what not to do in research

The Do’s and Don’ts of Writing Research Papers

Every researcher wants to submit an excellent research paper at the termination of their research. Your piece of writing is the only medium that conveys your hard work to the readers. Whether you write an abstract, a research paper, research proposals or thesis, your ways of presenting the data and your writing style all together create a holistic picture of you. Owing to the utter significance of a research paper, here are some tips that can ease the complicated process of writing .

The following is a list of Do’s and Don’ts to remember as you begin to pen down your work: The Do’s:

• Communicate your work clearly and precisely. Remember you are presenting a novel work done; you don’t have to write stories.
• Spotlight the ideas and methodologies involved. Discuss specific reasons to justify your research.
• Your innovative ideas and methodologies can be followed by future researchers, therefore, doubly verify the accuracy and correctness of the data you present.
• Your presented materials should give a thorough conception of the topic and all its aspects.
• Refer diverse sources of research for trustworthy and most up-to-date information.
• Do scrutinize your research stuff and information for reliability and present it with ample analysis and logic to show how it conveys and supports your research.
• Provide solid evidences and sufficient supporting arguments to reinforce your findings.
• Fill your paper with scientific terminologies. Write your paper with only enough detail about the research work .
• Maintain a track of the bibliography and references. Sort data by source or mark your notes so as to remember where individual facts came from.
• Proof read the paper several times. Do not hesitate to take help of your friends/peers/colleagues/professional editors in proof reading and fine tuning the paper.

And the Don’ts:

• Do not misrepresent yourself. Be honest to the readers.
• Don’t include anything that doesn’t answer the questions. It won’t lead to any new conclusion about your work.
• Don’t lengthen your paper unnecessarily. Relevant and to the point data is sufficient to frame your work and make your point.
• Don’t reveal incomplete or absurd reasons for doing the research.
• Don’t exceed the recommended word limits. This gives an impression that you don’t know how to follow guidelines, manage within limitations or systematize your findings.
• Don’t make too many generalizations. A paper full of overviews gives an impression that you do not have anything to say.
• Don’t write in a vacuum. Make sure that each of your findings support the cause.
• Don’t forget to reference any supporting material or related research done by other prominent researchers’ it augments and complements the research paper.
• Don’t cite Wikipedia.  Rather find an absolutely reliable source for your citations.
• Don’t plagiarize and always proof read your work before submission.

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Princeton Correspondents on Undergraduate Research

Do’s and Don’ts for Research Writing

In the thick of doing research, it’s easy to forget about the ultimate goal of writing and publishing. Thankfully, about once a month, the Princeton University Laser Sensing Lab holds what we call a “literature review”: Everyone brings in papers they’ve come across for their own research, and shares techniques that could be useful for the group at large.

At our last meeting, someone changed things up. Instead of bringing in a paper that contained interesting ideas, he brought one that he declared “the worst paper I’ve ever read”.

We all had a good laugh as the paper was passed around. He was right. The paper had numerous grammatical mistakes and many passages were indecipherable. But my adviser suggested that we not take it lightly. After all, this paper had somehow been published (though if he had any say in it, he would have it retracted). It served as a good example of what not to do—especially as the writing season falls upon us (hello to senior theses and final papers!).

As you write, here are other do’s and dont’s to keep in mind:

1. Don’t blow off grammar. Grammar mistakes look very unprofessional and immediately sink your standing in the eyes of your readers. The most common example? Its vs. it’s. Not only is this typo rampant in papers, but also in emails and online articles. Other examples I’ve come across: “development activities are currently been carried out ”, “in motion along our line of site ”… the list goes on. Don’t worry—we all make these typos when writing, and they’re easy to miss. I often don’t find my mistakes until I ask someone else to read over my work. But correcting grammar is a very simple fix, and can go a long way to help clean up your writing.

2. Don’t make excuses for poor writing. “Scientists aren’t known for being good writers, so it’s ok if my writing isn’t good either.” “This paper doesn’t count for much anyway, so it’s ok if it doesn’t make sense.” Yes, it’s tempting. But you don’t want to get into a habit of poor writing. And would you really want to be the TA or professor on the other end, reading a nonsensical paper?

3. Write something you would want to read. It sounds obvious, but it’s one of the most often ignored adages. Do you really need all that jargon up front? Are you giving your target audience, whether it’s fellow students or scientists, enough information to understand your writing? After you write your piece, the last thing you might feel like doing is reading it over again. But if you’re really aiming for a good paper, you should finish your draft with enough time—at least a day if possible—  before re-reading, to make sure your logical progressions are natural. And another round of proofreading is a great way to catch those rogue typos!

4. Have others check over your writing. If you can’t bring yourself to read your own writing, or can’t distance yourself enough to think about what may or may not make sense, have someone proofread for you. Even if you’ve read over your own paper, you might not be able to catch confusing or illogical wording — after all, you’re the one who wrote it. But having another person’s eyes on your paper can provide an important sanity check. The Writing Center is great for this, but if you want more tailored feedback, asking for help from a graduate student in your lab is not a bad idea, either!

And finally, do your research! Take note of papers you’ve found helpful in your literature review. What kind of language do they use? Is there anything confusing they’ve done that you think you can do better? Do try and actually read some of the papers you’re citing. After all, reading good writing will make you a better writer yourself.

–Stacey Huang, Engineering Correspondent

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15 Steps to Good Research

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

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

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• CAREER COLUMN
• 19 July 2019

What not to do in graduate school

• Buddini Karawdeniya 0

Buddini Karawdeniya is a postdoctoral fellow at the Kim lab in the Lyle School of Engineering at Southern Methodist University, Dallas, Texas.

You can also search for this author in PubMed   Google Scholar

During my time as a graduate student researching analytical sensors in the Dwyer laboratory at The University of Rhode Island in South Kingstown, I made a lot of mistakes — some of which matured into valuable lessons. If you are already in graduate school, or have decided to start, here are six things I recommend you do not do.

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Nature 572 , 553-554 (2019)

doi: https://doi.org/10.1038/d41586-019-02255-7

This is an article from the Nature Careers Community, a place for Nature readers to share their professional experiences and advice. Guest posts are encouraged. You can get in touch with the editor at [email protected].

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Five steps every researcher should take to ensure participants are not harmed and are fully heard

Professor of Physical Geography, University of the Witwatersrand

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Jasper Knight does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

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Academic research is not always abstract or theoretical. Nor does it take place in a vacuum. Research in many different disciplines is often grounded in the real world; it aims to understand and address problems that affect people and the environment, such as climate change, poverty, migration or natural hazards.

This means researchers often have to interact with and collect data from a wide range of different people in government, industry and civil society. These are known as research participants.

Over the last 50 years, the relationship between researcher and participant has fundamentally changed . Previously, research participants were viewed merely as objects of study. They had little input into the research process or its outcomes. Now, participants are increasingly viewed as collaborative partners and co-creators of knowledge. There are also many ways in which they can engage with researchers. This shift has been largely driven by the need for research that is relevant to today’s world as well as greater recognition of the diversity of people and cultures, and the internet, social media and other communication tools.

