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Application of optimized Kalman filtering in target tracking based on improved Gray Wolf algorithm

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Primacy of the research question, structure of the paper, writing a research article: advice to beginners.

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Thomas V. Perneger, Patricia M. Hudelson, Writing a research article: advice to beginners, International Journal for Quality in Health Care , Volume 16, Issue 3, June 2004, Pages 191–192, https://doi.org/10.1093/intqhc/mzh053

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Writing research papers does not come naturally to most of us. The typical research paper is a highly codified rhetorical form [ 1 , 2 ]. Knowledge of the rules—some explicit, others implied—goes a long way toward writing a paper that will get accepted in a peer-reviewed journal.

A good research paper addresses a specific research question. The research question—or study objective or main research hypothesis—is the central organizing principle of the paper. Whatever relates to the research question belongs in the paper; the rest doesn’t. This is perhaps obvious when the paper reports on a well planned research project. However, in applied domains such as quality improvement, some papers are written based on projects that were undertaken for operational reasons, and not with the primary aim of producing new knowledge. In such cases, authors should define the main research question a posteriori and design the paper around it.

Generally, only one main research question should be addressed in a paper (secondary but related questions are allowed). If a project allows you to explore several distinct research questions, write several papers. For instance, if you measured the impact of obtaining written consent on patient satisfaction at a specialized clinic using a newly developed questionnaire, you may want to write one paper on the questionnaire development and validation, and another on the impact of the intervention. The idea is not to split results into ‘least publishable units’, a practice that is rightly decried, but rather into ‘optimally publishable units’.

What is a good research question? The key attributes are: (i) specificity; (ii) originality or novelty; and (iii) general relevance to a broad scientific community. The research question should be precise and not merely identify a general area of inquiry. It can often (but not always) be expressed in terms of a possible association between X and Y in a population Z, for example ‘we examined whether providing patients about to be discharged from the hospital with written information about their medications would improve their compliance with the treatment 1 month later’. A study does not necessarily have to break completely new ground, but it should extend previous knowledge in a useful way, or alternatively refute existing knowledge. Finally, the question should be of interest to others who work in the same scientific area. The latter requirement is more challenging for those who work in applied science than for basic scientists. While it may safely be assumed that the human genome is the same worldwide, whether the results of a local quality improvement project have wider relevance requires careful consideration and argument.

Once the research question is clearly defined, writing the paper becomes considerably easier. The paper will ask the question, then answer it. The key to successful scientific writing is getting the structure of the paper right. The basic structure of a typical research paper is the sequence of Introduction, Methods, Results, and Discussion (sometimes abbreviated as IMRAD). Each section addresses a different objective. The authors state: (i) the problem they intend to address—in other terms, the research question—in the Introduction; (ii) what they did to answer the question in the Methods section; (iii) what they observed in the Results section; and (iv) what they think the results mean in the Discussion.

In turn, each basic section addresses several topics, and may be divided into subsections (Table 1 ). In the Introduction, the authors should explain the rationale and background to the study. What is the research question, and why is it important to ask it? While it is neither necessary nor desirable to provide a full-blown review of the literature as a prelude to the study, it is helpful to situate the study within some larger field of enquiry. The research question should always be spelled out, and not merely left for the reader to guess.

Typical structure of a research paper

The Methods section should provide the readers with sufficient detail about the study methods to be able to reproduce the study if so desired. Thus, this section should be specific, concrete, technical, and fairly detailed. The study setting, the sampling strategy used, instruments, data collection methods, and analysis strategies should be described. In the case of qualitative research studies, it is also useful to tell the reader which research tradition the study utilizes and to link the choice of methodological strategies with the research goals [ 3 ].

The Results section is typically fairly straightforward and factual. All results that relate to the research question should be given in detail, including simple counts and percentages. Resist the temptation to demonstrate analytic ability and the richness of the dataset by providing numerous tables of non-essential results.

The Discussion section allows the most freedom. This is why the Discussion is the most difficult to write, and is often the weakest part of a paper. Structured Discussion sections have been proposed by some journal editors [ 4 ]. While strict adherence to such rules may not be necessary, following a plan such as that proposed in Table 1 may help the novice writer stay on track.

References should be used wisely. Key assertions should be referenced, as well as the methods and instruments used. However, unless the paper is a comprehensive review of a topic, there is no need to be exhaustive. Also, references to unpublished work, to documents in the grey literature (technical reports), or to any source that the reader will have difficulty finding or understanding should be avoided.

Having the structure of the paper in place is a good start. However, there are many details that have to be attended to while writing. An obvious recommendation is to read, and follow, the instructions to authors published by the journal (typically found on the journal’s website). Another concerns non-native writers of English: do have a native speaker edit the manuscript. A paper usually goes through several drafts before it is submitted. When revising a paper, it is useful to keep an eye out for the most common mistakes (Table 2 ). If you avoid all those, your paper should be in good shape.

Common mistakes seen in manuscripts submitted to this journal

Huth EJ . How to Write and Publish Papers in the Medical Sciences , 2nd edition. Baltimore, MD: Williams & Wilkins, 1990 .

Browner WS . Publishing and Presenting Clinical Research . Baltimore, MD: Lippincott, Williams & Wilkins, 1999 .

Devers KJ , Frankel RM. Getting qualitative research published. Educ Health 2001 ; 14 : 109 –117.

Docherty M , Smith R. The case for structuring the discussion of scientific papers. Br Med J 1999 ; 318 : 1224 –1225.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

Cite this chapter

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

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

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Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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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?
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• 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 .

