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September 8, 2021

Explaining How Research Works

We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.

Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.

Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.

Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.

Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.

Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.

It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.

We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to  [email protected].

Below are some additional resources:

• Discoveries in Basic Science: A Perfectly Imperfect Process
• When Clinical Research Is in the News
• What is Basic Science and Why is it Important?
• ​ What is a Research Organism?
• What Are Clinical Trials and Studies?
• Basic Research – Digital Media Kit
• Decoding Science: How Does Science Know What It Knows? (NAS)
• Can Science Help People Make Decisions ? (NAS)

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This introductory program was created by  JSTOR  to help you get familiar with basic research concepts needed for success in school. The course contains three modules, each made up of three short lessons and three sets of practice quizzes. The topics covered are subjects that will help you prepare for college-level research. Each module ends with an assessment to test your knowledge.

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How To Write A Research Paper

Step-By-Step Tutorial With Examples + FREE Template

By: Derek Jansen (MBA) | Expert Reviewer: Dr Eunice Rautenbach | March 2024

For many students, crafting a strong research paper from scratch can feel like a daunting task – and rightly so! In this post, we’ll unpack what a research paper is, what it needs to do , and how to write one – in three easy steps. 🙂 

Overview: Writing A Research Paper

What (exactly) is a research paper.

• How to write a research paper
• Stage 1 : Topic & literature search
• Stage 2 : Structure & outline
• Stage 3 : Iterative writing
• Key takeaways

Let’s start by asking the most important question, “ What is a research paper? ”.

Simply put, a research paper is a scholarly written work where the writer (that’s you!) answers a specific question (this is called a research question ) through evidence-based arguments . Evidence-based is the keyword here. In other words, a research paper is different from an essay or other writing assignments that draw from the writer’s personal opinions or experiences. With a research paper, it’s all about building your arguments based on evidence (we’ll talk more about that evidence a little later).

Now, it’s worth noting that there are many different types of research papers , including analytical papers (the type I just described), argumentative papers, and interpretative papers. Here, we’ll focus on analytical papers , as these are some of the most common – but if you’re keen to learn about other types of research papers, be sure to check out the rest of the blog .

With that basic foundation laid, let’s get down to business and look at how to write a research paper .

Overview: The 3-Stage Process

While there are, of course, many potential approaches you can take to write a research paper, there are typically three stages to the writing process. So, in this tutorial, we’ll present a straightforward three-step process that we use when working with students at Grad Coach.

These three steps are:

• Finding a research topic and reviewing the existing literature
• Developing a provisional structure and outline for your paper, and
• Writing up your initial draft and then refining it iteratively

Let’s dig into each of these.

Need a helping hand?

Step 1: Find a topic and review the literature

As we mentioned earlier, in a research paper, you, as the researcher, will try to answer a question . More specifically, that’s called a research question , and it sets the direction of your entire paper. What’s important to understand though is that you’ll need to answer that research question with the help of high-quality sources – for example, journal articles, government reports, case studies, and so on. We’ll circle back to this in a minute.

The first stage of the research process is deciding on what your research question will be and then reviewing the existing literature (in other words, past studies and papers) to see what they say about that specific research question. In some cases, your professor may provide you with a predetermined research question (or set of questions). However, in many cases, you’ll need to find your own research question within a certain topic area.

Finding a strong research question hinges on identifying a meaningful research gap – in other words, an area that’s lacking in existing research. There’s a lot to unpack here, so if you wanna learn more, check out the plain-language explainer video below.

Once you’ve figured out which question (or questions) you’ll attempt to answer in your research paper, you’ll need to do a deep dive into the existing literature – this is called a “ literature search ”. Again, there are many ways to go about this, but your most likely starting point will be Google Scholar .

If you’re new to Google Scholar, think of it as Google for the academic world. You can start by simply entering a few different keywords that are relevant to your research question and it will then present a host of articles for you to review. What you want to pay close attention to here is the number of citations for each paper – the more citations a paper has, the more credible it is (generally speaking – there are some exceptions, of course).

Ideally, what you’re looking for are well-cited papers that are highly relevant to your topic. That said, keep in mind that citations are a cumulative metric , so older papers will often have more citations than newer papers – just because they’ve been around for longer. So, don’t fixate on this metric in isolation – relevance and recency are also very important.

Beyond Google Scholar, you’ll also definitely want to check out academic databases and aggregators such as Science Direct, PubMed, JStor and so on. These will often overlap with the results that you find in Google Scholar, but they can also reveal some hidden gems – so, be sure to check them out.

Once you’ve worked your way through all the literature, you’ll want to catalogue all this information in some sort of spreadsheet so that you can easily recall who said what, when and within what context. If you’d like, we’ve got a free literature spreadsheet that helps you do exactly that.

Step 2: Develop a structure and outline

With your research question pinned down and your literature digested and catalogued, it’s time to move on to planning your actual research paper .

It might sound obvious, but it’s really important to have some sort of rough outline in place before you start writing your paper. So often, we see students eagerly rushing into the writing phase, only to land up with a disjointed research paper that rambles on in multiple

Now, the secret here is to not get caught up in the fine details . Realistically, all you need at this stage is a bullet-point list that describes (in broad strokes) what you’ll discuss and in what order. It’s also useful to remember that you’re not glued to this outline – in all likelihood, you’ll chop and change some sections once you start writing, and that’s perfectly okay. What’s important is that you have some sort of roadmap in place from the start.

At this stage you might be wondering, “ But how should I structure my research paper? ”. Well, there’s no one-size-fits-all solution here, but in general, a research paper will consist of a few relatively standardised components:

• Introduction
• Literature review
• Methodology

Let’s take a look at each of these.

First up is the introduction section . As the name suggests, the purpose of the introduction is to set the scene for your research paper. There are usually (at least) four ingredients that go into this section – these are the background to the topic, the research problem and resultant research question , and the justification or rationale. If you’re interested, the video below unpacks the introduction section in more detail. 

The next section of your research paper will typically be your literature review . Remember all that literature you worked through earlier? Well, this is where you’ll present your interpretation of all that content . You’ll do this by writing about recent trends, developments, and arguments within the literature – but more specifically, those that are relevant to your research question . The literature review can oftentimes seem a little daunting, even to seasoned researchers, so be sure to check out our extensive collection of literature review content here .

With the introduction and lit review out of the way, the next section of your paper is the research methodology . In a nutshell, the methodology section should describe to your reader what you did (beyond just reviewing the existing literature) to answer your research question. For example, what data did you collect, how did you collect that data, how did you analyse that data and so on? For each choice, you’ll also need to justify why you chose to do it that way, and what the strengths and weaknesses of your approach were.

Now, it’s worth mentioning that for some research papers, this aspect of the project may be a lot simpler . For example, you may only need to draw on secondary sources (in other words, existing data sets). In some cases, you may just be asked to draw your conclusions from the literature search itself (in other words, there may be no data analysis at all). But, if you are required to collect and analyse data, you’ll need to pay a lot of attention to the methodology section. The video below provides an example of what the methodology section might look like.

