what to think of research

Exploring the World of 250+ Interesting Topics to Research

Research is a fascinating journey into the unknown, a quest for answers, and a process of discovery. Whether you’re an academic, a student, or just a curious mind, finding the right and interesting topics to research is paramount. Not only does it determine the success of your research project, but it can also make the experience enjoyable. 

In this blog, we’ll delve into the art of selecting interesting topics to research, particularly catering to the average reader.

How to Select Interesting Topics to Research?

Table of Contents

Choosing a research topic is like setting sail on a ship. It’s a decision that will dictate your course, so you must make it wisely. Here are some effective strategies to help you pick a captivating topic:

• Personal Interests: Researching a topic you’re genuinely passionate about can turn the entire process into an exciting adventure. Your enthusiasm will show in your work and make it more engaging for the reader.
• Current Trends and Issues: Current events and trends are always intriguing because they’re relevant. They often raise questions and uncertainties, making them excellent research candidates. Think of topics like the impact of a global pandemic on mental health or the evolution of renewable energy technologies in the face of climate change.
• Problem-Solving Approach: Identify a problem that needs a solution or an unanswered question. Researching with the aim to solve a real-world issue can be highly motivating. For instance, you could explore strategies to reduce plastic waste in your community.
• Impact and Relevance: Consider the significance of your topic. Will it impact people’s lives or contribute to existing knowledge? Research with a purpose tends to be more engaging. Topics like gender equality, public health, or environmental conservation often fall into this category.
• Unexplored or Unique Topics: Researching less-explored or unique topics can be exciting. It gives you the opportunity to contribute something new to your field. Remember, research isn’t limited to established subjects; there’s room for exploration in every discipline.

250+ Interesting Topics to Research: Popular Categories

Research topics come in various flavors. Let’s explore some popular categories, which are often engaging for average readers:

Science and Technology

• Artificial intelligence in healthcare.
• Quantum computing advancements.
• Space exploration and colonization.
• Genetic editing and CRISPR technology.
• Cybersecurity in the digital age.
• Augmented and virtual reality applications.
• Climate change and mitigation strategies.
• Sustainable energy sources.
• Internet of Things (IoT) innovations.
• Nanotechnology breakthroughs.
• 3D printing in various industries.
• Biotechnology in medicine.
• Autonomous vehicles and self-driving technology.
• Robotics in everyday life.
• Clean water technology.
• Renewable energy storage solutions.
• Wearable technology and health tracking.
• Green architecture and sustainable design.
• Bioinformatics and genomics.
• Machine learning in data analysis.
• Space tourism development.
• Advancements in quantum mechanics.
• Biometrics and facial recognition.
• Aerospace engineering innovations.
• Ethical considerations in AI development.
• Artificial organs and 3D bioprinting.
• Holography and holographic displays.
• Sustainable agriculture practices.
• Climate modeling and prediction.
• Advancements in battery technology.
• Neurotechnology and brain-computer interfaces.
• Space-based solar power.
• Green transportation options.
• Materials science and superconductors.
• Telemedicine and remote healthcare.
• Cognitive computing and AI ethics.
• Renewable energy policy and regulation.
• The role of 5G in the digital landscape.
• Precision medicine and personalized treatment.
• Advancements in quantum cryptography.
• Drone technology and applications.
• Environmental sensors and monitoring.
• Synthetic biology and bioengineering.
• Smart cities and urban planning.
• Quantum teleportation research.
• AI-powered virtual assistants.
• Space-based mining and resource extraction.
• Advancements in neuroprosthetics.
• Sustainable transportation solutions.
• Blockchain technology and applications.

Social Issues

• Gender inequality in the workplace.
• Racial discrimination and systemic racism.
• Income inequality and wealth gap.
• Climate change and environmental degradation.
• Mental health stigma and access to care.
• Access to quality education.
• Immigration and border control policies.
• Gun control and Second Amendment rights.
• Opioid epidemic and substance abuse.
• Affordable healthcare and insurance.
• LGBTQ+ rights and discrimination.
• Cyberbullying and online harassment.
• Homelessness and affordable housing.
• Police brutality and reform.
• Human trafficking and modern slavery.
• Voter suppression and electoral integrity.
• Access to clean water and sanitation.
• Child labor and exploitation.
• Aging population and healthcare for the elderly.
• Indigenous rights and land disputes.
• Bullying in schools and online.
• Obesity and public health.
• Access to reproductive healthcare.
• Income tax policies and fairness.
• Mental health support for veterans.
• Child abuse and neglect.
• Animal rights and cruelty.
• The digital divide and internet access.
• Youth unemployment and opportunities.
• Religious freedom and tolerance.
• Disability rights and accessibility.
• Affordable childcare and parental leave.
• Food insecurity and hunger.
• Drug policy and legalization.
• Human rights violations in conflict zones.
• Aging infrastructure and public safety.
• Cybersecurity and data privacy.
• Human rights in authoritarian regimes.
• Environmental racism and pollution.
• Discrimination against people with disabilities.
• Income and education disparities in rural areas.
• Freedom of the press and media censorship.
• Bullying and discrimination against the LGBTQ+ youth.
• Access to clean energy and sustainable practices.
• Child marriage and forced unions.
• Mental health in the workplace.
• Domestic violence and abuse.
• Education funding and quality.
• Childhood obesity and healthy habits.
• Poverty and economic development.

History and Culture

• The Rise and Fall of the Roman Empire
• Ancient Egyptian Civilization
• The Renaissance Period in Europe
• The Industrial Revolution
• The French Revolution
• The American Civil War
• The Silk Road and Cultural Exchange
• The Mayan Civilization
• The Byzantine Empire
• The Age of Exploration
• World War I: Causes and Consequences
• The Harlem Renaissance
• The Aztec Empire
• Ancient Greece: Democracy and Philosophy
• The Vietnam War
• The Cold War
• The Inca Empire
• The Enlightenment Era
• The Crusades
• The Spanish Inquisition
• The African Slave Trade
• The Suffragette Movement
• The Black Death in Europe
• The Apollo Moon Landing
• The Roaring Twenties
• The Chinese Cultural Revolution
• The Salem Witch Trials
• The Great Wall of China
• The Abolitionist Movement
• The Golden Age of Islam
• The Mesoamerican Ballgame
• The Age of Vikings
• The Ottoman Empire
• The Cultural Impact of the Beatles
• The Space Race
• The Fall of the Berlin Wall
• The History of Hollywood Cinema
• The Renaissance Art and Artists
• The British Empire
• The Age of Samurai in Japan
• The Ancient Indus Valley Civilization
• The Russian Revolution
• The Age of Chivalry
• The History of Native American Tribes
• The Cultural Significance of Greek Mythology
• The Etruscans in Ancient Italy
• The History of African Kingdoms
• The Great Famine in Ireland
• The Age of Invention and Innovation
• The Cultural Impact of Shakespeare’s Works

Business and Economics

• Impact of E-commerce on Traditional Retail
• Global Supply Chain Challenges
• Green Business Practices and Sustainability
• Strategies for Small Business Growth
• Cryptocurrency and Its Economic Implications
• Consumer Behavior in the Digital Age
• The Gig Economy and Its Future
• Economic Consequences of Climate Change
• The Role of AI in Financial Services
• Trade Wars and Their Effects on Global Markets
• Entrepreneurship in Emerging Markets
• Corporate Social Responsibility Trends
• The Economics of Healthcare
• The Impact of Inflation on Savings
• Startup Ecosystems and Innovation Hubs
• Financial Literacy and Education Initiatives
• Income Inequality and Economic Mobility
• The Sharing Economy and Collaborative Consumption
• International Trade Policies
• Behavioral Economics in Marketing
• Economic Effects of the COVID-19 Pandemic
• Fintech Innovations and Banking
• Real Estate Market Trends
• Public vs. Private Healthcare Systems
• Market Entry Strategies for New Businesses
• Global Economic Growth Prospects
• The Economics of Education
• Mergers and Acquisitions Trends
• Impact of Tax Reforms on Businesses
• Sustainable Investing and ESG Factors
• Monetary Policy and Interest Rates
• The Future of Work: Remote vs. Office
• Business Ethics and Corporate Governance
• The Economics of Artificial Intelligence
• Stock Market Volatility
• Supply and Demand Dynamics
• Entrepreneurial Finance and Fundraising
• Innovation and Technology Transfer
• Competition in the Digital Marketplace
• Economic Impacts of Aging Populations
• Economic Development in Developing Countries
• Regulatory Challenges in the Financial Sector
• The Economics of Healthcare Insurance
• Corporate Profitability and Market Share
• Energy Economics and Renewable Sources
• Economic Factors in Mergers and Acquisitions
• Financial Crises and Their Aftermath
• Economics of the Entertainment Industry
• Global Economic Trends Post-Pandemic
• Economic Consequences of Cybersecurity Threats
• The Impact of Online Learning
• Strategies for Inclusive Education
• Early Childhood Development
• The Role of Teachers in Student Motivation
• Educational Technology Trends
• Assessment Methods in Education
• The Importance of Multilingual Education
• Special Education Approaches
• Global Education Disparities
• Project-Based Learning
• Critical Thinking in the Classroom
• Educational Leadership
• Homeschooling vs. Traditional Education
• Education and Social Inequality
• Student Mental Health Support
• The Benefits of Student Extracurricular Activities
• The Montessori Approach
• STEM Education
• Educational Policy Reforms
• Education for Sustainable Development
• Educational Psychology
• Learning Disabilities
• Adult Education Programs
• The Role of Arts in Education
• The Flipped Classroom Model
• Educational Gamification
• School Bullying Prevention
• Inclusive Curriculum Design
• The Future of College Admissions
• Early Literacy Development
• Education and Gender Equity
• Teacher Training and Professional Development
• Homeschooling Challenges
• Gifted and Talented Education
• Education for Global Citizenship
• Virtual Reality in Education
• Outdoor and Environmental Education
• Education for Sustainable Agriculture
• Music Education Benefits
• Education and Technological Divide
• Cultural Competence in Education
• Education and Social Emotional Learning
• Personalized Learning
• Educational Equity
• Restorative Justice in Schools
• Study Abroad Programs
• Education for Digital Citizenship
• The Role of Parents in Education
• Vocational Education and Training
• The History of Education Movements

Techniques for Researching Interesting Topics

Once you’ve chosen the interesting topics to research, you’ll need effective techniques to delve deeper into it:

• Online Databases and Journals: Online academic databases like Google Scholar, JSTOR, or PubMed are invaluable resources. They provide access to a vast pool of academic research papers.
• Interviews and Surveys: If your topic involves human perspectives, conducting interviews or surveys can offer firsthand insights. Tools like Jotform Survey Maker , SurveyMonkey or Zoom can be helpful.
• Libraries and Archives: Traditional libraries still hold a treasure trove of information. Whether you visit in person or explore digital archives, libraries can provide a wealth of resources.
• Online Forums and Social Media: Online communities and forums can be excellent sources of information, particularly for trending topics. Sites like Reddit and Quora can connect you with experts and enthusiasts.
• Academic and Expert Sources: Seek out academic articles, books, and experts in your field. Don’t hesitate to reach out to professionals who may be willing to share their expertise.

How to Make Your Research Engaging?

Once you’ve conducted your research, it’s essential to present it in a way that captures the interest of your average reader:

1. Clear and Accessible Language

Avoid jargon and complex terminology. Use simple and straightforward language to ensure your research is accessible to a wide audience.

2. Storytelling and Anecdotes

Weave stories and anecdotes into your research to make it relatable and engaging. Personal narratives and real-life examples can resonate with readers.

3. Visual Aids (Images, Infographics)

Incorporate visuals like images, charts, and infographics to make your research visually appealing and easier to understand.

4. Real-Life Examples and Case Studies

Use real-life examples and case studies to illustrate the practical applications of your research findings. This makes the information tangible and relevant.

5. Relatable Examples from Popular Culture

Relate your research to pop culture, current events, or everyday experiences. This helps readers connect with the material on a personal level.

Examples of Interesting Topics to Research

To provide some inspiration, let’s explore a few intriguing research topics:

The Impact of Social Media on Mental Health

Examine the relationship between social media use and mental health, including topics like social comparison, cyberbullying, and the benefits of online support networks.

The Future of Renewable Energy

Research the latest advancements in renewable energy technologies, such as solar power, wind energy, and the feasibility of a global transition to sustainable energy sources.

The History of Women’s Suffrage

Delve into the historical struggles and milestones of the women’s suffrage movement, both in the United States and around the world.

The Role of Artificial Intelligence in Healthcare

Investigate the applications of AI in healthcare, from diagnosis algorithms to patient data analysis and the ethical implications of AI in medical practice.

Strategies for Sustainable Business Practices

Examine business sustainability practices , exploring how companies can balance profit and environmental responsibility in an increasingly eco-conscious world.

Challenges you Might Face in Research

While you are looking for interesting topics to research, it’s important to be aware of the challenges:

• Avoiding Bias and Misinformation: Ensure your research is unbiased and based on credible sources. Critical thinking is key to avoiding misinformation.
• Ethical Considerations: Research involving humans or animals should follow ethical guidelines. Always prioritize ethical research practices.
• Data Collection and Analysis: Data collection can be time-consuming and challenging. Make sure to use appropriate data collection methods and robust analysis techniques.
• Staying Updated with Latest Research: Research is an ongoing process. Stay up-to-date with the latest research in your field to ensure the relevance and accuracy of your work.

Research is a gateway to knowledge, innovation, and solutions. Choosing interesting topics to research is the first step in this exciting journey. Whether you’re exploring the depths of science, the intricacies of culture, or the dynamics of business, there’s a captivating research topic waiting for you. 

So, start your exploration, share your discoveries, and keep the flame of curiosity alive. The world is waiting to learn from your research.

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How To Find A High-Quality Research Topic

6 steps to find & evaluate high-quality dissertation/thesis topics.

By: Caroline Osella (PhD, BA)  and Derek Jansen (MBA) | July 2019

So, you’re finally nearing the end of your degree and it’s now time to find a suitable topic for your dissertation or thesis. Or perhaps you’re just starting out on your PhD research proposal and need to find a suitable area of research for your application proposal.

In this post, we’ll provide a straightforward 6-step process that you can follow to ensure you arrive at a high-quality research topic . Follow these steps and you will formulate a well-suited, well-defined core research question .

There’s a helpful clue already: your research ‘topic’ is best understood as a research question or a problem . Your aim is not to create an encyclopedia entry into your field, but rather to shed light on an acknowledged issue that’s being debated (or needs to be). Think research  questions , not research  topics  (we’ll come back to this later).

Overview: How To Find A Research Topic

• Get an understanding of the research process
• Review previous dissertations from your university
• Review the academic literature to start the ideation process
• Identify your potential research questions (topics) and shortlist
• Narrow down, then evaluate your research topic shortlist
• Make the decision (and stick with it!)

Step 1: Understand the research process

It may sound horribly obvious, but it’s an extremely common mistake – students skip past the fundamentals straight to the ideation phase (and then pay dearly for it).

Start by looking at whatever handouts and instructions you’ve been given regarding what your university/department expects of a dissertation. For example, the course handbook, online information and verbal in-class instructions. I know it’s tempting to just dive into the ideation process, but it’s essential to start with the prescribed material first.

There are two important reasons for this:

First , you need to have a basic understanding of the research process , research methodologies , fieldwork options and analysis methods before you start the ideation process, or you will simply not be equipped to think about your own research adequately. If you don’t understand the basics of  quantitative , qualitative and mixed methods BEFORE you start ideating, you’re wasting your time.

