where can i find previous research papers

• Corrections

Search Help

Get the most out of Google Scholar with some helpful tips on searches, email alerts, citation export, and more.

Finding recent papers

Your search results are normally sorted by relevance, not by date. To find newer articles, try the following options in the left sidebar:

• click "Since Year" to show only recently published papers, sorted by relevance;
• click "Sort by date" to show just the new additions, sorted by date;
• click the envelope icon to have new results periodically delivered by email.

Locating the full text of an article

Abstracts are freely available for most of the articles. Alas, reading the entire article may require a subscription. Here're a few things to try:

• click a library link, e.g., "FindIt@Harvard", to the right of the search result;
• click a link labeled [PDF] to the right of the search result;
• click "All versions" under the search result and check out the alternative sources;
• click "Related articles" or "Cited by" under the search result to explore similar articles.

If you're affiliated with a university, but don't see links such as "FindIt@Harvard", please check with your local library about the best way to access their online subscriptions. You may need to do search from a computer on campus, or to configure your browser to use a library proxy.

Getting better answers

If you're new to the subject, it may be helpful to pick up the terminology from secondary sources. E.g., a Wikipedia article for "overweight" might suggest a Scholar search for "pediatric hyperalimentation".

If the search results are too specific for your needs, check out what they're citing in their "References" sections. Referenced works are often more general in nature.

Similarly, if the search results are too basic for you, click "Cited by" to see newer papers that referenced them. These newer papers will often be more specific.

Explore! There's rarely a single answer to a research question. Click "Related articles" or "Cited by" to see closely related work, or search for author's name and see what else they have written.

Searching Google Scholar

Use the "author:" operator, e.g., author:"d knuth" or author:"donald e knuth".

Put the paper's title in quotations: "A History of the China Sea".

You'll often get better results if you search only recent articles, but still sort them by relevance, not by date. E.g., click "Since 2018" in the left sidebar of the search results page.

To see the absolutely newest articles first, click "Sort by date" in the sidebar. If you use this feature a lot, you may also find it useful to setup email alerts to have new results automatically sent to you.

Note: On smaller screens that don't show the sidebar, these options are available in the dropdown menu labelled "Year" right below the search button.

Select the "Case law" option on the homepage or in the side drawer on the search results page.

It finds documents similar to the given search result.

It's in the side drawer. The advanced search window lets you search in the author, title, and publication fields, as well as limit your search results by date.

Select the "Case law" option and do a keyword search over all jurisdictions. Then, click the "Select courts" link in the left sidebar on the search results page.

Tip: To quickly search a frequently used selection of courts, bookmark a search results page with the desired selection.

Access to articles

For each Scholar search result, we try to find a version of the article that you can read. These access links are labelled [PDF] or [HTML] and appear to the right of the search result. For example:

Academic Databases

ERIC research database: complete tutorial

The ERIC database is the premier education literature database for scholarly research. This guide covers search types and strategies, filters, and full text options.

How to efficiently search online databases for academic research

Academic research isn't difficult if you know where and how to search for scholarly articles and research papers. Here's how to do it.

How to use Google Scholar: the ultimate guide

Google Scholar is the number one academic search engine. Our detailed guide covers best practices for basic and advanced search strategies in Google Scholar.

Is Google Scholar a database or search engine? [Update 2024]

Google Scholar is the number one free resource to discover scientific literature, but is it an academic database or a search engine?

The best academic research databases [Update 2024]

Your research is stuck and you need to find new sources? Take a look at our compilation of academic research databases: Scopus, Web of Science, PubMed, ERIC, JSTOR, DOAJ, Science Direct, and IEEE Xplore.

The best academic search engines [Update 2024]

Your research is stuck, and you need to find new sources. Take a look at our compilation of free academic search engines: ✓ Google Scholar ✓ BASE ✓ CORE ✓ Science.gov

The best research databases for computer science [Update 2024]

The top 4 research databases specifically dedicated to computer science: ✓ ACM Digital Library ✓ IEEE Xplore ✓ dbpl ✓ Springer LNCS

The best research databases for healthcare and medicine [Update 2024]

We have compiled the top list of research databases for healthcare, medicine, and biomedical research: PubMed, EMBASE, PMC, and Cochrane Library.

Bibliometrics

Learn how to calculate your h-index on Google Scholar

Learn how to calculate your h-index using Google Scholar online for free, and which tools to use for a detailed analysis.

Learn how to calculate your h-index using Scopus [3 steps]

Learn how to assess your h-index on Scopus in 3 easy steps.

Learn how to calculate your h-index using Web of Science

Learn how to calculate your h-index using Web of Science in 3 easy steps.

The ultimate how-to-guide on the h-index

Learn what an h-index is, how to calculate it, and why it is important to know about it for your career as a scientist.

What is a good h-index? [with examples]

Curious to know what a good h-index is? Read this guide to learn when an h-index is considered good.

What is a good impact factor?

Do you want to find out what a good impact factor is? Read this guide to learn what an impact factor is, how it is calculated, and what impact factor is considered good.

Credible Sources

Can a blog be a credible source? [Update 2024]

You want to add a blog post to your research paper? In general, blogs are not considered to be credible sources: ➜ check out these reasons to learn more about it.

How can I find credible sources? [7 tips]

Sometimes it is hard to determine whether a source is credible or not. Read our guide to help you find credible sources.

Is Wikipedia a credible source?

We all ❤ Wikipedia, but can you cite it in your research paper? No. Wikipedia is not a credible source, and here is why you should only use it for preliminary research.

What are credible sources?

Credible sources are sources that are trustworthy and can be used as references in your academic papers. This guide will help you identify and evaluate sources for their credibility.

What are predatory journals?

This day and age, you have to be careful where you submit your work for publication. This article helps you spot a predatory journal and has tips and tricks on what to do if you accidentally submitted to one.

Primary and Secondary Sources

Interviews: are they a primary source?

Interviews can add tremendously to your research project. Read on to quickly learn when an interview is considered a primary source.

Is a documentary a secondary source? [with examples]

You are not sure if a documentary is a secondary source? We show you when and why is a documentary either a secondary or a tertiary source.

Is a letter a primary source?

Letters are frequently used in historical research. Read on to see when a letter qualifies as a primary source.

Is a map a primary source? [with examples]

Are you not sure if a map is a primary source? This guide will show you when and why a map is a primary or a secondary source.

Is a painting a primary source? [with examples]

Are you unsure if a painting is a primary source? This guide will show you when and why a painting is either a primary or a secondary source.

Is a textbook a secondary source?

Are you unsure if a textbook is a secondary source? Learn in this guide when and why a textbook is either a primary, secondary, or tertiary source.

Is an autobiography a primary source?

Are you not sure if an autobiography is a primary source? We show you when and why an autobiography is either a primary or secondary source.

Is an encyclopedia a primary source?

Are you unsure if an encyclopedia is a primary source? Find your answer and learn the right way to reference an encyclopedia in this guide.

Is census data a primary source?

You are not sure if census data is a primary source? We will show you when and why census data is a primary source and where to get it.

Is the US Constitution a primary source?

Are you wondering if the US Constitution is a primary source? Find the answer to your question in this guide.

Newspaper articles: primary or secondary sources?

Learn what questions to ask to see if a newspaper article really qualifies as a primary source.

Primary vs. secondary sources: how to distinguish them

Primary and secondary sources are the foundations of every research project. Learn about their differences and when to use them.

What is a primary source?

Primary sources are the most important sources when undertaking a research project. We answer the 5 most asked questions about primary sources.

What is a secondary source?

Secondary sources are your starting point when undertaking a research project. We answer the 5 most asked questions about secondary sources.

Research Methodology

How to do a content analysis [7 steps]

Content analysis is a research method you might come across when analyzing data. Learn what a content analysis is and how to do one in this step-by-step guide.

How to do a thematic analysis [6 steps]

A thematic analysis is a research method you might come across when analyzing qualitative data. Learn what a thematic analysis is and how to write one in this step-by-step guide.

How to write a rhetorical analysis [4 steps]

A rhetorical analysis explores the goals and motivations of an author, the techniques they’ve used to reach their audience, and how successful these techniques were. Learn how to write an excellent rhetorical analysis in this guide.

Qualitative vs. quantitative research - what’s the difference?

Qualitative and quantitative research are effective but very different approaches to study a subject. Learn the difference between them, what they are used for, and how to analyze qualitative and quantitative research in this guide.

What is research methodology? [Update 2024]

Having the right research methodology can be a make-or-break factor for your academic work. What is research methodology, and how can you get ahead?

Scholarly Sources

How to find a DOI [Update 2024]

Are you not sure where to find a DOI? Read this guide to learn exactly where to spot DOIs, and how to include them in your citations!

How to identify if a source is scholarly

Not sure if it is a scholarly source? Looking at these 6 identifiers can help differentiate scholarly from non-scholarly articles.

How to know if an article is peer reviewed [6 key features]

You don't know exactly what 'peer reviewed' articles are? Read this guide to learn all about peer reviewed articles, their features, and how to find them!

Is a book a scholarly source? [with checklist]

Sometimes it can be difficult to distinguish scholarly from non-scholarly books. This guide will help you with that. Learn how to identify scholarly books by following our simple guidelines.

What are peer reviewed journals? [Update 2024]

You don't know exactly what 'peer review' means? Read this guide to clear your doubts, and learn more about peer reviewed articles, its process and types!

What is a DOI [with examples]

What is a DOI? Learn what a digital object identifier is and how to include a DOI in APA and other styles.

What is a scholarly source?

Are you wondering what a scholarly source is and what makes it a scholarly source? Learn all about it in this guide, including what elements a scholarly source generally contains.

What is grey literature? [with examples]

Wondering what is grey literature and how to find it? Find all things grey literature in this quick and easy guide filled with sources for grey literature.

• Follow us on Facebook
• Follow us on Twitter
• Criminal Justice
• Environment
• Politics & Government
• Race & Gender

Expert Commentary

How to find an academic research paper

Looking for research on a particular topic? We’ll walk you through the steps we use here at Journalist's Resource.

Republish this article

This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License .

by David Trilling, The Journalist's Resource October 18, 2017

This <a target="_blank" href="https://journalistsresource.org/home/find-academic-research-paper-for-journalists/">article</a> first appeared on <a target="_blank" href="https://journalistsresource.org">The Journalist's Resource</a> and is republished here under a Creative Commons license.<img src="https://journalistsresource.org/wp-content/uploads/2020/11/cropped-jr-favicon-150x150.png" style="width:1em;height:1em;margin-left:10px;">

Journalists frequently contact us looking for research on a specific topic. While we have published a number of resources on how to understand an academic study and how to pick a good one — and why using social science research enriches journalism and public debate — we have little on the mechanics of how to search. This tip sheet will briefly discuss the resources we use.

Google Scholar

Let’s say we’re looking for papers on the opioid crisis. We often start with Google Scholar, a free service from Google that searches scholarly articles, books and documents rather than the entire web: scholar.google.com .

But a search for the keyword “opioids” returns almost half a million results, some from the 1980s. Let’s narrow down our search. On the left, you see options “anytime” (the default), “since 2013,” “since 2016,” etc. Try “since 2017” and the results are now about 17,000. You can also insert a custom range to search for specific years. And you can include patents or citations, if you like (unchecking these will slightly decrease the number of results).

Still too many results. To narrow the search further, try any trick you’d use with Google. (Here are some tips from MIT on how to supercharge your Google searches.) Let’s look for papers on opioids published in 2015 that look at race and exclude fentanyl (Google: “opioids +race -fentanyl”). Now we’re down to 2,750 results. Better.

Unless you tell Google to “sort by date,” the search engine will generally weight the papers that have been cited most often so you will see them first.

Try different keywords. If you’re looking for a paper that studies existing research, include the term “meta-analysis.” Try searching by the author’s name, if you know it, or title of the paper. Look at the endnotes in papers you like for other papers. And look at the papers that cited the paper you like; they’ll probably be useful for your project.

If you locate a study and it’s behind a paywall, try these steps:

• Click on “all versions.” Some may be available for free. (Though check the date, as this may include earlier drafts of a paper.)
• Reach out to the journal and the scholar. (The scholar’s email is often on the abstract page. Also, scholars generally have an easy-to-find webpage.) One is likely to give you a free copy of the paper, especially if you are a member of the press.
• In regular Google, search for the study by title and you might find a free version.

More tips on using Google Scholar from MIT and Google .

Other databases

• PubMed Central at the National Library of Medicine: If you are working on a topic that has a relationship to health, try this database run by the National Institutes of Health. This free site hosts articles or abstracts and links to free versions of a paper if they are available. Often Google Scholar will point you here.
• If you have online access to a university library or a local library, try that.
• Directory of Open Access Journals .
• Digital Public Library of America .
• Subscription services include org and Web of Science .

For more on efforts to make scholarly research open and accessible for all, check out SPARC , a coalition of university libraries.

Citations as a measure of impact

How do you know if a paper is impactful? Some scholars use the number of times the paper has been cited by other scholars. But that can be problematic: Some papers cite papers that are flawed simply to debunk them. Some topics will be cited more often than others. And new research, even if it’s high-quality, may not be cited yet.

The impact factor measures how frequently a journal, not a paper, is cited.

This guide from the University of Illinois, Chicago, has more on metrics.

Here’s a useful source of new papers curated by Boston Globe columnist Kevin Lewis for National Affairs.

Another way to monitor journals for new research is to set up an RSS reader like Feedly . Most journals have a media page where you can sign up for press releases or newsletters featuring the latest research.

Relevant tip sheets from Journalist’s Resource:

• 10 things we wish we’d known earlier about research
• How to tell good research from bad: 13 questions journalists should ask  (This post also discusses how to determine if a journal is good.)
• Lessons on online search techniques, reading studies, understanding data and methods
• Guide to critical thinking, research, data and theory: Overview for journalists

About The Author

David Trilling

Finding Current Research Using Free Online Resources

Even the most experienced scholars can find it difficult to keep up with new research in their fields. So much is being published in journals and online every day that it can be overwhelming. So I’ve put together a list of websites to help you wade through the rising tide of research. These resources are available free to anyone via the Internet and offer useful tools for discovering new research in a wide variety of subject areas.

Smithsonian Research Online While many people think of the Smithsonian as a huge museum (which it is), it also is home to nine research centers and a long list of research programs. Over the last few years, the output by Smithsonian researchers has averaged about 2000 articles, books and book chapters per year focused on our specialty areas within the fields of science, history, art and culture. Smithsonian Research Online (SRO) is a portal to that output and is managed by the Libraries.

On SRO’s landing page , you will find a number of search options, including a box for keyword searching. You also can limit your search by year and/or by museum/department, which helps to focus your search by broad subject areas as well. A link to an online version of the article is provided via our Digital Repository where possible. If you want to keep up with scientific research and reports from the Smithsonian, you can sign up for an RSS feed on the SRO page. Check out our previous blog post for more details about SRO.

Google Scholar Since it started back in 2004, Google Scholar has quickly become one of the primary (and fastest growing) places to find scholarly research online. Its search robots are constantly scanning the Internet looking for journal and conference papers, theses and dissertations, academic books, pre-prints, abstracts, technical reports and other scholarly literature in virtually any subject area, then adding them to its extensive database, often with a PDF copy available.

And once you’ve created a particularly productive search, be sure to create an email alert so that Google Scholar will let you know when any new items are added that match your search. You can do this by clicking the Create Alert envelope along the left side of your search results list. It’s a great way to stay up to date on your topic.

Mendeley I’ve discussed Mendeley in previous Library Hacks posts , explaining how this free reference management program helps you save websites and papers in your own online library and then create bibliographies and citations in your own papers. But I didn’t go into detail on another cool feature of Mendeley: its free online library listing millions – yes, millions – of research articles and papers. Mendeley has taken the citations added by its 2 million+ users worldwide and created a crowd-sourced research library that can be searched by anyone, even those who don’t have a Mendeley account.

