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What is Research: Definition, Methods, Types & Examples

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

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

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

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

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Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• Additional Help

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

• Next: Types of Research >>
• Last Updated: Dec 21, 2023 3:49 PM
• URL: https://guides.library.iit.edu/research_basics

Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

14k Accesses

Part of the book series: Research in Mathematics Education ((RME))

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.

Download chapter PDF

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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About 1 in 4 americans have unfavorable views of both biden and trump.

Joe Biden and Donald Trump each have enough delegates in the 2024 presidential primaries to secure their parties’ nominations for president . However, a sizable share of Americans are not particularly fond of either one.

Pew Research Center conducted this analysis to understand the American public’s opinion on presidential candidates Joe Biden and Donald Trump. Most of the data for this analysis comes from a survey of 12,693 respondents from Feb. 13 to 25, 2024.

Most of the respondents (10,642) are members of the American Trends Panel (ATP), an online survey panel recruited through national random sampling of residential addresses. This gives nearly all U.S. adults a chance of selection.

The other 2,051 respondents are members of three other panels: the Ipsos KnowledgePanel, the NORC AmeriSpeak panel and the SSRS opinion panel. All three are national survey panels recruited through random sampling (not “opt-in” polls). We used these additional panels to ensure that the survey would have enough respondents to report on the views of additional subgroups of adults.

For the part of the analysis focusing on the views of Republican primary supporters, we used the responses from a subset of the 4,792 U.S. adults who completed both the February survey and another ATP survey fielded from Nov. 27 to Dec. 3, 2023.

The surveys are weighted to be representative of the U.S. adult population by gender, race, ethnicity, partisan affiliation, education and other categories. Read more about the ATP’s methodology .

Here are the questions used for this analysis and its methodology .

Roughly a quarter of Americans (26%) hold unfavorable views of both Trump and Biden. This “double negative” sentiment is more common among younger adults than older adults. Those who reject partisan labels – identifying as independent or “something else” and instead leaning toward a party – are also more likely to hold this combination of views.

Most Americans view only one of the two men favorably: 37% have a favorable view of Trump and an unfavorable view of Biden, while 34% have a favorable view of Biden and an unfavorable view of Trump. Just 2% of Americans say they have a favorable view of both , according to a Pew Research Center survey of 12,693 adults conducted Feb. 13-25.

‘Double negatives’ are more common among some demographic groups

Younger adults are particularly likely to be “double negatives.” About four-in-ten adults ages 18 to 29 (41%) have an unfavorable opinion of both Biden and Trump. In comparison, three-in-ten adults ages 30 to 49 and fewer than two-in-ten of those 50 and older dislike both men.

Partisanship

Among Republicans and Republican-leaning independents, 72% have a favorable view of Trump only, while 4% have a favorable view of Biden only. About two-in-ten (22%) have unfavorable views of both .

But there are differences between those who identify as Republican and those who lean toward the Republican Party.

While Republican leaners are no more likely than Republican identifiers to view Biden favorably, they have less favorable views of Trump.

As a result, about a third of Republican leaners (35%) are double negatives, with an unfavorable view of both Biden and Trump. In comparison , just 15% of Republican identifiers are double negatives.

There is a similar dynamic among Democrats. About two-thirds of Democrats and Democratic-leaning independents say they have a favorable view of Biden and an unfavorable view of Trump. But among Democratic leaners, 38% have negative views of both Trump and Biden . That drops to 19% among those who identify as Democrats.

How many Haley, DeSantis supporters are ‘double negatives’?

Republican and Republican-leaning registered voters who supported a GOP candidate other than Trump for the 2024 presidential nomination are less favorable to the former president than those who backed him.

Nikki Haley’s primary supporters are especially likely to say they dislike both Trump and Biden. Among Republican and Republican-leaning registered voters who said in a December survey that they supported Haley for the Republican nomination, roughly half (53%) currently have unfavorable opinions of both Biden and Trump.

About four-in-ten (38%) of Haley’s supporters say they have a favorable view of Trump and not of Biden, while 9% say they have an unfavorable view of Trump but a favorable view of Biden.

Among Ron DeSantis’ December supporters, about three-in-ten are double negatives. Seven-in-ten have a favorable view of Trump and an unfavorable view of Biden.

An overwhelming majority of Trump’s primary supporters (96%) say they have a favorable view of Trump and an unfavorable view of Biden.

Biden and Trump favorability ratings

Overall, 62% of Americans have an unfavorable view of Biden – nearly identical to the 60% of Americans who view Trump negatively. (Read more about how Americans view Biden and Trump .)  

While Biden’s favorability ratings are little changed over the last year, they are more negative than they were in 2022. This shift has largely been within his own party: In July 2022, 75% of Democrats and Democratic leaners rated him positively. Today, 67% do.  

Republicans’ negative views of Biden have held steady over that time. Today, 94% say they have an unfavorable view of him.

Trump’s ratings are largely the same as they were in 2022. However, they are somewhat less negative than in 2023, as Republicans’ views have grown more positive.

Today, 73% of Republicans and Republican leaners hold a favorable view of the former president, up from 66% in July 2023.

Democrats continue to overwhelmingly view Trump negatively: 91% say they have an unfavorable view of him.

Views of Trump and Biden among racial and ethnic groups  

There have been only modest changes in views of Biden and Trump over the past two years among Black, Asian and White adults.

Among Hispanic adults, however, there are bigger changes. Hispanics are less likely to hold favorable views of Biden now than they were two years ago, and they are slightly more positive about Trump.

In July 2022, 54% of Hispanic adults held a favorable view of Biden. Today, that share has dropped to 37%.

At the same time, Trump’s favorability among Hispanic adults has ticked up, from 28% in July 2022 to 34% now.

Favorability by age

As has generally been the case for the past several years, younger Americans are less likely than older Americans to have favorable views of Biden or Trump.

Today, about three-in-ten adults under 30 say they have a favorable view of Biden (29%), while a nearly identical share (31%) have a favorable view of Trump.

Note: Here are the questions used for this analysis and its methodology .

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What is Research? – Purpose of Research

• By DiscoverPhDs
• September 10, 2020

The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. A research purpose is met through forming hypotheses, collecting data, analysing results, forming conclusions, implementing findings into real-life applications and forming new research questions.

What is Research

Simply put, research is the process of discovering new knowledge. This knowledge can be either the development of new concepts or the advancement of existing knowledge and theories, leading to a new understanding that was not previously known.

As a more formal definition of research, the following has been extracted from the Code of Federal Regulations :

While research can be carried out by anyone and in any field, most research is usually done to broaden knowledge in the physical, biological, and social worlds. This can range from learning why certain materials behave the way they do, to asking why certain people are more resilient than others when faced with the same challenges.

The use of ‘systematic investigation’ in the formal definition represents how research is normally conducted – a hypothesis is formed, appropriate research methods are designed, data is collected and analysed, and research results are summarised into one or more ‘research conclusions’. These research conclusions are then shared with the rest of the scientific community to add to the existing knowledge and serve as evidence to form additional questions that can be investigated. It is this cyclical process that enables scientific research to make continuous progress over the years; the true purpose of research.

What is the Purpose of Research

From weather forecasts to the discovery of antibiotics, researchers are constantly trying to find new ways to understand the world and how things work – with the ultimate goal of improving our lives.

The purpose of research is therefore to find out what is known, what is not and what we can develop further. In this way, scientists can develop new theories, ideas and products that shape our society and our everyday lives.

Although research can take many forms, there are three main purposes of research:

• Exploratory: Exploratory research is the first research to be conducted around a problem that has not yet been clearly defined. Exploration research therefore aims to gain a better understanding of the exact nature of the problem and not to provide a conclusive answer to the problem itself. This enables us to conduct more in-depth research later on.
• Descriptive: Descriptive research expands knowledge of a research problem or phenomenon by describing it according to its characteristics and population. Descriptive research focuses on the ‘how’ and ‘what’, but not on the ‘why’.
• Explanatory: Explanatory research, also referred to as casual research, is conducted to determine how variables interact, i.e. to identify cause-and-effect relationships. Explanatory research deals with the ‘why’ of research questions and is therefore often based on experiments.