By this stage of your paper, you will have explained what your research question is, what the existing literature has to say about that question, and how you analysed additional data to try to answer your question. So, the natural next step is to present your analysis of that data . This section is usually called the “results” or “analysis” section and this is where you’ll showcase your findings.

Depending on your school’s requirements, you may need to present and interpret the data in one section – or you might split the presentation and the interpretation into two sections. In the latter case, your “results” section will just describe the data, and the “discussion” is where you’ll interpret that data and explicitly link your analysis back to your research question. If you’re not sure which approach to take, check in with your professor or take a look at past papers to see what the norms are for your programme.

Alright – once you’ve presented and discussed your results, it’s time to wrap it up . This usually takes the form of the “ conclusion ” section. In the conclusion, you’ll need to highlight the key takeaways from your study and close the loop by explicitly answering your research question. Again, the exact requirements here will vary depending on your programme (and you may not even need a conclusion section at all) – so be sure to check with your professor if you’re unsure.

Step 3: Write and refine

Finally, it’s time to get writing. All too often though, students hit a brick wall right about here… So, how do you avoid this happening to you?

Well, there’s a lot to be said when it comes to writing a research paper (or any sort of academic piece), but we’ll share three practical tips to help you get started.

First and foremost , it’s essential to approach your writing as an iterative process. In other words, you need to start with a really messy first draft and then polish it over multiple rounds of editing. Don’t waste your time trying to write a perfect research paper in one go. Instead, take the pressure off yourself by adopting an iterative approach.

Secondly , it’s important to always lean towards critical writing , rather than descriptive writing. What does this mean? Well, at the simplest level, descriptive writing focuses on the “ what ”, while critical writing digs into the “ so what ” – in other words, the implications . If you’re not familiar with these two types of writing, don’t worry! You can find a plain-language explanation here.

Last but not least, you’ll need to get your referencing right. Specifically, you’ll need to provide credible, correctly formatted citations for the statements you make. We see students making referencing mistakes all the time and it costs them dearly. The good news is that you can easily avoid this by using a simple reference manager . If you don’t have one, check out our video about Mendeley, an easy (and free) reference management tool that you can start using today.

Recap: Key Takeaways

We’ve covered a lot of ground here. To recap, the three steps to writing a high-quality research paper are:

• To choose a research question and review the literature
• To plan your paper structure and draft an outline
• To take an iterative approach to writing, focusing on critical writing and strong referencing

Remember, this is just a b ig-picture overview of the research paper development process and there’s a lot more nuance to unpack. So, be sure to grab a copy of our free research paper template to learn more about how to write a research paper.

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• CAREER COLUMN
• 15 March 2019

A student’s guide to undergraduate research

• Shiwei Wang 0

Shiwei Wang is a junior undergraduate student studying Integrated Science and Chemistry at Northwestern University in Evanston, Illinois. Twitter: @W_Shiwei

You can also search for this author in PubMed   Google Scholar

I have thoroughly enjoyed my experience working in a materials-chemistry laboratory at Northwestern University in Evanston, Illinois, for the past two years. Being able to mix an undergraduate education with original research in a proper laboratory has been a fantastic opportunity.

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doi: https://doi.org/10.1038/d41586-019-00871-x

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].

Wang, S. et al. Preprint at ChemRxiv https://doi.org/10.26434/chemrxiv.7824707.v2 (2019).

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Home Market Research

What is Research: Definition, Methods, Types & Examples

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

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

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

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

Research is conducted with a purpose to:

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

What is the purpose of research?

There are three main purposes:

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

LEARN ABOUT: Descriptive Analysis

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

LEARN ABOUT: Best Data Collection Tools

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

Here is a comparative analysis chart for a better understanding:

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

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

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

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

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

Types of qualitative methods include:

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

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

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

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

It is essential to ensure that your data is:

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

Gather insights

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

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

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How to Do Research

Last Updated: March 13, 2023 References

This article was co-authored by Matthew Snipp, PhD and by wikiHow staff writer, Jennifer Mueller, JD . C. Matthew Snipp is the Burnet C. and Mildred Finley Wohlford Professor of Humanities and Sciences in the Department of Sociology at Stanford University. He is also the Director for the Institute for Research in the Social Science’s Secure Data Center. He has been a Research Fellow at the U.S. Bureau of the Census and a Fellow at the Center for Advanced Study in the Behavioral Sciences. He has published 3 books and over 70 articles and book chapters on demography, economic development, poverty and unemployment. He is also currently serving on the National Institute of Child Health and Development’s Population Science Subcommittee. He holds a Ph.D. in Sociology from the University of Wisconsin—Madison. This article has been viewed 227,475 times.

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

How do I find a topic to research?

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

How do I get started on my research?

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

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

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

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

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

How do I evaluate the quality of a source?

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

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

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

How do I organize my research for my paper?

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

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

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

How can I make sure I'm not plagiarizing?

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

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

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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

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

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Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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How to Make a Successful Research Presentation

Turning a research paper into a visual presentation is difficult; there are pitfalls, and navigating the path to a brief, informative presentation takes time and practice. As a TA for  GEO/WRI 201: Methods in Data Analysis & Scientific Writing this past fall, I saw how this process works from an instructor’s standpoint. I’ve presented my own research before, but helping others present theirs taught me a bit more about the process. Here are some tips I learned that may help you with your next research presentation:

More is more

In general, your presentation will always benefit from more practice, more feedback, and more revision. By practicing in front of friends, you can get comfortable with presenting your work while receiving feedback. It is hard to know how to revise your presentation if you never practice. If you are presenting to a general audience, getting feedback from someone outside of your discipline is crucial. Terms and ideas that seem intuitive to you may be completely foreign to someone else, and your well-crafted presentation could fall flat.

Less is more

Limit the scope of your presentation, the number of slides, and the text on each slide. In my experience, text works well for organizing slides, orienting the audience to key terms, and annotating important figures–not for explaining complex ideas. Having fewer slides is usually better as well. In general, about one slide per minute of presentation is an appropriate budget. Too many slides is usually a sign that your topic is too broad.

Limit the scope of your presentation

Don’t present your paper. Presentations are usually around 10 min long. You will not have time to explain all of the research you did in a semester (or a year!) in such a short span of time. Instead, focus on the highlight(s). Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

You will not have time to explain all of the research you did. Instead, focus on the highlights. Identify a single compelling research question which your work addressed, and craft a succinct but complete narrative around it.

Craft a compelling research narrative

After identifying the focused research question, walk your audience through your research as if it were a story. Presentations with strong narrative arcs are clear, captivating, and compelling.

• Introduction (exposition — rising action)

Orient the audience and draw them in by demonstrating the relevance and importance of your research story with strong global motive. Provide them with the necessary vocabulary and background knowledge to understand the plot of your story. Introduce the key studies (characters) relevant in your story and build tension and conflict with scholarly and data motive. By the end of your introduction, your audience should clearly understand your research question and be dying to know how you resolve the tension built through motive.