In this context, ethical research practices are more important than ever. However, guidelines and standards for research ethics vary between country and institution. Expectations may also vary between disciplines. So, it’s a good time to identify the key issues in human research ethics that transcend institutional or disciplinary differences.

Issues to consider

I am a long-time chair of one my institutions’ research ethics committees, and I do research ethics training for researchers and managers across southern Africa. I have also published on research ethics. Based on this experience and drawing from other work done on the topic , I suggest there are five critical ethics issues for researchers to consider.

Managing vulnerability: Research participants, especially in the developing world, may be potentially vulnerable to coercion, exploitation and the exertion of soft power.

This vulnerability may arise because of systemic social, economic, political and cultural inequalities, which are particularly marked in developing countries. And it may be amplified by inequalities in healthcare and education. Some groups in any society – among them minors, people with disabilities, prisoners, orphans, refugees, and those with stigmatised conditions like HIV and AIDS or albinism – may be more vulnerable than others.

This issue can be managed by considering what the participant group is like and by making sure that the data collection process does not increase any existing vulnerabilities.

Obtaining informed consent: This is a key precondition for participation in any study. Potential participants should first be informed about the nature of the study and the terms and conditions of their participation. That includes details about anonymity, confidentiality and their right to withdraw.

The researcher then needs to ensure that the potential participant understands this information and has the opportunity to ask questions. This should be done in a language and using words that the person can understand. After these steps are taken, the participant can give informed consent. Informal (verbal or any other non-written) consent is more appropriate if participants are not literate or are particularly vulnerable.

Protecting people: The overarching principle of protecting research participants was articulated in the landmark Belmont Report . The report emerged from a national commission in the US in the 1970s to consider research ethics principles. It called for researchers in any study to demonstrate non-maleficence (the principle of not doing harm) and ensure that they protect both participants and their data.

This can be done at different stages through the research process: by decreasing the potential for risk or harm through careful study design; by providing support or counselling services to participants during or after data collection; and by maintaining confidentiality and anonymity in data collection and reporting. Finally, personal data must be protected or de-identified if they are being stored for later analysis.

Managing risk: Potential sources of risk or harm to participants should, as far as possible, be identified and mitigated when the study is being designed. Risk may arise in any study, either at the time of data collection or afterwards. Sometimes this is unexpected, such as where data collection becomes more dangerous due to civil unrest or under COVID-19 restrictions.

It is important that researchers provide the details of support or counselling service for participants in case these are needed. Any trade-offs between risk and benefits can be considered through a risk-benefits analysis. But researchers should be realistic about any potential benefits that may result from their study.

Championing human rights: Researchers have responsibilities: to their disciplines, funders, institutions and participants. This means they should not merely be passive analysers of data. Instead they should be positive role models in society by seeking solutions, advocating for change and upholding human rights and social justice through their actions.

Research activities, especially those involving participants, should address and find solutions for local and global problems. They ought to result in positive societal and environmental outcomes. This should be the context for all types of research activities in a 21st century world.

Making it happen

Increasingly, there are national and international codes of research ethics, guiding researchers in different fields. An example is the 2010 Singapore Statement on Research Integrity . It emphasises the principles of honesty, accountability, professional courtesy and fairness, and good stewardship of data. These are the characteristics not just of ethical researchers, but of good researchers too.

These principles and processes should make research less risky and protect the rights of participants by building trust between researchers and participants. These principles can also help in making research more transparent, accountable and equitable – critical in an increasingly divided and unequal world.

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Writing a Good Research Title: Things to Avoid

When writing manuscripts , too many scholars neglect the research title. This phrase, along with the abstract, is what people will mostly see and read online. Title research of publications shows that the research paper title does matter a lot . Both bibliometrics and altmetrics tracking of citations are now, for better or worse, used to gauge a paper’s “success” for its author(s) and the journal publishing it. Interesting research topics coupled with good or clever yet accurate research titles can draw more attention to your work from peers and the public alike.

It would be helpful to have a list of what should never go into the title of a journal article. With this “don’ts” list, authors could have a handy tool to maximize the impact of their research. Titles for research manuscripts need not be complex. It can even have style. They can state the main result or idea of the paper (i.e., declarative). Alternatively, they can indicate the subject covered by the paper (i.e., descriptive). A third form, which should be used sparingly, conveys the research in the form of an open question.

A Handy List of Don’ts

• The period generally has no place in a title (even a declarative phrase can work without a period)
• Likewise, any kind of dashes to separates title parts (however, hyphens to link words is fine)
• Chemical formula, like H 2 O, CH 4 , etc. (instead use their common or generic names)
• Avoid roman numerals (e.g., III, IX, etc.)
• Semi-colons, as in “;” (the colon, however, is very useful to make two-part titles)
• The taxonomic hierarchy of species of plants, animals, fungi, etc. is not needed
• Abbreviations (except for RNA, DNA which is standard now and widely known)
• Initialisms and acronyms (e.g., “Ca” may get confused with CA, which denotes cancer)
• Avoid question marks (this tends to decrease citations, but posing a question is useful in economics and philosophy papers or when the results are not so clear-cut as hoped for)
• Uncommon words (a few are okay, but too many can influence altmetric scoring)
• Numerical exponents, or units (e.g. km -1 or km/hr)
• Vague terms (e.g., “with” could be re-written with a more specific verb; “amongst” rectified by simpler word ordering)
• Cryptic/complex drug names (use the generic name if allowed to)
• Obvious or non-specific openings with a conjunction: e.g., “Report on”, “A Study of”, “Results of”, “An Experimental Investigation of”, etc. (these don’t contribute meaning!)
• Italics, unless it is used for the species names of studied organisms
• Shorten scientific names (not coli , but write instead Escherichia coli )
• Keep it short. Aim for 50 to 100 characters, but not more (shorter titles are cited more often) or less than 13 words

Related: Finished preparing your manuscript? Check out this post now for additional points to consider submitting your manuscript!

Use the List

Take some time out to look at a good research title example. It could be one that you liked or a recognized collection of best research titles. Discuss these with your colleagues and co-authors. Write several title drafts in various forms, either in the declarative or descriptive form, with or without a colon. Then use the list above as a guide to polish and winnow your sample research title down to an effective title for your manuscript.

A great title should interest the reader enough to make him/her want to download your paper and actually read it. Importantly, in selecting the words, aim to both pique the reader’s curiosity and sum up the research work done. Bear mind, too, that a good title should also ensure your publication is easily found. This is now crucial for digital indexing and archiving purposes.

Research Titles in the 21 st Century

Remember, a good research paper title is now essential. However, it is no substitute for good quality science and scholarship. Exaggerated or sensational titles, especially those that make unwarranted generalizations, may well get more attention from the media. Given the growing use of Twitter and other social media platforms, the research paper title is clearly gaining value and importance. Title research, therefore, is critical to understand what effect a given type or use of a research title has on its readership.