Characteristics of Research

There are 8 core characteristics that all research projects should have. These are:

• Empirical  – based on proven scientific methods derived from real-life observations and experiments.
• Logical  – follows sequential procedures based on valid principles.
• Cyclic  – research begins with a question and ends with a question, i.e. research should lead to a new line of questioning.
• Controlled  – vigorous measures put into place to keep all variables constant, except those under investigation.
• Hypothesis-based  – the research design generates data that sufficiently meets the research objectives and can prove or disprove the hypothesis. It makes the research study repeatable and gives credibility to the results.
• Analytical  – data is generated, recorded and analysed using proven techniques to ensure high accuracy and repeatability while minimising potential errors and anomalies.
• Objective  – sound judgement is used by the researcher to ensure that the research findings are valid.
• Statistical treatment  – statistical treatment is used to transform the available data into something more meaningful from which knowledge can be gained.

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Types of Research

Research can be divided into two main types: basic research (also known as pure research) and applied research.

Basic Research

Basic research, also known as pure research, is an original investigation into the reasons behind a process, phenomenon or particular event. It focuses on generating knowledge around existing basic principles.

Basic research is generally considered ‘non-commercial research’ because it does not focus on solving practical problems, and has no immediate benefit or ways it can be applied.

While basic research may not have direct applications, it usually provides new insights that can later be used in applied research.

Applied Research

Applied research investigates well-known theories and principles in order to enhance knowledge around a practical aim. Because of this, applied research focuses on solving real-life problems by deriving knowledge which has an immediate application.

Methods of Research

Research methods for data collection fall into one of two categories: inductive methods or deductive methods.

Inductive research methods focus on the analysis of an observation and are usually associated with qualitative research. Deductive research methods focus on the verification of an observation and are typically associated with quantitative research.

Qualitative Research

Qualitative research is a method that enables non-numerical data collection through open-ended methods such as interviews, case studies and focus groups .

It enables researchers to collect data on personal experiences, feelings or behaviours, as well as the reasons behind them. Because of this, qualitative research is often used in fields such as social science, psychology and philosophy and other areas where it is useful to know the connection between what has occurred and why it has occurred.

Quantitative Research

Quantitative research is a method that collects and analyses numerical data through statistical analysis.

It allows us to quantify variables, uncover relationships, and make generalisations across a larger population. As a result, quantitative research is often used in the natural and physical sciences such as engineering, biology, chemistry, physics, computer science, finance, and medical research, etc.

What does Research Involve?

Research often follows a systematic approach known as a Scientific Method, which is carried out using an hourglass model.

A research project first starts with a problem statement, or rather, the research purpose for engaging in the study. This can take the form of the ‘ scope of the study ’ or ‘ aims and objectives ’ of your research topic.

Subsequently, a literature review is carried out and a hypothesis is formed. The researcher then creates a research methodology and collects the data.

The data is then analysed using various statistical methods and the null hypothesis is either accepted or rejected.

In both cases, the study and its conclusion are officially written up as a report or research paper, and the researcher may also recommend lines of further questioning. The report or research paper is then shared with the wider research community, and the cycle begins all over again.

Although these steps outline the overall research process, keep in mind that research projects are highly dynamic and are therefore considered an iterative process with continued refinements and not a series of fixed stages.

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The scope of the study is defined at the start of the study. It is used by researchers to set the boundaries and limitations within which the research study will be performed.

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What is Scientific Research and How Can it be Done?

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

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

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

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

Classification of Scientific Research

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

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

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

I. Descriptive research

• Case series
• Surveillance studies

II. Analytical research

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

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

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

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

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

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

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

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

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

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

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

Clinical Research

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

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

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

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

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

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

I. Calculation of the Number of Subjects

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

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

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

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

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

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

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

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

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

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

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

b. Difference between Groups and ES

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

c. Allocation Ratio of Groups

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

d. Direction of Hypothesis (H1)

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

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

II. Blinding (Masking) and Randomisation Methods

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

Blinding Method

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

Randomisation Method

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

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

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

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

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

Bias and Chicanery

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Peer-review: Externally peer-reviewed.

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

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

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

Module 1: Introduction: What is Research?

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

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 the Research Says about 1:1

As the principal of a school that implemented a one to one computer program, it has become a fascinating topic to discuss, examine, and research. It's fascinating because there is simply not the volume of information on this topic that most educators would assume. However, the summary outlined below has helped my associates and I have a better understanding of what themes have emerged.

All public schools are in a constant cycle of improvement for the betterment of their students. This frequent challenge can be both exciting and stressful for communities as schools’ staffs are continually asked to do more and more while resources seem to become consistently sparser. However, this past decade has seen enthusiastic determinations within the educational field that having a school implement a one to one technological tool is the essential means towards the quest for all students to become ready and able to take on 21st century learning expectations. This research is meant to decipher between subjective rhetoric and objective evidence on the impact this educational implementation truly has.

There has been a pseudo acceptance and criticism that evolving towards a one to one computer implementation is essentially the “silver bullet” that will fix all issues within a school building. Advocates will sometimes argue that when a school takes this direction it will result in immediate and substantial positive results, however, studies simply show that this may not be true. Two states employed a substantial and significant one to one initiative that did demonstrate the results that most would have assumed. The Main Learning and Technology Initiative spent almost $120 million, but research has shown that not all schools had implemented the program to the same degree and when 8th grade state assessment scores were examined no significant increase had been demonstrated (Weston & Bain, 2010)

Another significant one to one endeavor was the Texas Technology Immersion Pilot. This involved students in 22 schools receiving computers while the states invested nearly $14.5 million with a four year immersion goal. However, the same inconsistent findings were demonstrated similar to the implementation within the state of Maine. At the end of the fourth year, students’ access and usage of computers were below intended goals. Most concerning though, even with the significant financial investment there was still also no evidence that student performance or satisfaction of school had increased (Weston & Bain, 2010).

The above research makes the safe assumption that many educators assumed that just enabling a ubiquitous access to technology would lead towards positive results. However, the findings from Bebell and O’Dwyer (2010) pointed towards more positive outcomes when schools focus on more training and immersion rather than just implementation with one to one initiatives. In their study, they found that 7th grade students within the second year of implementation saw significant gains on the state assessments for ELA when compared to schools that had not provided one to one computer access to their students. The finding from this research was intriguing because the results were associated with the strength of the implementation rather than simply the implementation itself (Bebell & O’Dwyer, 2010).

It then becomes an interesting query on why some studies point towards such positive results as a result of an implementation of a one to one computer endeavor and other studies simply do not. Research that was done within the Ozarks Educational Research Initiative aims to address this question. The background behind this study is not much different than anything mentioned above. It is another program to add computer access to all students within 15 school districts within the state of Missouri, with the collaboration of both the state and local University. However, this research utilized a systematic review of prior studies on this topic to hopefully pin point particular criteria that would support the success of such an implementation (Sell, Cornelius-White, Chang, McLean, & Roworth, 2012).

The variety of best practices and approaches that are discussed are immense, but one item that correlates to the findings of Bebell and O’Dwyer (2010) relates to the professional development of staff. They found through their meta-synthesis analysis that professional development is not only essential but that it should not also just focus on new instructional skills. Instead, it should address teacher beliefs about instruction itself. The research found that by taking this approach teachers’ attitudes towards teaching becomes learner centered and they are more apt to become facilitators utilizing technology. However, in turn there was not sufficient evidence to find a correlation of increasing teachers’ computer literacy towards the success of a one to one computer program, and again, there were mixed results on increases in academic achievement (Sell, Cornelius-White, Chang, McLean, & Roworth, 2012).