Second , your university/department will have specific requirements for your research – for example, requirements in terms of topic originality, word count, data requirements, ethical adherence , methodology, etc. If you are not aware of these from the outset, you will again end up wasting a lot of time on irrelevant ideas/topics.

So, the most important first step is to get your head around both the basics of research (especially methodologies), as well as your institution’s specific requirements . Don’t give in to the temptation to jump ahead before you do this. As a starting point, be sure to check out our free dissertation course.

Step 2: Review past dissertations/theses

Unless you’re undertaking a completely new course, there will be many, many students who have gone through the research process before and have produced successful dissertations, which you can use to orient yourself. This is hugely beneficial – imagine being able to see previous students’ assignments and essays when you were doing your coursework!

Take a look at some well-graded (65% and above) past dissertations from your course (ideally more recent ones, as university requirements may change over time). These are usually available in the university’s online library. Past dissertations will act as a helpful model for all kinds of things, from how long a bibliography needs to be, to what a good literature review looks like, through to what kinds of methods you can use – and how to leverage them to support your argument.

As you peruse past dissertations, ask yourself the following questions:

• What kinds of topics did these dissertations cover and how did they turn the topic into questions?
• How broad or narrow were the topics?
• How original were the topics? Were they truly groundbreaking or just a localised twist on well-established theory?
• How well justified were the topics? Did they seem important or just nice to know?
• How much literature did they draw on as a theoretical base? Was the literature more academic or applied in nature?
• What kinds of research methods did they use and what data did they draw on?
• How did they analyse that data and bring it into the discussion of the academic literature?
• Which of the dissertations are most readable to you – why? How were they presented?
• Can you see why these dissertations were successful? Can you relate what they’ve done back to the university’s instructions/brief?

Seeing a variety of dissertations (at least 5, ideally in your area of interest) will also help you understand whether your university has very rigid expectations in terms of structure and format , or whether they expect and allow variety in the number of chapters, chapter headings, order of content, style of presentation and so on.

Some departments accept graphic novels; some are willing to grade free-flow continental-philosophy style arguments; some want a highly rigid, standardised structure.  Many offer a dissertation template , with information on how marks are split between sections. Check right away whether you have been given one of those templates – and if you do, then use it and don’t try to deviate or reinvent the wheel.

Step 3: Review the academic literature

Now that you (1) understand the research process, (2) understand your university’s specific requirements for your dissertation or thesis, and (3) have a feel for what a good dissertation looks like, you can start the ideation process. This is done by reviewing the current literature and looking for opportunities to add something original to the academic conversation.

Kick start the ideation process

So, where should you start your literature hunt? The best starting point is to get back to your modules. Look at your coursework and the assignments you did. Using your coursework is the best theoretical base, as you are assured that (1) the literature is of a high enough calibre for your university and (2) the topics are relevant to your specific course.

Start by identifying the modules that interested you the most and that you understood well (i.e. earned good marks for). What were your strongest assignments, essays or reports? Which areas within these were particularly interesting to you? For example, within a marketing module, you may have found consumer decision making or organisation trust to be interesting. Create a shortlist of those areas that you were both interested in and academically strong at. It’s no use picking an area that does not genuinely interest you – you’ll run out of motivation if you’re not excited by a topic.

Understand the current state of knowledge

Once you’ve done that, you need to get an understanding of the current state of the literature for your chosen interest areas. What you’re aiming to understand is this: what is the academic conversation here and what critical questions are yet unanswered? These unanswered questions are prime opportunities for a unique, meaningful research topic . A quick review of the literature on your favourite topics will help you understand this.

Grab your reading list from the relevant section of the modules, or simply enter the topics into Google Scholar . Skim-read 3-5 journal articles from the past 5 years which have at least 5 citations each (Google Scholar or a citations index will show you how many citations any given article has – i.e., how many other people have referred to it in their own bibliography). Also, check to see if your discipline has an ‘annual review’ type of journal, which gathers together surveys of the state of knowledge on a chosen topic. This can be a great tool for fast-tracking your understanding of the current state of the knowledge in any given area.

Start from your course’s reading list and work outwards. At the end of every journal article, you’ll find a reference list. Scan this reference list for more relevant articles and read those. Then repeat the process (known as snowballing) until you’ve built up a base of 20-30 quality articles per area of interest.

Absorb, don’t hunt

At this stage, your objective is to read and understand the current state of the theory for your area(s) of interest – you don’t need to be in topic-hunting mode yet. Don’t jump the gun and try to identify research topics before you are well familiarised with the literature.

As you read, try to understand what kinds of questions people are asking and how they are trying to answer them. What matters do the researchers agree on, and more importantly, what are they in disagreement about? Disagreements are prime research territory. Can you identify different ‘schools of thought’ or different ‘approaches’? Do you know what your own approach or slant is? What kinds of articles appeal to you and which ones bore you or leave you feeling like you’ve not really grasped them? Which ones interest you and point towards directions you’d like to research and know more about?

Once you understand the fundamental fact that academic knowledge is a conversation, things get easier.

Think of it like a party. There are groups of people in the room, enjoying conversations about various things. Which group do you want to join?  You don’t want to be that person in the corner, talking to themself. And you don’t want to be the hanger-on, laughing at the big-shot’s jokes and repeating everything they say.

Do you want to join a large group and try to make a small contribution to what’s going on, or are you drawn to a smaller group that’s having a more niche conversation, but where you feel you might more easily find something original to contribute? How many conversations can you identify? Which ones feel closer to you and more attractive? Which ones repel you or leave you cold? Are there some that, frankly, you just don’t understand?

Now, choose a couple of groups who are discussing something you feel interested in and where you feel like you might want to contribute. You want to make your entry into this group by asking a question – a question that will make the other people in the group turn around and look at you, listen to you, and think, “That’s interesting”.

Your dissertation will be the process of setting that question and then trying to find at least a partial answer to that question – but don’t worry about that now.  Right now, you need to work out what conversations are going on, whether any of them are related or overlapping, and which ones you might be able to walk into. I’ll explain how you find that question in the next step.

Need a helping hand?

Step 4: Identify potential research questions

Now that you have a decent understanding of the state of the literature in your area(s) of interest, it’s time to start developing your list of possible research topics. There are (at least) three approaches you can follow here, and they are not mutually exclusive:

Approach 1: Leverage the FRIN

Towards the end of most quality journal articles, you will find a section labelled “ further research ” or something similar. Generally, researchers will clearly outline where they feel further research is needed (FRIN), following on from their own research. So, essentially, every journal article presents you with a list of potential research opportunities.

Of course, only a handful of these will be both practical and of interest to you, so it’s not a quick-fix solution to finding a research topic. However, the benefit of going this route is that you will be able to find a genuinely original and meaningful research topic (which is particularly important for PhD-level research).

The upside to this approach is originality, but the downside is that you might not find something that really interests you , or that you have the means to execute. If you do go this route, make sure that you pay attention to the journal article dates, as the FRIN may already have been “solved” by other researchers if the article is old.

Approach 2: Put a context-based spin on an existing topic

The second option is to consider whether a theory which is already well established is relevant within a local or industry-specific context. For example, a theory about the antecedents (drivers) of trust is very well established, but there may be unique or uniquely important drivers within a specific national context or industry (for example, within the financial services industry in an emerging market).

If that industry or national context has not yet been covered by researchers and there is a good reason to believe there may be meaningful differences within that context, then you have an opportunity to take a unique angle on well-established theory, which can make for a great piece of research. It is however imperative that you have a good reason to believe that the existing theory may not be wholly relevant within your chosen context, or your research will not be justified.

The upside to this approach is that you can potentially find a topic that is “closer to home” and more relevant and interesting to you , while still being able to draw on a well-established body of theory. However, the downside is that this approach will likely not produce the level of originality as approach #1.

Approach 3: Uncensored brainstorming

The third option is to skip the FRIN, as well as the local/industry-specific angle and simply engage in a freeform brainstorming or mind-mapping session, using your newfound knowledge of the theory to formulate potential research ideas. What’s important here is that you do not censor yourself . However crazy, unfeasible, or plain stupid your topic appears – write it down. All that matters right now is that you are interested in this thing.

Next, try to turn the topic(s) into a question or problem. For example:

• What is the relationship between X, Y & Z?
• What are the drivers/antecedents of X?
• What are the outcomes of Y?
• What are the key success factors for Z?

Re-word your list of topics or issues into a list of questions .  You might find at this stage that one research topic throws up three questions (which then become sub-topics and even new separate topics in their own right) and in so doing, the list grows. Let it. Don’t hold back or try to start evaluating your ideas yet – just let them flow onto paper.

Once you’ve got a few topics and questions on paper, check the literature again to see whether any of these have been covered by the existing research. Since you came up with these from scratch, there is a possibility that your original literature search did not cover them, so it’s important to revisit that phase to ensure that you’re familiar with the relevant literature for each idea. You may also then find that approach #1 and #2 can be used to build on these ideas.

Try use all three approaches

As mentioned earlier, the three approaches discussed here are not mutually exclusive. In fact, the more, the merrier. Hopefully, you manage to utilise all three, as this will give you the best odds of producing a rich list of ideas, which you can then narrow down and evaluate, which is the next step.

Step 5: Narrow down, then evaluate

By this stage, you should have a healthy list of research topics. Step away from the ideation and thinking for a few days, clear your mind. The key is to get some distance from your ideas, so that you can sit down with your list and review it with a more objective view. The unbridled ideation phase is over and now it’s time to take a reality check .

Look at your list and see if any options can be crossed off right away .  Maybe you don’t want to do that topic anymore. Maybe the topic turned out to be too broad and threw up 20 hard to answer questions. Maybe all the literature you found about it was 30 years old and you suspect it might not be a very engaging contemporary issue . Maybe this topic is so over-researched that you’ll struggle to find anything fresh to say. Also, after stepping back, it’s quite common to notice that 2 or 3 of your topics are really the same one, the same question, which you’ve written down in slightly different ways. You can try to amalgamate these into one succinct topic.

Narrow down to the top 5, then evaluate

Now, take your streamlined list and narrow it down to the ‘top 5’ that interest you the most. Personal interest is your key evaluation criterion at this stage. Got your ‘top 5’?  Great!  Now, with a cool head and your best analytical mind engaged, go systematically through each option and evaluate them against the following criteria:

Research questions – what is the main research question, and what are the supporting sub-questions? It’s critically important that you can define these questions clearly and concisely. If you cannot do this, it means you haven’t thought the topic through sufficiently.

Originality – is the topic sufficiently original, as per your university’s originality requirements? Are you able to add something unique to the existing conversation? As mentioned earlier, originality can come in many forms, and it doesn’t mean that you need to find a completely new, cutting-edge topic. However, your university’s requirements should guide your decision-making here.

Importance – is the topic of real significance, or is it just a “nice to know”? If it’s significant, why? Who will benefit from finding the answer to your desired questions and how will they benefit? Justifying your research will be a key requirement for your research proposal , so it’s really important to develop a convincing argument here.

Literature – is there a contemporary (current) body of academic literature around this issue? Is there enough literature for you to base your investigation on, but not too much that the topic is “overdone”? Will you be able to navigate this literature or is it overwhelming?

Data requirements – What kind of data would you need access to in order to answer your key questions?  Would you need to adopt a qualitative, quantitative or mixed-methods approach to answer your questions? At this stage, you don’t need to be able to map out your exact research design, but you should be able to articulate how you would approach it in high-level terms. Will you use qual, quant or mixed methods? Why?

Feasibility – How feasible would it be to gather the data that would be needed in the time-frame that you have – and do you have the will power and the skills to do it? If you’re not confident with the theory, you don’t want something that’s going to draw you into a debate about the relative importance of epistemology and ontology. If you are shy, you won’t want to be doing ethnographic interviews. If you feel this question calls for a 100-person survey, do you have the time to plan, organise and conduct it and then analyse it? What will you do if you don’t get the response rate you expect? Be very realistic here and also ask advice from your supervisor and other experts – poor response rates are extremely common and can derail even the best research projects.

Personal attraction – On a scale of 1-10, how excited are you about this topic? Will addressing it add value to your life and/or career? Will undertaking the project help you build a skill you’ve previously wanted to work on (for example, interview skills, statistical analysis skills, software skills, etc.)?

The last point is particularly important. You will have to engage with your dissertation in a very sustained and deep way, face challenges and difficulties, and get it to completion. If you don’t start out enthusiastic about it, you’re setting yourself up for problems like ‘writer’s block’ or ‘burnout’ down the line. This is the reason personal interest was the sole evaluation criterion when we chose the top 5. So, don’t underestimate the importance of personal attraction to a topic – at the same time, don’t let personal attraction lead you to choose a topic that is not relevant to your course or feasible given your resources. 

Narrow down to 3, then get human feedback

We’re almost at the finishing line. The next step is to narrow down to 2 or 3 shortlisted topics. No more!  Write a short paragraph about each topic, addressing the following:

Firstly,  WHAT will this study be about? Frame the topic as a question or a problem. Write it as a dissertation title. No more than two clauses and no more than 15 words. Less than 15 is better (go back to good journal articles for inspiration on appropriate title styles).

Secondly, WHY this is interesting (original) and important – as proven by existing academic literature? Are people talking about this and is there an acknowledged problem, debate or gap in the literature?

Lastly,  HOW do you plan to answer the question? What sub-questions will you use? What methods does this call for and how competent and confident are you in those methods? Do you have the time to gather the data this calls for?

Show the shortlist and accompanying paragraphs to a couple of your peers from your course and also to an expert or two if at all possible (you’re welcome to reach out to us ), explaining what you will investigate, why this is original and important and how you will go about investigating it. 

Once you’ve pitched your ideas, ask for the following thoughts :

• Which is most interesting and appealing to them?
• Why do they feel this way?
• What problems do they foresee with the execution of the research?

Take advice and feedback and sit on it for another day. Let it simmer in your mind overnight before you make the final decision.  

Step 6: Make the decision (and stick with it!)

Then, make the commitment. Choose the one that you feel most confident about, having now considered both your opinion and the feedback from others.

Once you’ve made a decision, don’t doubt your judgement, don’t shift.  Don’t be tempted by the ones you left behind. You’ve planned and thought things through, checked feasibility and now you can start.  You have your research topic. Trust your own decision-making process and stick with it now. It’s time to get started on your research proposal!

Let’s recap…

In this post, I’ve proposed a straightforward 6-step plan to finding relevant research topic ideas and then narrowing them down to finally choose one winner. To recap:

• Understand the basics of academic research, as well as your university’s specific requirements for a dissertation, thesis or research project.
• Review previous dissertations for your course to get an idea of both topics and structure.
• Start the ideation process by familiarising yourself with the literature.
• Identify your potential research questions (topics).
• Narrow down your options, then evaluate systematically.
• Make your decision (and don’t look back!)

If you follow these steps, you’ll find that they also set you up for what’s coming next – both the proposal and the first three chapters of your dissertation. But that’s for future posts!

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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23 Comments

I would love to get a topic under teachers performance. I am a student of MSC Monitoring and Evaluations and I need a topic in the line of monitoring and evaluations

I just we put for some full notes that are payable

Thank you very much Dr Caroline

I need a project topics on transfer of learning

m a PhD Student I would like to be assisted inn formulating a title around: Internet of Things for online education in higher education – STEM (Science, technology, engineering and Mathematics, digital divide ) Thank you, would appreciate your guidance

Well structured guide on the topic… Good materials for beginners in research writing…

Hello Iam kindly seeking for help in formulating a researchable topic for masters degree program in line with teaching GRAPHIC ART

I read a thesis about a problem in a particular. Can I use the same topic just referring to my own country? Is that being original? The interview questions will mostly be the same as the other thesis.

