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"research of" vs "research on"

Last Updated: March 17, 2024

research of

This phrase is correct and commonly used in English when referring to the possession or ownership of the research.

• The book provides a detailed analysis of the research of the team.
• The research of the company has led to several breakthroughs.
• The professor discussed the research of his colleagues.
• The research of the department focuses on environmental issues.
• The research of the university covers a wide range of topics.

Alternatives:

• investigation of
• exploration of
• examination of
• analysis of

research on

This phrase is correct and commonly used in English when referring to the topic or subject of the research.

• She is conducting research on the effects of climate change.
• The research on renewable energy sources is extensive.
• Their research on the behavior of children is groundbreaking.
• The professor's research on artificial intelligence is well-known.
• The team is focused on research on cancer treatments.
• investigation on
• exploration on
• examination on
• analysis on

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Research On or In – Which Is Correct?

If you are conducting research, do you say that you are conducting research “on” a subject or “in” a subject? Prepositions often confuse people, so we’ll be answering that question here today.

Both “research on” and “research in” are correct in the right contexts. You can conduct research “in” a field of study, but you conduct research “on” a particular subject. This means both “on” and “in” are grammatically correct as long as you use them appropriately.

Don’t you just love the vague aspects of English? The truth is, “research” can be followed up by many prepositions. They can all be correct, as long as you use them in the right context. As far as “on” or “in” are concerned, the correct preposition depends on the scope of the research.

Let’s look at some examples:

• Diane conducts research in astronomy.
• Diane conducts research on black holes.

The distinction here is the difference between a field of study and a subject that is being studied. You can conduct research “in” a field of study, but you cannot conduct research “in” a subject. For example, consider this sentence:

• Diane conducts research in black holes.

Obviously, that doesn’t sound right at all. That would imply that Diane physically conducts research within black holes. That’s why she can conduct research “on” black holes”, not “in them”. However, when it comes to field of study, you can say “on” or “in”.

That’s because some things are a field of study, but also a standalone subject. For example, “astronomy” is a field of science that includes many things, such as planetary orbits, stars, and black holes. But you can also study the subject of astronomy as a whole, instead of something that falls under its umbrella.

The main point is to remember the following: you can conduct research “in” a field of study, but not “in” a subject. You can conduct research “on” a subject, or a field of study if it is a subject itself.

Research On

You can conduct research “on” a subject. This is appropriate because “on” specifies that you are “doing” research directly to the subject in question. This is why “research on” is the most common way of saying this. Most anything can be a subject, so it’s often appropriate to say “research on” a subject.

• Conducting research on heart failure has been very interesting for me.
• I don’t know why anyone would want to perform research on illnesses.
• Conducting research on various aspects of chemistry is my life’s work.
• Hey, how’s your research on the connection between smoking and lung cancer going?

In all of these examples, research is being done directly to a particular subject. That’s why you would say “research on” in these scenarios.

Research In

You can conduct research “in” a field of knowledge, but not “in” a subject. This can be a little confusing because fields of knowledge are also subjects. Not all subjects are fields of study, but all fields of study are subjects. Knowing the difference mostly comes down to how it sounds to say.

Consider these examples:

• Tana is conducting research in the field of sociology.
• For my project, I will be conducting research in the area of chemistry.
• Conducting research in mathematics sounds very tedious.
• I don’t have any interest regarding research in astrology.

In these examples, the thing being researched is a field of study. Thus, you can say that you are doing research “in” those fields.

Research About

You can do “research about” things. This phrase is more about what your research is seeking to accomplish, rather than what field or subject you are conducting research on. Consider the following examples:

• My research about the behavior of animals in captivity is going well.
• Conducting research about the link between humans and pets will be exciting.
• Research about how people behave without applied moral standards would be difficult.
• I want you to do research about how black holes really work.

In these sentences, “research about” is used to describe what specifically someone is researching. That said, most people would accept using it to say that you are researching “on” or “in” a field of study or a subject as well.

Research Into

“Research into” can be used mostly synonymously with all of the other options. You can conduct research “into” a field of study or “into” a subject. That means it can be used in all the same situations as those phrases. For example:

• Vlad is conducting research into the effects of drawing blood.
• Many scientists are conducting research into how dolphins communicate.
• I am conducting research into human nature.
• Research into the potential power of a Yellowstone volcano eruption is frightening.

You can conduct research in, on, into, or about a field of study. However you can only conduct research on, about, or into a subject. Basically, the only thing you need to remember is that you shouldn’t say “research in” a subject.

Martin holds a Master’s degree in Finance and International Business. He has six years of experience in professional communication with clients, executives, and colleagues. Furthermore, he has teaching experience from Aarhus University. Martin has been featured as an expert in communication and teaching on Forbes and Shopify. Read more about Martin here .

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Open Access

Research Matters

Research Matters articles provide a forum for scientists to communicate why basic research in their field matters.

See all article types »

Meta-research: Why research on research matters

* E-mail: [email protected]

Affiliations Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America, Department of Medicine, Department of Health Research and Policy, and Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California, United States of America, Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, California, United States of America

• John P. A. Ioannidis

Published: March 13, 2018

• https://doi.org/10.1371/journal.pbio.2005468
• Reader Comments

Meta-research is the study of research itself: its methods, reporting, reproducibility, evaluation, and incentives. Given that science is the key driver of human progress, improving the efficiency of scientific investigation and yielding more credible and more useful research results can translate to major benefits. The research enterprise grows very fast. Both new opportunities for knowledge and innovation and new threats to validity and scientific integrity emerge. Old biases abound, and new ones continuously appear as novel disciplines emerge with different standards and challenges. Meta-research uses an interdisciplinary approach to study, promote, and defend robust science. Major disruptions are likely to happen in the way we pursue scientific investigation, and it is important to ensure that these disruptions are evidence based.

Citation: Ioannidis JPA (2018) Meta-research: Why research on research matters. PLoS Biol 16(3): e2005468. https://doi.org/10.1371/journal.pbio.2005468

Copyright: © 2018 Ioannidis John P. A.. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: Laura and John Arnold Foundation. The Meta-Research Innovation Center at Stanford (METRICS) has been funded by the Laura and John Arnold Foundation. The work of John Ioannidis is funded by an unrestricted gift from Sue and Bob O’Donnell. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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

Abbreviations: NIH, National Institutes of Health; R&D, Research and Development; STEM, Science, Technology, Engineering, and Math

Provenance: Commissioned; not externally peer reviewed

Science, like all human endeavors, is prone to biases. Yet science can assess its own methods, reporting, reproducibility, evaluation, and incentives [ 1 ]. A relatively new discipline, called meta-research, covers a wide range of theoretical, observational, and experimental investigations designed to study research itself and its practices. The objective is to understand and improve how we perform, communicate, verify, evaluate, and reward research [ 1 ].

Before elaborating on a discipline that studies biases, I should disclose some of my own. First, all scientists are meta-researchers to some extent, though most usually work on focused subject matter disciplines. And though the advice of my early lab mentors—“focus, focus, focus”—still rings in my ears, the piles on my desk and the files in my computers can be notoriously unfocused. I don’t have attention-deficit disorder, but plain unconstrained curiosity. What attracted me to science was its vastness and diversity. In my early training years, I enjoyed roaming in libraries in Athens and Boston, discovering scientific journals with fancy names, encountering intriguing articles, drifting from my initial search. Without yet realizing it, I was interested primarily in research itself apparently, much as others were interested primarily in Caenorhabditis elegans , volcanic eruptions, or automata.

Science and its literature is a marvelous maze of data, arguments, biases, errors, and the greatest achievements of humans. What can be more rewarding to study scientifically? Thirty years later, I still feel like a researcher-in-training—actually, in early training—barely scratching the surface. However, much has changed. Thirty years ago, articles had to be handpicked like flowers one by one from their journal shelves and photocopied one page at a time. Now, one can text mine a million articles overnight. Good research, however, still takes time and focus. Take, for example, a recent project I worked on with my friend David Chavalarias. We text mined 12,821,790 abstracts and 843,884 full-text articles. We initially joked that it would take two days max. Eventually, it took four years of work with innumerable iterations, meticulous corrections, and repeated downloads.

My other personal bias is a heightened interest in methods rather than results. Result narratives are supposedly always exciting. I find them unbearably boring. Conversely, methods typically are missing in action, left unsung, or hidden in small print. Many researchers hope to clarify how to do experiments chatting in corridors or conferences. Study design and analysis are still mostly taught (if at all) in statistics-lite courses. Most of us have mastered how to write papers through reading other (mostly poorly reported) papers. We freely volunteer peer review but lack formal training on how to do it. In many fields, issues surrounding reproducibility were dormant until recently.

Science remains the key driver of human progress, yet we have little evidence on how to best fund science and incentivize high-quality work. We do know that leaving research practices to serendipity, biasing influences, methodological illiteracy, and statistical innumeracy is inefficient. Science needs science to avoid wasted effort and optimize resources. Amateur approaches face the current gigantic magnitudes of the research endeavor. Google Scholar currently includes about 180,000,000 documents, accruing approximately 4,000,000 new papers annually [ 2 ]. Along this universe of visible (published) matter, dark matter abounds; probably most observations and data analyses remain unpublished. Ulrich’s directory includes more than 40,000 refereed academic journals, and this is probably an underestimate [ 3 ]. Thousands of journals follow predatory practices or have uncertain value. The Science, Technology, Engineering, and Math (STEM) publishing business market size ($28 billion) roughly equals the National Institutes of Health (NIH) budget. Webometrics lists 26,368 research-producing universities [ 4 ], and many other entities generate research. Probably 100,000 biomedical conferences happen annually [ 5 ]. Global Research and Development (R&D) investment recently exceeded $2 trillion per year. Industry has the lion’s share, while public funding is limited for basic research and it is even more sparse for evidence-based evaluation research. Financial conflicts may shape research agendas, results, and interpretations [ 6 ]. Consider that the $1 trillion tobacco industry still runs “research” on its products despite killing millions of people who use them as directed. Big Pharma, another behemoth of similar financial magnitude, but which probably saves lives (albeit often at high cost), has to sponsor most research on its own products. Understanding who should do what and how in research needs better study.

Science is no longer the occupation of few intellectual dilettanti. Millions (co)author scientific papers. Even more people participate in research. Currently, health record databases engulf hundreds of millions of individuals. Social media databases generate the possibility of using data on billions—active monthly Facebook users, for example, exceeded 2 billion by July 2017.

Currently, generated research data are massive but also fragmented and often nontransparent. Full data sharing and preregistration of protocols are still uncommon in most fields [ 7 ]. We need to understand whether results and inferences are correct, modestly biased, or plain wrong. Comparing patterns of data and biases across the vast number of available studies, one can help answer this important question [ 8 ]. We have mapped 235 biases in biomedical research alone [ 9 ]. With increasing research complexity, multifarious choices emerge on how to design studies and analyze data. With 20 binary choices, 2 20 = 1,048,576 different ways exist to analyze the same data. Therefore, almost any result is possible, unless we safeguard methods and analysis standards. Surveys show that questionable research practices are used by most scientists: not fraud (which is rare) but “cutting corners” to achieve more interesting-looking results [ 10 ]. Understanding the boundaries between bias and creative exploration is important. Efforts to reproduce high-profile studies have shown high rates of nonreproducibility [ 11 ] and most scientists agree that a reproducibility crisis exists [ 12 ]. Meta-analyses—efforts to combine all data on a given question—become increasingly popular but face their own problems and biases [ 13 ].