• Methods (rising action)

The methods section should transition smoothly and logically from the introduction. Beware of presenting your methods in a boring, arc-killing, ‘this is what I did.’ Focus on the details that set your story apart from the stories other people have already told. Keep the audience interested by clearly motivating your decisions based on your original research question or the tension built in your introduction.

• Results (climax)

Less is usually more here. Only present results which are clearly related to the focused research question you are presenting. Make sure you explain the results clearly so that your audience understands what your research found. This is the peak of tension in your narrative arc, so don’t undercut it by quickly clicking through to your discussion.

• Discussion (falling action)

By now your audience should be dying for a satisfying resolution. Here is where you contextualize your results and begin resolving the tension between past research. Be thorough. If you have too many conflicts left unresolved, or you don’t have enough time to present all of the resolutions, you probably need to further narrow the scope of your presentation.

• Conclusion (denouement)

Return back to your initial research question and motive, resolving any final conflicts and tying up loose ends. Leave the audience with a clear resolution of your focus research question, and use unresolved tension to set up potential sequels (i.e. further research).

Use your medium to enhance the narrative

Visual presentations should be dominated by clear, intentional graphics. Subtle animation in key moments (usually during the results or discussion) can add drama to the narrative arc and make conflict resolutions more satisfying. You are narrating a story written in images, videos, cartoons, and graphs. While your paper is mostly text, with graphics to highlight crucial points, your slides should be the opposite. Adapting to the new medium may require you to create or acquire far more graphics than you included in your paper, but it is necessary to create an engaging presentation.

The most important thing you can do for your presentation is to practice and revise. Bother your friends, your roommates, TAs–anybody who will sit down and listen to your work. Beyond that, think about presentations you have found compelling and try to incorporate some of those elements into your own. Remember you want your work to be comprehensible; you aren’t creating experts in 10 minutes. Above all, try to stay passionate about what you did and why. You put the time in, so show your audience that it’s worth it.

For more insight into research presentations, check out these past PCUR posts written by Emma and Ellie .

— Alec Getraer, Natural Sciences Correspondent

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How to Become a Research Psychologist

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

Why Become a Research Psychologist

What do research psychologists do, where do research psychologists work, research psychology careers.

What do you do if you love psychology but have no interest in working in mental health? Fortunately, psychology is a very diverse field, and there are plenty of opportunities. One that you might consider is becoming a research psychologist.

Consider the following question from a reader:

" I love psychology, which is why I'm currently working on my bachelor's degree in psychology. I don't want to work in mental health, so my ultimate plan is to become a researcher. While I know that this means I will probably need to go to graduate school, I'm not quite sure exactly where to begin. What type of psychology degree do I need if I want to work in research?"

At a Glance

What do you do if you love psychology but don't want to become a therapist or work in mental health? You might consider becoming a research psychologist. These professionals conduct research and may work in various settings, including universities, research labs, the military, government agencies, and private businesses.

This scenario is not at all uncommon in psychology. Many students love the subject, yet are not at all interested in working in mental health settings. Experimental fields are great options for people who are fascinated by psychology and enjoy performing research.

As a psychology student , you've probably already gotten a taste of just how diverse the field can be. This can be a great thing because it allows for so many different career paths and options, but it can also be confusing for students as they struggle to select an educational path.

Just like many other areas of psychology , becoming a research psychologist is not a "one size fits all" career. There are actually many different degrees that you could potentially pursue.

Start by taking into account what type of research you want to perform and what specific topics interest you the most.

In order to decide if this field is right for you, it is important to first understand exactly what these professionals do:

• Also known as experimental psychologists , research psychologists study a broad range of human and animal behavior.
• They design and conduct experiments exploring how people act, think, behave, interact, learn, feel, and perform under different conditions.
• They also design studies and evaluate research for flaws and bias.
• This can encompass an enormous range of topics, including memory , attention, cognition, decision-making, perception, and just about any psychological topic you can think of!

If you enjoy research and still want to work in mental health, there are also mental health professionals who perform research and conduct studies in clinical settings.

Educational backgrounds and requirements for experimental psychologists can vary depending upon where you want to work.

In most cases, you will start by earning a bachelor's degree in psychology. Some students may then choose to earn a master's, but many will go on to receive a doctorate degree.

Bachelor's Degree

Many students interested in becoming research psychologists begin with a bachelor's in psychology . However, some come from a background in a related area such as social work or even from an entirely unrelated degree area altogether.

Remember, it is possible to switch to psychology for graduate school , even if your undergraduate degree is in an unrelated subject.

Master's Degree

In some cases, students might then choose to pursue a master's degree in experimental psychology. However, it is important to note that job opportunities are generally more limited with a master's degree, which is why many opt to instead go on to earn a doctorate degree in psychology .

Doctorate Degree

While you might think you are limited to earning a PhD in experimental psychology, there are actually many different options that you might opt to pursue.

For example, if you are interested in studying the human brain, you might earn a degree focused on neuropsychology. Have an active interest in social behavior? Then, you might want to consider a doctorate in social psychology .

How to Get Started

While you might not be exactly sure about what specialty you want to pursue, you can now do plenty of things to prepare for your future as a research psychologist . Start by taking as many undergraduate courses in research methods , statistics , and experimental design as possible.

Sign up for research opportunities through your school's psychology department and consider signing up as a research assistant. It's a great way to gain valuable experience while earning college credits.

As you can see, research is something that plays a significant role in virtually every field of psychology . Your goal now is to determine which particular specialty area interests you the most and exactly where you might want to work someday.

Research psychologists are employed in a wide range of sectors, including private research firms, universities, corporations, the military, and government agencies.

So what kind of jobs will you be able to get as a research psychologist? While there are many different options, a few that you might consider include:

College Professor

Many research psychologists work at colleges and universities, teaching undergraduate and graduate students and conducting research.

Research Analyst

A research analyst evaluates data that has been collected. This career involves performing statistical analyses and managing data to ensure it is collected, recorded, and analyzed properly.

Research Scientist

A research scientist conducted grant-funded research. They are often the lead investigators of a study and are responsible for hiring assistants, managing projects, designing experiments, writing journal articles, and sharing the results of their experiments.

If you enjoy research and aren't interested in working in the field of mental health, a career as a research psychologist might be a great choice for you. To enter this field, you should focus on earning an undergraduate degree in psychology before going to graduate school to get your doctorate. Taking coursework in statistics and research methodology can help, but you should also take advantage of any opportunity to participate in research.

Bishop DV. The psychology of experimental psychologists: Overcoming cognitive constraints to improve research: The 47th Sir Frederic Bartlett Lecture .  Q J Exp Psychol (Hove) . 2020;73(1):1-19. doi:10.1177/1747021819886519

Smith KV, Thew GR. Conducting research in clinical psychology practice: Barriers, facilitators, and recommendations .  Br J Clin Psychol . 2017;56(3):347-356. doi:10.1111/bjc.12142

Scholtz SE, de Klerk W, de Beer LT. The use of research methods in psychological research: A systematised review .  Front Res Metr Anal . 2020;5:1. doi:10.3389/frma.2020.00001

American Psychological Association.  Pursuing a Career in Experimental Psychology . Updated March 2014.