Hi, thanks I managed to listen to the video so helpful indeed. I am currently an MBA student looking for a specific topic and I have different ideas that not sure they can be turned to be a study.

I am doing a Master of Theology in Pastoral Care and Counselling and I felt like doing research on Spiritual problem cause by substance abuse among Youth. Can I get help to formulate the Thesis Title in line with it…please

Hello, I am kindly seeking help in formulating a researchable topic for a National diploma program

As a beginner in research, I am very grateful for this well-structured material on research writing.

Hello, I watched the video and its very helpful. I’m a student in Nursing (degree). May you please help me with any research problems (in Namibian society or Nursing) that need to be evaluate or solved?

I have been greatly impacted. Thank you.

more than useful… there will be no justification if someone fails to get a topic for his thesis

I watched the video and its really helpful.

How can i started discovery

Analysing the significance of Integrated reporting in Zimbabwe. A case of institutional investors. this is my topic for PHD Accounting sciences need help with research questions

Excellent session that cleared lots of doubts.

Excellent session that cleared lots of doubts

It was a nice one thank you

Wow, This helped a lot not only with how to find a research topic but inspired me to kick it off from now, I am a final year student of environmental science. And have to complete my project in the coming six months.

I was really stressed and thinking about different topics that I don’t know nothing about and having more than a hundred topics in the baggage, couldn’t make the tradeoff among them, however, reading this scrubbed the fuzzy layer off my head and now it seems like really easy.

Thanks GRADCOACH, you saved me from getting into the rabbit hole.

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Selecting a Research Topic: Overview

• Refine your topic
• Background information & facts
• Writing help

Here are some resources to refer to when selecting a topic and preparing to write a paper:

• MIT Writing and Communication Center "Providing free professional advice about all types of writing and speaking to all members of the MIT community."
• Search Our Collections Find books about writing. Search by subject for: english language grammar; report writing handbooks; technical writing handbooks
• Blue Book of Grammar and Punctuation Online version of the book that provides examples and tips on grammar, punctuation, capitalization, and other writing rules.
• Select a topic

Choosing an interesting research topic is your first challenge. Here are some tips:

• Choose a topic that you are interested in! The research process is more relevant if you care about your topic.
• If your topic is too broad, you will find too much information and not be able to focus.
• Background reading can help you choose and limit the scope of your topic. 
• Review the guidelines on topic selection outlined in your assignment.  Ask your professor or TA for suggestions.
• Refer to lecture notes and required texts to refresh your knowledge of the course and assignment.
• Talk about research ideas with a friend.  S/he may be able to help focus your topic by discussing issues that didn't occur to you at first.
• WHY did you choose the topic?  What interests you about it?  Do you have an opinion about the issues involved?
• WHO are the information providers on this topic?  Who might publish information about it?  Who is affected by the topic?  Do you know of organizations or institutions affiliated with the topic?
• WHAT are the major questions for this topic?  Is there a debate about the topic?  Are there a range of issues and viewpoints to consider?
• WHERE is your topic important: at the local, national or international level?  Are there specific places affected by the topic?
• WHEN is/was your topic important?  Is it a current event or an historical issue?  Do you want to compare your topic by time periods?

Table of contents

• Broaden your topic
• Information Navigator home
• Sources for facts - general
• Sources for facts - specific subjects

Start here for help

Ask Us Ask a question, make an appointment, give feedback, or visit us.

• Next: Refine your topic >>
• Last Updated: Jul 30, 2021 2:50 PM
• URL: https://libguides.mit.edu/select-topic

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

General Education

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

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

What Makes a Good Research Paper Topic?

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

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

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

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

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

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

#3: It Fits Your Teacher's Guidelines

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

113 Good Research Paper Topics

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

Arts/Culture

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

Current Events

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

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

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

Science/Environment

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

How to Write a Great Research Paper

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

#1: Figure Out Your Thesis Early

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

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

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

#2: Back Every Statement Up With Research

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

#3: Do Your Research Before You Begin Writing

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

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

What's Next?

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

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

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

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• 4. The Introduction
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

The introduction leads the reader from a general subject area to a particular topic of inquiry. It establishes the scope, context, and significance of the research being conducted by summarizing current understanding and background information about the topic, stating the purpose of the work in the form of the research problem supported by a hypothesis or a set of questions, explaining briefly the methodological approach used to examine the research problem, highlighting the potential outcomes your study can reveal, and outlining the remaining structure and organization of the paper.

Key Elements of the Research Proposal. Prepared under the direction of the Superintendent and by the 2010 Curriculum Design and Writing Team. Baltimore County Public Schools.

Importance of a Good Introduction

Think of the introduction as a mental road map that must answer for the reader these four questions:

• What was I studying?
• Why was this topic important to investigate?
• What did we know about this topic before I did this study?
• How will this study advance new knowledge or new ways of understanding?

According to Reyes, there are three overarching goals of a good introduction: 1) ensure that you summarize prior studies about the topic in a manner that lays a foundation for understanding the research problem; 2) explain how your study specifically addresses gaps in the literature, insufficient consideration of the topic, or other deficiency in the literature; and, 3) note the broader theoretical, empirical, and/or policy contributions and implications of your research.

A well-written introduction is important because, quite simply, you never get a second chance to make a good first impression. The opening paragraphs of your paper will provide your readers with their initial impressions about the logic of your argument, your writing style, the overall quality of your research, and, ultimately, the validity of your findings and conclusions. A vague, disorganized, or error-filled introduction will create a negative impression, whereas, a concise, engaging, and well-written introduction will lead your readers to think highly of your analytical skills, your writing style, and your research approach. All introductions should conclude with a brief paragraph that describes the organization of the rest of the paper.

Hirano, Eliana. “Research Article Introductions in English for Specific Purposes: A Comparison between Brazilian, Portuguese, and English.” English for Specific Purposes 28 (October 2009): 240-250; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide. Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Reyes, Victoria. Demystifying the Journal Article. Inside Higher Education.

Structure and Writing Style

I.  Structure and Approach

The introduction is the broad beginning of the paper that answers three important questions for the reader:

• What is this?
• Why should I read it?
• What do you want me to think about / consider doing / react to?

Think of the structure of the introduction as an inverted triangle of information that lays a foundation for understanding the research problem. Organize the information so as to present the more general aspects of the topic early in the introduction, then narrow your analysis to more specific topical information that provides context, finally arriving at your research problem and the rationale for studying it [often written as a series of key questions to be addressed or framed as a hypothesis or set of assumptions to be tested] and, whenever possible, a description of the potential outcomes your study can reveal.

These are general phases associated with writing an introduction: 1.  Establish an area to research by:

• Highlighting the importance of the topic, and/or
• Making general statements about the topic, and/or
• Presenting an overview on current research on the subject.

2.  Identify a research niche by:

• Opposing an existing assumption, and/or
• Revealing a gap in existing research, and/or
• Formulating a research question or problem, and/or
• Continuing a disciplinary tradition.

3.  Place your research within the research niche by:

• Stating the intent of your study,
• Outlining the key characteristics of your study,
• Describing important results, and
• Giving a brief overview of the structure of the paper.

NOTE:   It is often useful to review the introduction late in the writing process. This is appropriate because outcomes are unknown until you've completed the study. After you complete writing the body of the paper, go back and review introductory descriptions of the structure of the paper, the method of data gathering, the reporting and analysis of results, and the conclusion. Reviewing and, if necessary, rewriting the introduction ensures that it correctly matches the overall structure of your final paper.

II.  Delimitations of the Study

Delimitations refer to those characteristics that limit the scope and define the conceptual boundaries of your research . This is determined by the conscious exclusionary and inclusionary decisions you make about how to investigate the research problem. In other words, not only should you tell the reader what it is you are studying and why, but you must also acknowledge why you rejected alternative approaches that could have been used to examine the topic.

Obviously, the first limiting step was the choice of research problem itself. However, implicit are other, related problems that could have been chosen but were rejected. These should be noted in the conclusion of your introduction. For example, a delimitating statement could read, "Although many factors can be understood to impact the likelihood young people will vote, this study will focus on socioeconomic factors related to the need to work full-time while in school." The point is not to document every possible delimiting factor, but to highlight why previously researched issues related to the topic were not addressed.

Examples of delimitating choices would be:

• The key aims and objectives of your study,
• The research questions that you address,
• The variables of interest [i.e., the various factors and features of the phenomenon being studied],
• The method(s) of investigation,
• The time period your study covers, and
• Any relevant alternative theoretical frameworks that could have been adopted.

Review each of these decisions. Not only do you clearly establish what you intend to accomplish in your research, but you should also include a declaration of what the study does not intend to cover. In the latter case, your exclusionary decisions should be based upon criteria understood as, "not interesting"; "not directly relevant"; “too problematic because..."; "not feasible," and the like. Make this reasoning explicit!

NOTE:   Delimitations refer to the initial choices made about the broader, overall design of your study and should not be confused with documenting the limitations of your study discovered after the research has been completed.

ANOTHER NOTE: Do not view delimitating statements as admitting to an inherent failing or shortcoming in your research. They are an accepted element of academic writing intended to keep the reader focused on the research problem by explicitly defining the conceptual boundaries and scope of your study. It addresses any critical questions in the reader's mind of, "Why the hell didn't the author examine this?"

III.  The Narrative Flow

Issues to keep in mind that will help the narrative flow in your introduction :

• Your introduction should clearly identify the subject area of interest . A simple strategy to follow is to use key words from your title in the first few sentences of the introduction. This will help focus the introduction on the topic at the appropriate level and ensures that you get to the subject matter quickly without losing focus, or discussing information that is too general.
• Establish context by providing a brief and balanced review of the pertinent published literature that is available on the subject. The key is to summarize for the reader what is known about the specific research problem before you did your analysis. This part of your introduction should not represent a comprehensive literature review--that comes next. It consists of a general review of the important, foundational research literature [with citations] that establishes a foundation for understanding key elements of the research problem. See the drop-down menu under this tab for " Background Information " regarding types of contexts.
• Clearly state the hypothesis that you investigated . When you are first learning to write in this format it is okay, and actually preferable, to use a past statement like, "The purpose of this study was to...." or "We investigated three possible mechanisms to explain the...."
• Why did you choose this kind of research study or design? Provide a clear statement of the rationale for your approach to the problem studied. This will usually follow your statement of purpose in the last paragraph of the introduction.

IV.  Engaging the Reader

A research problem in the social sciences can come across as dry and uninteresting to anyone unfamiliar with the topic . Therefore, one of the goals of your introduction is to make readers want to read your paper. Here are several strategies you can use to grab the reader's attention:

• Open with a compelling story . Almost all research problems in the social sciences, no matter how obscure or esoteric , are really about the lives of people. Telling a story that humanizes an issue can help illuminate the significance of the problem and help the reader empathize with those affected by the condition being studied.
• Include a strong quotation or a vivid, perhaps unexpected, anecdote . During your review of the literature, make note of any quotes or anecdotes that grab your attention because they can used in your introduction to highlight the research problem in a captivating way.
• Pose a provocative or thought-provoking question . Your research problem should be framed by a set of questions to be addressed or hypotheses to be tested. However, a provocative question can be presented in the beginning of your introduction that challenges an existing assumption or compels the reader to consider an alternative viewpoint that helps establish the significance of your study. 
• Describe a puzzling scenario or incongruity . This involves highlighting an interesting quandary concerning the research problem or describing contradictory findings from prior studies about a topic. Posing what is essentially an unresolved intellectual riddle about the problem can engage the reader's interest in the study.
• Cite a stirring example or case study that illustrates why the research problem is important . Draw upon the findings of others to demonstrate the significance of the problem and to describe how your study builds upon or offers alternatives ways of investigating this prior research.

NOTE:   It is important that you choose only one of the suggested strategies for engaging your readers. This avoids giving an impression that your paper is more flash than substance and does not distract from the substance of your study.

Freedman, Leora  and Jerry Plotnick. Introductions and Conclusions. University College Writing Centre. University of Toronto; Introduction. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College; Introductions. The Writing Center. University of North Carolina; Introductions. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Introductions, Body Paragraphs, and Conclusions for an Argument Paper. The Writing Lab and The OWL. Purdue University; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70; Resources for Writers: Introduction Strategies. Program in Writing and Humanistic Studies. Massachusetts Institute of Technology; Sharpling, Gerald. Writing an Introduction. Centre for Applied Linguistics, University of Warwick; Samraj, B. “Introductions in Research Articles: Variations Across Disciplines.” English for Specific Purposes 21 (2002): 1–17; Swales, John and Christine B. Feak. Academic Writing for Graduate Students: Essential Skills and Tasks . 2nd edition. Ann Arbor, MI: University of Michigan Press, 2004 ; Writing Your Introduction. Department of English Writing Guide. George Mason University.

Writing Tip

Avoid the "Dictionary" Introduction

Giving the dictionary definition of words related to the research problem may appear appropriate because it is important to define specific terminology that readers may be unfamiliar with. However, anyone can look a word up in the dictionary and a general dictionary is not a particularly authoritative source because it doesn't take into account the context of your topic and doesn't offer particularly detailed information. Also, placed in the context of a particular discipline, a term or concept may have a different meaning than what is found in a general dictionary. If you feel that you must seek out an authoritative definition, use a subject specific dictionary or encyclopedia [e.g., if you are a sociology student, search for dictionaries of sociology]. A good database for obtaining definitive definitions of concepts or terms is Credo Reference .

Saba, Robert. The College Research Paper. Florida International University; Introductions. The Writing Center. University of North Carolina.

Another Writing Tip

When Do I Begin?

A common question asked at the start of any paper is, "Where should I begin?" An equally important question to ask yourself is, "When do I begin?" Research problems in the social sciences rarely rest in isolation from history. Therefore, it is important to lay a foundation for understanding the historical context underpinning the research problem. However, this information should be brief and succinct and begin at a point in time that illustrates the study's overall importance. For example, a study that investigates coffee cultivation and export in West Africa as a key stimulus for local economic growth needs to describe the beginning of exporting coffee in the region and establishing why economic growth is important. You do not need to give a long historical explanation about coffee exports in Africa. If a research problem requires a substantial exploration of the historical context, do this in the literature review section. In your introduction, make note of this as part of the "roadmap" [see below] that you use to describe the organization of your paper.

Introductions. The Writing Center. University of North Carolina; “Writing Introductions.” In Good Essay Writing: A Social Sciences Guide . Peter Redman. 4th edition. (London: Sage, 2011), pp. 63-70.

Yet Another Writing Tip

Always End with a Roadmap

The final paragraph or sentences of your introduction should forecast your main arguments and conclusions and provide a brief description of the rest of the paper [the "roadmap"] that let's the reader know where you are going and what to expect. A roadmap is important because it helps the reader place the research problem within the context of their own perspectives about the topic. In addition, concluding your introduction with an explicit roadmap tells the reader that you have a clear understanding of the structural purpose of your paper. In this way, the roadmap acts as a type of promise to yourself and to your readers that you will follow a consistent and coherent approach to addressing the topic of inquiry. Refer to it often to help keep your writing focused and organized.

Cassuto, Leonard. “On the Dissertation: How to Write the Introduction.” The Chronicle of Higher Education , May 28, 2018; Radich, Michael. A Student's Guide to Writing in East Asian Studies . (Cambridge, MA: Harvard University Writing n. d.), pp. 35-37.

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What Is Research, and Why Do People Do It?