So while you may not be able to get free access to everything you find in Mendeley’s free library, it is still a great resource. One reason I really like it – the items in Mendeley’s library are added by users . So you’re searching a list of what researchers are actually reading, not just what gets published! And you can see in a record how many users have added the same paper to their own library, which can give you an idea of how many of your colleagues are finding that paper interesting. One caveat: Mendeley is still more popular in the sciences than in other fields, so the papers available there skew toward scientific subjects. But the number of users from other fields continues to grow.

ResearchGate This one is definitely for all the scientists in the (virtual) house! ResearchGate serves as an information hub for more than 2.6 million researchers in science fields (which includes social sciences, computer/information sciences, and other so-called “soft” sciences). It provides a platform to communicate and share research with colleagues in your field, with the goal of helping the progress of scientific research.

However, ResearchGate’s search function does not offer many ways to refine your search, even when using the Advanced Search . For example, you can’t limit by publication date to find just the most recent articles, and you aren’t able to resort your results list by date either. You could try adding a year to your search terms — for example, enter “genetics 2012” in the search box (without the quotation marks) to try to find articles on genetics published in 2012 or including data from 2012. It’s not a perfect solution, but it may help to focus your search somewhat. As you’re finding useful articles, you can bookmark them to create your own collection of research materials in your ResearchGate account.

You have several options for saving your searches, including creating an RSS feed so that you can learn about new items fitting your search criteria. If you create a free DRIVER account , you can add items you find to your own collection within DRIVER or send items to your Dropbox account. You can even join or create communities with other DRIVER users around shared interests.

Even if you can’t access the article itself, this site can help you stay aware of who is researching what and what is getting published where. You can browse journals by subject to discover those that cover your areas of interest. And after setting up a free account , you can choose journals to follow and get their tables of contents sent to you by email or to your RSS reader as a new issue is published.

CiteULike is a similar source for journal tables of contents, covering about 13,500 journals in various subjects. While it doesn’t offer much in the way of search options, CiteULike includes some journals not available from JournalTOCs. And remember – you can always request copies of interesting articles not available online through the interlibrary loan service at your local public or academic library.

This list is by no means complete! I’ve concentrated on sharing some of the larger, more subject-diverse resources that help scholars find current research. Have you discovered other online sources for new research, maybe focused in your own subject area? If so, please share them in the Comment field. And be sure to visit public and/or academic libraries in your area to do more comprehensive research. They offer access to subscription research databases that aren’t available free online, and the librarians are standing by to help you use them!

This is the latest post in our series,  Library Hacks , where we take a look at cool and interesting online resources from the Smithsonian Libraries and the cyberworld at large.

Categories: homepage Library Hacks Research

35 Comments

[…] Smithsonian Libraries has an excellent blog post: Finding Current Research Using Free Online […]

Hi Trina, Yoour readers may also find my post Where to find new scholarly research papers: 30 key, free websites http://roddymacleod.wordpress.com/2011/11/10/where-to-find-new-scholarly-research-papers-30-key-free-websites/ of use.

Hi Roddy, I saw your great blog post when I was doing research for my own post! I found it very interesting and helpful. Thanks for sharing it here. Trina

the internet is very useful in the field of Research and Learning Thanks for great article

Thank for sharing this information. I, personally, find research report almost everyday and this article very helpful.

Thanks for great article

Hi. What sources do you recommend for legal information? Free resources, that is.

Thanks for finally writing about >Finding Current Research Using Free Online Resources – Smithsonian Libraries Blog <Liked it!

Thank you for the work.

Online search is very helpful in all areas especially in education

whats up guys?

Free online resources is very important for everyone wanting to make a research online. Students and people from the academe really like the internet because there is no limit to their research..

Information should be free, it should not be that we have to pay for it. but i guess no one wants to share secrets

What are your views on hackers though?

Trina, I had never heard of Google Scholar..it is huge! It is proving to be incredibly useful for my research on graphic design subjects (logo, brand identity, etc.). I think I understand now why Google has so many employees (this company is thinking of everything!). thanks

great article, Thank your information. http://www.centerklik.com

very nice resources you have shared with us thank a lot for sharing.

This is AMAZING! Also, thank you Trina for your recommendation to check out Google Scholar. That is going to be a time saver for sure!

You’re welcome, Bruce! If I can help more researchers discover useful resources like Google Scholar, then I’m doing my job as a librarian. 🙂 Trina

Thanks for the info. I’ve only just checked out Google Scholar now and it’s great! How come this isn’t more well known?

Great info, thank you for sharing. I’d like to recommend a website with free and interesting resources for teaching Spanish http://floatingpenguin.co.uk/translation-spanish.php

Thanks for the list, it’s really helpful for me 🙂

Wow great blog. I enjoyed every bit of the article on this blog. I will recommend it to other people. Keep on the good work.

I saw your great blog post when I was doing research for my own post! I found it very interesting and helpful. Thanks for sharing it here.

it is really hard to finding current research. really thanks for the info. i hope its free and the content is full. cause there so many sites that just give you the abstract.

I am really happy to read this weblog posts which contains tons of valuable data, thanks for providing these kinds of statistics.

thanks for sharing helpful information

Thanks Trina for sharing this very helpful article I can see you really have covered almost eveything. It helped me quite a lot as I’m sure it will many others Thanks again for posting this here Michelle http://excellentprinting.co.uk

Great info, thank you for sharing.

it so useful for student who needs references as a complement of its research. I like this blog also because always give us an inspiration to do something

Thanks for the info

Thanks for the list of great resources, i really love Google Scholar 🙂

Thank you for sharing this amazing compilation of resources. I would also recommend HowStuffWorks, USA.gov, and Data.gov for research purposes. Also, I’ll leave a link to more tools for researchers https://ivypanda.com/blog/how-to-research-ultimate-guide/

nice info, i also get good info from https://royalindonesia.id

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

How to find Research Papers: A Cheat Sheet for Graduate Students

• July 23, 2022
• PRODUCTIVITY

“I will read this paper later.” I thought to myself before adding another paper to my overflowing internet browser.

Of course, I didn’t read it later.

Since my workflow was unorganized, I missed out on reading many important papers.

This was a crucial period in my undergraduate career. I had been working with a company for my final year project and knew success would require a solid intellectual foundation. For many hours, I read papers, determined to master the literature in my field.

“How to find research papers quickly?” has been a never-ending question for me.

However, I was unable to succeed despite my best intentions, largely due to inefficiency. In addition, I did not have a system in place for keeping track of new papers being published daily in my topic area or checking if I had missed key studies.

Nothing is worse than forgetting where you saved an important research paper. If I couldn’t find that specific paper, I couldn’t do anything else, and sometimes a day would pass before I found it.

As I was about to begin my PhD, I convinced myself that I should be more organized.

This is the first post of the four-part blog series:  The Bulletproof Literature Management System . Follow the links below to read the other posts in the series:

• How to How to find Research Papers (You are here)
• How to Manage Research Papers
• How to Read Research Papers
• How to Organize Research Papers

My workflow has evolved through many iterations, and I have finally found a system that suits my needs after lots of trial and error.

These tips will help you how to find research papers quickly and more efficiently.

Get recommendations from your supervisor

You may have already received a folder of information from your supervisor regarding your thesis topic. Your supervisor should have already been working on the proposal before you were hired for a funded project.

My supervisor, for example, has a folder named “Literature” for each project folder that contains all the important papers one might need to complete that project.

Therefore, asking your supervisor is one of the most straightforward ways to find research papers.

Even though your supervisor has not put up a folder like that, you can still ask them for recommendations, and they can point out a couple of pertinent articles. From there, you can find the references in the papers they recommended.

Use feed aggregators

Feed aggregators, such as Feedly , Inoreader , and NewsBlur , help me organize my feeds. In the morning, I dedicate five minutes to scanning my feed. For most papers, I just glance at the title and scroll past. Whenever I come across something interesting, I add it to my ‘Read Later’ folder.

Instead of storing papers in an unsecured location, my papers are more secure. As a result, it is much easier for me to look at that folder later on.

Use literature mapping tools

ResearchRabbit , Inciteful , Litmaps , and Connected Papers are literature-mapping tools you can use to dig deeper into a topic. It lets you see which papers are the most groundbreaking in a given field based on their citation networks.

This might not be very helpful if you’re doing research in a relatively new area. Finding relevant research papers in such cases may be more challenging.

This is why checking research databases would be a better option.

Use standard research databases

Scopus has strong searching capabilities and publishes metrics that can measure the relative importance of papers in their fields. However, it may take up to 2 years before an article is included in Scopus.

It has more features for sorting and filtering, so you might not feel overwhelmed when searching.

Therefore, if you are just starting your research, SCOPUS might be an excellent option for finding research papers.

ResearchGate

In addition to traditional searching for publications, ResearchGate offers the following features:

• Follow researchers in your field, so you can keep up with their work.
• Keep up-to-date with the research projects of other researchers by following their research projects, and
• Comment on publications, ask questions, and send direct messages to interact with others.

As most of the comments on ResearchGate are coming from experts in their respective fields, the QnA section may be a great resource for finding the right paper for your research.

An RSS(Really Simple Syndication) feed, as the name implies, is a straightforward solution. By subscribing to RSS, users can access content from specific websites.

You can find RSS feeds for nearly every major journal and preprint server on their home pages – just look for the orange icon. As new articles are added to PubMed or Google Scholar, you can even subscribe to specific keywords.

Use academic textbooks the right way

If you are new to a particular research area, it would be best to start by reading textbooks to understand the topic better.

Despite the lack of depth and detail in a textbook, it can provide you with the basic concepts you need to read further. Furthermore, textbooks often include extensive lists of references as well as this information to get you started . Download the relevant articles from these references.

You might feel overwhelmed if you try to read an academic textbook from beginning to end. For this reason, read only the sections which contain the information you need for your project.

Review papers are game changers

A review paper on your topic is a great starting point for finding good references and getting a broad overview of your research topic.

After reading the review paper, you can read the references cited therein.

You are reading a much more comprehensive summary of the topic than you would have found reading ten individual research papers on the same topic if you found a highly relevant review paper for your research.

Look for technical reports and theses

Make sure you don’t limit yourself to research papers when looking for references. A technical report or code document on your topic may contain important citations (as well as practical information).

There is nothing that compares to a PhD thesis when it comes to the depth and extent of analytical work. See which references students have cited in their theses on your topic.

If you find a relevant thesis for your literature review, you will have extensive information about the research topic in one place, saving you a ton of time.

Google Scholar

The best for the last!

Due to its versatility and efficiency in finding academic papers, I decided to include Google Scholar separately from the database section.

I enjoy using Google Scholar among all the fancy databases available. One drawback to Google Scholar is that it lacks the ability to search for keywords and filter results.

Therefore, if you are just starting your research and aren’t sure what “keywords” to search for, Google Scholar might not be your first choice.

The advantage of Google Scholar is that if you are already familiar with your field of study and already know what you are doing, you will be able to find relevant research papers more quickly.

Use Google Scholar’s search function to locate relevant articles. Furthermore, you can subscribe to updates from colleagues in your field to access the latest references. The publisher of a journal paper may also report an article faster to Google Scholar than another database, which can take up to two years to include an article.

Images courtesy: Internet marketing vector created by jcomp – www.freepik.com

Aruna Kumarasiri

Founder at Proactive Grad, Materials Engineer, Researcher, and turned author. In 2019, he started his professional carrier as a materials engineer with the continuation of his research studies. His exposure to both academic and industrial worlds has provided many opportunities for him to give back to young professionals.

Did You Enjoy This?

Then consider getting the ProactiveGrad newsletter. It's a collection of useful ideas, fresh links, and high-spirited shenanigans delivered to your inbox every two weeks.

I accept the Privacy Policy

Hand-picked related articles

Why do graduate students struggle to establish a productive morning routine? And how to handle it?

• March 17, 2024

How to stick to a schedule as a graduate student?

• October 10, 2023

The best note-taking apps for graduate students: How to choose the right note-taking app

• September 20, 2022

Leave a Reply Cancel Reply

Your email address will not be published. Required fields are marked *

Name  *

Email  *

Add Comment  *

Notify me of follow-up comments by email.

Notify me of new posts by email.

Post Comment

The Best Method for Finding Research Papers

Do you struggle to find the relevant literature for your assignments or research projects? The search for academic sources works differently to the approach we might use when we search the Internet. In this article, we’ll learn how to search through academic databases effectively and identify the most relevant papers.

‍ The Database Search

First, you’ll need some keywords to limit your search. Which words or phrases relate best to the information you want to find? If you have papers already or other reading material, this can be a good place for keyword inspiration. Keep a note of these words so that you can expand on them later. You might come up with alternative words, such as synonyms and abbreviations, or try some spelling variations. Alternatively, you can make use of genei’s keywords section and use these results to guide your search. Second, take some time to learn about ‘power searching’ by using ‘Boolean operators’. These are simple techniques that involve adding words or symbols to narrow down or widen your search using your key terms. You can find some of these advanced techniques here . Additionally, you can limit the scope of your search by defining essential criteria such as date, language, publication type (ebook or journal article), or the journal you’re interested in. This can refine the results that get returned to you. Similarly, you can begin to make note of key journals or authors that come up, and conduct a separate search by defining that journal or look up the author to see their other work.

Tracking Logs

Now that you’ve made a start, it’s a good idea to keep track of the databases you have searched, and the key words or search techniques used. This will make it easier to keep up with what you have or haven’t done so far. If you’re working on a research project and need to conduct an extensive literature review, this is particularly important. Here’s an example of a literature search tracking log. While this documents the search process, it’s also important to store and organise the papers you want to check out. You could organise the papers using date, author names, or your keywords. Reference managers often have ‘tagging’ tools to organise papers. However, this could simply be picking a naming convention such as ‘Year_FirstAuthor_Keyword1’ in a folder on your computer.

Connected Papers

Another effective way to source out relevant literature is by identifying connecting papers. It can be useful to start with a recent research paper because this will point to older research on that topic. As mentioned earlier, this could also help you to identify key authors for your search. genei makes finding connected papers easy by generating a reference list of the sources used and their links. Likewise, in order to find out if the paper is relevant to your particular research interest, it’s best to read the abstract, then introduction and conclusion. However, genei generates AI-powered summaries that allow you to skim the entire paper, saving you time and allowing you to identify key topics within the paper.

Amirah Khan

Do you want to achieve more with your time?

98% of users say genei saves them time and helps them work more productively. Why don’t you join them?

About genei

genei is an AI-powered research tool built to help make the work and research process more efficient. Our studies show genei can help improve reading speeds by up to 70%! Revolutionise your research process.

Articles you may like:

Find out how genei can benefit you

Evidence-Based Research Series-Paper 1: What Evidence-Based Research is and why is it important?

Affiliations.

• 1 Johns Hopkins Evidence-based Practice Center, Division of General Internal Medicine, Department of Medicine, Johns Hopkins University, Baltimore, MD, USA.
• 2 Digital Content Services, Operations, Elsevier Ltd., 125 London Wall, London, EC2Y 5AS, UK.
• 3 School of Nursing, McMaster University, Health Sciences Centre, Room 2J20, 1280 Main Street West, Hamilton, Ontario, Canada, L8S 4K1; Section for Evidence-Based Practice, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, P.O.Box 7030 N-5020 Bergen, Norway.
• 4 Department of Sport Science and Clinical Biomechanics, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark; Department of Physiotherapy and Occupational Therapy, University Hospital of Copenhagen, Herlev & Gentofte, Kildegaardsvej 28, 2900, Hellerup, Denmark.
• 5 Musculoskeletal Statistics Unit, the Parker Institute, Bispebjerg and Frederiksberg Hospital, Copenhagen, Nordre Fasanvej 57, 2000, Copenhagen F, Denmark; Department of Clinical Research, Research Unit of Rheumatology, University of Southern Denmark, Odense University Hospital, Denmark.
• 6 Section for Evidence-Based Practice, Western Norway University of Applied Sciences, Inndalsveien 28, Bergen, P.O.Box 7030 N-5020 Bergen, Norway. Electronic address: [email protected].
• PMID: 32979491
• DOI: 10.1016/j.jclinepi.2020.07.020

Objectives: There is considerable actual and potential waste in research. Evidence-based research ensures worthwhile and valuable research. The aim of this series, which this article introduces, is to describe the evidence-based research approach.