The Princeton Review.  Experimental Psychology .

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

A new future of work: The race to deploy AI and raise skills in Europe and beyond

At a glance.

Amid tightening labor markets and a slowdown in productivity growth, Europe and the United States face shifts in labor demand, spurred by AI and automation. Our updated modeling of the future of work finds that demand for workers in STEM-related, healthcare, and other high-skill professions would rise, while demand for occupations such as office workers, production workers, and customer service representatives would decline. By 2030, in a midpoint adoption scenario, up to 30 percent of current hours worked could be automated, accelerated by generative AI (gen AI). Efforts to achieve net-zero emissions, an aging workforce, and growth in e-commerce, as well as infrastructure and technology spending and overall economic growth, could also shift employment demand.

By 2030, Europe could require up to 12 million occupational transitions, double the prepandemic pace. In the United States, required transitions could reach almost 12 million, in line with the prepandemic norm. Both regions navigated even higher levels of labor market shifts at the height of the COVID-19 period, suggesting that they can handle this scale of future job transitions. The pace of occupational change is broadly similar among countries in Europe, although the specific mix reflects their economic variations.

Businesses will need a major skills upgrade. Demand for technological and social and emotional skills could rise as demand for physical and manual and higher cognitive skills stabilizes. Surveyed executives in Europe and the United States expressed a need not only for advanced IT and data analytics but also for critical thinking, creativity, and teaching and training—skills they report as currently being in short supply. Companies plan to focus on retraining workers, more than hiring or subcontracting, to meet skill needs.

Workers with lower wages face challenges of redeployment as demand reweights toward occupations with higher wages in both Europe and the United States. Occupations with lower wages are likely to see reductions in demand, and workers will need to acquire new skills to transition to better-paying work. If that doesn’t happen, there is a risk of a more polarized labor market, with more higher-wage jobs than workers and too many workers for existing lower-wage jobs.

Choices made today could revive productivity growth while creating better societal outcomes. Embracing the path of accelerated technology adoption with proactive worker redeployment could help Europe achieve an annual productivity growth rate of up to 3 percent through 2030. However, slow adoption would limit that to 0.3 percent, closer to today’s level of productivity growth in Western Europe. Slow worker redeployment would leave millions unable to participate productively in the future of work.

Demand will change for a range of occupations through 2030, including growth in STEM- and healthcare-related occupations, among others

This report focuses on labor markets in nine major economies in the European Union along with the United Kingdom, in comparison with the United States. Technology, including most recently the rise of gen AI, along with other factors, will spur changes in the pattern of labor demand through 2030. Our study, which uses an updated version of the McKinsey Global Institute future of work model, seeks to quantify the occupational transitions that will be required and the changing nature of demand for different types of jobs and skills.

Our methodology

We used methodology consistent with other McKinsey Global Institute reports on the future of work to model trends of job changes at the level of occupations, activities, and skills. For this report, we focused our analysis on the 2022–30 period.

Our model estimates net changes in employment demand by sector and occupation; we also estimate occupational transitions, or the net number of workers that need to change in each type of occupation, based on which occupations face declining demand by 2030 relative to current employment in 2022. We included ten countries in Europe: nine EU members—the Czech Republic, Denmark, France, Germany, Italy, Netherlands, Poland, Spain, and Sweden—and the United Kingdom. For the United States, we build on estimates published in our 2023 report Generative AI and the future of work in America.

We included multiple drivers in our modeling: automation potential, net-zero transition, e-commerce growth, remote work adoption, increases in income, aging populations, technology investments, and infrastructure investments.

Two scenarios are used to bookend the work-automation model: “late” and “early.” For Europe, we modeled a “faster” scenario and a “slower” one. For the faster scenario, we use the midpoint—the arithmetical average between our late and early scenarios. For the slower scenario, we use a “mid late” trajectory, an arithmetical average between a late adoption scenario and the midpoint scenario. For the United States, we use the midpoint scenario, based on our earlier research.

We also estimate the productivity effects of automation, using GDP per full-time-equivalent (FTE) employee as the measure of productivity. We assumed that workers displaced by automation rejoin the workforce at 2022 productivity levels, net of automation, and in line with the expected 2030 occupational mix.

Amid tightening labor markets and a slowdown in productivity growth, Europe and the United States face shifts in labor demand, spurred not only by AI and automation but also by other trends, including efforts to achieve net-zero emissions, an aging population, infrastructure spending, technology investments, and growth in e-commerce, among others (see sidebar, “Our methodology”).

Our analysis finds that demand for occupations such as health professionals and other STEM-related professionals would grow by 17 to 30 percent between 2022 and 2030, (Exhibit 1).

By contrast, demand for workers in food services, production work, customer services, sales, and office support—all of which declined over the 2012–22 period—would continue to decline until 2030. These jobs involve a high share of repetitive tasks, data collection, and elementary data processing—all activities that automated systems can handle efficiently.

Up to 30 percent of hours worked could be automated by 2030, boosted by gen AI, leading to millions of required occupational transitions

By 2030, our analysis finds that about 27 percent of current hours worked in Europe and 30 percent of hours worked in the United States could be automated, accelerated by gen AI. Our model suggests that roughly 20 percent of hours worked could still be automated even without gen AI, implying a significant acceleration.

These trends will play out in labor markets in the form of workers needing to change occupations. By 2030, under the faster adoption scenario we modeled, Europe could require up to 12.0 million occupational transitions, affecting 6.5 percent of current employment. That is double the prepandemic pace (Exhibit 2). Under a slower scenario we modeled for Europe, the number of occupational transitions needed would amount to 8.5 million, affecting 4.6 percent of current employment. In the United States, required transitions could reach almost 12.0 million, affecting 7.5 percent of current employment. Unlike Europe, this magnitude of transitions is broadly in line with the prepandemic norm.

Both regions navigated even higher levels of labor market shifts at the height of the COVID-19 period. While these were abrupt and painful to many, given the forced nature of the shifts, the experience suggests that both regions have the ability to handle this scale of future job transitions.

Businesses will need a major skills upgrade

The occupational transitions noted above herald substantial shifts in workforce skills in a future in which automation and AI are integrated into the workplace (Exhibit 3). Workers use multiple skills to perform a given task, but for the purposes of our quantification, we identified the predominant skill used.

Demand for technological skills could see substantial growth in Europe and in the United States (increases of 25 percent and 29 percent, respectively, in hours worked by 2030 compared to 2022) under our midpoint scenario of automation adoption (which is the faster scenario for Europe).

Demand for social and emotional skills could rise by 11 percent in Europe and by 14 percent in the United States. Underlying this increase is higher demand for roles requiring interpersonal empathy and leadership skills. These skills are crucial in healthcare and managerial roles in an evolving economy that demands greater adaptability and flexibility.

Conversely, demand for work in which basic cognitive skills predominate is expected to decline by 14 percent. Basic cognitive skills are required primarily in office support or customer service roles, which are highly susceptible to being automated by AI. Among work characterized by these basic cognitive skills experiencing significant drops in demand are basic data processing and literacy, numeracy, and communication.