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• First Online: 03 December 2022

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

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

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Abstractspiepr Abs1

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

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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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Home Market Research

What is Research: Definition, Methods, Types & Examples

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

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

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

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

Research is conducted with a purpose to:

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

What is the purpose of research?

There are three main purposes:

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

LEARN ABOUT: Descriptive Analysis

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

LEARN ABOUT: Best Data Collection Tools

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

Here is a comparative analysis chart for a better understanding:

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

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

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

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods .

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

Types of qualitative methods include:

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

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

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

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

It is essential to ensure that your data is:

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

Gather insights

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

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

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Types of Research – Explained with Examples

• By DiscoverPhDs
• October 2, 2020

Types of Research

Research is about using established methods to investigate a problem or question in detail with the aim of generating new knowledge about it.

It is a vital tool for scientific advancement because it allows researchers to prove or refute hypotheses based on clearly defined parameters, environments and assumptions. Due to this, it enables us to confidently contribute to knowledge as it allows research to be verified and replicated.

Knowing the types of research and what each of them focuses on will allow you to better plan your project, utilises the most appropriate methodologies and techniques and better communicate your findings to other researchers and supervisors.

Classification of Types of Research

There are various types of research that are classified according to their objective, depth of study, analysed data, time required to study the phenomenon and other factors. It’s important to note that a research project will not be limited to one type of research, but will likely use several.

According to its Purpose

Theoretical research.

Theoretical research, also referred to as pure or basic research, focuses on generating knowledge , regardless of its practical application. Here, data collection is used to generate new general concepts for a better understanding of a particular field or to answer a theoretical research question.

Results of this kind are usually oriented towards the formulation of theories and are usually based on documentary analysis, the development of mathematical formulas and the reflection of high-level researchers.

Applied Research

Here, the goal is to find strategies that can be used to address a specific research problem. Applied research draws on theory to generate practical scientific knowledge, and its use is very common in STEM fields such as engineering, computer science and medicine.

This type of research is subdivided into two types:

• Technological applied research : looks towards improving efficiency in a particular productive sector through the improvement of processes or machinery related to said productive processes.
• Scientific applied research : has predictive purposes. Through this type of research design, we can measure certain variables to predict behaviours useful to the goods and services sector, such as consumption patterns and viability of commercial projects.

According to your Depth of Scope

Exploratory research.

Exploratory research is used for the preliminary investigation of a subject that is not yet well understood or sufficiently researched. It serves to establish a frame of reference and a hypothesis from which an in-depth study can be developed that will enable conclusive results to be generated.

Because exploratory research is based on the study of little-studied phenomena, it relies less on theory and more on the collection of data to identify patterns that explain these phenomena.

Descriptive Research

The primary objective of descriptive research is to define the characteristics of a particular phenomenon without necessarily investigating the causes that produce it.

In this type of research, the researcher must take particular care not to intervene in the observed object or phenomenon, as its behaviour may change if an external factor is involved.

Explanatory Research

Explanatory research is the most common type of research method and is responsible for establishing cause-and-effect relationships that allow generalisations to be extended to similar realities. It is closely related to descriptive research, although it provides additional information about the observed object and its interactions with the environment.

Correlational Research

The purpose of this type of scientific research is to identify the relationship between two or more variables. A correlational study aims to determine whether a variable changes, how much the other elements of the observed system change.

According to the Type of Data Used

Qualitative research.

Qualitative methods are often used in the social sciences to collect, compare and interpret information, has a linguistic-semiotic basis and is used in techniques such as discourse analysis, interviews, surveys, records and participant observations.

In order to use statistical methods to validate their results, the observations collected must be evaluated numerically. Qualitative research, however, tends to be subjective, since not all data can be fully controlled. Therefore, this type of research design is better suited to extracting meaning from an event or phenomenon (the ‘why’) than its cause (the ‘how’).

Quantitative Research

Quantitative research study delves into a phenomena through quantitative data collection and using mathematical, statistical and computer-aided tools to measure them . This allows generalised conclusions to be projected over time.

According to the Degree of Manipulation of Variables

Experimental research.

It is about designing or replicating a phenomenon whose variables are manipulated under strictly controlled conditions in order to identify or discover its effect on another independent variable or object. The phenomenon to be studied is measured through study and control groups, and according to the guidelines of the scientific method.

Non-Experimental Research

Also known as an observational study, it focuses on the analysis of a phenomenon in its natural context. As such, the researcher does not intervene directly, but limits their involvement to measuring the variables required for the study. Due to its observational nature, it is often used in descriptive research.

Quasi-Experimental Research

It controls only some variables of the phenomenon under investigation and is therefore not entirely experimental. In this case, the study and the focus group cannot be randomly selected, but are chosen from existing groups or populations . This is to ensure the collected data is relevant and that the knowledge, perspectives and opinions of the population can be incorporated into the study.

According to the Type of Inference

Deductive investigation.

In this type of research, reality is explained by general laws that point to certain conclusions; conclusions are expected to be part of the premise of the research problem and considered correct if the premise is valid and the inductive method is applied correctly.

Inductive Research

In this type of research, knowledge is generated from an observation to achieve a generalisation. It is based on the collection of specific data to develop new theories.

Hypothetical-Deductive Investigation

It is based on observing reality to make a hypothesis, then use deduction to obtain a conclusion and finally verify or reject it through experience.

According to the Time in Which it is Carried Out

Longitudinal study (also referred to as diachronic research).

It is the monitoring of the same event, individual or group over a defined period of time. It aims to track changes in a number of variables and see how they evolve over time. It is often used in medical, psychological and social areas .

Cross-Sectional Study (also referred to as Synchronous Research)

Cross-sectional research design is used to observe phenomena, an individual or a group of research subjects at a given time.

According to The Sources of Information

Primary research.

This fundamental research type is defined by the fact that the data is collected directly from the source, that is, it consists of primary, first-hand information.

Secondary research

Unlike primary research, secondary research is developed with information from secondary sources, which are generally based on scientific literature and other documents compiled by another researcher.

According to How the Data is Obtained

Documentary (cabinet).

Documentary research, or secondary sources, is based on a systematic review of existing sources of information on a particular subject. This type of scientific research is commonly used when undertaking literature reviews or producing a case study.

Field research study involves the direct collection of information at the location where the observed phenomenon occurs.

From Laboratory

Laboratory research is carried out in a controlled environment in order to isolate a dependent variable and establish its relationship with other variables through scientific methods.

Mixed-Method: Documentary, Field and/or Laboratory

Mixed research methodologies combine results from both secondary (documentary) sources and primary sources through field or laboratory research.

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An academic transcript gives a breakdown of each module you studied for your degree and the mark that you were awarded.

An abstract and introduction are the first two sections of your paper or thesis. This guide explains the differences between them and how to write them.

Nina’s in the first year of her PhD in the Department of Psychology at the University of Bath. Her project is focused on furthering our understanding of fatigue within adolescent depression.

Helena is a final year PhD student at the Natural History Museum in London and the University of Oxford. Her research is on understanding the evolution of asteroids through analysis of meteorites.

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• Thinking about Research

As you consider research, it is important to know why you want to make this commitment. Do you want to participate more actively in cutting edge science that you have learned about in the classroom? Is there a particular field or topic you would like to learn more about? Do you want to gain specific skills? Do you want to explore research as a potential career, or as a component of a career? Do you want to experience part of the work environment in a medical facility? Do you want to get course credit? Do you want to embark on a project that could evolve into a senior honors thesis?

Think also about the type of research you may find most useful for your individual goal or set of goals. Would you like to help run scientific experiments? Would you like to help design and/or analyze the results of experiments? Would you like to conduct survey research? Would you like to do archival research in a library setting? Would you prefer to be part of a research team or to work alone? Would you like to do research with humans or animals? Would you like to work with a specific species of animal, or a particular population of humans (e.g., children, elderly, persons with a certain disorder)?

It is helpful to consult with others as you think through these questions. Faculty you already know, other faculty including members of the MBB Board of Faculty Advisors [link here, <mbb/advising>], academic advisors (especially in your concentration but also including Shawn Harriman), teaching fellows and resident tutors (who are usually themselves researchers), and fellow students who are already involved in research are all great sounding boards and sources of additional information and perspectives.

Finally, give some thought about what you can offer a research team. This will help you when you come to applying for specific positions. A current resume is always valuable. You may have specific research experience already, or have taken relevant course work. You may be a good team player with a track record of responsibility and accomplishing goals. You do not usually need to have training in the specific techniques used in a laboratory or research program, as most researchers expect to train their undergraduate assistants. Positions that do have specific expectations will note them in their job description.

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Mental Health in the Workplace: A Conversation Bridging Research and Practice

How can we promote mental health in the workplace? 

This is a question that Zhiqing (Albert) Zhou , PhD, and Lawanda Lewis are constantly asking themselves in their work, just from different perspectives. As an associate professor in the Department of Mental Health , Zhou researches how employees’ work-related experiences impact their health, well-being, and safety. As an HR Business Partner who oversees multiple academic departments at the Bloomberg School, Lewis has firsthand experience with assessing the mental health needs of employees and the effectiveness of workplace mental health and wellness programs.

This Mental Health Awareness Month, Zhou and Lewis came together for a wide-ranging conversation about research, practice, program implementation, and what still needs to be learned to help workplaces manage and support the mental health of their employees.

This conversation has been edited and condensed for length and clarity.

Lawanda Lewis: Post-COVID, we’re seeing different work modalities, from fully remote work to hybrid work to a mix. Hybrid work seems to be the way of the world now. What approaches can organizations take to better promote mental wellness in a hybrid workforce?

Albert Zhou:  There is consistent evidence of the benefits of hybrid work, such as more flexibility, more autonomy, reducing commuting time, and better work-life balance. Meanwhile, there are reports of remote or hybrid workers feeling lonely or isolated, dealing with the added stress of shared spaces and family responsibilities, and feeling pressure to always appear available and present. We published a study in 2022 in the International Journal of Human Resource Management that found that workers’ mental health was negatively impacted when they felt too closely monitored by their managers.

One way to deal with this is to make sure managers are trained to prepare, support, and better communicate expectations and guidelines for hybrid and remote workers, while giving workers flexibility and more control over their time. Social and emotional support from coworkers and supervisors is also instrumental to building a healthy work life. People should be able to ask questions, connect with their colleagues, and access resources regardless of when and where they’re working. My collaborators and I are trying to understand how hybrid or remote work can be better managed so that workers can enjoy the benefits and reduce the negative consequences for their mental health.

LL: What has recent research revealed about the mental health benefits of transitioning to a four-day work week with no pay reduction?

AZ: We still need to do more research on the four-day work week, and we don’t yet have consistent solutions, even though this topic has been discussed for over 50 years. But there have been pilots and case studies in several countries that have shown evidence of increased productivity. Workers in these studies reported that they are more satisfied with their work, have better work life balance, and experience less stress and burnout. However, one issue that came up is scheduling problems. For example, I’m working for four days, but my clients are not, so how can we align our work? When we studied weekly work cycles , we found that Monday is already the most stressful day. Since working on Friday is off the table, how do I make sure too much work doesn’t pile up on Monday? 

It’s important to note that these pilot programs were tested with a small number of organizations who voluntarily participated, which means they were already open to the idea of a four-day work week. It's unclear, then, whether their practices can be generalized to other workplaces. The transition to a four-day work week may be easier for office workers, but it would be harder for people in industries where people’s work schedules are less flexible, like blue collar workers or healthcare workers. Again, more research is needed, especially with HR professionals like you, since a lot of these changes will be implemented through HR functions. You are at the front line of making sure that it works as planned, taking feedback, and continuously shaping the practice.

I’m learning a lot about HR practices, and I was wondering if you could give examples of programs you have implemented to promote workplace mental health. 

LL:  One of our most important programs is the Johns Hopkins Employee Assistance Program (JHEAP), which provides confidential counseling, resources, and referrals to employees and their families for personal and work-related issues. And we’ve implemented flexible work arrangements. Hybrid or modified hybrid schedules allow employees to meet the needs of their roles and divisions while still managing their personal and work lives. 

We also provide programs that can help employees manage their physical, emotional, social, and financial well-being, like meditation and yoga classes; premium memberships to tools that reduce stress and improve focus, like the Calm app; and the Healthy at Hopkins Wellness Initiative hub for resources and benefits.

Our leadership trainings raise awareness of mental health and unconscious bias and help supervisors recognize and manage employee stress. To reduce the stigma of talking about mental health, we regularly coach managers on how to create open dialogue with their employees about issues like stress and workloads. Through these kinds of initiatives, we want to help managers create a psychologically safe work environment. 

AZ:  Offering a variety of programs is great for addressing individuals’ different needs and creating psychologically safe relationships, while also caring about the overall work environment. Of all these different programs, what has worked well? 

LL:  Our Employee Assistance Program has evolved over the years. The University has been good at adapting it as work set-ups change and employee needs change, so that flexibility has led to a lot of reinventions over the years. Being flexible with our employees’ work arrangements has also worked really well. Everyone is dealing with day-to-day issues and unpredictable situations, so we want managers to balance knowing what needs to be done with caring for their employees. 

JHU’s supervisor trainings have helped managers lead fairly, create open communication, and provide timely feedback so that employees always know where they stand. We also think it’s important to show employees appreciation and recognition for their hard work.

AZ:  Definitely. We’ve seen in research that lack of recognition negatively affects productivity, performance, and mental well-being. It’s always good to see appreciation and recognition coming from the top down. Are there other workplace mental health topics that HR professionals like you are interested in right now?

LL:  I'm interested in learning more about efforts to reduce stigma and promote open dialogue, especially when it comes to relationships between supervisors and their subordinates. What should organizations look out for when managing that relationship?

AZ:  That's a great question because supervisors play an important role in employee mental health. From the research perspective, we develop specific, reliable, and valid measures to assess supervisor behaviors. The commonly used approach is asking workers to answer questions about the frequency of certain supervisor behaviors, such as rudeness or inattention. Those kinds of behaviors are subtle and sometimes low intensity but can affect people’s well-being if experienced regularly.

It's important to note that sometimes employees’ perceptions might not correspond to the actual behavior of the supervisor. The supervisor might not intentionally be rude, but their behaviors can still be perceived as rudeness or incivility. That's why when we study supervisor behaviors, it's important to calibrate across multiple direct reports of the same supervisor. That's an indication of a pattern of behavior and that action needs to be taken in terms of interventions or training. So, I think it’s important for organizations to continuously gather employees’ perceptions and combine data from multiple sources to get a more accurate reflection of supervisor behaviors. To prevent incivility in the workplace, it's important to build an environment where people are aware of their behavior and are mindful of their impact and talk about mental health.

LL:  Reducing mental health stigma is a major theme. We want to raise awareness of resources and make sure that people get the support they need. But when we start talking about illnesses, we start to trickle into the lines of protected health information and figuring out how to handle that information. We have an office that supports employees who need accommodations, but we are still learning. 

AZ: It’s great that HR is thinking about and prioritizing workplace mental health because that’s not the case everywhere. The research on workplace mental health is also still evolving. I’m doing a review piece with a student about disclosure of mental health conditions and how we can foster more open communication so support can be provided. But there’s still a long way to go. As a researcher, I want to keep providing evidence to help teams like yours who are doing actual implementation and supporting employee health and well-being. 

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• Copy URL https://www.pbs.org/newshour/politics/guilty-or-not-guilty-trump-verdict-wont-sway-most-voters-poll-shows

Guilty or not guilty, Trump verdict won’t sway most voters, poll shows

UPDATE: A jury convicted Donald Trump on all 34 counts in his hush money trial in New York on May 30. Judge Juan Merchan set Trump’s sentencing for July 11, days before the Republican National Convention and shortly after the first Biden-Trump presidential debate of the 2024 election. Find the latest updates here.