Did you like this post? Will it help you choose a good title for your next report/manuscript? Please share your comments in the section below.

Good article, but I think that before writing the title and the paper itself, you need to choose a topic for which to write and the topic should be simple and understandable for yourself.

Great post! Helped me in my research project title selection. Sharing it with my fellow classmates as well!

thank you. it helps me to choose my title as well.thanks

very useful and handy article

Yes its so very thankful to guided to me how to created research title

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In this didactic article, 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; and what is important is that you did your best. These myths appear to be 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.

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The Do's and Don'ts of Research Presentations

There's a basic formula for presentations of any kind: "Tell them what you are going to tell them; then tell them; and then tell them what you told them."

Of course, if you have ever sat through a 10-minute presentation with 50 slides covering every bit of data in the known world – or, if you've looked out from the podium and been able to count the number of dozing audience members – you know there are more nuances than that.

Here are 10 tips for giving a successful presentation that I share with younger hematologists and fellows new to the process – and the more senior researchers who might need a refresher course – learned from years of sitting in the audience of less-than-successful talks, as well as making my own mistakes as the brave soul up on the podium.

#1 Don't fall in love with your data. Many individuals feel that they have to address every single piece of evidence in existence to educate the audience on the topic being discussed. That's a great danger because, often, the presentation turns into a recitation of minutiae.

#2 Know your audience. Every audience is different – if you try to give the same talk to medical students and practicing physicians and your colleagues, you're making a grave mistake. Tailor your talk to your audience, their comprehension, and their training level.

In a similar vein, an academic presentation is not the time to test out some new jokes. Jokes tend to point out cultural ironies, so it's highly likely that your humor won't translate to a multicultural or international audience – and you also run the risk of alienating or insulting someone. If you want to inject humor into your presentation, stick to self-deprecatory jokes poking fun at yourself. Basically, though, you are given this time to deliver content, so deliver your content as best you can. Humor may make your presentation a little bit more memorable, but it's not something I recommend unless you understand the audience very well.

#3 Don't over-complicate things. Follow the "KISS" principle: Keep It Simple, Scientist. Experts tend to become so familiar with their data that they may overestimate what their audience really understands. When that's the case, talks can get very complex very quickly. Also, there is often a very broad range of knowledge among the audience members, so as a presenter, you are tasked with making a complex topic comprehensible to a wide variety of learners. That's not to say that you should dumb down your presentation, but try to find the balance between over-simplifying and making higher-level information understandable.

#4 Narrow your focus to key data. Most presentations tend to be too dense with respect to the data being presented: a speaker will work through the Kaplan-Meier survival curves, response rates, stringent response rates, and progression-free survival of 30 clinical trials. It all runs together. Similar to point #1, you need to focus on the key points that are representative of other published data.

#5 Don't forget to provide a learning objective. All talks should – and those that qualify for CME credit are required to – have learning objectives. Of course, if you do provide a learning objective, it should go without saying that you need to follow through with it. Many times, presenters will say, "Here's the purpose of my talk," but then they seem to forget about it as they progress through their talk. In other words, I view the learning objectives as the strategic plan for the presentation; it's another way of saying, "Here is what we are trying to achieve with this talk."

For instance, if you begin your talk by stating the objective of, "The learner will understand the newest therapies in myeloma," then don't spend half of your talk discussing the biology of the disease unless it relates directly to the function of the therapy. So, when you're reviewing your presentation, look at your learning objectives and ask yourself, "Have I met those objectives?"

#6 Follow the rule of "one minute per slide." I regularly see people who are scheduled for a 30-minute talk but show up with 70 slides. No matter how fast you talk, or how eloquent your data are, it is impossible to go through 70 slides in 30 minutes. The presenter typically ends up rushing through or omitting the data or running over the allotted time. This is a big mistake. It's important to discipline yourself – for your own sake, that of the audience, and that of the presenter following you whose time you might be cutting short.

#7 Don't forget to focus on the patient. It is very difficult, particularly with a clinical audience, to talk about data outside of the context of patients. To help the audience better understand the results of the research, I will always lead off my talks by discussing a patient who exemplifies a specific diagnostic or therapeutic dilemma. This provides a frame of reference at the launch of the discussion – and gives audience members the opportunity to see how the data could apply to their own practice.

#8 Incorporate questions in your talk. Questions are a great tool for audience engagement – it helps to break up the monotony of listening to a single person at the podium drone on for 40 minutes. Whether it's multiple-choice questions or an electronic audience response system, the questions should be oriented to your specific topic and cover relevant data.

After your presentation, the Q&A session is the opportunity for the audience to drive their own learning, so be mindful of the time you are allotted so you leave ample room for questions. During your presentation, you are following your own agenda and telling your audience what you think they need to know. By asking questions, the learners are delving more into what they want to know – which is far more important.

#9 Don't assume too much about your audience's background and knowledge. There's always the risk of assuming that the audience's knowledge of the topic being discussed is as sophisticated as your own – especially when you deliver the same talk over and over. This is simply a matter of researchers becoming inured to their own data; they may be breezing through their presentation, but they might have left the audience behind back on slide 4.

The most common manifestation of this problem is using abbreviations that no one in the audience can decipher. Many times, I've been watching a presentation and had to lean over to the person next to me to ask, "What does that stand for?" Sometimes, they don't know either! So, if there are two people sitting there that don't know, that's a real problem.

#10 Never apologize for a slide. How many times have you heard a speaker say, "This is a very busy slide – I'm really sorry about it, but if you just look in the lower right-hand corner…"? Never apologize for a slide. If you have to apologize, that slide should not be shown. Period. No one is forcing you to put three paragraphs on one slide! Aside from the nuts and bolts of making your slideshows effective and visually appealing (use san serif fonts, don't use the color red, etc.), it seems like common sense, but people do apologize for what they are showing the audience, and it's insane!

How to Know When You've Lost Your Audience

If you follow those tips, I hope you won't have to worry about your audience tuning you out. But it's always important to know when you're losing your audience so you can make the necessary adjustments to win them back.

I used to look out into the audience and count the people who were sleeping, but now I have a much more accurate method: I count how many people are on their smartphones. With the help of the illuminating blue light emanating from their screens, I can see who's texting, who's emailing, and who's browsing the Web looking for anything more interesting than what I'm discussing. If I see more than 10 percent of the audience on their phones, then I know I've done a bad job.

And, of course, if you finish your talk and there are no questions from the audience, you know you have failed miserably. When that happens to me, I actually feel sick. I think to myself, "I either lost them or they don't care anymore."

So, when I'm faced with an audience where people are lining up at the microphones to ask questions, that's the ideal situation. Handling those questions is another skill that's learned basically from trial and error.

As the presenter, the one on stage, you do have a certain amount of control over how the conversation will flow. If someone throws you a curveball that doesn't relate to the topic at hand, or that demonstrates a fundamental lack of understanding of the content that other members may have grasped, you have the ability to blow it off. Or, if you want to take the more diplomatic route, say, "That's an important question, but the answer is complex and I think we need to take it offline after my allotted time is done."