Study design and setting: In this first article of a three-article series, we introduce the evidence-based research approach. Evidence-based research is the use of prior research in a systematic and transparent way to inform a new study so that it is answering questions that matter in a valid, efficient, and accessible manner.

Results: We describe evidence-based research and provide an overview of the approach of systematically and transparently using previous research before starting a new study to justify and design the new study (article #2 in series) and-on study completion-place its results in the context with what is already known (article #3 in series).

Conclusion: This series introduces evidence-based research as an approach to minimize unnecessary and irrelevant clinical health research that is unscientific, wasteful, and unethical.

Keywords: Clinical health research; Clinical trials; Evidence synthesis; Evidence-based research; Medical ethics; Research ethics; Systematic review.

Copyright © 2020 Elsevier Inc. All rights reserved.

Publication types

• Research Support, Non-U.S. Gov't
• Biomedical Research* / methods
• Biomedical Research* / organization & administration
• Clinical Trials as Topic / ethics
• Clinical Trials as Topic / methods
• Clinical Trials as Topic / organization & administration
• Ethics, Research
• Evidence-Based Medicine / methods*
• Needs Assessment
• Reproducibility of Results
• Research Design* / standards
• Research Design* / trends
• Systematic Reviews as Topic
• Treatment Outcome

•   [email protected]
• Shapiro Library
• SNHU Library Frequently Asked Questions

FAQ: What is a research gap and how do I find one?

• 7 Academic Integrity & Plagiarism
• 63 Academic Support, Writing Help, & Presentation Help
• 29 Access/Remote Access
• 7 Accessibility
• 9 Building/Facilities
• 8 Career/Job Information
• 26 Catalog/Print Books
• 28 Circulation
• 128 Citing Sources
• 14 Copyright
• 310 Databases
• 24 Directions/Location
• 18 Faculty Resources/Needs
• 7 Hours/Contacts
• 1 Innovation Lab & Makerspace/3D Printing
• 25 Interlibrary Loan
• 43 IT/Computer/Printing Support
• 3 Library Instruction
• 40 Library Technology Help
• 8 Multimedia
• 17 Online Programs
• 20 Periodicals
• 25 Policies
• 8 RefWorks/Citation Managers
• 5 Research Guides (LibGuides)
• 221 Research Help
• 23 University Services

Last Updated: Jun 27, 2023 Views: 459011

What is a research gap.

A research gap is a question or a problem that has not been answered by any of the existing studies or research within your field. Sometimes, a research gap exists when there is a concept or new idea that hasn't been studied at all. Sometimes you'll find a research gap if all the existing research is outdated and in need of new/updated research (studies on Internet use in 2001, for example). Or, perhaps a specific population has not been well studied (perhaps there are plenty of studies on teenagers and video games, but not enough studies on toddlers and video games, for example). These are just a few examples, but any research gap you find is an area where more studies and more research need to be conducted. Please view this video clip from our Sage Research Methods database for more helpful information: How Do You Identify Gaps in Literature?

How do I find one?

It will take a lot of research and reading.  You'll need to be very familiar with all the studies that have already been done, and what those studies contributed to the overall body of knowledge about that topic. Make a list of any questions you have about your topic and then do some research to see if those questions have already been answered satisfactorily. If they haven't, perhaps you've discovered a gap!  Here are some strategies you can use to make the most of your time:

• One useful trick is to look at the “suggestions for future research” or conclusion section of existing studies on your topic. Many times, the authors will identify areas where they think a research gap exists, and what studies they think need to be done in the future.
• As you are researching, you will most likely come across citations for seminal works in your research field. These are the research studies that you see mentioned again and again in the literature.  In addition to finding those and reading them, you can use a database like Web of Science to follow the research trail and discover all the other articles that have cited these. See the FAQ: I found the perfect article for my paper. How do I find other articles and books that have cited it? on how to do this. One way to quickly track down these seminal works is to use a database like SAGE Navigator, a social sciences literature review tool. It is one of the products available via our SAGE Knowledge database.
• In the PsycINFO and PsycARTICLES databases, you can select literature review, systematic review, and meta analysis under the Methodology section in the advanced search to quickly locate these. See the FAQ: Where can I find a qualitative or quantitative study? for more information on how to find the Methodology section in these two databases.
• In CINAHL , you can select Systematic review under the Publication Type field in the advanced search. 
• In Web of Science , check the box beside Review under the Document Type heading in the “Refine Results” sidebar to the right of the list of search hits.
• If the database you are searching does not offer a way to filter your results by document type, publication type, or methodology in the advanced search, you can include these phrases (“literature reviews,” meta-analyses, or “systematic reviews”) in your search string.  For example, “video games” AND “literature reviews” could be a possible search that you could try.

Please give these suggestions a try and contact a librarian for additional assistance.

Content authored by: GS

• Share on Facebook

Was this helpful? Yes 373 No 151

Frequently Asked Questions (FAQs) are a self-serve option for users to search and find answers to their questions. 

Use the search box above to type your question to search for an answer or browse existing FAQs by group, topic, etc.

Tell Me More

Link to Question Form

More assistance.

Submit a Question

Related FAQs

Where to find peer reviewed articles for research

This is our ultimate guide to helping you get familiar with your research field and find peer reviewed articles in the Web of Science™. It forms part of our Research Smarter series. 

Finding relevant research and journal articles in your field is critical to a successful research project. Unfortunately, it can be one of the hardest, most time-consuming challenges for academics.

This blog outlines how you can leverage the Web of Science citation network to complete an in-depth, comprehensive search for literature. We share insights about how you can find a research paper and quickly assess its impact. We also explain how to create alerts to keep track of new papers in your field – whether you’re new to the topic or about to embark on a literature review.

• Choosing research databases for your search
• Where to find peer reviewed articles? Master the keyword search
• Filter your results and analyze for trends
• Explore the citation network
• Save your searches and set up alerts for new journal articles

1. Choosing research databases for your search

The myriad search engines, research databases and data repositories all differ in reliability, relevancy and organization of data. This can make it tricky to navigate and assess what’s best for your research at hand.

The Web of Science stands out the most powerful and trusted citation database. It helps you connect ideas and advance scientific research across all fields and disciplines. This is made possible with best-in-class publication and citation data for confident discovery and assessment of journal articles. The Web of Science is also publisher-neutral, carefully-curated by a team of expert editors and consists of 19 different research databases.

The Web of Science Core Collection™ is the single most authoritative source for how to find research articles, discover top authors , and relevant journals . It only includes journals that have met rigorous quality and impact criteria, and it captures billions of cited references from globally significant journals, books and proceedings ( check out its coverage ). Researchers and organizations use this research database regularly to track ideas across disciplines and time.

Explore the Web of Science Core Collection

We recommend spending time exploring the Core Collection specifically because its advanced citation network features are unparalleled. If you are looking to do an exhaustive search of a specific field, you might want to switch to one of the field-specific databases like MEDLINE and INSPEC. You can also select “All databases” from the drop-down box on the main search page. This will cover all research databases your institution subscribes to. IF you are still unsure about where to find scholarly journal articles, you can learn more in our Quick Reference Guide, here, or try it out today.

“We recommend spending time exploring the Core Collection specifically because its advanced citation network features are unparalleled.”

2. Where to find peer reviewed articles? Master the keyword search

A great deal of care and consideration is needed to find peer review articles for research. It starts with your keyword search.

Your chosen keywords or search phrases cannot be too inclusive or limiting. They also require constant iteration as you become more familiar with your research field. Watch this video on search tips to learn more:

It’s worth noting that a repeated keyword search in the same Web of Science database will retrieve almost identical results every time, save for newly-indexed research. Not all research databases do this. If you are conducting a literature review and require a reproducible keyword search, it is best to steer clear of certain databases. For example, a research database that lacks overall transparency or frequently changes its search algorithm may be detrimental to your research.

3. Filter your search results and analyze trends

Group, rank and analyze the research articles in your search results to optimize the relevancy and efficiency of your efforts. In the Web of Science, researchers can cut through the data in a number of creative ways. This will help you when you’re stuck wondering where to find peer reviewed articles, journals and authors. The filter and refine tools , as well as the Analyze Results feature, are all at your disposal for this.

“Group, rank and analyze the research papers in your search results to optimize the relevancy and efficiency of your efforts.”

Filter and Refine tools in the Web of Science

You can opt for basic filter and refine tools in the Web of Science. These include subject category, publication date and open access within your search results. You can also filter by highly-cited research and hot research papers. A hot paper is a journal article that has accumulated rapid and significant numbers of citations over a short period of time.

The Analyze Results tool does much of this and more. It provides an interactive visualization of your results by the most prolific author, institution and funding agency, for example. This, combined, will help you understand trends across your field.

4. Explore the citation network

Keyword searches are essentially an a priori view of the literature. Citation-based searching, on the other hand, leads to “systematic serendipity”. This term was used by Eugene Garfield, the founder of Web of Science. New scientific developments are linked to the global sphere of human knowledge through the citation network. The constantly evolving connections link ideas and lead to systematic serendipity, allowing for all sorts of surprising discoveries.

Exploring the citation network helps you to:

• Identify a seminal research paper in any field. Pay attention to the number of times a journal article is cited to achieve this.
• Track the advancement of research as it progresses over time by analyzing the research papers that cite the original source. This will also help you catch retractions and corrections to research.
• Track the evolution of a research paper backward in time by tracking the work that a particular journal article cites.
• View related references. A research paper may share citations with another piece of work (calculated from bibliographic coupling). That means it’s likely discussing a similar topic.

Visualizing the history discoveries in the citation network

The Web of Science Core Collection indexes every piece of content cover-to-cover. This creates a complete and certain view of more than 115 years of the highest-quality journal articles. The depth of coverage enables you to uncover the historical trail of a research paper in your field. By doing so, it helps you visualize how discoveries unfold through time. You can also learn where they might branch off into new areas of research.  Achieve this in your search by ordering your result set by date of publication.

As PhD student Rachel Ragnhild Carlson (Stanford University) recently wrote in a column for Nature: [1]

”As a PhD student, I’ve learnt to rely not just on my Web of Science research but on numerous conversations with seasoned experts. And I make sure that my reading includes literature from previous decades, which often doesn’t rise to the top of a web search. This practice is reinforced by mentors in my lab, who often find research gems by filtering explicitly for studies greater than ten years old.”

5. Save your search and set up alerts for new journal articles

Save time and keep abreast of new journal articles in your field by saving your searches and setting up email alerts . This means you can return to your search at any time. You can also stay up-to-date about a new research paper included in your search result. This will help you find an article more easily in the future. Head over to Web of Science to try it out today.

“Everyone should set up email alerts with keywords for PubMed, Web of Science, etc. Those keyword lists will evolve and be fine-tuned over time. However, it really helps to get an idea of recent publications.” Thorbjörn Sievert , PhD student, University of Jyväskylä

[1] Ragnhild Carlson, R. 2020 ‘How Trump’s embattled environment agency prepared me for a PhD’, Nature 579, 458

Related posts

Unlocking u.k. research excellence: key insights from the research professional news live summit.

For better insights, assess research performance at the department level

Getting the Full Picture: Institutional unification in the Web of Science

Understanding Interventions That Encourage Minorities to Pursue Research Careers: Summary of a Workshop (2007)

Chapter: 2 examples of previous research.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

2 Examples of Previous Research R esearchers have examined the processes involved in deci- sions to study science in college, enter graduate school in the sciences, and become a scientist. These research programs originate in a variety of disciplines and can have quite different perspectives, but they also complement each other in explaining the complex processes involved in making educational and career choices. Because many of those interested in these questions are biomedical researchers who may not be steeped in social science viewpoints on these issues, the planning committee constructed an early session in the workshop to provide a varied set of lenses for participants to think more broadly about this kind of work—and to help consider themes for future study. The perspectives offered at the workshop—and in this summary—do not provide an exhaus- tive set, but they help to provide a broader set of questions and approaches for thinking about these issues. Social Cognitive Career Theory Social cognitive career theory (SCCT) is an integrative theoreti- cal framework that explores the psychological and social factors that produce personal interests and lead to choices related to education and careers. The theory is also concerned with the network of factors that affect performance and persistence in a person’s educational and career paths and those that are responsible for an individual’s satisfaction in a particular job.  

 UNDERSTANDING INTERVENTIONS Personal interests are not the only factors that drive educa- tional and career choices and can be trumped by family expectations or other external influences. But interests are “strong motivational drivers of the choices that students make in their educational and career lives,” said Robert W. Lent, professor of counseling and per- sonnel services at the University of Maryland, College Park. Lent described his work on applying SCCT to the issue of expanding the science, technology, engineering, and mathematics (STEM) pipeline, noting that it serves as a template with which to view and develop interventions designed to encourage minorities to enter research careers. SCCT draws heavily from the more gen- eral social cognitive theory of the Stanford University psychologist Albert Bandura. The key construct in Bandura’s work is the concept of self-efficacy—people’s beliefs about their ability to perform specific behaviors or actions. In particular, it refers to domain-specific con- fidence in particular situations, not to self-confidence as a general trait. In the context of science and mathematics, said Lent, self-effi- cacy addresses “the fundamental question: Can I do this thing? Can I, for example, do well in math and science courses in middle or high school? Can I do well in a science or engineering-related major in college?” Self-efficacy beliefs, in turn, derive largely from four sources, according to Bandura’s theory. The first and most important is prior performance—the levels of mastery or failure that people have expe- rienced. “If I have done well in the past at a particular academic subject, for example, I am likely to expect in the future that I can do well in it as well,” said Lent. “Conversely, if I’ve not done so well, my self-efficacy beliefs are going to drop.” The three other sources of self-efficacy beliefs are also important. One is observations of others’ learning or the experience of models, especially models that one perceives as being similar to oneself. “For example, in terms of race, ethnicity, gender, social class, and so forth, viewing our models as being efficacious at things we want to do is a good way of raising self-efficacy—or lowering it, depending on the nature of the model,” Lent said. Another source of self-efficacy beliefs is the social messages that encourage or discourage participation in an activity. Students receive many messages from others and from the mass media that can influence their confidence about a particular activity. “But talk is cheap,” Lent reminded the workshop participants. “Sometimes, if we try to convince people that they are good at things that we are not so sure they are—or that their own performance experiences 

EXAMPLES OF PREVIOUS RESEARCH  disconfirm—then the source of that support may not be credible for very long, and people may not persist.” A final source of self-efficacy beliefs is physiological and affec- tive reactions. For example, if a person is so anxious in taking every math test that he or she does poorly, that person is likely to infer that math is a personal weakness. “So test anxiety can be, in that example, a negative influence on self-efficacy,” said Lent. This is one way in which gender can influence self-efficacy beliefs in science and mathematics, Lent noted. In a general popula- tion of college students, women at the college level and below tend to report significantly lower self-efficacy beliefs at math compared with men. However, the same tendency is not exhibited in more spe- cialized populations, such as engineering students. Also, if women have had similar experiences to men in terms of the four sources of self-efficacy beliefs, they tend to have the same self-efficacy beliefs as men. Interest in Bandura’s theory follows from a number of other factors, including the expectations surrounding particular outcomes. As Lent said, these beliefs “address the question: ‘If I do this, then what will happen? If I major, for example, in science or engineering, or if I choose to pursue a research career, what will be the outcomes? What will be the payoffs for me, and what will be the negative con- sequences? What will the salary be like? What will my co-workers be like? Prestige? Autonomy?’ These refer basically to career values that people want to fulfill.” Another factor is the goals that motivate people to engage in a particular activity or produce a particular outcome, such as trying to get an A grade in a particular math or science course. According to Lent, “Goals address the fundamental question of ‘how much do I want to do this course of action?’” Finally, within the theory, there are various kinds of social, financial, emotional, and other contextual supports and barriers that people encounter while pursuing their goals. These supports and barriers address the question of “‘how will the environment treat me if I try this particular course of action,’” Lent said. For example, “the phrase ‘chilly climate,’ which oftentimes refers to the experience of women and certain minority groups in science and engineering fields, refers to the perception of environmental barriers.” The importance of self-efficacy beliefs is often obvious among students studying science and engineering, Lent noted. For example, he has seen many students who did extremely well in high school lose confidence when they got poor grades on their first college mid- term examination. “All of a sudden their confidence levels plum- 