Thinking about becoming a nurse practitioner? Nurse practitioners have one of the fastest growing careers in the country, and we have all the information you need to know about what to expect from nurse practitioner school .

Want to know the fastest and easiest ways to convert between Fahrenheit and Celsius? We've got you covered! Check out our guide to the best ways to convert Celsius to Fahrenheit (or vice versa).

These recommendations are based solely on our knowledge and experience. If you purchase an item through one of our links, PrepScholar may receive a commission.

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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How to Do Research

Last Updated: March 13, 2023 References

This article was co-authored by Matthew Snipp, PhD and by wikiHow staff writer, Jennifer Mueller, JD . C. Matthew Snipp is the Burnet C. and Mildred Finley Wohlford Professor of Humanities and Sciences in the Department of Sociology at Stanford University. He is also the Director for the Institute for Research in the Social Science’s Secure Data Center. He has been a Research Fellow at the U.S. Bureau of the Census and a Fellow at the Center for Advanced Study in the Behavioral Sciences. He has published 3 books and over 70 articles and book chapters on demography, economic development, poverty and unemployment. He is also currently serving on the National Institute of Child Health and Development’s Population Science Subcommittee. He holds a Ph.D. in Sociology from the University of Wisconsin—Madison. This article has been viewed 226,384 times.

The idea of doing research may seem daunting, but as long as you keep yourself organized and focus on the question you want to answer, you'll be fine. If you're curious and interested in the topic, you might even find it fun! We here at wikiHow have gathered answers to all your most common questions about how to do research, from finding a good topic to identifying the best sources and writing your final paper.

How do I find a topic to research?

• For example, if you're researching in the political science field, you might be interested in determining what leads people to believe that the 2020 US presidential election was illegitimate.

How do I get started on my research?

• For example, if you're researching the 2020 election, you might find that "absentee ballots" and "voting by mail" come up frequently. Those are issues you could look into further to figure out how they impacted the final election results.
• You don't necessarily have to use the overview articles you look at as resources in your actual paper. Even Wikipedia articles can be a good way to learn more about a topic and you can check the references for more reputable sources that might work for your paper.

What's the best way to keep track of my sources?

• Research papers typically discuss 2 or 3 separate things that work together to answer the research question. You might also want to make a note on the front of which thing that source relates to. That'll make it easier for you to organize your sources later.
• For example, if you're researching the 2020 election, you might have a section of your paper discussing voting by mail. For the sources that directly address that issue, write "voting by mail" in the corner.

What kind of notes should I be taking as I research?

• If you find something that you think would make a good quote, copy it out exactly with quote marks around it, then add the page number where it appears so you can correctly cite it in your paper without having to go back and hunt for it again.

How do I evaluate the quality of a source?

• Does the article discuss or reference another article? (If so, use that article instead.)
• What expertise or authority does the author have?
• When was the material written? (Is it the most up-to-date reference you could use?)
• Why was the article published? (Is it trying to sell you something or persuade you to adopt a certain viewpoint?)
• Are the research methods used consistent and reliable? (Appropriate research methods depend on what was studied.)

What if I'm having a hard time finding good sources?

• For example, if you're writing about the 2020 election, you might find tons of stories online, but very little that is reputable enough for you to use in your paper. Because the election happened so recently, it might be too soon for there to be a lot of solid academic research on it. Instead, you might focus on the 2016 election.
• You can also ask for help. Your instructor might be able to point you toward good sources. Research librarians are also happy to help you.

How do I organize my research for my paper?

• For example, if you're researching the effect of the COVID-19 pandemic on the 2020 election, you might have sections on social distancing and cleaning at in-person voting locations, the accessibility of mail-in ballots, and early voting.

What's the best way to start writing my paper?

• Include an in-text citation for everything that needs one, even in your initial rough draft. That'll help you make sure that you don't inadvertently misattribute or fail to cite something as you work your way through substantive drafts.
• Write your introduction and conclusion only after you're satisfied that the body of your paper is essentially what you want to turn in. Then, you can polish everything up for the final draft.

How can I make sure I'm not plagiarizing?

• If you have any doubt over whether you should cite something, go ahead and do it. You're better off to err on the side of over-citing than to look like you're taking credit for an idea that isn't yours.
• ↑ https://www.nhcc.edu/student-resources/library/doinglibraryresearch/basic-steps-in-the-research-process
• ↑ Matthew Snipp, PhD. Sociology Professor, Stanford University. Expert Interview. 26 March 2020.
• ↑ https://library.taylor.edu/eng-212/research-paper
• ↑ http://www.butte.edu/departments/cas/tipsheets/research/research_paper.html
• ↑ https://www.potsdam.edu/sites/default/files/documents/support/tutoring/cwc/6-Simple-Steps-for-Writing-a-Research-Paper.pdf
• ↑ https://www.umgc.edu/current-students/learning-resources/writing-center/online-guide-to-writing/tutorial/chapter4/ch4-05.html

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About This Article

If you need to do research on a particular topic, start by searching the internet for any information you can find on the subject. In particular, look for sites that are sourced by universities, scientists, academic journals, and government agencies. Next, visit your local library and use the electric card catalog to research which books, magazines, and journals will have information on your topic. Take notes as you read, and write down all of the information you’ll need to cite your sources in your final project. To learn how interviewing a first-hand source can help you during your research, read on! Did this summary help you? Yes No

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Research: Where to Begin

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

Choosing and Narrowing Your Research Topic

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

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

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

Writing Your Research Question

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

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

Knowing What Types of Sources You Need

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

Asking Productive Questions

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

What kind of information are you looking for?

