research based

• Skip to primary navigation
• Skip to main content
• Plan Your Visit

Evidence-Based? Research-Based? What does it all Mean?

Have you ever felt puzzled by trying to discern the difference between the terms, evidence-based and research-based ? Or have you ever found yourself feeling intimidated when someone asked you, “But is that program/practice evidence-based?” I know I have. To help me clarify my understanding, I reached out to my colleagues here at the Center and my old friend, Google. I’ve come to the following understandings and a bit of friendly advice – stay curious! Please keep reading if you’re feeling as perplexed as I am.

Clarifying the Difference between Research-Based and Evidence-Based

My current working definition of research-based instruction has come to mean those practices/programs that are based on well-supported and documented theories of learning. The instructional approach is based on research that supports the principles it incorporates, but there may not be specific research or its own evidence to directly demonstrate its effectiveness.

Defining evidence-based practice has been more headache-inducing as the term is frequently and widely used to mean a myriad of things. Currently, I have come to understand that evidence-based practices are those that have been researched with either experimental studies (think randomly assigned control groups), quasi-experimental studies (comparison groups that are not randomized), or studies that were well-designed and well-implemented correlational studies with statistical controls for selection bias. In brief, a specific study (or studies) has been done to test its effectiveness.

By no means are these definitions ready for Merriam-Webster, but they are helping me to make sense of the terms.

So what do you say or ask when “research” is thrown your way?

Recently, I met with a group of literacy coaches and we discussed how to respond when a fellow educator approaches them with “research” either supporting or refuting an instructional practice or program. My best advice to them probably sounded like a Viking River Cruise commercial – “Be curious!” Below are some examples of ways to respond to demonstrate that you are open to learning more.

• Thank you for bringing that information to my attention. Can you share your source of information or the article so I can read it too and we can talk about it together?
• Please talk more about what you have learned (or read or heard). I’m curious to learn more about: a. Whether the research was published in a peer-reviewed journal or if the research was sponsored by a publisher or other interested party. b. The sample size or the number of schools/students involved in the study. c. The demographics of the subjects involved in the study. d. The type of research conducted.

3. I’m wondering how many studies have been conducted that replicate those results. 4. That research sounds important. Can you share the source with me? Perhaps it will be helpful for our grade level team to read it and discuss the findings together.

As educators, we are always looking for the most effective ways to support our students. Stay open to new findings and be sure to slow the process down so you probe deeper to learn if there truly is current research to back what people are claiming. Then be sure to evaluate the credibility of the source of information, the methods or processes used to critique or research, and don’t forget to rely upon trusted sources like What Works Clearinghouse . You might also appreciate a lecture presented by Maren Aukerman that discusses comprehensive, research-informed literacy instruction . The more you dig, the more you may find that many practices and programs touted as evidence-based are either based on personal anecdotes and stories or the research base is flimsy at best.

You might also be interested in

Advice for Coaches: Helping Teachers Navigate the End-of-Year Homestretch

How can coaches help empower teachers to navigate the end-of-year homestretch? By creating opportunities for reflection.   

Comprehension and Building Knowledge: From Acquiring Knowledge to Actively Using It

Literacy expert Stephanie Harvey explains the relationship between comprehension and knowledge building.

Seeking to Understand the Science of Reading

The 2024 book, “Fact-checking the Science of Reading: Opening Up the Conversation,” is an unbiased, accessible review of ten major claims associated with the Science of Reading movement.

If you have any questions, please contact the Lesley University Center for Reading Recovery and Literacy Collaborative.

Phone: 617.349.8424

Hours: 8:00 am–5:00 pm

Mailing Address Lesley University 29 Everett Street Cambridge, MA 02138

• Personalized Learning: MTSS, PBIS & Student Support
• Mental Health, Wellbeing & Resilience
• College & Career Readiness
• School Climate & Culture
• DESSA Aperture System
• DESSA Student Self-Report
• DESSA Second Step® Assessments
• Strategies and Interventions
• Aperture Academy
• Aperture Advisors
• Customer Success and Implementation
• Professional Learning and Training
• Support Portal
• Case Studies
• Parent Resources
• Research & White Papers

“Evidence-Based” vs. “Research-Based”: Understanding the Differences

Often, when reviewing resources, programs, or assessments, we might come across terms like “evidence-based” or “research-based.” These terms each tell us something about the resources that they describe and the evidence supporting them. Understanding each term’s meaning can help us make informed decisions when selecting and implementing resources.

So what do these terms mean, exactly?

Typically, the terms  Evidence-Based   Practices  or  Evidence-Based   Programs  refer to individual practices (for example, single lessons or in-class activities) or programs (for example, year-long curricula) that are considered effective based on scientific evidence. To deem a program or practice “evidence-based,” researchers will typically study the impact of the resource(s) in a controlled setting – for example, they may study differences in skill growth between students whose educators used the resources and students whose educators did not. If sufficient research suggests that the program or practice is effective, it may be deemed “evidence-based.”

Evidence-Informed  (or  Research-Based )  Practices  are practices that were developed based on the best research available in the field. This means that users can feel confident that the strategies and activities included in the program or practice have a strong scientific basis for their use. Unlike Evidence-Based Practices or Programs, Research-Based Practices have not been researched in a controlled setting.

What about assessment?

Terms like “evidence-based” and “research-based” are often used to describe  intervention activities,  like strategies or curricula designed to build skills in specific areas. But the process of measuring skills with assessment tools can be evidence-based as well. An assessment process can be considered  Evidence-Based Assessment  if:

• The choice of skills to be measured by the assessment was informed by research;
• The assessment method and measurement tools used are informed by scientific research and theory and meet the relevant standards for their intended uses; and
• The way that the assessment is implemented and interpreted is backed by research.

Using evidence-based assessment to guide or evaluate an intervention gives us confidence that the process is well-suited for our purpose, is grounded in scientific theory, and will be effective for our students.

What Standards Exist for Educational Assessments?

The process of Evidence-Based Assessment involves the use of a measurement tool that “meets the relevant standards for their intended uses.” What are the relevant standards, and how can we know if a tool meets them?