10 UNDERSTANDING INTERVENTIONS meted, and they were convinced they were in the wrong field,” Lent said. “They had never gotten Bs, or worse, before, and all of a sudden it was time to change majors and career paths.” The ongoing experience of success or failure subsequently modi- fies or stabilizes self-efficacy and outcome beliefs “in a never-ending loop,” said Lent. Changes to these beliefs also can occur through outside influences like “technological advances, parenting, and other life experiences that may formulate changes in interest patterns because of their impact on self-efficacy and outcome expectations.” Each person has what SCCT theorists call “person inputs”— factors like personality, ability, gender, race, ethnicity, disability, and health status. These factors interact with background factors such as social class and the quality of early educational experiences. “Depending on who one is, and what one looks like, the environ- ment may selectively provide or withhold certain opportunities,” said Lent. Lent and others have applied this framework in several major research projects. In a study of students at a predominantly white university, Lent and his colleagues found “that SCCT variables were well predictive of goals and actual persistence in engineering over a three-semester sequence.” This model was equally good at predicting choice and persistence goals in engineering majors when extended to two historically black universities. Lent and his coworkers are now conducting a large-scale longitudinal study of computer science and computer engineering students at multiple predominantly white and historically black colleges and universities around the country. This theoretical work has suggested particular intervention points and approaches, according to Lent. One possibility is to work at expanding vocational interests, especially in high-aptitude areas, and “getting people to rethink areas they might be able to do well at but have prematurely foreclosed upon because they don’t believe they have the ability to do well or don’t know enough about the field to want to pursue it.” Other options are clarifying career goals, sup- porting career goals, strengthening self-efficacy, instilling realistic   R.W. Lent, S.D. Brown, J. Schmidt, B. Brenner, H. Lyons, and D. Treistman. 2003. Relation of contextual supports and barriers to choice behavior in engineering ma- jors: Test of alternative social cognitive models. Journal of Counseling Psychology 50: 458-465.   R.W. Lent, S.D. Brown, H. Sheu, J. Schmidt, B.R. Brenner, C.S. Gloster, G. Wilkins, L. Schmidt, H. Lyons, and D. Treistman. 2005. Social cognitive predictors of academic interests and goals in engineering: Utility for women and students at historically Black universities. Journal of Counseling Psychology 52: 84-92. 

EXAMPLES OF PREVIOUS RESEARCH 11 outcome expectations, and helping people to manage environmental barriers and build effective support systems. Past work has also emphasized how much more could be learned through further research that applies this model. A particular need, said Lent, is for more longitudinal, multiyear, and multisite research. Also, according to Lent, the basic theory needs to be studied in rela- tion to women and underrepresented minorities in STEM fields, and more theory-based interventions and experimental studies are needed. “There has been some of this and I think it holds much promise for the future, but we need much more of it,” said Lent. Lent noted, by the way, that many individuals and groups out- side of academia are interested in applying this approach to the issues they face. In addition to his university position, Lent is a visiting scholar at the U.S. Department of Homeland Security, and he said that the department views this issue as important to national security as well as economic prosperity. “There are some things that we probably don’t want to outsource to other countries,” Lent said. Human Capital Theory Another theoretical perspective, this one rooted in economics, is known as human capital theory. As described by Anne Preston, asso- ciate professor of economics at Haverford College, each individual has a stock of skills, knowledge, abilities, and other characteristics that determine his or her wage-earning potential. Individuals can invest in increases in their own human capital through education, on-the-job training, and other activities. “Human capital theory basi- cally allows us to understand under what circumstances an indi- vidual will decide to invest in further acquisition of human capital and [in] what types,” said Preston. “So you can think of it as a pure cost-benefit calculation made by what we in economics always talk about—the rational and perfectly informed actor.” Of course, as Preston noted, “we do understand that not every individual is totally rational or perfectly informed.” Costs, which Preston said are relatively easy to estimate, are for tangible expenses— such as tuition, room, board, books, and foregone earnings—and they occur at the time of the investment. Benefits, which can include future wages and future income streams, in contrast, can be much harder to predict. In addition, these cost-benefit calculations often require dis- counting future income versus current costs. “Some people value future income differently than others,” said Preston. “It depends on 

12 UNDERSTANDING INTERVENTIONS your current family income, maybe your family structure, the sorts of needs that your family has in terms of income now versus in the future, and the expected duration of the work life.” Some of these factors can differ for different populations. Finally, economists know that people do not always act in per- fectly rational and perfectly informed ways. Methods exist to take a lack of information or irrational decision-making into account, but these methods may introduce additional levels of uncertainty. Human capital theory can be used to provide insights into how interventions might lead to different decisions, Preston said. For example, mentoring programs can increase knowledge and change expectations. Better job placement programs might lead to better returns on an investment in human capital. Fellowships, research assistantships, teaching assistantships, and other forms of financial support can reduce the costs of the investment. Better information about the opportunities that investments give an individual can make a difference. Methodologically, human capital theory is a strategy in which economists quantify variables and seek to determine the relation- ships among those variables. Some of these variables have discrete values, such as whether a person stays in a field or leaves it, or a per- son’s race, sex, or type of school; others are continuous, like wages. Some variables are measured by proxies, as when the number of publications or number of citations are used as measures of research productivity. Preston explained that economists add variables to an analysis with the goal of explaining away the effect. If all vari- ables that can be identified—except for the one under study—fail to explain away the effect, researchers have an indication that the variable under investigation plays an important role. Economists also try to measure the quantitative effects of inter- ventions. If mentoring programs are thought to make a difference, for example, economists will try to analyze whether being men- tored influences the probability of investment in human capital. This could be done for majority and for minority students to see if there are differences in the effects of mentoring. Studies such as this introduce what economists call “selectiv- ity.” If the individuals being mentored differ from those who are not mentored in some important way, the effect ascribed to mentoring may actually arise from personal characteristics, not the mentoring program. Economists can try to reduce these effects using various complex mathematical techniques, but Preston said that “person- ally, I find them not very reliable or satisfying.” An alternative, she said, is “to move from these big national data sets [to create] indi- 

EXAMPLES OF PREVIOUS RESEARCH 13 vidualized data sets.” Approaches such as randomized trials, where individuals are selected to receive or not receive an intervention and the effects of the intervention measured, diminish issues of selectiv- ity; however, they are seldom feasible and may even be unethical in such a setting, where some individuals are prevented from engaging in what is believed to be a positive intervention. Another possibility is to collect data from natural experiments, using existing variation in the population of study. For example, student outcomes could be measured from different schools, some of which have an institutionalized mentoring system and others that do not. Such experiments require thought, time, creativity, and fund- ing, said Preston, but “economists can really make some interesting inroads if they take up this challenge.” Social Identity and Stereotype Threat Claude Steele, director of the Center for Advanced Study in the Behavioral Sciences, professor of psychology and Lucie Stern Professor in the Social Sciences at Stanford University, and his col- leagues have focused their research on two main themes. The first is underperformance in school by groups whose abilities are nega- tively stereotyped in the broader society—an issue closely related to the persistence of members of these groups in pursuing research careers. The second is the set of broader issues posed by diversity. “It is one thing to integrate a school setting or work place,” Steele said. “It is another thing to make that setting a place where everybody seems to flourish—where they feel like they belong.” Unlike many psychologists, Steele stresses the importance of context. “When we talk about schools and other environments of that sort, we tend to think of them as homogeneous environments— environments that are essentially the same for everybody. If there is one thing I hope you get from my remarks today, it is that they are different for people with different identities. The very same rooms with the same pictures on the wall, the same test items, the same teachers, can be very different as a function of social identities that a person has.” Each individual has many different social identities. These iden- tities can be based on age, sex, race, religion, ethnicity, and so on. Different identities generate what Steele calls “contingencies”— reactions by others to a particular identity. “You have to deal with certain things because you have certain identities,” he said. An individual’s social identities can change. During the great migration of African Americans from the southern to the north- 

14 UNDERSTANDING INTERVENTIONS ern United States, an estimated 10,000 to 30,000 African Ameri- cans “passed” from being “black” to being “white,” asserted Steele. “[That’s] what I mean by contingencies,” he said. “They were avoiding the things that went with that identity.” Another example includes changing a foreign-sounding name to one that sounds more American. Some contingencies are threatening. An example might be an African American seeking to excel in an endeavor where members of that group are stereotyped as underperforming. When someone is threatened by the contingencies of a social identity, that person might seek to conceal or disguise that identity. But threats to an identity tend to make it central to your functioning, said Steele. Stereotype threat is a good example of a contingency. Stereo- type threat arises when a person is in a situation where a negative stereotype applies. A good example is women in mathematics. In a series of experiments done by Steele and his colleagues, women and men who were equally skilled in math were given a very difficult math test one at a time in a testing room. Women in this situation tended to underperform. When they experienced the frustration of a difficult test, the stereotype that women have weaker mathematical abilities suggested to women that they may lack ability. Men who are frustrated by the test may also believe that they don’t have the necessary ability, but it’s because of factors other than their male- ness. “So for a woman in that situation, there is extra pressure— especially if that woman cares about math, has high expectations for her performance, or is committed to it,” said Steele. In one recent study, simply mentioning the word “genetics” in the preamble to a math test worsened women’s performance in math. However, when the researchers told the women before they took the test that “for this particular test, women always do as well as men,” the women’s performance was higher than when they were experiencing stereotype threat. Interestingly, the performance of men tends to drop somewhat under these circumstances. “We can be advantaged by stereotypes,” said Steele, describing stereotype   C.M. Steele, S.J. Spencer, and J. Aronson. 2002. Contending with group image: The psychology of stereotype and social identity threat. Advances in Experimental Social Psychology 24: 379-440. C.M. Steele and J. Aronson. 1995. Stereotype threat and the intellectual test performance of African Americans. Journal of Personality and Social Psychology 69(5): 797-811.   S.J. Spencer, C.M. Steele, and D.M. Quinn. 1999. Stereotype threat and women’s math performance. Journal of Experimental Social Psychology 35(1): 4-28.   Dar-Nimrod and S.J. Heine. 2006. Exposure to scientific theories affects women’s I. math performance. Science 314(5798): 435. 

EXAMPLES OF PREVIOUS RESEARCH 15 lift, in which one group can be “on the upside of somebody else’s negative stereotype.” Men may do worse on the test because “it isn’t plausible to them that they lack the ability to do the work. It doesn’t make sense. So the experience of frustration is less. If you take that advantage away from them, . . . then you may see some decrements in performance.” The effects of stereotype threat also were observed among Afri- can Americans taking a test using Raven’s Progressive Matrices, a type of IQ test. When told that the test was to measure IQ, African American students dramatically underperformed compared with white students. But when African American students were told that the test was simply a puzzle, their performance rose dramatically. One remarkable finding from studies such as these is that the strongest students are often more susceptible to stereotype threat. “You have to care about [the domain] to experience stereotype threat,” Steele said. “One protection against stereotype threat is not to care about it. [If you] dis-identify with the domain, then you don’t care that much that your group is negatively stereotyped in that domain because you don’t care that much about the domain.” These studies also emphasize the importance of cues in the environment that accentuate or lessen threats, Steele pointed out. “Cues that signal threatening contingencies foster vigilance,” he said. “They hamper a sense of belonging in the setting, and this in turn impairs learning.” One such cue is the number of other people in a setting with whom you share a social identity. For example, when women are greatly outnumbered by men in taking a math test, they tend to perform worse than if men are absent. This kind of marginalization through small numbers can have a powerful effect on identity threat. The profound segregation that exists on many college campuses can heighten a sense of difference. The effects of cues on attitudes were tested in an experiment performed by Steele in collaboration with Mary Murphy, using stu- dents who were waiting to be interviewed for admission to a sum- mer workshop on science and engineering. While waiting for the interview, they watched a videotape about the summer workshop that showed students working together. In one videotape, men and women were balanced one to one. In the other, men outnumbered the women three to one. The women who watched the video with the unbalanced genders had a much better memory of the inciden-   R.P. Brown and E.A. Day. 2006. The difference isn’t black and white: Stereotype threat and the race gap on Raven’s Advanced Progressive Matrices. Journal of Applied Psychology 91(4): 979-985. 

16 UNDERSTANDING INTERVENTIONS tal details of that videotape. Steele hypothesized that the cues in the videotape were making female viewers aware of their gender identity, which made them more aware of the situation than they would be otherwise. “Think of any time any of you have ever been in a situation where you are one of a kind,” Steele said. “You pay attention.” This awareness has effects not only on memory, but also on physiology. In fact, when the students were hooked up to physi- ological recording equipment (under the pretense that they would need it for a different experiment they would soon undertake), the women who watched the unbalanced videotape had much higher cardiovascular activity than the men. Steele takes several messages away from this research. One is that these kinds of cues and reactions are virtually unavoidable in a diverse society such as ours. “Any diverse setting holds these iden- tity threats,” he said. “This is sort of an American challenge. I think at one level we should be proud of it because we are a society that publicly holds on to the idea that all of society should be integrated. . . . But one of the challenges behind that commitment . . . is making integration work. It is making these settings, these schools, these programs work for a truly diverse population.” Also, these cues do not arise solely from discrimination. On the contrary, he said, they can exist in the absence of discrimination. “These are contextual factors that make identities function in certain ways,” he said. The importance of cues also suggests ways to promote learning. If the cues change, performance can change. The most important change that has to happen, according to Steele, is for women and minorities to have a sense that they belong in a particular setting. “For instruction to work—and for the decisions we want them to make to be made—they have to have a sense of belonging. As a soci- ety, [we have to] understand that that has to come first.” In fact, said Steele, without changing this sense of belonging, other interventions can be counterproductive: “If you push other things, like try to motivate [students], expose them to strong skill-focused programs, without at the same time addressing the sense of belonging, you can really get a backfire effect. Things may not work at all.” One cue is what people say. “What do the university president, the department chair, [and other] people say about the ‘belong- ingness’ of groups? Do they avoid the issue and see it as a minor issue and not something of importance? Or do they really own it and make the proclamation that everybody belongs intellectually in these settings?” he asked. Making the presence of particular groups 

EXAMPLES OF PREVIOUS RESEARCH 17 the norm can relieve the tension in a setting and enable students to feel that they belong. Similarly, a critical mass of people with a particular social iden- tity is also pivotal, claimed Steele. Individuals are always looking around and counting how many other people share their social identities in a particular setting. “People do respond to numbers,” he said. Particular interventions can dramatically shape how students respond to cues. In a study done by Gregory Walton and Geoffrey Cohen at Yale University, African American and white students watched and then discussed a videotape of an African American student talking about how alienated and out of place he had felt at Yale. But the student went on to recount how, during a trip home, his father reminded him what a superb opportunity it was to be attend- ing Yale and that he needed to take advantage of it. The student described becoming active in a singing group and in academics, and he concluded that he was now very happy at Yale and that he was enjoying and learning from Yale’s rich environment. Just watching the videotape and talking about it raised the grade point average of African American students by two-thirds of a letter grade in the subsequent semester. “Why does that work?” asked Steele. “Because it gives [the students] an interpretation of the cues in the environment that [is] hopeful. . . . Everybody has those feel- ings [of not belonging], but if you’re a group that the whole society negatively stereotypes in this way, those feelings are really a weight. So you need an interpretation that makes your sense of not belong- ing normal. This guy in the videotape makes it normal, and then he offers light at the end of the tunnel. Wow.” In another intervention, having African Americans talk with members of other minority and majority groups in informal settings greatly improved their grade point average. “They found out that things that were happening to them were not things that were just happening to black kids. They were happening to every kid. They got the data, the evidence that their experience was not racially based, and then when their experience was not racially based in this environment, the whole environment changed. It wasn’t nearly as threatening. All those cues that might otherwise suggest threat were seen as much less threat.” Steele recounted from his own personal experiences that having an advisor during graduate school who believed in him was enough for him to overcome the many negative   G.M. Walton and G.L. Cohen. 2007. A question of belonging: Race, social fit, and achievement. Journal of Personality and Social Psychology 92(1): 82-96. 