The same is true if you're asked a question that requires a four-minute answer during your five-minute Q&A session – you can decline to answer, rather than shutting out four other people's questions. Think about the audience as a whole and what you would like for them to get out of your presentation.

Last, But Not Least…

Have a conclusion. At the end of your presentation, it is important to emphasize what was just discussed – essentially, "tell them what you told them." The conclusion is your listeners' take-home message. It's the "elevator speech" that they can carry with them after they've listened to your (and probably 10 more) presentations.

So, here's mine. Overall, effectively delivering a research presentation boils down to a basic principle: understand what you can and cannot do in the time you've been given. There is not a topic in the world, no matter what it is, that you can't talk about for two-and-a-half hours. But you have 10 minutes, so do it in 10 minutes – no matter what you are presenting. It could be earth-shattering research that's going to win the Nobel Prize, but you are getting 10 minutes for that abstract presentation, so work with it.

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

General Education

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

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

What Makes a Good Research Paper Topic?

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

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

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

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

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

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

#3: It Fits Your Teacher's Guidelines

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

113 Good Research Paper Topics

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

Arts/Culture

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

Current Events

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

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

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

Science/Environment

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

How to Write a Great Research Paper

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

#1: Figure Out Your Thesis Early

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

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

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

#2: Back Every Statement Up With Research

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

#3: Do Your Research Before You Begin Writing

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

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

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

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

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

What Is Research, and Why Do People Do It?

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

15k Accesses

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

Abstractspiepr Abs1

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

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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Research Objectives | Definition & Examples

Published on July 12, 2022 by Eoghan Ryan . Revised on November 20, 2023.

Research objectives describe what your research is trying to achieve and explain why you are pursuing it. They summarize the approach and purpose of your project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement . They should:

• Establish the scope and depth of your project
• Contribute to your research design
• Indicate how your project will contribute to existing knowledge

Table of contents

What is a research objective, why are research objectives important, how to write research aims and objectives, smart research objectives, other interesting articles, frequently asked questions about research objectives.

Research objectives describe what your research project intends to accomplish. They should guide every step of the research process , including how you collect data , build your argument , and develop your conclusions .

Your research objectives may evolve slightly as your research progresses, but they should always line up with the research carried out and the actual content of your paper.

Research aims

A distinction is often made between research objectives and research aims.

A research aim typically refers to a broad statement indicating the general purpose of your research project. It should appear at the end of your problem statement, before your research objectives.

Your research objectives are more specific than your research aim and indicate the particular focus and approach of your project. Though you will only have one research aim, you will likely have several research objectives.

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Research objectives are important because they:

• Establish the scope and depth of your project: This helps you avoid unnecessary research. It also means that your research methods and conclusions can easily be evaluated .
• Contribute to your research design: When you know what your objectives are, you have a clearer idea of what methods are most appropriate for your research.
• Indicate how your project will contribute to extant research: They allow you to display your knowledge of up-to-date research, employ or build on current research methods, and attempt to contribute to recent debates.

Once you’ve established a research problem you want to address, you need to decide how you will address it. This is where your research aim and objectives come in.

Step 1: Decide on a general aim

Your research aim should reflect your research problem and should be relatively broad.

Step 2: Decide on specific objectives

Break down your aim into a limited number of steps that will help you resolve your research problem. What specific aspects of the problem do you want to examine or understand?

Step 3: Formulate your aims and objectives

Once you’ve established your research aim and objectives, you need to explain them clearly and concisely to the reader.

You’ll lay out your aims and objectives at the end of your problem statement, which appears in your introduction. Frame them as clear declarative statements, and use appropriate verbs to accurately characterize the work that you will carry out.

The acronym “SMART” is commonly used in relation to research objectives. It states that your objectives should be:

• Specific: Make sure your objectives aren’t overly vague. Your research needs to be clearly defined in order to get useful results.
• Measurable: Know how you’ll measure whether your objectives have been achieved.
• Achievable: Your objectives may be challenging, but they should be feasible. Make sure that relevant groundwork has been done on your topic or that relevant primary or secondary sources exist. Also ensure that you have access to relevant research facilities (labs, library resources , research databases , etc.).
• Relevant: Make sure that they directly address the research problem you want to work on and that they contribute to the current state of research in your field.
• Time-based: Set clear deadlines for objectives to ensure that the project stays on track.

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

Methodology

• Sampling methods
• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Your research objectives indicate how you’ll try to address your research problem and should be specific:

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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What Researchers Discovered When They Sent 80,000 Fake Résumés to U.S. Jobs

Some companies discriminated against Black applicants much more than others, and H.R. practices made a big difference.

By Claire Cain Miller and Josh Katz

A group of economists recently performed an experiment on around 100 of the largest companies in the country, applying for jobs using made-up résumés with equivalent qualifications but different personal characteristics. They changed applicants’ names to suggest that they were white or Black, and male or female — Latisha or Amy, Lamar or Adam.

On Monday, they released the names of the companies . On average, they found, employers contacted the presumed white applicants 9.5 percent more often than the presumed Black applicants.

Yet this practice varied significantly by firm and industry. One-fifth of the companies — many of them retailers or car dealers — were responsible for nearly half of the gap in callbacks to white and Black applicants.

Two companies favored white applicants over Black applicants significantly more than others. They were AutoNation, a used car retailer, which contacted presumed white applicants 43 percent more often, and Genuine Parts Company, which sells auto parts including under the NAPA brand, and called presumed white candidates 33 percent more often.

In a statement, Heather Ross, a spokeswoman for Genuine Parts, said, “We are always evaluating our practices to ensure inclusivity and break down barriers, and we will continue to do so.” AutoNation did not respond to a request for comment.

Companies With the Largest and Smallest Racial Contact Gaps

Of the 97 companies in the experiment, two stood out as contacting presumed white job applicants significantly more often than presumed Black ones. At 14 companies, there was little or no difference in how often they called back the presumed white or Black applicants.

Source: Patrick Kline, Evan K. Rose and Christopher R. Walters

Known as an audit study , the experiment was the largest of its kind in the United States: The researchers sent 80,000 résumés to 10,000 jobs from 2019 to 2021. The results demonstrate how entrenched employment discrimination is in parts of the U.S. labor market — and the extent to which Black workers start behind in certain industries.

“I am not in the least bit surprised,” said Daiquiri Steele, an assistant professor at the University of Alabama School of Law who previously worked for the Department of Labor on employment discrimination. “If you’re having trouble breaking in, the biggest issue is the ripple effect it has. It affects your wages and the economy of your community going forward.”

Some companies showed no difference in how they treated applications from people assumed to be white or Black. Their human resources practices — and one policy in particular (more on that later) — offer guidance for how companies can avoid biased decisions in the hiring process.

A lack of racial bias was more common in certain industries: food stores, including Kroger; food products, including Mondelez; freight and transport, including FedEx and Ryder; and wholesale, including Sysco and McLane Company.