Demand for work in which higher cognitive skills predominate could also decline slightly, according to our analysis. While creativity is expected to remain highly sought after, with a potential increase of 12 percent by 2030, work activities characterized by other advanced cognitive skills such as advanced literacy and writing, along with quantitative and statistical skills, could decline by 19 percent.

Demand for physical and manual skills, on the other hand, could remain roughly level with the present. These skills remain the largest share of workforce skills, representing about 30 percent of total hours worked in 2022. Growth in demand for these skills between 2022 and 2030 could come from the build-out of infrastructure and higher investment in low-emissions sectors, while declines would be in line with continued automation in production work.

Business executives report skills shortages today and expect them to worsen

A survey we conducted of C-suite executives in five countries shows that companies are already grappling with skills challenges, including a skills mismatch, particularly in technological, higher cognitive, and social and emotional skills: about one-third of the more than 1,100 respondents report a shortfall in these critical areas. At the same time, a notable number of executives say they have enough employees with basic cognitive skills and, to a lesser extent, physical and manual skills.

Within technological skills, companies in our survey reported that their most significant shortages are in advanced IT skills and programming, advanced data analysis, and mathematical skills. Among higher cognitive skills, significant shortfalls are seen in critical thinking and problem structuring and in complex information processing. About 40 percent of the executives surveyed pointed to a shortage of workers with these skills, which are needed for working alongside new technologies (Exhibit 4).

Companies see retraining as key to acquiring needed skills and adapting to the new work landscape

Surveyed executives expect significant changes to their workforce skill levels and worry about not finding the right skills by 2030. More than one in four survey respondents said that failing to capture the needed skills could directly harm financial performance and indirectly impede their efforts to leverage the value from AI.

To acquire the skills they need, companies have three main options: retraining, hiring, and contracting workers. Our survey suggests that executives are looking at all three options, with retraining the most widely reported tactic planned to address the skills mismatch: on average, out of companies that mentioned retraining as one of their tactics to address skills mismatch, executives said they would retrain 32 percent of their workforce. The scale of retraining needs varies in degree. For example, respondents in the automotive industry expect 36 percent of their workforce to be retrained, compared with 28 percent in the financial services industry. Out of those who have mentioned hiring or contracting as their tactics to address the skills mismatch, executives surveyed said they would hire an average of 23 percent of their workforce and contract an average of 18 percent.

Occupational transitions will affect high-, medium-, and low-wage workers differently

All ten European countries we examined for this report may see increasing demand for top-earning occupations. By contrast, workers in the two lowest-wage-bracket occupations could be three to five times more likely to have to change occupations compared to the top wage earners, our analysis finds. The disparity is much higher in the United States, where workers in the two lowest-wage-bracket occupations are up to 14 times more likely to face occupational shifts than the highest earners. In Europe, the middle-wage population could be twice as affected by occupational transitions as the same population in United States, representing 7.3 percent of the working population who might face occupational transitions.

Enhancing human capital at the same time as deploying the technology rapidly could boost annual productivity growth

About quantumblack, ai by mckinsey.

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Organizations and policy makers have choices to make; the way they approach AI and automation, along with human capital augmentation, will affect economic and societal outcomes.

We have attempted to quantify at a high level the potential effects of different stances to AI deployment on productivity in Europe. Our analysis considers two dimensions. The first is the adoption rate of AI and automation technologies. We consider the faster scenario and the late scenario for technology adoption. Faster adoption would unlock greater productivity growth potential but also, potentially, more short-term labor disruption than the late scenario.

The second dimension we consider is the level of automated worker time that is redeployed into the economy. This represents the ability to redeploy the time gained by automation and productivity gains (for example, new tasks and job creation). This could vary depending on the success of worker training programs and strategies to match demand and supply in labor markets.

We based our analysis on two potential scenarios: either all displaced workers would be able to fully rejoin the economy at a similar productivity level as in 2022 or only some 80 percent of the automated workers’ time will be redeployed into the economy.

Exhibit 5 illustrates the various outcomes in terms of annual productivity growth rate. The top-right quadrant illustrates the highest economy-wide productivity, with an annual productivity growth rate of up to 3.1 percent. It requires fast adoption of technologies as well as full redeployment of displaced workers. The top-left quadrant also demonstrates technology adoption on a fast trajectory and shows a relatively high productivity growth rate (up to 2.5 percent). However, about 6.0 percent of total hours worked (equivalent to 10.2 million people not working) would not be redeployed in the economy. Finally, the two bottom quadrants depict the failure to adopt AI and automation, leading to limited productivity gains and translating into limited labor market disruptions.

Four priorities for companies

The adoption of automation technologies will be decisive in protecting businesses’ competitive advantage in an automation and AI era. To ensure successful deployment at a company level, business leaders can embrace four priorities.

Understand the potential. Leaders need to understand the potential of these technologies, notably including how AI and gen AI can augment and automate work. This includes estimating both the total capacity that these technologies could free up and their impact on role composition and skills requirements. Understanding this allows business leaders to frame their end-to-end strategy and adoption goals with regard to these technologies.

Plan a strategic workforce shift. Once they understand the potential of automation technologies, leaders need to plan the company’s shift toward readiness for the automation and AI era. This requires sizing the workforce and skill needs, based on strategically identified use cases, to assess the potential future talent gap. From this analysis will flow details about the extent of recruitment of new talent, upskilling, or reskilling of the current workforce that is needed, as well as where to redeploy freed capacity to more value-added tasks.

Prioritize people development. To ensure that the right talent is on hand to sustain the company strategy during all transformation phases, leaders could consider strengthening their capabilities to identify, attract, and recruit future AI and gen AI leaders in a tight market. They will also likely need to accelerate the building of AI and gen AI capabilities in the workforce. Nontechnical talent will also need training to adapt to the changing skills environment. Finally, leaders could deploy an HR strategy and operating model to fit the post–gen AI workforce.

Pursue the executive-education journey on automation technologies. Leaders also need to undertake their own education journey on automation technologies to maximize their contributions to their companies during the coming transformation. This includes empowering senior managers to explore automation technologies implications and subsequently role model to others, as well as bringing all company leaders together to create a dedicated road map to drive business and employee value.

AI and the toolbox of advanced new technologies are evolving at a breathtaking pace. For companies and policy makers, these technologies are highly compelling because they promise a range of benefits, including higher productivity, which could lift growth and prosperity. Yet, as this report has sought to illustrate, making full use of the advantages on offer will also require paying attention to the critical element of human capital. In the best-case scenario, workers’ skills will develop and adapt to new technological challenges. Achieving this goal in our new technological age will be highly challenging—but the benefits will be great.

Eric Hazan is a McKinsey senior partner based in Paris; Anu Madgavkar and Michael Chui are McKinsey Global Institute partners based in New Jersey and San Francisco, respectively; Sven Smit is chair of the McKinsey Global Institute and a McKinsey senior partner based in Amsterdam; Dana Maor is a McKinsey senior partner based in Tel Aviv; Gurneet Singh Dandona is an associate partner and a senior expert based in New York; and Roland Huyghues-Despointes is a consultant based in Paris.