Some foundational standards for educational assessments, as compiled by experts in the educational, psychological, and assessment fields, include:

• Validity for an Intended Use:  the tool should have been researched to determine that it is valid, or appropriate, for the decisions we may make based on its results. Just like we wouldn’t use a math quiz to inform whether a student needs additional practice with reading comprehension, we shouldn’t use an assessment for purposes outside of those that research has deemed “valid.”
• Reliability:  the tool should have been researched to ensure that it meets expectations for reliability, or consistency. For example, researchers might explore whether the tool produces similar results if it is completed twice in a short period of time. Reliability can be explored via a variety of methods, depending on the measurement tool.
• Fairness:  the tool should have been researched to explore how fair, or unbiased, it is among different subgroups of students, such as subgroups based on race, ethnicity, or cultural background. Using a biased measurement tool can lead to biased decision-making and threaten our ability to provide equitable services.

Specific standards within each of these domains, and others, are compiled in the handbook, “Standards for Educational and Psychological Testing” (2014), written by the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education. This handbook can be a useful companion when reviewing the specific evidence behind measurement tools.

In Conclusion

Terms like “evidence-based” or “research-based” are useful indicators of the type of evidence behind programs, practices, or assessments – however, they can only tell us so much about the specific research behind each tool. For situations where more information on a resource’s evidence base would be beneficial, it may be helpful to request research summaries or articles from the resource’s publisher for further review.

Further Reading

• Hunsley, J., & Mash, E. J. (2007). Evidence-based assessment. Rev. Clin. Psychol., 3, 29-51 .
• Joint Committee on the Standards for Educational and Psychological Testing of the American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education (2014). Standards for Educational and Psychological Testing. The American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education .
• S. Department of Education (2016). Using Evidence to Strengthen Education Investments .

Interested in learning more about Aperture Education's research-based universal screeners and assessments? Contact our expert advisors today !

What can we help you with?

Discover more from aperture education.

Subscribe now to keep reading and get access to the full archive.

Type your email…

Continue reading

32 Research-Based Instructional Strategies

32 Research-Based Instructional Strategies For Teachers

by TeachThought Staff

You want to teach with what’s been proven to work. That makes sense.

In the ‘data era’ of education that’s mean research-based instructional strategies to drive data-based teaching, and while there’s a lot to consider here we’d love to explore more deeply, for now we’re just going to take a look at the instructional strategies themselves.

A post is not the best way to share this kind of information, honestly. A video for each one might be better, so maybe a YouTube channel? Or a book, as Hattie and Marzano and others have done. Professional development may also work–take 12 strategies or so and work with teachers to integrate them into different kinds of lessons may be useful. (See Hattie’s Index Of Effect Sizes .)

But upside to sharing this information as a post is that it can act a starting point to research the above, which is why we’ve tried to include links, related content, and suggested reading for many of the strategies, and are trying to add citations for all  of them that reference the original study that demonstrated that strategy’s effectiveness. (This is an ongoing process.)

How should you use a list like this? In 6 Questions Hattie Didn’t Ask , Terry Heick wondered the same.

“In lieu of any problems, this much data has to be useful. Right? Maybe. But it might be that so much effort is required to localize and recalibrate it a specific context, that’s it’s just not–especially when it keeps schools and districts from becoming ‘researchers’ on their own terms, leaning instead on Hattie’s list. Imagine ‘PDs’ where this book has been tossed down in the middle of every table in the library and teachers are told to ‘come up with lessons’ that use those strategies that appear in the ‘top 10.’ Then, on walk-throughs for the next month, teachers are constantly asked about ‘reciprocal teaching’ (.74 ES after all). If you consider the analogy of a restaurant, Hattie’s book is like a big book of cooking practices that have been shown to be effective within certain contexts: Use of Microwave (.11 ES) Chefs Academic Training (.23 ES), Use of Fresh Ingredients (.98). The problem is, without the macro-picture of instructional design, they are simply contextual-less, singular items.”

In short, these instructional strategies have been demonstrated to, in at least one study, be ‘effective.’ As implied above, it’s not that simple–and it doesn’t mean it will work well in your next lesson. But as a place to begin taking a closer look at what seems to work–and more importantly how and why it works–feel free to begin your exploring with the list below.

1. Setting Objectives

2. Reinforcing Effort/Providing Recognition

3. Cooperative Learning

4. Cues, Questions & Advance Organizers

5. Nonlinguistic Representations (see Teaching With Analogies )

6. Summarizing & Note Taking

7. Identifying Similarities and Differences

8. Generating & Testing Hypotheses

9. Instructional Planning Using the Nine Categories of Strategies

10. Rewards based on a specific performance standard (Wiersma 1992)

11. Homework for later grades (Ross 1998) with minimal parental involvement (Balli 1998) with a clear purpose (Foyle 1985)

12. Direct Instruction

13. Scaffolding Instruction

14. Provide opportunities for student practice

15. Individualized Instruction

16. Inquiry-Based Teaching (see  20 Questions To Guide Inquiry-Based Learnin g )

See also The 40 Best Classroom Management Apps & Tools

17. Concept Mapping

18. Reciprocal Teaching

19. Promoting student metacognition (see  5o Questions That Promote Metacognition In Students )

20. Developing high expectations for each student

21. Providing clear and effective learning feedback (see 13 Concrete Examples Of Effective Learning Feedback )

22. Teacher clarity (learning goals, expectations, content delivery, assessment results, etc.)

23. Setting goals or objectives (Lipset & Wilson 1993)

24. Consistent, ‘low-threat’ assessment (Bangert-Drowns, Kulik, & Kulik 1991; Fuchs & Fuchs 1986)

25. Higher-level questioning (Redfield & Rousseau 1981) (see  Questions Stems For Higher Level Discussion )

26. Learning feedback that is detailed and specific (Hattie & Temperly 2007)

27. The Directed Reading-Thinking Activity (Stauffer 1969)

28. Question-Answer Relationship (QAR) (Raphael 1982)

29. KWL Chart (Ogle 1986)

30. Comparison Matrix (Marzano 2001)

31. Anticipation Guides (Buehl 2001)

32. Response Notebooks (Readence, Moore, Rickelman, 2002)

Sources: Marzano Research ; Visible Learning ; http://www.sde.ct.gov/sde/lib/sde/pdf/curriculum/section7.pdf ;  32 Research-Based Instructional Strategies

About The Author

Teachthought staff.

• Our Mission

6 Evidence-Based Instructional Practices Drawn From Cognitive Science

These research-backed strategies have the capacity to help students learn and retain more information.