How should a scientist best train, work, collaborate, and contribute to scientific and broader communities? Researchers spend most of their time on grants [ 14 ] and administrative chores of unclear utility. Journal peer review takes another 64 million hours annually for biomedical papers alone [ 15 ]. Justifiably, we all despise bureaucracy and obstructions. Poor research practices make things worse.

Thousands of new scientific fields emerge, merge, split, and evolve [ 16 ]. Different disciplines may differ in research standards and challenges ( Box 1 ). Meta-research can help us disseminate efficient research practices and abandon wasteful ones. Publication and peer review models, scientific education, funding, and academic reward systems need to adapt successfully to a rapidly changing world. Some predict [ 17 ] that even researchers may disappear within decades, replaced by artificial intelligence. While this sounds extreme, several aspects of current “business as usual” in research will face disruption. Even 1% improvement in the yield and translation of useful discoveries effected through better research practices reflects value equivalent of many Nobel or Breakthrough prizes.

Box 1. Features of research practices, opportunities, and threats that vary across fields.

• ◦ Type of mix of research (basic, applied translational, evaluation, implementation)
• ◦ Types of study designs commonly used or misused
• ◦ Types of experimental/measurement tools commonly used or misused
• ◦ Types of statistical methods commonly used or misused
• ◦ Types of common biases encountered and whether they are easy to fix or not
• ◦ Extent of use of methods to prevent or correct for biases
• ◦ Prevalence of different types of questionable/detrimental research practices
• ◦ Distribution of effect sizes observed
• ◦ Typical heterogeneity of results across studies
• ◦ Proportion of results that are true, exaggerated, or entirely false
• ◦ Reputational impact for bias or wrong, refuted results
• ◦ Proportion of studies and analyses that are published
• ◦ Number and types of available publication venues
• ◦ Implementation of prepublication peer review (e.g., preprints)
• ◦ Implementation of postpublication peer review
• ◦ Extent from adoption of various research reporting standards
• ◦ Commonly accepted authorship and contributorship norms
• ◦ Extent of adoption of team science and consortia
• ◦ Type of training for scientists in the field
• ◦ Extent of methodological and statistical literacy/numeracy
• ◦ Extent and enforcement of preregistration of protocols
• ◦ Extent of use of replication studies
• ◦ Extent of use of exact replication versus corroboration or triangulation
• ◦ Extent of sharing of primary raw data and/or processed data
• ◦ Extent of sharing of software and code
• ◦ Extent and types of evidence synthesis used
• ◦ Main funders (government, industry, other) and types of studies that they fund
• ◦ Project-based versus person-based funding
• ◦ Mix and interplay of institutions performing research (university, industry, other)
• ◦ Types of metrics and criteria used for assessing researchers and institutions
• ◦ Typical conflicts of interest operating in the field
• ◦ Completeness of disclosure of conflicts of interest
• ◦ Extent and fidelity of dissemination of research findings to the general public
• ◦ Extent of public misperceptions about the field
• ◦ Threats from antiscience advocates attacking the field

Meta-research is interdisciplinary. For example, it benefits from better tools and methods in statistics and informatics. Complex issues of behavior change converge on modeling, psychology, sociology, and behavioral economics. Newly introduced, sophisticated measurement tools and techniques in various disciplines introduce new, peculiar errors and biases; their understanding requires combining expertise in biology, bioengineering, and data sciences. Properly communicating science and its value requires combining expertise in multiple fields and has become increasingly critical nowadays, when mistrust of science runs high and multiple interests hold a stake in influencing research results. Some interests set out to manipulate science and cause damage when their intentional bias pollutes the scientific record (e.g., tobacco companies or climate change deniers). Meta-research may be our best chance to defend science, gain public support for research, and counter antiscience movements. It may help provide a correcting mechanism closer to real time than the self-correcting scientific process that otherwise may take much longer.

Moreover, bird’s-eye metaviews of science are not separate and detached from focused field-specific research. In my experience, inspiration for new projects has often come from mistakes, shortcomings, or difficulties that I encountered while doing field-specific research. It is sometimes difficult to convey a message that something is wrong. However, it is paradoxically easier when the message says that thousands or millions of papers are doing something wrong rather than arousing personal animosity for a single failed paper. It is also easier when the constructive critique comes from within a field, recognized as necessary improvement rather than intrusion. Learning by collaborating with researchers in diverse disciplines and trying to understand the daily challenges in a specific field can be a highly rewarding experience for a meta-researcher. We need scientific curiosity but also intellectual humility and commitment to improve our efforts.

• View Article
• PubMed/NCBI
• Google Scholar
• 4. Webometrics. List of universities (as of January 2017). [Cited 21 January 2018]. Available from: http://www.webometrics.info/en/node/54 .

Research Basics

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

A good working definition of research might be:

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

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

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

So What Do We Mean By “Formal Research?”

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

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

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

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

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

What Is Research, and Why Do People Do It?

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

15k Accesses

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

Abstractspiepr Abs1

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

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

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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research on or about

• Thread starter tfmoraes
• Start date Nov 8, 2014
• Nov 8, 2014

Hello, Is there any difference between on or about in the sentence: It is important to state that the current amount of researches available to academic and general public on thermal effects on electrical switches is not as wide as on the universe of applications of those switches. And It is important to state that the current amount of researches available to academic and general public about thermal effects on electrical switches is not as wide as about the universe of applications of those switches. Thank you!  

Florentia52

Modwoman in the attic.

It's a little confusing, because you've emphasized the preposition after "effects" in the first sentence and the one after "public" in the second. That said, I think the second sentence is better. "Research" should be singular, in either case.  

Great, got it! Thank you again, Florentia52!  

Senior Member

We research things or we research into things . Context decides which is better. When research already exists (in the form of written papers) then that research is about or concerning the subject. My version It is important to state that the current amount of research available to academics and the general public into thermal effects on electrical switches is not as wide as that concerning the applications of those switches. It is still long-winded but now it makes sense!  

Understood that! Thank you, Biffo!  

• Oct 16, 2019

Biffo said: We research things or we research into things . Context decides which is better. Click to expand...

entangledbank

I think plain 'research' is much more common, with a direct object. Nouns can't take objects, so you need a preposition with the noun, so you do research into (or on) things.  

• Aug 22, 2020

entangledbank said: I think plain 'research' is much more common, with a direct object. Nouns can't take objects, so you need a preposition with the noun, so you do research into (or on) things. Click to expand...

nh01 said: But if I don't use do research, can I use "about" with "research"? When he researches about this topic, he will have to sit at the computer too much. Click to expand...

• Sep 30, 2020

entangledbank said: so you need a preposition with the noun, so you do research into (or on) things. Click to expand...

nh01 said: So, is the sentence below correct? And can we use "in" also instead of "on"? Thanks. I do a lot of research on this area/field/ discipline. Click to expand...

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• Knowledge Base

Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base. This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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• Cambridge Dictionary +Plus

Meaning of research in English

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• He has dedicated his life to scientific research.
• He emphasized that all the people taking part in the research were volunteers .
• The state of Michigan has endowed three institutes to do research for industry .
• I'd like to see the research that these recommendations are founded on.
• It took months of painstaking research to write the book .
• absorptive capacity
• dream something up
• modularization
• nanotechnology
• non-imitative
• operational research
• think outside the box idiom
• think something up
• uninventive
• study What do you plan on studying at university?
• major US She majored in philosophy at Harvard.
• cram She's cramming for her history exam.
• revise UK I'm revising for tomorrow's test.
• review US We're going to review for the test tomorrow night.
• research Scientists are researching possible new treatments for cancer.
• The amount of time and money being spent on researching this disease is pitiful .
• We are researching the reproduction of elephants .
• She researched a wide variety of jobs before deciding on law .
• He researches heart disease .
• The internet has reduced the amount of time it takes to research these subjects .
• adjudication
• interpretable
• interpretive
• interpretively
• investigate
• reinvestigate
• reinvestigation
• risk assessment
• run over/through something
• run through something

You can also find related words, phrases, and synonyms in the topics:

Related word

Research | american dictionary, research | business english, examples of research, collocations with research.

These are words often used in combination with research .

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Translations of research

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a type of singing in which four, usually male, voices in close combination perform popular romantic songs, especially from the 1920s and 1930s

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Definition of research noun from the Oxford Advanced Learner's Dictionary

• scientific/medical/academic research
• They are raising money for cancer research.
• to do/conduct/undertake research
• I've done some research to find out the cheapest way of travelling there.
• research into something He has carried out extensive research into renewable energy sources.
• research on something/somebody Recent research on deaf children has produced some interesting findings about their speech.
• Research on animals has led to some important medical advances.
• according to research According to recent research, more people are going to the movies than ever before.
• Their latest research project will be funded by the government.
• Are you hoping to get a research grant ?
• a research fellow/assistant/scientist
• a research institute/centre/laboratory
• The research findings were published in the Journal of Environmental Quality.
• formulate/​advance a theory/​hypothesis
• build/​construct/​create/​develop a simple/​theoretical/​mathematical model
• develop/​establish/​provide/​use a theoretical/​conceptual framework
• advance/​argue/​develop the thesis that…
• explore an idea/​a concept/​a hypothesis
• make a prediction/​an inference
• base a prediction/​your calculations on something
• investigate/​evaluate/​accept/​challenge/​reject a theory/​hypothesis/​model
• design an experiment/​a questionnaire/​a study/​a test
• do research/​an experiment/​an analysis
• make observations/​measurements/​calculations
• carry out/​conduct/​perform an experiment/​a test/​a longitudinal study/​observations/​clinical trials
• run an experiment/​a simulation/​clinical trials
• repeat an experiment/​a test/​an analysis
• replicate a study/​the results/​the findings
• observe/​study/​examine/​investigate/​assess a pattern/​a process/​a behaviour
• fund/​support the research/​project/​study
• seek/​provide/​get/​secure funding for research
• collect/​gather/​extract data/​information
• yield data/​evidence/​similar findings/​the same results
• analyse/​examine the data/​soil samples/​a specimen
• consider/​compare/​interpret the results/​findings
• fit the data/​model
• confirm/​support/​verify a prediction/​a hypothesis/​the results/​the findings
• prove a conjecture/​hypothesis/​theorem
• draw/​make/​reach the same conclusions
• read/​review the records/​literature
• describe/​report an experiment/​a study
• present/​publish/​summarize the results/​findings
• present/​publish/​read/​review/​cite a paper in a scientific journal
• a debate about the ethics of embryonic stem cell research
• For his PhD he conducted field research in Indonesia.
• Further research is needed.
• Future research will hopefully give us a better understanding of how garlic works in the human body.
• Dr Babcock has conducted extensive research in the area of agricultural production.
• the funding of basic research in biology, chemistry and genetics
• Activists called for a ban on animal research.
• Work is under way to carry out more research on the gene.
• She returned to Jamaica to pursue her research on the African diaspora.
• Bad punctuation can slow down people's reading speeds, according to new research carried out at Bradford University.
• He focused his research on the economics of the interwar era.
• Most research in the field has concentrated on the effects on children.
• One paper based on research conducted at Oxford suggested that the drug may cause brain damage.
• Research demonstrates that women are more likely than men to provide social support to others.
• She's doing research on Czech music between the wars.
• The research does not support these conclusions.
• They are carrying out research into the natural flow patterns of water.
• They lack the resources to do their own research.
• What has their research shown?
• Funding for medical research has been cut quite dramatically.
• a startling piece of historical research
• pioneering research into skin disease
• They were the first to undertake pioneering research into the human genome.
• There is a significant amount of research into the effects of stress on junior doctors.
• He's done a lot of research into the background of this story.
• research which identifies the causes of depression
• spending on military research and development
• the research done in the 1950s that linked smoking with cancer
• The children are taking part in a research project to investigate technology-enabled learning.
• The Lancet published a research paper by the scientist at the centre of the controversy.
• Who is directing the group's research effort?
• She is chief of the clinical research program at McLean Hospital.
• James is a 24-year-old research student from Iowa.
• You will need to describe your research methods.
• Before a job interview, do your research and find out as much as you can about the company.
• Most academic research is carried out in universities.
• This is a piece of research that should be taken very seriously.
• This is an important area of research.
• There's a large body of research linking hypertension directly to impaired brain function.
• In the course of my researches, I came across some of my grandfather's old letters.
• demonstrate something
• find something
• identify something
• programme/​program
• research in
• research into
• research on
• an area of research
• focus your research on something
• somebody’s own research

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Through articles, reports, working papers, experiments and practical tools, RoRI aims to supply the evidence and data that people and organisations need to change research for the better. All RoRI publications and resources are openly available to everyone.