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

Where do you need to look for your research?

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

How much information do you need?

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

How timely does your research need to be?

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

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Methodology

Research Methods | Definitions, Types, Examples

Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.

First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :

• Qualitative vs. quantitative : Will your data take the form of words or numbers?
• Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
• Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?

Second, decide how you will analyze the data .

• For quantitative data, you can use statistical analysis methods to test relationships between variables.
• For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.

Table of contents

Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.

Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.

Qualitative vs. quantitative data

Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.

For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .

If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .

You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.

Primary vs. secondary research

Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).

If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.

Descriptive vs. experimental data

In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .

In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .

To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.

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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.

Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.

Qualitative analysis methods

Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:

• From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
• Using non-probability sampling methods .

Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .

Quantitative analysis methods

Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).

You can use quantitative analysis to interpret data that was collected either:

• During an experiment .
• Using probability sampling methods .

Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.

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

• Chi square test of independence
• Statistical power
• Descriptive statistics
• Degrees of freedom
• Pearson correlation
• Null hypothesis
• Double-blind study
• Case-control study
• Research ethics
• Data collection
• Hypothesis testing
• Structured interviews

Research bias

• Hawthorne effect
• Unconscious bias
• Recall bias
• Halo effect
• Self-serving bias
• Information bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

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How to Do Research: A Step-By-Step Guide: 2d. Articles

• Get Started
• 1a. Select a Topic
• 1b. Develop Research Questions
• 1c. Identify Keywords
• 1d. Find Background Information
• 1e. Refine a Topic
• 2a. Search Strategies
• 2d. Articles
• 2e. Videos & Images
• 2f. Databases
• 2g. Websites
• 2h. Grey Literature
• 2i. Open Access Materials
• 3a. Evaluate Sources
• 3b. Primary vs. Secondary
• 3c. Types of Periodicals
• 4a. Take Notes
• 4b. Outline the Paper
• 4c. Incorporate Source Material
• 5a. Avoid Plagiarism
• 5b. Zotero & MyBib
• 5c. MLA Formatting
• 5d. MLA Citation Examples
• 5e. APA Formatting
• 5f. APA Citation Examples
• 5g. Annotated Bibliographies

Periodicals Defined

Periodicals are publications which are issued at regular intervals, such as journals, magazines, and newspapers.

To help you identify different periodicals, refer to the Types of Periodicals guide.

Article Formats

Depending on the database you are using, articles may be displayed in different formats:

• Index : Includes only the article citation (i.e., author, title, date, etc.). Neither a summary, nor the full-text of the article are available.
• Abstract : Includes the citation and a summary of the article's content. It does not include the full-text article.
• Full-text : Includes the citation and full-text article. This may be in HTML, .pdf, or both formats.

Library Search

Tutorial: Using Library Search (video)

For resources not searchable through Library Search, see the orange-flagged items ( ) on the  Databases page .

Where to Find Articles

Consider the many options for locating journal articles at the GTL:

• Library Search : Search all databases in a single search. Click on the "Articles" icon for articles-only results.
• Databases by Title : Find the database you need, or use the descriptions to select an appropriate one.
• Databases by Subject : Search from the recommended databases listed in the box at the left-hand side of the screen.
• Article Linking: If the database lists the article title, but does not provide the full text, click on the linking button to find the article in another database.
• Browse Journal Titles : Type in the title, or part of the title of a journal to view a list of GTL databases that include the journal.
• Google Scholar : Search for academic articles. Available articles will be linked to Elmira College resources.
• Interlibrary Loan : If the GTL does not have access to the article, submit a request to acquire it from another library.

Finding Articles in Databases

The Using Databases guide offers information about choosing a database, types of databases, database features, searching, and troubleshooting.

About Peer Reviewed and Scholarly Articles

Peer-reviewed articles are reviewed by objective peer scholars (referees) in the field prior to publication to guarantee that the research is credible and sound. Scholarly articles are written by experts, but do not necessarily go through the peer review process.

Scholarly articles usually:

• are written by one or several experts (scholar, professor, researcher) in the field
• are written for experts using technical language
• include an abstract, literature review of related research, methodology, data, conclusion
• contain notes and bibliographic references

By using scholarly articles you support your arguments with expert documentation, and engage in the scholarly exchange of ideas.

Many databases include only scholarly articles. Others allow you to select them in your search results.

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The best AI tools for research papers and academic research (Literature review, grants, PDFs and more)

As our collective understanding and application of artificial intelligence (AI) continues to evolve, so too does the realm of academic research. Some people are scared by it while others are openly embracing the change. 

Make no mistake, AI is here to stay!

Instead of tirelessly scrolling through hundreds of PDFs, a powerful AI tool comes to your rescue, summarizing key information in your research papers. Instead of manually combing through citations and conducting literature reviews, an AI research assistant proficiently handles these tasks.

These aren’t futuristic dreams, but today’s reality. Welcome to the transformative world of AI-powered research tools!

The influence of AI in scientific and academic research is an exciting development, opening the doors to more efficient, comprehensive, and rigorous exploration.

This blog post will dive deeper into these tools, providing a detailed review of how AI is revolutionizing academic research. We’ll look at the tools that can make your literature review process less tedious, your search for relevant papers more precise, and your overall research process more efficient and fruitful.

I know that I wish these were around during my time in academia. It can be quite confronting when trying to work out what ones you should and shouldn’t use. A new one seems to be coming out every day!

Here is everything you need to know about AI for academic research and the ones I have personally trialed on my Youtube channel.