“We want to bring people’s attention not only to the fact that racism is real, sexism is real, some are discriminating, but also that it’s possible to do better, and there’s something to be learned from those that have been doing a good job,” said Patrick Kline, an economist at the University of California, Berkeley, who conducted the study with Evan K. Rose at the University of Chicago and Christopher R. Walters at Berkeley.

The researchers first published details of their experiment in 2021, but without naming the companies. The new paper, which is set to run in the American Economic Review, names the companies and explains the methodology developed to group them by their performance, while accounting for statistical noise.

Sample Résumés From the Experiment

Fictitious résumés sent to large U.S. companies revealed a preference, on average, for candidates whose names suggested that they were white.

To assign names, the researchers started with a prior list that had been assembled using Massachusetts birth certificates from 1974 to 1979. They then supplemented this list with names found in a database of speeding tickets issued in North Carolina between 2006 and 2018, classifying a name as “distinctive” if more than 90 percent of people with that name were of a particular race.

The study includes 97 firms. The jobs the researchers applied to were entry level, not requiring a college degree or substantial work experience. In addition to race and gender, the researchers tested other characteristics protected by law , like age and sexual orientation.

They sent up to 1,000 applications to each company, applying for as many as 125 jobs per company in locations nationwide, to try to uncover patterns in companies’ operations versus isolated instances. Then they tracked whether the employer contacted the applicant within 30 days.

A bias against Black names

Companies requiring lots of interaction with customers, like sales and retail, particularly in the auto sector, were most likely to show a preference for applicants presumed to be white. This was true even when applying for positions at those firms that didn’t involve customer interaction, suggesting that discriminatory practices were baked in to corporate culture or H.R. practices, the researchers said.

Still, there were exceptions — some of the companies exhibiting the least bias were retailers, like Lowe’s and Target.

The study may underestimate the rate of discrimination against Black applicants in the labor market as a whole because it tested large companies, which tend to discriminate less, said Lincoln Quillian, a sociologist at Northwestern who analyzes audit studies. It did not include names intended to represent Latino or Asian American applicants, but other research suggests that they are also contacted less than white applicants, though they face less discrimination than Black applicants.

The experiment ended in 2021, and some of the companies involved might have changed their practices since. Still, a review of all available audit studies found that discrimination against Black applicants had not changed in three decades. After the Black Lives Matter protests in 2020, such discrimination was found to have disappeared among certain employers, but the researchers behind that study said the effect was most likely short-lived.

Gender, age and L.G.B.T.Q. status

On average, companies did not treat male and female applicants differently. This aligns with other research showing that gender discrimination against women is rare in entry-level jobs, and starts later in careers.

However, when companies did favor men (especially in manufacturing) or women (mostly at apparel stores), the biases were much larger than for race. Builders FirstSource contacted presumed male applicants more than twice as often as female ones. Ascena, which owns brands like Ann Taylor, contacted women 66 percent more than men.

Neither company responded to requests for comment.

The consequences of being female differed by race. The differences were small, but being female was a slight benefit for white applicants, and a slight penalty for Black applicants.

The researchers also tested several other characteristics protected by law, with a smaller number of résumés. They found there was a small penalty for being over 40.

Overall, they found no penalty for using nonbinary pronouns. Being gay, as indicated by including membership in an L.G.B.T.Q. club on the résumé, resulted in a slight penalty for white applicants, but benefited Black applicants — although the effect was small, when this was on their résumés, the racial penalty disappeared.

Under the Civil Rights Act of 1964, discrimination is illegal even if it’s unintentional . Yet in the real world, it is difficult for job applicants to know why they did not hear back from a company.

“These practices are particularly challenging to address because applicants often do not know whether they are being discriminated against in the hiring process,” Brandalyn Bickner, a spokeswoman for the Equal Employment Opportunity Commission, said in a statement. (It has seen the data and spoken with the researchers, though it could not use an academic study as the basis for an investigation, she said.)

What companies can do to reduce discrimination

Several common measures — like employing a chief diversity officer, offering diversity training or having a diverse board — were not correlated with decreased discrimination in entry-level hiring, the researchers found.

But one thing strongly predicted less discrimination: a centralized H.R. operation.

The researchers recorded the voice mail messages that the fake applicants received. When a company’s calls came from fewer individual phone numbers, suggesting that they were originating from a central office, there tended to be less bias . When they came from individual hiring managers at local stores or warehouses, there was more. These messages often sounded frantic and informal, asking if an applicant could start the next day, for example.

“That’s when implicit biases kick in,” Professor Kline said. A more formalized hiring process helps overcome this, he said: “Just thinking about things, which steps to take, having to run something by someone for approval, can be quite important in mitigating bias.”

At Sysco, a wholesale restaurant food distributor, which showed no racial bias in the study, a centralized recruitment team reviews résumés and decides whom to call. “Consistency in how we review candidates, with a focus on the requirements of the position, is key,” said Ron Phillips, Sysco’s chief human resources officer. “It lessens the opportunity for personal viewpoints to rise in the process.”

Another important factor is diversity among the people hiring, said Paula Hubbard, the chief human resources officer at McLane Company. It procures, stores and delivers products for large chains like Walmart, and showed no racial bias in the study. Around 40 percent of the company’s recruiters are people of color, and 60 percent are women.

Diversifying the pool of people who apply also helps, H.R. officials said. McLane goes to events for women in trucking and puts up billboards in Spanish.

So does hiring based on skills, versus degrees . While McLane used to require a college degree for many roles, it changed that practice after determining that specific skills mattered more for warehousing or driving jobs. “We now do that for all our jobs: Is there truly a degree required?” Ms. Hubbard said. “Why? Does it make sense? Is experience enough?”

Hilton, another company that showed no racial bias in the study, also stopped requiring degrees for many jobs, in 2018.

Another factor associated with less bias in hiring, the new study found, was more regulatory scrutiny — like at federal contractors, or companies with more Labor Department citations.

Finally, more profitable companies were less biased, in line with a long-held economics theory by the Nobel Prize winner Gary Becker that discrimination is bad for business. Economists said that could be because the more profitable companies benefit from a more diverse set of employees. Or it could be an indication that they had more efficient business processes, in H.R. and elsewhere.

Claire Cain Miller writes about gender, families and the future of work for The Upshot. She joined The Times in 2008 and was part of a team that won a Pulitzer Prize in 2018 for public service for reporting on workplace sexual harassment issues. More about Claire Cain Miller

Josh Katz is a graphics editor for The Upshot, where he covers a range of topics involving politics, policy and culture. He is the author of “Speaking American: How Y’all, Youse, and You Guys Talk,” a visual exploration of American regional dialects. More about Josh Katz

From The Upshot: What the Data Says

Analysis that explains politics, policy and everyday life..

Employment Discrimination: Researchers sent 80,000 fake résumés to some of the largest companies in the United States. They found that some discriminated against Black applicants much more than others .