No parent sends their child to school to develop stress, experience anxiety, or lose their self-esteem. Every educator aspires for their students to learn what they are being taught. Yet, both teachers and students, albeit for different reasons, had to teach themselves to learn. This is not a criticism of our education but instead a nod of understanding. More can be done to ensure that each student and teacher is set up to succeed. This is inclusive of improving each student’s ability to learn and curiosity for learning rather than ranking them against each other or attributing their struggles to poor habits.

Successful educators constantly refine how they teach by utilizing evidence-based instructional practices and scientific discoveries about learning.

Learning is complex. It depends on a myriad of cognitive skills, emotions, and behaviors alongside prior knowledge. Fortunately, the field of cognitive science has accumulated a rich body of evidence of how we learn (science of learning) and how to teach to promote learning (science of teaching). Unfortunately, this mountain of evidence remains mostly trapped within academia, siloed from professional development, and primarily absent from where it is needed most: in our classrooms. Here are some tips for applying this knowledge in your classroom.

6 Strategies to Promote Learning based on cognitive science

1. Prime the brain for learning. While tests are commonly used to measure performance, they are also powerful tools to foster learning. What does the science of learning say about testing before we even teach the material? It helps students learn the material better because it signals to their brains what is important and forthcoming . This is referred to as priming . Look for opportunities to incorporate priming questions into already-established classroom routines.

2. Activate your students’ attentional filter. Learning requires “active processing” because information is processed and remembered only if it makes it through our attentional filters . The learner must then choose what pieces of information to bring into their working memory , to actively engage with that material and be able to learn it.

Realize that only a fraction of what you teach is actively being processed, and more information is processed when you engage students with the material you are teaching. Capture your students’ attention with prediction, purposeful novelty, and cues that denote importance.

3. Preteach jargon to avoid cognitive overload. Teaching often involves new terms. Cognitive multimedia theory teaches us that students learn better when the terms are introduced in advance of the content . This helps avoid cognitive overload, which occurs when new content is paired with new terms that prevent the student from effectively grasping both simultaneously. Consider using flash cards at the start of a unit instead of waiting until the end.

4. Incorporate frequent, spaced opportunities to retrieve information and skills. We begin forgetting what we’ve learned shortly after we learn it. The forgetting curve illustrates how learned information decays over time . We can use retrieval practice to combat forgetting and form reliable learning .

Retrieval practice is pulling a stored memory out of long-term memory into working memory for continued processing and use. Spacing refers to the strategic use of delays across learning trials. It works because it requires extra cognitive effort to recall material and creates multiple retrieval routes to aid remembering . Ask questions from last year, last month, and last week.

5. Alternate between subjects after moderate review of one. We are used to working in a blocked fashion. Our textbooks are designed this way. We study this way by cramming, and we often teach this way by introducing material sequentially; these methods aren’t as helpful to learning. Interleaving challenges the conventional approach to teaching and learning with a far more effective approach. As you might suspect, it requires the opposite.

When tasks or skills are mixed within a single class period or study session, the brain must work harder, and memory associations are strengthened . The key here is alternating after moderate review; avoid switching too often. How much is too often? There is no formula that is universally appropriate across age groups, subject matter, and cognitive profiles. Generally speaking, consider interleaving no more than two to three topics per session and switching topics roughly every 15–20 minutes.

6. Prompt students to actively generate information. Students are often tasked with answering questions that can be found in the text or other content provided. Elaboration, another type of retrieval, asks students to look beyond what is provided by describing using many details. It works by activating and making connections to prior knowledge . Create opportunities for students to make connections between different ideas and concepts and consider what makes them similar or different.

These strategies only scratch the surface of the daunting tasks that many teachers and students have faced and continue to face today. By studying cognitive science more closely, teachers can become experts on how to teach (science of learning) and on what they teach (academic content). The science of learning supports the important work that is already being done by so many.

Research-Based Teacher Education

• Living reference work entry
• First Online: 13 July 2019
• Cite this living reference work entry

• Elaine Munthe 2  

146 Accesses

1 Citations

Introduction

Conceptions of what teacher education is and should prepare for have changed along with growing social complexity, a stronger focus on accountability, and deepening understanding of how people learn and what teachers need to know (Cochran-Smith et al. 2016 ). Teachers face an increasingly wide range of student learning differences in their classrooms, they are expected to develop new ways of teaching and learning, and technology is being introduced at a rapid pace, often with little support. In many countries, this has led to a call for teacher education programs that help teachers to become innovators and researchers in education, laying a foundation for continuous learning and change in the workplace (Brouwer and Korthagen 2005 ).

Research-based teacher education is one key to this development, but how research-based teacher education is understood can vary, and there can be varying degrees of strength in a research-based teacher education program. In the following,...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

British Educational Research Association/BERA. (2014). Research and the teaching profession, final report of the BERA-RSA inquiry into the role of research in teacher education, BERA. Downloaded from: https://www.bera.ac.uk/project/research-and-teacher-education

Brouwer, N., & Korthagen, F. A. (2005). Can teacher education make a difference? American Educational Research Journal, 42 (1), 153–224.

Article   Google Scholar  

Cochran-Smith, M., Villegas, A. M., with Abrams, L. W., Chavez-Moreno, L. C., Mills, T., & Stern, R. (2016). Research on teacher preparation; charting the landscape of a sprawling field. In D. H. Gitomer & C. A. Bell (Eds.), Handbook of research on teaching (5th ed., pp. 439–548). Washington, DC: American Educational Research Association.

Chapter   Google Scholar  

Griffiths, R. (2004). Knowledge production and the research – Teaching nexus: The case of the built environment disciplines. Studies in Higher Education, 29 (6), 709–726.

Healey, M. (2005). Linking Research and Teaching to Benefit Student Learning, Journal of Geography in Higher Education, 29 (2), 183–201. https://doi.org/10.1080/03098260500130387

Healey, M., & Jenkins, A. (2009). Developing undergraduate research and inquiry . York: The Higher Education Academy. Downloaded from: https://www.heacademy.ac.uk/system/files/developingundergraduate_final.pdf .

Google Scholar  

Munthe, E., & Rogne, M. (2015). Research based teacher education. Teaching and Teacher Education, 46 , 17–24. https://doi.org/10.1016/j.tate.2014.10.006 .

Niemi, H. (2016). Academic and practical: Research-based teacher education in Finland. In B. Moon (Ed.), Do universities have a role in the education and training of teachers? An international analysis of policy and practice (pp. 19–34). Cambridge, UK: Cambridge University Press.