Best ChatGPT interface – Chat with PDFs/websites and more

I get more out of ChatGPT with HeyGPT . It can do things that ChatGPT cannot which makes it really valuable for researchers.

Use your own OpenAI API key ( h e re ). No login required. Access ChatGPT anytime, including peak periods. Faster response time. Unlock advanced functionalities with HeyGPT Ultra for a one-time lifetime subscription

AI literature search and mapping – best AI tools for a literature review – elicit and more

Harnessing AI tools for literature reviews and mapping brings a new level of efficiency and precision to academic research. No longer do you have to spend hours looking in obscure research databases to find what you need!

AI-powered tools like Semantic Scholar and elicit.org use sophisticated search engines to quickly identify relevant papers.

They can mine key information from countless PDFs, drastically reducing research time. You can even search with semantic questions, rather than having to deal with key words etc.

With AI as your research assistant, you can navigate the vast sea of scientific research with ease, uncovering citations and focusing on academic writing. It’s a revolutionary way to take on literature reviews.

• Elicit –  https://elicit.org
• Supersymmetry.ai: https://www.supersymmetry.ai
• Semantic Scholar: https://www.semanticscholar.org
• Connected Papers –  https://www.connectedpapers.com/
• Research rabbit – https://www.researchrabbit.ai/
• Laser AI –  https://laser.ai/
• Litmaps –  https://www.litmaps.com
• Inciteful –  https://inciteful.xyz/
• Scite –  https://scite.ai/
• System –  https://www.system.com

If you like AI tools you may want to check out this article:

• How to get ChatGPT to write an essay [The prompts you need]

AI-powered research tools and AI for academic research

AI research tools, like Concensus, offer immense benefits in scientific research. Here are the general AI-powered tools for academic research. 

These AI-powered tools can efficiently summarize PDFs, extract key information, and perform AI-powered searches, and much more. Some are even working towards adding your own data base of files to ask questions from. 

Tools like scite even analyze citations in depth, while AI models like ChatGPT elicit new perspectives.

The result? The research process, previously a grueling endeavor, becomes significantly streamlined, offering you time for deeper exploration and understanding. Say goodbye to traditional struggles, and hello to your new AI research assistant!

• Bit AI –  https://bit.ai/
• Consensus –  https://consensus.app/
• Exper AI –  https://www.experai.com/
• Hey Science (in development) –  https://www.heyscience.ai/
• Iris AI –  https://iris.ai/
• PapersGPT (currently in development) –  https://jessezhang.org/llmdemo
• Research Buddy –  https://researchbuddy.app/
• Mirror Think – https://mirrorthink.ai

AI for reading peer-reviewed papers easily

Using AI tools like Explain paper and Humata can significantly enhance your engagement with peer-reviewed papers. I always used to skip over the details of the papers because I had reached saturation point with the information coming in. 

These AI-powered research tools provide succinct summaries, saving you from sifting through extensive PDFs – no more boring nights trying to figure out which papers are the most important ones for you to read!

They not only facilitate efficient literature reviews by presenting key information, but also find overlooked insights.

With AI, deciphering complex citations and accelerating research has never been easier.

• Open Read –  https://www.openread.academy
• Chat PDF – https://www.chatpdf.com
• Explain Paper – https://www.explainpaper.com
• Humata – https://www.humata.ai/
• Lateral AI –  https://www.lateral.io/
• Paper Brain –  https://www.paperbrain.study/
• Scholarcy – https://www.scholarcy.com/
• SciSpace Copilot –  https://typeset.io/
• Unriddle – https://www.unriddle.ai/
• Sharly.ai – https://www.sharly.ai/

AI for scientific writing and research papers

In the ever-evolving realm of academic research, AI tools are increasingly taking center stage.

Enter Paper Wizard, Jenny.AI, and Wisio – these groundbreaking platforms are set to revolutionize the way we approach scientific writing.

Together, these AI tools are pioneering a new era of efficient, streamlined scientific writing.

• Paper Wizard –  https://paperwizard.ai/
• Jenny.AI https://jenni.ai/ (20% off with code ANDY20)
• Wisio – https://www.wisio.app

AI academic editing tools

In the realm of scientific writing and editing, artificial intelligence (AI) tools are making a world of difference, offering precision and efficiency like never before. Consider tools such as Paper Pal, Writefull, and Trinka.

Together, these tools usher in a new era of scientific writing, where AI is your dedicated partner in the quest for impeccable composition.

• Paper Pal –  https://paperpal.com/
• Writefull –  https://www.writefull.com/
• Trinka –  https://www.trinka.ai/

AI tools for grant writing

In the challenging realm of science grant writing, two innovative AI tools are making waves: Granted AI and Grantable.

These platforms are game-changers, leveraging the power of artificial intelligence to streamline and enhance the grant application process.

Granted AI, an intelligent tool, uses AI algorithms to simplify the process of finding, applying, and managing grants. Meanwhile, Grantable offers a platform that automates and organizes grant application processes, making it easier than ever to secure funding.

Together, these tools are transforming the way we approach grant writing, using the power of AI to turn a complex, often arduous task into a more manageable, efficient, and successful endeavor.

• Granted AI – https://grantedai.com/
• Grantable – https://grantable.co/

Free AI research tools

There are many different tools online that are emerging for researchers to be able to streamline their research processes. There’s no need for convience to come at a massive cost and break the bank.

The best free ones at time of writing are:

• Elicit – https://elicit.org
• Connected Papers – https://www.connectedpapers.com/
• Litmaps – https://www.litmaps.com ( 10% off Pro subscription using the code “STAPLETON” )
• Consensus – https://consensus.app/

Wrapping up

The integration of artificial intelligence in the world of academic research is nothing short of revolutionary.