Fortunately, there does seem to at least be a few emerging themes within this topic. Different research studies point towards varying amounts of success or increases within the realm of academic achievement. Another theme is the significance of professional development, and more importantly, how it is approached when implementing computers to each student within a school. The success of a one to one initiative is dependent upon so many characteristics and approaches, and not merely creating the infrastructure and providing the resource (Lemke, Coughlin, & Reifsneider, 2009).

Each of the research articles either directly or indirectly discussed the existence of a subjective educational belief that giving each child in a school a computer will not only increase academic achievement, but it is becoming an absolute necessity due to the nature of an ever changing technological society. Professor Steven Higgins, ZhiMin Xiao, and Maria Katsipataki of Durham University (2012) speak of several fallacies about technology usage within public education. However, one such myth seems to have a direct correlation to the summary of information found within this paper. They speak of a generally accepted myth that children of this day and age learning differently than in past generations and that computers are somehow necessary to achieve academic success. There are countless examples of varying levels of academic success within schools implementing one to one computer usage programs, and because of this it seems very safe to come to the conclusion that simply giving a child a computer will not automatically guarantee academic improvement (Higgins, Xiao, & Katsipataki, 2012). Ultimately, a piece of technology is a resource and how that technology is embraced and utilized by the teachers themselves is the ultimate variable of success.

Bebell, D. & O’Dwyer, L.M. (2010). Educational outcomes and research from 1:1 computing settings. Journal of Technology, Learning, and Assessment, 9(1). 5-13.

Higgins, S., Xiao, X., & Katsipataki, M. (2012). The impact of digital technology on learning: a summary for the education endowment foundation. Education Endowment Foundation.Retrieved from https://larrycuban.files.wordpress.com/2013/12/the_impact_of_digital_technologies_on_learning_full_report_2012.pdf

Lemke, C., Coughlin, E., & Reifsneider, D. (2009). Technology in schools: What the research says: An update. Culver City, CA: Commissioned by Cisco.

Sell, G., Cornelius-White, J., Chang, C., McLean, A., & Roworth., W. (2012). A met-synthesis of research on 1:1 technology initiatives in k-12 education. Ozarks Educational Research Initiative. Retrieved from http://education.missouristate.edu/assets/clse/Final_Report_of_One-to-One_Meta-Synthesis__April_2012_.pdf

Weston, M.E. & Bain, A. (2010). The end of techno-critique: the naked truth about 1:1 laptop initiatives and educational change. Journal of Technology, Learning, and Assessment, 9(6). 5-10.

This piece was originally submitted to our community forums by a reader. Due to audience interest, we’ve preserved it. The opinions expressed here are the writer’s own.

Choose Your Test

Sat / act prep online guides and tips, 113 great research paper topics.

General Education

One of the hardest parts of writing a research paper can be just finding a good topic to write about. Fortunately we've done the hard work for you and have compiled a list of 113 interesting research paper topics. They've been organized into ten categories and cover a wide range of subjects so you can easily find the best topic for you.

In addition to the list of good research topics, we've included advice on what makes a good research paper topic and how you can use your topic to start writing a great paper.

What Makes a Good Research Paper Topic?

Not all research paper topics are created equal, and you want to make sure you choose a great topic before you start writing. Below are the three most important factors to consider to make sure you choose the best research paper topics.

#1: It's Something You're Interested In

A paper is always easier to write if you're interested in the topic, and you'll be more motivated to do in-depth research and write a paper that really covers the entire subject. Even if a certain research paper topic is getting a lot of buzz right now or other people seem interested in writing about it, don't feel tempted to make it your topic unless you genuinely have some sort of interest in it as well.

#2: There's Enough Information to Write a Paper

Even if you come up with the absolute best research paper topic and you're so excited to write about it, you won't be able to produce a good paper if there isn't enough research about the topic. This can happen for very specific or specialized topics, as well as topics that are too new to have enough research done on them at the moment. Easy research paper topics will always be topics with enough information to write a full-length paper.

Trying to write a research paper on a topic that doesn't have much research on it is incredibly hard, so before you decide on a topic, do a bit of preliminary searching and make sure you'll have all the information you need to write your paper.

#3: It Fits Your Teacher's Guidelines

Don't get so carried away looking at lists of research paper topics that you forget any requirements or restrictions your teacher may have put on research topic ideas. If you're writing a research paper on a health-related topic, deciding to write about the impact of rap on the music scene probably won't be allowed, but there may be some sort of leeway. For example, if you're really interested in current events but your teacher wants you to write a research paper on a history topic, you may be able to choose a topic that fits both categories, like exploring the relationship between the US and North Korea. No matter what, always get your research paper topic approved by your teacher first before you begin writing.

113 Good Research Paper Topics

Below are 113 good research topics to help you get you started on your paper. We've organized them into ten categories to make it easier to find the type of research paper topics you're looking for.

Arts/Culture

• Discuss the main differences in art from the Italian Renaissance and the Northern Renaissance .
• Analyze the impact a famous artist had on the world.
• How is sexism portrayed in different types of media (music, film, video games, etc.)? Has the amount/type of sexism changed over the years?
• How has the music of slaves brought over from Africa shaped modern American music?
• How has rap music evolved in the past decade?
• How has the portrayal of minorities in the media changed?

Current Events

• What have been the impacts of China's one child policy?
• How have the goals of feminists changed over the decades?
• How has the Trump presidency changed international relations?
• Analyze the history of the relationship between the United States and North Korea.
• What factors contributed to the current decline in the rate of unemployment?
• What have been the impacts of states which have increased their minimum wage?
• How do US immigration laws compare to immigration laws of other countries?
• How have the US's immigration laws changed in the past few years/decades?
• How has the Black Lives Matter movement affected discussions and view about racism in the US?
• What impact has the Affordable Care Act had on healthcare in the US?
• What factors contributed to the UK deciding to leave the EU (Brexit)?
• What factors contributed to China becoming an economic power?
• Discuss the history of Bitcoin or other cryptocurrencies  (some of which tokenize the S&P 500 Index on the blockchain) .
• Do students in schools that eliminate grades do better in college and their careers?
• Do students from wealthier backgrounds score higher on standardized tests?
• Do students who receive free meals at school get higher grades compared to when they weren't receiving a free meal?
• Do students who attend charter schools score higher on standardized tests than students in public schools?
• Do students learn better in same-sex classrooms?
• How does giving each student access to an iPad or laptop affect their studies?
• What are the benefits and drawbacks of the Montessori Method ?
• Do children who attend preschool do better in school later on?
• What was the impact of the No Child Left Behind act?
• How does the US education system compare to education systems in other countries?
• What impact does mandatory physical education classes have on students' health?
• Which methods are most effective at reducing bullying in schools?
• Do homeschoolers who attend college do as well as students who attended traditional schools?
• Does offering tenure increase or decrease quality of teaching?
• How does college debt affect future life choices of students?
• Should graduate students be able to form unions?