As a jury deliberates on former President Donald Trump’s criminal hush money charges in New York, 2 in 3 registered U.S. voters say a guilty verdict would have no effect on whom they plan to vote for in the presidential election, according to a PBS NewsHour/NPR/Marist poll .

Overall, 67 percent of voters said a conviction would make no difference for them in November, including 74 percent of independents. That’s a significantly higher number than the percentage of either Republicans or Democrats who said it wouldn’t change their vote.

In fact, 25 percent of Republicans said they would be even more likely to vote for Trump if he were found guilty by a jury, while 27 percent of Democrats said they would be less likely to vote for him – a split that underscores hardened partisan perspectives on candidate Trump.

Chart by Megan McGrew/PBS NewsHour

To Democratic strategist Simon Rosenberg, if Trump “ends up getting found guilty, I think it makes it much less likely for him to win. But [Democrats] don’t need it in order to win.”

Republican strategist Douglas Heye said he thought a guilty verdict would “give a subset of voters something to think about, but not a ton of voters.”

Narrow slivers of Republicans – 10 percent – and independent voters – 11 percent – said they would be less likely to vote for Trump if he is found guilty.

The latest poll also asked voters whether Trump being acquitted would affect their vote preference. A large majority – 76 percent – seemed to see a not-guilty verdict as keeping the status quo, saying that outcome would make no difference to them on Election Day.

Because the Manhattan court proceedings are not televised, Trump’s trial has gone largely unnoticed by many Americans, said Heye, who noted he has traveled extensively around the country in recent weeks.

“No one is talking about the trial except people in Washington, D.C. and New York City,” he said. “I’m not surprised anymore.”

Survey data supports that claim. Last month, 55 percent of Americans said they were paying little to no attention to Trump’s hush money trial, according to a May 1 PBS NewsHour/NPR/Marist poll.

The lack of public interest in Trump’s latest trial is indisputable, Rosenberg said, adding that it highlights how disengaged people remain at this point in the election cycle. “People don’t know the economy is growing, not shrinking. Why do we think they’re going to know about Trump’s trials?”

But that is likely to change as Election Day looms closer, he said.

“As the American people check in and go through the process of going from being disinterested to becoming a voter, the information they gain is far more likely to benefit [Democrats] than Republicans,” Rosenberg said.

Biden vs. Trump (and the 3rd party factor)

If the election were held today, 50 percent of national registered voters said they would vote for President Joe Biden, according to this latest poll. Meanwhile, 48 percent said they would vote to reelect former president Donald Trump.

This head-to-head has remained essentially unchanged for the last two months, and is in line with what many national polls have shown – “a close, competitive election,” Rosenberg said.

To Heye, it’s significant that despite Trump being embroiled in scandal for years, the public’s overall enthusiasm for the former president is statistically tied with that for Biden – a reminder, he said, that both candidates are deeply unpopular. In this latest poll, Biden’s approval rating remains at 41 percent among Americans overall, while his disapproval rating is stuck at 54 percent.

“That ought to send an alarm bell to Democrats more than Republicans,” Heye said.

“I think the notion that Trump has a lead is not consistent with the data in front of us,” Rosenberg said. A headline-grabbing New York Times/Siena poll in mid-May found Trump had an advantage in a handful of battleground states, such as Arizona and Michigan. More recently, Trump received support from former Republican rival Nikki Haley, who has continued to sap primary votes from the presumptive nominee despite leaving the race in March. Haley’s supporters are seen as a key group to win over, and the Biden campaign has been trying .

Six months ahead of the 2024 presidential election, voters still have a lot of time to decide who should lead the country. About one in four Americans said in this poll that they were not following this election closely, if at all. Rosenberg, who also called the current race a “true toss-up right now,” said that once voters begin to examine the records of both presumptive nominees, they will see that Biden “has been a good president” and that “the country is better off.”

At the same time, he predicted, voters will find Trump’s “performance on the stump is far more erratic and disturbing” than in 2016 or 2020, and that “his agenda is far more extreme and dangerous.”

When voters were asked to choose from a wider field of candidates, this poll found Trump had a small advantage: 44 percent of voters said they would vote for Trump, 40 percent said they would vote for Biden and 8 percent would vote for independent candidate Robert F. Kennedy Jr. Kennedy – who, in the days since this latest poll was conducted, was rejected by Libertarians for their party nomination and also said he opposed the removal of Confederate monuments – received support from 17 percent of independent voters.

“He’s not a strong candidate, and he’s not going to be a major public presence in the election,” Rosenberg said, adding that beyond his famous last name, the candidate and his agenda do not appeal to most Democrats. However, “fringe candidates can impact the election,” he said.

Eight in 10 national registered voters said they will be “definitely voting” in November, though that enthusiasm was less likely among younger voters. Sixty-nine percent of Gen Z and millennials said they will vote without fail, compared to 93 percent of the Silent Generation. In this poll, those younger voters (who seemed in this poll less sure as a generation that they would vote compared to others) favored Biden to Trump in a head-to-head match (52 percent to 46 percent), while the oldest voters preferred Trump to Biden (53 percent to 47 percent).

A growing share of national registered voters say they know who they will vote for in the 2024 presidential election and nothing will change their minds. In this latest poll, 66 percent of national registered voters say they already know which candidate has their vote, up 6 percentage points since April. In another show of the youth enthusiasm gap, Gen Z and millennial registered voters were the most likely to be still weighing their options.

Public attitudes about the war in Gaza

The percentage of Americans who think the U.S. provides too much military aid to Israel (35 percent) has risen 4 percentage points since November, according to this latest poll, which was conducted days before a deadly Israeli strike on a civilian camp in the Gazan city of Rafah.

At the same time, a growing number of Americans feel the U.S. is being overly generous to Palestinians. Twenty-nine percent think the country is doing too much to provide humanitarian aid to Palestinians, up from 22 percent last November. Meanwhile, the percentage who feel the U.S. should do more on humanitarian aid is shrinking, dropping from 40 percent to 29 percent in the last six months.

And yet, overall, more than a third of Americans (36 percent) said the U.S. is offering the right amount of humanitarian relief, according to this latest poll.

When given a range of options about what the U.S. role should be in the war, roughly half of Americans (48 percent) said the U.S. should support Israel’s right to defend itself against Hamas while also using its influence to encourage Israel to protect Palestinian civilians. To Mary McCord, a former Justice Department acting assistant attorney general for national security, the data suggests there may be more consensus around U.S. policy on the current Israel-Gaza war than some might think.

“Polarization is driven at the margins by people who are more vocal and whose positions get more publicity,” said McCord, a professor of law at Georgetown University. “They drive the extremes. That creates an impression that’s absorbed by the population that we’re farther apart than the bulk of the population is.”

Though polls are always a snapshot in time – a survey taken this week, for instance, in the aftermath of the widely condemned Rafah blast, could have shown different results – the percentage of Americans who say they support Israel defending itself far outflanks the 25 percent of Americans who say the U.S. should withdraw all support for Israel until a cease-fire is secured in Gaza (something 38 percent of Gen Z and millennials supported). It is also substantially more than 23 percent of Americans who say the U.S. should fully support Israel’s military actions against Hamas (an idea supported by 32 percent of the Silent Generation).

The PBS NewsHour, NPR and Marist Poll conducted a survey on May 21 through May 23 that polled 1,261 U.S. adults with a margin of error of 3.4 percentage points, 1,122 registered voters with a margin of error of 3.7 percentage points and 907 registered voters who definitely plan to vote in November’s general election with a margin of error of 4.1 percentage points.

Laura Santhanam is the Health Reporter and Coordinating Producer for Polling for the PBS NewsHour, where she has also worked as the Data Producer. Follow @LauraSanthanam

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Public’s Positive Economic Ratings Slip; Inflation Still Widely Viewed as Major Problem

1. views of the nation’s economy, table of contents.

• Views of top problems facing the nation
• Americans’ views of the state of the nation
• Similar shares in both parties view personal financial situation positively
• Americans’ views on the future of the economy and their financial situation
• Changes in views of the country’s top problems
• Acknowledgments
• The American Trends Panel survey methodology

Fewer than a quarter of Americans (23%) currently rate the country’s economic conditions as excellent or good, while 36% say they are poor and about four-in-ten (41%) view conditions as “only fair.”

While positive ratings of the economy have slowly climbed since the summer of 2022, there has been a slight drop  since the start of the year – when 28% rated economic conditions as excellent or good.

This change has been largely driven by Democrats and Democratic leaners: In January of this year, 44% of Democrats rated the economy positively, compared with 37% now.

Still, ratings among Democrats remain higher than they were last year.

Views among Republicans and GOP leaners remain negative: Just one-in-ten rate economic conditions as excellent or good, while half say they are poor and another four-in-ten view them as “only fair.”

Views of the nation’s economy have long been partisan.

• Republicans expressed far more positive views of the economy than did Democrats throughout most of Donald Trump’s presidency.
• Democrats have been consistently more likely than Republicans to rate conditions as excellent or good during Biden’s presidency. However, their ratings have been far less positive than Republicans’ ratings of the economy were when Trump was president. 

There also are wide differences in views of the economy by age and race and ethnicity – especially among Democrats.

Age, race and ethnicity

As in the past, Democrats under age 50 express much less positive views of the nation’s economy than do Democrats 50 and older:

• Just 21% of Democrats under 30 rate economic conditions positively, as do 29% of those 30 to 49.
• By contrast, nearly half of Democrats ages 50 to 64 (47%) and a majority of those 65 and older (55%) say conditions are excellent or good.

However, since January there has been a steeper decline in positive views among Democrats 65 and older (from 70% to 55%) than among Democrats in younger age groups.

Republicans are much less likely to view current economic conditions in positive terms across age groups.

There are also significant differences among Democrats by race and ethnicity. White Democrats are more likely than Black, Hispanic and Asian Democrats to rate the economy positively. However, ratings have dropped across these groups since January.

Views of personal finances and national economic ratings

As might be expected, those who rate their personal finances positively also are more likely to rate national economic conditions as excellent or good.

Among the roughly four-in-ten Americans (41%) who rate their own finances positively, 40% rate the national economy positively. Among those who say their finances are only fair or poor, far fewer say national economic conditions are excellent or good (14% among only fair, 6% among poor).

However, partisanship is a factor here as well. Among Democrats who have a positive evaluation of their finances, 58% rate economic conditions positively. That compares with just 19% of Republicans who give similarly positive ratings of their financial situation.

Overall, personal financial ratings have fluctuated less dramatically than national ratings.

However, the share of Americans who rate their personal finances as excellent or good declined from about 50% in 2021 to about 40% in 2022 and has remained at about that level since then.

About four-in-ten say their financial situation is in excellent or good shape (41%), while a similar share say their situation is in “only fair” shape (39%). Another 19% say their situation is in poor shape.

Americans’ ratings of their personal finances are considerably less partisan than their views of the nation’s economy. Roughly four-in-ten Democrats (44%) say their financial situation is in excellent or good shape.

When asked for their expectations of the country’s economic conditions a year from now, 43% of Americans say they expect it to be about the same as it currently is. About a quarter (24%) say they expect the economy will be better a year from now, and nearly a third (32%) expect conditions to worsen.

And when asked for their expectations of their own family’s financial situation a year from now, 49% of adults say they expect it to be about the same. Roughly a third (34%) say they expect their financial situation will be better a year from now, and 16% expect their situation to worsen.

The shares of the public who expect economic conditions to worsen on either a national level or personal level is smaller than in recent years .

Among partisans, similar shares expect economic conditions of the country to be better a year from now (23% of Republicans, 26% of Democrats). However, a larger share of Republicans than Democrats expect the country’s economic conditions to worsen (38% vs. 25%).

Republicans remain less hopeful than Democrats about the future of their personal financial situation. About three-in-ten Republicans (29%) say their family’s personal finances will be better a year from now, compared with 39% of Democrats who say the same. And twice as many Republicans as Democrats say they expect their own financial situation to worsen (22% vs. 11%).

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Most AAPI adults think the history of racism should be taught in schools, an AP-NORC poll finds

W ASHINGTON (AP) — U.S. schools should teach about issues related to race, most Asian Americans, Native Hawaiians and Pacific Islanders believe. They also oppose efforts to restrict what subjects can be discussed in the classroom, according to a new poll.

In the survey from AAPI Data and The Associated Press-NORC Center for Public Affairs Research , 71% of AAPI adults favor teaching about the history of slavery, racism and segregation in K-12 public schools. The same share also said they support teaching about the history of Asian American and Pacific Islander communities in the United States, while about half support teaching about issues related to sex and sexuality.

AAPI Democrats are more supportive of these topics being taught in classrooms than AAPI Republicans.

Still, only 17% of AAPI adults think school boards should be able to limit what subjects students and teachers talk about in the classroom, and about one-quarter of AAPI Republicans are in favor of these restrictions.

The results indicate that efforts to politicize education through culture war issues have not gained strong inroads in Asian American communities, said Karthick Ramakrishnan, a public policy professor at the University of California, Riverside, and founder of AAPI Data. Across the country, conservative members of state legislatures and local school boards have made efforts to restrict teaching about race and gender in classrooms.

“Even as parents are concerned and engaged in various ways with K-12 education, the culture wars are not something that resonate with AAPI parents,” he said. “I think that's important because there's so much news coverage of it and certainly a lot of policy activity.”

AAPI Americans are a fast-growing demographic , but small sample sizes and linguistic barriers often prevent their views from being analyzed in other surveys.

Glenn Thomas, a 53-year-old father to three children in public schools who identifies as a political independent and is Japanese and white, said that while he does not oppose classrooms covering topics like race and gender, he does not think they should be the sole focus of how curriculums are designed.

“I'm kind of old-school, reading, writing, arithmetic,” he said of how schools approach topics like gender and race. “I don't think it necessarily needs to be taught as separate curriculums.”

Thomas, whose family has lived all over the country because of his career in the military, said the influence of politics and external actors in public schools varied greatly depending on where they lived. In Florida, where he currently lives, he thinks the state government too heavily influences local schools.

Nationally, 39% of AAPI adults say that they follow news about their school boards, while just 13% say they have attended a local school board meeting and 18% have communicated in-person or online with a local school board member. When it comes to elections, 28% have voted in a local school board election.

While those percentages are roughly consistent with the general public, AAPI adults are slightly less likely to say they have voted in a local school board election.

Because a high percentage of Asian Americans are immigrants, Ramakrishnan said, many did not grow up in the same political system as the United States, where there is a high level of local control and influence over schools. A lack of outreach from mainstream institutions may also contribute to a lower level of engagement, he added.

“It takes a fair amount of effort to learn how the system works and how to have influence in that system," he said. “Given the high level of interest that (Asian American and Pacific Islander) parents place in education, you would expect higher rates of participation.”

Varisa Patraporn, a Thai American mother of two public school children in California, said that she is a consistent voter in local elections, given the importance of those individuals in making decisions that affect schools. In Cerritos, where she lives, candidates tend to host events and send out mailers during elections, reflecting a robust campaign for seats on the school board.

Patraporn said that while she has communicated with school board members, she has not attended a school board meeting. Part of that, she said, is because the meetings happen in the evening and are harder to attend for parents who have young children or other obligations. That means the parents who do attend and speak up can have a disproportionate amount of sway.