With the array of AI tools we’ve explored today – from research and mapping, literature review, peer-reviewed papers reading, scientific writing, to academic editing and grant writing – the landscape of research is significantly transformed.

The advantages that AI-powered research tools bring to the table – efficiency, precision, time saving, and a more streamlined process – cannot be overstated.

These AI research tools aren’t just about convenience; they are transforming the way we conduct and comprehend research.

They liberate researchers from the clutches of tedium and overwhelm, allowing for more space for deep exploration, innovative thinking, and in-depth comprehension.

Whether you’re an experienced academic researcher or a student just starting out, these tools provide indispensable aid in your research journey.

And with a suite of free AI tools also available, there is no reason to not explore and embrace this AI revolution in academic research.

We are on the precipice of a new era of academic research, one where AI and human ingenuity work in tandem for richer, more profound scientific exploration. The future of research is here, and it is smart, efficient, and AI-powered.

Before we get too excited however, let us remember that AI tools are meant to be our assistants, not our masters. As we engage with these advanced technologies, let’s not lose sight of the human intellect, intuition, and imagination that form the heart of all meaningful research. Happy researching!

Thank you to Ivan Aguilar – Ph.D. Student at SFU (Simon Fraser University), for starting this list for me!

Dr Andrew Stapleton has a Masters and PhD in Chemistry from the UK and Australia. He has many years of research experience and has worked as a Postdoctoral Fellow and Associate at a number of Universities. Although having secured funding for his own research, he left academia to help others with his YouTube channel all about the inner workings of academia and how to make it work for you.

Thank you for visiting Academia Insider.

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A basic introduction to research: how not to do research

Andrew j. vickers.

Department of Epidemiology and Biostatistics, Memorial Sloan-Kettering Cancer Center

In this didactic paper, I review some prevalent “myths” about clinical research: anyone can do research; you can learn how to do research from a book or journal articles; all you need to do statistics is the right software (although Excel will also do); you can do good quality research at your kitchen sink; what is important is that you did your best. These myths appear particularly prevalent in the complementary and alternative medicine communities. They are based on a clear double standard: most clinicians would express shock and horror at the very thought that someone without appropriate clinical training and qualifications might treat a patient; meanwhile many clinicians do research with no research qualifications whatsoever. But clinical research can guide clinical decisions that affect the health and well-being of millions of people: it is therefore arguable that poorly conducted research is potentially far more harmful than poor medical practice. As such, it is doubly important that clinical research is conducted by those with appropriate training, statistical help and institutional support.

Introduction

In this didactic paper, I offer some personal reflections on perhaps the most common mistake made by beginning researchers: that clinical research is not particularly difficult and can be done by pretty much anyone, regardless of training and experience. I will explain my views by examining several “myths” about research that I believe to be particularly common in the complementary and alternative medicine community. I will end by making some practical suggestions to counter each of these myths.

Myth 1: anyone can do clinical research

A friend of mine who is a professional mountaineer recently received the following Email from a local hiking group: “Dear Sir, we would like to climb a mountain in the Himalayas, perhaps something 22 – 25,000 feet high. We understand from the literature that it is important to take bottled oxygen and were wondering what brand you would recommend.” I am joking of course: I don’t know any mountaineers and I doubt anyone has ever sent such an Email. That is because it is obvious to anyone: try to climb a Himalayan mountain without an experienced leader and you are going to get yourself killed.

Clinical research appears to be a different matter, however. There is a widespread impression that clinical research can be done by almost anyone, regardless of prior skills or experience. I get Emails similar in form to the one above regularly; for a recent example, “I want to do some research on massage and need an outcome measure. What would you suggest?” What I suggested was that the enquirer find an experienced researcher with whom to work. Similarly, a statistician friend received a call from a doctor: “my statistical software has given me an error message: data failed to converge. What does this mean?”’ My friend gave the only possible answer: “it means you need to see a statistician.”

Many clinicians I have met have a double standard: on the one hand, those engaged in clinical activities must have the proper training and experience; on the other hand, anyone can do research. Most clinicians express shock and horror at the very thought that someone without appropriate clinical training and qualifications might treat a patient; indeed, there is plenty enough finger-pointing even at those who do have qualifications (e.g. “doctor acupuncturists don’t do proper acupuncture”). Meanwhile many clinicians do research with no research qualifications whatsoever.

This is perhaps most clearly brought home at ‘research days’ where complementary practitioners, acupuncturists say, attend a few seminars hoping to learn how to do clinical research. Now compare this to an ‘acupuncture day’ at which statisticians without prior knowledge are taught a few techniques so that they can practice acupuncture. Yet whilst ‘research days’ continue to proliferate and ‘acupuncture days’ are unheard of, it is arguable that it is medical research that requires more training (see table ).

Training and qualifications for acupuncture compared to research

Myth 2: you can learn how to do research from a book or journal articles

I was recently asked to review a paper that described an ‘n-of-1’ trial of a complementary therapy. The paper contained numerous important flaws and required major revision. It was not hard to see why the authors had gone so badly wrong: they had no formal training in research methods, they had never previously conducted an n-of-1 trial and they were not working at an institution where such trials (or anything remotely similarly) had been conducted. The authors had based their methods on a chapter in a complementary medicine research textbook. The first problem, which is fairly typical of those writing about complementary medicine research, is that the author of this chapter had no experience whatsoever of n-of-1 methodology (similarly, the journal that asked me to review this paper published one entitled ‘how to conduct a survey’, written by an author with no significant survey publications). The second problem is that science is not cookery and scientific texts are not cookbooks. The reason most of us are able to make ratatouille from a recipe is that we all have a stove, have previously chopped an onion and know what a stew is meant to look like. The same is not generally true of research. You cannot expect to throw a cookbook at someone who has never seen a kitchen before and expect to get a Spanish omelet. Give an inexperienced researcher a methodology textbook and similarly, all you’ll end up with is broken eggs.