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Advice on aligning schedules, fostering culture, and ensuring productivity.

Managers struggling to implement hybrid work policies confront three key challenges: scheduling, culture, and productivity. Research into companies allowing employees to be both remote and in-person suggest these obstacles can be overcome. In scheduling, shift to a focus not on how often workers are in, but which activities are better done in the office. To build and maintain culture, encourage employees to come in not for the organization or themselves but for their colleagues. And to ensure productivity, avoid surveillance in favor of support.

More than four years after the Covid-19 pandemic accelerated an immediate shift to remote knowledge work, it’s clear that, despite some organizations’ attempts to lure employees back to the office full-time, hybrid work arrangements are here to stay . And yet employers are still struggling with  implementation. In particular, the managers we have talked to point to three key issues: scheduling, culture, and productivity.

• Mark C. Bolino is the David L. Boren Professor and the Michael F. Price Chair in International Business at the University of Oklahoma’s Price College of Business. His research focuses on understanding how an organization can inspire its employees to go the extra mile without compromising their personal well-being.
• CP Corey Phelps is the dean, the Fred E. Brown Chair of Business, and a professor of entrepreneurship and strategy at the University of Oklahoma’s Price College of Business. His research explores how organizations innovate, grow, and adapt to changing competitive conditions.

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Bezos Earth Fund Grant Creates Sustainable Protein Research Hub at NC State

For Immediate Release

The Bezos Center for Sustainable Protein launched today at North Carolina State University. The Bezos Earth Fund awarded NC State $30 million over five years to lead a center of excellence to create a biomanufacturing hub for dietary proteins that are environmentally friendly, healthy, tasty and affordable. The Earth Fund has committed $100 million to establish a network of open-access research and development centers focused on sustainable protein alternatives, expanding consumer choices.

The center will engage partners from academia and industry to research, create, and commercialize new technologies, provide training for the emerging industry workforce, and gauge consumers’ protein preferences.

“As a land-grant university in a state with significant animal agriculture, NC State is uniquely positioned to help shape the future of sustainable food production,” said Chancellor Randy Woodson. “We’re thankful for the support from the Bezos Earth Fund that will help drive economic and workforce development in this critical area of sustainable protein production in order to feed a growing world population in an economically and environmentally sustainable way. The state legislature’s funding of the Food Innovation Lab in Kannapolis and new facilities in the College of Engineering have made NC State incredibly competitive for this grant.”

Protein is essential to human health, whether it comes from animal or plant sources. Without the amino acids in protein, our cells, tissues and organs can’t function. And as the global population expands, the health of both humans and the planet will increasingly depend on widespread availability of proteins that taste good and are produced in ways that reduce greenhouse gas emissions and protect nature.

“Food production is the second largest source of greenhouse gas emissions, so it’s critical we find ways to feed a growing population without degrading the planet,” said Andrew Steer, President and CEO of the Earth Fund. “Sustainable protein has tremendous potential but more research is needed to reduce the price and boost the flavor and texture to ensure nutritious, affordable products are available. It’s about choice.”

The grant funding will support research on three types of sustainable proteins: plant-based products; precision fermentation to produce proteins and nutrients that can be used in food formulations; and cultivated meat grown from animal cells.

“Feeding a growing world requires producing tasty proteins that won’t further degrade nature,” said Andy Jarvis, the Earth Fund’s Director of Future of Food. “These centers will advance open-source, sustainable protein R&D to benefit consumer choice while protecting our planet.”

“This effort is all about expanding the sustainable protein knowledge base and ecosystem,” said Rohan Shirwaiker, James T. Ryan Professor of Industrial and Systems Engineering at NC State, principal investigator on the grant, and co-director of the center. “The center’s capabilities and partnerships will add a new dimension to expand NC State’s biotechnology and advanced manufacturing expertise.”

The center will also provide more reasons for biomanufacturing firms to locate in North Carolina, generating jobs and economic growth. The grant funding will help prepare the workforce for jobs in advanced food technology through various university and community college partnerships, while industry partnerships will support food production and processing, including small companies and start-ups.

“This is a significant opportunity for North Carolina to not only be a state with a thriving animal-sourced foods sector, but also one where it is a powerhouse in complementary proteins, building new industry and driving economic growth for the state,” said Bill Aimutis, co-principal investigator on the grant and co-director of the new center who has extensive experience working with sustainable protein producers and start-up companies. “With the center we are looking to develop solutions that will provide greater diversity of choices for consumers that are both tasty and sustainable.”

NC State will work with academic partners N.C. A&T State University, the University of North Carolina at Pembroke, Duke University, and Forsyth Tech Community College on the research, workforce development, and community engagement efforts. More than 20 industry partners will also be part of the center, which will facilitate technology transfer and student internships and mentorships.

This announcement builds on the Bezos Earth Fund’s $1 billion grant commitment to help transform food and agricultural systems to support healthy lives without degrading the planet, which also includes efforts to reduce emissions from livestock.

More information on the center and its work is available.

• Driving Food, Water and Energy Solutions
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The real dolphin tale: They’re smart, sometimes vicious and highly sexed

The longest-running wild dolphin research study paints a fuller picture of the marine mammals; they’re not “humans in wet suits.”

SARASOTA, Fla. — The research vessel Martha Jane glided slowly across the teal waters of Sarasota Bay on Florida’s Gulf Coast under a cloudless sky tailor-made for tourists on a recent day. “There’s 2094!” one of the scientists on the boat called out. “She’s still with us!”

The bottlenose dolphin known to researchers as 2094 had poked her dorsal fin out of the water for only a few seconds, but that was enough to identify her as a young female that had been the focus of a dramatic rescue from a fishing line a year ago.

No. 2094 is one of thousands of dolphins registered in the Sarasota Dolphin Research Program ’s database, each individual identified by the nicks and notches on their dorsal — or back — fins.

The world’s longest-running study of a wild dolphin population, the Sarasota effort has sighted and recorded more than 5,750 dolphins and made the shallow waters of Sarasota Bay a living laboratory for 53 years.

Among the program’s key findings: The individual dolphins here live in specific “neighborhoods” generation after generation, forming a mosaic of adjacent communities along Florida’s west coast. Many males forge buddy pairs for protection and stay together for life. And hetero- and same-sex interactions are used to establish and maintain social bonds over dolphin life spans that can stretch well past the age of 60.

Not ‘humans in wet suits’

In 1970, when the Sarasota Dolphin Research Program launched, dolphins were the subject of numerous romantic myths, including that they were intelligent and kind — animals that could be friends and even movie stars.

People viewed them as “humans in wet suits,” said Randy Wells, the director of the program, which is administered by the Brookfield Zoo Chicago.

People viewed them as “humans in wet suits.” — Randy Wells, director of the Sarasota Dolphin Research Program

But research has shown that, while they are highly intelligent, they have sensory systems very different from those of humans and a complex and unique means of communication. Listening stations the program installed around Sarasota Bay have recorded thousands of hours of dolphin vocalizations, and the team’s work with collaborators has shown that each dolphin has its own whistle, used for life like a name.