Woods HB and Pinfield S. Incentivising research data sharing: a scoping review. Wellcome Open Research 2022, 6:355

Rzayeva n, henriques so, pinfield s, waltman l. 2023. the experiences of covid-19 preprint authors: a survey of researchers about publishing and receiving feedback on their work during the pandemic. peer j 11:e15864, waltman, l., kaltenbrunner, w., pinfield, s., & woods, h. b. (2023). how to improve scientific peer review: four schools of thought. learned publishing., stafford t, rombach i, hind d et al. where next for partial randomisation of research funding the feasibility of rcts and alternatives. wellcome open research 2023, 8:309, kaltenbrunner, w., pinfield, s., waltman, l., woods hb and brumberg, j. (2022). innovating peer review, reconfiguring scholarly communication: an analytical overview of ongoing peer review innovation activities. journal of documentation. vol. 78 no. 7, pp.429-449, woods hb, brumberg j, kaltenbrunner, w et al. an overview of innovations in the external peer review of journal manuscripts. wellcome open research 2023, 7: 82, hook, d. w. & wilsdon, j. r. (2023). the pandemic veneer: covid-19 research as a mobilisation of collective intelligence by the global research community. collective intelligence, 2(1)., research on research institute: independent review of pilot phase (2019-2021), scholarly communication in times of crisis: the response of the scholarly communication system to the covid-19 pandemic.

This report analyses how the scholarly communication system – involving the production, evaluation, and dissemination of research outputs – has responded to the COVID-19 pandemic crisis.

The experimental research funder’s handbook The experimental research funder’s handbook (2nd edition)

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Harnessing the Metric Tide: indicators, infrastructures & priorities for UK responsible research assessment

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

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

Examples of research in a Sentence

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

Word History

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

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• market research
• operations research
• translational research
• marketing research

research and development

• oppo research
• research park

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 11 Apr. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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The Research-Backed Benefits of Daily Rituals

• Michael I. Norton

A survey of more than 130 HBR readers asked how they use rituals to start their days, psych themselves up for stressful challenges, and transition when the workday is done.

While some may cringe at forced corporate rituals, research shows that personal and team rituals can actually benefit the way we work. The authors’ expertise on the topic over the past decade, plus a survey of nearly 140 HBR readers, explores the ways rituals can set us up for success before work, get us psyched up for important presentations, foster a strong team culture, and help us wind down at the end of the day.

“Give me a W ! Give me an A ! Give me an L ! Give me a squiggly! Give me an M ! Give me an A ! Give me an R ! Give me a T !”

• Michael I. Norton is the Harold M. Brierley Professor of Business Administration at the Harvard Business School. He is the author of The Ritual Effect and co-author of Happy Money: The Science of Happier Spending . His research focuses on happiness, well-being, rituals, and inequality. See his faculty page here .

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Loneliness Matters: A Theoretical and Empirical Review of Consequences and Mechanisms

Louise c. hawkley.

Center for Cognitive and Social Neuroscience, University of Chicago, Chicago, IL, USA. Department of Psychology, University of Chicago, 940 E. 57th St, Chicago, IL 60637, USA

John T. Cacioppo

Center for Cognitive and Social Neuroscience, University of Chicago, Chicago, IL, USA

As a social species, humans rely on a safe, secure social surround to survive and thrive. Perceptions of social isolation, or loneliness, increase vigilance for threat and heighten feelings of vulnerability while also raising the desire to reconnect. Implicit hypervigilance for social threat alters psychological processes that influence physiological functioning, diminish sleep quality, and increase morbidity and mortality. The purpose of this paper is to review the features and consequences of loneliness within a comprehensive theoretical framework that informs interventions to reduce loneliness. We review physical and mental health consequences of loneliness, mechanisms for its effects, and effectiveness of extant interventions. Features of a loneliness regulatory loop are employed to explain cognitive, behavioral, and physiological consequences of loneliness and to discuss interventions to reduce loneliness. Loneliness is not simply being alone. Interventions to reduce loneliness and its health consequences may need to take into account its attentional, confirmatory, and memorial biases as well as its social and behavioral effects.

Introduction

Loneliness is a common experience; as many as 80% of those under 18 years of age and 40% of adults over 65 years of age report being lonely at least sometimes [ 1 – 3 ], with levels of loneliness gradually diminishing through the middle adult years, and then increasing in old age (i.e., ≥70 years) [ 2 ]. Loneliness is synonymous with perceived social isolation, not with objective social isolation. People can live relatively solitary lives and not feel lonely, and conversely, they can live an ostensibly rich social life and feel lonely nevertheless. Loneliness is defined as a distressing feeling that accompanies the perception that one’s social needs are not being met by the quantity or especially the quality of one’s social relationships [ 2 , 4 – 6 ]. Loneliness is typically measured by asking individuals to respond to items such as those on the frequently used UCLA Loneliness Scale [ 7 ]: “I feel isolated,” “There are people I can talk to,” and “I feel part of a group of friends.” The result is a continuum of scores that range from highly socially connected to highly lonely.

Each of us is capable of feeling lonely, and loneliness is an equal opportunity tenant for good reason. We have posited that loneliness is the social equivalent of physical pain, hunger, and thirst; the pain of social disconnection and the hunger and thirst for social connection motivate the maintenance and formation of social connections necessary for the survival of our genes [ 8 , 9 ]. Feelings of loneliness generally succeed in motivating connection or reconnection with others following geographic relocation or bereavement, for instance, thereby diminishing or abolishing feelings of social isolation. For as many as 15–30% of the general population, however, loneliness is a chronic state [ 10 , 11 ]. Left untended, loneliness has serious consequences for cognition, emotion, behavior, and health. Here, we review physical and mental health consequences of perceived social isolation and then introduce mechanisms for these outcomes in the context of a model that takes into consideration the cognitive, emotional, and behavioral characteristics of loneliness.

Loneliness Matters for Physical Health and Mortality

A growing body of longitudinal research indicates that loneliness predicts increased morbidity and mortality [ 12 – 19 ]. The effects of loneliness seem to accrue over time to accelerate physiological aging [ 20 ]. For instance, loneliness has been shown to exhibit a dose–response relationship with cardiovascular health risk in young adulthood [ 12 ]. The greater the number of measurement occasions at which participants were lonely (i.e., childhood, adolescence, and at 26 years of age), the greater their number of cardiovascular health risks (i.e., BMI, systolic blood pressure (SBP), total, and HDL cholesterol levels, glycated hemoglobin concentration, maximum oxygen consumption). Similarly, loneliness was associated with increased systolic blood pressure in a population-based sample of middle-aged adults [ 21 ], and a follow-up study of these same individuals showed that a persistent trait-like aspect of loneliness accelerated the rate of blood pressure increase over a 4-year follow-up period [ 22 ]. Loneliness accrual effects are also evident in a study of mortality in the Health and Retirement Study; all-cause mortality over a 4-year follow-up was predicted by loneliness, and the effect was greater in chronically than situationally lonely adults [ 17 ]. Penninx et al. [ 15 ] showed that loneliness predicted all-cause mortality during a 29-month follow-up after controlling for age, sex, chronic diseases, alcohol use, smoking, self-rated health, and functional limitations. Sugisawa et al. [ 18 ] also found a significant effect of loneliness on mortality over a 3-year period, and this effect was explained by chronic diseases, functional status, and self-rated health. Among women in the National Health and Nutrition Survey, chronic high frequency loneliness (>3 days/week at each of two measurement occasions about 8 years apart) was prospectively associated with incident coronary heart disease (CHD) over a 19-year follow-up in analyses that adjusted for age, race, socioeconomic status, marital status, and cardiovascular risk factors [ 19 ]. Depressive symptoms have been associated with loneliness and with adverse health outcomes, but loneliness continued to predict CHD in these women after also controlling for depressive symptoms. Finally, loneliness has also been shown to increase risk for cardiovascular mortality; individuals who reported often being lonely exhibited significantly greater risk than those who reported never being lonely [ 14 ]. In sum, feelings of loneliness mark increased risk for morbidity and mortality, a phenomenon that arguably reflects the social essence of our species.

Loneliness Matters for Mental Health and Cognitive Functioning

The impact of loneliness on cognition was assessed in a recent review of the literature [ 9 ]. Perhaps, the most striking finding in this literature is the breadth of emotional and cognitive processes and outcomes that seem susceptible to the influence of loneliness. Loneliness has been associated with personality disorders and psychoses [ 23 – 25 ], suicide [ 26 ], impaired cognitive performance and cognitive decline over time [ 27 – 29 ], increased risk of Alzheimer’s Disease [ 29 ], diminished executive control [ 30 , 31 ], and increases in depressive symptoms [ 32 – 35 ]. The causal nature of the association between loneliness and depressive symptoms appears to be reciprocal [ 32 ], but more recent analyses of five consecutive annual assessments of loneliness and depressive symptoms have shown that loneliness predicts increases in depressive symptoms over 1-year intervals, but depressive symptoms do not predict increases in loneliness over those same intervals [ 36 ]. In addition, experimental evidence, in which feelings of loneliness (and social connectedness) were hypnotically induced, indicates that loneliness not only increases depressive symptoms but also increases perceived stress, fear of negative evaluation, anxiety, and anger, and diminishes optimism and self-esteem [ 8 ]. These data suggest that a perceived sense of social connectedness serves as a scaffold for the self—damage the scaffold and the rest of the self begins to crumble.