Myth 3: All you need to do statistics is the right software (although Excel will also do)

The other day I sat down in front of Microsoft Word and typed ‘Now is the winter of our discontent.’ When the rest of Richard III did not flash up on screen I rang Microsoft technical support. They weren’t that helpful so I cut and pasted a few things and sent the results to a literary magazine for publication.

The statistical equivalent is so commonplace as to be cliche. Just as a recent example, I peer reviewed a paper in which many of the p values were given as ‘p=0.000’. This is obviously absurd, on the grounds that any conceivable clinical trial result has a non-zero probability. When I pointed this out to the authors, their defense was that they had cut and pasted from the statistical software so their result must be true. Again we see the double standard: to be a clinician takes years of training; to be a statistician, all you need is some software and familiarity with the ‘paste’ key.

When I read any medical paper, one of the first things I do is to glance over the list of authors. I always want to see whether at least one author is affiliated to a statistics department or has an appropriate qualification (PhD, MPH, MSc). Having a statistician as an author does not necessarily mean that the statistics are correct, just as doctors can give bad clinical advice. Similarly, the absence of a statistician does not mean that the statistics will be incorrect: my neighbor isn’t a doctor but he does sometimes say sensible things about health. On balance though, if I’m sick, I want to see someone with a plaque on the door.

Myth 4: You can do good quality research at your kitchen sink

It is almost impossible to enumerate in full the physical and intellectual resources that are taken for granted by those working in large research institutions. But to take just a couple of examples, if you work at a hospital with over 400 active clinical trials, the complex computer programing required for data entry and randomization databases has already been completed by a specialist team. Working at such a hospital also means that research protocols are evaluated by expert committees of researchers who can offer guidance and advice.

Is it really possible that the isolated practitioner, working alone without expert help or any significant research facilities, can really produce good clinical science? I can’t say I’m 100% sure, but it is difficult to think of many examples.

Myth 5: What is important is that you did your best

I was once asked to read a report of a clinical trial conducted by a medical student. When I remarked that the trial was badly flawed, I was told not to be so critical: it was only a student project, she had done pretty well, considering, and the paper deserved to get published on that basis. Similarly, when I criticized a published paper in a book, I received a nasty letter from the author of the paper saying, in short, “how could you be so mean, it was my first try!”

Now singing a few flat notes on karaoke night at the local bar does not spoil the fun, as long as everyone tries their best and has a good time. The problem with the odd flat note in medicine is that it can ruin everything entirely. Every clinician recognizes this: put a catheter in the wrong place and, unlike singing in the wrong key, someone could die as a result. A medical researcher who tried to treat a sick patient and messed up through lack of skills, knowledge and training would rightly be excoriated; “it was my first patient” or “I did my best” would be no defense, and no comfort, to the injured party. So why is this not recognized for research too? Why the double standard such that it is somehow okay to mess up research, but not medicine, through inexperience, ignorance and lack of resources?

Good clinical research can guide clinical decisions that affect the health and well-being of millions of people. Bad research can therefore be just as harmful as bad medicine, perhaps even more so. Until this is more widely realized, and clinicians accordingly pay much more attention to the skills, training and resources needed for high quality science, it is likely that much of the research we see in journals will continue to be little more than the intellectual equivalent of karaoke night.

“How not how not to do research” isn’t very catchy, but when you think about it, that pretty much describes the scientific process: we find out what leads us astray (contaminated test tubes, uncontrolled studies) and try to avoid it (wash glassware, randomize). Here are some practical suggestions to counter each of the myths I discuss in this paper.

Myth 1: Anyone can do research irrespective of skills and experience

We can take the mountain climbing analogy one step further. You wouldn’t try to climb Annapurna without going along with someone who had already some experience of Himalayan mountaineering; accordingly, if you want to do a clinical trial, have a researcher on your team who has done a trial previously; if you want to do molecular marker study, include someone with a track record of publications in that area.

Myth 2: You can learn how to do research from a book or journal articles

Most researchers will tell you that some kind of formal training in research methods is an essential basis for a research career, but also that hands-on experience is critical. If you really want an active research career, you will need to consider a higher degree. Someone who wants to specialize in research should think about a doctorate, otherwise, a Masters’ in clinical epidemiology, public health or biostatistics would provide an excellent foundation for subsequent clinical research activities. In either case, you will need to get experience on as many different research studies as possible during and after you complete your studies.

Myth 3: All you need to do statistics is the right software

Incorporation of biostatistical help is cited by experienced investigators as one of the key determinants of the success or failure of a research program. The ideal is to develop a long-term collaboration with a biostatistician who understands your work, and why it is important. This can be difficult unless the biostatistician and you are members of the same institution (see myth 4).

Obtain a position at an institution that has a good research infrastructure and high-quality scientists.

Constantly ask whether you are doing research for the right reasons: why is it that you want that grant? Or that paper published? If your answers deviate much from a passionate commitment to improve human health, then stop and go and do something else. Medical research shouldn’t be about the researcher, it should be about the people who might be helped by the researcher’s science.