• What are different ways to lower gun-related deaths in the US?
• How and why have divorce rates changed over time?
• Is affirmative action still necessary in education and/or the workplace?
• Should physician-assisted suicide be legal?
• How has stem cell research impacted the medical field?
• How can human trafficking be reduced in the United States/world?
• Should people be able to donate organs in exchange for money?
• Which types of juvenile punishment have proven most effective at preventing future crimes?
• Has the increase in US airport security made passengers safer?
• Analyze the immigration policies of certain countries and how they are similar and different from one another.
• Several states have legalized recreational marijuana. What positive and negative impacts have they experienced as a result?
• Do tariffs increase the number of domestic jobs?
• Which prison reforms have proven most effective?
• Should governments be able to censor certain information on the internet?
• Which methods/programs have been most effective at reducing teen pregnancy?
• What are the benefits and drawbacks of the Keto diet?
• How effective are different exercise regimes for losing weight and maintaining weight loss?
• How do the healthcare plans of various countries differ from each other?
• What are the most effective ways to treat depression ?
• What are the pros and cons of genetically modified foods?
• Which methods are most effective for improving memory?
• What can be done to lower healthcare costs in the US?
• What factors contributed to the current opioid crisis?
• Analyze the history and impact of the HIV/AIDS epidemic .
• Are low-carbohydrate or low-fat diets more effective for weight loss?
• How much exercise should the average adult be getting each week?
• Which methods are most effective to get parents to vaccinate their children?
• What are the pros and cons of clean needle programs?
• How does stress affect the body?
• Discuss the history of the conflict between Israel and the Palestinians.
• What were the causes and effects of the Salem Witch Trials?
• Who was responsible for the Iran-Contra situation?
• How has New Orleans and the government's response to natural disasters changed since Hurricane Katrina?
• What events led to the fall of the Roman Empire?
• What were the impacts of British rule in India ?
• Was the atomic bombing of Hiroshima and Nagasaki necessary?
• What were the successes and failures of the women's suffrage movement in the United States?
• What were the causes of the Civil War?
• How did Abraham Lincoln's assassination impact the country and reconstruction after the Civil War?
• Which factors contributed to the colonies winning the American Revolution?
• What caused Hitler's rise to power?
• Discuss how a specific invention impacted history.
• What led to Cleopatra's fall as ruler of Egypt?
• How has Japan changed and evolved over the centuries?
• What were the causes of the Rwandan genocide ?

• Why did Martin Luther decide to split with the Catholic Church?
• Analyze the history and impact of a well-known cult (Jonestown, Manson family, etc.)
• How did the sexual abuse scandal impact how people view the Catholic Church?
• How has the Catholic church's power changed over the past decades/centuries?
• What are the causes behind the rise in atheism/ agnosticism in the United States?
• What were the influences in Siddhartha's life resulted in him becoming the Buddha?
• How has media portrayal of Islam/Muslims changed since September 11th?

Science/Environment

• How has the earth's climate changed in the past few decades?
• How has the use and elimination of DDT affected bird populations in the US?
• Analyze how the number and severity of natural disasters have increased in the past few decades.
• Analyze deforestation rates in a certain area or globally over a period of time.
• How have past oil spills changed regulations and cleanup methods?
• How has the Flint water crisis changed water regulation safety?
• What are the pros and cons of fracking?
• What impact has the Paris Climate Agreement had so far?
• What have NASA's biggest successes and failures been?
• How can we improve access to clean water around the world?
• Does ecotourism actually have a positive impact on the environment?
• Should the US rely on nuclear energy more?
• What can be done to save amphibian species currently at risk of extinction?
• What impact has climate change had on coral reefs?
• How are black holes created?
• Are teens who spend more time on social media more likely to suffer anxiety and/or depression?
• How will the loss of net neutrality affect internet users?
• Analyze the history and progress of self-driving vehicles.
• How has the use of drones changed surveillance and warfare methods?
• Has social media made people more or less connected?
• What progress has currently been made with artificial intelligence ?
• Do smartphones increase or decrease workplace productivity?
• What are the most effective ways to use technology in the classroom?
• How is Google search affecting our intelligence?
• When is the best age for a child to begin owning a smartphone?
• Has frequent texting reduced teen literacy rates?

How to Write a Great Research Paper

Even great research paper topics won't give you a great research paper if you don't hone your topic before and during the writing process. Follow these three tips to turn good research paper topics into great papers.

#1: Figure Out Your Thesis Early

Before you start writing a single word of your paper, you first need to know what your thesis will be. Your thesis is a statement that explains what you intend to prove/show in your paper. Every sentence in your research paper will relate back to your thesis, so you don't want to start writing without it!

As some examples, if you're writing a research paper on if students learn better in same-sex classrooms, your thesis might be "Research has shown that elementary-age students in same-sex classrooms score higher on standardized tests and report feeling more comfortable in the classroom."

If you're writing a paper on the causes of the Civil War, your thesis might be "While the dispute between the North and South over slavery is the most well-known cause of the Civil War, other key causes include differences in the economies of the North and South, states' rights, and territorial expansion."

#2: Back Every Statement Up With Research

Remember, this is a research paper you're writing, so you'll need to use lots of research to make your points. Every statement you give must be backed up with research, properly cited the way your teacher requested. You're allowed to include opinions of your own, but they must also be supported by the research you give.

#3: Do Your Research Before You Begin Writing

You don't want to start writing your research paper and then learn that there isn't enough research to back up the points you're making, or, even worse, that the research contradicts the points you're trying to make!

Get most of your research on your good research topics done before you begin writing. Then use the research you've collected to create a rough outline of what your paper will cover and the key points you're going to make. This will help keep your paper clear and organized, and it'll ensure you have enough research to produce a strong paper.

What's Next?

Are you also learning about dynamic equilibrium in your science class? We break this sometimes tricky concept down so it's easy to understand in our complete guide to dynamic equilibrium .

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Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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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

research and development

• market research
• translational research
• marketing research
• operations research
• research park
• oppo research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 21 Mar. 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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15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

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Type 1 diabetes articles from across Nature Portfolio

Type 1 diabetes (also known as diabetes mellitus) is an autoimmune disease in which immune cells attack and destroy the insulin-producing cells of the pancreas. The loss of insulin leads to the inability to regulate blood sugar levels. Patients are usually treated by insulin-replacement therapy.

METTL3 restrains autoimmunity in β-cells

Activation of innate immunity has been linked to the progression of type 1 diabetes. A study now shows that overexpression of METTL3, a writer protein of the m 6 A machinery that modifies mRNA, restrains interferon-stimulated genes when expressed in pancreatic β-cells, identifying it as a promising therapeutic target.

• Balasubramanian Krishnamurthy
• Helen E. Thomas

Latest Research and Reviews

Dynamic associations between glucose and ecological momentary cognition in Type 1 Diabetes

• Z. W. Hawks
• L. T. Germine

Generative deep learning for the development of a type 1 diabetes simulator

Mujahid et al. develop a type 1 diabetes patient simulator using a conditional sequence-to-sequence deep generative model. Their approach captures causal relationships between insulin, carbohydrates, and blood glucose levels, producing virtual patients with similar responses to real patients in open and closed-loop insulin therapy scenarios.

• Omer Mujahid
• Ivan Contreras

High dose cholecalciferol supplementation causing morning blood pressure reduction in patients with type 1 diabetes mellitus and cardiovascular autonomic neuropathy

• João Felício
• Lorena Moraes
• Karem Felício

Does minimed 780G TM insulin pump system affect energy and nutrient intake?: long-term follow-up study

• Yasemin Atik-Altinok
• Yelda Mansuroglu
• Damla Goksen

Redox regulation of m 6 A methyltransferase METTL3 in β-cells controls the innate immune response in type 1 diabetes

De Jesus et al. describe the redox-mediated regulation of m 6 A writer methyltransferase 3, which blunts innate immune responses by modification of RNA sensor and editor component mRNAs during the onset of type 1 diabetes in β-cells.

• Dario F. De Jesus
• Zijie Zhang
• Rohit N. Kulkarni

Metabolomic associations of impaired awareness of hypoglycaemia in type 1 diabetes

• R. D. M. Varkevisser
• M. M. van der Klauw

News and Comment

Reply to ‘slowly progressive insulin dependent diabetes mellitus in type 1 diabetes endotype 2’.