18 UNDERSTANDING INTERVENTIONS cues he encountered. “With this one big cue that said I did belong in that setting, the significance of the other cues tended to wane away,” he recalled. An especially powerful way to undercut stereotype threat is to attack and undermine people’s theories of intelligence, Steele said, citing the work of Carol Dweck and Joshua Aronson. Many Americans tend to think that each individual has a particular level of intelligence and that one cannot perform beyond that level. But others, such as those from Asian and Eastern European cultures, see intelligence much differently. Students in those countries are more often taught that abilities are incremental and can be expanded through learning. They do not see math ability, for example, as something that is fixed and genetically determined but as something that people can improve. “I think this has huge effects on people’s choices of majors and persistence in graduate school,” said Steele. He felt that intervention on this topic would be especially valuable in entry-level, technical, and quantitatively based courses where stu- dents may receive their first sub-par grades, especially with faculty members who discourage students by telling them that many will drop out of or fail their courses. Survey Research Carefully conducted surveys can explore the attitudes, expe- riences, and thought processes that underlie the theoretical per- spectives described above. With his colleague Catherine M. Millett, Michael T. Nettles, senior vice president and Edmund W. Gordon Chair of the Policy Evaluation and Research Center at Educational Testing Service, conducted a 28-page survey of about 9,000 doctoral students at 21 U.S. universities. (The research team used a variety of incentives to achieve a 72 percent response rate, Nettles noted, including a raffle for cash payments.) The survey asked students about their background, undergraduate and doctoral program expe- riences, finances, aspirations, and expectations for graduate study. Conclusions drawn from the survey were published in the book Three Magic Letters: Getting to Ph.D. One critical factor Nettles and Millett examined was how stu- dents are supported during their doctoral education. In particular, they contrasted fellowships (money, tuition, or fee waivers given to students with no expectation of repayment or of services to be rendered), research assistantships (tuition, fee waivers, or a stipend   M.T. Nettles and C.M. Millett. 2006. Baltimore: Johns Hopkins University Press. 

EXAMPLES OF PREVIOUS RESEARCH 19 given to students with the expectation of research services to be ren- dered), and teaching assistantships (tuition, fee waivers, or a stipend given to students with the expectation of teaching services to be rendered). Nettles and Millett found that in the sciences, mathemat- ics, and engineering, African American students were less likely than white students to be research assistants during their doctoral programs, even when background characteristics and student expe- riences are taken into account. Yet being a research assistant can have a profound effect on a student’s experiences in graduate school. For students with a research assistantship, Nettles pointed out, “we observe an increase in students’ social interactions with peers, their academic interac- tions with faculty, their interactions with their faculty advisers, their presenting papers and publishing articles, and their overall research productivity.” Somewhat surprisingly, a research assistantship did not influence students’ time to degree, their overall satisfaction with their doctoral programs, or social interactions with faculty. Nettles noted that universities often use fellowships to attract students to their institutions. While fellowships can be attractive to prospective students, they can have other consequences once students arrive on campus. Because students on fellowship are not always engaged in research or teaching activities from the beginning of graduate work, Nettles said, fellowships “can lead to the social isolation or the neglect on the part of faculty of students who are not actually engaged in the production of [that teaching and research]. . . . This is not to suggest that fellowships are not a good idea, but I think that what universities are experiencing is trying to figure out the right balance.” Another critical factor identified in the surveys is whether stu- dents have a mentor. Nettles distinguished sharply between an advisor—who acts in an official capacity to give a student advice about academic programs or coursework—and a mentor—who is a faculty member to whom a student turns for advice about intellectual and academic processes as well as general support and encourage- ment. One of the good messages to emerge from the survey, Nettles said, was that race was not a major factor in whether a doctoral stu- dent had a mentor (possibly the same person as a faculty advisor). Furthermore, of the students who had mentors, three-quarters were able to find them within the first year of their doctoral experiences. Having a mentor influences social interactions between students and faculty, unlike having a research assistantship. Having a mentor also influences the rate of scholarly publishing, degree completion, 

20 UNDERSTANDING INTERVENTIONS and even time to degree. However, it did not influence satisfaction with doctoral programs or whether students left the program. A third key finding that emerged from their study was the importance of research productivity. Publishing in a refereed journal is a strong measure of this productivity, but the study showed that many other measures of research productivity are also important, such as presenting a paper at a research conference, publishing a book chapter, or being a member of a roundtable discussion at a professional meeting. As Nettles said, “many students pursuing research careers get on the train in different places.” Over half of the students surveyed had presented a paper at a conference, published an article in a refereed journal, published a chapter in an edited vol- ume, or published a book. Publishing in a journal “has become an extremely important endeavor for students,” Nettles said. “In fact, many people feel that they can’t complete [their degrees] without doing this because their first entry into the academic profession is going to be enhanced by their performance in conducting this activity.” However, the percentage of African American students publish- ing refereed journal articles in science and mathematics was signifi- cantly lower than for other groups (although that was not the case in engineering). Again, this was true even after controlling for factors such as student backgrounds and experiences. Before doing the study, Nettles thought that research productiv- ity might compete with time to degree because students would be devoting time and attention to producing articles and publishing. However, “we found just the opposite,” he said. Publishing articles actually was associated with an increased rate of progress in their doctoral programs and reduced the time to degree. Research on Existing Interventions Existing intervention programs can have research components that produce broadly applicable information. An example is the Alli- ance for Graduate Education in the Professoriate (AGEP), funded by the National Science Foundation. Yolanda S. George, deputy director for education and human resources at the American Association for the Advancement of Science (AAAS), which has provided evalua- tion capacity-building activities and research resources for the AGEP program, explained that the goal of AGEP is to increase the number of underrepresented minority students pursuing advanced studies, obtaining doctorate degrees, and entering the professoriate in STEM fields, including the social sciences. 

EXAMPLES OF PREVIOUS RESEARCH 21 AGEP has identified several factors that facilitate progression of minorities into STEM post-secondary studies: • Taking high-intensity and high-quality advanced high school STEM courses • STEM pre-college programs • Post-secondary support programs in core STEM courses • Financial aid packages that reduce debt burden • STEM pre-graduate-school bridging programs. The institutions that participate in AGEP “are expected to engage in comprehensive institutional cultural changes that will lead to sustained increases in the conferral of STEM doctoral degrees, significantly exceeding historical levels of performance,” George explained. She discussed several of the important lessons AGEP has demonstrated in seeking to achieve this goal. One lesson, accord- ing to George, is that admission and selection committees need to be conscious of diversity issues. The AGEP program tries to have a diversity coordinator or diversity-conscious faculty member sit in on admissions and selection. “You will get a behavior change if you get an advocate there,” said George. AGEP programs have also found that following up with applicants and linking financial aid to admissions helps with recruitment. At the same time, AGEP has found that it is important to work closely with university administrators on what can and cannot be done with recruitment and retention programs. George said, “You have to start talking to counsel about diversity-conscious and legally defensible student admission selection criteria, financial aid, and programs before you get that letter from that group that is threaten- ing to shut you down.” Furthermore, these discussions need to be ongoing, said George, since challenges will continue to arise. AGEP has conducted meetings and workshops to explore par- ticular topics. For example, a 2003 meeting on mentoring found that relatively little was known about mentoring specifically for STEM students. “We know that STEM core mentoring appears to be more prevalent in the after-school programs at the middle and high school level, but the level of systematic STEM career and workforce mentoring is not high in undergraduate research programs,” George said. However, support networks for women, including students, in STEM areas in academia, industry, and government are useful in   AAAS created a Science Mentoring Research website that followed on the 2003 meeting: <http://ehrweb.aaas.org/sciMentoring/>. 

22 UNDERSTANDING INTERVENTIONS helping to balance family and career, negotiating organizational or departmental challenges, and advancing in a career. George also observed that, through its program evaluation capacity-building project, AAAS has helped AGEP awardees build comprehensive evaluation and assessment infrastructures to exam- ine their graduate education enterprises. The framework for mak- ing change includes collecting and using disaggregated data for decision-making and leadership development within the faculty and administration. The goal of AAAS’s AGEP program is “to help the leaders in these projects, [who] are the deans and provosts in some cases, faculty members, and people who run the program, to figure out how to evaluate and assess the infrastructure in order to get the types of effects that they want,” George said. A particularly important task is to help faculty and administrators understand the research that has been conducted so that they can engage faculty members in the process of institutional change. Other Research Initiatives Several other important lines of research were mentioned more briefly by presenters and attendees at the workshop. Two described here are conducted by current grantees of the Efficacy of Interven- tions program; additional interventions and research studies are discussed elsewhere in this summary.10 For example, Reba Page, professor of education at the University of California, Riverside, conducts ethnographic studies of mentor- ing, journal clubs, research in labs, and so forth to understand how those components of intervention programs play out in practice. She wants to know “not what do people tell us they are, not what does the brochure tell us they are, but what do they actually look like in real time, as people, students and teachers together, enact the com- ponents.” By studying these situations and the processes they entail, Page is able to examine “the assumptions that undergird those pro- cesses and what holds them in place, and what we might want to target if we wanted to change them.” A prominent question in her work is why outcomes seem so resistant to change. The conclusion she has drawn is that outcomes depend not only on the culture of science but on the culture of the broader society. To understand sci- ence, including science education, “we have to see that science is embedded in our society,” Page said. Another line of research focuses specifically on the attitudes 10  See, in particular, Chapter 4 for discussion of initiatives by educational stage. 

EXAMPLES OF PREVIOUS RESEARCH 23 and preferences of students. Merna Villarejo, professor emerita at the University of California, Davis, has asked students in interviews about the motivating factors that caused them to make particular career decisions. Students who went to medical school tended to say that they want to give back to the community. But “that is not what researchers say,” Villarejo observed. “The most frequent answer for researchers for ‘why did you choose your profession’ is ‘because I really love science; it just turns me on; it is exciting; it is great.’” According to Rick McGee, associate dean for faculty affairs at Northwestern University’s Feinberg School of Medicine, another distinguishing characteristic was between students who wanted a fairly predictable future and those who were willing to live with more uncertainty. The students most likely to go into research were the ones who said, when asked about their future, “‘I don’t know, I might be doing this, I might be doing that, I might do this for awhile, I might do that for awhile.’ . . . They really are quite different people,” McGee said. As Daryl E. Chubin, planning committee member and director of the AAAS Center for Advancing Science and Engineering Capacity, said, many kinds of investigations can produce information needed to advance minorities in research careers. “Where does knowledge come from? We know it comes from data and we know it comes from research. But it also comes from evaluation and it comes from technical assistance and it comes from first-person reports. . . . The challenge here is to learn from all of these interventions and then try to apply that in our own context.” 

Welcome to OpenBook!

You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

Do you want to take a quick tour of the OpenBook's features?

Show this book's table of contents , where you can jump to any chapter by name.

...or use these buttons to go back to the previous chapter or skip to the next one.

Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

To search the entire text of this book, type in your search term here and press Enter .

Share a link to this book page on your preferred social network or via email.

View our suggested citation for this chapter.

Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

Get Email Updates

Do you enjoy reading reports from the Academies online for free ? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.

• Hirsh Health Sciences
• Lilly Music
• Webster Veterinary
• Hirsh Health Sciences Library

Finding and Creating Surveys/Questionnaires

• Published Questionnaires
• Finding Info about Tests

Look for a Test

• PsycTESTS Provides access to abstracts and some full-text of psychological tests, measures, scales, surveys, and other assessments as well as descriptive information about the test and its development and administration. Most records include the actual instrument.

Searching for Surveys/Questionnaires

Some questionnaires or surveys are published within an article. To find them, conduct an article search in a bibliographic database on the topic of interest and add in the Keywords: survey* or questionnaire*

In some cases the actual questionnaire or survey is not published with the article, but referred to within the text. In this case look at the bibliography and find the reference to the questionnaire/survey itself, or to the original article where the instrument was published. From that information track down the instrument.

Some instruments are not free. They can be purchased from the developer.

Tips for searching for an already established survey or questionnaire. Be sure to read the definition if there is one and also look to see if there is a broader or narrower concept that might be more specific to your needs.

• In Ovid MEDLINE or PubMed using MeSH terminology
• Use the subheading for Statistics and Numerical Data (sn) with your topic
• In conjunction with your topic use the term AND to combine the topic with any of the terms below.
• There is a broader term Data Collection or a narrower term Self Report , if those are more appropriate.
• There are more discipline specific subject headings or MeSH, such as the ones below. Be sure to read the definitions of these terms to make sure they are correct for what you want to find.
• Health Surveys
• Dental Health Surveys
• Health Status Indicators
• Nutrition Surveys
• Diet Surveys
• Health Care Surveys
• Nutrition Assessment
• Questionnaires
• In PsycInfo, these terms might be helpful:
• Attitude Measures
• Opinion Survey
• General Health Questionnaires
• Mail Surveys
• Consumer Surveys
• Telephone Surveys
• Test Construction
• In Sociological Abstracts
•  Attitudes
• In ERIC (education research)
• Evaluation Methods
• << Previous: Surveys
• Next: Finding Info about Tests >>

Desk Hours: M-F 7:45am-5pm

• Last Updated: Nov 22, 2023 10:58 AM
• URL: https://researchguides.library.tufts.edu/surveys

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base
• Research paper

Writing a Research Paper Introduction | Step-by-Step Guide

Published on September 24, 2022 by Jack Caulfield . Revised on March 27, 2023.

The introduction to a research paper is where you set up your topic and approach for the reader. It has several key goals:

• Present your topic and get the reader interested
• Provide background or summarize existing research
• Position your own approach
• Detail your specific research problem and problem statement
• Give an overview of the paper’s structure

The introduction looks slightly different depending on whether your paper presents the results of original empirical research or constructs an argument by engaging with a variety of sources.

Instantly correct all language mistakes in your text

Upload your document to correct all your mistakes in minutes

Table of contents

Step 1: introduce your topic, step 2: describe the background, step 3: establish your research problem, step 4: specify your objective(s), step 5: map out your paper, research paper introduction examples, frequently asked questions about the research paper introduction.

The first job of the introduction is to tell the reader what your topic is and why it’s interesting or important. This is generally accomplished with a strong opening hook.

The hook is a striking opening sentence that clearly conveys the relevance of your topic. Think of an interesting fact or statistic, a strong statement, a question, or a brief anecdote that will get the reader wondering about your topic.

For example, the following could be an effective hook for an argumentative paper about the environmental impact of cattle farming:

A more empirical paper investigating the relationship of Instagram use with body image issues in adolescent girls might use the following hook:

Don’t feel that your hook necessarily has to be deeply impressive or creative. Clarity and relevance are still more important than catchiness. The key thing is to guide the reader into your topic and situate your ideas.

The only proofreading tool specialized in correcting academic writing - try for free!

The academic proofreading tool has been trained on 1000s of academic texts and by native English editors. Making it the most accurate and reliable proofreading tool for students.

Try for free

This part of the introduction differs depending on what approach your paper is taking.

In a more argumentative paper, you’ll explore some general background here. In a more empirical paper, this is the place to review previous research and establish how yours fits in.

Argumentative paper: Background information

After you’ve caught your reader’s attention, specify a bit more, providing context and narrowing down your topic.

Provide only the most relevant background information. The introduction isn’t the place to get too in-depth; if more background is essential to your paper, it can appear in the body .

Empirical paper: Describing previous research

For a paper describing original research, you’ll instead provide an overview of the most relevant research that has already been conducted. This is a sort of miniature literature review —a sketch of the current state of research into your topic, boiled down to a few sentences.

This should be informed by genuine engagement with the literature. Your search can be less extensive than in a full literature review, but a clear sense of the relevant research is crucial to inform your own work.

Begin by establishing the kinds of research that have been done, and end with limitations or gaps in the research that you intend to respond to.

