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The 10 Most Significant Education Studies of 2021

From reframing our notion of “good” schools to mining the magic of expert teachers, here’s a curated list of must-read research from 2021.

It was a year of unprecedented hardship for teachers and school leaders. We pored through hundreds of studies to see if we could follow the trail of exactly what happened: The research revealed a complex portrait of a grueling year during which persistent issues of burnout and mental and physical health impacted millions of educators. Meanwhile, many of the old debates continued: Does paper beat digital? Is project-based learning as effective as direct instruction? How do you define what a “good” school is?

Other studies grabbed our attention, and in a few cases, made headlines. Researchers from the University of Chicago and Columbia University turned artificial intelligence loose on some 1,130 award-winning children’s books in search of invisible patterns of bias. (Spoiler alert: They found some.) Another study revealed why many parents are reluctant to support social and emotional learning in schools—and provided hints about how educators can flip the script.

1. What Parents Fear About SEL (and How to Change Their Minds)

When researchers at the Fordham Institute asked parents to rank phrases associated with social and emotional learning , nothing seemed to add up. The term “social-emotional learning” was very unpopular; parents wanted to steer their kids clear of it. But when the researchers added a simple clause, forming a new phrase—”social-emotional & academic learning”—the program shot all the way up to No. 2 in the rankings.

What gives?

Parents were picking up subtle cues in the list of SEL-related terms that irked or worried them, the researchers suggest. Phrases like “soft skills” and “growth mindset” felt “nebulous” and devoid of academic content. For some, the language felt suspiciously like “code for liberal indoctrination.”

But the study suggests that parents might need the simplest of reassurances to break through the political noise. Removing the jargon, focusing on productive phrases like “life skills,” and relentlessly connecting SEL to academic progress puts parents at ease—and seems to save social and emotional learning in the process.

2. The Secret Management Techniques of Expert Teachers

In the hands of experienced teachers, classroom management can seem almost invisible: Subtle techniques are quietly at work behind the scenes, with students falling into orderly routines and engaging in rigorous academic tasks almost as if by magic. 

That’s no accident, according to new research . While outbursts are inevitable in school settings, expert teachers seed their classrooms with proactive, relationship-building strategies that often prevent misbehavior before it erupts. They also approach discipline more holistically than their less-experienced counterparts, consistently reframing misbehavior in the broader context of how lessons can be more engaging, or how clearly they communicate expectations.

Focusing on the underlying dynamics of classroom behavior—and not on surface-level disruptions—means that expert teachers often look the other way at all the right times, too. Rather than rise to the bait of a minor breach in etiquette, a common mistake of new teachers, they tend to play the long game, asking questions about the origins of misbehavior, deftly navigating the terrain between discipline and student autonomy, and opting to confront misconduct privately when possible.

3. The Surprising Power of Pretesting

Asking students to take a practice test before they’ve even encountered the material may seem like a waste of time—after all, they’d just be guessing.

But new research concludes that the approach, called pretesting, is actually more effective than other typical study strategies. Surprisingly, pretesting even beat out taking practice tests after learning the material, a proven strategy endorsed by cognitive scientists and educators alike. In the study, students who took a practice test before learning the material outperformed their peers who studied more traditionally by 49 percent on a follow-up test, while outperforming students who took practice tests after studying the material by 27 percent.

The researchers hypothesize that the “generation of errors” was a key to the strategy’s success, spurring student curiosity and priming them to “search for the correct answers” when they finally explored the new material—and adding grist to a 2018 study that found that making educated guesses helped students connect background knowledge to new material.

Learning is more durable when students do the hard work of correcting misconceptions, the research suggests, reminding us yet again that being wrong is an important milestone on the road to being right.

4. Confronting an Old Myth About Immigrant Students

Immigrant students are sometimes portrayed as a costly expense to the education system, but new research is systematically dismantling that myth.

In a 2021 study , researchers analyzed over 1.3 million academic and birth records for students in Florida communities, and concluded that the presence of immigrant students actually has “a positive effect on the academic achievement of U.S.-born students,” raising test scores as the size of the immigrant school population increases. The benefits were especially powerful for low-income students.

While immigrants initially “face challenges in assimilation that may require additional school resources,” the researchers concluded, hard work and resilience may allow them to excel and thus “positively affect exposed U.S.-born students’ attitudes and behavior.” But according to teacher Larry Ferlazzo, the improvements might stem from the fact that having English language learners in classes improves pedagogy , pushing teachers to consider “issues like prior knowledge, scaffolding, and maximizing accessibility.”

5. A Fuller Picture of What a ‘Good’ School Is

It’s time to rethink our definition of what a “good school” is, researchers assert in a study published in late 2020.⁣ That’s because typical measures of school quality like test scores often provide an incomplete and misleading picture, the researchers found.

The study looked at over 150,000 ninth-grade students who attended Chicago public schools and concluded that emphasizing the social and emotional dimensions of learning—relationship-building, a sense of belonging, and resilience, for example—improves high school graduation and college matriculation rates for both high- and low-income students, beating out schools that focus primarily on improving test scores.⁣

“Schools that promote socio-emotional development actually have a really big positive impact on kids,” said lead researcher C. Kirabo Jackson in an interview with Edutopia . “And these impacts are particularly large for vulnerable student populations who don’t tend to do very well in the education system.”

The findings reinforce the importance of a holistic approach to measuring student progress, and are a reminder that schools—and teachers—can influence students in ways that are difficult to measure, and may only materialize well into the future.⁣

6. Teaching Is Learning

One of the best ways to learn a concept is to teach it to someone else. But do you actually have to step into the shoes of a teacher, or does the mere expectation of teaching do the trick?

In a 2021 study , researchers split students into two groups and gave them each a science passage about the Doppler effect—a phenomenon associated with sound and light waves that explains the gradual change in tone and pitch as a car races off into the distance, for example. One group studied the text as preparation for a test; the other was told that they’d be teaching the material to another student.

The researchers never carried out the second half of the activity—students read the passages but never taught the lesson. All of the participants were then tested on their factual recall of the Doppler effect, and their ability to draw deeper conclusions from the reading.

The upshot? Students who prepared to teach outperformed their counterparts in both duration and depth of learning, scoring 9 percent higher on factual recall a week after the lessons concluded, and 24 percent higher on their ability to make inferences. The research suggests that asking students to prepare to teach something—or encouraging them to think “could I teach this to someone else?”—can significantly alter their learning trajectories.

7. A Disturbing Strain of Bias in Kids’ Books

Some of the most popular and well-regarded children’s books—Caldecott and Newbery honorees among them—persistently depict Black, Asian, and Hispanic characters with lighter skin, according to new research .

Using artificial intelligence, researchers combed through 1,130 children’s books written in the last century, comparing two sets of diverse children’s books—one a collection of popular books that garnered major literary awards, the other favored by identity-based awards. The software analyzed data on skin tone, race, age, and gender.

Among the findings: While more characters with darker skin color begin to appear over time, the most popular books—those most frequently checked out of libraries and lining classroom bookshelves—continue to depict people of color in lighter skin tones. More insidiously, when adult characters are “moral or upstanding,” their skin color tends to appear lighter, the study’s lead author, Anjali Aduki,  told The 74 , with some books converting “Martin Luther King Jr.’s chocolate complexion to a light brown or beige.” Female characters, meanwhile, are often seen but not heard.

Cultural representations are a reflection of our values, the researchers conclude: “Inequality in representation, therefore, constitutes an explicit statement of inequality of value.”

8. The Never-Ending ‘Paper Versus Digital’ War

The argument goes like this: Digital screens turn reading into a cold and impersonal task; they’re good for information foraging, and not much more. “Real” books, meanwhile, have a heft and “tactility”  that make them intimate, enchanting—and irreplaceable.

But researchers have often found weak or equivocal evidence for the superiority of reading on paper. While a recent study concluded that paper books yielded better comprehension than e-books when many of the digital tools had been removed, the effect sizes were small. A 2021 meta-analysis further muddies the water: When digital and paper books are “mostly similar,” kids comprehend the print version more readily—but when enhancements like motion and sound “target the story content,” e-books generally have the edge.

Nostalgia is a force that every new technology must eventually confront. There’s plenty of evidence that writing with pen and paper encodes learning more deeply than typing. But new digital book formats come preloaded with powerful tools that allow readers to annotate, look up words, answer embedded questions, and share their thinking with other readers.

We may not be ready to admit it, but these are precisely the kinds of activities that drive deeper engagement, enhance comprehension, and leave us with a lasting memory of what we’ve read. The future of e-reading, despite the naysayers, remains promising.

9. New Research Makes a Powerful Case for PBL

Many classrooms today still look like they did 100 years ago, when students were preparing for factory jobs. But the world’s moved on: Modern careers demand a more sophisticated set of skills—collaboration, advanced problem-solving, and creativity, for example—and those can be difficult to teach in classrooms that rarely give students the time and space to develop those competencies.

Project-based learning (PBL) would seem like an ideal solution. But critics say PBL places too much responsibility on novice learners, ignoring the evidence about the effectiveness of direct instruction and ultimately undermining subject fluency. Advocates counter that student-centered learning and direct instruction can and should coexist in classrooms.

Now two new large-scale studies —encompassing over 6,000 students in 114 diverse schools across the nation—provide evidence that a well-structured, project-based approach boosts learning for a wide range of students.

In the studies, which were funded by Lucas Education Research, a sister division of Edutopia , elementary and high school students engaged in challenging projects that had them designing water systems for local farms, or creating toys using simple household objects to learn about gravity, friction, and force. Subsequent testing revealed notable learning gains—well above those experienced by students in traditional classrooms—and those gains seemed to raise all boats, persisting across socioeconomic class, race, and reading levels.

10. Tracking a Tumultuous Year for Teachers

The Covid-19 pandemic cast a long shadow over the lives of educators in 2021, according to a year’s worth of research.

The average teacher’s workload suddenly “spiked last spring,” wrote the Center for Reinventing Public Education in its January 2021 report, and then—in defiance of the laws of motion—simply never let up. By the fall, a RAND study recorded an astonishing shift in work habits: 24 percent of teachers reported that they were working 56 hours or more per week, compared to 5 percent pre-pandemic.

The vaccine was the promised land, but when it arrived nothing seemed to change. In an April 2021 survey  conducted four months after the first vaccine was administered in New York City, 92 percent of teachers said their jobs were more stressful than prior to the pandemic, up from 81 percent in an earlier survey.

It wasn’t just the length of the work days; a close look at the research reveals that the school system’s failure to adjust expectations was ruinous. It seemed to start with the obligations of hybrid teaching, which surfaced in Edutopia ’s coverage of overseas school reopenings. In June 2020, well before many U.S. schools reopened, we reported that hybrid teaching was an emerging problem internationally, and warned that if the “model is to work well for any period of time,” schools must “recognize and seek to reduce the workload for teachers.” Almost eight months later, a 2021 RAND study identified hybrid teaching as a primary source of teacher stress in the U.S., easily outpacing factors like the health of a high-risk loved one.

New and ever-increasing demands for tech solutions put teachers on a knife’s edge. In several important 2021 studies, researchers concluded that teachers were being pushed to adopt new technology without the “resources and equipment necessary for its correct didactic use.” Consequently, they were spending more than 20 hours a week adapting lessons for online use, and experiencing an unprecedented erosion of the boundaries between their work and home lives, leading to an unsustainable “always on” mentality. When it seemed like nothing more could be piled on—when all of the lights were blinking red—the federal government restarted standardized testing .

Change will be hard; many of the pathologies that exist in the system now predate the pandemic. But creating strict school policies that separate work from rest, eliminating the adoption of new tech tools without proper supports, distributing surveys regularly to gauge teacher well-being, and above all listening to educators to identify and confront emerging problems might be a good place to start, if the research can be believed.

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
• Evidence Synthesis/Systematic Reviews
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Home Market Research

What is Research: Definition, Methods, Types & Examples

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

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

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

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

Research is conducted with a purpose to:

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

What is the purpose of research?

There are three main purposes:

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

LEARN ABOUT: Descriptive Analysis

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

LEARN ABOUT: Best Data Collection Tools

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

Here is a comparative analysis chart for a better understanding:

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

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

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

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods.

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

Types of qualitative methods include:

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

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

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

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

It is essential to ensure that your data is:

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

Gather insights

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

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

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Population Interference in Panel Experiments

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

Division of General Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA, USA

Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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All authors made substantial contributions to conception and design, data acquisition and interpretation, drafting the manuscript, and providing critical revisions. All authors approve this final version of the manuscript.

Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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Methodology

Research Methods | Definitions, Types, Examples

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

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

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

Second, decide how you will analyze the data .

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

Table of contents

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

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

Qualitative vs. quantitative data

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

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

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

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

Primary vs. secondary research

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

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

Descriptive vs. experimental data

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

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

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

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

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

Qualitative analysis methods

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

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

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

Quantitative analysis methods

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

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

• During an experiment .
• Using probability sampling methods .

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

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

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

Research bias

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

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

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

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

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

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

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

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

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

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

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

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

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

What Is Research, and Why Do People Do It?

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

14k Accesses

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

Abstractspiepr Abs1

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

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Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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Module 1: Introduction: What is Research?

Learning Objectives

By the end of this module, you will be able to:

• Explain how the scientific method is used to develop new knowledge
• Describe why it is important to follow a research plan

The Scientific Method consists of observing the world around you and creating a  hypothesis  about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the  hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a  manipulation  that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.

Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.

No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the  Principal Investigator  (PI). The PI is in charge of all aspects of the research and creates what is called a  protocol  (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.

Module 1: Discussion Questions

• How is a hypothesis like a road map?
• Who is ultimately responsible for the design and conduct of a research study?
• How does following the research protocol contribute to informing public health practices?

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Research and trends in STEM education: a systematic review of journal publications

• Yeping Li 1 ,
• Ke Wang 2 ,
• Yu Xiao 1 &
• Jeffrey E. Froyd 3  

International Journal of STEM Education volume  7 , Article number:  11 ( 2020 ) Cite this article

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With the rapid increase in the number of scholarly publications on STEM education in recent years, reviews of the status and trends in STEM education research internationally support the development of the field. For this review, we conducted a systematic analysis of 798 articles in STEM education published between 2000 and the end of 2018 in 36 journals to get an overview about developments in STEM education scholarship. We examined those selected journal publications both quantitatively and qualitatively, including the number of articles published, journals in which the articles were published, authorship nationality, and research topic and methods over the years. The results show that research in STEM education is increasing in importance internationally and that the identity of STEM education journals is becoming clearer over time.

Introduction

A recent review of 144 publications in the International Journal of STEM Education ( IJ - STEM ) showed how scholarship in science, technology, engineering, and mathematics (STEM) education developed between August 2014 and the end of 2018 through the lens of one journal (Li, Froyd, & Wang, 2019 ). The review of articles published in only one journal over a short period of time prompted the need to review the status and trends in STEM education research internationally by analyzing articles published in a wider range of journals over a longer period of time.

With global recognition of the growing importance of STEM education, we have witnessed the urgent need to support research and scholarship in STEM education (Li, 2014 , 2018a ). Researchers and educators have responded to this on-going call and published their scholarly work through many different publication outlets including journals, books, and conference proceedings. A simple Google search with the term “STEM,” “STEM education,” or “STEM education research” all returned more than 450,000,000 items. Such voluminous information shows the rapidly evolving and vibrant field of STEM education and sheds light on the volume of STEM education research. In any field, it is important to know and understand the status and trends in scholarship for the field to develop and be appropriately supported. This applies to STEM education.