Patraporn said that she wants the school curriculum to be more diverse and inclusive, despite pushback from some parents who do not want discussions of race in the classroom. She said she often supplements her children's reading to expose them to a wider range of perspectives beyond what they get from their assignments.

“Those conversations have started, but there's a lot of resistance in our community to that,” she said. “There's a lot of resistance in terms of being fearful of what it means to actually talk about race.”

Ramakrishnan said the polling data indicates an opening to engage AAPI communities more intensely with their local educational institutions. According to the poll, about two-thirds of AAPI adults see the schools that children attend as extremely or very important to their success in adulthood. And about half say parents and teachers have too little influence on the curriculum in public schools, similar to the general population.

“This is a community that still sees college as a good deal, as an important pathway toward mobility and success, and is concerned about the quality of K-12 education as well,” he said. “We have a ripe opportunity to engage and boost participation in these Asian American Pacific Islander communities when it comes to educational policy.”

The poll of 1,068 U.S. adults who are Asian American, Native Hawaiian and Pacific Islanders was conducted from April 8-17, 2024, using a sample drawn from NORC’s probability-based Amplify AAPI Panel, designed to be representative of the Asian American, Native Hawaiian, and Pacific Islander population. Online and telephone interviews were offered in English, the Chinese dialects of Mandarin and Cantonese, Vietnamese and Korean. The margin of sampling error for all respondents is plus or minus 4.7 percentage points.

The Associated Press’ education coverage receives financial support from multiple private foundations. AP is solely responsible for all content. Find AP’s standards for working with philanthropies, a list of supporters and funded coverage areas at AP.org .

Q&A: These researchers examined 20 years of data same-sex marriage. Here’s what they found

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Twenty years ago this month, Marcia Kadish and Tanya McCloskey exchanged wedding vows at Cambridge City Hall in Massachusetts and became the first same-sex couple to legally marry in the United States .

The couple had been together since 1986, but their decision to wed was radical for its time. In 2004, only 31% of Americans supported same-sex marriage, while 60% were opposed , according to a Pew Research Center poll.

Much of that opposition was fueled by fears that expanding the definition of marriage beyond the traditional union of a man and a women would undermine the institution and be destabilizing to families. Researchers at the Rand Corp. decided to find out if those predictions turned out to be true.

A team from the Santa Monica-based think tank spent a year poring over the data. The result is a 186-page report that should be reassuring to supporters of marriage equality.

“If there were negative consequences in the last 20 years of the decision to legalize marriage for same-sex couples, no one has yet been able to measure them,” said Benjamin Karney , an adjunct behavioral scientist at Rand.

Karney, who is also a social psychologist at UCLA, led the report with Melanie Zaber , a labor economist and economic demographer at Rand. They spoke with The Times about what they learned.

Does marriage make people better off?

Benjamin Karney: On average, yes. People who are married experience fewer health problems , they live years longer , they make more money , and they accumulate more wealth than people who marry and divorce or who don’t marry at all. People who are married also experience more stable and positive psychological health , and they have sex more frequently than people who are not married.

All those benefits accrue primarily to people who are in happy marriages. Unhappy marriage is very, very harmful. But most people who are married are happy — that’s why they stay married.

What prompted you to examine same-sex marriage now?

BK: At the time that these policies were changing, there were a lot of arguments on both sides about whether the consequences would be positive or negative. Twenty years is a long time, and during that time, a lot of research has been conducted. It seemed like a good time to ask the question: What did happen as a consequence of legalizing marriage for same-sex couples? So that’s one reason.

The second reason is that in the Dobbs decision that overturned Roe vs. Wade , Justice Clarence Thomas in his concurring opinion said explicitly that this Supreme Court should consider reviewing and potentially overturning other decisions , and he named the 2015 Obergefell vs. Hodges decision that legalized marriage for same-sex couples by name. Given that people may be wondering about the merits of that decision, it seemed like a good time to evaluate the consequences of that decision, and that’s what we’ve done.

What did you find?

BK: We found 96 studies across a range of disciplines. Some are in economics. Some are in psychology. Some are in medicine. Some are in public health.

Melanie Zaber: We wanted it to be research that actually measured something. There were a number of more qualitative or theoretical or legal arguments that we excluded.

BK: What I found most notable is that all of the studies drew the same conclusions: There was either no effect or beneficial effects on any outcome you could look at. That’s 20 years of research, 96 studies, and no harms.

On same-sex marriage, ‘the country has caught up with California’

Gov. Gavin Newsom and Vice President Kamala Harris were at the vanguard in pushing for marriage equality, which will soon be signed into federal law.

Dec. 12, 2022

Does it seem plausible that the results could be so one-sided?

BK: I was not surprised. There’s a lot of good theory in family science and relationship science to argue that if you extend rights to a group that’s been stigmatized, that group should do better, and the majority group should not be affected. Indeed, that’s what we found.

MZ: I don’t find it particularly surprising. When we say there are no harms, that doesn’t mean everything’s coming up sunshine and roses — it means sunshine and roses or nothing. In this case, where the prediction was something negative, then nothing still feels like sunshine and roses.

What sorts of things did these studies measure?

BK: There were three general categories. The largest group was looking at outcomes for LGBT individuals and same-sex couples. The second bucket looked at the children of same-sex parents. And the third bucket was the effect on everybody else.

There was no evidence of harms anywhere.

That’s interesting because opponents of these policy changes very strongly — and very explicitly — predicted there would be harms. They predicted it in front of the Supreme Court , arguing that if we allow same-sex couples to marry, the consequences for the country will be negative and severe and unavoidable and irreversible.

Who benefits the most from legalizing same-sex marriage?

BK: Same-sex couples. Their relationships last longer when they are able to marry and cement their commitment. Their incomes go up. Their mental health improves.

That mental health improvement extends to LGBT individuals whether or not they are married. Even if you’re not married , if you’re a member of a sexual minority and live in a world that validates same-sex relationships, that relieves a stressor and has measurable benefits on physical and mental health.

What’s behind these improvements?

BK: The effects on health seem like they operate partly through employer-based health insurance being extended to spouses.

The mechanisms for mental health have been described by minority stress theory . Living in a society that is constantly sending you a message that you are less worthy of equal treatment is stressful, partly because it leads to discrimination. Being the target of discrimination is stressful , and that stress has real mental and physical consequences .

You found 96 studies about gay marriage. Why did you conduct your own research as well?

MZ: Some of those studies were conducted when only a few states had marriage for same-sex couples. A state like West Virginia or Wyoming might say, “Well that’s all well and good that you have evidence from Massachusetts or Vermont, but New England isn’t the center of the universe.”

By looking at a broader range of years, we’re better able to capture some of those states that did allow same-sex couples to marry but weren’t among the first to do so. We have reason to think those states may be very different environments. Our approach was to use each state as a quasi-experiment.

What did all that data tell you?

MZ: The headline from our new analysis is no negative impacts and some positive ones.

We see an increase in marriage, and that increase is driven not just by newly marrying same-sex couples, but also by an increase in marriage among different-sex couples. That was a bit surprising to us.

World & Nation

Hearing threat to Roe vs. Wade, I thought of my gay marriage — and Jim Obergefell’s fight

Like LGBTQ people nationwide, I can’t help but worry that the legal logic that might topple Roe will be used against my marriage.

May 16, 2022

What do you think was going on?

MZ: There are a few different mechanisms for this, none of which we can explicitly test.

One could be allyship . There are individuals who identify as cisgender straight individuals, but they want to show their allyship so they delay marriage until everyone’s able to marry.

There’s an increasing number of individuals who identify as bisexual in the United States. Even if they’re marrying a different-sex partner, they may be trying to have validation of their broader identity.

The argument we find most compelling is that having people loudly clamoring for all the great things that come along with marriage made people in the broader population say, “Oh hey, getting married means people can go visit me in the hospital, and that if I’m in an accident there’s no concern about who my property will go to, and we have more access to health insurance.” Talking about that may have made some people realize, “You know, marriage actually is pretty helpful.”

BK: If you hear about a restaurant that everyone’s trying to get into, you want to eat at that restaurant.

MZ: That is an excellent way of putting it!

Do you think this research will persuade those who were concerned that same-sex marriage would have terrible consequences?

MZ: That’s our goal — to put evidence out to the public so policymakers can make informed choices.

BK: I’d like to believe so. At the time those arguments were made, they were speculative. People were trying to predict the future. Now we don’t have to predict the future. Twenty years have passed and we have the data. We can document what has happened.

This interview has been edited for length and clarity.

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In this piece

What does the public in six countries think of generative ai in news.

An electronic screen displaying Japan's Nikkei share average. Credit: Reuters / Issei Kato

DOI: 10.60625/risj-4zb8-cg87

Executive summary.

Based on an online survey focused on understanding if and how people use generative artificial intelligence (AI), and what they think about its application in journalism and other areas of work and life across six countries (Argentina, Denmark, France, Japan, the UK, and the USA), we present the following findings.

Findings on the public’s use of generative AI

ChatGPT is by far the most widely recognised generative AI product – around 50% of the online population in the six countries surveyed have heard of it. It is also by far the most widely used generative AI tool in the six countries surveyed. That being said, frequent use of ChatGPT is rare, with just 1% using it on a daily basis in Japan, rising to 2% in France and the UK, and 7% in the USA. Many of those who say they have used generative AI have used it just once or twice, and it is yet to become part of people’s routine internet use.

In more detail, we find:

• While there is widespread awareness of generative AI overall, a sizable minority of the public – between 20% and 30% of the online population in the six countries surveyed – have not heard of any of the most popular AI tools.
• In terms of use, ChatGPT is by far the most widely used generative AI tool in the six countries surveyed, two or three times more widespread than the next most widely used products, Google Gemini and Microsoft Copilot.
• Younger people are much more likely to use generative AI products on a regular basis. Averaging across all six countries, 56% of 18–24s say they have used ChatGPT at least once, compared to 16% of those aged 55 and over.
• Roughly equal proportions across six countries say that they have used generative AI for getting information (24%) as creating various kinds of media, including text but also audio, code, images, and video (28%).
• Just 5% across the six countries covered say that they have used generative AI to get the latest news.

Findings on public opinion about the use of generative AI in different sectors

Most of the public expect generative AI to have a large impact on virtually every sector of society in the next five years, ranging from 51% expecting a large impact on political parties to 66% for news media and 66% for science. But, there is significant variation in whether people expect different sectors to use AI responsibly – ranging from around half trusting scientists and healthcare professionals to do so, to less than one-third trusting social media companies, politicians, and news media to use generative AI responsibly.

• Expectations around the impact of generative AI in the coming years are broadly similar across age, gender, and education, except for expectations around what impact generative AI will have for ordinary people – younger respondents are much more likely to expect a large impact in their own lives than older people are.
• Asked if they think that generative AI will make their life better or worse, a plurality in four of the six countries covered answered ‘better’, but many have no strong views, and a significant minority believe it will make their life worse. People’s expectations when asked whether generative AI will make society better or worse are generally more pessimistic.
• Asked whether generative AI will make different sectors better or worse, there is considerable optimism around science, healthcare, and many daily routine activities, including in the media space and entertainment (where there are 17 percentage points more optimists than pessimists), and considerable pessimism for issues including cost of living, job security, and news (8 percentage points more pessimists than optimists).
• When asked their views on the impact of generative AI, between one-third and half of our respondents opted for middle options or answered ‘don’t know’. While some have clear and strong views, many have not made up their mind.

Findings on public opinion about the use of generative AI in journalism

Asked to assess what they think news produced mostly by AI with some human oversight might mean for the quality of news, people tend to expect it to be less trustworthy and less transparent, but more up to date and (by a large margin) cheaper for publishers to produce. Very few people (8%) think that news produced by AI will be more worth paying for compared to news produced by humans.

• Much of the public think that journalists are currently using generative AI to complete certain tasks, with 43% thinking that they always or often use it for editing spelling and grammar, 29% for writing headlines, and 27% for writing the text of an article.
• Around one-third (32%) of respondents think that human editors check AI outputs to make sure they are correct or of a high standard before publishing them.
• People are generally more comfortable with news produced by human journalists than by AI.
• Although people are generally wary, there is somewhat more comfort with using news produced mostly by AI with some human oversight when it comes to soft news topics like fashion (+7 percentage point difference between comfortable and uncomfortable) and sport (+5) than with ‘hard’ news topics, including international affairs (-21) and, especially, politics (-33).
• Asked whether news that has been produced mostly by AI with some human oversight should be labelled as such, the vast majority of respondents want at least some disclosure or labelling. Only 5% of our respondents say none of the use cases we listed need to be disclosed.
• There is less consensus on what uses should be disclosed or labelled. Around one-third think ‘editing the spelling and grammar of an article’ (32%) and ‘writing a headline’ (35%) should be disclosed, rising to around half for ‘writing the text of an article’ (47%) and ‘data analysis’ (47%).
• Again, when asked their views on generative AI in journalism, between a third and half of our respondents opted for neutral middle options or answered ‘don’t know’, reflecting a large degree of uncertainty and/or recognition of complexity.

Introduction

The public launch of OpenAI’s ChatGPT in November 2022 and subsequent developments have spawned huge interest in generative AI. Both the underlying technologies and the range of applications and products involving at least some generative AI have developed rapidly (though unevenly), especially since the publication in 2017 of the breakthrough ‘transformers’ paper (Vaswani et al. 2017) that helped spur new advances in what foundation models and Large Language Models (LLMs) can do.

These developments have attracted much important scholarly attention, ranging from computer scientists and engineers trying to improve the tools involved, to scholars testing their performance against quantitative or qualitative benchmarks, to lawyers considering their legal implications. Wider work has drawn attention to built-in limitations, issues around the sourcing and quality of training data, and the tendency of these technologies to reproduce and even exacerbate stereotypes and thus reinforce wider social inequalities, as well as the implications of their environmental impact and political economy.

One important area of scholarship has focused on public use and perceptions of AI in general, and generative AI in particular (see, for example, Ada Lovelace Institute 2023; Pew 2023). In this report, we build on this line of work by using online survey data from six countries to document and analyse public attitudes towards generative AI, its application across a range of different sectors in society, and, in greater detail, in journalism and the news media specifically.

We go beyond already published work on countries including the USA (Pew 2023; 2024), Switzerland (Vogler et al. 2023), and Chile (Mellado et al. 2024), both in terms of the questions we cover and specifically in providing a cross-national comparative analysis of six countries that are all relatively privileged, affluent, free, and highly connected, but have very different media systems (Humprecht et al. 2022) and degrees of platformisation of their news media system in particular (Nielsen and Fletcher 2023).

The report focuses on the public because we believe that – in addition to economic, political, and technological factors – public uptake and understanding of generative AI will be among the key factors shaping how these technologies are being developed and are used, and what they, over time, will come to mean for different groups and different societies (Nielsen 2024). There are many powerful interests at play around AI, and much hype – often positive salesmanship, but sometimes wildly pessimistic warnings about possible future risks that might even distract us from already present issues. But there is also a fundamental question of whether and how the public at large will react to the development of this family of products. Will it be like blockchain, virtual reality, and Web3? All promoted with much bombast but little popular uptake so far. Or will it be more like the internet, search, and social media – hyped, yes, but also quickly becoming part of billions of people’s everyday media use.

To advance our understanding of these issues, we rely on data from an online survey focused on understanding if and how people use generative AI, and what they think about its application in journalism and other areas of work and life. In the first part of the report, we present the methodology, then we go on to cover public awareness and use of generative AI, expectations for generative AI’s impact on news and beyond, how people think AI is being used by journalists right now, and how people think about how journalists should use generative AI, before offering a concluding discussion.