Toom, A., Kynäslahti, H., Krokfors, L., Jyrhämä, R., Byman, R., Stenberg, K., et al. (2010). Experiences of a research based approach to teacher education: Suggestions for future policies. European Journal of Education, 45 (2), Part II, 331–Part II, 344.

Download references

Author information

Authors and affiliations.

Faculty of Arts and Education, University of Stavanger, Stavanger, Norway

Elaine Munthe

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Elaine Munthe .

Editor information

Editors and affiliations.

WMIER, The University of Waikato WMIER, Hamilton, Waikato, New Zealand

Michael A. Peters

Section Editor information

No affiliation provided

Douwe Beijaard

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this entry

Cite this entry.

Munthe, E. (2019). Research-Based Teacher Education. In: Peters, M. (eds) Encyclopedia of Teacher Education. Springer, Singapore. https://doi.org/10.1007/978-981-13-1179-6_53-1

Download citation

DOI : https://doi.org/10.1007/978-981-13-1179-6_53-1

Received : 27 May 2019

Accepted : 27 May 2019

Published : 13 July 2019

Publisher Name : Springer, Singapore

Print ISBN : 978-981-13-1179-6

Online ISBN : 978-981-13-1179-6

eBook Packages : Springer Reference Education Reference Module Humanities and Social Sciences Reference Module Education

• Publish with us

Policies and ethics

• Find a journal
• Track your research

Solution Tree Blog

Research Based Learning: a Lifelong Learning Necessity

  “Give a person a fish and he will eat for a day; teach a person to fish and she will eat for a lifetime.” – Adapted from a saying by an unknown author

What is Research-Based Learning? Research-based learning (RBL) consists of a framework that helps to prepare students to be lifelong inquirers and learners. The term “research,” which often conjures up a picture of students writing research reports, is here defined as a way of thinking about teaching and learning, a perspective, a paradigm. It is a specific approach to classroom teaching that places less emphasis on teacher-centered learning of content and facts and greater emphasis on students as active researchers.

In a research-based learning approach, students actively search for and then use multiple resources, materials, and texts in order to explore important, relevant, and interesting questions and challenges. They find, process, organize and evaluate information and ideas as they build reading skills and vocabulary. They learn how to read for understanding, form interpretations, develop and evaluate hypotheses, and think critically and creatively. They learn how to solve problems, challenges, and dilemmas. Finally, they develop communication skills through writing and discussion.

In the five stages of research-based learning, students:

a. Identify and clarify issues, questions, challenges, and puzzles. A key component of research-based learning is the identification and clarification of issues, problems, challenges and questions for discussion and exploration. The learner is able to seek relevancy in the work they are doing and to become deeply involved in the learning process. b. Find and process information. Students are tasked with searching for, finding, closely reading, processing, and using information related to the identified issue and question from one or more sources. As they seek out resources and read information, and then organize, classify, categorize, define, and conceptualize data. In the process, they become better readers. c. Think critically and creatively. Students are provided with the opportunity to use their researched information to compare and contrast, interpret, apply, infer, analyze, synthesize, and think creatively. d. Apply knowledge and ideas and draw conclusions. Students use what they have learned to draw conclusions, complete an authentic task, summarize results, solve problems, make decisions, or answer key questions. e. Communicate results. Students communicate results of their research activities in a number of possible ways, such as through a written research report, a persuasive essay, a book designed to teach younger students, a math problem solution, a plan of action, or a slide presentation to members of the community.

The Teacher’s Role Teachers play a key role in the success of research-based instruction by engaging and involving students in information gathering and processing. While teachers might occasionally provide information through lectures, and textbooks are used as a source of information, there is an emphasis placed on students learning how to seek out and process resources themselves. A teacher provides a climate that supports student curiosity and questioning . Teachers enable students to ask questions and pose problems. Students are invited to ask and answer questions. The classroom climate is conducive to using higher-order thinking and problem-solving skills to apply knowledge to solve problems. Teachers attempt to build ways for students to take ownership of their learning, to create a value and a purpose for learning.

In a research-based learning classroom, teachers often act more like a coach, guiding students as they develop questions and problems, helping students to find, read, sort, and evaluate information, giving students the opportunity to draw their own conclusions, and providing the time and the opportunity for students to communicate results.

Finally, one of the most important components of a successful research-based learning program is the ability to help students understand and apply this approach consistently, by providing them with research-based opportunities for learning. Thus students are encouraged to bring in additional materials and resources to help the class understand a topic, choose and complete projects and performance tasks as part of their units of study, and discuss issues using evidence from sources of information. The classroom climate and environment continually encourage students to express their opinions, problem solve, and think at higher levels.

Student Outcomes Significant outcomes occur when this approach is utilized over time. Learning how to search for and find reliable information and resources is a skill that is important for a lifetime of learning, Reading many different kinds of texts strengthens reading skills and builds vocabulary. Thinking skills are developed as students classify, organize, and synthesize information. “Habits of mind,” such as perseverance and resilience are strengthened through long-term projects. Writing skills are developed through note-taking, reflection activities, and many different types of writing tasks.

In addition, students feel greater ownership for their learning and the learning process and thus develop greater self-esteem with regard to learning. There is greater interest in and curiosity about learning and a willingness to work harder to learn. Students are more likely to retain information longer because it is more meaningful to them and organized in a more interesting fashion.

Finally, students are able to learn the difference between reliable and unreliable information, ideas, and resources, a key need in today’s world with so much misleading and erroneous information.

Summary The stages of research-based learning, key activities, and student outcomes are summarized in chart one, below. This framework also fits nicely with the four-phase model of instruction examined in my book Teaching for Lifelong Learning: How to Prepare Students for a Changing World (Solution Tree Press, 2021) and in a previous Solution Tree blog post: Using a Four-Phase Instructional Model to Plan and Teach for Lifelong Learning .

Teachers who provide a structure for research-based learning as part of their regular teaching routine should experience greater interest and involvement on the part of their students, and help students develop both skills and a fundamental knowledge base that are important for a lifetime of learning.

Zorfass, Judith and Copel. Harriet. The I-Search: Guiding Students Toward Relevant Research. In Educational Leadership, Volume 53, Number 1, September 1995, pp. 48-51.

Zorfass, Judith and Copel, Harriet (1998) Teaching Middle School Students to be Active Researchers. Alexandria, VA: Association for Supervision and Curriculum Development.