A particularly devastating consequence of feeling socially isolated is cognitive decline and dementia. Feelings of loneliness at age 79 predicted “lifetime cognitive change” as indicated by lower IQ at age 79 adjusting for IQ at age 11, living arrangements at age 11 and at age 79, sex, marital status, and ideal level of social support [ 27 ]. This finding does not rule out a reverse causal direction; cognitive impairments may hamper social interactions, prompt social withdrawal, and thus lead to loneliness. Other studies, however, have indicated that loneliness is a precursor of cognitive decline. For instance, the cognitive functioning of 75–85-year-olds (as assessed by the Mini-Mental State Examination) did not differ as a function of loneliness at baseline but diminished to a greater extent among those high than low in loneliness over a 10-year follow-up [ 28 ]. In a prospective study by Wilson et al. [ 29 ], loneliness was inversely associated with performance on a battery of cognitive measures in a sample of 823 initially dementia-free older adults. Moreover, loneliness at baseline was associated with a faster decline in cognitive performance on most of these measures over a 4-year follow-up. This was not true of the converse: cognitive status at baseline did not predict changes in loneliness. In addition, incidence of Alzheimer’s disease (76 individuals) was predicted by degree of baseline loneliness after adjusting for age, sex, and education; those in the top decile of loneliness scores were 2.1 times as likely to develop Alzheimer’s disease than those in the bottom decile of loneliness scores. Depressive symptoms had a modest effect on Alzheimer’s disease risk, but loneliness continued to exert a significant and much larger influence on Alzheimer’s disease than depressive symptoms when depressive symptoms were included in the model [ 29 ]. Overall, it appears that something about our sense of connectedness with others penetrates the physical organism and compromises the integrity of physical and mental health and well-being. What that “something” might be is the topic to which we next turn.

How Loneliness Matters: Mechanisms

The loneliness model.

Our model of loneliness [ 8 , 9 ] posits that perceived social isolation is tantamount to feeling unsafe, and this sets off implicit hypervigilance for (additional) social threat in the environment. Unconscious surveillance for social threat produces cognitive biases: relative to nonlonely people, lonely individuals see the social world as a more threatening place, expect more negative social interactions, and remember more negative social information. Negative social expectations tend to elicit behaviors from others that confirm the lonely persons’ expectations, thereby setting in motion a self-fulfilling prophecy in which lonely people actively distance themselves from would-be social partners even as they believe that the cause of the social distance is attributable to others and is beyond their own control [ 37 ]. This self-reinforcing loneliness loop is accompanied by feelings of hostility, stress, pessimism, anxiety, and low self-esteem [ 8 ] and represents a dispositional tendency that activates neurobiological and behavioral mechanisms that contribute to adverse health outcomes.

Health behaviors

One of the consequences of loneliness and implicit vigilance for social threat is a diminished capacity for self-regulation. The ability to regulate one’s thoughts, feelings, and behavior is critical to accomplish personal goals or to comply with social norms. Feeling socially isolated impairs the capacity to self-regulate, and these effects are so automatic as to seem outside of awareness. In a dichotic listening task, for instance, right-handed individuals quickly and automatically attend preferentially to the pre-potent right ear. Latency to respond to stimuli presented to the non-dominant ear can be enhanced, however, by instructing participants to attend to their left ear. Among young adults who were administered this task, the lonely and nonlonely groups did not differ in performance when directed to attend to their pre-potent right ear, but the lonely group performed significantly worse than the nonlonely group when directed to shift attention to their non-prepotent left ear [ 30 ]. In other words, automatic attentional processes may be unimpaired, but effortful attentional processes are compromised in lonely relative to socially connected individuals.

Of relevance for health is the capacity for self-regulation in the arena of lifestyle behaviors. Regulation of emotion can enhance the ability to regulate other self-control behaviors [ 38 ], as is evident from research showing that positive affect predicts increased physical activity [ 39 ]. In middle-aged and older adults, greater loneliness was associated with less effort applied to the maintenance and optimization of positive emotions [ 31 ]. Compromised regulation of emotion in lonely individuals explained their diminished likelihood of performing any physical activity, and loneliness also predicted a decrease in physical activity over time [ 31 ]. Physical activity is a well-known protective factor for physical health, mental health, and cognitive functioning [ 40 ], suggesting that poorer self-regulation may contribute to the greater health risk associated with loneliness via diminished likelihood of engaging in health-promoting behaviors. A related literature shows that loneliness is also a risk factor for obesity [ 41 ] and health-compromising behavior, including a greater propensity to abuse alcohol [ 42 ]. To the extent that self-regulation accounts for poorer health behaviors in lonely people, better health behaviors may be more easily accomplished in the actual or perceived company of others. Interestingly, animal research has shown that social isolation dampens the beneficial effects of exercise on neurogenesis [ 43 ], implying that health behaviors may better serve their purpose or have greater effect among those who feel socially connected than those who feel lonely. This hypothesis remains to be tested, but research on the restorative effects of sleep is consistent with this notion.

Countering the physiological effects of the challenge of daily emotional, cognitive, and behavioral experiences, sleep offers physiological restoration. Experimental sleep deprivation has adverse effects on cardiovascular functioning, inflammatory status, and metabolic risk factors [ 44 ]. In addition, short sleep duration has been associated with risk for hypertension [ 45 ], incident coronary artery calcification [ 46 ], and mortality [ 47 ].

What is less appreciated is that sleep quality may also be important in accomplishing sleep’s restorative effects. Nonrestorative sleep (i.e., sleep that is non-refreshing despite normal sleep duration) results in daytime impairments such as physical and intellectual fatigue, role impairments, and cognitive and memory problems [ 48 ]. We have noted that loneliness heightens feelings of vulnerability and unconscious vigilance for social threat, implicit cognitions that are antithetical to relaxation and sound sleep. Indeed, loneliness and poor quality social relationships have been associated with self-reported poor sleep quality and daytime dysfunction (i.e., low energy, fatigue), but not with sleep duration [ 49 – 52 ]. In young adults, greater daytime dysfunction, a marker of poor sleep quality, was accompanied by more nightly micro-awakenings, an objective index of sleep continuity obtained from Sleep-Caps worn by participants during one night in the hospital and seven nights in their own beds at home [ 53 ]. The conjunction of daytime dysfunction and micro-awakenings is consistent with polysomnography studies showing a conjunction, essentially an equivalence, between subjective sleep quality and sleep continuity [ 54 ], and substantiates the hypothesis that loneliness impairs sleep quality.

In an extension of these findings, loneliness was associated with greater daytime dysfunction in a 3-day diary study of middle-age adults, an association that was independent of age, gender, race/ethnicity, household income, health behaviors, BMI, chronic health conditions, daily illness symptom severity, and related feelings of stress, hostility, poor social support, and depressive symptoms. Cross-lagged panel analyses of the three consecutive days indicated potentially reciprocal causal roles for loneliness and daytime dysfunction: lonely feelings predicted daytime dysfunction the following day, and daytime dysfunction exerted a small but significant effect on lonely feelings the following day [ 55 ], effects that were independent of sleep duration. In other words, the same amount of sleep is less salubrious in individuals who feel more socially isolated and, ironically, less salubrious sleep feeds forward to further exacerbate feelings of social isolation. This recursive loop operates outside of consciousness and speaks to the relative impenetrability of loneliness to intervention.

Physiological functioning

The association between loneliness and cardiovascular disease and mortality [ 13 , 14 , 19 ] may have its roots in physiological changes that begin early in life. As noted earlier, chronic social isolation, rejection, and/or feelings of loneliness in early childhood, adolescence, and young adulthood cumulated in a dose–response fashion to predict cardiovascular health risk factors in young adulthood (26 years old), including elevated blood pressure [ 12 ]. In our study of young adults, loneliness was associated with elevated levels of total peripheral resistance (TPR [ 49 , 56 ]). TPR is the primary determinant of SBP until at least 50 years of age [ 57 ], which suggests that loneliness-related elevations in TPR in early to middle-adulthood may lead to higher blood pressure in middle and older age. Consistent with this hypothesis, loneliness was associated with elevated SBP in an elderly convenience sample [ 49 ], and in a population-based sample of 50–68-year-old adults in the Chicago Health, Aging, and Social Relations Study [ 21 ]. The association between loneliness and elevated SBP was exaggerated in older relative to younger lonely adults in this sample [ 21 ], suggesting an accelerated physiological decline in lonely relative to nonlonely individuals. Our recent study of loneliness and SBP in these same individuals over five annual assessments supported this hypothesis. Short-term (i.e., 1 year) fluctuations in loneliness were not significant predictors of SBP changes over 1-year intervals, but a trait-like component of loneliness present at study onset contributed to greater increases in SBP over 2-, 3-, and 4-year intervals [ 22 ]. These increases were cumulative such that higher initial levels of loneliness were associated with greater increases in SBP over a 4-year period. The prospective effect of loneliness on SBP was independent of age, gender, race/ethnicity, cardiovascular risk factors, medications, health conditions, and the effects of depressive symptoms, social support, perceived stress, and hostility [ 22 ]. Elevated SBP is a well-known risk factor for chronic cardiovascular disease, and these data suggest that the effects of loneliness accrue to accelerate movement along a trajectory toward serious health consequences [ 20 ].

The physiological determinants responsible for the cumulative effect of loneliness on blood pressure have yet to be elucidated. TPR plays a critical role in determining SBP in early to mid-adulthood, but other mechanisms come into play with increasing age. Candidate mechanisms include age-related changes in vascular physiology, including increased arterial stiffness [ 58 ], diminished endothelial cell release of nitric oxide, enhanced vascular responsivity to endothelial constriction factors, increases in circulating catecholamines, and attenuated vasodilator responses to circulating epinephrine due to decreased beta-adrenergic sensitivity in vascular smooth muscle [ 59 – 61 ]. In turn, many of these mechanisms are influenced by lifestyle factors such as diet, physical inactivity, and obesity—factors that alter blood lipids and inflammatory processes that have known consequences for vascular health and functioning [ 62 , 63 ].

Neuroendocrine Effects

Changes in TPR levels are themselves influenced by a variety of physiological processes, including activity of the autonomic nervous system and the hypothalamic-pituitary adrenocortical (HPA) axis. The sympathetic branch of the autonomic nervous system plays a major role in maintaining basal vascular tone and TPR [ 64 , 65 ] and elevated sympathetic tone is responsible for the development and maintenance of many forms of hypertension [ 66 ]. To date, loneliness has not been shown to correlate with SNS activity at the myocardium (i.e., pre-ejection period [ 21 , 56 ]) but was associated with a greater concentration of epinephrine in overnight urine samples in a middle-aged and older adult sample [ 21 ]. At high concentrations, circulating epinephrine binds α-1 receptors on vascular smooth muscle cells to elicit vasoconstriction and could thereby serve as a mechanism for increased SBP in lonely individuals.