Pandemic School Closures: ​A variety of data about children’s academic outcomes and about the spread of Covid-19 has accumulated since the start of the pandemic. Here is what we learned from it .

Affirmative Action: The Supreme Court effectively ended race-based preferences in admissions. But will selective schools still be able to achieve diverse student bodies? Here is how they might try .

N.Y.C. Neighborhoods: We asked New Yorkers to map their neighborhoods and to tell us what they call them . The result, while imperfect, is an extremely detailed map of the city .

Dialect Quiz:  What does the way you speak say about where you’re from? Answer these questions to find out .

Read our research on: Gun Policy | International Conflict | Election 2024

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Political typology quiz.

Notice: Beginning April 18th community groups will be temporarily unavailable for extended maintenance. Thank you for your understanding and cooperation.

Where do you fit in the political typology?

Are you a faith and flag conservative progressive left or somewhere in between.

Take our quiz to find out which one of our nine political typology groups is your best match, compared with a nationally representative survey of more than 10,000 U.S. adults by Pew Research Center. You may find some of these questions are difficult to answer. That’s OK. In those cases, pick the answer that comes closest to your view, even if it isn’t exactly right.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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4 Reasons Why Managers Fail

• Swagatam Basu,
• Atrijit Das,
• Vitorio Bretas,
• Jonah Shepp

Nearly half of all managers report buckling under the stress of their role and struggling to deliver.

Gartner research has found that managers today are accountable for 51% more responsibilities than they can effectively manage — and they’re starting to buckle under the pressure: 54% are suffering from work-induced stress and fatigue, and 44% are struggling to provide personalized support to their direct reports. Ultimately, one in five managers said they would prefer not being people managers given a choice. Further analysis found that 48% of managers are at risk of failure based on two criteria: 1) inconsistency in current performance and 2) lack of confidence in the manager’s ability to lead the team to future success. This article offers four predictors of manager failure and offers suggestions for organizations on how to address them.

The job of the manager has become unmanageable. Organizations are becoming flatter every year. The average manager’s number of direct reports has increased by 2.8 times over the last six years, according to Gartner research. In the past few years alone, many managers have had to make a series of pivots — from moving to remote work to overseeing hybrid teams to implementing return-to-office mandates.

• Swagatam Basu is senior director of research in the Gartner HR practice and has spent nearly a decade researching leader and manager effectiveness. His work spans additional HR topics including learning and development, employee experience and recruiting. Swagatam specializes in research involving extensive quantitative analysis, structured and unstructured data mining and predictive modeling.
• Atrijit Das is a senior specialist, quantitative analytics and data science, in the Gartner HR practice. He drives data-based research that produces actionable insights on core HR topics including performance management, learning and development, and change management.
• Vitorio Bretas is a director in the Gartner HR practice, supporting HR executives in the execution of their most critical business strategies. He focuses primarily on leader and manager effectiveness and recruiting. Vitorio helps organizations get the most from their talent acquisition and leader effectiveness initiatives.
• Jonah Shepp is a senior principal, research in the Gartner HR practice. He edits the Gartner  HR Leaders Monthly  journal, covering HR best practices on topics ranging from talent acquisition and leadership to total rewards and the future of work. An accomplished writer and editor, his work has appeared in numerous publications, including  New York   Magazine ,  Politico   Magazine ,  GQ , and  Slate .

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Creative Australia's report into the music festival sector shows how many of the country's big events are struggling

More than one-third of Australian music festivals are losing money as they face skyrocketing operational costs and dwindling younger audiences, according to a new report from Creative Australia.

Billed as the first widespread report of its kind, Soundcheck: Insights into Australia's music festival sector  delves into the cultural, social and economic impacts of Australian music festivals, and paints a clear picture of the landscape as it stood in the 2022-23 financial year.

Spanning the 535 music festivals held nationwide in that time — that's almost 1.5 festivals per day — the 116-page report reflects the scope, scale and diversity of the Australian music festival landscape.

Given the highly publicised recent struggles festivals have faced, it's timely research that looks to help Australian audiences and funding bodies understand the challenges these events face.

How much money do music festivals make?

Just 56 per cent of music festivals reported a profit in the 2022-23 financial year, with more than one third of festivals reporting a deficit and eight per cent breaking even.

The median average cost to stage a music festival is $3.3 million, and those events that do make a profit pull in a median average of $731,569 per event.

When looking at the mean average of the same data, though, that figure skyrockets to $2.6 million — confirming that some festivals are in a much better financial position and stand to gain far more than some of their contemporaries.

For instance, the highest profit for a festival surveyed for this data was $47.4 million, while the smallest profit was just $20,000.

What are the biggest challenges festivals face?

Rising operational costs had the most severe impact on almost half of festival organisers (47 per cent) — overheads like artist fees, production, suppliers, freight, transportation and insurance.

Other major barriers included a lack of funding and grants, as well as extreme weather events. Almost one third of festivals said skyrocketing insurance costs were a major challenge.

Australian live music venues' public liability insurance policies increased 10-fold in the past financial year, climbing from $20,000 per year to as much as $120,000.

One festival organiser noted that necessary event cancellation insurance costs had "pretty much doubled" since the COVID-19 pandemic.

"The excess used to be like a standard commercial policy, which is like $4,000 or $5,000. Our excess for this year is $250,000."

Another organiser said navigating insurance paperwork had become an "absolute minefield" after making the tough call to cancel their festival.

"We had to wait until the morning of the show to make the final determination to cancel, otherwise there's the possibility that the insurance company could have said we could have worked out other alternatives.

"You're left with this real balancing act of, do you let your patrons know … who may have been booking accommodation, may have been getting drivers, getting babysitters, outlaying some money to attend the festival?"

The rising costs of securing police and security was another sore point. More than a quarter of festivals noted the challenges of navigating police and security requirements, and the difficulties of dealing with different government and council regulations across different states and jurisdictions.

"There's not enough consistency," said one logistics/operations worker from New South Wales.

"Whether you do an event in the metro area, or you do an event in Newcastle, or you do an event down the South Coast, or whatever the case may be, all these authorities have different expectations in regards to what they want from security and from the event. That makes it hard because some of the implications are more costs for the event promoter."

By contrast, most festivals found health, medical and liquor licensing requirements were the least challenging regulatory challenge, with around seven per cent reporting these elements had an impact.

Ongoing festival cancellations have created a vicious cycle where the more events pull the plug or lose headliners last-minute, the more hesitation it creates in the wider market — from both the industry and from punters holding off on purchasing tickets.

Who's buying festival tickets?

While music festival revenue comes from various avenues — from corporate sponsorship to hospitality services to merchandise and more — it's ticket sales that determine the ultimate feasibility of a music festival.

There is some good news on that front, with average ticket sales in 2022-23 higher than pre-COVID levels.

The average festival sold 8,116 tickets in 2018-19, which ballooned to 9,506 for 2022-23, indicating that the industry is slowly recovering from the decimating impacts of the COVID-19 pandemic.

The research suggests that young people are no longer the main consumer of music festivals, nor are they attending as much as they have in the past.