Solution Tree

Here's some awesome bio info about me! Short codes are not allowed, but perhaps we can work something else out.

Leave a Reply Cancel reply

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

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

Welcome to a collaborative resource that offers practical guidance and inspiring stories from authors and expert practitioners.

Subscribe to Our Blog

3 Key Learning Outcomes and How to Question Them

Getting to Know Differentiation in the Classroom

3 Books to Fire Up Your Imagination This Summer

How to Foster Civil Mindsets in Classroom Communities

Iowa Reading Research Center

Evidence-based vs. research-based interventions—update.

Have you ever wondered about the terms  evidence-based  and  research-based  interventions? Educators and schools often wrestle with these terms as they consider programs or intervention to implement.

Evidence-Based vs. Research-Based: What is the Difference?

Many publishers tout their programs as being research-based or evidence-based, and oftentimes people use those terms interchangeably.   Have you ever wondered if there was a difference between  evidence-based  and  research-based  and, if so, what that difference might be?

Dr. Sally Shaywitz, Co-Director of the Yale Center for Dyslexia and Creativity, provides a simple explanation in a brief  video clip  about the difference between the two terms and how they relate to reading programs. She discusses how research-based means there are theories behind it, but that they aren’t always proven true. She tells how evidence-based means there is efficacy to back it up.

We can help explain what Dr. Shaywitz meant by “efficacy” in several ways. First, it requires that the program was studied by researchers who were not involved in creating the program. In addition, the researchers cannot stand to profit from the outcomes. Finally, the study the researchers conducted should have the following characteristics:

• The program was compared to another type of program or a different kind of instruction.
• Improvements in students’ reading abilities were measured with valid and reliable instruments.
• There was a thorough description of how the program was implemented so that others could follow those same procedures and include the same elements.
• The effect sizes were reported, and those revealed an improvement that was significantly greater than any improvement in the comparison condition.

Understanding what creates an evidence base is helpful when thinking about intervention programs, particularly for those students with serious reading difficulties. As Dr. Shaywitz commented, “Our children’s reading is too important to be left to theoretical, but unproven, practices and methods. We must replace anecdotal and common, but not evidence-based practices with those that are proven; that is, they are evidence-based.”

Reviews of Intervention Programs

The  Iowa Department of Education  has commissioned a review of the evidence base for literacy interventions commonly used in the state. Studies that examined the effectiveness of these programs were evaluated against rigorous criteria such as those used by the  National Center on Intensive Interventions  (NCII) and the What Works Clearinghouse (WWC). The NCII maintains an  Academic Intervention Tools Chart  to summarize their reviews of individual studies on programs.

The  What Works Clearinghouse  (WWC), part of the Institute of Education Science, is another source for determining which programs and practices have  evidence of effectiveness . The WWC reviews research designed to answer the question, “What works in education?” You can visit the WWC and use the filters to locate reviews specific to reading skills.

• evidence-based practice
• interventions
• research-based practice

6 Spring Break Literacy Activities for Families

Created by the Great Schools Partnership , the GLOSSARY OF EDUCATION REFORM is a comprehensive online resource that describes widely used school-improvement terms, concepts, and strategies for journalists, parents, and community members. | Learn more »

Research-Based

see Evidence-Based

Alphabetical Search

• American Career College
• ACC Library

What is Research and Evidence-based Practice?

• Research Basics
• Peer-reviewed Sources
• Evidence-Based Practice
• Types of Sources

What is Research?

In the medical and allied health fields, researchers conduct scientifically-designed experiments to answer   specific research questions.  E ach study  builds on previous research to add a new piece of evidence to the larger puzzle.   The goal of health research is to improve understanding of a particular field or subject area in order to improve technology, care, and decision-making .  

What is Evidence-based Practice?

As a practitioner, it's important to develop an evidence-based practice (EBP).  EBP focuses on using  current evidence  derived from  clinical research studies  and analysis, in combination with a practitioner's own  experience  and  judgement .  It's a three-part approach called the three-legged stool of EBP .   The three legs are clinical research, clinical experience, and the patient's preferences.  

Clinical research can usually be found in peer-reviewed sources , such as peer-reviewed journal databases.  

Read more  about EBP in a peer-reviewed article from Credo Reference..

What Does Peer-Reviewed Mean?

Peer-reviewed sources  are sources that have been reviewed for quality by experts and professionals according to industry best practices.  Peer reviewed sources are reviewed before they are published , which is different than many websites.  All of the links to library collections are peer-reviewed.   This means that you can generally trust information from the library's collections more than the information on the Web.  

Reference Sources

Reference sources summarize research information in easy-to-understand articles.  You're probably familiar with reference sources like Wikipedia and imdb.com.  For evidence-based practice, you'll have to use peer-reviewed health care reference sites.  Check out our online reference collections .

Images & Media

Of course we find images and stream videos online all the time from YouTube and social media sites.  You want to make sure health care images and videos are professionally created by experts in the field.  Check out our online image and media collections .

Peer-reviewed Journals

Peer-reviewed journals are the best source of research evidence.  They're where health researchers publish results of research studies testing the effectiveness of drugs, interventions, and practices.  

The best peer-reviewed journals aren't usually free, though, so you can't find them through Google.  Since the school buys subscriptions, you can access them for free through the library.  Check out our peer-reviewed journal collections ..

• Next: Peer-reviewed Sources >>
• Last Updated: Dec 23, 2021 1:48 PM
• URL: https://guides.americancareercollege.edu/evidence

Warning: The NCBI web site requires JavaScript to function. more...

An official website of the United States government

The .gov means it's official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you're on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• Browse Titles

NCBI Bookshelf. A service of the National Library of Medicine, National Institutes of Health.

Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr.

Patient Safety and Quality: An Evidence-Based Handbook for Nurses.

Chapter 7 the evidence for evidence-based practice implementation.

Marita G. Titler .

Affiliations

Overview of evidence-based practice.

