilibrium model column rates.

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Chaffin, M.S., Cangi, E.M., Gregory, B.S. et al. Venus water loss is dominated by HCO + dissociative recombination. Nature 629 , 307–310 (2024). https://doi.org/10.1038/s41586-024-07261-y

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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 ...

  10. Understanding Research Study Designs

    Ranganathan P. Understanding Research Study Designs. Indian J Crit Care Med 2019;23 (Suppl 4):S305-S307. Keywords: Clinical trials as topic, Observational studies as topic, Research designs. We use a variety of research study designs in biomedical research. In this article, the main features of each of these designs are summarized. Go to:

  11. What Is a Research Design

    A research design is a strategy for answering your research question using empirical data. Creating a research design means making decisions about: Your overall research objectives and approach. Whether you'll rely on primary research or secondary research. Your sampling methods or criteria for selecting subjects. Your data collection methods.

  12. (PDF) What is research? A conceptual understanding

    Research is a systematic endeavor to acquire understanding, broaden knowledge, or find answers to unanswered questions. It is a methodical and structured undertaking to investigate the natural and ...

  13. RESEARCH

    RESEARCH definition: 1. a detailed study of a subject, especially in order to discover (new) information or reach a…. Learn more.

  14. Research

    Research Definition. Research is a careful and detailed study into a specific problem, concern, or issue using the scientific method. It's the adult form of the science fair projects back in ...

  15. Research

    Research. Definition: Research refers to the process of investigating a particular topic or question in order to discover new information, develop new insights, or confirm or refute existing knowledge.It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail. ...

  16. What Is Quantitative Research?

    Quantitative research methods. You can use quantitative research methods for descriptive, correlational or experimental research. In descriptive research, you simply seek an overall summary of your study variables.; In correlational research, you investigate relationships between your study variables.; In experimental research, you systematically examine whether there is a cause-and-effect ...

  17. What Are Cancer Research Studies?

    Epidemiological research is the study of the patterns, causes, and effects of cancer in a group of people of a certain background. This research encompasses both observational population-based studies but also includes clinical epidemiological studies where the relationship between a population's risk factors and treatments are tested.

  18. Limitations of the Study

    Definition. The limitations of the study are those characteristics of design or methodology that impacted or influenced the interpretation of the findings from your research. ... Lack of prior research studies on the topic-- citing prior research studies forms the basis of your literature review and helps lay a foundation for understanding the ...

  19. Definitions of Research and Development: An Annotated Compilation of

    This document provides definitions of research and development from U.S. and international sources. The first section (I) presents statistical definitions of R&D from the Organisation for Economic Co-operation and Development (OECD) Frascati Manual 2015: Guidelines for Collecting and Reporting Data on Research and Experimental Development. The next three sections are organized by sectors of ...

  20. Validity of claims-based definition of number of remaining teeth in

    Background Secondary healthcare data use has been increasing in the dental research field. The validity of the number of remaining teeth assessed from Japanese dental claims data has been reported in several studies, but has not been tested in the general population in Japan. Objectives To evaluate the validity of the number of remaining teeth assessed from Japanese dental claims data and ...

  21. Exercise for weight loss: Calories burned in 1 hour

    To lose weight, most people need to cut the number of calories they eat and move more. This is according to the 2020-2025 Dietary Guidelines for Americans. Most often, that means cutting daily calories by 500 to 750 to lose 1 1/2 pounds (0.7 kilograms) a week. Other factors might be involved in losing weight. Because of changes to the body over ...

  22. What Is Action Research?

    Action research is a research method that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research conducts research and takes action at the same time. It was first coined as a term in 1944 by MIT professor Kurt Lewin.A highly interactive method, action research is often used in the social ...

  23. Venus water loss is dominated by HCO+ dissociative recombination

    Water loss to space late in Venus history is shown to be more active than previously thought, with unmeasured HCO+ dissociative recombination dominating present-day H loss.