People also once believed that dolphins liked being near humans and benefited from food handouts. But the researchers have found that interactions with people can have dire consequences — including raising risks of the marine mammals ingesting inappropriate food, being exposed to spinning boat propellers and becoming entangled in fishing gear.

When the program started, no one knew whether dolphins generally ranged widely or stayed local — key information for wildlife managers. Using radio tracking devices and other tools, the researchers found that the roughly 170 dolphins that live in Sarasota Bay are organized in a definable range that is their home for life.

Generation after generation also stay in the same area and raise families. One 67-year-old female has given birth in a particular neighborhood at least 12 times, the program says. Before the study began, scientists had no idea bottlenose dolphins could live into their 60s in the wild.

A dolphin’s day

A day in the life of a Sarasota Bay dolphin is one of constant motion in which they feed on a variety of fish, travel, socialize with others and, finally, rest. Program scientists have observed the dolphins moving fluidly in and out of groups, depending on whom they encounter.

Nurseries made up of mothers and their youngest calves will swim together for a while, and independent juveniles join up with each other to practice skills needed later in life. During these activities, the dolphins are seeking prey while also keeping an eye out for predatory sharks and boat traffic as well as other disruptive human activities.

Sarasota Bay dolphins dine on a wide variety of fish, the data shows. They use their superb hearing to target prey fish such as toadfish and sea trout, which produce sounds.

Wells said that over the years, the team consistently documented pairs of the same males surfacing together, in a sort of buddy system that begins around the age of 10 and can last a lifetime. The pairs — which are unusual among mammals — protect the animals from predators when they’re resting. And during mating, one dolphin often stands guard while the other spends time with a female. When temporarily separated, the dolphins sometimes call to each other, apparently to maintain contact.

Bottlenose dolphins are very active sexually, Wells says. Both hetero- and homosexual interactions are used to create social bonds, he says, not just for procreation.

The greatest threats

The Sarasota Bay study animals are urban dolphins, living among a burgeoning human population and nearly constant exposure to boat traffic.

“Dolphins can be big, mean jerks.” — Gretchen Lovewell, program manager of Mote Marine Laboratory’s Stranding Investigations Program

Fifty thousand boats are registered in the dolphins’ home range within the bay, and boats pass within 100 yards of a dolphin an average of every six minutes during the day. Program staff were among the first to document the threats of death and serious injury to the dolphins caused by interactions with recreational fishing.

“Interaction with fisheries is the most common cause of death,” said Gretchen Lovewell, program manager of Mote Marine Laboratory’s Stranding Investigations Program , based in Sarasota. Lovewell works closely with Wells’s team to help fill in the dolphins’ life story, studying the animals’ skeletons to determine cause of death — and how they lived.

The bones sometimes reflect a darker side of dolphin behavior, one that belies the smiling caricature perpetuated by sympathetic images. The animals have powerful tails and beaks and use them against each other during conflicts. With males reaching more than nine feet in length and weighing as much as 660 pounds, such conflicts can be lethal.

Some of the bones of calves that Lovewell has examined show signs of being bashed by adult dolphins — deep teeth marks, broken bones and bruising around the babies’ jaws where adults apparently rammed them.

“Dolphins can be big, mean jerks,” Lovewell says.

Besides tangling with recreational fishing, the dolphins increasingly grapple with other threats. After recent severe outbreaks of a harmful algal bloom known as red tide, the dolphins altered their ranging and social patterns, interacting with anglers and boaters more often, with sometimes fatal results.

Dolphin encounters with sharks also rose, probably because red tide’s lethal effects on the fish that sharks normally consume caused them to prey on dolphins instead. However, researchers have documented more healed shark bite marks on paired males than single males, leading scientists to believe wounded paired dolphins survive attacks more often.

Climate change and blubber

Climate change has scientists concerned for the dolphins’ future. The animals’ blubber thickness and lipid content go up and down in response to seasonal temperature changes, the program team has found. “With climate change, rising water temperatures in areas where they live come close to the dolphins’ body temperature, and there’s a limit to how much blubber they can shed to adapt,” Wells said.

In some ways, dolphins can serve as canaries in a global ocean coal mine.

“Understanding dolphin health, behavior and biology helps us conserve dolphins in the wild and better protect their populations,” said Michael Adkesson, president and CEO of the Brookfield Zoo Chicago, which oversees animal conservation projects around the world, including the Sarasota program. “It also provides valuable information on the overall health of the oceans and marine landscapes that impact countless other species, including humans.”

Techniques developed by the team in Sarasota Bay have been used to help other scientists unravel the structure of dolphin populations and conserve them across the country and around the world, including endangered bottlenose dolphins in Greece and Mekong River dolphins in Cambodia.

Small franciscana dolphins that were dying in local fishermen’s nets in two Argentina bays were tracked in collaboration with Argentine scientists using the program’s satellite-linked transmitters, determining that the animals’ range closely matched the fishing zone. The findings have been used by the fishermen and the Argentine government to help protect the dolphins.

Data gathered by the program over the years has contributed to National Oceanic and Atmospheric Administration management plans for the species and has guided officials’ handling of environmental disasters such as the 2010 Deepwater Horizon oil spill.

The Sarasota-based method of temporarily restraining wild dolphins for health assessments was central to understanding the impact of the spill in Louisiana’s Barataria Bay, which was heavily oiled by the spill. The dolphins were found to have significant levels of adrenal toxicity and lung disease, among other disorders related to petroleum hydrocarbon exposure and toxicity.

“The techniques and long-term data coming from Sarasota served as the baseline for the data obtained in Barataria Bay,” said Michael Moore, senior scientist at the Woods Hole Oceanographic Institution in Massachusetts.

“Teams and tools developed by the Sarasota Dolphin Research Program were deployed in the spill area and led to a whole new understanding of how these disasters impact marine mammals,” Moore added. “None of this would have happened without the tools Randy Wells and his team developed.”

More From Forbes

Answering 'what is your ideal work environment' in a job interview.

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Employers ask about your ideal work environment to assess fit.

Predicting what you'll be asked in a job interview is challenging. One common question that may leave you stumped if caught off guard is, “What is your ideal work environment?” Another version of this question is, "What type of work environment do you prefer?" Obviously, there is no perfect workplace. But preparing an answer to this question in advance will accomplish two things. First, it will force you to dig deep to identify what’s important to you. Best of all, articulating your vision in a clear and succinct way that aligns with the company’s values will also leave a lasting impression on your future employer.

Your ideal work environment refers to the type of workplace where you will be the most productive and satisfied. Employers ask this question for several reasons. One is to assess cultural fit. They want to know that your desires match what they have to offer. Why? Employees who fit well within an organization are more likely to feel motivated and engaged, resulting in higher productivity. It also gives the hiring manager insight into your personality—something more difficult to glean from a résumé.