The 18-24-year-old group is no longer the biggest ticket-buying demographic, with people in their mid-to-late twenties overtaking them. The younger crowd slumped from 41 per cent of all ticket buyers in 2018/19 to 27 per cent in 2022/23. 

Genre specific events faring better

The report arrives amid a feast or famine crisis for the Australian music festival scene.

There's been a growing list of festival cancellations, from major events like Splendour In The Grass , Groovin The Moo and Mona Foma , to newer players like This That, Summerground ,  Vintage Vibes , Tent Pole , Valleyways, Costal Jam and more.

Amid those reports, however, genre-focused events — such as Good Things, Knotfest, Listen Out, CMC Rocks — are still proving popular, and summer staples — like Laneway Festival, Beyond The Valley and Field Day — are adapting to current challenges with great success.

The vast majority of Australian festivals predominantly feature homegrown line-ups, with four out of five acts being Australian. The most popular genre offering was electronic music, accounting for almost a quarter of Australian festivals. Other popular genres included rock (21 per cent) country (19 per cent) and indie (17 per cent).

Georgie McClean of Creative Australia says she hopes this research will serve as both a tool for those in the industry, as well as a way to exhibit the contributions music festivals make to Australia's creative sector.

"We hope this report will help us to better understand the role and contribution of festivals within the broader creative industries as they face multiple challenges.

"To inform the future work of Music Australia, we will be undertaking further research into how Australians discover, engage with and consume music, in order to better understand the broader ecosystem that underpins live music including festivals."

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What Research Institutions Can Do to Foster Research Integrity

1 Department of Epidemiology and Biostatistics, Amsterdam University Medical Centers, Amsterdam, The Netherlands

2 Department of Philosophy, Faculty of Humanities, Vrije Universiteit, Amsterdam, The Netherlands

In many countries attention for fostering research integrity started with a misconduct case that got a lot of media exposure. But there is an emerging consensus that questionable research practices are more harmful due to their high prevalence. QRPs have in common that they can help to make study results more exciting, more positive and more statistically significant. That makes them tempting to engage in. Research institutions have the duty to empower their research staff to steer away from QRPs and to explain how they realize that in a Research Integrity Promotion Plan. Avoiding perverse incentives in assessing researchers for career advancement is an important element in that plan. Research institutions, funding agencies and journals should make their research integrity policies as evidence-based as possible. The dilemmas and distractions researchers face are real and universal. We owe it to society to collaborate and to do our utmost best to prevent QRPs and to foster research integrity.

Introduction

Traditionally research integrity has focussed on the prevention, identification and handling of the three deadly sins of scientific and scholarly research: fabrication, falsification and plagiarism (National Academies of Sciences 2017 ). In many countries the attention for research integrity was fuelled by a misconduct case that got a lot of media exposure. In The Netherlands it was the Diederik Stapel case (Levelt, Noort and Drenth Committees 2012 ) that served as a call to arms. It shocked many within and outside Academia but turned out to be a blessing in disguise as well. Stapel’s successor as dean of the faculty of social sciences of Tilburg University acted according to the dictum ‘never waste a good crisis’ (Sijtsma 2017 ). The other Dutch universities followed and local and national measures were taken. This sequence of events seems typical for many countries.

In recent years attention has shifted to the lesser breaches of research integrity that are commonly referred to as questionable research practices or QRPs (Bouter et al. 2016 ; Haven et al. 2019 ). The idea is that these are much more prevalent and thus collectively do more harm to the validity of and the trust in the results of research (National Academies of Sciences 2017 ; Editorial 2019 ; Macleod and Mohan 2019 ). Examples are selective reporting, P-hacking, and hypothesising-after-the-results-are-known or HARK-ing. In an excellent paper from the Meta-Research Center of Tilburg University 34 QRPs are identified as researcher degrees of freedom that should be avoided in hypothesis-testing research (Wicherts et al. 2016 ). QRPs have in common that they can help to make study results more exciting, more positive and more statistically significant, which in its turn increases the likelihood to be accepted by a high impact journal, to get many citations, and to obtain the next grant or academic tenure.

Almost all researchers want to deliver good quality science, to avoid QRPs, and to follow their moral compass to steer a course of research integrity. Like any compass the functioning of a moral compass depends on its quality and on external factors. The quality is determined by the virtuousness of the individual at issue. Major external factors that can corrupt the moral compass concern the local research climate and the perverse incentives of the science system as a whole. Researchers need help from their research institution in optimising the functioning of their moral compass. That help involves adequate education and skills training, good facilities and expert help, and clear codes and procedures. That being said we should realize that research institutions experience perverse incentives that concern the way research is financed and evaluated by governments and research funders which ultimately trickle down the researchers themselves (Anderson 2019 ; Bagioli et al. 2019 ). Consequently research institutions need help from the other stakeholders in the research system (Bouter 2018 ).

Duties of Care

The Netherlands Code of Conduct for Research Integrity specifies 61 standards for good research that mirror in fact as many QRPs to be avoided (Netherlands code of conduct on research integrity 2018 ). An unique feature of the code is that it also contains a chapter on the duties of care research institutions have to empower their research staff to steer away from QRPs. This idea is not new and was already contained in the Singapore Statement ( 2010 ) that says in responsibility 13: ‘Research institutions should create and sustain environments that encourage integrity through education, clear policies, and reasonable standards for advancement, while fostering work environments that support research integrity’. In other words: research institutions need to have a Research Integrity Promotion Plan. The Horizon 2020 funded consortium Standard Operating Procedures for Research Integrity (SOPs4RI 2020 ) will offer research institutions help to formulate this plan. Having implemented such a plan might become a contractual obligation for institutions accepting grants from the next EU framework program Horizon Europe.

The idea is that the Research Integrity Promotion Plan explains what the research institution sets out to do—in the context of its mission, disciplinary focus and type of research it performs—to promote research integrity. The plan needs to cover a set of mandatory topics and will typically describe a mix of education programs, codes, manuals, policy measures, regulations, facilities, audit schemes, and support systems. SOPs4RI will produce a toolbox filled with Standard Operating Procedures (SOPs) and guidelines that can help research institutions to formulate their Research Integrity Promotion Plan (e.g. ORI 1995 ; ENRIO 2020 ; Forsberg et al. 2018 ; Penders et al. 2018 ). A preliminary version of the SOPS4RI toolbox will become available by the end of 2020 and the final version will be ready in 2022. The difference between a SOP and a guideline is gradual, with SOPs being more strict step-by-step recipes and guidelines offering some freedom of choice. It’s important to make this not another box ticking exercise, but to ensure that researchers appreciate and use the guidance offered by their institution.

Initiatives of research institutions and other stakeholders to improve the quality of research and research integrity are by no means unique to The Netherlands. Some important initiatives are the USA Centre for Open Science (COS 2020 ), the UK Reproducibility Network (UKRN 2020 ), the European Quality In Preclinical Data Innovative Medicine Initiative (EQIPD IMI 2020 ), and the German Quality, Ethics, Open Science, Translation Center (QUEST 2020 ). Taken together currently there is a lack of solid guidance for research institutions that want to improve responsible research practices. The examples are scattered and not all evidence-based and fit for application. The hope is that the results of the SOPs4RI consortium will improve this situation substantially.