The next step is to clarify how your own research fits in and what problem it addresses.

Argumentative paper: Emphasize importance

In an argumentative research paper, you can simply state the problem you intend to discuss, and what is original or important about your argument.

Empirical paper: Relate to the literature

In an empirical research paper, try to lead into the problem on the basis of your discussion of the literature. Think in terms of these questions:

• What research gap is your work intended to fill?
• What limitations in previous work does it address?
• What contribution to knowledge does it make?

You can make the connection between your problem and the existing research using phrases like the following.

Now you’ll get into the specifics of what you intend to find out or express in your research paper.

The way you frame your research objectives varies. An argumentative paper presents a thesis statement, while an empirical paper generally poses a research question (sometimes with a hypothesis as to the answer).

Argumentative paper: Thesis statement

The thesis statement expresses the position that the rest of the paper will present evidence and arguments for. It can be presented in one or two sentences, and should state your position clearly and directly, without providing specific arguments for it at this point.

Empirical paper: Research question and hypothesis

The research question is the question you want to answer in an empirical research paper.

Present your research question clearly and directly, with a minimum of discussion at this point. The rest of the paper will be taken up with discussing and investigating this question; here you just need to express it.

A research question can be framed either directly or indirectly.

• This study set out to answer the following question: What effects does daily use of Instagram have on the prevalence of body image issues among adolescent girls?
• We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls.

If your research involved testing hypotheses , these should be stated along with your research question. They are usually presented in the past tense, since the hypothesis will already have been tested by the time you are writing up your paper.

For example, the following hypothesis might respond to the research question above:

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

The final part of the introduction is often dedicated to a brief overview of the rest of the paper.

In a paper structured using the standard scientific “introduction, methods, results, discussion” format, this isn’t always necessary. But if your paper is structured in a less predictable way, it’s important to describe the shape of it for the reader.

If included, the overview should be concise, direct, and written in the present tense.

• This paper will first discuss several examples of survey-based research into adolescent social media use, then will go on to …
• This paper first discusses several examples of survey-based research into adolescent social media use, then goes on to …

Full examples of research paper introductions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.

• Argumentative paper
• Empirical paper

Are cows responsible for climate change? A recent study (RIVM, 2019) shows that cattle farmers account for two thirds of agricultural nitrogen emissions in the Netherlands. These emissions result from nitrogen in manure, which can degrade into ammonia and enter the atmosphere. The study’s calculations show that agriculture is the main source of nitrogen pollution, accounting for 46% of the country’s total emissions. By comparison, road traffic and households are responsible for 6.1% each, the industrial sector for 1%. While efforts are being made to mitigate these emissions, policymakers are reluctant to reckon with the scale of the problem. The approach presented here is a radical one, but commensurate with the issue. This paper argues that the Dutch government must stimulate and subsidize livestock farmers, especially cattle farmers, to transition to sustainable vegetable farming. It first establishes the inadequacy of current mitigation measures, then discusses the various advantages of the results proposed, and finally addresses potential objections to the plan on economic grounds.

The rise of social media has been accompanied by a sharp increase in the prevalence of body image issues among women and girls. This correlation has received significant academic attention: Various empirical studies have been conducted into Facebook usage among adolescent girls (Tiggermann & Slater, 2013; Meier & Gray, 2014). These studies have consistently found that the visual and interactive aspects of the platform have the greatest influence on body image issues. Despite this, highly visual social media (HVSM) such as Instagram have yet to be robustly researched. This paper sets out to address this research gap. We investigated the effects of daily Instagram use on the prevalence of body image issues among adolescent girls. It was hypothesized that daily Instagram use would be associated with an increase in body image concerns and a decrease in self-esteem ratings.

The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

The way you present your research problem in your introduction varies depending on the nature of your research paper . A research paper that presents a sustained argument will usually encapsulate this argument in a thesis statement .

A research paper designed to present the results of empirical research tends to present a research question that it seeks to answer. It may also include a hypothesis —a prediction that will be confirmed or disproved by your research.

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

Caulfield, J. (2023, March 27). Writing a Research Paper Introduction | Step-by-Step Guide. Scribbr. Retrieved March 23, 2024, from https://www.scribbr.com/research-paper/research-paper-introduction/

Is this article helpful?

Jack Caulfield

Other students also liked, writing strong research questions | criteria & examples, writing a research paper conclusion | step-by-step guide, research paper format | apa, mla, & chicago templates, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Perspective
• Published: 06 March 2024

Artificial intelligence and illusions of understanding in scientific research

• Lisa Messeri   ORCID: orcid.org/0000-0002-0964-123X 1   na1 &
• M. J. Crockett   ORCID: orcid.org/0000-0001-8800-410X 2 , 3   na1  

Nature volume  627 ,  pages 49–58 ( 2024 ) Cite this article

20k Accesses

3 Citations

707 Altmetric

Metrics details

• Human behaviour
• Interdisciplinary studies
• Research management
• Social anthropology

Scientists are enthusiastically imagining ways in which artificial intelligence (AI) tools might improve research. Why are AI tools so attractive and what are the risks of implementing them across the research pipeline? Here we develop a taxonomy of scientists’ visions for AI, observing that their appeal comes from promises to improve productivity and objectivity by overcoming human shortcomings. But proposed AI solutions can also exploit our cognitive limitations, making us vulnerable to illusions of understanding in which we believe we understand more about the world than we actually do. Such illusions obscure the scientific community’s ability to see the formation of scientific monocultures, in which some types of methods, questions and viewpoints come to dominate alternative approaches, making science less innovative and more vulnerable to errors. The proliferation of AI tools in science risks introducing a phase of scientific enquiry in which we produce more but understand less. By analysing the appeal of these tools, we provide a framework for advancing discussions of responsible knowledge production in the age of AI.

This is a preview of subscription content, access via your institution

Access options

Access Nature and 54 other Nature Portfolio journals

Get Nature+, our best-value online-access subscription

24,99 € / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

185,98 € per year

only 3,65 € per issue

Rent or buy this article

Prices vary by article type

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

Nobel Turing Challenge: creating the engine for scientific discovery

Hiroaki Kitano

Accelerating science with human-aware artificial intelligence

Jamshid Sourati & James A. Evans

On scientific understanding with artificial intelligence

Mario Krenn, Robert Pollice, … Alán Aspuru-Guzik

Crabtree, G. Self-driving laboratories coming of age. Joule 4 , 2538–2541 (2020).

Article   CAS   Google Scholar  

Wang, H. et al. Scientific discovery in the age of artificial intelligence. Nature 620 , 47–60 (2023). This review explores how AI can be incorporated across the research pipeline, drawing from a wide range of scientific disciplines .

Article   CAS   PubMed   ADS   Google Scholar  

Dillion, D., Tandon, N., Gu, Y. & Gray, K. Can AI language models replace human participants? Trends Cogn. Sci. 27 , 597–600 (2023).

Article   PubMed   Google Scholar  

Grossmann, I. et al. AI and the transformation of social science research. Science 380 , 1108–1109 (2023). This forward-looking article proposes a variety of ways to incorporate generative AI into social-sciences research .

Gil, Y. Will AI write scientific papers in the future? AI Mag. 42 , 3–15 (2022).

Google Scholar  

Kitano, H. Nobel Turing Challenge: creating the engine for scientific discovery. npj Syst. Biol. Appl. 7 , 29 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Benjamin, R. Race After Technology: Abolitionist Tools for the New Jim Code (Oxford Univ. Press, 2020). This book examines how social norms about race become embedded in technologies, even those that are focused on providing good societal outcomes .

Broussard, M. More Than a Glitch: Confronting Race, Gender, and Ability Bias in Tech (MIT Press, 2023).

Noble, S. U. Algorithms of Oppression: How Search Engines Reinforce Racism (New York Univ. Press, 2018).

Bender, E. M., Gebru, T., McMillan-Major, A. & Shmitchell, S. On the dangers of stochastic parrots: can language models be too big? in Proc. 2021 ACM Conference on Fairness, Accountability, and Transparency 610–623 (Association for Computing Machinery, 2021). One of the first comprehensive critiques of large language models, this article draws attention to a host of issues that ought to be considered before taking up such tools .

Crawford, K. Atlas of AI: Power, Politics, and the Planetary Costs of Artificial Intelligence (Yale Univ. Press, 2021).

Johnson, D. G. & Verdicchio, M. Reframing AI discourse. Minds Mach. 27 , 575–590 (2017).

Article   Google Scholar  

Atanasoski, N. & Vora, K. Surrogate Humanity: Race, Robots, and the Politics of Technological Futures (Duke Univ. Press, 2019).

Mitchell, M. & Krakauer, D. C. The debate over understanding in AI’s large language models. Proc. Natl Acad. Sci. USA 120 , e2215907120 (2023).

Kidd, C. & Birhane, A. How AI can distort human beliefs. Science 380 , 1222–1223 (2023).

Birhane, A., Kasirzadeh, A., Leslie, D. & Wachter, S. Science in the age of large language models. Nat. Rev. Phys. 5 , 277–280 (2023).

Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4 , 100804 (2023).

Hullman, J., Kapoor, S., Nanayakkara, P., Gelman, A. & Narayanan, A. The worst of both worlds: a comparative analysis of errors in learning from data in psychology and machine learning. In Proc. 2022 AAAI/ACM Conference on AI, Ethics, and Society (eds Conitzer, V. et al.) 335–348 (Association for Computing Machinery, 2022).

Rudin, C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat. Mach. Intell. 1 , 206–215 (2019). This paper articulates the problems with attempting to explain AI systems that lack interpretability, and advocates for building interpretable models instead .

Crockett, M. J., Bai, X., Kapoor, S., Messeri, L. & Narayanan, A. The limitations of machine learning models for predicting scientific replicability. Proc. Natl Acad. Sci. USA 120 , e2307596120 (2023).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Lazar, S. & Nelson, A. AI safety on whose terms? Science 381 , 138 (2023).

Article   PubMed   ADS   Google Scholar  

Collingridge, D. The Social Control of Technology (St Martin’s Press, 1980).

Wagner, G., Lukyanenko, R. & Paré, G. Artificial intelligence and the conduct of literature reviews. J. Inf. Technol. 37 , 209–226 (2022).

Hutson, M. Artificial-intelligence tools aim to tame the coronavirus literature. Nature https://doi.org/10.1038/d41586-020-01733-7 (2020).

Haas, Q. et al. Utilizing artificial intelligence to manage COVID-19 scientific evidence torrent with Risklick AI: a critical tool for pharmacology and therapy development. Pharmacology 106 , 244–253 (2021).

Article   CAS   PubMed   Google Scholar  

Müller, H., Pachnanda, S., Pahl, F. & Rosenqvist, C. The application of artificial intelligence on different types of literature reviews – a comparative study. In 2022 International Conference on Applied Artificial Intelligence (ICAPAI) https://doi.org/10.1109/ICAPAI55158.2022.9801564 (Institute of Electrical and Electronics Engineers, 2022).

van Dinter, R., Tekinerdogan, B. & Catal, C. Automation of systematic literature reviews: a systematic literature review. Inf. Softw. Technol. 136 , 106589 (2021).

Aydın, Ö. & Karaarslan, E. OpenAI ChatGPT generated literature review: digital twin in healthcare. In Emerging Computer Technologies 2 (ed. Aydın, Ö.) 22–31 (İzmir Akademi Dernegi, 2022).

AlQuraishi, M. AlphaFold at CASP13. Bioinformatics 35 , 4862–4865 (2019).

Jumper, J. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596 , 583–589 (2021).

Article   CAS   PubMed   PubMed Central   ADS   Google Scholar  

Lee, J. S., Kim, J. & Kim, P. M. Score-based generative modeling for de novo protein design. Nat. Computat. Sci. 3 , 382–392 (2023).

Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15 , 1120–1127 (2016).

Krenn, M. et al. On scientific understanding with artificial intelligence. Nat. Rev. Phys. 4 , 761–769 (2022).

Extance, A. How AI technology can tame the scientific literature. Nature 561 , 273–274 (2018).

Hastings, J. AI for Scientific Discovery (CRC Press, 2023). This book reviews current and future incorporation of AI into the scientific research pipeline .

Ahmed, A. et al. The future of academic publishing. Nat. Hum. Behav. 7 , 1021–1026 (2023).

Gray, K., Yam, K. C., Zhen’An, A. E., Wilbanks, D. & Waytz, A. The psychology of robots and artificial intelligence. In The Handbook of Social Psychology (eds Gilbert, D. et al.) (in the press).

Argyle, L. P. et al. Out of one, many: using language models to simulate human samples. Polit. Anal. 31 , 337–351 (2023).

Aher, G., Arriaga, R. I. & Kalai, A. T. Using large language models to simulate multiple humans and replicate human subject studies. In Proc. 40th International Conference on Machine Learning (eds Krause, A. et al.) 337–371 (JMLR.org, 2023).

Binz, M. & Schulz, E. Using cognitive psychology to understand GPT-3. Proc. Natl Acad. Sci. USA 120 , e2218523120 (2023).

Ornstein, J. T., Blasingame, E. N. & Truscott, J. S. How to train your stochastic parrot: large language models for political texts. Github , https://joeornstein.github.io/publications/ornstein-blasingame-truscott.pdf (2023).

He, S. et al. Learning to predict the cosmological structure formation. Proc. Natl Acad. Sci. USA 116 , 13825–13832 (2019).

Article   MathSciNet   CAS   PubMed   PubMed Central   ADS   Google Scholar  

Mahmood, F. et al. Deep adversarial training for multi-organ nuclei segmentation in histopathology images. IEEE Trans. Med. Imaging 39 , 3257–3267 (2020).

Teixeira, B. et al. Generating synthetic X-ray images of a person from the surface geometry. In Proc. IEEE Conference on Computer Vision and Pattern Recognition 9059–9067 (Institute of Electrical and Electronics Engineers, 2018).

Marouf, M. et al. Realistic in silico generation and augmentation of single-cell RNA-seq data using generative adversarial networks. Nat. Commun. 11 , 166 (2020).

Watts, D. J. A twenty-first century science. Nature 445 , 489 (2007).

boyd, d. & Crawford, K. Critical questions for big data. Inf. Commun. Soc. 15 , 662–679 (2012). This article assesses the ethical and epistemic implications of scientific and societal moves towards big data and provides a parallel case study for thinking about the risks of artificial intelligence .

Jolly, E. & Chang, L. J. The Flatland fallacy: moving beyond low–dimensional thinking. Top. Cogn. Sci. 11 , 433–454 (2019).

Yarkoni, T. & Westfall, J. Choosing prediction over explanation in psychology: lessons from machine learning. Perspect. Psychol. Sci. 12 , 1100–1122 (2017).

Radivojac, P. et al. A large-scale evaluation of computational protein function prediction. Nat. Methods 10 , 221–227 (2013).

Bileschi, M. L. et al. Using deep learning to annotate the protein universe. Nat. Biotechnol. 40 , 932–937 (2022).

Barkas, N. et al. Joint analysis of heterogeneous single-cell RNA-seq dataset collections. Nat. Methods 16 , 695–698 (2019).

Demszky, D. et al. Using large language models in psychology. Nat. Rev. Psychol. 2 , 688–701 (2023).

Karjus, A. Machine-assisted mixed methods: augmenting humanities and social sciences with artificial intelligence. Preprint at https://arxiv.org/abs/2309.14379 (2023).

Davies, A. et al. Advancing mathematics by guiding human intuition with AI. Nature 600 , 70–74 (2021).

Peterson, J. C., Bourgin, D. D., Agrawal, M., Reichman, D. & Griffiths, T. L. Using large-scale experiments and machine learning to discover theories of human decision-making. Science 372 , 1209–1214 (2021).

Ilyas, A. et al. Adversarial examples are not bugs, they are features. Preprint at https://doi.org/10.48550/arXiv.1905.02175 (2019)

Semel, B. M. Listening like a computer: attentional tensions and mechanized care in psychiatric digital phenotyping. Sci. Technol. Hum. Values 47 , 266–290 (2022).

Gil, Y. Thoughtful artificial intelligence: forging a new partnership for data science and scientific discovery. Data Sci. 1 , 119–129 (2017).