Activation of the HPA axis involves a cascade of signals that results in release of ACTH from the pituitary and cortisol from the adrenal cortex. Vascular integrity and functioning are beholden, in part, to well-regulated activity of the HPA axis. Dysregulation of the HPA axis contributes to inflammatory processes that play a role in hypertension, atherosclerosis, and coronary heart disease [ 67 – 69 ]. Loneliness has been associated with urinary excretion of significantly higher concentrations of cortisol [ 70 ], and, in more recent studies, with higher levels of salivary or plasma cortisol [ 71 , 72 ]. Pressman et al. [ 72 ] found that loneliness was associated with higher early morning and late night levels of circulating cortisol in young adult university students, and Steptoe et al. [ 71 ] found that chronically high levels of trait loneliness in middle-aged adults (M=52.4 years) predicted greater increases in salivary cortisol during the first 30 min after awakening (i.e., cortisol awakening response) such that the cortisol awakening response in individuals in the highest loneliness tertile was 21% greater than that in the lowest tertile. In our study of middle-aged and older adults, day-today fluctuations in feelings of loneliness were associated with individual differences in the cortisol awakening response. For this study, diary reports of daily psychosocial, emotional, and physical states were completed at bedtime on each of three consecutive days, and salivary cortisol levels were measured at wakeup, 30 min after awakening, and at bedtime each day. Parallel multilevel causal models revealed that prior-day feelings of loneliness and related feelings of sadness, threat, and lack of control were associated with a higher cortisol awakening response the next day, but morning cortisol awakening response did not predict experiences of these psychosocial states later the same day [ 73 ]. Social evaluative threat is known to be a potent elicitor of cortisol [ 74 ], and our theory that loneliness is characterized by chronic threat of and hypervigilance for negative social evaluation [ 9 ] is consistent with the finding that loneliness predicts increased cortisol awakening response. The relevance of the association between loneliness and HPA regulation is particularly noteworthy given recent evidence that loneliness-related alterations in HPA activity may occur at the level of the gene, a topic to which we turn next.

Gene Effects

Cortisol regulates a wide variety of physiological processes via nuclear hormone receptor-mediated control of gene transcription. Cortisol activation of the glucocorticoid receptor, for instance, exerts broad anti-inflammatory effects by inhibiting pro-inflammatory signaling pathways. Given that loneliness is associated with elevated cortisol levels, loneliness might be expected to reduce risk for inflammatory diseases. However, as we have noted above, feelings of loneliness and social isolation are associated with increased risk for inflammatory disease. This finding may be attributable to impaired glucocorticoid receptor-mediated signal transduction; failure of the cellular genome to “hear” the anti-inflammatory signal sent by circulating glucocorticoids permits inflammatory processes to continue relatively unchecked. We found evidence consistent with glucocorticoid insensitivity in our examination of gene expression rates in chronically lonely versus socially connected older adults [ 75 ]. Genome-wide microarray analyses revealed that 209 transcripts, representing 144 distinct genes, were differentially expressed in these two groups. Markers of immune activation and inflammation (e.g., pro-inflammatory cytokines and inflammatory mediators) were over-expressed in genes of the lonely relative to the socially connected group (37% of the 209 differentially expressed transcripts). Markers of cell cycle inhibitors and an inhibitor of the potent pro-inflammatory NF–κB transcript were under-expressed in genes of the lonely relative to the socially connected group (63% of the differentially expressed transcripts). The net functional implication of the differential gene transcription favored increased cell cycling and inflammation in the lonely group [ 75 ].

Subsequent bioinformatic analyses indicated that loneliness-associated differences in gene expression could be attributable to increased activity of the NF–κB transcription factor. NF–κB is known to up-regulate inflammation-related genes, and its activity is antagonized by the glucocorticoid receptor. Bioinformatic analyses also indicated a possible decrease in glucocorticoid receptor-mediated transcription in the lonely group, despite the fact that there were no group differences in circulating glucocorticoid levels. These results are consistent with the hypothesis that adverse social conditions result in functional desensitization of the glucocorticoid receptor, which permits increased NF–κB activity and thereby induces a pro-inflammatory bias in gene expression. Group differences in NF–κB/glucocorticoid receptor-mediated transcription activity were not attributable to objective indices of social isolation, nor were they explained by demographic, psychosocial (i.e., perceived stress, depression, hostility), or medical risk factors [ 75 ]. These results suggest that feelings of loneliness may exert a unique transcriptional influence that has potential relevance for health.

In an extension of this work, a recent study showed that feelings of social isolation were associated with a proxy measure of functional glucocorticoid insensitivity [ 76 ]. The composition of the leukocyte population in circulation is subject to the regulatory influence of glucocorticoids; high cortisol levels increase circulating concentrations of neutrophils and simultaneously decrease concentrations of lymphocytes and monocytes. In a study of older Taiwanese adults, this relationship was reflected in a positive correlation between cortisol levels and the ratio of neutrophil percentages relative to lymphocyte or monocyte percentages. However, in lonely individuals, this correlation was attenuated and nonsignificant, consistent with a diminished effect of cortisol at the level of leukocytes.

The precise molecular site of glucocorticoid insensitivity in the pro-inflammatory transcription cascade has yet to be identified, and additional longitudinal and experimental research are needed to determine the degree to which chronic feelings of social isolation play a causal role in differential gene expression. However, the association between subjective social isolation and gene expression corresponds well to gene expression differences in animal models of social isolation (e.g., [ 77 – 79 ]), suggesting that a subjective sense of social connectedness is important for genomic expression and normal immunoregulation in humans. Impaired transcription of glucocorticoid response genes and increased activity of pro-inflammatory transcription control pathways provide a functional genomic explanation for elevated risk of inflammatory disease in individuals who experience chronically high levels of loneliness.

Immune Functioning

Loneliness differences in immunoregulation extend beyond inflammation processes. Loneliness has been associated with impaired cellular immunity as reflected in lower natural killer (NK) cell activity and higher antibody titers to the Epstein Barr Virus and human herpes viruses [ 70 , 80 – 82 ]. In addition, loneliness among middle-age adults has been associated with a smaller increase in NK cell numbers in response to the acute stress of a Stroop task and a mirror tracing task [ 71 ]. In young adults, loneliness was associated with poorer antibody response to a component of the flu vaccine [ 72 ], suggesting that the humoral immune response may also be impaired in lonely individuals. Among HIV-positive men without AIDS, loneliness was associated with a lower count of CD4 T-lymphocytes in one study [ 83 ] but was not associated with the CD4 count in another study [ 84 ]. However, in the latter study, loneliness predicted a slower rate of decline in levels of CD4 T-lymphocytes over a 3-year period [ 84 ]. These data suggest that loneliness protects against disease progression, but no association was observed between loneliness and time to AIDS diagnosis or AIDS-related mortality [ 84 ]. Additional research is needed to examine the role of loneliness chronicity, age, life stress context, genetic predispositions, and interactions among these factors to determine when and how loneliness operates to impair immune functioning.

Future Loneliness Matters

Interventions for loneliness.

Six qualitative reviews of the loneliness intervention literature have been published since 1984 [ 85 – 90 ], and all explicitly or implicitly addressed four main types of interventions: (1) enhancing social skills, (2) providing social support, (3) increasing opportunities for social interaction, and (4) addressing maladaptive social cognition. All but one of these reviews concluded that loneliness interventions have met with success, particularly interventions which targeted opportunities for social interaction. Findlay [ 87 ] was more cautious in his review, noting that only six of the 17 intervention studies in his review employed a randomized group comparison design, with the remaining 11 studies subject to the shortcomings and flaws of pre-post and nonrandomized group comparison designs.

We recently completed a meta-analysis of loneliness intervention studies published between 1970 and September 2009 to test the magnitude of the intervention effects within each type of study design and to determine whether the intervention target moderated effect sizes (Masi et al., unpublished). Of the 50 studies eligible for inclusion in the meta-analysis, 12 were pre-post studies, 18 were non-randomized group comparison studies, and 20 were randomized group comparison studies. Effect sizes were significantly different from zero within each study design group, but randomized group comparison studies produced the smallest effect overall (pre-post=−0.37, 95% CI −.55, −.18; non-randomized control=−0.46, 95% CI −0.72, −0.20; randomized control=−0.20, 95% CI −0.32, −0.08).

Our model of loneliness holds that implicit hypervigilance for social threat exerts a powerful influence on perceptions, cognitions, and behaviors, and that loneliness may be diminished by reducing automatic perceptual and cognitive biases that favor over-attention to negative social information in the environment. Accordingly, we posited that interventions that targeted maladaptive social cognition (e.g., cognitive behavioral therapy that involved training to identify automatic negative thoughts and look for disconfirming evidence, to decrease biased cognitions, and/or to reframe perceptions of loneliness and personal control) would be more effective than interventions that targeted social support, social skills, or social access. Moderational analyses of the randomized group comparison studies supported our hypothesis: the effect size for social cognition interventions (−0.60, 95% CI −0.96, −0.23, N = 4) was significantly larger than the effect size for social support (−0.16, 95% CI −0.27, −0.06, N =12), social skills (0.02, 95% CI −0.24, 0.28, N =2), and social access (−0.06, 95% CI −0.35, 0.22, N =2); the latter three types of interventions did not differ significantly from each other. The results for social cognitive therapy are promising, but this intervention type appears not to have been widely employed to date relative to other types of loneliness therapy. Moreover, existing social cognitive therapies have had a small effect overall (0.20) relative to the meta-analytic mean effect of over 300 other interventions in the social and behavioral domains (0.50) [ 91 ]. A social cognitive approach to loneliness reduction outlined in a recent book [ 92 ] may encourage therapists to develop a treatment that focuses on the specific affective, cognitive, and behavioral propensities that afflict lonely individuals.

Implications for Health

Reducing feelings of loneliness and enhancing a sense of connectedness and social adhesion are laudable goals in their own right, but a critical question is whether modifying perceptions of social isolation or connectedness have any impact on health. VanderWeele et al. (unpublished) recently examined the reduction in depressive symptoms that could be expected if loneliness were successfully reduced and found there would be significant benefits that would accrue for as long as two years following the intervention. Would a successful intervention to lower loneliness produce corresponding benefits in physiological mechanisms and physical health outcomes? The only extant data to address this question comes from a recent study in which 235 lonely home-dwelling older adults (>74 years) were randomly assigned to an intervention or control group. In the treatment arm of the study, closed small groups of seven to eight individuals met with two professional facilitators once a week for 3 months to participate in group activities in art, exercise, or therapeutic writing. The control group continued to receive usual community care. Relative to the control group, individuals in the treatment group became more socially active, found new friends, and experienced an increase in feeling needed [ 93 ]. This was accompanied by a significant improvement in self-rated health, fewer health care services and lower costs, and greater survival at 2-year follow-up [ 94 ]. Feelings of loneliness did not differ between the groups, however [ 93 ], indicating that changes in loneliness were not responsible for improvements in health. According to our theory of loneliness, the interventions targeted by the treatment study would not be expected to influence loneliness dramatically because they fail to address the hypervigilance to social threat and the related cognitive biases that characterize lonely individuals. That is, group activities such as those introduced in this intervention provide new social opportunities but do not alter how individuals approach and think about their social relationships more generally. An intervention study of loneliness and health has yet to be designed that addresses the maladaptive social cognitions that make loneliness the health risk factor it increasingly appears to be. Beyond that, additional research is needed to determine the mechanisms through which successful loneliness interventions enhance health and survival, and to examine whether the type of loneliness intervention moderates its health benefits.