Conducting systematic reviews to explore the status and trends in specific disciplines is common in educational research. For example, researchers surveyed the historical development of research in mathematics education (Kilpatrick, 1992 ) and studied patterns in technology usage in mathematics education (Bray & Tangney, 2017 ; Sokolowski, Li, & Willson, 2015 ). In science education, Tsai and his colleagues have conducted a sequence of reviews of journal articles to synthesize research trends in every 5 years since 1998 (i.e., 1998–2002, 2003–2007, 2008–2012, and 2013–2017), based on publications in three main science education journals including, Science Education , the International Journal of Science Education , and the Journal of Research in Science Teaching (e.g., Lin, Lin, Potvin, & Tsai, 2019 ; Tsai & Wen, 2005 ). Erduran, Ozdem, and Park ( 2015 ) reviewed argumentation in science education research from 1998 to 2014 and Minner, Levy, and Century ( 2010 ) reviewed inquiry-based science instruction between 1984 and 2002. There are also many literature reviews and syntheses in engineering and technology education (e.g., Borrego, Foster, & Froyd, 2015 ; Xu, Williams, Gu, & Zhang, 2019 ). All of these reviews have been well received in different fields of traditional disciplinary education as they critically appraise and summarize the state-of-art of relevant research in a field in general or with a specific focus. Both types of reviews have been conducted with different methods for identifying, collecting, and analyzing relevant publications, and they differ in terms of review aim and topic scope, time period, and ways of literature selection. In this review, we systematically analyze journal publications in STEM education research to overview STEM education scholarship development broadly and globally.

The complexity and ambiguity of examining the status and trends in STEM education research

A review of research development in a field is relatively straight forward, when the field is mature and its scope can be well defined. Unlike discipline-based education research (DBER, National Research Council, 2012 ), STEM education is not a well-defined field. Conducting a comprehensive literature review of STEM education research require careful thought and clearly specified scope to tackle the complexity naturally associated with STEM education. In the following sub-sections, we provide some further discussion.

Diverse perspectives about STEM and STEM education

STEM education as explicated by the term does not have a long history. The interest in helping students learn across STEM fields can be traced back to the 1990s when the US National Science Foundation (NSF) formally included engineering and technology with science and mathematics in undergraduate and K-12 school education (e.g., National Science Foundation, 1998 ). It coined the acronym SMET (science, mathematics, engineering, and technology) that was subsequently used by other agencies including the US Congress (e.g., United States Congress House Committee on Science, 1998 ). NSF also coined the acronym STEM to replace SMET (e.g., Christenson, 2011 ; Chute, 2009 ) and it has become the acronym of choice. However, a consensus has not been reached on the disciplines included within STEM.

To clarify its intent, NSF published a list of approved fields it considered under the umbrella of STEM (see http://bit.ly/2Bk1Yp5 ). The list not only includes disciplines widely considered under the STEM tent (called “core” disciplines, such as physics, chemistry, and materials research), but also includes disciplines in psychology and social sciences (e.g., political science, economics). However, NSF’s list of STEM fields is inconsistent with other federal agencies. Gonzalez and Kuenzi ( 2012 ) noted that at least two US agencies, the Department of Homeland Security and Immigration and Customs Enforcement, use a narrower definition that excludes social sciences. Researchers also view integration across different disciplines of STEM differently using various terms such as, multidisciplinary, interdisciplinary, and transdisciplinary (Vasquez, Sneider, & Comer, 2013 ). These are only two examples of the ambiguity and complexity in describing and specifying what constitutes STEM.

Multiple perspectives about the meaning of STEM education adds further complexity to determining the extent to which scholarly activity can be categorized as STEM education. For example, STEM education can be viewed with a broad and inclusive perspective to include education in the individual disciplines of STEM, i.e., science education, technology education, engineering education, and mathematics education, as well as interdisciplinary or cross-disciplinary combinations of the individual STEM disciplines (English, 2016 ; Li, 2014 ). On the other hand, STEM education can be viewed by others as referring only to interdisciplinary or cross-disciplinary combinations of the individual STEM disciplines (Honey, Pearson, & Schweingruber, 2014 ; Johnson, Peters-Burton, & Moore, 2015 ; Kelley & Knowles, 2016 ; Li, 2018a ). These multiple perspectives allow scholars to publish articles in a vast array and diverse journals, as long as journals are willing to take the position as connected with STEM education. At the same time, however, the situation presents considerable challenges for researchers intending to locate, identify, and classify publications as STEM education research. To tackle such challenges, we tried to find out what we can learn from prior reviews related to STEM education.

Guidance from prior reviews related to STEM education

A search for reviews of STEM education research found multiple reviews that could suggest approaches for identifying publications (e.g., Brown, 2012 ; Henderson, Beach, & Finkelstein, 2011 ; Kim, Sinatra, & Seyranian, 2018 ; Margot & Kettler, 2019 ; Minichiello, Hood, & Harkness, 2018 ; Mizell & Brown, 2016 ; Thibaut et al., 2018 ; Wu & Rau, 2019 ). The review conducted by Brown ( 2012 ) examined the research base of STEM education. He addressed the complexity and ambiguity by confining the review with publications in eight journals, two in each individual discipline, one academic research journal (e.g., the Journal of Research in Science Teaching ) and one practitioner journal (e.g., Science Teacher ). Journals were selected based on suggestions from some faculty members and K-12 teachers. Out of 1100 articles published in these eight journals from January 1, 2007, to October 1, 2010, Brown located 60 articles that authors self-identified as connected to STEM education. He found that the vast majority of these 60 articles focused on issues beyond an individual discipline and there was a research base forming for STEM education. In a follow-up study, Mizell and Brown ( 2016 ) reviewed articles published from January 2013 to October 2015 in the same eight journals plus two additional journals. Mizell and Brown used the same criteria to identify and include articles that authors self-identified as connected to STEM education, i.e., if the authors included STEM in the title or author-supplied keywords. In comparison to Brown’s findings, they found that many more STEM articles were published in a shorter time period and by scholars from many more different academic institutions. Taking together, both Brown ( 2012 ) and Mizell and Brown ( 2016 ) tended to suggest that STEM education mainly consists of interdisciplinary or cross-disciplinary combinations of the individual STEM disciplines, but their approach consisted of selecting a limited number of individual discipline-based journals and then selecting articles that authors self-identified as connected to STEM education.

In contrast to reviews on STEM education, in general, other reviews focused on specific issues in STEM education (e.g., Henderson et al., 2011 ; Kim et al., 2018 ; Margot & Kettler, 2019 ; Minichiello et al., 2018 ; Schreffler, Vasquez III, Chini, & James, 2019 ; Thibaut et al., 2018 ; Wu & Rau, 2019 ). For example, the review by Henderson et al. ( 2011 ) focused on instructional change in undergraduate STEM courses based on 191 conceptual and empirical journal articles published between 1995 and 2008. Margot and Kettler ( 2019 ) focused on what is known about teachers’ values, beliefs, perceived barriers, and needed support related to STEM education based on 25 empirical journal articles published between 2000 and 2016. The focus of these reviews allowed the researchers to limit the number of articles considered, and they typically used keyword searches of selected databases to identify articles on STEM education. Some researchers used this approach to identify publications from journals only (e.g., Henderson et al., 2011 ; Margot & Kettler, 2019 ; Schreffler et al., 2019 ), and others selected and reviewed publications beyond journals (e.g., Minichiello et al., 2018 ; Thibaut et al., 2018 ; Wu & Rau, 2019 ).

The discussion in this section suggests possible reasons contributing to the absence of a general literature review of STEM education research and development: (1) diverse perspectives in existence about STEM and STEM education that contribute to the difficulty of specifying a scope of literature review, (2) its short but rapid development history in comparison to other discipline-based education (e.g., science education), and (3) difficulties in deciding how to establish the scope of the literature review. With respect to the third reason, prior reviews have used one of two approaches to identify and select articles: (a) identifying specific journals first and then searching and selecting specific articles from these journals (e.g., Brown, 2012 ; Erduran et al., 2015 ; Mizell & Brown, 2016 ) and (b) conducting selected database searches with keywords based on a specific focus (e.g., Margot & Kettler, 2019 ; Thibaut et al., 2018 ). However, neither the first approach of selecting a limited number of individual discipline-based journals nor the second approach of selecting a specific focus for the review leads to an approach that provides a general overview of STEM education scholarship development based on existing journal publications.

Current review

Two issues were identified in setting the scope for this review.

What time period should be considered?

What publications will be selected for review?

Time period

We start with the easy one first. As discussed above, the acronym STEM did exist until the early 2000s. Although the existence of the acronym does not generate scholarship on student learning in STEM disciplines, it is symbolic and helps focus attention to efforts in STEM education. Since we want to examine the status and trends in STEM education, it is reasonable to start with the year 2000. Then, we can use the acronym of STEM as an identifier in locating specific research articles in a way as done by others (e.g., Brown, 2012 ; Mizell & Brown, 2016 ). We chose the end of 2018 as the end of the time period for our review that began during 2019.

Focusing on publications beyond individual discipline-based journals

As mentioned before, scholars responded to the call for scholarship development in STEM education with publications that appeared in various outlets and diverse languages, including journals, books, and conference proceedings. However, journal publications are typically credited and valued as one of the most important outlets for research exchange (e.g., Erduran et al., 2015 ; Henderson et al., 2011 ; Lin et al., 2019 ; Xu et al., 2019 ). Thus, in this review, we will also focus on articles published in journals in English.

The discourse above on the complexity and ambiguity regarding STEM education suggests that scholars may publish their research in a wide range of journals beyond individual discipline-based journals. To search and select articles from a wide range of journals, we thought about the approach of searching selected databases with keywords as other scholars used in reviewing STEM education with a specific focus. However, existing journals in STEM education do not have a long history. In fact, IJ-STEM is the first journal in STEM education that has just been accepted into the Social Sciences Citation Index (SSCI) (Li, 2019a ). Publications in many STEM education journals are practically not available in several important and popular databases, such as the Web of Science and Scopus. Moreover, some journals in STEM education were not normalized due to a journal’s name change or irregular publication schedule. For example, the Journal of STEM Education was named as Journal of SMET Education when it started in 2000 in a print format, and the journal’s name was not changed until 2003, Vol 4 (3 and 4), and also went fully on-line starting 2004 (Raju & Sankar, 2003 ). A simple Google Scholar search with keywords will not be able to provide accurate information, unless you visit the journal’s website to check all publications over the years. Those added complexities prevented us from taking the database search as a viable approach. Thus, we decided to identify journals first and then search and select articles from these journals. Further details about the approach are provided in the “ Method ” section.

Research questions

Given a broader range of journals and a longer period of time to be covered in this review, we can examine some of the same questions as the IJ-STEM review (Li, Froyd, & Wang, 2019 ), but we do not have access to data on readership, articles accessed, or articles cited for the other journals selected for this review. Specifically, we are interested in addressing the following six research questions:

What were the status and trends in STEM education research from 2000 to the end of 2018 based on journal publications?

What were the patterns of publications in STEM education research across different journals?

Which countries or regions, based on the countries or regions in which authors were located, contributed to journal publications in STEM education?

What were the patterns of single-author and multiple-author publications in STEM education?

What main topics had emerged in STEM education research based on the journal publications?

What research methods did authors tend to use in conducting STEM education research?

Based on the above discussion, we developed the methods for this literature review to follow careful sequential steps to identify journals first and then identify and select STEM education research articles published in these journals from January 2000 to the end of 2018. The methods should allow us to obtain a comprehensive overview about the status and trends of STEM education research based on a systematic analysis of related publications from a broad range of journals and over a longer period of time.

Identifying journals

We used the following three steps to search and identify journals for inclusion:

We assumed articles on research in STEM education have been published in journals that involve more than one traditional discipline. Thus, we used Google to search and identify all education journals with their titles containing either two, three, or all four disciplines of STEM. For example, we did Google search of all the different combinations of three areas of science, mathematics, technology Footnote 1 , and engineering as contained in a journal’s title. In addition, we also searched possible journals containing the word STEAM in the title.

Since STEM education may be viewed as encompassing discipline-based education research, articles on STEM education research may have been published in traditional discipline-based education journals, such as the Journal of Research in Science Teaching . However, there are too many such journals. Yale’s Poorvu Center for Teaching and Learning has listed 16 journals that publish articles spanning across undergraduate STEM education disciplines (see https://poorvucenter.yale.edu/FacultyResources/STEMjournals ). Thus, we selected from the list some individual discipline-based education research journals, and also added a few more common ones such as the Journal of Engineering Education .

Since articles on research in STEM education have appeared in some general education research journals, especially those well-established ones. Thus, we identified and selected a few of those journals that we noticed some publications in STEM education research.

Following the above three steps, we identified 45 journals (see Table  1 ).

Identifying articles

In this review, we will not discuss or define the meaning of STEM education. We used the acronym STEM (or STEAM, or written as the phrase of “science, technology, engineering, and mathematics”) as a term in our search of publication titles and/or abstracts. To identify and select articles for review, we searched all items published in those 45 journals and selected only those articles that author(s) self-identified with the acronym STEM (or STEAM, or written as the phrase of “science, technology, engineering, and mathematics”) in the title and/or abstract. We excluded publications in the sections of practices, letters to editors, corrections, and (guest) editorials. Our search found 798 publications that authors self-identified as in STEM education, identified from 36 journals. The remaining 9 journals either did not have publications that met our search terms or published in another language other than English (see the two separate lists in Table 1 ).

Data analysis

To address research question 3, we analyzed authorship to examine which countries/regions contributed to STEM education research over the years. Because each publication may have either one or multiple authors, we used two different methods to analyze authorship nationality that have been recognized as valuable from our review of IJ-STEM publications (Li, Froyd, & Wang, 2019 ). The first method considers only the corresponding author’s (or the first author, if no specific indication is given about the corresponding author) nationality and his/her first institution affiliation, if multiple institution affiliations are listed. Method 2 considers every author of a publication, using the following formula (Howard, Cole, & Maxwell, 1987 ) to quantitatively assign and estimate each author’s contribution to a publication (and thus associated institution’s productivity), when multiple authors are included in a publication. As an example, each publication is given one credit point. For the publication co-authored by two, the first author would be given 0.6 and the second author 0.4 credit point. For an article contributed jointly by three authors, the three authors would be credited with scores of 0.47, 0.32, and 0.21, respectively.

After calculating all the scores for each author of each paper, we added all the credit scores together in terms of each author’s country/region. For brevity, we present only the top 10 countries/regions in terms of their total credit scores calculated using these two different methods, respectively.

To address research question 5, we used the same seven topic categories identified and used in our review of IJ-STEM publications (Li, Froyd, & Wang, 2019 ). We tested coding 100 articles first to ensure the feasibility. Through test-coding and discussions, we found seven topic categories could be used to examine and classify all 798 items.

K-12 teaching, teacher, and teacher education in STEM (including both pre-service and in-service teacher education)

Post-secondary teacher and teaching in STEM (including faculty development, etc.)

K-12 STEM learner, learning, and learning environment

Post-secondary STEM learner, learning, and learning environments (excluding pre-service teacher education)

Policy, curriculum, evaluation, and assessment in STEM (including literature review about a field in general)

Culture and social and gender issues in STEM education

History, epistemology, and perspectives about STEM and STEM education

To address research question 6, we coded all 798 publications in terms of (1) qualitative methods, (2) quantitative methods, (3) mixed methods, and (4) non-empirical studies (including theoretical or conceptual papers, and literature reviews). We assigned each publication to only one research topic and one method, following the process used in the IJ-STEM review (Li, Froyd, & Wang, 2019 ). When there was more than one topic or method that could have been used for a publication, a decision was made in choosing and assigning a topic or a method. The agreement between two coders for all 798 publications was 89.5%. When topic and method coding discrepancies occurred, a final decision was reached after discussion.

Results and discussion

In the following sections, we report findings as corresponding to each of the six research questions.

The status and trends of journal publications in STEM education research from 2000 to 2018

Figure  1 shows the number of publications per year. As Fig.  1 shows, the number of publications increased each year beginning in 2010. There are noticeable jumps from 2015 to 2016 and from 2017 to 2018. The result shows that research in STEM education had grown significantly since 2010, and the most recent large number of STEM education publications also suggests that STEM education research gained its own recognition by many different journals for publication as a hot and important topic area.