• Maria J. Redondo
• Noel G. Morgan

Slowly progressive insulin-dependent diabetes mellitus in type 1 diabetes endotype 2

• Tetsuro Kobayashi
• Takashi Kadowaki

Type 1 diabetes mellitus: a brave new world

One hundred years after the Nobel prize was bestowed on Banting and McLeod for the ‘discovery’ of insulin, we are again seeing major evolutions in the management of type 1 diabetes mellitus, with the prospect of achieving disease control beyond mere management now becoming real. Here, we discuss the latest, most notable developments.

• Pieter-Jan Martens
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β-cells protected from T1DM by early senescence programme

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Antivirals in the treatment of new-onset T1DM

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Biden Signs Executive Order to Expand Research on Women’s Health

The president said that improving women’s health was crucial to ensuring a healthy, stable economy.

Biden Signs Executive Order to Boost Women’s Health Research

The executive order is aimed at addressing the underrepresentation of women in health research..

We’ve launched the first ever White House initiative on women’s health research to pioneer the next generation of scientific research and discovery in women’s health. Think of all the breakthroughs we’ve made in medicine across the board, but women have not been the focus. And today — [applause] today, we’re jumpstarting that investment by dedicating $200 million in the National Institutes of Health to tackle some of the most pressing health problems facing women today. With the executive order I’m about to sign, I’m directing the most comprehensive set of executive actions ever taken to improve women’s health — ever taken. And I’m going to ensure that women’s health is integrated and prioritized across the entire federal government. It’s not just in women’s health, not just at N.I.H., the National Science Foundation or the Defense Department, the Environmental Protection Agency. I mean, across the board. This is really serious.

By Zolan Kanno-Youngs

Reporting from Washington

President Biden on Monday signed an executive order to expand the federal government’s research into women’s health, including midlife conditions like menopause, arthritis and heart disease, as well as issues specifically affecting women in the military.

In what the White House described as the “most comprehensive” action by a president on women’s health research, Mr. Biden directed federal agencies to ensure that they are using federal funds to research health conditions and diseases that disproportionately affect women.

Standing alongside the first lady, Jill Biden, and Vice President Kamala Harris, Mr. Biden said improving women’s health was crucial to guaranteeing a healthy, stable economy.

“There’s not a damn thing a man can do a woman can’t do,” Mr. Biden said. “To state the obvious, if you want to have the strongest economy in the world, you can’t leave half of the country behind.”

Carolyn M. Mazure, a psychologist and a professor at the Yale School of Medicine, who is the chairwoman of the White House initiative on Women’s Health Research, told reporters on Sunday night that health conditions like heart disease, Alzheimer’s, menopause and fibroids would be a focus of the expanded research effort.

“I’m not even a betting woman,” said Maria Shriver, the former first lady of California, who also attended the event, “but I’ll bet today that this is the first time a president of the United States has ever signed an executive order that mentions the words ‘menopause’ and ‘women’s midlife health’ in it.”

After the U.S. Supreme Court overturned Roe v. Wade in 2022 and the Alabama Supreme Court ruled last month that frozen embryos should be considered children , threatening in vitro fertilization, the Biden campaign has increasingly accused Republicans of undermining women’s health. During his State of the Union address this month, Mr. Biden said such decisions would motivate women to vote in the November election, while also saying his White House would commit to investing in women’s health in the year ahead.

“You can’t lead America with old ideas and take us backwards,” Mr. Biden said, adding, “To lead the land of possibilities, you need a vision for the future laying out what we can and should do and what we’re going to do.”

Mr. Biden’s executive order will require agencies to report annually their investments in women’s health research and to study ways that artificial intelligence can be used to advance such research. The National Institutes of Health will increase by 50 percent investments in small businesses focused on women’s health. The Defense Department also plans to invest $10 million to learn more about cancers and mental health issues affecting women in active military service.

The White House has called on Congress to pass a plan to invest $12 billion to create a new fund for women’s health research at the National Institutes of Health. In the meantime, the executive order signed on Monday directed the N.I.H. to spend $200 million on women’s health research. Dr. Biden traveled to Cambridge, Mass., last month to announce the first step of the women’s health initiative: $100 million to support women’s health researchers and start-up companies.

Zolan Kanno-Youngs is a White House correspondent, covering President Biden and his administration. More about Zolan Kanno-Youngs

UMass Amherst Libraries to Host Exhibit Featuring Public Interest Technology Research Across Campus

UMass Amherst Libraries will host “PIT x UMass: Developing Technology for the Public Interest,” an exhibit in the  Science and Engineering Library , during regular library hours from April 1-May 19. A reception will take place in the exhibit space, located in the Lederle Graduate Research Center (GRC) Lowrise, on Tuesday, April 2, from 4-6 p.m.

Public Interest Technology (PIT)  focuses on the development and realization of socially responsible solutions to the challenges of a technology-driven world. This emerging field brings together researchers, technologists, policymakers and more from across disciplines and sectors.

The exhibit will feature PIT-related research and advocacy that is taking place across the UMass Amherst community as conducted by UMass faculty and students. The faculty projects featured in the exhibit are supported by the  PIT@UMass initiative, which is a growing hub of PIT-related work on campus and beyond.

“UMass Amherst is a pioneer in rebooting higher education to promote social responsibility in our tech-powered world,” says  Fran Berman , director of Public Interest Technology and PIT@UMass co-founder. “It’s tremendously exciting to see faculty, students, staff and leadership from all over campus take the lead in creating public interest tech-focused coursework, certificates, events and clubs to build needed 21st century literacies and skills.”

Berman added, “This exhibit celebrates the launch and extraordinary impact of the PIT@UMass initiative and the UMass Public Interest Technology community and ushers in new opportunities for impact on campus, within the region and nationally.”

“The Libraries are the ideal place to showcase the meaningful and vital work occurring as part of the PIT@UMass initiative,”  Nandita Mani , dean of University Libraries says. “As a critical partner for the creation and dissemination of research and scholarship at UMass Amherst, this is in keeping with our land-grant mission to create positive impact on the Commonwealth and the broader society we serve.”

“We are delighted to host the exhibit and introduce the broader community to the incredible work PIT is doing to foster the socially responsible and ethical development and use of technology for society,” adds  Rebecca Reznik-Zellen , head of the Science and Engineering Library. 

The exhibit is supported through the combined efforts of the PIT@UMass initiative, Responsible Technology Coalition graduate student club, PIT-Club undergraduate student club, Sophia Schweik of BDIC and the UMass Amherst Libraries. For more information, email Reznik-Zellen at  [email protected] .

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Apple Publishes Details About New 'MM1' AI Model

Apple researchers have developed a new method for training large language models (LLMs) that seamlessly integrates both text and visual information.

Apple's research focuses on the combination of different types of training data and model architectures, which enables the AI to understand and generate language based on a mix of visual and linguistic cues. This capability is vital for tasks that require a nuanced comprehension of the world, such as interpreting complex images or answering questions that involve visual elements.

The paper also highlights the MM1 model's exceptional in-context learning abilities, particularly in the largest 30 billion parameter configuration of the model. This version apparently exhibits remarkable capabilities for multi-step reasoning over multiple images using few-shot "chain-of-thought" prompting, a technique that allows the AI to perform complex, open-ended problem solving based on minimal examples.

This research emerges as part of Apple's broader initiative to enhance its AI capabilities amid growing competition. Earlier today, Bloomberg 's Mark Gurman reported that Apple is in discussions with Google to license Google's Gemini generative large-language models to power new features coming to the iPhone as part of iOS 18 .

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Top Rated Comments

Again this is all propaganda, Apple is not going to release the key to their secret sauce until at least WWDC, and maybe not even then. Cook needs any goodwill the media will give him. I see him running from department to department asking, "What can we release through the backdoor, I'm sinking here!!!!"