Conclusions

Human beings are thoroughly social creatures. Indeed, human survival in difficult physical environments seems to have selected for social group living [ 95 ]. Consider that the reproductive success of the human species hinges on offspring surviving to reproductive age. Social connections with a mate, a family, and a tribe foster social affiliative behaviors (e.g., altruism, cooperation) that enhance the likelihood that utterly dependent offspring reach reproductive age, and connections with others at the individual and collective levels improve our chances of survival in difficult or hostile environments. These behaviors co-evolved with supporting genetic, neural, and hormonal mechanisms to ensure that humans survived, reproduced, and cared for offspring sufficiently long that they, too, could reproduce [ 96 – 98 ]. Human sociality is prominent even in contemporary individualistic societies. Almost 80% of our waking hours are spent with others, and on average, time spent with friends, relatives, spouse, children, and coworkers is rated more inherently rewarding than time spent alone [ 99 , 100 ]. Humans are such meaning-making creatures that we perceive social relationships where no objectifiable relationship exists (e.g., between author and reader, between an individual and God) or where no reciprocity is possible (e.g., in parasocial relationships with television characters). Conversely, we perceive social isolation when social opportunities and relationships do exist but we lack the capacity to harness the power of social connectedness in everyday life. Chronic perceived isolation (i.e., loneliness) is characterized by impairments in attention, cognition, affect, and behavior that take a toll on morbidity and mortality through their impact on genetic, neural, and hormonal mechanisms that evolved as part and parcel of what it means to be human. Future interventions to alleviate the health burden of loneliness will do well to take into account our evolutionary design as a social species.

Acknowledgments

This research was supported by Grant R01-AG036433-01 and R01-AG034052 from the National Institute on Aging and by the John Templeton Foundation.

Contributor Information

Louise C. Hawkley, Center for Cognitive and Social Neuroscience, University of Chicago, Chicago, IL, USA. Department of Psychology, University of Chicago, 940 E. 57th St, Chicago, IL 60637, USA.

John T. Cacioppo, Center for Cognitive and Social Neuroscience, University of Chicago, Chicago, IL, USA.

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Stanford Medicine study flags unexpected cells in lung as suspected source of severe COVID

A previously overlooked type of immune cell allows SARS-CoV-2 to proliferate, Stanford Medicine scientists have found. The discovery has important implications for preventing severe COVID-19.

April 10, 2024 - By Bruce Goldman

In an uninfected interstitial macrophage, the nucleus (purple) and outer cell membrane (blue) are intact. In an infected interstitial macrophage, the nucleus is shattered, copious newly made viral components (red) clump together, and the cell broadcasts inflammatory and scar-tissue-inducing chemical signals (yellow). Emily Moskal

The lung-cell type that’s most susceptible to infection by SARS-CoV-2, the virus that causes COVID-19, is not the one previously assumed to be most vulnerable. What’s more, the virus enters this susceptible cell via an unexpected route. The medical consequences may be significant.

Stanford Medicine investigators have implicated a type of immune cell known as an interstitial macrophage in the critical transition from a merely bothersome COVID-19 case to a potentially deadly one. Interstitial macrophages are situated deep in the lungs, ordinarily protecting that precious organby, among other things, engorging viruses, bacteria, fungi and dust particles that make their way down our airways. But it’s these very cells, the researchers have shown in a study published online April 10 in the  Journal of Experimental Medicine , that of all known types of cells composing lung tissue are most susceptible to infection by SARS-CoV-2.

SARS-CoV-2-infected interstitial macrophages, the scientists have learned, morph into virus producersand squirt out inflammatory and scar-tissue-inducing chemical signals, potentially paving the road to pneumonia and damaging the lungs to the point where the virus, along with those potent secreted substances, can break out of the lungs and wreak havoc throughout the body.

The surprising findings point to new approaches in preventing a SARS-CoV-2 infection from becoming a life-threatening disease. Indeed, they may explain why monoclonal antibodies meant to combat severe COVID didn’t work well, if at all — and when they did work, it was only when they were administered early in the course of infection, when the virus was infecting cells in the upper airways leading to the lungs but hadn’t yet ensconced itself in lung tissue.

The virus surprises

“We’ve overturned a number of false assumptions about how the virus actually replicates in the human lung,” said  Catherine Blish , MD, PhD, a professor of infectious diseases and of microbiology and immunology and the George E. and Lucy Becker Professor in Medicine and associate dean for basic and translational research.

Blish is the co-senior author of the study, along with  Mark Krasnow , MD, PhD, the Paul and Mildred Berg Professor of biochemistry and the Executive Director of the Vera Moulton Wall Center for pulmonary vascular disease.

“The critical step, we think, is when the virus infects interstitial macrophages, triggering a massive inflammatory reaction that can flood the lungs and spread infection and inflammation to other organs,” Krasnow said. Blocking that step, he said, could prove to be a major therapeutic advance. But there’s a plot twist: The virus has an unusual way of getting inside these cells — a route drug developers have not yet learned how to block effectively — necessitating a new focus on that alternative mechanism, he added.

Catherine Blish

In a  paper  published in  Nature  in early 2020, Krasnow and his colleagues including then-graduate student Kyle Travaglini, PhD — who is also one of the new study’s co-lead authors along with MD-PhD student  Timothy Wu  — described a technique they’d worked out for isolating fresh human lungs; dissociating the cells from one another; and characterizing them, one by one, on the basis of which genes within each cell were active and how much so. Using that technique, the Krasnow lab and collaborators were able to discern more than 50 distinct cell types, assembling an atlas of healthy lung cells.

“We’d just compiled this atlas when the COVID-19 pandemic hit,” Krasnow said. Soon afterward, he learned that Blish and  Arjun Rustagi , MD, PhD, instructor of infectious diseases and another lead co-author of the study, were building an ultra-safe facility where they could safely grow SARS-CoV-2 and infect cells with it.

A collaboration ensued. Krasnow and Blish and their associates obtained fresh healthy lung tissue excised from seven surgical patients and five deceased lung donors whose lungs were virus-free but for one reason or another not used in transplants. After infecting the lung tissue with SARS-CoV-2 and waiting one to three days for the infection to spread, they separated and typed the cells to generate an infected-lung-cell atlas, analogous to the one Krasnow’s team had created with healthy lung cells. They saw most of the cell types that Krasnow’s team had identified in healthy lung tissue.

Now the scientists could compare pristine versus SARS-CoV-2-infected lungs cells of the same cell type and see how they differed: They wanted to know which cells the virus infected, how easily SARS-CoV-2 replicated in infected cells, and which genes the infected cells cranked up or dialed down compared with their healthy counterparts’ activity levels. They were able to do this for each of the dozens of different cell types they’d identified in both healthy and infected lungs.

“It was a straightforward experiment, and the questions we were asking were obvious,” Krasnow said. “It was the  answers  we weren’t prepared for.”

It’s been assumed that the cells in the lungs that are most vulnerable to SARS-CoV-2 infection are those known as alveolar type 2 cells. That’s because the surfaces of these cells, along with those of numerous other cell types in the heart, gut and other organs, sport many copies of a molecule known as ACE2. SARS-CoV-2 has been shown to be able to grab onto ACE2 and manipulate it in a way that allows the virus to maneuver its way into cells.

Alveolar type 2 cells are somewhat vulnerable to SARS-CoV-2, the scientists found. But the cell types that were by far the most frequently infected turned out to be two varieties of a cell type called a macrophage.

Virus factories

The word “macrophage” comes from two Greek terms meaning, roughly, “big eater.” This name is not unearned. The air we inhale carries not only oxygen but, unfortunately, tiny airborne dirt particles, fungal spores, bacteria and viruses. A macrophage earns its keep by, among other things, gobbling up these foreign bodies.

Mark Krasnow

The airways leading to our lungs culminate in myriad alveoli, minuscule one-cell-thick air sacs, whichare abutted by abundant capillaries. This interface, called the interstitium, is where oxygen in the air we breathe enters the bloodstream and is then distributed to the rest of the body by the circulatory system.

The two kinds of SARS-CoV-2-susceptible lung-associated macrophages are positioned in two different places. So-called alveolar macrophages hang out in the air spaces within the alveoli. Once infected, these cells smolder, producing and dribbling out some viral progeny at a casual pace but more or less keeping a stiff upper lip and maintaining their normal function. This behavior may allow them to feed SARS-CoV-2’s progression by incubating and generating a steady supply of new viral particles that escape by stealth and penetrate the layer of cells enclosing the alveoli.

Interstitial macrophages, the other cell type revealed to be easily and profoundly infected by SARS-CoV-2, patrol the far side of the alveoli, where the rubber of oxygen meets the road of red blood cells. If an invading viral particle or other microbe manages to evade alveolar macrophages’ vigilance, infect and punch through the layer of cells enclosing the alveoli, jeopardizing not only the lungs but the rest of the body, interstitial macrophages are ready to jump in and protect the neighborhood.

At least, usually. But when an interstitial macrophage meets SARS-CoV-2, it’s a different story. Rather than get eaten by the omnivorous immune cell, the virus infects it.

And an infected interstitial macrophage doesn’t just smolder; it catches on fire. All hell breaks loose as the virus literally seizes the controls and takes over, hijacking a cell’s protein- and nucleic-acid-making machinery. In the course of producing massive numbers of copies of itself, SARS-CoV-2 destroys the boundaries separating the cell nucleus from the rest of the cell like a spatula shattering and scattering the yolk of a raw egg. The viral progeny exit the spent macrophage and move on to infect other cells.

But that’s not all. In contrast to alveolar macrophages, infected interstitial macrophages pump out substances that signal other immune cells elsewhere in the body to head for the lungs. In a patient, Krasnow suggested, this would trigger an inflammatory influx of such cells. As the lungs fill with cells and fluid that comes with them, oxygen exchange becomes impossible. The barrier maintaining alveolar integrity grows progressively damaged. Leakage of infected fluids from damaged alveoli propels viral progeny into the bloodstream, blasting the infection and inflammation to distant organs.

Yet other substances released by SARS-CoV-2-infected interstitial macrophages stimulate the production of fibrous material in connective tissue, resulting in scarring of the lungs. In a living patient, the replacement of oxygen-permeable cells with scar tissue would further render the lungs incapable of executing oxygen exchange.

“We can’t say that a lung cell sitting in a dish is going to get COVID,” Blish said. “But we suspect this may be the point where, in an actual patient, the infection transitions from manageable to severe.”

Another point of entry

Compounding this unexpected finding is the discovery that SARS-CoV-2 uses a different route to infect interstitial macrophages than the one it uses to infect the other types.

Unlike alveolar type 2 cells and alveolar macrophages, to which the virus gains access by clinging to ACE2 on their surfaces, SARS-CoV-2 breaks into interstitial macrophages using a different receptor these cells display. In the study, blocking SARS-CoV-2’s binding to ACE2 protected the former cells but failed to dent the latter cells’ susceptibility to SARS-CoV-2 infection.

“SARS-CoV-2 was not using ACE2 to get into interstitial macrophages,” Krasnow said. “It enters via another receptor called CD209.”

That would seem to explain why monoclonal antibodies developed specifically to block SARS-CoV-2/ACE2 interaction failed to mitigate or prevent severe COVID-19 cases.

It’s time to find a whole new set of drugs that can impede SARS-CoV-2/CD209 binding. Now, Krasnow said.

The study was funded by the National Institutes of Health (grants K08AI163369, T32AI007502 and T32DK007217), the Bill & Melinda Gates Foundation, Chan Zuckerberg Biohub, the Burroughs Wellcome Fund, Stanford Chem-H, the Stanford Innovative Medicine Accelerator, and the Howard Hughes Medical Institute.