The distribution of STEM education publications over the years

Among the 798 articles, there were 549 articles with the word “STEM” (or STEAM, or written with the phrase of “science, technology, engineering, and mathematics”) included in the article’s title or both title and abstract and 249 articles without such identifiers included in the title but abstract only. The results suggest that many scholars tended to include STEM in the publications’ titles to highlight their research in or about STEM education. Figure  2 shows the number of publications per year where publications are distinguished depending on whether they used the term STEM in the title or only in the abstract. The number of publications in both categories had significant increases since 2010. Use of the acronym STEM in the title was growing at a faster rate than using the acronym only in the abstract.

The trends of STEM education publications with vs. without STEM included in the title

Not all the publications that used the acronym STEM in the title and/or abstract reported on a study involving all four STEM areas. For each publication, we further examined the number of the four areas involved in the reported study.

Figure  3 presents the number of publications categorized by the number of the four areas involved in the study, breaking down the distribution of these 798 publications in terms of the content scope being focused on. Studies involving all four STEM areas are the most numerous with 488 (61.2%) publications, followed by involving one area (141, 17.7%), then studies involving both STEM and non-STEM (84, 10.5%), and finally studies involving two or three areas of STEM (72, 9%; 13, 1.6%; respectively). Publications that used the acronym STEAM in either the title or abstract were classified as involving both STEM and non-STEM. For example, both of the following publications were included in this category.

Dika and D’Amico ( 2016 ). “Early experiences and integration in the persistence of first-generation college students in STEM and non-STEM majors.” Journal of Research in Science Teaching , 53 (3), 368–383. (Note: this article focused on early experience in both STEM and Non-STEM majors.)

Sochacka, Guyotte, and Walther ( 2016 ). “Learning together: A collaborative autoethnographic exploration of STEAM (STEM+ the Arts) education.” Journal of Engineering Education , 105 (1), 15–42. (Note: this article focused on STEAM (both STEM and Arts).)

Publication distribution in terms of content scope being focused on. (Note: 1=single subject of STEM, 2=two subjects of STEM, 3=three subjects of STEM, 4=four subjects of STEM, 5=topics related to both STEM and non-STEM)

Figure  4 presents the number of publications per year in each of the five categories described earlier (category 1, one area of STEM; category 2, two areas of STEM; category 3, three areas of STEM; category 4, four areas of STEM; category 5, STEM and non-STEM). The category that had grown most rapidly since 2010 is the one involving all four areas. Recent growth in the number of publications in category 1 likely reflected growing interest of traditional individual disciplinary based educators in developing and sharing multidisciplinary and interdisciplinary scholarship in STEM education, as what was noted recently by Li and Schoenfeld ( 2019 ) with publications in IJ-STEM.

Publication distribution in terms of content scope being focused on over the years

Patterns of publications across different journals

Among the 36 journals that published STEM education articles, two are general education research journals (referred to as “subject-0”), 12 with their titles containing one discipline of STEM (“subject-1”), eight with journal’s titles covering two disciplines of STEM (“subject-2”), six covering three disciplines of STEM (“subject-3”), seven containing the word STEM (“subject-4”), and one in STEAM education (“subject-5”).

Table  2 shows that both subject-0 and subject-1 journals were usually mature journals with a long history, and they were all traditional subscription-based journals, except the Journal of Pre - College Engineering Education Research , a subject-1 journal established in 2011 that provided open access (OA). In comparison to subject-0 and subject-1 journals, subject-2 and subject-3 journals were relatively newer but still had quite many years of history on average. There are also some more journals in these two categories that provided OA. Subject-4 and subject-5 journals had a short history, and most provided OA. The results show that well-established journals had tended to focus on individual disciplines or education research in general. Multidisciplinary and interdisciplinary education journals were started some years later, followed by the recent establishment of several STEM or STEAM journals.

Table 2 also shows that subject-1, subject-2, and subject-4 journals published approximately a quarter each of the publications. The number of publications in subject-1 journals is interested, because we selected a relatively limited number of journals in this category. There are many other journals in the subject-1 category (as well as subject-0 journals) that we did not select, and thus it is very likely that we did not include some STEM education articles published in subject-0 or subject-1 journals that we did not include in our study.

Figure  5 shows the number of publications per year in each of the five categories described earlier (subject-0 through subject-5). The number of publications per year in subject-5 and subject-0 journals did not change much over the time period of the study. On the other hand, the number of publications per year in subject-4 (all 4 areas), subject-1 (single area), and subject-2 journals were all over 40 by the end of the study period. The number of publications per year in subject-3 journals increased but remained less than 30. At first sight, it may be a bit surprising that the number of publications in STEM education per year in subject-1 journals increased much faster than those in subject-2 journals over the past few years. However, as Table 2 indicates these journals had long been established with great reputations, and scholars would like to publish their research in such journals. In contrast to the trend in subject-1 journals, the trend in subject-4 journals suggests that STEM education journals collectively started to gain its own identity for publishing and sharing STEM education research.

STEM education publication distribution across different journal categories over the years. (Note: 0=subject-0; 1=subject-1; 2=subject-2; 3=subject-3; 4=subject-4; 5=subject-5)

Figure  6 shows the number of STEM education publications in each journal where the bars are color-coded (yellow, subject-0; light blue, subject-1; green, subject-2; purple, subject-3; dark blue, subject-4; and black, subject-5). There is no clear pattern shown in terms of the overall number of STEM education publications across categories or journals, but very much individual journal-based performance. The result indicates that the number of STEM education publications might heavily rely on the individual journal’s willingness and capability of attracting STEM education research work and thus suggests the potential value of examining individual journal’s performance.

Publication distribution across all 36 individual journals across different categories with the same color-coded for journals in the same subject category

The top five journals in terms of the number of STEM education publications are Journal of Science Education and Technology (80 publications, journal number 25 in Fig.  6 ), Journal of STEM Education (65 publications, journal number 26), International Journal of STEM Education (64 publications, journal number 17), International Journal of Engineering Education (54 publications, journal number 12), and School Science and Mathematics (41 publications, journal number 31). Among these five journals, two journals are specifically on STEM education (J26, J17), two on two subjects of STEM (J25, J31), and one on one subject of STEM (J12).

Figure  7 shows the number of STEM education publications per year in each of these top five journals. As expected, based on earlier trends, the number of publications per year increased over the study period. The largest increase was in the International Journal of STEM Education (J17) that was established in 2014. As the other four journals were all established in or before 2000, J17’s short history further suggests its outstanding performance in attracting and publishing STEM education articles since 2014 (Li, 2018b ; Li, Froyd, & Wang, 2019 ). The increase was consistent with the journal’s recognition as the first STEM education journal for inclusion in SSCI starting in 2019 (Li, 2019a ).

Publication distribution of selected five journals over the years. (Note: J12: International Journal of Engineering Education; J17: International Journal of STEM Education; J25: Journal of Science Education and Technology; J26: Journal of STEM Education; J31: School Science and Mathematics)

Top 10 countries/regions where scholars contributed journal publications in STEM education

Table  3 shows top countries/regions in terms of the number of publications, where the country/region was established by the authorship using the two different methods presented above. About 75% (depending on the method) of contributions were made by authors from the USA, followed by Australia, Canada, Taiwan, and UK. Only Africa as a continent was not represented among the top 10 countries/regions. The results are relatively consistent with patterns reported in the IJ-STEM study (Li, Froyd, & Wang, 2019 )

Further examination of Table 3 reveals that the two methods provide not only fairly consistent results but also yield some differences. For example, Israel and Germany had more publication credit if only the corresponding author was considered, but South Korea and Turkey had more publication credit when co-authors were considered. The results in Table 3 show that each method has value when analyzing and comparing publications by country/region or institution based on authorship.

Recognizing that, as shown in Fig. 1 , the number of publications per year increased rapidly since 2010, Table  4 shows the number of publications by country/region over a 10-year period (2009–2018) and Table 5 shows the number of publications by country/region over a 5-year period (2014–2018). The ranks in Tables  3 , 4 , and 5 are fairly consistent, but that would be expected since the larger numbers of publications in STEM education had occurred in recent years. At the same time, it is interesting to note in Table 5 some changes over the recent several years with Malaysia, but not Israel, entering the top 10 list when either method was used to calculate author's credit.

Patterns of single-author and multiple-author publications in STEM education

Since STEM education differs from traditional individual disciplinary education, we are interested in determining how common joint co-authorship with collaborations was in STEM education articles. Figure  8 shows that joint co-authorship was very common among these 798 STEM education publications, with 83.7% publications with two or more co-authors. Publications with two, three, or at least five co-authors were highest, with 204, 181, and 157 publications, respectively.

Number of publications with single or different joint authorship. (Note: 1=single author; 2=two co-authors; 3=three co-authors; 4=four co-authors; 5=five or more co-authors)

Figure  9 shows the number of publications per year using the joint authorship categories in Fig.  8 . Each category shows an increase consistent with the increase shown in Fig. 1 for all 798 publications. By the end of the time period, the number of publications with two, three, or at least five co-authors was the largest, which might suggest an increase in collaborations in STEM education research.

Publication distribution with single or different joint authorship over the years. (Note: 1=single author; 2=two co-authors; 3=three co-authors; 4=four co-authors; 5=five or more co-authors)

Co-authors can be from the same or different countries/regions. Figure  10 shows the number of publications per year by single authors (no collaboration), co-authors from the same country (collaboration in a country/region), and co-authors from different countries (collaboration across countries/regions). Each year the largest number of publications was by co-authors from the same country, and the number increased dramatically during the period of the study. Although the number of publications in the other two categories increased, the numbers of publications were noticeably fewer than the number of publications by co-authors from the same country.

Publication distribution in authorship across different categories in terms of collaboration over the years

Published articles by research topics

Figure  11 shows the number of publications in each of the seven topic categories. The topic category of goals, policy, curriculum, evaluation, and assessment had almost half of publications (375, 47%). Literature reviews were included in this topic category, as providing an overview assessment of education and research development in a topic area or a field. Sample publications included in this category are listed as follows:

DeCoito ( 2016 ). “STEM education in Canada: A knowledge synthesis.” Canadian Journal of Science , Mathematics and Technology Education , 16 (2), 114–128. (Note: this article provides a national overview of STEM initiatives and programs, including success, criteria for effective programs and current research in STEM education.)

Ring-Whalen, Dare, Roehrig, Titu, and Crotty ( 2018 ). “From conception to curricula: The role of science, technology, engineering, and mathematics in integrated STEM units.” International Journal of Education in Mathematics Science and Technology , 6 (4), 343–362. (Note: this article investigates the conceptions of integrated STEM education held by in-service science teachers through the use of photo-elicitation interviews and examines how those conceptions were reflected in teacher-created integrated STEM curricula.)

Schwab et al. ( 2018 ). “A summer STEM outreach program run by graduate students: Successes, challenges, and recommendations for implementation.” Journal of Research in STEM Education , 4 (2), 117–129. (Note: the article details the organization and scope of the Foundation in Science and Mathematics Program and evaluates this program.)

Frequencies of publications’ research topic distributions. (Note: 1=K-12 teaching, teacher and teacher education; 2=Post-secondary teacher and teaching; 3=K-12 STEM learner, learning, and learning environment; 4=Post-secondary STEM learner, learning, and learning environments; 5=Goals and policy, curriculum, evaluation, and assessment (including literature review); 6=Culture, social, and gender issues; 7=History, philosophy, Epistemology, and nature of STEM and STEM education)

The topic with the second most publications was “K-12 teaching, teacher and teacher education” (103, 12.9%), followed closely by “K-12 learner, learning, and learning environment” (97, 12.2%). The results likely suggest the research community had a broad interest in both teaching and learning in K-12 STEM education. The top three topics were the same in the IJ-STEM review (Li, Froyd, & Wang, 2019 ).

Figure  11 also shows there was a virtual tie between two topics with the fourth most cumulative publications, “post-secondary STEM learner & learning” (76, 9.5%) and “culture, social, and gender issues in STEM” (78, 9.8%), such as STEM identity, students’ career choices in STEM, and inclusion. This result is different from the IJ-STEM review (Li, Froyd, & Wang, 2019 ), where “post-secondary STEM teacher & teaching” and “post-secondary STEM learner & learning” were tied as the fourth most common topics. This difference is likely due to the scope of journals and the length of the time period being reviewed.

Figure  12 shows the number of publications per year in each topic category. As expected from the results in Fig.  11 the number of publications in topic category 5 (goals, policy, curriculum, evaluation, and assessment) was the largest each year. The numbers of publications in topic category 3 (K-12 learner, learning, and learning environment), 1 (K-12 teaching, teacher, and teacher education), 6 (culture, social, and gender issues in STEM), and 4 (post-secondary STEM learner and learning) were also increasing. Although Fig.  11 shows the number of publications in topic category 1 was slightly more than the number of publications in topic category 3 (see Fig.  11 ), the number of publications in topic category 3 was increasing more rapidly in recent years than its counterpart in topic category 1. This may suggest a more rapidly growing interest in K-12 STEM learner, learning, and learning environment. The numbers of publications in topic categories 2 and 7 were not increasing, but the number of publications in IJ-STEM in topic category 2 was notable (Li, Froyd, & Wang, 2019 ). It will be interesting to follow trends in the seven topic categories in the future.

Publication distributions in terms of research topics over the years

Published articles by research methods

Figure  13 shows the number of publications per year by research methods in empirical studies. Publications with non-empirical studies are shown in a separate category. Although the number of publications in each of the four categories increased during the study period, there were many more publications presenting empirical studies than those without. For those with empirical studies, the number of publications using quantitative methods increased most rapidly in recent years, followed by qualitative and then mixed methods. Although there were quite many publications with non-empirical studies (e.g., theoretical or conceptual papers, literature reviews) during the study period, the increase of the number of publications in this category was noticeably less than empirical studies.

Publication distributions in terms of research methods over the years. (Note: 1=qualitative, 2=quantitative, 3=mixed, 4=Non-empirical)

Concluding remarks

The systematic analysis of publications that were considered to be in STEM education in 36 selected journals shows tremendous growth in scholarship in this field from 2000 to 2018, especially over the past 10 years. Our analysis indicates that STEM education research has been increasingly recognized as an important topic area and studies were being published across many different journals. Scholars still hold diverse perspectives about how research is designated as STEM education; however, authors have been increasingly distinguishing their articles with STEM, STEAM, or related words in the titles, abstracts, and lists of keywords during the past 10 years. Moreover, our systematic analysis shows a dramatic increase in the number of publications in STEM education journals in recent years, which indicates that these journals have been collectively developing their own professional identity. In addition, the International Journal of STEM Education has become the first STEM education journal to be accepted in SSCI in 2019 (Li, 2019a ). The achievement may mark an important milestone as STEM education journals develop their own identity for publishing and sharing STEM education research.

Consistent with our previous reviews (Li, Froyd, & Wang, 2019 ; Li, Wang, & Xiao, 2019 ), the vast majority of publications in STEM education research were contributed by authors from the USA, where STEM and STEAM education originated, followed by Australia, Canada, and Taiwan. At the same time, authors in some countries/regions in Asia were becoming very active in the field over the past several years. This trend is consistent with findings from the IJ-STEM review (Li, Froyd, & Wang, 2019 ). We certainly hope that STEM education scholarship continues its development across all five continents to support educational initiatives and programs in STEM worldwide.

Our analysis has shown that collaboration, as indicated by publications with multiple authors, has been very common among STEM education scholars, as that is often how STEM education distinguishes itself from the traditional individual disciplinary based education. Currently, most collaborations occurred among authors from the same country/region, although collaborations across cross-countries/regions were slowly increasing.

With the rapid changes in STEM education internationally (Li, 2019b ), it is often difficult for researchers to get an overall sense about possible hot topics in STEM education especially when STEM education publications appeared in a vast array of journals across different fields. Our systematic analysis of publications has shown that studies in the topic category of goals, policy, curriculum, evaluation, and assessment have been the most prevalent, by far. Our analysis also suggests that the research community had a broad interest in both teaching and learning in K-12 STEM education. These top three topic categories are the same as in the IJ-STEM review (Li, Froyd, & Wang, 2019 ). Work in STEM education will continue to evolve and it will be interesting to review the trends in another 5 years.