Will be extremely funny to read all this PR from Apple about the big important ML research they are doing and then find out at WWDC they are just gonna outsource Siri to Microsoft, lmao

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Pokémon Go Tour 2024: Road to Sinnoh – all Research tasks

T he Pokémon Go Tour 2024: Sinnoh is coming up quickly, but before the grand event is opening its doors players can prepare themselves with the Road to Sinnoh event starting on February 19, 2024, at 10am local time. Users should take advantage of this event to gather useful resources ahead of the great hunt for Sinnoh's rarest beasties.

One free Special Research has already been available since February 15, 2024, for every player of the mobile game, allowing them to select the Diamond or Pearl version for the upcoming Go Tour event. With the proper start of the Road to Sinnoh event, some paid Research tasks as well as an attractive Raid Challenge have been made available for trainers.

Here are all Pokémon Go Tour 2024: Road to Sinnoh Research tasks and rewards.

Go Tour 2024: Road to Sinnoh – all free Special Research tasks

You’ll be able to complete this Special Research whenever you want once you’ve claimed it, so make sure you get access to it by logging in after February 15, 2024, at 10am local time. It’s a pretty short string of missions and some of its rewards can only be described as Niantic trolling players, but you’ll also get a Legendary Pokémon during Go Tour and lots of Mega Energy out of it.

Step 1 of 3

• Catch a Pokémon originally discovered in the Sinnoh region : Go Tour Sticker x10
• Complete all tasks in this step : XP x2,024, Stardust x2,024, Bidoof

Choose a Path

You can choose between Diamond and Pearl in this step. Both options provide different rewards during Go Tour 2024 as well as for the remainder of this Special Research, so make your selection with care.

Choose Diamond Version to obtain the following bonuses during the global portion of Go Tour 2024:

• Encounter Dialga (Origin Forme) with Roar of Time as a guaranteed move.
• Double duration for the Adventure Effect of Roar of Time.
• Obtain additional Dialga Candy.

Choose Pearl Version to obtain the following bonuses during the global portion of Go Tour 2024:

• Encounter Palkia (Origin Forme) with Spacial Rend as a guaranteed move.
• Double duration for the Adventure Effect of Spacial Rend.
• Obtain additional Palkia Candy.

Related: Pokémon Go is making a beloved glitch into an expensive feature

Step 2 of 3

Diamond version.

• Feed your buddy : Larvitar Candy x10
• Feed your buddy : Tyranitar Mega Energy x100
• Complete all tasks in this step : Diamond Badge, Dialga Candy x3, Larvitar

Pearl Version

• Feed your buddy : Bagon Candy x10
• Feed your buddy : Salamence Mega Energy x100
• Complete all tasks in this step : Pearl Badge, Palkia Candy x3, Bagon

Step 3 of 3

Diamond and pearl version.

• Catch a Pokémon originally discovered in the Sinnoh region : Bidoof Candy x10

We can’t really advise you on whether to choose Diamond or Pearl here, since this totally depends on your personal preferences – when it comes down to the two Legendaries and perhaps which of the two mega evolutions you still need to conduct.

Go Tour 2024: Road to Sinnoh – all Raid Challenge tasks

This Raid Challenge can be tackled from the start of the event until February 23, 2024, at 8pm local time. You can get some pretty rare Pokémon out of this one, as you can see below:

Step 1 of 1

• Win 2 raids : Cresselia
• Win 4 raids : Heatran
• Win 6 raids : Giratina (Origin Forme)
• Win 8 raids : Darkrai
• Complete all tasks in this step : Dialga Candy x5, Palkia Candy x5, Giratina Candy x5

Here's an important tip: Catching these Pokémon during their respective Raid Hours this week will guarantee you a specimen that knows a specific move, so make sure to claim your reward encounters at the right time:

• Darkrai: February 19, 2024, 6 to 7pm local time – Sludge Bomb
• Cresselia: February 20, 2024, 6 to 7pm local time – Grass Knot
• Heatran: February 22, 2024, 6 to 7pm local time – Magma Storm
• Giratina (Origin Forme): February 23, 2024, 6 to 7pm local time – Shadow Force

Related: Pokémon Go: all 5-Star Raids in February 2024

Go Tour 2024: Road to Sinnoh – all Field Research tasks

As usual, Field Research is available for the duration of the event and can be obtained by spinning PokéStops. You have no time pressure to complete the tasks once you got them, as long as you're not tight on storage space. Here are all Field Research tasks you can find and complete during Road to Sinnoh:

• Spin 10 PokéStops or Gyms : Turtwig Candy x10, Chimchar Candy x10, or Piplup Candy x10
• Hatch an Egg : Poké Ball x10
• Hatch 2 Eggs : Great Ball x10
• Hatch 3 Eggs : Ultra Ball x10 or Sinnoh Stone x1

Go Tour 2024: Road to Sinnoh – all Timed Research tasks

Two Timed Research stories are available for players during Road to Sinnoh as well, though both are gated behind $4.99 USD tickets from the in-app store. You'll need to complete these tasks before February 25, 2024, at 8pm local time in order to claim every reward associated with them.

Road to Sinnoh: Raids – Step 1 of 1

• Win 2 raids : Cresselia Candy XL x5
• Win 4 raids : Heatran Candy XL x5
• Win 6 raids : Giratina Candy XL x5
• Win 8 raids : Darkrai Candy XL x5
• Complete all tasks in this step : Dialga Candy x5, Palkia Candy x5, Eevee Mask

Purchasing this ticket will also grant you access to some raid-related bonuses such as bonus XP and Candy as well as two additional free daily Raid Passes from spinning Gyms.

Road to Sinnoh: Hatch – Step 1 of 1

• Hatch an Egg : Chatot
• Hatch 2 Eggs : Pachirisu
• Hatch 3 Eggs : Carnivine
• Hatch 4 Eggs : Chatot
• Hatch 5 Eggs : Pachirisu
• Hatch 6 Eggs : Carnivine
• Complete all tasks in this step : Super Incubator x1, Stardust x4,000, Pikachu Mask

Buying this ticket doubles your XL, Candy, and Stardust from hatching Eggs.

Related: Pokémon Go: all Spotlight Hours in February 2024

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Google Research unveils AI-driven global flood forecasting system

• By Brian Fagioli
• Published 1 day ago

Google Research has made a significant breakthrough in global flood forecasting, utilizing artificial intelligence (AI) to expand access to reliable flood forecasts, particularly in regions where data is scarce. Floods, being the most common natural disaster, inflict approximately $50 billion in financial damages annually and affect nearly 1.5 billion people worldwide. The advent of this AI-based system marks a crucial step in improving early warning systems, potentially saving thousands of lives each year.

The research showcases the use of machine learning (ML) technologies to enhance flood forecasting on a global scale. This advancement has extended the reliability of nowcasts from zero to five days on average and has improved forecasts across Africa and Asia to levels comparable to those in Europe. The evaluation of these models was conducted in collaboration with the European Center for Medium Range Weather Forecasting (ECMWF).

Google’s Flood Hub tool, powered by the ML models, provides real-time river forecasts up to seven days in advance, covering over 80 countries. This information is vital for communities, governments, and international organizations to take proactive measures to protect vulnerable populations.

The AI-based flood forecasting initiative began in 2017 and has evolved through years of research and collaboration with academic institutions, governments, and NGOs. The system employs Long Short-Term Memory (LSTM) models, a type of state-space model, to predict river floods using publicly available weather data and physical watershed information. The models are trained on data from diverse watershed streamflow gauges and can make predictions for any river location, even those without streamflow gauges.

In comparison with the current state-of-the-art global flood forecasting system, GloFAS version 4, Google’s ML-based model has shown to provide earlier and more accurate warnings for larger and more impactful events. The system achieves reliability scores at up to 4-day or 5-day lead times that are comparable to or better than the reliability of GloFAS nowcasts.