Evidence-based health care practices are available for a number of conditions such as asthma, heart failure, and diabetes. However, these practices are not always implemented in care delivery, and variation in practices abound. 1–4 Traditionally, patient safety research has focused on data analyses to identify patient safety issues and to demonstrate that a new practice will lead to improved quality and patient safety. 5 Much less research attention has been paid to how to implement practices. Yet, only by putting into practice what is learned from research will care be made safer. 5 Implementing evidence-based safety practices are difficult and need strategies that address the complexity of systems of care, individual practitioners, senior leadership, and—ultimately—changing health care cultures to be evidence-based safety practice environments. 5

Nursing has a rich history of using research in practice, pioneered by Florence Nightingale. 6–9 Although during the early and mid-1900s, few nurses contributed to this foundation initiated by Nightingale, 10 the nursing profession has more recently provided major leadership for improving care through application of research findings in practice. 11

Evidence-based practice (EBP) is the conscientious and judicious use of current best evidence in conjunction with clinical expertise and patient values to guide health care decisions. 12–15 Best evidence includes empirical evidence from randomized controlled trials; evidence from other scientific methods such as descriptive and qualitative research; as well as use of information from case reports, scientific principles, and expert opinion. When enough research evidence is available, the practice should be guided by research evidence in conjunction with clinical expertise and patient values. In some cases, however, a sufficient research base may not be available, and health care decision making is derived principally from nonresearch evidence sources such as expert opinion and scientific principles. 16 As more research is done in a specific area, the research evidence must be incorporated into the EBP. 15

Models of Evidence-Based Practice

Multiple models of EBP are available and have been used in a variety of clinical settings. 16–36 Although review of these models is beyond the scope of this chapter, common elements of these models are selecting a practice topic (e.g., discharge instructions for individuals with heart failure), critique and syntheses of evidence, implementation, evaluation of the impact on patient care and provider performance, and consideration of the context/setting in which the practice is implemented. 15 , 17 The learning that occurs during the process of translating research into practice is valuable information to capture and feed back into the process, so that others can adapt the evidence-based guideline and/or the implementation strategies.

A recent conceptual framework for maximizing and accelerating the transfer of research results from the Agency for Healthcare Research and Quality (AHRQ) patient safety research portfolio to health care delivery was developed by the dissemination subcommittee of the AHRQ Patient Safety Research Coordinating Committee. 37 This model is a synthesis of concepts from scientific information on knowledge transfer, social marketing, social and organizational innovation, and behavior change (see Figure 1 ). 37 Although the framework is portrayed as a series of stages, the authors of this framework do not believe that the knowledge transfer process is linear; rather, activities occur simultaneously or in different sequences, with implementation of EBPs being a multifaceted process with many actors and systems.

AHRQ Model of Knowledge Transfer Adapted from Nieva, V., Murphy, R., Ridley, N., et al. Used with permission. http://www.ahrq.gov/qual/advances/

Steps of Evidence-Based Practice

Steps of promoting adoption of EBPs can be viewed from the perspective of those who conduct research or generate knowledge, 23 , 37 those who use the evidence-based information in practice, 16 , 31 and those who serve as boundary spanners to link knowledge generators with knowledge users. 19

Steps of knowledge transfer in the AHRQ model 37 represent three major stages: (1) knowledge creation and distillation, (2) diffusion and dissemination, and (3) organizational adoption and implementation. These stages of knowledge transfer are viewed through the lens of researchers/creators of new knowledge and begin with determining what findings from the patient safety portfolio or individual research projects ought to be disseminated.

Knowledge creation and distillation is conducting research (with expected variation in readiness for use in health care delivery systems) and then packaging relevant research findings into products that can be put into action—such as specific practice recommendations—thereby increasing the likelihood that research evidence will find its way into practice. 37 It is essential that the knowledge distillation process be informed and guided by end users for research findings to be implemented in care delivery. The criteria used in knowledge distillation should include perspectives of the end users (e.g., transportability to the real-world health care setting, feasibility, volume of evidence needed by health care organizations and clinicians), as well as traditional knowledge generation considerations (e.g., strength of the evidence, generalizability).

Diffusion and dissemination involves partnering with professional opinion leaders and health care organizations to disseminate knowledge that can form the basis of action (e.g., essential elements for discharge teaching for hospitalized patient with heart failure) to potential users. Dissemination partnerships link researchers with intermediaries that can function as knowledge brokers and connectors to the practitioners and health care delivery organizations. Intermediaries can be professional organizations such as the National Patient Safety Foundation or multidisciplinary knowledge transfer teams such as those that are effective in disseminating research-based cancer prevention programs. In this model, dissemination partnerships provide an authoritative seal of approval for new knowledge and help identify influential groups and communities that can create a demand for application of the evidence in practice. Both mass communication and targeted dissemination are used to reach audiences with the anticipation that early users will influence the latter adopters of the new usable, evidence-based research findings. Targeted dissemination efforts must use multifaceted dissemination strategies, with an emphasis on channels and media that are most effective for particular user segments (e.g., nurses, physicians, pharmacists).

End user adoption, implementation, and institutionalization is the final stage of the knowledge transfer process. 37 This stage focuses on getting organizations, teams, and individuals to adopt and consistently use evidence-based research findings and innovations in everyday practice. Implementing and sustaining EBPs in health care settings involves complex interrelationships among the EBP topic (e.g., reduction of medication errors), the organizational social system characteristics (such as operational structures and values, the external health care environment), and the individual clinicians. 35 , 37–39 A variety of strategies for implementation include using a change champion in the organization who can address potential implementation challenges, piloting/trying the change in a particular patient care area of the organization, and using multidisciplinary implementation teams to assist in the practical aspects of embedding innovations into ongoing organizational processes. 35 , 37 Changing practice takes considerable effort at both the individual and organizational level to apply evidence-based information and products in a particular context. 22 When improvements in care are demonstrated in the pilot studies and communicated to other relevant units in the organization, key personnel may then agree to fully adopt and sustain the change in practice. Once the EBP change is incorporated into the structure of the organization, the change is no longer considered an innovation but a standard of care. 22 , 37

In comparison, other models of EBP (e.g., Iowa Model of Evidence-based Practice to Promote Quality of Care 16 ) view the steps of the EBP process from the perspective of clinicians and/or organizational/clinical contexts of care delivery. When viewing steps of the EBP process through the lens of an end user, the process begins with selecting an area for improving care based on evidence (rather than asking what findings ought to be disseminated); determining the priority of the potential topic for the organization; formulating an EBP team composed of key stakeholders; finding, critiquing, and synthesizing the evidence; setting forth EBP recommendations, with the type and strength of evidence used to support each clearly documented; determining if the evidence findings are appropriate for use in practice; writing an EBP standard specific to the organization; piloting the change in practice; implementing changes in practice in other relevant practice areas (depending on the outcome of the pilot); evaluating the EBP changes; and transitioning ongoing quality improvement (QI) monitoring, staff education, and competency review of the EBP topic to appropriate organizational groups as defined by the organizational structure. 15 , 40 The work of EBP implementation from the perspective of the end user is greatly facilitated by efforts of AHRQ, professional nursing organizations (e.g., Oncology Nursing Society), and others that distill and package research findings into useful products and tools for use at the point of care delivery.