Encouraged by our recent IJ-STEM review, we began this review with an ambitious goal to provide an overview of the status and trends of STEM education research. In a way, this systematic review allowed us to achieve our initial goal with a larger scope of journal selection over a much longer period of publication time. At the same time, there are still limitations, such as the decision to limit the number of journals from which we would identify publications for analysis. We understand that there are many publications on STEM education research that were not included in our review. Also, we only identified publications in journals. Although this is one of the most important outlets for scholars to share their research work, future reviews could examine publications on STEM education research in other venues such as books, conference proceedings, and grant proposals.

Availability of data and materials

The data and materials used and analyzed for the report are publicly available at the various journal websites.

Journals containing the word "computers" or "ICT" appeared automatically when searching with the word "technology". Thus, the word of "computers" or "ICT" was taken as equivalent to "technology" if appeared in a journal's name.

Abbreviations

Information and Communications Technology

International Journal of STEM Education

Kindergarten–Grade 12

Science, Mathematics, Engineering, and Technology

Science, Technology, Engineering, Arts, and Mathematics

Science, Technology, Engineering, and Mathematics

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Scientists Collaborate to Study the Cause and Development of Liver Abscesses in Cattle

Contact: Maribel Alonso Email: [email protected]

March 20, 2024

To effectively prevent liver abscesses in cattle, it is crucial to gain a complete understanding of the development of this condition and the bacteria responsible for causing the infection.

A collaborative effort between scientists from the USDA’s Agricultural Research Service (ARS), Texas Tech University, Kansas State University, and West Texas A&M University work on reliable and repeatable liver abscess models to learn the triggers for this costly condition in cattle that not only negatively impacts animal well-being but also causes liver condemnations and may lead to increased carcass trimming and an overall decrease in profitability.

The first successful model study , recently published in the Journal of Animal Science ,  is part of a series of studies conducted at the USDA-ARS Livestock Issues Research Unit in Lubbock, Texas, that aim to find solutions to a problem of concern for animal well-being that is costing the industry millions of dollars.

Cattle with liver abscesses can experience health problems and reduced growth and feed efficiency. However, cattle with liver abscesses don’t show clinical signs and are generally identified too late –at harvest. The economic losses associated with this condition in cattle can be as high as 400 million dollars annually. The knowledge gained from these models will help develop preventive interventions.

"After decades of studies, researchers haven't found an accurate way to predict nor diagnose liver abscesses, because of the complexity of the disease," said Rand Broadway , a research scientist with the Livestock Issues Research Unit. "Our study is a huge collaborative effort between USDA and academic partners to develop a liver abscess model in cattle that can help us better understand how liver abscess formation begins. We are constantly learning about the causes and development of these abscesses."

This model consistently showed a 50 percent prevalence, which is important for researchers to study liver abscess development and prevention strategies, particularly in calves entering the beef supply chain from dairy origin. In addition, the model continues to be improved in an effort to mimic “real-world” disease etiology while examining the physiological changes in the animal to better understand root causes of the disease.  

Currently, the primary treatment to prevent liver abscesses in cattle has been in-feed antibiotics. However, antibiotics have come under more scrutiny by the general public, and alternatives to antibiotics are being sought.

"We are trying to ensure this model is effective and applicable to test non-antibiotic interventions in the future," added Broadway. "We seek to use the knowledge gained from these models to develop different alternative interventions, such as nutrition management strategies."

In the study, scientists work with dairy and ‘beef-dairy’ cross steers, the population of cattle that most commonly suffer from this infection. They tested two diets (a high grain-based and forage-based) and three bacteria commonly found in liver abscesses [ Fusobacterium necrophorum subsp. necrophorum, Trueperella pyogenes , and Salmonella enterica serovar Lubbock]. The results from the high-grain diet model were found to be more reliable, leading scientists to focus more on this model.

Most people associate liver abscesses in cattle with a high-energy diet. The theory is that when cattle are fed elevated grain levels, highly fermentable starch in the rumen is rapidly fermented by bacteria, causing a drop in rumen pH. This acidity causes damage to the rumen lining, allowing bacteria to travel into the blood, reaching the liver and other organs where they can cause infection. However, it is still unknown with accuracy the exact route that these bacteria take to cause infection or injury to the liver.

Scientists discovered that the bacteria associated with liver abscesses in cattle may not always originate from the rumen. An alternative route may be bacterial travel from the lower gastrointestinal tract. The research showed that in some cases, when these bacteria were not detected in the acidic rumen environment caused by a high-grain diet, no liver abscesses were detected. However, when scientists introduced bacteria directly to the rumen, they observed the formation of liver abscesses and were able to isolate the bacteria from the infected sites.

The study confirms that an acidotic diet, combined with bacterial inoculation in the rumen, can be used as a model to induce liver abscesses. However, further research is being conducted at USDA to determine the consistency of the model before it can be used to evaluate new interventions to prevent this complex infection.

The Agricultural Research Service is the U.S. Department of Agriculture's chief scientific in-house research agency. Daily, ARS focuses on solutions to agricultural problems affecting America. Each dollar invested in U.S. agricultural research results in $20 of economic impact.

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Study About Purported Ancient ‘Pyramid’ in Indonesia Is Retracted

The study, based on research featured in a Netflix documentary, fueled debate over a site that is used for Islamic and Hindu rituals.

By Mike Ives

Reporting from Seoul

The American publisher of a study that challenged scientific orthodoxy by claiming that an archaeological site in Indonesia may be the world’s “oldest pyramid” says it has been retracted.

The October 2023 study in the journal Archaeological Prospection made the explosive claim that the deepest layer of the site, Gunung Padang, appears to have been “sculpted” by humans up to 27,000 years ago.

The study’s critics say that it incorrectly dated the human presence at Gunung Padang based on radiocarbon measurements of soil from drilling samples, not artifacts. The journal’s American publisher, Wiley, cited that exact reasoning in the retraction notice it issued on Monday.

Gunung Padang is widely considered a dormant volcano, and archaeologists say that ceramics recovered there so far suggest that humans have been using it for several hundred years or more — not anything close to 27,000 years. The pyramids of Giza in Egypt are only about 4,500 years old.

The retraction, based on a monthslong investigation, said that the study was flawed because its soil samples “were not associated with any artifacts or features that could be reliably interpreted as anthropogenic or ‘man-made.’”

Some archaeologists said in interviews that they welcomed the retraction. But the study’s authors called it “unjust,” saying in a statement on Wednesday that their soil samples had been “unequivocally established as man-made constructions or archaeological features,” in part because the soil layers included artifacts.

“We urge the academic community, scientific organizations, and concerned individuals to stand with us in challenging this decision and upholding the principles of integrity, transparency, and fairness in scientific research and publishing,” the authors wrote.

The study ’s lead author, Danny Hilman Natawidjaja, an earthquake geologist, did not immediately respond to a request for comment. Neither did Wiley or the editors of Archaeological Prospection, Eileen Ernenwein and Gregory Tsokas.

One prominent supporter of Mr. Natawidjaja’s research, the journalist Graham Hancock, said in a statement he did not see the retraction as “fair, justified or good science.” He said that instead of issuing a retraction, the journal should have published critiques of the paper, a move he said would have allowed readers to make up their own minds.

“Science should not be about suppression,” said Mr. Hancock, who interviewed Mr. Natawidjaja for an episode about Gunung Padang on “ Ancient Apocalypse, ” his 2022 Netflix documentary series.

The Society for American Archaeology has said that Mr. Hancock’s Netflix show “devalues the archaeological profession on the basis of false claims and disinformation.” He has vigorously rejected that argument, arguing that archaeologists should be more open to theories that challenge academic orthodoxy. Netflix did not respond to a request for comment on the retraction.

People from Indonesia have long traveled to Gunung Padang, a hilltop site dotted with stone terraces, to hold Islamic and Hindu rituals. A domestic narrative portraying it as a very, very old pyramid had support, and financing, from the central government during the administration of President Susilo Bambang Yudhoyono, who left office in 2014. His successor, President Joko Widodo, cut off the funding.

Archaeologists said in interviews on Wednesday that they welcomed the retraction.

One of them, Noel Hidalgo Tan, an archaeologist in Bangkok who had relayed his concerns about the study to Wiley, said that he considered the retraction “entirely appropriate” because the study’s evidence did not support its conclusions.

“It was unfortunate that the paper had to get to this stage,” said Dr. Tan, who works at the Southeast Asian Regional Center for Archaeology and Fine Arts. “But it was better to be retracted than to have nothing said about it at all.”

Dwi Ratna Nurhajarini, the head of the Cultural Heritage Conservation Office in West Java Province, the location of the site, said the study’s conclusions should be re-examined in light of the retraction.

“The structures at Gunung Padang are indeed layered and terraced, reminiscent of civilizations from Indonesia’s distant past,” she said by phone on Wednesday. “But their age might not be as old as suggested.”

Rin Hindryati contributed reporting.

Mike Ives is a reporter for The Times based in Seoul, covering breaking news around the world. More about Mike Ives

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Executive Order on Advancing Women’s Health Research and   Innovation

    By the authority vested in me as President by the Constitution and the laws of the United States of America, it is hereby ordered as follows:

     Section 1.  Policy.  My Administration is committed to getting women the answers they need about their health.  For far too long, scientific and biomedical research excluded women and undervalued the study of women’s health.  The resulting research gaps mean that we know far too little about women’s health across women’s lifespans, and those gaps are even more prominent for women of color, older women, and women with disabilities.  

     The notion of including women in clinical trials used to be revolutionary — which means many diagnostics and treatments were developed without women in mind and thus failed to account for women’s health.  Over 30 years ago, the Congress passed the landmark National Institutes of Health Revitalization Act of 1993 (Public Law 103-43) to direct the National Institutes of Health (NIH), the largest public funder of biomedical research in the world, to include women and people of color in NIH-funded clinical research.  In 2016, the Congress built on these requirements in the 21st Century Cures Act (Public Law 114-255), which directed the NIH to further its pursuit of women’s health research, including by strengthening clinical trial inclusion and data analysis, developing research and data standards to advance the study of women’s health, and improving NIH-wide coordination on women’s health research.

     These policies led to significant increases in women’s participation in clinical trials, and ongoing investments in biomedical research have supported breakthroughs in women’s health.  Through the discovery of genetic factors that increase the risk of breast cancer and innovations in mammography, we have transformed our approach to prevention, early detection, and treatment, and have improved outcomes for women facing a breast cancer diagnosis.  We have improved access to life-saving treatments for women with severe heart failure by ensuring that the devices they need are the right size for a woman’s body.  We have also identified some of the most characteristic symptoms of heart attack in women, which are different from those in men — discoveries that have helped deliver faster treatment to women when every second counts.  This is what we can achieve when we invest in women’s health research.

     It is time, once again, to pioneer the next generation of discoveries in women’s health.  My Administration seeks to fundamentally change how we approach and fund women’s health research in the United States.  That is why I established the first-ever White House Initiative on Women’s Health Research (Initiative) — which is within the Office of the First Lady and includes a wide array of executive departments and agencies (agencies) and White House offices — to accelerate research that will provide the tools we need to prevent, diagnose, and treat conditions that affect women uniquely, disproportionately, or differently.

     Together with the First Lady’s tireless efforts, the Initiative is already galvanizing the Federal Government to advance women’s health, including through investments in innovation and improved coordination within and across agencies.  We are also mobilizing leaders across a wide range of sectors, including industry, philanthropy, and the medical and research communities, to improve women’s health.

     It is the policy of my Administration to advance women’s health research, close health disparities, and ensure that the gains we make in research laboratories are translated into real-world clinical benefits for women.  It is also the policy of my Administration to ensure that women have access to high-quality, evidence-based health care and to improve health outcomes for women across their lifespans and throughout the country.

     I will continue to call on the Congress to provide the transformative investments necessary to help our researchers and scientists answer today’s most pressing questions related to women’s health.  Investing in innovation in women’s health is an investment in the future of American families and the economy.  At the same time, agencies must use their existing authorities to advance and integrate women’s health across the Federal research portfolio, close research gaps, and make investments that maximize our ability to prevent, diagnose, and treat health conditions in women. 

     Sec. 2.  Definitions.  For purposes of this order:

     (a)  The term “women’s health research” means research aimed at expanding knowledge of women’s health across their lifespans, which includes the study and analysis of conditions specific to women, conditions that disproportionately impact women, and conditions that affect women differently.

     (b)  The term “White House Initiative on Women’s Health Research” means the interagency, advisory body established by the Presidential Memorandum of November 13, 2023 (White House Initiative on Women’s Health Research), to advance women’s health research.

     (c)  The term “agency Members of the Initiative” refers to the Secretary of Defense, the Secretary of Agriculture, the Secretary of Health and Human Services, the Secretary of Veterans Affairs, the Administrator of the Environmental Protection Agency, and the Director of the National Science Foundation.

     Sec. 3.  Further Integrating Women’s Health Research in Federal Research Programs.  (a)  Building on research and data standards issued by the NIH in 2016, agency Members of the Initiative shall consider actions to develop or strengthen research and data standards that enhance the study of women’s health across all relevant, federally funded research and other Federal funding opportunities.  Agency Members of the Initiative shall consider issuing new guidance, application materials, reporting requirements, and research dissemination strategies to advance the study of women’s health, including to:

(i)    require applicants for Federal research funding, as appropriate, to explain how their proposed study designs will consider and advance our knowledge of women’s health, including through the adoption of standard application language;

(ii)   consider women’s health, as appropriate, during the evaluation of research proposals that address medical conditions that may affect women differently or disproportionately;

(iii)  improve accountability for grant recipients, including, as appropriate, by requiring regular reporting on their implementation of, and compliance with, research and data standards related to women’s health, including compliance with recruitment milestones; and

(iv)   improve the recruitment, enrollment, and retention of women in clinical trials, including, as appropriate, by reducing barriers through technological and data sciences advances.

(b)  Within 30 days of the date of this order, the Chair of the Initiative and the Director of the NIH Office of Research on Women’s Health, in consultation with the Director of the Office of Management and Budget (OMB), shall establish and co-chair a subgroup of the Initiative to promote interagency alignment and consistency in the development of agency research and data standards to enhance the study of women’s health.

     (c)  Within 90 days of the date of this order, agency Members of the Initiative shall report to the Chair of the Initiative on actions taken to strengthen research and data standards to enhance the study and analysis of women’s health and related conditions.

(d)  Within 180 days of the date of this order and on an annual basis thereafter, agency Members of the Initiative shall report to the President on the status of implementation of research and data standards.

     Sec. 4.  Prioritizing Federal Investments in Women’s Health Research.  (a)  Agency Members of the Initiative shall identify and, as appropriate and consistent with applicable law, prioritize grantmaking and other awards to advance women’s health research, with an emphasis on:

(i)    promoting collaborative, interdisciplinary research across fields and areas of expertise;

(ii)   addressing health disparities and inequities affecting women, including those related to race, ethnicity, age, socioeconomic status, disability, and exposure to environmental factors and contaminants that can directly affect health; and

(iii)  supporting the translation of research advancements into improved health outcomes.

(b)  Agency Members of the Initiative shall take steps to promote the availability of federally funded research and other Federal funding opportunities to advance women’s health, including through the development and inclusion of standard language related to women’s health, as appropriate, in all relevant notices of funding opportunity and through better facilitating potential grant applicants’ access to information about funding opportunities related to women’s health research.