Ultimately, Google is not only advancing the field of hydrology but also providing a lifeline to those in the path of potential floods. With the ability to predict flood events days in advance, this system offers a crucial window for preparation and evacuation, potentially saving countless lives. As Google continues to refine and expand its flood forecasting capabilities, the future looks brighter for millions who live under the constant threat of natural disasters.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

If you want to know more about the research process , methodology , research bias , or statistics , make sure to check out some of our other articles with explanations and examples.

Methodology

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Research bias

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McCombes, S. (2023, October 19). 10 Research Question Examples to Guide your Research Project. Scribbr. Retrieved March 20, 2024, from https://www.scribbr.com/research-process/research-question-examples/

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Pokémon Go ‘Rogue of the Jungle’ Zarude ticket Special Research

Is the “Rogue of the Jungle” ticket worth buying?

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From March 21-25 , Pokémon Go has a ticket in the shop to unlock Special Research that rewards Zarude , the mythical monkey Pokémon. For $7.99, you can buy the “ Rogue of the Jungle ” ticket, which gives a five-step research set that does not expire.

This ticketed research launched alongside the “ Verdant Wonders ” event.

It can be tough to figure out if it’s worth forking over the money to get this pass, so we break it down and list out all the “Rogue of the Jungle” steps below.

Is the ‘Rogue of the Jungle’ Zarude ticket worth it in Pokémon Go?

Here’s a quick and dirty list of everything you get for completing the research:

• A Zarude encounter
• A Seedot encounter
• A Galarian Zigzagoon encounter
• A Cacnea encounter
• A Scraggy encounter
• An Absol encounter
• 10 Ultra Balls
• 10 Great Balls
• 20 Poké Balls
• 4 Golden Razz Berries
• 6 Silver Pinap Berries
• 10 Razz Berries
• 5 Hyper Potions
• 2 Charge TMs
• 2 Premium Raid Passes
• 5 Rare Candy
• 10 Zarude Candy
• 3 Zarude Candy XL
• 3,000 Stardust

You also get special, boosted Incense spawns in the “Verdant Wonders” event for Pansage, Pansear, Panpour, Zorua, Cutiefly, and Flabébé specifically.

All of that said, you will get your money’s worth in stuff back from the $7.99 pass if you finish it all. There’s no time limit or anything, so you can take your time, too.

I think this pass is specifically worth it if you still need a Zarude (or you want a second one). Zarude is a Mythical Pokémon, meaning it’s only usually available from special distribution events that go away after a certain period of time. That said, this means Zarude is particularly valuable on the trading market — even more so with the Pokémon Go seal on it, since that little emblem is unable to be generated by cheating sources (as of this writing, at least).

Pokémon Go ‘Rogue of the Jungle’ Special Research and rewards

Step 1 of 5.

• Complete 5 Field Research tasks (2 Silver Pinap Berries)
• Catch 25 grass-type Pokémon (Seedot encounter)
• Evolve 3 grass-type Pokémon (20 Poké Balls)

Rewards : 1,000 Stardust, 10 Razz Berries, 2 Fast TMs

Step 2 of 5

• Explore 5 km (2 Golden Razz Berries)
• Catch 20 dark-type Pokémon (Galarian Zigzagoon encounter)
• Evolve 3 dark-type Pokémon (10 Great Balls)

Rewards : 1,000 XP, 5 Hyper Potions, 2 Charge TMs

Step 3 of 5

• Spin 25 PokéStops or gyms (2 Silver Pinap Berries)
• Power up 5 grass-type Pokémon (Cacnea encounter)
• Make an excellent throw (10 Ultra Balls)

Rewards : 1,000 Stardust, 5 Revives, 1 Incense

Step 4 of 5

• Use 5 supereffective charged attacks (2 Golden Razz Berries)
• Power up 5 dark-type Pokémon (Scraggy encounter)
• Use 10 berries to help catch Pokémon (5 Rare Candy)

Rewards : 1,000 XP, 2 Premium Raid Passes, 1,000 Stardust

Step 5 of 5

• Win 3 raids (2 Silver Pinap Berries)
• Catch 15 different species of Pokémon (Absol encounter)
• Defeat 5 Team Go Rocket grunts (10 Zarude Candy)

Rewards : 1,000 Stardust, Zarude encounter, 3 Zarude Candy XL

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• Wonder Ticket
• Ditto disguises

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Celebrating Women's History Month

Published March 19, 2024

March is Women’s History Month! Read about some of the women working in EPA’s Office of Research and Development to protect human health and the environment.

Maureen Gwinn , Ph.D., DABT, Principal Deputy Assistant Administrator and Chief Scientist for EPA’s Office of Research and Development

How does your work at epa help protect public health and the environment.

My work as the Principal Deputy Assistant Administrator for Research and Development is focused on the development of the science needed to inform Agency (and partner) decision-making. The mission of the Office of Research and Development (ORD) is to provide the best available science and technology for protecting human health and the environment.  Our work covers the breadth of topics important to environmental protection – addressing the impacts of environmental stressors (chemical and non-chemical) to human and ecological health following exposure through air, water and land. We are not just working to understand what is happening now, but to learn from it to avoid potential impacts in the future.

This means everything we do in ORD is to help develop the foundational science necessary to protect human health and the environment. I am fortunate to work with committed colleagues – including scientists, engineers, administrative professionals, computer and IT experts, lawyers, science communicators, facility managers, and a variety of other specialists—that believe in this mission as much as I do. 

What was your most challenging experience in school or work and how did you overcome it?

This is not an easy question to answer. There are moments in your life that seemed like the biggest hurdle you will ever face, but then you conquer it and move on with (if you are lucky) increased personal strength. 

To answer this, I’d have to say it was my first big career shift at EPA. I had been in ORD working on human health hazard assessments for about seven years. My background and experience at the Agency was largely focused on review and synthesis of toxicology and cancer research in order to develop human hazard assessments to support environmental decision-making. I was asked to shift gears to take on a leadership role in the Sustainable and Healthy Communities national research program focused on oversight of the public health research portfolio. Taking this detail on not only exposed me to an entire new aspect of ORD’s research portfolio, but also a new group of colleagues – most of whom worked in labs geographically distant from my office in Washington, DC. One of the great things about the EPA is the ability to try new things by taking on details – I highly recommend it! As one of my tasks was to better coordinate the public health portfolio with the ecological portfolio, it soon became apparent I had a steep learning curve to better understand the ecological research. I was faced with a four-month detail that was going to require a huge lift just to get up to speed – both in getting to know the science and the people, along with learning the actual job itself. I often questioned if I had made a huge mistake! So, what did I do? I thought about what I needed to succeed: hard work, good communication, supportive colleagues, and a strong mentor. I was lucky enough to have all of these, and I took the time to really understand the subjects and the colleagues I was working with on this project. My four-month detail extended to over two years, and this experience has always stuck with me when faced with what seems like an insurmountable challenge. 

Do you have any advice for young people interested in careers like yours?

I often share key pieces of advice that I have learned through the years, either through my own experiences or from the wisdom of others.

Very rarely will you find someone who had a career path that was exactly as they planned when leaving school. Look to each opportunity as a step that you can learn from – even if what you learn is that it is not the step for you. These opportunities may be hard, but they are so often worth it. Even if it ends up not being the role for you, knowing that you are capable of taking on a challenge and succeeding will serve you well in the future. 

And finally, I cannot stress enough the importance of mentors. They don’t have to be formal, you don’t have to meet every week, but have a sense of who you can call to help you work through any issues or big decisions and think about those that will be there to help you celebrate your achievements. I have mentors from a variety of periods of my life, each with a different focus, each with an immense value to me. 