Checco, A., Bracciale, L., Loreti, P., Pinfield, S. & Bianchi, G. AI-assisted peer review. Humanit. Soc. Sci. Commun. 8 , 25 (2021).

Thelwall, M. Can the quality of published academic journal articles be assessed with machine learning? Quant. Sci. Stud. 3 , 208–226 (2022).

Dhar, P. Peer review of scholarly research gets an AI boost. IEEE Spectrum spectrum.ieee.org/peer-review-of-scholarly-research-gets-an-ai-boost (2020).

Heaven, D. AI peer reviewers unleashed to ease publishing grind. Nature 563 , 609–610 (2018).

Conroy, G. How ChatGPT and other AI tools could disrupt scientific publishing. Nature 622 , 234–236 (2023).

Nosek, B. A. et al. Replicability, robustness, and reproducibility in psychological science. Annu. Rev. Psychol. 73 , 719–748 (2022).

Altmejd, A. et al. Predicting the replicability of social science lab experiments. PLoS ONE 14 , e0225826 (2019).

Yang, Y., Youyou, W. & Uzzi, B. Estimating the deep replicability of scientific findings using human and artificial intelligence. Proc. Natl Acad. Sci. USA 117 , 10762–10768 (2020).

Youyou, W., Yang, Y. & Uzzi, B. A discipline-wide investigation of the replicability of psychology papers over the past two decades. Proc. Natl Acad. Sci. USA 120 , e2208863120 (2023).

Rabb, N., Fernbach, P. M. & Sloman, S. A. Individual representation in a community of knowledge. Trends Cogn. Sci. 23 , 891–902 (2019). This comprehensive review paper documents the empirical evidence for distributed cognition in communities of knowledge and the resultant vulnerabilities to illusions of understanding .

Rozenblit, L. & Keil, F. The misunderstood limits of folk science: an illusion of explanatory depth. Cogn. Sci. 26 , 521–562 (2002). This paper provided an empirical demonstration of the illusion of explanatory depth, and inspired a programme of research in cognitive science on communities of knowledge .

Hutchins, E. Cognition in the Wild (MIT Press, 1995).

Lave, J. & Wenger, E. Situated Learning: Legitimate Peripheral Participation (Cambridge Univ. Press, 1991).

Kitcher, P. The division of cognitive labor. J. Philos. 87 , 5–22 (1990).

Hardwig, J. Epistemic dependence. J. Philos. 82 , 335–349 (1985).

Keil, F. in Oxford Studies In Epistemology (eds Gendler, T. S. & Hawthorne, J.) 143–166 (Oxford Academic, 2005).

Weisberg, M. & Muldoon, R. Epistemic landscapes and the division of cognitive labor. Philos. Sci. 76 , 225–252 (2009).

Sloman, S. A. & Rabb, N. Your understanding is my understanding: evidence for a community of knowledge. Psychol. Sci. 27 , 1451–1460 (2016).

Wilson, R. A. & Keil, F. The shadows and shallows of explanation. Minds Mach. 8 , 137–159 (1998).

Keil, F. C., Stein, C., Webb, L., Billings, V. D. & Rozenblit, L. Discerning the division of cognitive labor: an emerging understanding of how knowledge is clustered in other minds. Cogn. Sci. 32 , 259–300 (2008).

Sperber, D. et al. Epistemic vigilance. Mind Lang. 25 , 359–393 (2010).

Wilkenfeld, D. A., Plunkett, D. & Lombrozo, T. Depth and deference: when and why we attribute understanding. Philos. Stud. 173 , 373–393 (2016).

Sparrow, B., Liu, J. & Wegner, D. M. Google effects on memory: cognitive consequences of having information at our fingertips. Science 333 , 776–778 (2011).

Fisher, M., Goddu, M. K. & Keil, F. C. Searching for explanations: how the internet inflates estimates of internal knowledge. J. Exp. Psychol. Gen. 144 , 674–687 (2015).

De Freitas, J., Agarwal, S., Schmitt, B. & Haslam, N. Psychological factors underlying attitudes toward AI tools. Nat. Hum. Behav. 7 , 1845–1854 (2023).

Castelo, N., Bos, M. W. & Lehmann, D. R. Task-dependent algorithm aversion. J. Mark. Res. 56 , 809–825 (2019).

Cadario, R., Longoni, C. & Morewedge, C. K. Understanding, explaining, and utilizing medical artificial intelligence. Nat. Hum. Behav. 5 , 1636–1642 (2021).

Oktar, K. & Lombrozo, T. Deciding to be authentic: intuition is favored over deliberation when authenticity matters. Cognition 223 , 105021 (2022).

Bigman, Y. E., Yam, K. C., Marciano, D., Reynolds, S. J. & Gray, K. Threat of racial and economic inequality increases preference for algorithm decision-making. Comput. Hum. Behav. 122 , 106859 (2021).

Claudy, M. C., Aquino, K. & Graso, M. Artificial intelligence can’t be charmed: the effects of impartiality on laypeople’s algorithmic preferences. Front. Psychol. 13 , 898027 (2022).

Snyder, C., Keppler, S. & Leider, S. Algorithm reliance under pressure: the effect of customer load on service workers. Preprint at SSRN https://doi.org/10.2139/ssrn.4066823 (2022).

Bogert, E., Schecter, A. & Watson, R. T. Humans rely more on algorithms than social influence as a task becomes more difficult. Sci Rep. 11 , 8028 (2021).

Raviv, A., Bar‐Tal, D., Raviv, A. & Abin, R. Measuring epistemic authority: studies of politicians and professors. Eur. J. Personal. 7 , 119–138 (1993).

Cummings, L. The “trust” heuristic: arguments from authority in public health. Health Commun. 29 , 1043–1056 (2014).

Lee, M. K. Understanding perception of algorithmic decisions: fairness, trust, and emotion in response to algorithmic management. Big Data Soc. 5 , https://doi.org/10.1177/2053951718756684 (2018).

Kissinger, H. A., Schmidt, E. & Huttenlocher, D. The Age of A.I. And Our Human Future (Little, Brown, 2021).

Lombrozo, T. Explanatory preferences shape learning and inference. Trends Cogn. Sci. 20 , 748–759 (2016). This paper provides an overview of philosophical theories of explanatory virtues and reviews empirical evidence on the sorts of explanations people find satisfying .

Vrantsidis, T. H. & Lombrozo, T. Simplicity as a cue to probability: multiple roles for simplicity in evaluating explanations. Cogn. Sci. 46 , e13169 (2022).

Johnson, S. G. B., Johnston, A. M., Toig, A. E. & Keil, F. C. Explanatory scope informs causal strength inferences. In Proc. 36th Annual Meeting of the Cognitive Science Society 2453–2458 (Cognitive Science Society, 2014).

Khemlani, S. S., Sussman, A. B. & Oppenheimer, D. M. Harry Potter and the sorcerer’s scope: latent scope biases in explanatory reasoning. Mem. Cognit. 39 , 527–535 (2011).

Liquin, E. G. & Lombrozo, T. Motivated to learn: an account of explanatory satisfaction. Cogn. Psychol. 132 , 101453 (2022).

Hopkins, E. J., Weisberg, D. S. & Taylor, J. C. V. The seductive allure is a reductive allure: people prefer scientific explanations that contain logically irrelevant reductive information. Cognition 155 , 67–76 (2016).

Weisberg, D. S., Hopkins, E. J. & Taylor, J. C. V. People’s explanatory preferences for scientific phenomena. Cogn. Res. Princ. Implic. 3 , 44 (2018).

Jerez-Fernandez, A., Angulo, A. N. & Oppenheimer, D. M. Show me the numbers: precision as a cue to others’ confidence. Psychol. Sci. 25 , 633–635 (2014).

Kim, J., Giroux, M. & Lee, J. C. When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations. Psychol. Mark. 38 , 1140–1155 (2021).

Nguyen, C. T. The seductions of clarity. R. Inst. Philos. Suppl. 89 , 227–255 (2021). This article describes how reductive and quantitative explanations can generate a sense of understanding that is not necessarily correlated with actual understanding .

Fisher, M., Smiley, A. H. & Grillo, T. L. H. Information without knowledge: the effects of internet search on learning. Memory 30 , 375–387 (2022).

Eliseev, E. D. & Marsh, E. J. Understanding why searching the internet inflates confidence in explanatory ability. Appl. Cogn. Psychol. 37 , 711–720 (2023).

Fisher, M. & Oppenheimer, D. M. Who knows what? Knowledge misattribution in the division of cognitive labor. J. Exp. Psychol. Appl. 27 , 292–306 (2021).

Chromik, M., Eiband, M., Buchner, F., Krüger, A. & Butz, A. I think I get your point, AI! The illusion of explanatory depth in explainable AI. In 26th International Conference on Intelligent User Interfaces (eds Hammond, T. et al.) 307–317 (Association for Computing Machinery, 2021).

Strevens, M. No understanding without explanation. Stud. Hist. Philos. Sci. A 44 , 510–515 (2013).

Ylikoski, P. in Scientific Understanding: Philosophical Perspectives (eds De Regt, H. et al.) 100–119 (Univ. Pittsburgh Press, 2009).

Giudice, M. D. The prediction–explanation fallacy: a pervasive problem in scientific applications of machine learning. Preprint at PsyArXiv https://doi.org/10.31234/osf.io/4vq8f (2021).

Hofman, J. M. et al. Integrating explanation and prediction in computational social science. Nature 595 , 181–188 (2021). This paper highlights the advantages and disadvantages of explanatory versus predictive approaches to modelling, with a focus on applications to computational social science .

Shmueli, G. To explain or to predict? Stat. Sci. 25 , 289–310 (2010).

Article   MathSciNet   Google Scholar  

Hofman, J. M., Sharma, A. & Watts, D. J. Prediction and explanation in social systems. Science 355 , 486–488 (2017).

Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: people prefer algorithmic to human judgment. Organ. Behav. Hum. Decis. Process. 151 , 90–103 (2019).

Nguyen, C. T. Cognitive islands and runaway echo chambers: problems for epistemic dependence on experts. Synthese 197 , 2803–2821 (2020).

Breiman, L. Statistical modeling: the two cultures. Stat. Sci. 16 , 199–215 (2001).

Gao, J. & Wang, D. Quantifying the benefit of artificial intelligence for scientific research. Preprint at arxiv.org/abs/2304.10578 (2023).

Hanson, B. et al. Garbage in, garbage out: mitigating risks and maximizing benefits of AI in research. Nature 623 , 28–31 (2023).

Kleinberg, J. & Raghavan, M. Algorithmic monoculture and social welfare. Proc. Natl Acad. Sci. USA 118 , e2018340118 (2021). This paper uses formal modelling methods to demonstrate that when companies all rely on the same algorithm to make decisions (an algorithmic monoculture), the overall quality of those decisions is reduced because valuable options can slip through the cracks, even when the algorithm performs accurately for individual companies .

Article   MathSciNet   CAS   PubMed   PubMed Central   Google Scholar  

Hofstra, B. et al. The diversity–innovation paradox in science. Proc. Natl Acad. Sci. USA 117 , 9284–9291 (2020).

Hong, L. & Page, S. E. Groups of diverse problem solvers can outperform groups of high-ability problem solvers. Proc. Natl Acad. Sci. USA 101 , 16385–16389 (2004).

Page, S. E. Where diversity comes from and why it matters? Eur. J. Soc. Psychol. 44 , 267–279 (2014). This article reviews research demonstrating the benefits of cognitive diversity and diversity in methodological approaches for problem solving and innovation .

Clarke, A. E. & Fujimura, J. H. (eds) The Right Tools for the Job: At Work in Twentieth-Century Life Sciences (Princeton Univ. Press, 2014).

Silva, V. J., Bonacelli, M. B. M. & Pacheco, C. A. Framing the effects of machine learning on science. AI Soc. https://doi.org/10.1007/s00146-022-01515-x (2022).

Sassenberg, K. & Ditrich, L. Research in social psychology changed between 2011 and 2016: larger sample sizes, more self-report measures, and more online studies. Adv. Methods Pract. Psychol. Sci. 2 , 107–114 (2019).

Simon, A. F. & Wilder, D. Methods and measures in social and personality psychology: a comparison of JPSP publications in 1982 and 2016. J. Soc. Psychol. https://doi.org/10.1080/00224545.2022.2135088 (2022).

Anderson, C. A. et al. The MTurkification of social and personality psychology. Pers. Soc. Psychol. Bull. 45 , 842–850 (2019).

Latour, B. in The Social After Gabriel Tarde: Debates and Assessments (ed. Candea, M.) 145–162 (Routledge, 2010).

Porter, T. M. Trust in Numbers: The Pursuit of Objectivity in Science and Public Life (Princeton Univ. Press, 1996).

Lazer, D. et al. Meaningful measures of human society in the twenty-first century. Nature 595 , 189–196 (2021).

Knox, D., Lucas, C. & Cho, W. K. T. Testing causal theories with learned proxies. Annu. Rev. Polit. Sci. 25 , 419–441 (2022).

Barberá, P. Birds of the same feather tweet together: Bayesian ideal point estimation using Twitter data. Polit. Anal. 23 , 76–91 (2015).

Brady, W. J., McLoughlin, K., Doan, T. N. & Crockett, M. J. How social learning amplifies moral outrage expression in online social networks. Sci. Adv. 7 , eabe5641 (2021).

Article   PubMed   PubMed Central   ADS   Google Scholar  

Barnes, J., Klinger, R. & im Walde, S. S. Assessing state-of-the-art sentiment models on state-of-the-art sentiment datasets. In Proc. 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (eds Balahur, A. et al.) 2–12 (Association for Computational Linguistics, 2017).

Gitelman, L. (ed.) “Raw Data” is an Oxymoron (MIT Press, 2013).

Breznau, N. et al. Observing many researchers using the same data and hypothesis reveals a hidden universe of uncertainty. Proc. Natl Acad. Sci. USA 119 , e2203150119 (2022). This study demonstrates how 73 research teams analysing the same dataset reached different conclusions about the relationship between immigration and public support for social policies, highlighting the subjectivity and uncertainty involved in analysing complex datasets .

Gillespie, T. in Media Technologies: Essays on Communication, Materiality, and Society (eds Gillespie, T. et al.) 167–194 (MIT Press, 2014).

Leonelli, S. Data-Centric Biology: A Philosophical Study (Univ. Chicago Press, 2016).

Wang, A., Kapoor, S., Barocas, S. & Narayanan, A. Against predictive optimization: on the legitimacy of decision-making algorithms that optimize predictive accuracy. ACM J. Responsib. Comput. , https://doi.org/10.1145/3636509 (2023).

Athey, S. Beyond prediction: using big data for policy problems. Science 355 , 483–485 (2017).

del Rosario Martínez-Ordaz, R. Scientific understanding through big data: from ignorance to insights to understanding. Possibility Stud. Soc. 1 , 279–299 (2023).

Nussberger, A.-M., Luo, L., Celis, L. E. & Crockett, M. J. Public attitudes value interpretability but prioritize accuracy in artificial intelligence. Nat. Commun. 13 , 5821 (2022).

Zittrain, J. in The Cambridge Handbook of Responsible Artificial Intelligence: Interdisciplinary Perspectives (eds. Voeneky, S. et al.) 176–184 (Cambridge Univ. Press, 2022). This article articulates the epistemic risks of prioritizing predictive accuracy over explanatory understanding when AI tools are interacting in complex systems.

Shumailov, I. et al. The curse of recursion: training on generated data makes models forget. Preprint at arxiv.org/abs/2305.17493 (2023).

Latour, B. Science In Action: How to Follow Scientists and Engineers Through Society (Harvard Univ. Press, 1987). This book provides strategies and approaches for thinking about science as a social endeavour .

Franklin, S. Science as culture, cultures of science. Annu. Rev. Anthropol. 24 , 163–184 (1995).

Haraway, D. Situated knowledges: the science question in feminism and the privilege of partial perspective. Fem. Stud. 14 , 575–599 (1988). This article acknowledges that the objective ‘view from nowhere’ is unobtainable: knowledge, it argues, is always situated .