     (c)  To advance innovation, commercialization, and risk mitigation, agency Members of the Initiative shall:

(i)    identify and, as appropriate and consistent with applicable law, seek ways to use innovation funds, challenges, prizes, and other mechanisms to spur innovation in women’s health;

(ii)   invest in innovation to accelerate women’s health research, including through or in collaboration with the Advanced Research Projects Agency for Health and the Congressionally Directed Medical Research Programs;

(iii)  support the role of small businesses and entrepreneurs in advancing innovation in women’s health research, including through Small Business Innovation Research Programs and Small Business Technology Transfer Programs; and

(iv)   invest in translational science to convert research findings and discoveries into treatments and interventions that improve women’s health outcomes and reduce health disparities, including through the Department of Agriculture National Institute of Food and Agriculture research programs.

(d)  In implementing section 8(b) of Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence), the Secretary of Health and Human Services, in consultation with the Director of the National Science Foundation, shall consider the opportunities for and challenges that affect women’s health research in the responsible deployment and use of artificial intelligence (AI) and AI-enabled technologies in the health and human services sector.

     Sec. 5.  Galvanizing Research on Women’s Midlife Health.  (a)  Within 90 days of the date of this order, to address research gaps in understanding women’s health and diseases and conditions associated with women’s midlife and later years, the Secretary of Health and Human Services shall:

(i)    launch a comprehensive assessment of the current state of the science on menopause to develop an evidence-based research agenda that will guide Federal and private sector investments in menopause-related research;

(ii)   evaluate evidence-based interventions and strategies to improve women’s experiences in the menopausal and perimenopausal periods, including the delivery of treatments for women experiencing menopause in clinical care settings;

(iii)  consider developing new common data elements and survey tools to expand the ethical and equitable collection of data on issues related to women’s midlife health; and

(iv)   develop new comprehensive resources to help ensure that the public has evidence-based information about menopause, including menopause-related research initiatives, findings, and symptom-prevention and treatment options.

(b)  The Secretary of Defense and the Secretary of Veterans Affairs shall evaluate the needs of women service members and veterans related to midlife health and shall develop recommendations to support improved treatment and targeted research of midlife health issues, including menopausal symptoms.

     Sec. 6.  Assessing Unmet Needs to Support Women’s Health Research.  The Director of OMB and the Assistant to the President and Director of the Gender Policy Council (Directors) shall lead an effort, in collaboration with the Initiative, to identify current gaps in Federal funding for women’s health research and shall submit recommendations to the President describing the additional funding and programming necessary to catalyze research on women’s health, including in priority areas within women’s health as identified by the Initiative, as follows:

     (a)  Within 90 days of the date of this order, the Directors shall, in consultation with the Initiative, develop guidance for assessing additional funding that agencies need to close research gaps in women’s health.

     (b)  Within 180 days of the date of this order, Members of the Initiative shall consult the guidance described in subsection (a) of this section and shall each submit a report to the Directors that identifies the funding needed to catalyze research on women’s health.

     (c)  Based on the reports described in subsection (b) of this section, the Directors shall develop and submit recommendations to the President on steps the Federal Government should take to catalyze research on women’s health.  These recommendations shall identify any statutory, regulatory, budgetary, or other changes that may be necessary to ensure that Federal laws, policies, practices, and programs support women’s health research more effectively.

     (d)  Following the submission of the recommendations described in subsection (c) of this section, each Member of the Initiative shall report annually to the Directors on progress made in response to those recommendations and to improve the study of women’s health.  The Director of OMB shall provide a summary of Members’ progress and any new recommendations to the President on an annual basis, consult with each Member on their women’s health research funding needs during the annual budget process, and calculate Federal funding for women’s health research on an annual basis.

     Sec. 7.  General Provisions.  (a)  Nothing in this order shall be construed to impair or otherwise affect: (i)   the authority granted by law to an executive department or agency, or the head thereof; or

(ii)  the functions of the Director of the Office of Management and Budget relating to budgetary, administrative, or legislative proposals.

     (b)  This order shall be implemented consistent with applicable law and subject to the availability of appropriations.

     (c)  This order is not intended to, and does not, create any right or benefit, substantive or procedural, enforceable at law or in equity by any party against the United States, its departments, agencies, or entities, its officers, employees, or agents, or any other person.                                    JOSEPH R. BIDEN JR. THE WHITE HOUSE,     March 18, 2024.

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Regions & Countries

How hispanic americans get their news, u.s.-born latinos overwhelmingly prefer to get their news in english; about half of immigrant latinos prefer it in spanish.

Pew Research Center conducted this study to understand Hispanic Americans’ habits around news and information, including the languages in which they consume news and their engagement with Hispanic media outlets.

Most of the questions in this report are from Pew Research Center’s 2023 National Survey of Latinos, a survey of 5,078 U.S. Hispanic adults conducted Nov. 6-19, 2023. This includes 1,524 Hispanic adults on the Center’s  American Trends Panel  (ATP) and 3,554 Hispanic adults on Ipsos’ KnowledgePanel . Respondents on both panels are recruited through national, random sampling of residential addresses. Recruiting panelists by phone or mail ensures that nearly all U.S. adults have a chance of selection. This gives us confidence that any sample can represent the whole population, or in this case the whole U.S. Hispanic population. (For more information, watch our  Methods 101 explainer  on random sampling.)

To further ensure the survey reflects a balanced cross-section of the nation’s Hispanic adults, the data is weighted to match the U.S. Hispanic adult population by age, gender, education, nativity, Hispanic origin group and other categories. Read more about the  ATP’s methodology . Refer to the topline for the questions used for our National Survey of Latinos , along with responses, and to methodology for more details.

The questions about how often people get news from various platforms, which platforms they prefer for getting news, and which social media sites people get news from are from an ATP survey of 8,842 U.S. adults, including 1,193 Hispanic adults, conducted Sept. 25-Oct. 1, 2023. Refer to the topline for t he questions used for this survey , along with responses, and to the methodology for more details.

Pew Research Center is a subsidiary of The Pew Charitable Trusts, its primary funder. This is the latest report in Pew Research Center’s ongoing investigation of the state of news, information and journalism in the digital age, a research program funded by The Pew Charitable Trusts, with generous support from the John S. and James L. Knight Foundation.

The terms  Hispanic  and  Latino  are used interchangeably in this report.

Hispanic/Latino Americans, Hispanic/Latino adults , and Hispanics/Latinos are used interchangeably in this report to refer to survey respondents who self-identify as Hispanic or Latino in the United States. They include those who say their race is White, Black, Asian or some other race and those who identify as multiracial. Hispanic/Latino Americans live in the U.S. but are not necessarily U.S. citizens.

U.S. born  refers to people born in the 50 states or the District of Columbia.

Immigrant  refers to people born outside the 50 states or D.C. For the purposes of this report, immigrants include those born in Puerto Rico or another U.S. territory. Although individuals born in Puerto Rico are U.S. citizens by birth, they are grouped with immigrant respondents because they were born into a Spanish-dominant culture and because on many points their attitudes, views and beliefs more closely resemble those of Hispanics born outside the U.S. than Hispanics born in the 50 states or D.C., and even U.S.-born Hispanics who identify as being of Puerto Rican origin.

Second generation  refers to people born in the 50 states or D.C. who have at least one parent born in a different country, Puerto Rico or another U.S. territory.

Third generation   or higher refers to people born in the 50 states or D.C. who have two parents born in the 50 states or D.C.

Language dominance  is a composite measure based on self-described assessments of speaking and reading abilities.  Spanish-dominant  people are more proficient in Spanish than in English (i.e., they speak and read Spanish “very well” or “pretty well” but rate their English ability lower).  Bilingual  refers to people who are proficient in both English and Spanish.  English-dominant  people are more proficient in English than in Spanish.

“Middle income” is defined here as two-thirds to double the median annual family income for panelists on the American Trends Panel. “Lower income” falls below that range; “upper income” falls above it. Refer to the methodology for more details.

Hispanic news outlets are those outlets that focus on providing news and information specifically to Hispanic audiences. These can include newspapers, radio or TV stations, podcasts, or social media accounts created for and by Hispanic people. Their content could be in Spanish, English, both languages or another language.

Country of origin refers to the country that survey respondents, their parents or their Hispanic ancestors came from.

Just over half of U.S. Hispanic adults (54%) get their news mostly in English – far higher than the share who get their news mostly in Spanish (21%). About a quarter of Hispanic Americans (23%) say they consume news in both languages about equally.

There is an almost identical pattern on the question of preferred language for news: 51% prefer to get their news in English, 24% prefer Spanish and 23% say they do not have a preference.

But a new Pew Research Center survey of adults who identify as Hispanic or Latino finds major differences in news consumption habits between U.S.-born Hispanics and those who immigrated from other countries .

While U.S.-born Latinos overwhelmingly get their news in English, and prefer it in English, those born outside the United States have much more varied habits: 41% get their news mostly in Spanish, 26% get it primarily in English and 31% do both about equally. Similarly, 47% of Latino immigrants prefer to get their news in Spanish, while 22% prefer English and 31% do not express a preference.

Among Latino immigrants, those who have spent more years in the U.S. are less inclined than more recent arrivals to get news in Spanish, and more inclined to get it in English. There is little difference in the shares who get news in both languages about equally.

Jump to more information on the languages in which U.S. Latinos consume news.

We asked these questions to better understand how a group that makes up nearly one-in-five Americans stays informed, especially as its demographics and use of Spanish continue to change. Immigrants are declining as a share of all U.S. Hispanics , and the share of Hispanics who speak Spanish at home has also dropped – even though the number of Hispanics who speak Spanish at home has increased due to overall growth in the Hispanic population.

Other key findings about Hispanics’ news consumption include:

Most Latino adults prefer digital devices for news

Latinos get their news from a variety of sources, but most say they prefer to use digital devices over other platforms. Nearly nine-in-ten (87%) say they get news from digital devices at least sometimes, and 65% say they prefer this form of news over TV, radio or print. Digital devices have become an increasingly common source for news among Latinos – and among Americans overall – in recent decades, a shift driven by the rise of the internet .

Latinos are more likely than White Americans (55%) and Black Americans (50%) to prefer getting news from digital devices. Latinos also are more likely than White and Black adults to get news from social media, at least in part because Latino adults tend to be younger than other groups, and young adults are more inclined to use social media for news.

Nearly three-quarters of Latino adults under 50 (73%) prefer to get their news on digital devices, including 27% who prefer social media specifically.

Jump to more information on the platforms where U.S. Latinos get news.

Attention to news is declining among U.S. Latinos

About one-in-five Latino adults (22%) say they follow the news all or most of the time, while an additional 36% follow the news some of the time. The share of Latinos who follow the news all or most of the time has fluctuated in recent years but has dropped by 9 percentage points between 2020 (31%) and 2023 (22%), similar to a pattern seen across the general U.S. public .

In recent years, Hispanic Americans have followed the news less closely than Black and White Americans. Again, the high share of young adults within the Hispanic population plays a role, because young people are less likely to follow the news closely. Among Hispanic adults ages 18 to 29, just 10% say they follow the news all or most of the time – far below the share of Hispanics ages 65 and older who do so (44%).

Jump to more information on U.S. Hispanics’ news consumption habits.

Half of Hispanic adults get news from Hispanic news outlets

Half of U.S. Hispanic adults say they at least sometimes get news from Hispanic news outlets – those that specifically cater to Hispanic audiences. This includes 21% who say they do this extremely or very often. Just over half of Hispanics (54%) get news about their or their family’s country of origin at least sometimes, including 24% who do this often. 

Hispanic immigrants are much more likely than U.S.-born Hispanics to get news from Hispanic outlets and about their origin country. In both cases, about seven-in-ten immigrants say they at least sometimes get these types of news: 69% get news from Hispanic outlets and 72% get news about their country of origin. Among Hispanic adults who were born in the U.S., 33% at least sometimes get news from Hispanic outlets, and 38% get news about their family’s country of origin.

There are further differences among U.S.-born Hispanics: Those whose parents were also born in the U.S. are even less likely than those with one or more immigrant parent to get these types of news.

Jump to more information on Hispanic news outlets and news about Hispanic Americans’ origin countries.

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

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Nasa to send research to station aboard 30th spacex resupply mission.

Tiernan P. Doyle

Nasa headquarters.

Editor’s note: This release was updated March 18, 2024, to include NASA+ links for live mission coverage.

New research and technology demonstrations for NASA are set to launch aboard the agency’s SpaceX 30th commercial resupply services mission to the International Space Station. Launch is targeted for 4:55 p.m. EDT Thursday, March 21, lifting off from Space Launch Complex 40 at Cape Canaveral Space Force Station in Florida.

Live launch coverage will air on NASA+ , NASA Television, the NASA app , and the agency’s website, with prelaunch events starting Tuesday, March 19. Learn how to stream NASA TV through a variety of platforms.

SpaceX’s Dragon spacecraft will deliver new scientific investigations, food, supplies, and equipment to the international crew. NASA and its partners will send studies aboard the mission on plant metabolism in space and a set of new sensors for free-flying Astrobee robots to provide 3D mapping capabilities. Other research includes a fluid physics study that could benefit solar cell technology and a university project from CSA (Canadian Space Agency) that will monitor sea ice and ocean conditions.

Arrival at the station is scheduled for approximately 7:30 a.m. Saturday, March 23. Live coverage begins at 5:30 a.m. The SpaceX Dragon spacecraft will dock autonomously to the zenith port of the station’s Harmony module.

The spacecraft is expected to spend about a month attached to the orbiting outpost before it returns to Earth with research and cargo, splashing down off the coast of Florida.

Full mission coverage is as follows (all times Eastern and subject to change based on operations):

Tuesday, March 19

3 p.m. – Prelaunch media teleconference with the following participants:

• Kristi Duplichen, deputy manager, NASA’s International Space Station Transportation Integration Office
• Heidi Parris, associate program scientist, NASA’s International Space Station Program Research Office
• Sarah Walker, director, SpaceX Dragon mission management
• Melody Lovin, launch weather officer, Cape Canaveral Space Force Station’s 45th Weather Squadron

Media may ask questions during the media teleconference by phone only. For the dial-in number and passcode, please contact NASA’s Kennedy Space Center in Florida no later than 2 p.m. EDT March 19, at: [email protected].

Thursday, March 21

• 4:35 p.m. – Launch coverage begins
• 4:55 p.m. – Launch

Saturday , March 23

• 5:30 a.m. – NASA arrival coverage begins
• 7:30 a.m. – Targeted docking to the zenith port of the station’s Harmony module

NASA’s coverage is subject to change based on real-time operational activities. Follow the International Space Station blog for updates.

NASA Television launch coverage

Live coverage of the launch on NASA Television will begin at 4:35 p.m. Thursday, March 21. For downlink information, schedules, and links to streaming video, visit:

https://www.nasa.gov/nasatv

Audio only of the news conferences and launch coverage will be carried on the NASA “V” circuits, which may be accessed by dialing 321-867-1220, -1240, or -7135. On launch day, the full mission broadcast can be heard on -1220 and -1240, while the countdown net only can be heard on -7135 beginning approximately one hour before the mission broadcast begins.

On launch day, live coverage of the launch without NASA Television commentary will be carried on the NASA Television media channel.

NASA website launch coverage

Launch day coverage of the mission will be available on the NASA website. Coverage will include live streaming and blog updates beginning no earlier than 4:35 p.m. Thursday, March 21, as the countdown milestones occur. On-demand streaming video and photos of the launch will be available shortly after liftoff. For questions about countdown coverage, contact the Kennedy newsroom at 321-867-2468. Follow countdown coverage on our commercial resupply services mission blog for updates.

Attend launch virtually

Members of the public can register to attend this launch virtually. Registrants will receive mission updates and activities by email. NASA’s virtual guest program for this mission also includes curated launch resources, notifications about related opportunities, and a virtual guest passport stamp following a successful launch.

Watch, engage on social media

Let people know you’re watching the mission on X, Facebook, and Instagram by following and tagging these accounts:

Twitter:  @ NASA,  @ NASAKennedy , @ NASASocial , @ Space_Station ,  ISS_Research , @ ISS National Lab

Facebook:  NASA ,  NASAKennedy ,  ISS ,  ISS National Lab

Instagram:  @ NASA , @ NASAKennedy , @ ISS , @ ISSNationalLab

Para obtener información sobre cobertura en español en el Centro Espacial Kennedy o si desea solicitor entrevistas en español, comuníquese con Antonia Jaramillo o Messod Bendayan a: [email protected] o [email protected] .