Acknowledgments

Dr Vickers’ work on this research was funded by a P50-CA92629 SPORE from the National Cancer Institute

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Relationships Articles & More

How often do couples express compassion to each other, according to new research, partners show compassion about twice an hour—but our reasons for doing so can sometimes be counterintuitive..

Imagine you’re coming home from work at the end of a long day. You feel tired and distracted, and you might be looking forward to an evening of relaxing on the couch—but you also know you need to put dinner on the table, care for children or pets, and complete household tasks.

In situations like this, during the hustle and bustle of everyday life with our families, how much compassion do couples express? A recent study sought to test out this question—and found that expressions of compassion are surprisingly common, but the patterns behind when we express compassion are somewhat unexpected.

How often do people express compassion?

In the paper, published recently in the Journal of Family Psychology , researchers examined how often married couples express compassion to each other in their daily lives. As part of a larger study on family life, trained videographers recorded families interacting over four days. The results were drawn from 29 families with children from the greater Los Angeles area.

The researchers collected over 1,200 hours of recordings, including morning and evening routines, as well as full days on the weekends. To identify potential instances of compassion, the research team first identified words that might indicate compassion and searched through the transcripts of the recordings for each time couples used these words. This included words more directly related to helping (such as the words “help” or “care” or the phrase “you OK?”) as well as words that might come up in the expression of compassion (such as “good” or “wrong”). Then, they looked at these interactions more closely to determine whether an expression of compassion had occurred.

Focusing on the parents, the researchers found that expressions of compassion were quite common—when interacting, couples expressed compassion to each other about twice per hour on average. These expressions of compassion included a range of actions, such as offering an apology when someone noticed their partner feeling stressed, or asking, “What’s wrong?” after hearing their partner take a potentially stressful work call.

The researchers also found that there weren’t gender differences here—husbands and wives expressed compassion to each other about equally often.

Compassion Meditation

Strengthen feelings of concern for the suffering of others

Given the wide-ranging benefits of compassion , the fact that couples express compassion to each other readily is good news. And, importantly, these expressions of compassion didn’t occur in the research lab (where you might expect couples to be especially attuned to their partner’s behavior), but in everyday life. Galen McNeil, staff psychologist and research scientist at UCLA and lead author of the study, explains that it’s one thing to express compassion in the relatively distraction-free environment of a research lab. But that might not be reflective of how much compassion couples actually express at home, when lots of other things—such as household tasks or an influx of notifications from our phones—are also competing for our attention.

Who expresses more compassion?

In addition to measuring how much compassion partners expressed on average, the researchers looked at what might explain the differences in how often particular individuals showed compassion. Participants had filled out surveys measuring their depressive symptoms and neuroticism (the tendency to experience negative emotions), as well as how they perceived the quality of their relationship. The researchers found that, when wives felt more depressed, their husbands expressed more compassion, suggesting that husbands extended more support when their wives were in need.

However, some of the other findings were more counterintuitive. When husbands perceived their relationship to be lower-quality , they showed more compassion toward their wives. Similarly, when husbands were more neurotic, they also engaged in more compassion. For women, their perception of the relationship, neuroticism, and depression were not related to how much compassion they expressed to their partners.

Given the wide range of benefits compassion has, it might seem surprising that men who were expressing more compassion were more neurotic and thought their relationships were in worse shape. One explanation, according to the researchers, is that these men might be especially vigilant for signs of potential problems in their relationships, and that expressing compassion might be a response to their worries about how the relationship is going.

Interestingly, this interpretation suggests that, when people express compassion, it may sometimes have more to do with their own levels of stress. One implication of this, McNeil explains, is that if your partner is expressing a lot of compassion to you, it might be worthwhile to also check in with them : Showing compassion to others could be a sign that someone also needs support for themselves.

The researchers suggest that one direction for future studies could be to look at the context in which compassion occurs. Since partners who expressed more compassion weren’t more satisfied with their relationships in the current study, McNeil wants to investigate why. Perhaps, for example, there are some contexts in which expressing compassion is more beneficial, and others where it is less so. Additionally, the authors point out that compassion can also be expressed nonverbally, and one potential direction for future research could be to analyze nonverbal behaviors in addition to the words people use.

Overall, the research finds that people express compassion relatively frequently—about twice an hour when interacting with their spouse. However, it also suggests that our reasons for expressing compassion aren’t always as straightforward and other-focused as we might think.

About the Author

Elizabeth Hopper

Elizabeth Hopper, Ph.D. , received her Ph.D. in psychology from UC Santa Barbara and currently works as a freelance science writer specializing in psychology and mental health.

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White-sounding names get called back for jobs more than Black ones, a new study finds

Joe Hernandez

A sign seeking job applicants is seen in the window of a restaurant in Miami, Florida, on May 5, 2023. Joe Raedle/Getty Images hide caption

A sign seeking job applicants is seen in the window of a restaurant in Miami, Florida, on May 5, 2023.

Twenty years ago, two economists responded to a slew of help-wanted ads in Boston and Chicago newspapers using a set of fictitious names to test for racial bias in the job market.

The watershed study found that applicants with names suggesting they were white got 50% more callbacks from employers than those whose names indicated they were Black.

Researchers at the University of California, Berkeley and the University of Chicago recently took that premise and expanded on it, filing 83,000 fake job applications for 11,000 entry-level positions at a variety of Fortune 500 companies.

Their working paper , published this month and titled "A Discrimination Report Card," found that the typical employer called back the presumably white applicants around 9% more than Black ones. That number rose to roughly 24% for the worst offenders.

The research team initially conducted its experiment in 2021, but their new paper names the 97 companies they included in the study and assigns them grades representing their level of bias, thanks to a new methodology the researchers developed.