Finding an ideal culture match matters just as much to you, the job seeker, as to the employer. So much so that a Glassdoor survey polling over 5,000 respondents from the U.S., U.K., France and Germany found that 73% said they wouldn’t even apply to a company unless its values align with their own. The next time you prepare for an interview, follow these steps to respond to the question, “What is your ideal work environment?”

Reflect on past experiences

The first step is to define your preferences. Look back on past work experiences to identify the environments in which you thrived. Remember, it’s about more than just describing the physical location. Think about factors such as:

• Flexibility
• Work-life balance
• Opportunities for growth
• Collaboration vs. working independently
• Structured vs. ambiguous environments

Then, make a list and prioritize these attributes. Are there any elements on which you could be flexible? Also, note any characteristics you consider deal breakers.

NSA Warns iPhone And Android Users To Turn It Off And On Again

Will trump go to prison here’s what happens now that he’s been found guilty in hush money case., can trump vote as a convicted felon here s what rights he could lose, research the company.

Some employers ask about your ideal work environment to ensure you researched the company. Check the job description for keywords like creative, fast-paced or team-oriented. To learn more about the company culture , review the corporate website. Pay special attention to the mission statement and careers section. Also, look at social media channels to get a glimpse into the organization’s priorities. Another idea is to create a Google alert to stay on top of breaking news or announcements. Finally, talk to current employees. By scheduling informational interviews, you can get an insider perspective on what it’s like to work there.

Prepare your response

In a job interview, you always want to appear energetic and enthusiastic about the role. So, when you respond, frame your answer in a positive light. For example, instead of describing how you hated working for your micromanaging boss who tracked your every move, focus on the fact that you’re a self-starter who thrives on flexibility. Highlight what is most important to you and connect it to the organization you’re interviewing with. To make your response more compelling, use real-life examples. By using a storytelling approach, your interview will be engaging and memorable.

Example answers

Here are a few sample responses to this increasingly common interview question:

You enjoy a team-based environment

My ideal work environment is one where I can express my creativity while using my problem-solving skills to overcome obstacles. I enjoy collaborating with team members on challenging assignments. Working in a rewarding environment is also important to me. That’s why I was impressed that you recently created a program to recognize employees who go above and beyond. I find that I’m most productive and motivated when I’m part of a team that celebrates each other’s wins.

You prefer a balance between group and independent projects

I prefer working both in a group setting and independently at times. When I researched your company, I learned that many employees collaborate on projects and also focus on their own responsibilities. I’ve found that this balance is what makes me thrive as an advertising executive. While I enjoy brainstorming sessions, I also like spending time alone to strategize and focus on my day-to-day responsibilities.

You thrive in a remote setting

My ideal work environment centers around working for an organization that empowers its employees. When I read that you are a global company that prioritizes a sense of belonging, I was excited. I am most energized and productive when I am given the flexibility to work remotely for fast-paced, high-growth companies. Given that you promote transparency, work-life balance and asynchronous work, I can make an immediate contribution in this role.

Job interviews are a two-way conversation. If you determine that the company culture and your expectations don’t align, that’s okay. The role may not be a good fit. However, if there is overlap, you can decide whether some preferences are worth compromising. Most importantly, be authentic. It will make you a more attractive candidate and increase the likelihood of finding a job opportunity that is the best fit for you.

Are you a woman who needs help changing careers? Download my FREE 22-page e-book: How Professional Women Can Master Career Change!

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Melissa L. Gaskill

Tracking tiny stowaways, getting there and back, refining radiation detection, robot helpers.

Space, the saying goes, is hard. And the farther humans go, the harder it can get.

Some of the challenges on missions to explore the Moon and Mars include preventing microbial contamination of these destinations, navigating there safely, protecting crew members and hardware from radiation, and maintaining and repairing equipment.

Research on the International Space Station is helping NASA scientists develop tools and processes to ensure success on these important missions. Here are highlights from some of the investigations making space a little easier.

Bacteria and fungi live in and on all humans and all around us on Earth. Most of these microorganisms are beneficial or harmless but introducing them to other celestial bodies could adversely affect our ability to study ecosystems on those other worlds.

Crew members will conduct a spacewalk to collect samples near space station life support system vents for ISS External Microorganisms , an investigation to assess whether the orbiting laboratory releases microorganisms into space. Results could provide insight into the potential for organisms to survive and reproduce in space and help researchers determine which microbes would most likely contaminate other planetary bodies visited by crewed missions.

A miniature, hand-held digital microscope designed to make in-flight medical diagnoses, the Moon Microscope , also can test water, food, and surfaces for contamination. The device images samples at high resolution and processes data on web-enabled devices such as phones or tablets. Multiple users can access the microscope simultaneously, and some applications run autonomously.

Spacecraft must have sophisticated high-tech systems for navigating. Sextant Navigation tests the function of sextants in microgravity as an emergency backup navigation technique for Artemis and other future exploration missions. These mechanical devices have guided navigators for centuries, and Gemini and Apollo missions demonstrated they were useful for astronauts.

Missions beyond low Earth orbit increase exposure to radiation, which can pose a hazard to human health and interfere with equipment operation. As NASA prepares for future missions, providing adequate protection is vital.

The Hybrid Electronic Radiation Assessor, or HERA, was built to serve as a primary radiation detection system for the Orion spacecraft, which will carry crews into orbit around the Moon. The International Space Station Hybrid Electronic Radiation Assessor investigation modified the system to operate on the space station to provide researchers input for use on future exploration missions.

Artemis HERA on Space Station further modified the radiation detection system so researchers could continue to evaluate the hardware in the space radiation environment prior to Artemis II.

Active-Dosimeters , an investigation led by ESA (European Space Agency), tested a wearable system to measure radiation exposure to crew members on the space station and how it changes with the station’s orbit and altitude. Data from the wearable dosimeter improved radiation risk assessments and could lead to better protection for astronauts, including the ability to quickly respond to changes in exposure throughout future exploration missions.

On future exploration missions, robotic technology can help crew members with basic tasks, monitor and maintain equipment, and conduct operations such as sample collection, reducing the need to expose astronauts to harsh environments. Integrated System for Autonomous and Adaptive Caretaking demonstrates using autonomous robots to transfer and unpack cargo and to track and respond to maintenance issues such as leaks and fires, which could protect valuable equipment and reduce costly repairs on future missions. The investigation uses the space station’s Astrobee and Robonaut robots.

Multi-Resolution Scanning uses the station’s Astrobees to test sensors and robotics to support automated 3D sensing, mapping, and situational awareness functions. On future Gateway and lunar surface missions, such systems could automatically detect defects and conduct remote maintenance and autonomous operation of vehicles such as rovers.

Surface Avatar evaluates crew operation of multiple autonomous robots in space. The investigation also assesses crew member responsiveness to feedback on the consoles used to operate robots remotely, which supports design of effective setups for operating robots on the ground from a spacecraft orbiting above. Results contribute to the development of other uses of robotic assistance such as returning samples from Mars and asteroids.

Melissa Gaskill International Space Station Research Communications Team NASA’s Johnson Space Center

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