When the clinical questions of end users can be addressed through use of existing evidence that is packaged with end users in mind, steps of the EBP process take less time and more effort can be directed toward the implementation, evaluation, and sustainability components of the process. For example, finding, critiquing, and synthesizing the evidence; setting forth EBP recommendations with documentation of the type and strength of evidence for each recommendation; and determining appropriateness of the evidence for use in practice are accelerated when the knowledge-based information is readily available. Some distilled research findings also include quick reference guides that can be used at the point of care and/or integrated into health care information systems, which also helps with implementation. 41 , 42

Translation Science: An Overview

Translation science is the investigation of methods, interventions, and variables that influence adoption by individuals and organizations of EBPs to improve clinical and operational decisionmaking in health care. 35 , 43–46 This includes testing the effect of interventions on promoting and sustaining adoption of EBPs. Examples of translation studies include describing facilitators and barriers to knowledge uptake and use, organizational predictors of adherence to EBP guidelines, attitudes toward EBPs, and defining the structure of the scientific field. 11 , 47–49

Translation science must be guided by a conceptual model that organizes the strategies being tested, elucidates the extraneous variables (e.g., behaviors and facilitators) that may influence adoption of EBPs (e.g., organizational size, characteristics of users), and builds a scientific knowledge base for this field of inquiry. 15 , 50 Conceptual models used in the translating-research-into-practice studies funded by AHRQ were adult learning, health education, social influence, marketing, and organizational and behavior theories. 51 Investigators have used Rogers’s Diffusion of Innovation model, 35 , 39 , 52–55 the Promoting Action on Research Implementation in Health Services (PARIHS) model, 29 the push/pull framework, 23 , 56 , 57 the decisionmaking framework, 58 and the Institute for Healthcare Improvement (IHI) model 59 in translation science.

Study findings regarding evidence-based practices in a diversity of health care settings are building an empirical foundation of translation science. 19 , 43 , 51 , 60–83 These investigations and others 18 , 84–86 provide initial scientific knowledge to guide us in how to best promote use of evidence in practice. To advance knowledge about promoting and sustaining adoption of EBPs in health care, translation science needs more studies that test translating research into practice (TRIP) interventions: studies that investigate what TRIP interventions work, for whom, in what circumstances, in what types of settings; and studies that explain the underlying mechanisms of effective TRIP interventions. 35 , 49 , 79 , 87 Partnership models, which encourage ongoing interaction between researchers and practitioners, may be the way forward to carry out such studies. 56 Challenges, issues, methods, and instruments used in translation research are described elsewhere. 11 , 19 , 49 , 78 , 88–97

• Research Evidence

What Is Known About Implementing Evidence-Based Practices?

Multifaceted implementation strategies are needed to promote use of research evidence in clinical and administrative health care decisionmaking. 15 , 22 , 37 , 45 , 64 , 72 , 77 , 79 , 98 , 99 Although Grimshaw and colleagues 65 suggest that multifaceted interventions are no more effective than single interventions, context (site of care delivery) was not incorporated in the synthesis methodology. As noted by others, the same TRIP intervention may meet with varying degrees of effectiveness when applied in different contexts. 35 , 49 , 79 , 80 , 87 , 100 , 101 Implementation strategies also need to address both the individual practitioner and organizational perspective. 15 , 22 , 37 , 64 , 72 , 77 , 79 , 98 When practitioners decide individually what evidence to use in practice, considerable variability in practice patterns result, 71 potentially resulting in adverse patient outcomes.

For example, an “individual” perspective of EBP would leave the decision about use of evidence-based endotracheal suctioning techniques to each nurse and respiratory therapist. Some individuals may be familiar with the research findings for endotracheal suctioning while others may not. This is likely to result in different and conflicting practices being used as people change shifts every 8 to 12 hours. From an organizational perspective, endotracheal suctioning policies and procedures based on research are written, the evidence-based information is integrated into the clinical information systems, and adoption of these practices by nurses and other practitioners is systematically promoted in the organization. This includes assuring that practitioners have the necessary knowledge, skills, and equipment to carry out the evidence-based endotracheal suctioning practice. The organizational governance supports use of these practices through various councils and committees such as the Practice Committee, Staff Education Committee, and interdisciplinary EBP work groups.

The Translation Research Model, 35 built on Rogers’s seminal work on diffusion of innovations, 39 provides a guiding framework for testing and selecting strategies to promote adoption of EBPs. According to the Translation Research Model, adoption of innovations such as EBPs are influenced by the nature of the innovation (e.g., the type and strength of evidence, the clinical topic) and the manner in which it is communicated (disseminated) to members (nurses) of a social system (organization, nursing profession). 35 Strategies for promoting adoption of EBPs must address these four areas (nature of the EBP topic; users of the evidence; communication; social system) within a context of participative change (see Figure 2 ). This model provided the framework for a multisite study that tested the effectiveness of a multifaceted TRIP intervention designed to promote adoption of evidence-based acute pain management practices for hospitalized older adults. The intervention improved the quality of acute pain management practices and reduced costs. 81 The model is currently being used to test the effectiveness of a multifaceted TRIP intervention to promote evidence-based cancer pain management of older adults in home hospice settings. * This guiding framework is used herein to overview what is known about implementation interventions to promote use of EBPs in health care systems (see Evidence Table ).

*Implementation Model Redrawn from Rogers EM. Diffusion of innovations. 5th ed. New York: The Free Press; 2003; Titler MG, Everett LQ. Translating research into practice: considerations for critical care investigators. Crit Care Nurs Clin North Am 2001a;13(4):587-604. (more...)