Learn more about NASA commercial resupply services missions at:

https://www.nasa.gov/international-space-station/commercial-resupply/

Julian Coltre / Claire O’Shea Headquarters, Washington 202-358-1100 [email protected] / claire.a.o’[email protected]

Stephanie Plucinsky / Steven Siceloff Kennedy Space Center, Florida 321-876-2468 [email protected] / [email protected]

Sandra Jones Johnson Space Center, Houston 281-483-5111 [email protected]

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Research on Adolescence in the Twenty-First Century

Robert crosnoe.

1 Department of Sociology and Population Research Center, University of Texas at Austin, Austin, Texas 78712

Monica Kirkpatrick Johnson

2 Department of Sociology, Washington State University, Pullman, Washington 99164-4020

Recent methodological advances have allowed empirical research on adolescence to do better justice to theoretical models. Organized by a life course framework, this review covers the state of contemporary research on adolescents' physical, psychological, interpersonal, and institutional pathways; how these pathways connect within primary ecological contexts; and how they relate to broader patterns of societal stratification and historical change. Looking forward, it also emphasizes three future challenges/opportunities, including efforts to illuminate biosocial processes, link adolescence to other life stages, and account for the influence of major social changes (e.g., the new media).

Introduction

First coined by Hall (1904) only a century ago, adolescence was “created” by the convergence of multiple trends, including labor and schooling laws, that extended dependency beyond childhood and delayed entry into adult roles ( Modell & Goodman 1990 ). Adolescence as a period of dependency and preparation for adulthood has since been reinforced through more recent social changes, including economic restructuring and changing cultural norms about parenting ( Goldin & Katz 2008 , Settersten et al. 2005 ). Research on adolescence has also changed dramatically. This review discusses recent developments in this literature, being cognizant of their historical underpinnings while focusing on the future. Given our background in the life course tradition, as well as the inherent importance of transitions, trajectories, and context to understanding this life stage, we use a life course framework to organize our review. Owing to space constraints, we focus primarily on American adolescents.

In his 1989 review, Dornbusch wrote that research on adolescence was turning from psychologists studying “individual adolescents carrying out their developmental tasks” (p. 233) to contextual approaches emphasizing transactions between adolescents and their environments. This trend has since intensified, reflecting refinements of theoretical models, including human ecology ( Bronfenbrenner & Morris 1998 ) and the life course paradigm ( Elder 1998 ). A central imagery of the latter, our focus here, is of lives as a tapestry of three threads—developmental trajectories (physical and psychological growth), social pathways (sequences of institutional roles and activities), and social convoys (continuity and change in interpersonal relations)—situated in settings of daily life, larger structures of society, and the broader sweep of history.

Unfortunately, studying this dynamic, multilayered model of adolescence has taxed methodological and data resources ( Elder & Giele 2009 ). Recently, however, data sets on children have aged into adolescence (e.g., Children of the National Longitudinal Survey of Youth), and data sets on adolescents have aged into adulthood (e.g., National Longitudinal Study of Adolescent Health, or Add Health). Data sets are also integrating biological, behavioral, and setting data. At the same time, significant advances in longitudinal and multilevel modeling have allowed researchers to capture individual and population trajectories and to identify person-context effects ( Bollen & Curran 2006 , Bryk et al. 2002 ). Of note is that qualitative and mixed methods research projects are also increasingly emphasizing context, longitudinal change, and the multiple strands of adolescent development ( Giordano et al. 2006 ).

In other words, sociologists are making progress on a more holistic understanding of adolescents in society. In reviewing this progress, we focus on each of the three main strands of the adolescent life course before turning to the ways in which their interweaving is embedded in ecological contexts and both reflects and contributes to population-level inequalities.

Three Threads in the Life Course Tapestry

Exhaustively reviewing recent research on the three main strands of the life course is impossible. As just one example, the United States has a high teen pregnancy rate that contributes to the overall rate of nonmarital fertility, and early parenthood relates to past and future socioeconomic disadvantages in complex ways ( Mollborn 2007 ). As a result, this topic could fill an entire review article, and in fact has ( Furstenberg 2003 ). Also regrettably left out are issues such as disordered eating and bullying. Consequently, this review should be viewed as an effort to put forward a limited set of illustrative examples of new ways to think about old issues.

Developmental Trajectories

Adolescence is a period of rapid change. This change is dramatically crystallized in the flood of hormonal activity and rapid physiological development that constitutes puberty ( Susman et al. 2003 ). Great psychological and emotional change also occurs during adolescence. In the years following puberty, adolescents are faced with the task of establishing their own identity separate from their parents, which may be stressful ( Kroger 2007 ). At the same time, rates of risky behavior (e.g., substance use, delinquency, sexual activity) also rise markedly, especially among boys ( Bachman et al. 2002 ). A hallmark of adolescence is that maturation can occur at different velocities in different domains of development, so that youth may look or feel like adults in some ways but not in others.

Beginning with puberty, a great deal of attention has been focused on pubertal timing, following earlier work outside the United States ( Stättin & Magnusson 1990 ). Primarily among girls, going through puberty earlier than the norm is associated with a host of adjustment problems, including risky sex and delinquency. Sociologists have elucidated many mechanisms underlying these patterns, which are not entirely hormonal or biological in nature. One concerns premature self- and other-perceptions of early maturers, especially girls, as adults. In other words, they are adult-like physically and, as a result, may engage in actions or put themselves in situations that are ahead of their emotional or cognitive capacities ( Cavanagh 2004 , Haynie 2003 ). Another mechanism is increased distress related to growing size in the context of strict norms about female body weight ( Ge et al. 2001 ). These socioemotional difficulties in early adolescence can then disrupt academic functioning ( Cavanagh et al. 2007 ). Pubertal timing, therefore, represents an intersection of biological, emotional, social, and institutional processes.

Turning to mental health, adolescence marks the emergence of gender differences in depression—with girls higher than boys—that persist for decades ( Hankin et al. 2007 ). Efforts to explain this trend have focused primarily on social psychological phenomena, including gender differences in self-concept, management of daily stressors, experiences of puberty, and the rigidity and enforcement of societal standards of appearance and behavior ( Martin 1996 , Rosenfield et al. 2000 ). A particularly insightful sociological approach to adolescent depression, regardless of gender, concerns how it is interpreted by others. For example, depression can be strongly stigmatized in social groups when it is perceived as mental illness as opposed to a health problem, leading depressed youth to be isolated from others just when they need more support ( Martin et al. 2007 ). Indeed, social responses to adolescent distress influence whether it can have long-term effects on other areas of life, including education ( McLeod & Fettes 2007 ). Thus, socialized perspectives on depression and other psychological constructs reveal insight into the complex dance between self and other that characterizes adolescence.

Identity development is another psychological process that has been studied from a variety of angles. The consensus is that it is a highly social process, with young people slowly integrating the different pieces of themselves that they come to understand through social interactions into a cohesive sense of who they are and where they fit in the world ( Kroger 2007 ). In the past two decades, considerable research has centered on the development of group-based identities. For example, racial identity taps into the significance and meaning attached to race within individuals' overall senses of self. According to work by Sellers and associates (1998) on African American youth, racial identity has four dimensions: ( a ) salience (how much race is part of one's self-concept), ( b ) centrality (whether one defines him or herself through race), ( c ) regard (the degree of positive or negative feelings about one's race), and ( d ) ideology (beliefs about how someone of a certain race should act). Across minority groups, these dimensions tend to increase as adolescence unfolds and are strongly related to mental health ( Mandara et al. 2009 , Umaña-Taylor et al. 2009 ). For the most part, the benefits of racial identity are strongest when minority adolescents have reached the achieved stage of identity development, meaning that they have committed to a particular identity after exploring what it means and what alternative identities might be possible ( Seaton et al. 2006 ). Similar research has been done on sexual identity, tracing the gradual process by which adolescents come to see themselves as homosexual and the role that this process plays in healthy development ( Russell & Sigler-Andrews 2003 ).

As for risky behavior, understanding why adolescents become more reckless even as they develop critical thinking skills has long been a major activity of adolescence researchers. One explanation is that adolescence is a time of heightened sensitivity to social influences and greater propensity toward emotional stimulation. These developmental changes have traditionally been viewed as by-products of identity development, but recent neurological research is shedding new light on this phenomenon ( Dahl & Spear 2004 ). Specifically, magnetic resonance imaging (MRI) studies suggest that increased risky behavior during adolescence reflects different rates of growth in the brain's socioemotional and cognitive control systems. After puberty, dopamine receptors increase rapidly in regions that control sensation-seeking, which encourages behaviors that bring some emotional or sensory reward ( Steinberg 2008 ). Peer approval is one such reward, and ample evidence indicates that engaging in some level of dangerous behavior can elicit peer esteem and popularity ( Allen et al. 2005 , Kreager & Staff 2009 ). Importantly, structural changes of equivalent magnitude do not occur in the prefrontal cortex, which controls cognition, until adolescents approach young adulthood. That enhanced self-regulation skills tend to come after the increased propensity toward sensation-seeking helps to explain the increase in risky behavior that characterizes the years between the end of childhood and the start of adulthood ( Dahl & Spear 2004 , Steinberg 2008 ). Clearly, other factors are also at work, including changing cultural norms about permissible behavior and increasing opportunities for engaging in certain behaviors, but neurological development is certainly a piece of the puzzle.

As is evident even from this very selective discussion, adolescent development crosses many different psychological, physiological, cognitive, and behavioral domains. As a result, understanding one domain often requires consideration of others.

Social Pathways

Beyond the family, two key institutions structure the social pathways of adolescence. Beginning with schools, sociologists have traditionally studied the organization of high schools via academic tracks (e.g., vocational, college preparatory). As formal tracking has been largely dismantled ( Lucas 2001 ), new organizational schemas have been identified, including patterns of course-taking, critical courses (e.g., advanced math), and course trajectories ( Gamoran & Hannigan 2000 , McFarland 2006 , Riegle-Crumb 2006 ). Math is a clear example, as it is highly structured and strongly predicts educational and occupational attainment ( Adelman 2006 , Frank et al. 2008 , Riegle-Crumb 2006 ). Case studies have yielded new insights on the implications of curricular structure. McFarland (2006) , for example, examined student flow across math courses in two high schools, one characterized by five math trajectories with fewer lower-ability courses over time, and the other with a branching tree structure in which students move from a single trunk into four eventual trajectories of increasing differentiation. In each structure, specific courses represent critical junctions between trajectories and in math persistence altogether.

Thus, students' school pathways are far more complex than the traditional view of tracking suggests. Studying these pathways also reveals new insights into gender differences in education. Girls have been surpassing boys in most academic domains in secondary and postsecondary education for some time, especially among African Americans. Research on coursework trajectories suggests that girls have now also closed the gap with boys in math and science in terms of the course credits they accrue in high school. However, despite their advantage in college enrollment and graduation ( Buchman & DiPrete 2006 ), they remain underrepresented in these curricula in college ( Riegle-Crumb 2006 ).

Studies in the past decade have also emphasized the changing role of higher education in adolescents' lives. Expectations to earn four-year and graduate degrees have risen dramatically, faster than actual attainment ( Jacob & Wilder 2010 , Reynolds et al. 2006 ). In line with a “college for all” norm ( Rosenbaum 2001 ), expectations to complete college have become less tied to social class and previous achievement ( Goyette 2008 , Reynolds et al. 2006 , Schneider & Stevenson 1999 ). More than 80% of high school seniors in 2008 reported that they would probably or definitely earn a four-year college degree ( Bachman et al. 2009 ). Some of the least educationally ambitious students may have dropped out of school before senior year and, therefore, would be absent from such statistics, but educational expectations are actually even higher when measured at eighth or tenth grade instead ( Goyette 2008 , Jacob & Wilder 2010 ). The rise in expectations to earn a college degree has been even steeper among girls, who now expect BAs at higher rates than boys, with little difference within gender between blacks and whites ( Jacob & Wilder 2010 ).

Commonly cited explanations for rising educational expectations include ( a ) an increase in the earnings payoff to college (versus high school) graduation; ( b ) expanding higher education options, including online degrees and community colleges; and ( c ) trends in the educational attainment of parents ( Berg 2007 , Goldin & Katz 2008 , Goyette 2008 , Schneider & Stevenson 1999 ). Regarding the latter, the relative risk aversion thesis suggests that adolescents strive for at least as much education as their parents have. As parents' average education levels rise across cohorts, therefore, so do adolescents' educational expectations ( Breen & Goldthorpe 1997 ).

Paid work is another institution shaping adolescence, with nearly all high school students employed during the school year at some point while in high school ( Apel et al. 2007 , Mortimer 2003 , National Research Council 1998 ). Building on foundational studies from the 1980s and 1990s, recent research has elucidated the mix of risks and benefits of paid work for adolescents. Although adolescent work often starts earlier, most studies focus on high school, when employment is more likely to occur in the formal sector and for longer hours. Moreover, school-year employment continues to garner the most attention, despite higher rates of summer employment ( Mortimer 2003 , Perreira et al. 2007 ). These foci reflect concerns about potentially competing demands of school and employment, key institutions that structure the social pathways of adolescence. The question is whether (or under what conditions) employment facilitates educational attainment and builds human capital useful later in the labor market or whether employment, especially working 20 h or more per week, can distract from academic pursuits and foster various problem behaviors, including delinquency and substance use ( Lee & Staff 2007 , McMorris & Uggen 2000 , Mortimer 2003 , Paternoster et al. 2003 ).

In the past two decades, a major activity has been in understanding the variable meaning and consequence of paid work in adolescence. For example, the outcomes linked to work hours depend on the goal of working, including saving for college and supporting the self or family ( Marsh 1991 , Newman 1996 ). Recent evidence indicates that work can promote educational attainment among those with low academic promise ( Staff & Mortimer 2007 ) and among poor and/or minority students ( Entwisle et al. 2005 ). For example, teenagers in Newman's (1999) ethnography of fast food workers often rejected the delinquency of peers in choosing to work, and their jobs brought them coworkers and supervisors that supported and rewarded their educational pursuits. Along these same lines, Lee & Staff (2007) compared adolescents who work intensively and those who do not do so but who share similar preexisting background characteristics. They found no effect on dropout among adolescents with backgrounds indicative of a high propensity to work intensively. These students tended to be from socioeconomically disadvantaged families and have weaker school performance. Additional studies indicate that the association between intensive employment and substance use is largely limited to whites ( Johnson 2004 ) and that intensive employment can actually help curb substance use and delinquency for adolescents with earlier histories of these problem behaviors ( Apel et al. 2007 ).

More effort also has been devoted to promoting causal inference in research on adolescent employment. Both spuriousness and bidirectionality are concerns in studies of work hour effects on adolescent behavior. Longitudinal studies adjusting for known covariates, including lagged measures of the outcome, often indicate that that preexisting differences account for many observed effects ( Schoenhals et al. 1998 , Warren et al. 2000 ). Links to substance use and some academic outcomes, however, persist ( McMorris & Uggen 2000 , Mortimer & Johnson 1998 , Paternoster et al. 2003 , Schoenhals et al. 1998 ). Other techniques, such as fixed and random effects models and propensity score matching, have revealed no evidence of work hour effects on adolescent behavior or effects only on adolescents with low or moderate propensities to work ( Lee & Staff 2007 , Paternoster et al. 2003 ), but they have been applied to a limited set of behavioral dimensions to date.

Thus, research is moving toward a clearer picture of how developmental, educational, human capital, and behavioral outcomes are linked to employment in adolescence. The same can be said of studies on other social pathways of adolescents (e.g., academic pathways).

Social Convoys

Adolescence is a time of both quantitative and qualitative change in the matrix of social relationships. In particular, the push and pull between parents and peers has been a dominant theme of research on adolescence for years.