Kaytee Canfield , Ph.D., Social Scientist

I am a social scientist with expertise primarily in inclusive science communication, tourism, and coastal environments. My work on the Translational Science Team in the Office of Science Advisor, Policy, and Engagement at the EPA is focused on finding ways to make our translational research more useful to people across the country. Translational research projects engage with partners, both within and beyond the agency, to identify priority environmental and public health problems and work collaboratively to find solutions that meet partners’ needs. For example, an ongoing project integrates agency researchers’ expertise and community knowledge to experiment with wastewater, aquaculture, and restoration techniques to manage water pollution from nutrients in coastal waterways. I look forward each day to finding ways to improve the usability of our work to better support all residents of the U.S. in tackling the complex environmental and public health problems we face in the twenty-first century.

  What was your most challenging experience in school or work and how did you overcome it?

I was lucky and privileged to be in very supportive, inclusive, and largely women-led science programs throughout my education. Because I primarily felt my gender (along with my race, sexuality, and other identities) belonged in science through the examples of my teachers, professors, and colleagues, my most challenging school and career experience was finding where my passion for environmental justice and my academic training could align to make a meaningful difference. This came up for me repeatedly through my time at university and early after finishing my doctoral work. As a self-identified geek, and a notorious introvert, many of the expected paths for “making a difference” didn’t quite fit with my personality and skillset. Finding my way to inclusive science communication research and practice in the final year of my doctorate was a game changer. Working closely with Sunshine Menezes and other leaders helped me find my place in this movement, working towards equity, inclusion, and justice in science and science communication. Inclusive science communication has been central to both my career and personal evolution, in finding ways I can be my full self and create a positive difference within academic and research environments.

Give yourself space and grace to grow, expect challenges, find a mentor you trust, and ask questions. I started college in chemical engineering with a goal of creating truly biodegradable plastic. While I miss calculus (see, I said I’m a geek), I’m so happy with the qualitative, environmental, and inclusive science communication career in which I’ve landed. When I leaped into environmental social science, it felt like I was disappointing the women in science and engineering before me and taking the easier path. In my first environmental studies course, I found my first mentor, Dr. Kristen Weiss, who introduced me to qualitative social science, social network analysis, and science communication, and reminded me that social science is indeed science. While I’m still relatively early in my career, I have many people to be grateful to for the way they’ve helped grow my confidence and sense of belonging in my career and field. Ask those people who help you feel you belong lots of questions, and don’t hesitate to come back with more. They are happy to help you find where you belong, and I would be happy to help you, too.

Rebecca (Becky) Nachman,  Ph.D., Epidemiologist

My work as an epidemiologist helps us to understand how chemicals in the environment impact the health of people. Much of the evidence I work with comes from observational studies, which means the people in the studies were not intentionally exposed to anything. Instead, researchers use creative methods to determine whether some people are more exposed to a chemical than others and whether the rates of disease are different between different groups of people depending on their exposure level. This kind of evidence adds an important dimension to EPA’s risk assessments, which are used to make decisions about regulations like water and air standards, because epidemiology evidence shows what is happening in a real-life scenario with real people. I enjoy the challenges of interpreting epidemiologic evidence, but I especially like knowing that the work I do contributes directly to protecting people’s health.

My greatest challenge was taking the research that I conducted over a period of years as a doctoral student and describing it clearly in a paper for publication. When you spend a lot of time thinking about a problem, all of the details and how they fit together become very familiar to you. You have become an expert in something, but the results of your research only have an impact if they can be communicated to others. Doing scientific work often involves working with a lot of information and complex ideas, but in the end, it is the scientist’s responsibility to communicate the purpose of the research, how it was done, what the results were, and how those results advance what we knew before the research was conducted. Scientific journals often have a limit on the number of words allowed in a single article, which makes doing this all the more challenging. However, I find that it is when we are working within limitations that we often do our greatest work.

My advice to young people is to keep trying new things, keep taking action. If you talk to me or any of my colleagues, many of us did not know that we would someday be scientists working at EPA when we were first starting our careers. Some of us started in careers that seem unrelated. For example, before I went to graduate school, I studied English and worked for several years as an editor in a publishing house. Young people should know that at every step they take, they will gain skills that they may apply in ways they never imagined, and they will learn something about themselves and where they want to go next.

Lindsay Stanek,  Ph.D., Physcial Scientist

My research, as well as that of my branch (the Exposure Indicators Branch), is focused on human exposure to environmental contaminants and microbes. I am very interested in understanding the many determinants of exposure that influence where and when we come into contact with chemicals in our everyday lives. It is critical to recognize the importance of place in a person’s overall exposure, while acknowledging that many environmental issues are common across the country; yet every community is unique in its combinations of exposures and the actions that could mitigate them. My most recent projects have been focused on lead (Pb) and whether we can use surrogates of exposure to predict where children may be affected by Pb in their homes and neighborhoods.

There have been a few times where my motivation was questioned that was in direct opposition with a main value of mine – integrity. In each case, after the initial exchange, I took some time to reflect and work through my response, so it would be less emotional. From those early career experiences, I’ve learned to be open and transparent in sharing my rationale for making decisions.

My biggest piece of advice is to take advantage of opportunities presented to you and to put yourself out there. There were many times throughout my career where others saw potential in me and gave me the encouragement I needed to step into roles that were outside of my comfort zone. Through doing things that stretched my knowledge and training, I realized that I brought unique perspectives and experiences that were just as valuable as my credentials. 

Emily Trentacoste,  Ph.D., Biologist

One of my favorite parts about working at EPA is that every person makes a difference in supporting our Agency’s mission. Since joining EPA, I’ve worked in several different roles across the Agency, including writing rules for wastewater in the Office of Water and directly working with states and communities in the Chesapeake Bay Program to help them use science to curb nutrient and sediment pollution. Since joining ORD, I’ve worked to support ORD’s leadership. In my current role, I feel I’m protecting public health and the environment by helping to ensure EPA’s and our partners’ decisions are supported by the best available science. I love being able to support ORD researchers at a high level across all our organization and communicate the great work we do.

My most challenging experience in school or work was the “imposter syndrome” I felt while in my first year of graduate school. I had just come from undergrad into a PhD program, and although I had done well in research in school previously, I had never done it as independently. My first year I felt like I was doing everything wrong, I wasn’t producing anything, and so I must not deserve to be there. But I continued to work hard, build skills, and learn as much as I could, and I quickly became confident in myself, my knowledge, and my abilities. Although the experience was challenging, it made me a better researcher.

Yes, two pieces of advice! The first is to talk to people. You can find out so much information about different types of careers within the fields of environmental science, research, and protection by just talking to people in different positions. There are more options out there than you might think. Don’t be afraid to contact someone you’ve never met to talk to them about what they do. The second is to approach every position in your career as an opportunity, even if it’s not where you want to ultimately want to end up. If you find yourself in a position that you know you’ll want to move on from eventually, work hard at whatever skills or experience you can build while you’re in it, and remember that discovering what you don’t want from your job can be equally as helpful as discovering what you do want.

Nicolle Tulve ,  Ph.D., Research Physcial Scientist

My work is focused on understanding the links between chemical and non-chemical stressors and how those interactions may affect our health, well-being, and quality of life. Fundamental to my research is a strong interdisciplinary background and training, and within that background the field of exposure science. Exposure science is fundamental to our research because it is everything that we are exposed to and how those exposures occur, when they occur, and how all these stressors interact that influence our health, well-being, and quality of life. My research is primarily focused on children’s environmental health, but what we learn about protecting children’s health is applicable to all humans. It has been very exciting to work on Agency priority research throughout my career.

Gosh, there have been so many challenges throughout my career. One of the biggest challenges I faced was being one of only a couple of women in my graduate school program. I am an environmental engineer by training, and breaking through those barriers and graduating with high honors took a lot of perseverance, strength, and thick skin. That thick skin has served me well throughout my career.

Learn as much as you can and keep learning. I truly believe that the most pressing issues of our current society can only be solved through interdisciplinary collaborations. And always recognize when you need others to help work on a problem and seek them out.

Editor's Note:  The opinions expressed herein are those of the researcher alone. EPA does not endorse the opinions or positions expressed.  

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