As with all survey-based work, we are reliant on people’s own understanding and recall. This means that many responses here will draw on broad conceptions of what AI is and might mean, and that, when it comes to generative AI in particular, people are likely to answer based on their experience of using free-standing products explicitly marketed as being based on generative AI, like ChatGPT. Most respondents will be less likely to be thinking about incidents where they may have come across functionalities that rely in part on generative AI, but do not draw as much attention to it – a version of what is sometimes called ‘invisible AI’ (see, for example, Alm et al. 2020). We are also aware that these data reflect a snapshot of public opinion, which can fluctuate over time.

We hope the analysis and data published here will help advance scholarly analysis by complementing the important work done on the use of AI in news organisations (for example, Beckett and Yaseen 2023; Caswell 2024; Diakopoulos 2019; Diakopoulos et al 2024; Newman 2024; Simon 2024), including its limitations and inequities (see, for example, Broussard 2018, 2023; Bender et al. 2021), and help centre the public as a key part of how generative AI will develop and, over time, potentially impact many different sectors of society, including journalism and the news media.

Methodology

The report is based on a survey conducted by YouGov on behalf of the Reuters Institute for the Study of Journalism (RISJ) at the University of Oxford. The main purpose is to understand if and how people use generative AI, and what they think about its application in journalism and other areas of work and life.

The data were collected by YouGov using an online questionnaire fielded between 28 March and 30 April 2024 in six countries: Argentina, Denmark, France, Japan, the UK, and the USA.

YouGov was responsible for the fieldwork and provision of weighted data and tables only, and RISJ was responsible for the design of the questionnaire and the reporting and interpretation of the results.

Samples in each country were assembled using nationally representative quotas for age group, gender, region, and political leaning. The data were weighted to targets based on census or industry-accepted data for the same variables.

Sample sizes are approximately 2,000 in each country. The use of a non-probability sampling approach means that it is not possible to compute a conventional ‘margin of error’ for individual data points. However, differences of +/- 2 percentage points (pp) or less are very unlikely to be statistically significant and should be interpreted with a very high degree of caution. We typically do not regard differences of +/- 2pp as meaningful, and as a general rule we do not refer to them in the text.

It is important to note that online samples tend to under-represent the opinions and behaviours of people who are not online (typically those who are older, less affluent, and have limited formal education). Moreover, because people usually opt in to online survey panels, they tend to over-represent people who are well educated and socially and politically active.

Some parts of the survey require respondents to recall their past behaviour, which can be flawed or influenced by various biases. Additionally, respondents’ beliefs and attitudes related to generative AI may be influenced by social desirability bias, and when asked about complex socio-technical issues, people will not always be familiar with the terminology experts rely on or understand the terms the same way. We have taken steps to mitigate these potential biases and sources of error by implementing careful questionnaire design and testing.

1. Public awareness and use of generative AI

Most of our respondents have, by now, heard of at least some of the most popular generative AI tools. ChatGPT is by far the most widely recognised of these, with between 41% (Argentina) and 61% (Denmark) saying they’d heard of it.

Other tools, typically those built by incumbent technology companies – such as Google Gemini, Microsoft Copilot, and Snapchat My AI – are some way behind ChatGPT, even with the boost that comes from being associated with a well-known brand. They are, with the exception of Grok from X, each recognised by roughly 15–25% of the public.

Tools built by specialised AI companies, such as Midjourney and Perplexity, currently have little to no brand recognition among the public at large. And there’s little national variation here, even when it comes to brands like Mistral in France; although it is seen by some commentators as a national champion, it clearly hasn’t yet registered with the wider French population.

We should also remember that a sizable minority of the public – between 19% of the online population in Japan and 30% in the UK – have not heard of any of the most popular AI tools (including ChatGPT) despite nearly two years of hype, policy conversations, and extensive media coverage.

While our Digital News Report (Newman et al. 2023) shows that in most countries the news market is dominated by domestic brands that focus on national news, in contrast, the search and social platform space across countries tends to feature the same products from large technology companies such as Google, Meta, and Microsoft. At least for now, it seems like the generative AI space will follow the pattern from the technology sector, rather than the more nationally oriented one of news providers serving distinct markets defined in part by culture, history, and language.

The pattern we see for awareness in Figure 1 extends to use, with ChatGPT by far the most widely used generative AI tool in the six countries surveyed. Use of ChatGPT is roughly two or three times more widespread than the next products, Google Gemini and Microsoft Copilot. What’s also clear from Figure 2 is that, even when it comes to ChatGPT, frequent use is rare, with just 1% using it on a daily basis in Japan, rising to 2% in France and the UK, and 7% in the USA. Many of those who say they have used generative AI have only used it once or twice, and it is yet to become part of people’s routine internet use.

Use of ChatGPT is slightly more common among men and those with higher levels of formal education, but the biggest differences are by age group, with younger people much more likely to have ever used it, and to use it on a regular basis (Figure 3). Averaging across all six countries, 16% of those aged 55 and over say they have used ChatGPT at least once, compared to 56% of 18–24s. But even among this age group infrequent use is the norm, with just over half of users saying they use it monthly or less.

Although people working in many different industries – including news and journalism – are looking for ways of deploying generative AI, people in every country apart from Argentina are slightly more likely to say they are using it in their private life rather than at work or school (Figure 4). If providers of AI products convince more companies and organisations that these tools can deliver great efficiencies and new opportunities this may change, with professional use becoming more widespread and potentially spilling over to people’s personal lives – a dynamic that was part of how the use of personal computers, and later the internet, spread. However, at this stage private use is more widespread.

Averaging across six countries, roughly equal proportions say that they have used generative AI for getting information (24%) as creating media (28%), which as a category includes creating images (9%), audio (3%), video (4%), code (5%), and generating text (Figure 5). When it comes to creating text more specifically, people report using generative AI to write emails (9%) and essays (8%), and for creative writing (e.g. stories and poems) (7%). But it’s also clear that many people who say they have used generative AI for creating media have just been playing around or experimenting (11%) rather than looking to complete a specific real-world task. This is also true when it comes to using generative AI to get information (9%), but people also say they have used it for answering factual questions (11%), advice (10%), generating ideas (9%), and summarisation (8%).

An average of 5% across the six countries say that they have used generative AI to get the latest news, making it less widespread than most of the other uses that were mentioned previously. One reason for this is that the free version of the most widely used generative AI product – ChatGPT – is not yet connected to the web, meaning that it cannot be used for the latest news. Furthermore, our previous research has shown that around half of the most widely used news websites are blocking ChatGPT (Fletcher 2024), and partly as a result, it is rarely able to deliver the latest news from specific outlets (Fletcher et al. 2024).

The figures for using generative AI for news vary by country, from just 2% in the UK and Denmark to 10% in the USA (Figure 6). The 10% figure in the USA is probably partly due to the fact that Google has been trialling Search Generative Experiences (SGE) there for the last year, meaning that people who use Google to search for a news-related topic – something that 23% of Americans do each week (Newman et al. 2023) – may see some generative AI text that attempts to provide an answer. However, given the documented limitations of generative AI when it comes to factual precision, companies like Google may well approach news more cautiously than other types of content and information, and the higher figure in the USA may also simply be because generative AI is more widely used there generally.

Numerous examples have been documented of generative AI giving incorrect answers when asked factual questions, as well as other forms of so-called ‘hallucination’ that result in poor- quality outputs (e.g. Angwin et al. 2024). Although some are quick to point out that it is wrong to expect generative AI to be good at information-based tasks – at least at its current state of development – some parts of the public are experimenting with doing exactly that.

Given the known problems when it comes to reliability and veracity, it is perhaps concerning that our data also show that users seem reasonably content with the performance – most of those (albeit a rather small slice of the online population) who have tried to use generative AI for information-based tasks generally say they trusted the outputs (Figure 7).

In interpreting this, it is important to keep in mind two important caveats.

First, the vast majority of the public has not used generative AI for information-based tasks, so we do not know about their level of trust. Other evidence suggests that trust among the large part of the public that has not used generative AI is low, meaning overall trust levels are likely to be low (Pew 2024).

Second, people are more likely to say that they ‘somewhat trust’ the outputs rather than ‘strongly trust’, which indicates a degree of scepticism – their trust is far from unconditional. However, this may also mean that from the point of view of members of the public who have used the tools, information from generative AI while clearly not perfect is already good enough for many purposes, especially tasks like generating ideas.

When we ask people who have used generative AI to create media whether they think the product they used did it well or badly, we see a very similar picture. Most of those who have tried to use generative AI to create media think that it did it ‘very’ or ‘somewhat’ well, but again, we can only use this data to know what users of the technology think.

The general population’s views on the media outputs may look very different, and while early adopters seem to have some trust in generative AI, and feel these technologies do a somewhat good job for many tasks, it is not certain that everyone will feel the same, even if or when they start using generative AI tools.

2. Expectations for generative AI’s impact on news and beyond

We now move from people’s awareness and use of generative AI products to their expectations around what the development of these technologies will mean. First, we find that most of the public expect generative AI to have a large impact on virtually every sector of society in the next five years (Figure 8). For every sector, there is a smaller number who expect low impact (compared to a large impact), and a significant number of people (roughly between 15% and 20%) who answer ‘don’t know’.

Averaging across six countries, we find that around three-quarters of respondents think generative AI will have a large impact on search and social media companies (72%), while two-thirds (66%) think that it will have a large impact on the news media – strikingly, the same proportion who think it will have a large impact upon the work of scientists (66%). Around half think that generative AI will have a large impact upon national governments (53%) and politicians and political parties (51%).

Interestingly, there are generally fewer people who expect it will have a large impact on ordinary people (48%). Much of the public clearly thinks the impact of generative AI will be mediated by various existing social institutions.

Bearing in mind how different the countries we cover are in many respects, including in terms of how people use and think about news and media (see, for example, Newman et al. 2023), it is striking that we find few cross-country differences in public expectations around the impact of generative AI. There are a few minor exceptions. For example, expectations around impact for politicians and political parties are a bit higher than average in the USA (60% vs 51%) and a bit lower in Japan (44% vs 51%) – but, for the most part, views across countries are broadly similar.

For almost all these sectors, there is little variation across age and gender, and the main difference when it comes to different levels of education is that respondents with lower levels of formal education are more likely to respond with ‘don’t know’, and those with higher levels of education are more likely to expect a large impact. The number who expect a small impact remains broadly stable across levels of education.

The only exception to this relative lack of variation by demographic factors is expectations around what impact generative AI will have for ordinary people. Younger respondents, who, as we have shown in earlier sections, are much more likely to have used generative AI tools, are also much more likely to expect a large impact within the next five years than older people, who often have little or no personal experience of using generative AI (Figure 9).

Expectations around the impact of generative AI, whether large or small, in themselves say nothing about how people think about whether this impact will, on balance, be for better or for worse.

Because generative AI use is highly mediated by institutions, and our data document that much of the public clearly recognise this, a useful additional way to think about expectations is to consider whether members of the public trust different sectors to make responsible use of generative AI.

We find that public trust in different institutions to make responsible use of generative AI is generally quite low (Figure 10). While around half in most of the six counties trust scientists and healthcare professionals to use generative AI responsibly, the figures drop below 40% for most other sectors in most countries. Figures for social media companies are lower than many other sectors, as are those for news media, ranging from 12% in the UK to 30% in Argentina and the USA.

There is more cross-country variation in public trust and distrust in different institutions’ potential use of generative AI, partly in line with broader differences from country to country in terms of trust in institutions.

But there are also some overarching patterns.

First, younger people, while still often sceptical, are for many sectors more likely to say they trust a given institution to use generative AI responsibly, and less likely to express distrust. This tendency is most pronounced in the sectors viewed with greatest scepticism by the public at large, including the government, politicians, and ordinary people, as well as news media, social media, and search engines.

Second, a significant part of the public does not have a firm view on whether they trust or distrust different institutions to make responsible use of generative AI. Varying from sector to sector and from country to country, between roughly one-quarter and half of respondents answer ‘neither trust nor distrust’ or ‘don’t know’ when asked. There is much uncertainty and often limited personal experience; in that sense, the jury is still out.

Leaving aside country differences for a moment and looking at the aggregate across all six countries, we can combine our data on public expectations around the size of the impact that generative AI will have with expectations around whether various sectors will use these technologies responsibly. This will provide an overall picture of how people think about these issues across different social institutions (Figure 11).

If we compare public perceptions relative to the average percentage of respondents who expect a large impact across all sectors (58%, marked by the vertical dashed line in Figure 11) and the average percentage of respondents who distrust actors in a given sector to make responsible use of generative AI (33%, marked by the horizontal dashed line), we can group expectations from sector to sector into four quadrants.

• First, there are those sectors where people expect generative AI to have a relatively large impact, but relatively few expect it will be used irresponsibly (e.g. healthcare and science).
• Second, there are sectors where people expect the impact may not be as great, and relatively fewer fear irresponsible use (e.g. ordinary people and retailers).
• Third, there are sectors where relatively few people expect a large impact, and relatively more people are worried about irresponsible use (e.g. government and political parties).
• Finally, there are sectors where more people expect large impact, and more people fear irresponsible use by the actors involved (e.g. social media and the news media, who are viewed very similarly by the public in this respect).

It is important to keep this quite nuanced and differentiated set of expectations in mind in interpreting people’s general expectations around what impact they think generative AI will have for them personally, as well as for society at large.

Asked if they think that generative AI will make their life better or worse, more than half of our respondents answer ‘neither better nor worse’ or ‘don’t know’, with a plurality in four of the six countries covered answering ‘better’, and a significant minority ‘worse’ (Figure 12). The large number of people with no strong expectations either way is consistent across countries, but the balance between more optimistic responses and more pessimistic ones varies.

People’s expectations when asked whether generative AI will make society better or worse are more pessimistic on average. There are about the same number of optimists, but significantly more pessimists who believe generative AI will make society worse. Expectations around what generative AI might mean for society are more varied across the six countries we cover. In two (France and the UK), there are more who expect it will make society worse than better. In another two (Denmark and the USA), there are as many pessimists as optimists. And in the remaining two (Argentina and Japan) more respondents expect generative AI products will make society better than expect them to make society worse.

Looking more closely at people’s expectations, both in terms of their own life and in terms of society, younger people and people with more formal education also often opt for ‘neither better nor worse’ or ‘don’t know’, but in most countries – Argentina being the exception – they are more likely to answer ‘better’ (Figure 13).

Asked whether they think the use of generative AI will make different areas of life better or worse, again, much of the public is undecided, either opting for ‘neither better nor worse’ or answering ‘don’t know’, underlining that it is still early days.

We now look specifically at the percentage point difference between optimists who expect AI to make things better and pessimists who expect it to make them worse gives a sense of public expectations across different areas (Figure 14). Large parts of the public think generative AI will make science (net ‘better’ of +44 percentage points), healthcare (+36), and many daily routine activities, including transportation (+26), shopping (+22), and entertainment (+17), better, even though there is much less optimism when it comes to core areas of the rule of law, including criminal justice (+1) and more broadly legal rights and due process (-3), and considerable pessimism for some very bread-and-butter issues, including cost of living (-6), equality (-6), and job security (-18).

News and journalism is also an area where, on balance, there is more pessimism than optimism (-8) – a striking contrast to another area involving the media, namely entertainment (+17). But there is a lot of national variation here. In countries that are more optimistic about the potential effects of generative AI, namely Argentina (+19) and Japan (+8), the proportion that think it will make news and journalism better is larger than the proportion that think it will become worse. The UK public are particularly negative about the effect of generative AI on journalism, with a net score of -35. There is a similar lack of consensus across different countries on whether crime and justice, legal rights and due process, cost of living, equality, and job security will be made better or worse.