Over time, the normative break with parents in adolescence has been reconceptualized as a renegotiation of parent and child roles, not disengagement. In other words, adolescents may spend less time with, and seek more autonomy from, parents, but they typically do so in the context of stable strong connections and parental influence ( Larson et al. 1996 ). Similar trends extend to other family relationships (e.g., with siblings and grandparents), which may loosen more in terms of shared time than in emotional bonds ( Crouter et al. 2004 , King et al. 2003 ).

The idea of parent-adolescent renegotiation has led to new ways of thinking about oft-studied issues. One of the best examples concerns parental monitoring. The general consensus has long been that adolescents engage in fewer problem behaviors when their parents keep close track of what they do and with whom they associate, in part because monitoring constrains opportunities to engage in such behaviors and in part because it helps to develop adolescent self-control ( Browning et al. 2005 , Hay 2006 ). Yet, Stättin & Kerr (2000) have argued that the most common indicator of parental monitoring—parental knowledge about adolescents' activities and peers—may be an effect of adolescent behavior more than a cause. In other words, well-behaved adolescents share their lives with their parents, creating the appearance of monitoring being behaviorally protective. More likely, this link is reciprocal—monitoring promoting prosocial behavior that, in turn, increases parent-adolescent relationship quality, adolescents' openness to parental monitoring, and adolescents' willingness to self-disclose to parents ( Fletcher et al. 2004 , Yau et al. 2009 ). This debate has driven home the need to think of adolescents' developmental trajectories and social convoys as intertwined over time.

Along these same lines, adolescents are increasingly viewed as eliciting parenting, not just being shaped by it. For example, changes in U.S. antipoverty policy that emphasize the role of fathers have brought attention to nonresident fathers ( Furstenberg 2007 ). Although the assumption is that having involved fathers is good for adolescents, this link partially reflects the tendency for nonresident fathers to be more involved in the lives of well-adjusted adolescents ( Hawkins et al. 2007 ). As another example, the normative increase in parent-child conflict during adolescence is less pronounced for second- or later-born children, as parents learn what to expect from their first-born children ( Shanahan et al. 2007 ). Another line of research that views both sides of the parent-adolescent relationship concerns the degree to which the characteristics and behaviors of parents and adolescents are aligned. Consider that religious mismatches within the family (e.g., religious mother and nonreligious adolescent, or vice versa) appear to engender adolescent problem behavior ( Pearce & Haynie 2004 ). Approaching parent-adolescent relationships as evolving, two-sided, and mutually influential, therefore, is crucial.

Of course, peers continue to be a primary focus of research on adolescence. Much of this research concerns how friends influence each other and how adolescents select into different kinds of friendships, but more attention is now being paid to the larger peer groupings in which these friendships are embedded. For example, boys are at greater risk for emotional distress when they are members of networks that are large and cohesive, but girls are at greater risk in networks that are large and noncohesive. This gendered pattern reflects differences in the interpersonal styles of girls and boys ( Falci & McNeely 2009 ). As another example, friendships tend to have greater influence on adolescent delinquency when they are embedded in dense networks ( Haynie 2001 ). Many social and institutional settings, such as schools and neighborhoods, can also be thought of as peer contexts, in that they organize the friendship market and serve as a center of youth culture ( Harding 2009 ). Peer relations and dynamics within such contexts may be better characterized by qualitative groupings of youth (e.g., crowds) as opposed to quantitatively measurable collectives (e.g., networks). Indeed, many meaningful peer groups are fluid but matter because they provide common identity and serve as the practical universe of potential friends ( Akerlof & Kranton 2002 , Brown & Klute 2003 ). Barber and associates (2001) , for example, used the archetypal characters from the movie The Breakfast Club (e.g., the jock, the rebel, the princess) as a way of organizing data collection on such peer crowds. Importantly, interpersonal processes that occur within larger bands of peers seem to do as much, if not more, to predict the positive and negative mental health and educational outcomes of adolescents than intimate friendships, especially in the long term.

Historically, scholars studied another key peer relation—romantic relationships—in terms of major developmental tasks (e.g., preparation for adult relationships), leading to a focus on their benefits ( Shulman & Collins 1998 ). Later, risks took the spotlight, including links of girls' dating with depression, stress, and abuse, and more attention was paid to the consequences of stricter norms about appropriate dating (and sexual) behavior for girls ( Hagan & Foster 2001 , Joyner & Udry 2000 , Kreager & Staff 2009 ). Increasingly, however, scholars have recognized that adolescent romance may be developmentally positive or negative depending on the characteristics of the partners, the quality of the relationship, and the context in which it occurs. For example, romantic relationships may foster early sexual activity but also reduce the psychological strain of sex and increase contraceptive use. They may be especially important as buffers against the potential harm of weak bonds with parents or as a stand-ins for close friends ( Giordano et al. 2006 , Manlove et al. 2007 , McCarthy & Casey 2008 ). Importantly, although boys were long thought to be less oriented to and affected by romance, emerging evidence suggests that boys may have equally strong ties to their partners as girls and be more influenced by them. Along with their lower confidence in their romantic skills, these qualities might leave boys vulnerable emotionally to the vicissitudes of adolescent romance ( Giordano et al. 2006 ).

An emerging task is to add a wider variety of extrafamilial and other familial relationships to this traditional focus on parents and peers. Taking such a holistic view of overlapping relationships as they evolve is the best way to capture the concept of social convoys.

The Social Embeddedness of Adolescence

As alluded to throughout the prior discussion, the three main strands of the life course play out—and come together—within social contexts, ranging from small primary and secondary groups (e.g., families) to larger societal institutions (e.g., schools) to macro-level social structures, such as stratification systems based on gender, race, and class. Here, we highlight some recent explorations of this social embeddedness of adolescence.

The Ecological Contexts of Adolescence

Because adolescents have limited mobility, neighborhoods can powerfully structure their lives physically and socially. As a result, studies of neighborhood effects have proliferated in recent years, aided by neighborhood data in specific locales (e.g., Project on Human Development in Chicago Neighborhoods, L.A. Family and Neighborhood Survey), on the national level (e.g., Add Health), and through demonstration projects moving low-income families to new communities (e.g., the Moving to Opportunity, or MTO, experiment), as well as by qualitative studies of neighborhoods and communities. Most of these studies focus on neighborhood disadvantage and adolescent risk-taking ( Bellair & Roscigno 2000 , Browning et al. 2005 , Dance 2002 , Harding 2003 , Kling et al. 2007 ).

Motivating much of this research is Wilson's (1996) perspective on spatially concentrated disadvantage, which is thought to disrupt networks of social capital that socialize and supervise youth and to hinder the effectiveness of local institutions (e.g., schools, churches) and informal networks in providing social control. Contemporary scholars have sought to identify the mechanisms involved in these processes. For example, Browning and associates (2005) reported that adolescents in neighborhoods of concentrated poverty experienced sexual onset earlier than others but that higher neighborhood collective efficacy delayed sexual onset, at least among adolescents experiencing lower levels of parental monitoring. These findings suggest conditional effects between neighborhood conditions and family functioning, appearing to contradict prior studies downplaying the possibility of multiplicative contextual influences ( Cook et al. 2002 ). Importantly, studies such as another by Browning and associates (2008) raise the issue of rates of risky sex in turn affecting the concentration of STD risk in neighborhoods, with individuals shaping context. Such micro-to-macro examples are rare but need more attention.

As in all examinations of contextual effects, causality has been a concern in neighborhood research. Browning and associates (2005) have argued that findings varying by level of neighborhood exposure suggest true effects. In line with this argument, Harding (2003) reported that neighborhood poverty effects on adolescents persisted when propensity score matching was employed. He also noted that controlling for individual-level factors may obscure real neighborhood effects if they are affected by neighborhood features themselves, a point echoed by Chuang and associates (2005) in arguing that parents may adjust their parenting based on neighborhood conditions. Indeed, instead of isolating neighborhood effects by controlling individual, economic, and family factors, Bellair & Roscigno (2000) have advocated for viewing labor market opportunities as preceding neighborhood disadvantage, family income, and adolescent attachments, all of which affect adolescent behavior. In other words, instead of controlling for family income, family structure, and adolescents' attachments to family, school, and peers to evaluate the link between local labor market conditions and delinquency, they map the effects of local labor market conditions on delinquency through its effects on family income and structure and adolescents' attachments.

With an experimental design, MTO revealed compelling findings about the implications of switching from low-income to middle-income neighborhoods for adolescents. Interestingly, the benefits of such moves were limited to girls, including improvements in mental health and decreases in delinquency. The qualitative components of the experiment suggested several mechanisms underlying these gendered effects, including girls' greater freedom from sexual fears, boys' (especially minority boys') greater difficulty integrating into new peer networks, and boys' continued strong ties to peers from their former communities ( Clampet-Lundquist et al. 2006 , Kling et al. 2007 ). Outside MTO, other qualitative studies of minority youth have detailed the gendered dilemma of youth adaptation to neighborhood disadvantage, especially crime. Girls must live up to feminized social expectations of them while trying to survive often violent conditions, and boys must develop fearsome personae that protect them on the streets but may disadvantage them in other contexts ( Dance 2002 , Jones 2010 ).

Another major ecological setting of adolescence is the school, where young people spend a large proportion of their waking hours. Scholars continue to decipher the effects of the organizational structure of schools (e.g., size, sector, and racial and socioeconomic composition) on student outcomes (see Arum 2000 for a recent review). Yet, the past decade has witnessed considerably more interest in the normative and social climate of schools, as captured by the rates of behaviors and social characteristics in the student body as a whole. These aggregated aspects of the student body tap into the value systems and opportunity structures to which adolescents are exposed on a daily basis, socializing them as well as affecting their ability to act on or against their own proclivities ( Crosnoe 2011 ). For example, adolescents attending schools in which a high proportion of their fellow students come from single-parent homes transition to first sex earlier than others, as this feature of the student body indicates reduced parental supervision of adolescents and their peers and also speaks to normative understandings of sexual relationships and families among students ( Harris et al. 2002 ). As another example, the average body size of students in a school sets the standard of comparison for adolescents' self-evaluations, affecting whether their own body size has implications for their socioemotional functioning ( Crosnoe 2011 ). As a final example, behavioral patterns in the student body as a whole can constrain or strengthen close friends' similarities on substance use ( Cleveland & Wiebe 2003 ). The peer culture of the school, therefore, provides opportunities that condition selection and socialization processes. Importantly, schools do not just expose students to a student body, they also organize peer subsets within the student body through activity and curricular offerings. Consider that the aforementioned Breakfast Club groups ( Barber et al. 2001 ) often arise from extracurricular activities. Moreover, Frank and associates (2008) used school transcripts to identify adolescents sharing the same social and academic space in school, peer groups that were significantly related to student outcomes.

As for the connection between neighborhoods and schools, ethnographic work has been especially insightful. For example, several studies have illuminated the unique challenges faced by working class and low-income African American youth, especially boys, as they simultaneously navigate their neighborhoods and their schools with very different sets of racialized expectations for youth. For such boys, the tough and seemingly defiant posture that they develop among peers in their neighborhoods is often misconstrued and viewed negatively by the middle-class personnel in their schools, leading to academic marginalization and fueling pernicious ideas about the oppositional culture of minority youth ( Dance 2002 , Carter 2006 ).

The point of this neighborhood and school research is that ecological settings create social networks and contexts in which the powerful peer and family processes of adolescence operate. Thus, going beyond structural dimensions of such settings to capture social processes is important.

Adolescence and Social Stratification

The adolescent population is quite diverse in terms of race/ethnicity, social class, and other markers of social location. Especially among sociologists, such diversity has motivated a great deal of research concerning the ways that adolescents' experiences are both a product of and contributor to major systems of social stratification ( Morgan 2005 ).

In part because adolescence is a relatively healthy period in the life course, major health disparities are less common and consistent during this stage compared with others ( Crockett & Peterson 1993 ). Indeed, adolescents from historically disadvantaged minority groups often are similar to or lower than whites in rates of many risky health behaviors, such as drinking ( Harris et al. 2006 ). Yet, the recent rise in obesity has also been problematic from a long-term health perspective, particularly for African American and Latino/a youth ( Ogden et al. 2010 ). Thus, adolescence may play a positive and negative role in race/ethnic disparities across the life course.

On a structural level, school segregation continues to be an issue of great interest ( Rothstein 2004 ). Studies of school composition suggest that racial desegregation has academic benefits for both white and non-white students by exposing them to different ways of thinking and by leading to greater equity in school resources. Yet, students may also feel lowered senses of belonging and perceive more discrimination in diverse schools ( Goldsmith 2004 , Johnson et al. 2001 , Rumberger & Palardy 2005 ). Progress in school desegregation has also often come with increased within-school segregation ( Mickelson 2001 ). Recently, Parents Involved , in which the Supreme Court curtailed use of race in school assignment, has shifted attention from race to socioeconomic status. Efforts to socioeconomically desegregate schools, however, also demonstrate a mixture of benefits and risks, with research suggesting that academic gains might be accompanied by psychosocial problems and that socioeconomically integrating schools would not alter levels of racial segregation ( Crosnoe 2009 , Reardon et al. 2006 ).

On an interpersonal level, Ogbu's (1997) oppositional culture thesis—which, among other things, argues that African American and Latino/a peers de-emphasize achievement and equate it with acting white—has continued to generate debate. Quantitative examinations have provided little evidence of this phenomenon ( Ainsworth-Darnell & Downey 1998 , Harris 2006 ). Mixed methods examinations have suggested that it does occur occasionally but with some important caveats: ( a ) It is rooted in schools' long-term misunderstanding of minority group culture, and ( b ) it is not racialized but instead happens in youth culture more generally, in ways that are manifested differently by race and class ( Carter 2006 , Tyson et al. 2005 ). In total, research on oppositional culture has probably done less to unpack race/ethnic achievement gaps than it has to illuminate the nexus of youth culture and schooling.

For the most part, research on socioeconomic disparities has continued to focus on socioeconomic disadvantage (e.g., poverty), especially after the contentious public debate about welfare reform ( Gennetian et al. 2008 ). Much of this research suggests that poverty is clearly detrimental to adolescents but perhaps less so than it is for children ( Duncan et al. 1998 ). At the same time, the past decade has also witnessed significant advances in our understanding of socioeconomic advantage. Lareau's (2004) work has been enormously influential. This research has demonstrated that middle-class parents tend to follow an approach to parenting, concerted cultivation, that prioritizes providing children cognitively and socially stimulating activities at home and in formal organizations that develop skills, enhance their senses of entitlement, and teach them how to work institutional systems. Such parenting is so well aligned with the American educational system that it gives their children a competitive edge in school. Initially, Lareau focused on elementary school, but her basic insights have been replicated in studies of adolescence ( Crosnoe & Huston 2007 , Kim & Schneider 2005 ). Moreover, Lareau's recent follow-up of her sample in young adulthood revealed that parenting-related socioeconomic advantages persisted into adolescence and beyond.

Turning to immigration, traditional assimilation perspectives posited improved outcomes for the descendants of immigrants compared with immigrants themselves. Yet, newer research suggests that the foreign- and U.S.-born children of immigrants outperform third-plus-generation youth, despite higher levels of socioeconomic disadvantage among immigrants. Evidence of this immigrant paradox is more consistent among adolescents than children ( Glick & Hohmann-Marriott 2007 , Kao 1999 , Portes & Rumbaut 2001 , Suarez-Orozco et al. 2009 ). This age difference could reflect adolescents' greater time to adapt to American schools and culture. It could also reflect biases in high school data sets, as immigrants from many regions are more likely to drop out of or bypass school ( Oropesa & Landale 2009 ). Perhaps more importantly, evidence of the immigrant paradox varies widely according to national origin, race/ethnicity, and socioeconomic status. For example, adolescents whose parents emigrated from Asia best illustrate the immigrant paradox, at least in the academic realm. Asia is a region in which migration is positively selective on education and income, but, beyond socioeconomic status, Asian immigrant parents tend to have high standards of academic success, go to great lengths to secure educational opportunities for adolescents, and are highly planful (especially financially) about education ( Kao 2004 , Zhou 2009 ). This diversity in immigrant outcomes has supported theoretical reconceptualizations, such as segmented assimilation ( Portes & Zhou 1993 ), contending that the outcomes of assimilation depend on the context in which it occurs.