"Putting the names out there in the public domain is to move away from a lot of the performative allyship that you see with these companies, saying, 'Oh, we value inclusivity and diversity,'" said Pat Kline, a University of California, Berkeley economics professor who worked on the study. "We're trying to create kind of an objective ground truth here."

America Reckons With Racial Injustice

From jobs to homeownership, protests put spotlight on racial economic divide.

The names that researchers tested include some used in the 2004 study as well as others culled from a database of speeding tickets in North Carolina. A name was classified as "racially distinctive" if more than 90% of people with that name shared the same race.

Applicants with names such as Brad and Greg were up against Darnell and Lamar. Amanda and Kristen competed for jobs with Ebony and Latoya.

What the researchers found was that some firms called back Black applicants considerably less, while race played little to no factor in the hiring processes at other firms.

Dorianne St Fleur, a career coach and workplace consultant, said she wasn't surprised by the findings showing fewer callbacks for presumed Black applicants at some companies.

"I know the study focused on entry-level positions. Unfortunately it doesn't stop there. I've seen it throughout the organization all the way up into the C-suite," she said.

St Fleur, who primarily coaches women of color, said many of her clients have the right credentials and experience for certain jobs but aren't being hired.

"They are sending out dozens, hundreds of resumes and receiving nothing back," she said.

What the researchers found

Much of a company's bias in hiring could be explained by its industry, the study found. Auto dealers and retailers of car parts were the least likely to call back Black applicants, with Genuine Auto Parts (which distributes NAPA products) and the used car retailer AutoNation scoring the worst on the study's "discrimination report card."

"We are always evaluating our practices to ensure inclusivity and break down barriers, and we will continue to do so," Heather Ross, vice president of strategic communications at Genuine Parts Company, said in an email.

AutoNation did not reply to a request for comment.

The companies that performed best in the analysis included Charter/Spectrum, Dr. Pepper, Kroger and Avis-Budget.

Workplace Diversity Goes Far Past Hiring. How Leaders Can Support Employees Of Color

Several patterns emerged when the researchers looked at the companies that had the lowest "contact gap" between white and Black applicants

Federal contractors and more profitable companies called back applicants from the two racial groups at more similar rates. Firms with more centralized human resources departments and policies also exhibited less racial bias, which Kline says may indicate that a standardized hiring workflow involving multiple employees could help reduce discrimination.

When it came to the sex of applicants, most companies didn't discriminate when calling back job-seekers.

Still, some firms preferred one sex over another in screening applicants. Manufacturing companies called back people with male names at higher rates, and clothing stores showing a bias toward female applicants.

What can workplaces — and workers — do

Kline said the research team hoped the public would focus as much on companies doing a bad job as those doing a good one, since they have potentially found ways to remove or limit racial bias from the hiring process.

"Even if it's true, from these insights in psychology and behavioral economics, that individuals are inevitably going to carry biases along with them, it's not automatic that those individual biases will translate into organizational biases, on average," he said.

St Fleur said there are several strategies companies can use to cut down on bias in the hiring process, including training staff and involving multiple recruiters in callback decisions.

Companies should also collect data about which candidates make it through the hiring process and consider standardizing or anonymizing that process, she added.

St Fleur also said she often tells her job-seeking clients that it's not their fault that they aren't getting called back for open positions they believe they're qualified for.

"The fact that you're not getting callbacks does not mean you suck, you're not a good worker, you don't deserve this thing," she said. "It's just the nature of the systemic forces at play, and this is what we have to deal with."

Still, she said job candidates facing bias in the hiring process can lean on their network for new opportunities, prioritize inclusive companies when applying for work and even consider switching industries or locations.

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Azrieli Research Fellowship for PhD Candidates and Early-Career Scholars, 2024-2025

The Division of Academic Programs at the USC Shoah Foundation invites applications for its inaugural Azrieli Research Fellowships for PhD candidates and early-career scholars during the spring 2025 semester.

One applicant will be chosen to pursue their original research on the Holocaust and/or historical and contemporary antisemitism using the USC Shoah Foundation’s Visual History Archive (VHA), which currently holds over 55,000 video testimonies of survivors and witnesses of the Holocaust and other genocides. The incumbents may also wish to complement their testimony-based research in the VHA with the primary and secondary source collections related to the Holocaust at USC’s Doheny Library.

The fellowship will provide $25,000 support for one semester-long research stay at the USC Shoah Foundation. Fellows must reside in Los Angeles for the duration of their award and will hold one public lecture at USC about their research. Fellows are responsible for securing their own housing, which may be available on USC’s campus, and health insurance.

Eligibility requirements: Candidates must be enrolled in an accredited PhD program or be scholars who are either untenured or in the first five years of their career. Although any person may apply, preference will be given to Canadian scholars, those at institutions located in Canada, or research related to Canada.

This fellowship i awarded on a competitive basis. Applications must be submitted no later than midnight PT on July 31, 2024. Applications will consist of the following:

• Letter of Intent
• Project proposal of approximately 1,000 words
• Curriculum vitae
• Reference letter from dissertation committee member (for graduate students) or senior colleague (for early-career scholars)

Please note: only applications that have submitted each of the required materials will be considered for these fellowships. Applicants will be notified of the outcome by August 15, 2024. Fellowship recipients may begin their tenure at the USC Shoah Foundation in January 2025.

Please direct both questions regarding the fellowship or application process and reference letters to Dr. Jennifer L. Rodgers, Director of Academic Programs to jr77905[@]usc.edu.

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