About Stanford Medicine

Stanford Medicine is an integrated academic health system comprising the Stanford School of Medicine and adult and pediatric health care delivery systems. Together, they harness the full potential of biomedicine through collaborative research, education and clinical care for patients. For more information, please visit med.stanford.edu .

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No sign of greenhouse gases increases slowing in 2023

• April 5, 2024

Levels of the three most important human-caused greenhouse gases – carbon dioxide (CO 2 ), methane and nitrous oxide – continued their steady climb during 2023, according to NOAA scientists. 

While the rise in the three heat-trapping gases recorded in the air samples collected by NOAA’s Global Monitoring Laboratory (GML) in 2023 was not quite as high as the record jumps observed in recent years, they were in line with the steep increases observed during the past decade. 

“NOAA’s long-term air sampling program is essential for tracking causes of climate change and for supporting the U.S. efforts to establish an integrated national greenhouse gas measuring, monitoring and information system,” said GML Director Vanda Grubišić. “As these numbers show, we still have a lot of work to do to make meaningful progress in reducing the amount of greenhouse gases accumulating in the atmosphere.” 

The global surface concentration of CO 2 , averaged across all 12 months of 2023, was 419.3 parts per million (ppm), an increase of 2.8 ppm during the year. This was the 12th consecutive year CO 2 increased by more than 2 ppm, extending the highest sustained rate of CO 2 increases during the 65-year monitoring record. Three consecutive years of CO 2  growth of 2 ppm or more had not been seen in NOAA’s monitoring records prior to 2014. Atmospheric CO 2 is now more than 50% higher than pre-industrial levels.

This graph shows the globally averaged monthly mean carbon dioxide abundance measured at the Global Monitoring Laboratory’s global network of air sampling sites since 1980. Data are still preliminary, pending recalibrations of reference gases and other quality control checks. Credit: NOAA GML

“The 2023 increase is the third-largest in the past decade, likely a result of an ongoing increase of fossil fuel CO 2 emissions, coupled with increased fire emissions possibly as a result of the transition from La Nina to El Nino,” said Xin Lan, a CIRES scientist who leads GML’s effort to synthesize data from the NOAA Global Greenhouse Gas Reference Network for tracking global greenhouse gas trends .

Atmospheric methane, less abundant than CO 2 but more potent at trapping heat in the atmosphere, rose to an average of 1922.6 parts per billion (ppb). The 2023 methane increase over 2022 was 10.9 ppb, lower than the record growth rates seen in 2020 (15.2 ppb), 2021(18 ppb)  and 2022 (13.2 ppb), but still the 5th highest since renewed methane growth started in 2007. Methane levels in the atmosphere are now more than 160% higher than their pre-industrial level.

This graph shows globally-averaged, monthly mean atmospheric methane abundance determined from marine surface sites for the full NOAA time-series starting in 1983. Values for the last year are preliminary, pending recalibrations of standard gases and other quality control steps. Credit: NOAA GM

In 2023, levels of nitrous oxide, the third-most significant human-caused greenhouse gas, climbed by 1 ppb to 336.7 ppb. The two years of highest growth since 2000 occurred in 2020 (1.3 ppb) and 2021 (1.3 ppb). Increases in atmospheric nitrous oxide during recent decades are mainly from use of nitrogen fertilizer and manure from the expansion and intensification of agriculture. Nitrous oxide concentrations are 25% higher than the pre-industrial level of 270 ppb.

Taking the pulse of the planet one sample at a time NOAA’s Global Monitoring Laboratory collected more than 15,000 air samples from monitoring stations around the world in 2023 and analyzed them in its state-of-the-art laboratory in Boulder,

Colorado. Each spring, NOAA scientists release preliminary calculations of the global average levels of these three primary long-lived greenhouse gases observed during the previous year to track their abundance, determine emissions and sinks, and understand carbon cycle feedbacks.

Measurements are obtained from air samples collected from sites in NOAA’s Global Greenhouse Gas Reference Network , which includes about 53 cooperative sampling sites around the world, 20 tall tower sites, and routine aircraft operation sites from North America. 

Carbon dioxide emissions remain the biggest problem 

By far the most important contributor to climate change is CO 2 , which is primarily emitted by burning of fossil fuels. Human-caused CO 2 pollution increased from 10.9 billion tons per year in the 1960s – which is when the measurements at the Mauna Loa Observatory in Hawaii began – to about 36.8 billion tons per year in 2023. This sets a new record, according to the Global Carbon Project , which uses NOAA’s Global Greenhouse Gas Reference Network measurements to define the net impact of global carbon emissions and sinks.

The amount of CO 2 in the atmosphere today is comparable to where it was around 4.3 million years ago during the mid- Pliocene epoch , when sea level was about 75 feet higher than today, the average temperature was 7 degrees Fahrenheit higher than in pre-industrial times, and large forests occupied areas of the Arctic that are now tundra. 

About half of the CO 2 emissions from fossil fuels to date have been absorbed at the Earth’s surface, divided roughly equally between oceans and land ecosystems, including grasslands and forests. The CO 2 absorbed by the world’s oceans contributes to ocean acidification, which is causing a fundamental change in the chemistry of the ocean, with impacts to marine life and the people who depend on them. The oceans have also absorbed an estimated 90% of the excess heat trapped in the atmosphere by greenhouse gases. 

Research continues to point to microbial sources for rising methane

NOAA’s measurements show that atmospheric methane increased rapidly during the 1980s, nearly stabilized in the late-1990s and early 2000s, then resumed a rapid rise in 2007. 

A 2022 study by NOAA and NASA scientists and additional NOAA research in 2023 suggests that more than 85% of the increase from 2006 to 2021 was due to increased microbial emissions generated by livestock, agriculture, human and agricultural waste, wetlands and other aquatic sources. The rest of the increase was attributed to increased fossil fuel emissions. 

“In addition to the record high methane growth in 2020-2022, we also observed sharp changes in the isotope composition of the methane that indicates an even more dominant role of microbial emission increase,” said Lan. The exact causes of the recent increase in methane are not yet fully known. 

NOAA scientists are investigating the possibility that climate change is causing wetlands to give off increasing methane emissions in a feedback loop. 

To learn more about the Global Monitoring Laboratory’s greenhouse gas monitoring, visit: https://gml.noaa.gov/ccgg/trends/.

Media Contact: Theo Stein, [email protected] , 303-819-7409

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5. party identification among religious groups and religiously unaffiliated voters.

The relationship between partisanship and voters’ religious affiliation continues to be strong – especially when it comes to whether they belong to any organized religion at all.

The gap between voters who identify with an organized religion and those who do not has grown much wider in recent years.

Protestants mostly align with the Republican Party. Protestants remain the largest single religious group in the United States. As they have for most of the past 15 years, a majority of Protestant registered voters (59%) associate with the GOP, though as recently as 2009 they were split nearly equally between the two parties.

Partisan identity among Catholics had been closely divided, but the GOP now has a modest advantage among Catholics. About half of Catholic voters identify as Republicans or lean toward the Republican Party, compared with 44% who identify as Democrats or lean Democratic.

Members of the Church of Jesus Christ of Latter-day Saints remain overwhelmingly Republican. Three-quarters of voters in this group, widely known as Mormons, identify as Republicans or lean Republican. Only about a quarter (23%) associate with the Democratic Party.

Jewish voters continue to mostly align with the Democrats. About seven-in-ten Jewish voters (69%) associate with the Democratic Party, while 29% affiliate with the Republican Party. The share of Jewish voters who align with the Democrats has increased 8 percentage points since 2020.

Muslims associate with Democrats over Republicans by a wide margin. Currently, 66% of Muslim voters say they are Democrats or independents who lean Democratic, compared with 32% who are Republicans or lean Republican. (Data for Muslim voters is not available for earlier years because of small sample sizes.)

Democrats maintain a wide advantage among religiously unaffiliated voters. Religious “nones” have become more Democratic over the past few decades as their size in the U.S. population overall and in the electorate has grown significantly. While 70% of religiously unaffiliated voters align with the Democratic Party, just 27% identify as Republicans or lean Republican.

Related: Religious “nones” in America: Who they are and what they believe

Religion, race and ethnicity, and partisanship

Over the past few decades, White evangelical Protestant voters have moved increasingly toward the GOP.

• Today, 85% of White evangelical voters identify with or lean toward the GOP; just 14% align with the Democrats.

• Over the past three decades, there has been a 20 percentage point rise in the share of White evangelicals who associate with the GOP – and a 20-point decline in the share identifying as or leaning Democratic. 

Over the past 15 years, the GOP also has made gains among White nonevangelical and White Catholic voters.

About six-in-ten White nonevangelicals (58%) and White Catholics (61%) align with the GOP.    Voters in both groups were equally divided between the two parties in 2009.

Partisanship among Hispanic voters varies widely among Catholics and Protestants.

• 60% of Hispanic Catholic voters identify as Democrats or lean Democratic, but that share has declined over the past 15 years.
• Hispanic Protestant voters are evenly divided: 49% associate with the Republican Party, while 45% identify as Democrats or lean Democratic.

A large majority of Black Protestants identify with the Democrats (84%), but that share is down 9 points from where it was 15 years ago (93%).

Party identification among atheists, agnostics and ‘nothing in particular’

Atheists and agnostics, who make up relatively small shares of all religiously unaffiliated voters, are heavily Democratic.

Among those who identify their religion as “nothing in particular” – and who comprise a majority of all religious “nones” – Democrats hold a smaller advantage in party identification.

• More than eight-in-ten atheists (84%) align with the Democratic Party, as do 78% of agnostics.
• 62% of voters who describe themselves as “nothing in particular” identify as Democrats or lean Democratic, while 34% align with the GOP.

Partisanship and religious service attendance

Voters who regularly attend religious services are more likely to identify with or lean toward the Republican Party than voters who attend less regularly.

In 2023, 62% of registered voters who attended religious services once a month or more aligned with Republicans, compared with 41% of those who attend services less often.

This pattern has been evident for many years. However, the share of voters who identify as Republicans or lean Republican has edged up in recent years.

For White, Hispanic and Asian voters, regular attendance at religious services is linked to an increase in association with the Republican Party.

However, this is not the case among Black voters.

Only about one-in-ten Black voters who are regular attenders (13%) and a similar share (11%) of those who attend less often identify as Republicans or Republican leaners.

Higher GOP association among regular attenders of religious services is seen across most denominations.

For example, among Catholic voters who attend services monthly or more often, 61% identify as Republicans or lean toward the Republican Party.

Among less frequent attenders, 47% align with the GOP.

Black Protestants are an exception to this pattern: Black Protestant voters who attend religious services monthly or more often are no more likely to associate with the Republican Party than less frequent attenders.

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Prestigious cancer research institute has retracted 7 studies amid controversy over errors

Seven studies from researchers at the prestigious Dana-Farber Cancer Institute have been retracted over the last two months after a scientist blogger alleged that images used in them had been manipulated or duplicated.

The retractions are the latest development in a monthslong controversy around research at the Boston-based institute, which is a teaching affiliate of Harvard Medical School. 

The issue came to light after Sholto David, a microbiologist and volunteer science sleuth based in Wales, published a scathing post on his blog in January, alleging errors and manipulations of images across dozens of papers produced primarily by Dana-Farber researchers . The institute acknowledged errors and subsequently announced that it had requested six studies to be retracted and asked for corrections in 31 more papers. Dana-Farber also said, however, that a review process for errors had been underway before David’s post. 