Of course, the adolescent population is stratified by factors beyond race and family background that also shape trajectories into adulthood. Two examples are obesity and homosexuality. Because of the stigma of obesity in American culture, obese youth are at heightened risk for psychosocial difficulties, which appear to disrupt their educational trajectories ( Crosnoe 2011 ). Similarly, same-sex-attracted youth often face strong social sanctions during high school that can filter into multiple domains, including academic progress ( Pearson et al. 2007 , Russell & Joyner 2001 ). In both cases, adolescents' characteristics position them on a social hierarchy to create short-term problems with long-term consequences. These stratifying processes are similar to gender, race, class, and immigration in that their significance in adolescence may create and reinforce unequal life chances.

New Directions for Research on Adolescence

Attempts by sociologists and other scholars over the past two decades to answer many of the tough questions about adolescence have raised additional questions. Having looked back, therefore, we now look forward. Given the space allowed, we have decided to focus on three specific future directions that touch on particularly provocative and timely debates and discussions in the field.

Biosocial Processes

In recent years, the integration of biomarkers with psychological and social data has helped empirical activity catch up with developmental theory. The sociological value of this activity is not in establishing genetic effects on adolescent behavior but instead in understanding the interplay of genes and environment at work in adolescent behavior ( Guo et al. 2008 ).

Understanding latent genetic influences has been aided by the creation of sibling samples, which allow assessments of sibling similarity in behavioral or other outcomes across sibling pairs of different degrees of genetic relatedness. Analyses of data from one such sample, the Nonshared Environment in Adolescent Development project, have elucidated the ways in which genetic traits select adolescents into different relationships and elicit different kinds of parenting. They have also demonstrated how the experiences that siblings have outside the home differentiate them on developmental outcomes, despite their genetic relatedness ( Reiss et al. 2000 ). Research on Add Health's diverse sibling pairs subsample has been particularly insightful about variability in shared environment and observed heritability of behaviors across social settings ( Boardman et al. 2008 ). For example, adolescent aggression is genetically influenced in both socioeconomically advantaged and disadvantaged communities, but the effects of shared environments (e.g., social influences experienced by both siblings) are significantly stronger in disadvantaged communities ( Cleveland 2003 ).

Turning to specific genetic influences, the collection of genetic data in behavioral studies has encouraged deeper exploration of gene-environment interactions. For example, Caspi and associates (2003) , drawing on biological and psychosocial data from New Zealand, reported that stressful life events had a larger impact on depression among youth with short alleles of the 5-HTT promoter polymorphism, which reduces efficiency of serotonin reuptake in the brain. Research by Guo and associates (2008) with the genetic data in Add Health demonstrated that the significance for delinquency of DRD2 alleles, which reduce efficiencies in the dopaminergetic system, is weaker in families with well-organized routines. Such studies push for a transactional view of biology, development, and environment.

Particularly important are genetically informed studies comparing adolescence with other life stages. For example, Dick and associates (2006) , working with genetic and psychosocial data from the United States, reported that the presence of a gene-regulating neurotransmitters, GABRA2, was associated with conduct disorder in adolescence and then with alcohol use in young adulthood. Thus, a genetic predisposition toward risky behavior is manifested differently across stages. A sociological interpretation is that entry into new settings across the transition from adolescence to adulthood might account for such changes.

As for other biomarkers, cortisol is a central hormone in stress response. Because cortisol levels tend to decline over the day, flatter diurnal patterns may signal health risks through the overactivation of physiological stress response ( Susman 2006 ). Efforts to integrate saliva samples and time diaries have revealed that minority youth report higher levels of chronic stress and demonstrate flatter cortisol patterns across the day than whites. Thus, identifying biological mechanisms underlying links between environmental stress and adolescent health may shed light on the role of adolescence in health disparities ( DeSantis et al. 2007 ). Similarly, immunological processes provide a window into environmental effects on youth. For example, McDade (2001) has combined samples of Epstein-Barr virus antibodies with lifestyle data. This work indicates that the stress that adolescents feel from modernization in developing countries is manifested in reduced immune functioning. Like neuroscience, this biomarker research is more common outside sociology, but it touches on core sociological questions, such as the effects of social integration on life chances, thereby representing a growth area for sociologists.

The nature versus nurture debate, therefore, seems to be dying. Indeed, research on adolescence is turning to the synergistic interplay between nature and nurture. Sociologists interested in adolescence have a significant role to play in uncovering the complexities of this interplay moving forward.

Linking Life Stages

Adolescence is better understood when it is viewed within the full life course, and we are now well poised to theorize and empirically evaluate linkages between adolescence and other life stages ( Johnson et al. 2011 ). As noted in the opening section of this review, advances in longitudinal sampling and modeling have facilitated asking and answering questions that involve processes unfolding over time and across contexts. At least in some domains (e.g., education, work), scholars of adolescence are accustomed to thinking about how adolescent experiences affect adult life. Both looking back to childhood and looking forward to adulthood, however, will enable us to elucidate the role of adolescence in the life course.

For example, initial curricular placements and academic achievement in high school are recognized as important to concurrent and future well-being. Yet, the past decade has also witnessed greater emphasis on understanding proximate and distal factors involved in producing varying levels of high school achievement. By following Baltimore schoolchildren from first grade into early adulthood, Alexander and associates (2007) were able to capture the full educational career and, in the process, identify critical periods. Socioeconomic disparities in academic progress at the start of high school were traced back to corresponding disparities in place at the start of first grade and to summer learning differences by socioeconomic status during the elementary school years. These ninth grade differences were then linked to curricular track, high school completion, and college attendance. Their interpretation emphasized how foundational the skills are that are learned in the early years of schooling and the ways in which the in-school and out-of-school settings and experiences that stratify early learning can have lasting, even accumulating, consequences for the life course. Exclusive focus on the adolescent years, and particularly the high school years, misses these processes set in motion much earlier and likely obscures the best points of intervention ( Heckman 2006 ).

As another example, pubertal timing may be a conduit in the connection between disadvantage in childhood and adulthood. Consider that family adversity is among the myriad biological and environmental factors accelerating pubertal timing ( Belsky et al. 2007 ). Cavanagh and associates (2007) have reported that early pubertal timing during middle school is linked to lower grades and the likelihood of course failure at the start of high school, and that as a result, high school completion and the grades of those who graduate are also affected. By stepping back to view longer-term processes, we see additional mechanisms through which family disadvantage impacts children's success in adolescence and adulthood, operating via biological and social processes, as well as their complex interactions.

Finally, charting individual trajectories over time provides important context for understanding what is observed in adolescence. The influential differentiation between life course persistence and adolescence desistance in criminal behavior is one example ( Moffitt 1993 ). Another concerns adolescent substance use, which is embedded within a variety of long-term trajectories that have distinct meaning and consequence. Following cohorts of adolescents in the Monitoring the Future Surveys into adulthood, Schulenberg and associates (2005) linked different patterns of substance use to the pathways through which adolescents transition into adulthood. Levels of substance use in adolescence anticipated the configuration of role transitions young people experienced in the years immediately following, but were also shaped by them. Young people who worked and did not attend school during these years binge drank more frequently during high school. Those who moved away from home for college were less frequent binge drinkers in high school but quickly caught up. These patterns suggest the varied settings and conditions that different adult statuses bring but also the potential for psychosocial preparation for these statuses during adolescence.

The life course paradigm emphasizes that development is lifelong and that no life stage can be understood in isolation. These examples highlight the advances that can be made if we rise to the challenge posed in this life course principle.

Social Change

Of course, linkages among life stages are also shaped by broader changes in the structure of society. Economic restructuring, for example, is dramatically affecting education and employment in young adulthood and beyond, and we need to better understand what this means for adolescents. Changes in the relative size of the manufacturing and service sectors have occurred in such a way as to reduce the availability of jobs with benefits, increase the income premium of higher education, and create greater fluidity between jobs ( Goldin & Katz 2008 ). Furthermore, Fullerton & Wallace (2007) characterized a set of interrelated changes occurring since the 1970s, including declining unionization, downsizing, growing use of contingent labor, and organizational restructuring as a “flexible turn in U.S. labor relations (p. 201),” which has eroded workers' perceptions of job security. Such changes are increasing the importance of adolescents' educational experiences in the status attainment process, thereby magnifying the significance of all of the factors discussed in this review that matter to these experiences.

In this context, the process of becoming adult has clearly changed, with scholars suggesting that adolescence has been extended to older ages or even that a new life stage should be recognized ( Settersten et al. 2005 ). Demands for and returns to education have risen, and relatedly, the period of dependency and semi-autonomy has lengthened. We know young people are staying in school longer, more often combining employment with higher education, and marrying later ( Bernhardt et al. 2001 , Fitzpatrick & Turner 2007 , U.S. Census Bureau 2006 ). Although race/ethnic and socioeconomic variability in these patterns has long been recognized ( Settersten et al. 2005 ), we are only just beginning to address a number of other important questions related to these broad social changes, including what they mean for the achievement of social and financial autonomy and relationships with others, including parents, and what we need to equip adolescents with in order for them to successfully navigate the transition to adulthood.

Two major collaborative efforts have laid an important foundation for understanding the implications of these social changes for adolescence. The first is the MacArthur Research Network on Transitions to Adulthood and Public Policy ( Settersten et al. 2005 ). It has reported, among other things, that parents, especially those with more resources, increasingly support their children through the transition to adulthood financially and otherwise. About one-third of 18–34 year olds in the Panel Study of Income Dynamics received cash support from their parents, at an average of just over $3,400 per year ( Schoeni & Ross 2005 ). This and other forms of ongoing material assistance (e.g., support of higher education pursuits, allowing children to remain in the home, assistance in establishing independent households, providing childcare) may be yet another way in which parents' level of education and income can affect the status attainment of their children. The second effort is the Society for Research on Adolescence's Study Group on Adolescence in the Twenty-First Century ( Larson et al. 2002 ). One of its reports has argued that changes in family size, structure, and relationships; increased participation in school and after-school activities; and the advent of the Internet have created new opportunities for adolescents to develop more flexible social skills and capacities to move between diverse social worlds. It also notes, however, that those from families in poverty and those in elite families are more deprived when it comes to the social experience that builds these skills.

Another major social change concerns the transformation of social interaction though new media and technologies. The Pew Internet and American Life Project has reported that, in 2009, 75% of 12–17 year olds had cell phones and 93% went online ( Lenhart et al. 2010 ). The report also indicated that 76% of families with adolescents now had broadband access at home. Adolescents also go online via cell phones and portable gaming devices, in addition to computers at home. These technologies offer new opportunities for leisure, shopping, and staying in touch with others, as well as broader access to information and support. Roughly one-third of adolescents in the Pew study who went online used the Internet to gather information on health, dieting, or physical fitness, and 17% looked online for information about sensitive health topics, such as those related to drug use, mental health, and sex ( Lenhart et al. 2010 ).

Importantly, the Internet can also be thought of as a new kind of peer context. A recent study of 800 American youth revealed that, for most youth, new media technologies are used primarily to maintain and extend friendship networks ( Ito et al. 2010 ). Moreover, the Internet provides opportunities for socially isolated youth to connect with others in meaningful ways while also enabling the peer cultures of high school—including negative dimensions, such as bullying and gossiping—to follow young people home ( Crosnoe 2011 , Raskauskas & Stoltz 2007 ). Thus, new media represent a potential context of resource and risk related to peers.

Other potential risks ranging from driving accidents related to texting or talking on cell phones to exposure to questionable online content or social interactions (e.g., pornography, gambling, sexual solicitation) have concerned parents, educators, and lawmakers. Debates continue over whether risks can also accrue from the potentially sensitive or identifying information, including pictures and videos, adolescents post online about themselves and one another, or whether this is a healthy part of adolescents' self-expression and identity exploration. Nearly three-quarters of online youth use a social networking Web site such as Facebook or MySpace ( Lenhart et al. 2010 ). A recent content analysis of teenagers' MySpace pages indicated that 40% of adolescent users restricted access to their pages to identified friends. Yet, 10% of adolescent users who did not restrict access posted their full name, and many more listed their hometowns and the name of their schools, which could be used to identify them ( Hinduja & Patchin 2008 ).

Relevant empirical evidence about these concerns, however, is rare, and the topic is notably absent from the top sociological publishing outlets. Yet, sociologists have much to contribute, as adolescents' use of new media raises important questions about social networks, personal relationships, and identity development. What are the implications of cell phone use or online communication for parental monitoring and peer interactions? Do electronic interactions replace or complement face-to-face interactions? Do online venues provide a safe or dangerous place for identity work? As an example highlighting the potential work to be done, Blais and associates (2008) reported that Internet activity was related to changes in the quality of best friendships and romantic relationships over the course of a year among Canadian adolescents. Specifically, use of instant messaging, which occurs with known others, enhanced these relationships, but visits to chat rooms, which primarily involve communicating with strangers, were associated with worsening relationship quality over time. As these findings suggest, we will need to be specific about the types of media being used when we attempt to understand their implications for today's adolescents.

As is evident from this review, the rich sociological tradition of research on adolescence has continued into the new century. Still, the sociology of adolescence may be at something of a crossroads. The mapping of the human genome and the increasing sophistication of brain imaging are reshaping the scientific agenda in ways that, at first glance, do not tap into the traditional strengths of sociologists. At the same time, the renewed interest in childhood as a critical period—generated by findings that early interventions bring greater long-term returns to investments than those targeting adolescence ( Heckman 2006 )—has shifted attention to earlier stages. Another way of looking at these developments, however, is that they are opportunities. Indeed, sociologists are well-positioned to demonstrate how biological processes cannot be understood absent a firm sociological understanding of the environment in which they play out over time, explain how the long reach of childhood is channeled through adolescence, and identify ways in which adolescence produces turning points and deflections in the life course.

Summary Points

• Research on adolescence has moved in a sociological direction by emphasizing the role of context in shaping adolescents' lives and the link between adolescent development and societal inequality, fueled in part by recent advances in data collection and methodology.
• Early childhood experiences are very important to long-term health, educational, and behavioral trajectories, but adolescent experiences play key roles in this process by magnifying or deflecting children's trajectories.
• Many of the major developmental trajectories of adolescence, including those related to puberty, risky behavior, academic achievement, health, and identity development, reflect a complex interplay of biology, personal agency, and environment.
• Adolescents' navigation of institutional systems, such as school and work, have become increasingly complex and interrelated, with high school coursework more consequential to long-term outcomes in the globalized economy and paid work during adolescence becoming more common and potentially either risky or beneficial for educational attainment depending on motivation, background, and academic competence.
• Adolescents tend to spend less time with parents and other relatives and seek more autonomy while becoming more immersed in expanding peer networks, including romantic networks, but they typically do so while maintaining strong emotional ties to their families.
• Although much of the research on school and neighborhood effects on adolescent behavior has focused on the structural features of these contexts, more attention is being paid to the ways in which they organize peer groups that differ widely in terms of norms, values, and behavioral opportunities, as well as the ways families affect and respond within them.
• Gender, race, social class, and immigration stratify adolescents' lives, with poor and/or minority youth particularly vulnerable in the educational system, through a variety of structural inequalities and interpersonal processes, but immigrant youth often demonstrate a high level of resilience in the face of similar risks.

Acknowledgments

Support for R.C. came from a faculty scholar award from the William T. Grant Foundation, as well as a center grant to the Population Research Center at the University of Texas at Austin from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (R24 HD042849; PI: Mark Hayward). The authors thank Anna Thornton for her help with this review.

Disclosure Statement : The authors are not aware of any affiliations, memberships, funding, or financial holdings that might be perceived as affecting the objectivity of this review.

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