Harding, S. Objectivity and Diversity: Another Logic of Scientific Research (Univ. Chicago Press, 2015).

Longino, H. E. Science as Social Knowledge: Values and Objectivity in Scientific Inquiry (Princeton Univ. Press, 1990).

Daston, L. & Galison, P. Objectivity (Princeton Univ. Press, 2007). This book is a historical analysis of the shifting modes of ‘objectivity’ that scientists have pursued, arguing that objectivity is not a universal concept but that it shifts alongside scientific techniques and ambitions .

Prescod-Weinstein, C. Making Black women scientists under white empiricism: the racialization of epistemology in physics. Signs J. Women Cult. Soc. 45 , 421–447 (2020).

Mavhunga, C. What Do Science, Technology, and Innovation Mean From Africa? (MIT Press, 2017).

Schiebinger, L. The Mind Has No Sex? Women in the Origins of Modern Science (Harvard Univ. Press, 1991).

Martin, E. The egg and the sperm: how science has constructed a romance based on stereotypical male–female roles. Signs J. Women Cult. Soc. 16 , 485–501 (1991). This case study shows how assumptions about gender affect scientific theories, sometimes delaying the articulation of what might be considered to be more accurate descriptions of scientific phenomena .

Harding, S. Rethinking standpoint epistemology: What is “strong objectivity”? Centen. Rev. 36 , 437–470 (1992). In this article, Harding outlines her position on ‘strong objectivity’, by which clearly articulating one’s standpoint can lead to more robust knowledge claims .

Oreskes, N. Why Trust Science? (Princeton Univ. Press, 2019). This book introduces the reader to 20 years of scholarship in science and technology studies, arguing that the tools the discipline has for understanding science can help to reinstate public trust in the institution .

Rolin, K., Koskinen, I., Kuorikoski, J. & Reijula, S. Social and cognitive diversity in science: introduction. Synthese 202 , 36 (2023).

Hong, L. & Page, S. E. Problem solving by heterogeneous agents. J. Econ. Theory 97 , 123–163 (2001).

Sulik, J., Bahrami, B. & Deroy, O. The diversity gap: when diversity matters for knowledge. Perspect. Psychol. Sci. 17 , 752–767 (2022).

Lungeanu, A., Whalen, R., Wu, Y. J., DeChurch, L. A. & Contractor, N. S. Diversity, networks, and innovation: a text analytic approach to measuring expertise diversity. Netw. Sci. 11 , 36–64 (2023).

AlShebli, B. K., Rahwan, T. & Woon, W. L. The preeminence of ethnic diversity in scientific collaboration. Nat. Commun. 9 , 5163 (2018).

Campbell, L. G., Mehtani, S., Dozier, M. E. & Rinehart, J. Gender-heterogeneous working groups produce higher quality science. PLoS ONE 8 , e79147 (2013).

Nielsen, M. W., Bloch, C. W. & Schiebinger, L. Making gender diversity work for scientific discovery and innovation. Nat. Hum. Behav. 2 , 726–734 (2018).

Yang, Y., Tian, T. Y., Woodruff, T. K., Jones, B. F. & Uzzi, B. Gender-diverse teams produce more novel and higher-impact scientific ideas. Proc. Natl Acad. Sci. USA 119 , e2200841119 (2022).

Kozlowski, D., Larivière, V., Sugimoto, C. R. & Monroe-White, T. Intersectional inequalities in science. Proc. Natl Acad. Sci. USA 119 , e2113067119 (2022).

Fehr, C. & Jones, J. M. Culture, exploitation, and epistemic approaches to diversity. Synthese 200 , 465 (2022).

Nakadai, R., Nakawake, Y. & Shibasaki, S. AI language tools risk scientific diversity and innovation. Nat. Hum. Behav. 7 , 1804–1805 (2023).

National Academies of Sciences, Engineering, and Medicine et al. Advancing Antiracism, Diversity, Equity, and Inclusion in STEMM Organizations: Beyond Broadening Participation (National Academies Press, 2023).

Winner, L. Do artifacts have politics? Daedalus 109 , 121–136 (1980).

Eubanks, V. Automating Inequality: How High-Tech Tools Profile, Police, and Punish the Poor (St. Martin’s Press, 2018).

Littmann, M. et al. Validity of machine learning in biology and medicine increased through collaborations across fields of expertise. Nat. Mach. Intell. 2 , 18–24 (2020).

Carusi, A. et al. Medical artificial intelligence is as much social as it is technological. Nat. Mach. Intell. 5 , 98–100 (2023).

Raghu, M. & Schmidt, E. A survey of deep learning for scientific discovery. Preprint at arxiv.org/abs/2003.11755 (2020).

Bishop, C. AI4Science to empower the fifth paradigm of scientific discovery. Microsoft Research Blog www.microsoft.com/en-us/research/blog/ai4science-to-empower-the-fifth-paradigm-of-scientific-discovery/ (2022).

Whittaker, M. The steep cost of capture. Interactions 28 , 50–55 (2021).

Liesenfeld, A., Lopez, A. & Dingemanse, M. Opening up ChatGPT: Tracking openness, transparency, and accountability in instruction-tuned text generators. In Proc. 5th International Conference on Conversational User Interfaces 1–6 (Association for Computing Machinery, 2023).

Chu, J. S. G. & Evans, J. A. Slowed canonical progress in large fields of science. Proc. Natl Acad. Sci. USA 118 , e2021636118 (2021).

Park, M., Leahey, E. & Funk, R. J. Papers and patents are becoming less disruptive over time. Nature 613 , 138–144 (2023).

Frith, U. Fast lane to slow science. Trends Cogn. Sci. 24 , 1–2 (2020). This article explains the epistemic risks of a hyperfocus on scientific productivity and explores possible avenues for incentivizing the production of higher-quality science on a slower timescale .

Stengers, I. Another Science is Possible: A Manifesto for Slow Science (Wiley, 2018).

Lake, B. M. & Baroni, M. Human-like systematic generalization through a meta-learning neural network. Nature 623 , 115–121 (2023).

Feinman, R. & Lake, B. M. Learning task-general representations with generative neuro-symbolic modeling. Preprint at arxiv.org/abs/2006.14448 (2021).

Schölkopf, B. et al. Toward causal representation learning. Proc. IEEE 109 , 612–634 (2021).

Mitchell, M. AI’s challenge of understanding the world. Science 382 , eadm8175 (2023).

Sartori, L. & Bocca, G. Minding the gap(s): public perceptions of AI and socio-technical imaginaries. AI Soc. 38 , 443–458 (2023).

Download references

Acknowledgements

We thank D. S. Bassett, W. J. Brady, S. Helmreich, S. Kapoor, T. Lombrozo, A. Narayanan, M. Salganik and A. J. te Velthuis for comments. We also thank C. Buckner and P. Winter for their feedback and suggestions.

Author information

These authors contributed equally: Lisa Messeri, M. J. Crockett

Authors and Affiliations

Department of Anthropology, Yale University, New Haven, CT, USA

Lisa Messeri

Department of Psychology, Princeton University, Princeton, NJ, USA

M. J. Crockett

University Center for Human Values, Princeton University, Princeton, NJ, USA

You can also search for this author in PubMed   Google Scholar

Contributions

The authors contributed equally to the research and writing of the paper.

Corresponding authors

Correspondence to Lisa Messeri or M. J. Crockett .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature thanks Cameron Buckner, Peter Winter and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Cite this article.

Messeri, L., Crockett, M.J. Artificial intelligence and illusions of understanding in scientific research. Nature 627 , 49–58 (2024). https://doi.org/10.1038/s41586-024-07146-0

Download citation

Received : 31 July 2023

Accepted : 31 January 2024

Published : 06 March 2024

Issue Date : 07 March 2024

DOI : https://doi.org/10.1038/s41586-024-07146-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

Ai is no substitute for having something to say.

Nature Reviews Physics (2024)

Perché gli scienziati si fidano troppo dell'intelligenza artificiale - e come rimediare

Nature Italy (2024)

Why scientists trust AI too much — and what to do about it

Nature (2024)

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

• CBSSports.com
• Fanatics Sportsbook
• CBS Sports Home
• NCAA Tournament
• W. Tournament
• Champions League
• Motor Sports
• High School
• Horse Racing 

Men's Brackets

Women's Brackets

Fantasy Baseball

Fantasy football, football pick'em, college pick'em, fantasy basketball, fantasy hockey, franchise games, 24/7 sports news network.

• CBS Sports Golazo Network
• March Madness Live
• PGA Tour on CBS
• UEFA Champions League
• UEFA Europa League
• Italian Serie A
• Watch CBS Sports Network
• TV Shows & Listings

The Early Edge

A Daily SportsLine Betting Podcast

With the First Pick

NFL Draft is coming up!

• Podcasts Home
• Eye On College Basketball
• The First Cut Golf
• NFL Pick Six
• Cover 3 College Football
• Fantasy Football Today
• Morning Kombat
• My Teams Organize / See All Teams Help Account Settings Log Out

2024 NCAA Tournament bracket predictions: March Madness expert picks, favorites to win, winners, upsets

Our experts have filled out their brackets, so check who they predict will be cutting down the nets.

Has your NCAA Tournament bracket been busted before the second weekend of March Madness has arrived? Take solace, my friend, because you are not alone. In addition to Bijan Robinson's bid for a perfect bracket ending on Friday, even our team of experts – who watched hundreds of games collectively this season – haven't been 100% accurate.

So whether you've watched zero games all season or hundreds like us, keep in mind what we love most about March is the madness synonymous with the sport. Upsets, Cinderellas and shockers are all part of the game, and the only predictable element is unpredictability.

We like to think we've gotten some things right along the way, though. For transparency purposes, we're airing out all the good and the bad together in the space below. You can see each of our brackets and how busted they've already become to help give you comfort ... and perhaps use it as a guide for what to expect in the days ahead.

OK, let's dive into the good stuff: The brackets. ...  

2024 NCAA Tournament bracket predictions

Click each bracket to enlarge.

Gary Parrish

Watching UConn become the first back-to-back national champion since Florida in 2006 and 2007 would be a blast. And let the record show that the Huskies are the betting-market favorites. So I realize picking against them might prove dumb. But, that acknowledged, I'm going to continue to do what I've been doing most of this season and put my faith in the Boilermakers. Wouldn't that be a great story -- Purdue winning the 2024 NCAA Tournament after losing to a No. 16 seed in the opening round of the 2023 NCAA Tournament? Zach Edey holding the championship trophy as a two-time National Player of the Year? Matt Painter shedding his label as the best coach yet to make a Final Four by becoming the first coach to take Purdue to the final weekend of the season since 1980? It's all such good stuff. Just getting to the Final Four will be challenging considering Tennessee, Creighton and Kansas are also in the Midwest Region. But I'm still taking the Boilermakers to make it to Arizona. And then, once they get there, I think they'll win two more games and cut nets on the second Monday in April.

Matt Norlander

A locomotive screaming down the tracks. The 31-3 reigning national champions enter this NCAA Tournament as the strongest team with the best chance to repeat of any squad since Florida in 2007. Dan Hurley's Huskies are led by All-American guard Tristen Newton (15.2 ppg, 7.0 rpg, 6.0 apg), who holds the school record for triple-doubles. In the middle is 7-foot-2 "Cling Kong," Donvan Clingan, a menace of a defender and the type of player you can't simulate in practice. The Huskies boast the nation's most efficient offense (126.6 adjusted points per 100 possessions, via KenPom.com) and overwhelm teams in a variety of ways. Sophomore Alex Karaban (39.5%) and senior Cam Spencer (44.4%) are both outstanding 3-point shooters. The Huskies have been beaten by Kansas, Seton Hall and Creighton, but all of those were road games, and there are no more road games left this season. UConn will try to become the fourth No. 1 overall seed to win the national title, joining 2007 Florida, 2012 Kentucky and 2013 Louisville.

The antagonistic side of me initially picked Purdue over UConn in the title game. But I sat and thought about it and couldn't make any reasonable case to pick any team other than UConn as champion. Of course, that doesn't guarantee the Huskies win it all and become the first repeat champs since Florida in 2007. There's a lot that can happen in the next few weeks. But they have the electric offense, the guard depth, the size down low, the shooting [takes breath] .. the passing and the pizzazz of a team that's best in the country and knows it. Every top team in this field has a high level at which they can play but no one has a top gear like UConn.

Get every pick, every play, every upset and fill out your bracket with our help! Visit SportsLine now to see which teams will make and break your bracket, and see who will cut down the nets , all from the model that nailed a whopping 20 first-round upsets by double-digit seeds.

Purdue is set for redemption after an embarrassing 2023 loss to No. 16 seed Fairleigh Dickinson in the first round. This time around, the Boilermakers are a much better 3-point shooting team and have a more favorable path than No. 1 overall seed UConn. The Huskies were the most dominant team leading up to the Big Dance the East Region bracket is filled with peril.

This is not the Purdue you have seen the last few years. Braden Smith has made a big jump from last season to this one. Fletcher Loyer is better. Lance Jones gives Purdue defense, shooting and another ball handler. And Zach Edey is better too. This is a team on a mission. This is the year they accomplish it.

Dennis Dodd

What is there not to like? The Heels won the ACC regular season. They beat Tennessee and swept Duke. RJ Davis is an elite guard and ACC Player of the Year. Hubert Davis has settled in after going to the national championship game in his first season and missing the tournament in his second. This is his best team. There will be/and always is pressure to win it all. 

Armando Bacot is not as dominating as previous. Harrison Ingram (Stanford) and Cormac Ryan (Notre Dame) have been big additions in the portal. The West Region is friendly, assuming here that Alabama and Michigan State don't get in the way before the regional in L.A. An interesting regional final against Arizona looms. In the end, sometimes you go with chalk. UNC has been to the most Final Fours (21) and No. 1 seeds (18) all-time. It is tied with Kentucky for the most tournament wins ever (131). This is what the Heels do.

Chip Patterson

The selection committee set up plenty of stumbling blocks for the reigning champs, placing what I believe to be the best No. 1 seed, the best No. 2 seed (Iowa State), the best No. 3 seed (Illinois) and the best No. 4 seed (Auburn) in the Huskies bracket. And if accomplishing a historic feat like the first back-to-back title runs since 2007 is going to require that kind of epic journey, UConn has every skill and tool needed to make it back to the top of the mountain. UConn can win in all different ways, overwhelming teams with their offense in high-scoring track meets or out-executing the opponent in low-possession grinders, and it has a handful of key contributors who could each step up as needed during a title run.

Cameron Salerno

Defense wins championships. That is part of the reason why I'm picking Houston to win it all. The Cougars have the top-ranked scoring defense in the country and terrific guard play on offense to complement it. Jamal Shead is arguably the best point guard in the nation, and J'wan Roberts is an X-Factor on both ends of the floor. Houston's path to the Final Four is favorable. The Cougars weren't able to reach the Final Four in their home state last spring, but this will be the year they run the table and win their first national championship in program history.

Our Latest College Basketball Stories

Tennessee vs. Texas odds, NCAA Tournament picks, bets

Cbs sports staff • 3 min read.

Clark, Iowa take care of Holy Cross in first round

Jack maloney • 2 min read.

2024 NCAA Tournament schedule, dates, times

Gary parrish • 4 min read.

Storylines, how to stream March Madness games Saturday

Chip patterson • 6 min read.

2024 NCAA Tournament schedule, scores by region

Cameron salerno • 3 min read.

2024 NCAA Tournament: How to watch live, TV tip times

Matt norlander • 3 min read.

Expert brackets: Predictions for NCAA Tournament 2024

Expert picks: Who wins Saturday?

Storylines: UNC takes on Michigan State

NCAA Tournament: Tipoff times, second-round TV info

Louisville, Michigan interested in FAU's May

Yale roars back to shock Auburn in first-round game

Baylor's use of freshman could work for other teams

Simpson's late shot lifts Colorado over Florida

NIT: Ohio State, Cincinnati in action on Saturday

Winners & losers: Dukes shock Badgers in Rd. 1