Now, at least one more study has been retracted than Dana-Farber initially indicated, and David said he has discovered an additional 30 studies from authors affiliated with the institute that he believes contain errors or image manipulations and therefore deserve scrutiny.

The episode has imperiled the reputation of a major cancer research institute and raised questions about one high-profile researcher there, Kenneth Anderson, who is a senior author on six of the seven retracted studies. 

Anderson is a professor of medicine at Harvard Medical School and the director of the Jerome Lipper Multiple Myeloma Center at Dana-Farber. He did not respond to multiple emails or voicemails requesting comment. 

The retractions and new allegations add to a larger, ongoing debate in science about how to protect scientific integrity and reduce the incentives that could lead to misconduct or unintentional mistakes in research. 

The Dana-Farber Cancer Institute has moved relatively swiftly to seek retractions and corrections. 

“Dana-Farber is deeply committed to a culture of accountability and integrity, and as an academic research and clinical care organization we also prioritize transparency,” Dr. Barrett Rollins, the institute’s integrity research officer, said in a statement. “However, we are bound by federal regulations that apply to all academic medical centers funded by the National Institutes of Health among other federal agencies. Therefore, we cannot share details of internal review processes and will not comment on personnel issues.”

The retracted studies were originally published in two journals: One in the Journal of Immunology and six in Cancer Research. Six of the seven focused on multiple myeloma, a form of cancer that develops in plasma cells. Retraction notices indicate that Anderson agreed to the retractions of the papers he authored.

Elisabeth Bik, a microbiologist and longtime image sleuth, reviewed several of the papers’ retraction statements and scientific images for NBC News and said the errors were serious. 

“The ones I’m looking at all have duplicated elements in the photos, where the photo itself has been manipulated,” she said, adding that these elements were “signs of misconduct.” 

Dr.  John Chute, who directs the division of hematology and cellular therapy at Cedars-Sinai Medical Center and has contributed to studies about multiple myeloma, said the papers were produced by pioneers in the field, including Anderson. 

“These are people I admire and respect,” he said. “Those were all high-impact papers, meaning they’re highly read and highly cited. By definition, they have had a broad impact on the field.” 

Chute said he did not know the authors personally but had followed their work for a long time.

“Those investigators are some of the leading people in the field of myeloma research and they have paved the way in terms of understanding our biology of the disease,” he said. “The papers they publish lead to all kinds of additional work in that direction. People follow those leads and industry pays attention to that stuff and drug development follows.”

The retractions offer additional evidence for what some science sleuths have been saying for years: The more you look for errors or image manipulation, the more you might find, even at the top levels of science. 

Scientific images in papers are typically used to present evidence of an experiment’s results. Commonly, they show cells or mice; other types of images show key findings like western blots — a laboratory method that identifies proteins — or bands of separated DNA molecules in gels. 

Science sleuths sometimes examine these images for irregular patterns that could indicate errors, duplications or manipulations. Some artificial intelligence companies are training computers to spot these kinds of problems, as well. 

Duplicated images could be a sign of sloppy lab work or data practices. Manipulated images — in which a researcher has modified an image heavily with photo editing tools — could indicate that images have been exaggerated, enhanced or altered in an unethical way that could change how other scientists interpret a study’s findings or scientific meaning. 

Top scientists at big research institutions often run sprawling laboratories with lots of junior scientists. Critics of science research and publishing systems allege that a lack of opportunities for young scientists, limited oversight and pressure to publish splashy papers that can advance careers could incentivize misconduct. 

These critics, along with many science sleuths, allege that errors or sloppiness are too common , that research organizations and authors often ignore concerns when they’re identified, and that the path from complaint to correction is sluggish. 

“When you look at the amount of retractions and poor peer review in research today, the question is, what has happened to the quality standards we used to think existed in research?” said Nick Steneck, an emeritus professor at the University of Michigan and an expert on science integrity.

David told NBC News that he had shared some, but not all, of his concerns about additional image issues with Dana-Farber. He added that he had not identified any problems in four of the seven studies that have been retracted. 

“It’s good they’ve picked up stuff that wasn’t in the list,” he said. 

NBC News requested an updated tally of retractions and corrections, but Ellen Berlin, a spokeswoman for Dana-Farber, declined to provide a new list. She said that the numbers could shift and that the institute did not have control over the form, format or timing of corrections. 

“Any tally we give you today might be different tomorrow and will likely be different a week from now or a month from now,” Berlin said. “The point of sharing numbers with the public weeks ago was to make clear to the public that Dana-Farber had taken swift and decisive action with regard to the articles for which a Dana-Farber faculty member was primary author.” 

She added that Dana-Farber was encouraging journals to correct the scientific record as promptly as possible. 

Bik said it was unusual to see a highly regarded U.S. institution have multiple papers retracted. 

“I don’t think I’ve seen many of those,” she said. “In this case, there was a lot of public attention to it and it seems like they’re responding very quickly. It’s unusual, but how it should be.”

Evan Bush is a science reporter for NBC News. He can be reached at [email protected].

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Watch your garden glow with new genetically modified bioluminescent petunias

Sasa Woodruff

A long exposure photo of Firefly petunias, which are genetically modified to produce their own light through bioluminescence Sasa Woodruff/Boise State Public Radio hide caption

A long exposure photo of Firefly petunias, which are genetically modified to produce their own light through bioluminescence

Keith Wood, Ph.D. spent most of his career in pharmaceutical research in molecular and chemical biology, using his work with bioluminescence to understand how molecules interacted with diseases. His work started as a graduate student when the team he was on inserted a firefly gene into a tobacco plant.

It was a small plant and couldn't sustain light without the addition of a substrate. It wasn't something a consumer would buy, but it was good for understanding pathways within an organism.

Now, about 40 years after that first plant, Wood and his company in Ketchum, Light Bio, are marketing a garden petunia with a twist: it glows in the dark.

View this post on Instagram A post shared by Alexandra L. Woodruff (@trowelandfork)

"People don't think about science as just bringing joy to our lives," Wood said, "We thought we could do something really special here. We could create a kind of decorative plant that was really just enjoyment, just bringing a kind of magic into our lives."

Scientist Keith Wood stands in his Ketchum home with a photo of a tobacco plant modified with a firefly gene Sasa Woodruff/Boise State Public Radio hide caption

Scientist Keith Wood stands in his Ketchum home with a photo of a tobacco plant modified with a firefly gene

The petunia with bright, white flowers looks like something you'd buy in spring at a garden nursery. But, when the lights are turned out, the petals slowly start lighting up with a greenish, white glow. The plant is always glowing, it's just our eyes that need to adjust to see the light. The newest buds are the brightest and punctuate the glowing flowers.

"That's why we call it the Firefly Petunia. Because these bright buds resemble fireflies sitting on top of the plant.," Wood explained.

And despite its name, this plant doesn't have any firefly genes, rather four genes from a bioluminescent mushroom and a fifth from a fungi.

"The first gene takes a metabolite and turns it into an intermediate," Wood explained, "The second gene takes the intermediate and turns it into the actual fuel for the bioluminescence. The third gene is what actually makes the light. And then the last gene takes the product from the light reaction and recycles it back to the starting point."

This cycle is self-sustaining, which means it shines brightly and doesn't need an extra chemical like the tobacco plant did to light up.

"The [firefly] gene was functional, but it didn't connect seamlessly into the natural metabolic processes," Wood said.

"You've got glow, but it was a weak glow. Not satisfying at all."

Petunia approval paperwork

It took about 10 years to go from development to approval from the U.S. Department of Agriculture last fall.

The plants went on sale online in February and the first ones were shipped out this week.

Diane Blazek, the executive director of the National Garden Bureau, an educational nonprofit, says customers are always looking for the next new plant and petunias are a guaranteed bestseller.

"Grandma grew petunias, but oh, look, now I've got a petunia that glows in the dark. So, this is really cool," Blazek said.

The Firefly Petunia emanates light because it's been modified with genes from a bioluminescent mushroom Sasa Woodruff/Boise State Public Radio hide caption

The Firefly Petunia emanates light because it's been modified with genes from a bioluminescent mushroom

She doesn't think that the fact that it's genetically modified will affect customers buying it because there's a precedent.

Seven years ago, an orange petunia modified with a maize gene showed up in gardens and nurseries in Europe and the U.S. The plant was never supposed to leave a closed lab but somehow ended up in lots of gardens. Regulators eventually asked people to destroy the plants and seeds.

"Overwhelmingly, the response was, wait a minute, it's a petunia. We're not eating it. The orange gene came from maize. Why? Why can't we plant this?" Blazek remembered.

Eventually, regulators approved the plants in the U.S.

Chris Beytes, at Ball Publishing, who oversees several horticulture publications, said the Firefly Petunia could open up gardening to new customers.

"If you buy your first plant because it glows in the dark or it's dyed pink, your second and third and 100th plant may be the traditional stuff. You never know," Beytes said. "Anything that creates excitement around flowers and plants. I'm all for it."

The Firefly Petunia may not have practical implications for things like drug advances or crop production, but for Wood this petunia is transcendent.

"There's something magical about seeing this living presence, this glowing vitality coming from a living plant that in person gives a kind of magical experience that you just can't see in a photograph.

And this summer, that magic could be sitting on the patio watching your garden glow from the light of a petunia.

Space Station

Station Orbits into Eclipse, Crew Works Research and Spacesuits

The International Space Station soared into the Moon’s shadow during the solar eclipse on Monday afternoon. The Expedition 71 crew members had an opportunity to view the shadow at the end of their workday filled with cargo transfers, spacesuit maintenance, and microgravity research.

The windows on the cupola, the orbital outpost’s “window to the world,” were open and NASA Flight Engineers Matthew Dominick and Jeanette Epps were inside photographing and videotaping the Moon’s shadow on Earth, or umbra, beneath them. They were orbiting 260 miles above southeastern Canada as the Moon’s umbra was moving from New York state into Newfoundland.

The space station experienced a totality of about 90% during its flyover period. Views of the solar eclipse itself, the Moon orbiting directly between the sun and the Earth, were only accessible through a pair of windows in the space station’s Roscosmos segment which may not have been accessible due to cargo constraints.

Before the eclipse activities began on Monday, Dominick worked on orbital plumbing, serviced a pair of science freezers and swapped cargo in and out of the SpaceX Dragon spacecraft. Dominick then joined NASA astronaut Mike Barratt inspecting spacesuit tethers and organizing spacewalking tools.

Epps installed a small satellite orbital deployer inside the Kibo laboratory module ’s airlock and also participated in the Dragon cargo work. NASA Flight Engineer Tracy C. Dyson assisted Epps with the small satellite installations and cargo transfers. Dyson also reviewed operations with the BioFabrication Facility and prepared research hardware for an upcoming session to print cardiac tissue cell samples.

Station Commander Oleg Kononenko spent Monday on inspection tasks in the aft end of the Zvezda service module and Progress 87 resupply ship. Flight Engineer Nikolai Chub focused his attention on electronics and ventilation maintenance. Chub also spent a few moments assisting Flight Engineer Alexander Grebenkin as he attached sensors to himself measuring his heart activity for a long-running Roscosmos space cardiac investigation. He later turned on an ultrasound device and scanned surfaces inside Zvezda.

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