3. How people think generative AI is being used by journalists right now

Many of the conversations around generative AI and journalism are about what might happen in the future – speculation about what the technology may or may not be able to do one day, and how this will shape the profession as we know it. But it is important to remember that some journalists and news organisations are using generative AI right now, and they have been using some form of AI in the newsroom for several years.

We now focus on how much the public knows about this, what they think journalists currently use generative AI for, and what processes they think news media have in place to ensure quality.

In the survey, we showed respondents a list of journalistic tasks and asked them how often they think journalists perform them ‘using artificial intelligence with some human oversight’. The tasks ranged from behind-the-scenes work like ‘editing the spelling and grammar of an article’ and ‘data analysis’ through to much more audience-facing outputs like ‘writing the text of an article’ and ‘creating a generic image/illustration to accompany the text of an article’.

We specifically asked about doing these ‘using artificial intelligence with some human oversight’ because we know that some newsrooms are already performing at least some tasks in this way, while few are currently doing them entirely using AI without a human in the loop. Even tasks that may seem fanciful to some, like ‘creating an artificial presenter or author’, are not without precedent. In Germany, for example, the popular regional newspaper Express has created a profile for an artificial author called Klara Indernach, 1 which it uses as the byline for its articles created with the help of AI, and several news organisations across the world already use AI-generated artificial presenters for various kinds of video and audio.

Figure 15 shows that a substantial minority of the public believe that journalists already always or often use generative AI to complete a wide range of different tasks. Around 40% believe that journalists often or always use AI for translation (43%), checking spelling and grammar (43%), and data analysis (40%). Around 30% think that journalists often or always use AI for re-versioning – whether it’s rewriting the same article for different people (28%) or turning text into audio or video (30%) – writing headlines (29%), or creating stock images (30%).

In general, the order of the tasks in Figure 15 reflects the fact that people – perhaps correctly – believe that journalists are more likely to employ AI for behind-the-scenes work like spellchecking and translation than they are for more audience-facing outputs. This may be because people understand that some tasks carry a greater reputational risk for journalists, and/or that the technology is simply better at some things than others.

The results may also reveal a degree of cynicism about journalism from some parts of the public. The fact that around a quarter think that journalists always or often use AI to create an image if a real photograph is not available (28%) and 17% think they create an artificial presenter or author may say more about their attitudes towards journalism as an institution than about how they think generative AI is actually being used. However unwelcome they might be – and however wrong they are about how many news media use AI – these perceptions are a social reality, shaping how parts of the public think about the intersection between journalism and AI.

Public perceptions of what journalists and news media already use AI for are quite consistent across different genders and age groups, but there are some differences by country, with respondents in Argentina and the USA a little more likely to believe that AI is used for each of these tasks, and respondents in Denmark and the UK less likely.

Among those news organisations that have decided to implement generative AI for certain tasks, the importance of ‘having a human in the loop’ to oversee processes and check errors is often stressed. Human oversight is nearly always mentioned in public-facing guidelines on the use of AI for editorial work, and journalists themselves mention it frequently (Becker et al. 2024).

Large parts of the public, however, do not think this is happening (Figure 16). Averaging across the six countries, around one-third think that human editors ‘always’ or ‘often’ check AI outputs to make sure they are correct or of a high standard before publishing them. Nearly half think that journalists ‘sometimes’, ‘rarely’, or ‘never’ do this – again, perhaps, reflecting a level of cynicism about the profession among the public, or a tendency to judge the whole profession and industry on the basis of how some parts of it act.

The proportion that think checking is commonplace is lowest in the UK, where only one-third of the population say they ‘trust most news most of the time’ (Newman et al. 2023), but we also see similarly low figures in Denmark, where trust in the news is much higher. The results may, therefore, also partly reflect more than just people’s attitudes towards journalism and the news media.

4. What does the public think about how journalists should use generative AI?

Various forms of AI have long been used to produce news stories by publishers including, for example, Associated Press, Bloomberg, and Reuters. And content produced with newer forms of generative AI has, with mixed results, been published by titles including BuzzFeed, the Los Angeles Times , the Miami Herald , USA Today , and others.

Publishers may be more or less comfortable with how they are using these technologies to produce various kinds of content, but our data suggest that much of the public is not – at least not yet. As we explore in greater detail in our forthcoming 2024 Reuters Institute Digital News Report (Newman et al. 2024), people are generally more comfortable with news produced by human journalists than by AI.

However, averaging across six countries, younger people are significantly more likely to say they are comfortable with using news produced in whole or in part by AI (Figure 17). The USA and Argentina have somewhat higher levels of comfort with news made by generative AI, but there too, much of the public remains sceptical.

We also asked respondents whether they are comfortable or uncomfortable using news produced mostly by AI with some human oversight on a range of different topics. Figure 18 shows the net percentage point difference between those that selected ‘very’ or ‘somewhat’ comfortable and those that selected ‘very’ or ‘somewhat’ uncomfortable (though, as ever, a significant minority selected the ‘neither’ or ‘don’t know’ options). Looking across different topics, there is somewhat more comfort with using news produced mostly by AI with some human oversight when it comes to ‘softer’ news topics, like fashion (+7) and sports (+5), than ‘hard’ news topics including politics (-33) and international affairs (-21).

But in every area, at this point in time, only for a very small number of topics are there more people uncomfortable with relying on AI-generated news than comfortable. As with overall comfort, there is somewhat greater acceptance of the use of AI for generating various kinds of news with at least some human oversight in the USA and Argentina.

Putting aside country differences, there is again a marked difference between our respondents overall and younger respondents. Among respondents overall, there are only three topic areas out of ten where slightly more respondents are comfortable with news made mostly by AI with some human oversight than are uncomfortable with this. Among respondents aged 18 to 24, this rises to six out of ten topic areas.

It is important to remember that much of the public does not have strong views either way, at least at this stage. Between one-quarter and one-third of respondents answer either ‘neither comfortable nor uncomfortable’ or ‘don’t know’ when asked the general questions about comfort with different degrees of reliance on generative AI versus human journalists, and between one-third and half of respondents do the same when asked about generative AI news for specific topics. It is an open question as to how these less clearly formed views will evolve.

One way to assess what the public expects it will mean if and when AI comes to play a greater role in news production is to gauge people’s views on how it will change news, compared to a baseline of news produced entirely by human journalists.

We map this by asking respondents if they think that news produced mostly by AI with some human oversight will differ from what most are used to across a range of different qualities and attributes.

Between one-third and half of our respondents do not have a strong view either way. Focusing on those respondents who do have a view, we can look at the net percentage point difference between how many respondents think AI will make the news somewhat more or much more (e.g. more ‘up to date’ or more ‘transparent’), versus somewhat less or much less, of each, helping to provide an overarching picture of public expectations.

On balance, more respondents expect news produced mostly by AI with some human oversight to be less trustworthy (-17) and less transparent (-8), but more up to date (+22) and – by a large margin – cheaper to make (+33) (Figure 19). There is considerable national variation here, but with the exception of Argentina, the balance of public opinion (net positive or negative) is usually the same for these four attributes. For the others, the balance often varies.

Essentially our data suggest that the public, at this stage, primarily think that the use of AI in news production will help publishers by cutting costs, but identify few, if any, ways in which they expect it to help them – and several key areas where many expect news made with AI to be worse.

In light of this, it makes sense that, when asked if news produced mostly by AI with some human oversight is more or less worth paying for than news produced entirely by a human journalist, an average of 41% across six countries say less worth paying for (Figure 20). Just 8% say they think that news made in this way will be more valuable.

There is some variation here by country and by age, but even among the generally more AI-positive younger respondents aged 18–24, most say either less worth paying for (33%) or about the same (38%). The implications of the spread of generative AI and how it is used by publishers for people’s willingness to pay for news will be interesting to follow going forward, as tensions may well mount between the ‘pivot to pay’ we have seen from many news media in recent years and the views we map here.

Looking across a range of different tasks that journalists and news media might use generative AI for, and in many cases already are using generative AI for, we can again gauge how comfortable the public is by looking at the balance between how many are comfortable with a particular use case and how many are uncomfortable.

As with several of the questions above, about a third have no strong view either way at this stage – but many others do. Across six countries, the balance of public opinion ranges from relatively high levels of comfort with back-end tasks, including editing spelling and grammar (+38), translation (+35), and the making of charts (+28), to widespread net discomfort with synthetic content, including creating an image if a real photo is not available (-13) and artificial presenters and authors (-24) (Figure 21).

When asked if it should be disclosed or labelled as such if news has been produced mostly by AI with some human oversight, only 5% of our respondents say none of the use cases included above need to be disclosed, and the vast majority of respondents say they want some form of disclosure or labelling in at least some cases. Research on the effect of labelling AI-generated news is ongoing, but early results suggest that although labelling may be desired by audiences, it may have a negative effect on trust (Toff and Simon 2023).

There is, however, less consensus on what exactly should be disclosed or labelled, except for somewhat lower expectations around the back-end tasks people are frequently comfortable with AI completing (Figure 22). Averaging across six countries, around half say that ‘creating an image if a real photograph is not available’ (49%), ‘writing the text of an article’ (47%), and ‘data analysis’ (47%) should be labelled as such if generative AI is used. However, this figure drops to around one-third for ‘editing the spelling and grammar of an article’ (32%) and ‘writing a headline’ (35%). Again, variation exists between both countries and demographic groups that are generally more positive about AI.

Based on online surveys of nationally representative samples in six countries, we have, with a particular focus on journalism and news, documented how aware people are of generative AI, how they use it, and their expectations on the magnitude of impact it will have in different sectors – including whether it will be used responsibly.

We find that most of the public are aware of various generative AI products, and that many have used them, especially ChatGPT. But between 19% and 30% of the online population in the six countries surveyed have not heard of any of the most popular generative AI tools, and while many have tried using various of them, only a very small minority are, at this stage, frequent users. Going forward, some use will be driven by people seeking out and using stand-alone generative AI tools such as ChatGPT, but it seems likely that much of it will be driven by a combination of professional adaptation, through products used in the workplace, and the introduction of more generative AI-powered elements into platforms already widely used in people’s private lives, including social media and search engines, as illustrated with the recent announcements of much greater integration of generative AI into Google Search.

When it comes to public expectations around the impact of generative AI and whether these technologies are likely to be used responsibly, we document a differentiated and nuanced picture. First, there are sectors where people expect generative AI will have a greater impact, and relatively fewer people expect it will be used irresponsibly (including healthcare and science). Second, there are sectors where people expect the impact may not be as great, and relatively fewer fear irresponsible use (including from ordinary people and retailers). Third, there are sectors where relatively fewer people expect large impact, and relatively more people are worried about irresponsible use (including government and political parties). Fourth, there are sectors where more people expect large impact, and more people fear irresponsible use by the actors involved (this includes social media and the news media).

Much of the public is still undecided on what the impact of generative AI will be. They are unsure whether, on balance, generative AI will make their own lives and society better or worse. This is understandable, given many are not aware of any of these products, and few have personal experience of using them frequently. Younger people and those with higher levels of formal education – who are also more likely to have used generative AI – are generally more positive.

Expectations around what generative AI might mean for society are more varied across the six countries we cover. In two, there are more who expect it will make society worse than better, in another two, there are as many pessimists as optimists, and in the final two, more respondents expect generative AI products will make society better than expect them to make society worse. These differences may also partly reflect the current situation societies find themselves in, and whether people think AI can fundamentally change the direction of those societies. To some extent we also see this pattern reflected in how people think about AI in news. Across a range of measures, in some countries people are generally more optimistic, but in others more pessimistic.

Looking at journalism and news media more closely, we have found that many believe generative AI is already relatively widely used for many different tasks, but that they are, in most cases, not convinced these uses of AI make news better – they mostly expect it to make it cheaper to produce.

While there is certainly curiosity, openness to new approaches, and some optimism in parts of the public (especially when it comes to the use of these technologies in the health sector and by scientists), generally, the role of generative AI in journalism and news media is seen quite negatively compared to many other sectors – in some ways similar to how much of the public sees social media companies. Basically, we find that the public primarily think that the use of generative AI in news production will help publishers cut costs, but identify few, if any, ways in which they expect it to help them as audiences, and several key areas where many expect news made with AI to be worse.

These views are not solely informed by how people think generative AI will impact journalism in the future. A substantial minority of the public believe that journalists already always or often use generative AI to complete a wide range of different tasks. Some of these are tasks that most are comfortable with, and are within the current capabilities of generative AI, like checking spelling and grammar. But many others are not. More than half of our respondents believe that news media at least sometimes use generative AI to create images if no real photographs are available, and as many believe that news media at least sometimes create artificial authors or presenters. These are forms of use that much of the public are uncomfortable with.

Every individual journalist and every news organisation will need to make their own decisions about which, if any, uses of generative AI they believe are right for them, given their editorial principles and their practical imperatives. Public opinion cannot – and arguably should not – dictate these decisions. But public opinion provides a guide on which uses are likely to influence how people judge the quality of news and their comfort with relying on it, and thus helps, among other things, to identify areas where it is particularly important for journalists and news media to communicate and explain their use of AI to their target audience.

It is still early days, and it remains to be seen how public use and perception of generative AI in general, and its role in journalism and news specifically, will evolve. On many of the questions asking respondents to evaluate AI in different sectors and for different uses, between roughly a quarter and half of respondents pick relatively neutral middle options or answer ‘don’t know’. There is still much uncertainty around what role generative AI should and will have, in different sectors, and for different purposes. And, especially in light of how many have limited personal experience of using these products, it makes sense that much of the public has not made up their minds.

Public debate, opinion commentary, and news coverage will be among the factors influencing how this evolves. So will people’s own experience of using generative AI products, whether for private or professional purposes. Here, it is important to note two things. First, younger respondents generally are much more open to, and in many cases optimistic about, generative AI than respondents overall. Second, despite the many documented limitations and problems with state-of-the-art generative AI products, those respondents who use these tools themselves tend to offer a reasonably positive assessment of how well they work, and how much they trust them. This does not necessarily mean that future adopters will feel the same. But if they do, and use becomes widespread and routine, overall public opinion will change – in some cases perhaps towards a more pessimistic view, but, at least if our data are anything to go by, in a more grounded and cautiously optimistic direction.

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1 https://www.express.de/autor/klara-indernach-594809

About the authors

Dr Richard Fletcher is Director of Research at the Reuters Institute for the Study of Journalism. He is primarily interested in global trends in digital news consumption, the use of social media by journalists and news organisations, and, more broadly, the relationship between computer-based technologies and journalism.

Professor Rasmus Kleis Nielsen is Director of the Reuters Institute for the Study of Journalism, Professor of Political Communication at the University of Oxford, and served as Editor-in-Chief of the International Journal of Press/Politics from 2015 to 2018. His work focuses on changes in the news media, political communication, and the role of digital technologies in both.

Acknowledgements

We would like to thank Caryhs Innes, Xhoana Beqiri, and the rest of the team at YouGov for their work on fielding the survey. We would also like to thank Felix Simon for his help with the data analysis. We are grateful to the other members of the research team at RISJ for their input on the questionnaire and interpretation of the results, and to Kate Hanneford-Smith, Alex Reid, and Rebecca Edwards for helping to move this project forward and keeping us on track.

Funding acknowledgement

Report published by the Reuters Institute for the Study of Journalism (2024) as part of our work on AI and the Future of News , supported by seed funding from Reuters News and made possible by core funding from the Thomson Reuters Foundation.

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