Perverse Incentives

Arguably one of the most important things research institutions should do is to avoid perverse incentives in assessing researchers for career advancement. The current dominant focus on bibliometric indicators derived from publication and citation counts sends a strong message that only these things really matter (Moher et al. 2018 ). During recent years the myopic use of quantitative indicators in research evaluations has been criticised. This led to initiatives like the Leiden Manifesto (Hicks et al. 2015 ) and the San Francisco Declaration on Research Assessment (DORA 2020 ). In line with this the Hong Kong Principles for assessing researchers (Moher et al. 2019 ) were formulated and endorsed at the 6th World Conference on Research Integrity (WCRIF 2020 ). These principles will help research institutions that adopt them to minimise perverse incentives that invite to engage in questionable research practices or worse.

The Hong Kong Principles are chosen with a view to explicitly recognise and reward researchers for behaviour that leads to trustworthy research by avoiding QRPs. The principles have been developed with the idea in mind that their implementation could assist in how researchers are assessed for career advancement with a focus on behaviours that strengthen research integrity. Five principles were formulated: assess responsible research practices, value complete reporting, reward the practice of open science, acknowledge a broad range of research activities, and recognise essential other tasks like peer review and mentoring. For each principle a rationale for its inclusion is provided and examples of research institutions where these principles are already being adopted are given.

Meta-research

The little empirical evidence on interventions to improve responsible research practices we have is often of poor quality, negative or both. This is illustrated by the Cochrane review that summarizes the evidence on interventions to prevent misconduct and promote integrity in research and publication (Marusic et al. 2016 ). Research institutions should make their research integrity policies as evidence-based as possible. In hindsight it’s difficult to understand why it took us so long to establish a solid tradition in research on research integrity. That only started to happen recently and was fuelled by granting programs like the Horizon 2020 Science with and for Society (SwafS) calls for research ethics and research integrity (EC 2020 ). In the Netherlands the programs on Fostering Responsible Research Practices (ZonMw 2020 ) and Replication Studies (NWO 2020 ) contributed to the emerging field of research on research. At the 5th World Conference on Research Integrity the Amsterdam Agenda was adopted that strongly encourages research on research integrity especially focusing on solutions that really work and effect change (Amsterdam Agenda 2015 ; Mayer et al. 2017 ).

That being said there is still a lot we don’t know about research integrity in research institutions. To fill this gap in May 2020 all researchers in Dutch universities and university medical centres will be invited to participate in the National Survey on Research Integrity. The survey is expected to provide valid and reliable knowledge on how often specific QRPs occur and what their underlying explanatory variables are. This will provide insights that help research institutions to improve their policies and to fulfil their duties of care in fostering research integrity better. Given the sensitivity of some of the questions, the survey will pay particular attention to ensuring the protection of the identity of the participants and their research institutions. The Randomised Response technique that will be used is expected to elicit a strong sense of trust in respondents because their answers can never be linked to them (Lentsvelt-Mulders et al. 2005 ). And to keep the time to complete the survey short we make use of missingness by design.

But let me be clear: surveys, focus group interviews and Delphi studies can only guide us towards potentially effective measures research institutions can take to improve responsible research practices. How good for instance SOPs and guidelines (SOPs4RI 2020 ) or the Hong Kong Principles (Moher et al. 2019 ) really are in comparison to alternative approaches needs to be sorted out in future studies designed to demonstrate effectiveness in terms of outcomes that matter.

Finally it’s important to note that there are many stakeholders with a responsibility to foster research integrity. First and foremost the researchers themselves are responsible to behave well and to refrain from QRPs. Researchers should also be a good role model and help others to keep on track. Second, research institutions must empower researchers to act according to the standards of good research. But also funding agencies and scientific journals have important roles to play. There is no magic pill or a quick fix. The dilemmas and distractions researchers face are real and universal. We owe it to society to collaborate and to do our utmost best to prevent QRPs and to foster research integrity.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Trump says migrants are fueling violent crime. Here is what the research shows

• Medium Text

WHAT IS TRUMP SAYING ABOUT IMMIGRANTS AND CRIME?

How has biden responded, do immigrants commit more crime than the native born.

• The report, which used data from the Texas Department of Public Safety between 2012-2018, found a lower felony arrest rate for immigrants in the U.S. illegally compared to legal immigrants and native-born U.S. citizens and no evidence of increasing criminality among immigrants.
• Light published a study New Tab , opens new tab in 2017 that found illegal immigration does not increase violent crime. The study used data from all 50 U.S. states and Washington, D.C., from 1990-2014. A separate study found New Tab , opens new tab no link between increased illegal immigration and drunk-driving deaths.
• The libertarian think tank has published multiple New Tab , opens new tab reports New Tab , opens new tab that show immigrants in the country commit crimes at lower rates than the native-born. In a recent USA Today op-ed New Tab , opens new tab , Nowrasteh previewed new research that found immigrants in the U.S. illegally in Texas were about 26% less likely to be convicted of homicide than native-born Americans from 2013-2022.

DO ANY STUDIES FIND IMMIGRANTS MORE LIKELY TO COMMIT CRIMES?

Is it possible that trends have shifted recently.

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Reporting by Ted Hesson in Washington and Mica Rosenberg in New York; Editing by Mary Milliken and Aurora Ellis

Our Standards: The Thomson Reuters Trust Principles. New Tab , opens new tab

Thomson Reuters

Ted Hesson is an immigration reporter for Reuters, based in Washington, D.C. His work focuses on the policy and politics of immigration, asylum and border security. Prior to joining Reuters in 2019, Ted worked for the news outlet POLITICO, where he also covered immigration. His articles have appeared in POLITICO Magazine, The Atlantic and VICE News, among other publications. Ted holds a master's degree from the Columbia University Graduate School of Journalism and bachelor's degree from Boston College.

Mica Rosenberg leads the immigration team at Reuters, reporting her own projects while helping edit and coordinate cross-border coverage. An investigation she published with colleagues into child labor in the United States – exposing migrant children manufacturing car parts and working in chicken processing in Alabama – was a finalist for the Pulitzer Prize and won a George Polk award among other honors. She was a foreign correspondent reporting from nearly a dozen countries across Latin America and also covered legal affairs and white-collar crime in New York. She completed a Knight Bagehot Fellowship in business journalism and earned a master’s from Columbia’s School of International and Public Affairs. She is originally from New Mexico and is based in Brooklyn.

World Chevron

Trump makes history with new york hush money criminal trial.

Donald Trump becomes the first former president to face a criminal trial on Monday when jury selection begins in Manhattan in a case involving hush money paid to porn star Stormy Daniels, with the U.S. election looming in less than seven months as he seeks a return to the White House.