Evidence Table

Evidence-Based Practice in Nursing

Nature of the Innovation or Evidence-Based Practice

Characteristics of an innovation or EBP topic that affect adoption include the relative advantage of the EBP (e.g., effectiveness, relevance to the task, social prestige); the compatibility with values, norms, work, and perceived needs of users; and complexity of the EBP topic. 39 For example, EBP topics that are perceived by users as relatively simple (e.g., influenza vaccines for older adults) are more easily adopted in less time than those that are more complex (acute pain management for hospitalized older adults). Strategies to promote adoption of EBPs related to characteristics of the topic include practitioner review and “reinvention” of the EBP guideline to fit the local context, use of quick reference guides and decision aids, and use of clinical reminders. 53 , 59 , 60 , 65 , 74 , 82 , 102–107 An important principle to remember when planning implementation of an EBP is that the attributes of the EBP topic as perceived by users and stakeholders (e.g., ease of use, valued part of practice) are neither stable features nor sure determinants of their adoption. Rather it is the interaction among the characteristics of the EBP topic, the intended users, and a particular context of practice that determines the rate and extent of adoption. 22 , 35 , 39

Studies suggest that clinical systems, computerized decision support, and prompts that support practice (e.g., decisionmaking algorithms, paper reminders) have a positive effect on aligning practices with the evidence base. 15 , 51 , 65 , 74 , 80 , 82 , 102 , 104 , 107–110 Computerized knowledge management has consistently demonstrated significant improvements in provider performance and patient outcomes. 82 Feldman and colleagues, using a just-in-time e-mail reminder in home health care, have demonstrated (1) improvements in evidence-based care and outcomes for patients with heart failure, 64 , 77 and (2) reduced pain intensity for cancer patients. 75 Clinical information systems should deploy the evidence base to the point of care and incorporate computer decision-support software that integrates evidence for use in clinical decisionmaking about individual patients. 40 , 104 , 111–114 There is still much to learn about the “best” manner of deploying evidence-based information through electronic clinical information systems to support evidence-based care. 115

Methods of Communication

Interpersonal communication channels, methods of communication, and influence among social networks of users affect adoption of EBPs. 39 Use of mass media, opinion leaders, change champions, and consultation by experts along with education are among strategies tested to promote use of EBPs. Education is necessary but not sufficient to change practice, and didactic continuing education alone does little to change practice behavior. 61 , 116 There is little evidence that interprofessional education as compared to discipline-specific education improves EBP. 117 Interactive education, used in combination with other practice-reinforcing strategies, has more positive effects on improving EBP than didactic education alone. 66 , 68 , 71 , 74 , 118 , 119 There is evidence that mass media messages (e.g., television, radio, newspapers, leaflets, posters and pamphlets), targeted at the health care consumer population, have some effect on use of health services for the targeted behavior (e.g., colorectal cancer screening). However, little empirical evidence is available to guide framing of messages communicated through planned mass media campaigns to achieve the intended change. 120

Several studies have demonstrated that opinion leaders are effective in changing behaviors of health care practitioners, 22 , 68 , 79 , 100 , 116 , 121–123 especially in combination with educational outreach or performance feedback. Opinion leaders are from the local peer group, viewed as a respected source of influence, considered by associates as technically competent, and trusted to judge the fit between the innovation and the local situation. 39 , 116 , 121 , 124–127 With their wide sphere of influence across several microsystems/units, opinion leaders’ use of the innovation influences peers and alters group norms. 39 , 128 The key characteristic of an opinion leader is that he or she is trusted to evaluate new information in the context of group norms. Opinion leadership is multifaceted and complex, with role functions varying by the circumstances, but few successful projects to implement innovations in organizations have managed without the input of identifiable opinion leaders. 22 , 35 , 39 , 81 , 96 Social interactions such as “hallway chats,” one-on-one discussions, and addressing questions are important, yet often overlooked components of translation. 39 , 59 Thus, having local opinion leaders discuss the EBPs with members of their peer group is necessary to translate research into practice. If the EBP that is being implemented is interdisciplinary in nature, discipline-specific opinion leaders should be used to promote the change in practice. 39

Change champions are also helpful for implementing innovations. 39 , 49 , 81 , 129–131 They are practitioners within the local group setting (e.g., clinic, patient care unit) who are expert clinicians, passionate about the innovation, committed to improving quality of care, and have a positive working relationship with other health care professionals. 39 , 125 , 131 , 132 They circulate information, encourage peers to adopt the innovation, arrange demonstrations, and orient staff to the innovation. 49 , 130 The change champion believes in an idea; will not take “no” for an answer; is undaunted by insults and rebuffs; and, above all, persists. 133 Because nurses prefer interpersonal contact and communication with colleagues rather than Internet or traditional sources of practice knowledge, 134–137 it is imperative that one or two change champions be identified for each patient care unit or clinic where the change is being made for EBPs to be enacted by direct care providers. 81 , 138 Conferencing with opinion leaders and change champions periodically during implementation is helpful to address questions and provide guidance as needed. 35 , 66 , 81 , 106

Because nurses’ preferred information source is through peers and social interactions, 134–137 , 139 , 140 using a core group in conjunction with change champions is also helpful for implementing the practice change. 16 , 110 , 141 A core group is a select group of practitioners with the mutual goal of disseminating information regarding a practice change and facilitating the change by other staff in their unit/microsystem. 142 Core group members represent various shifts and days of the week and become knowledgeable about the scientific basis for the practice; the change champion educates and assists them in using practices that are aligned with the evidence. Each member of the core group, in turn, takes the responsibility for imparting evidence-based information and effecting practice change with two or three of their peers. Members assist the change champion and opinion leader with disseminating the EBP information to other staff, reinforce the practice change on a daily basis, and provide positive feedback to those who align their practice with the evidence base. 15 Using a core-group approach in conjunction with a change champion results in a critical mass of practitioners promoting adoption of the EBP. 39

Educational outreach, also known as academic detailing, promotes positive changes in practice behaviors of nurses and physicians. 22 , 64 , 66 , 71 , 74 , 75 , 77 , 81 , 119 , 143 Academic detailing is done by a topic expert, knowledgeable of the research base (e.g., cancer pain management), who may be external to the practice setting; he or she meets one-on-one with practitioners in their setting to provide information about the EBP topic. These individuals are able to explain the research base for the EBPs to others and are able to respond convincingly to challenges and debates. 22 This strategy may include providing feedback on provider or team performance with respect to selected EBP indicators (e.g., frequency of pain assessment). 66 , 81 , 119

Users of the Innovation or Evidence-Based Practice

Members of a social system (e.g., nurses, physicians, clerical staff) influence how quickly and widely EBPs are adopted. 39 Audit and feedback, performance gap assessment (PGA), and trying the EBP are strategies that have been tested. 15 , 22 , 65 , 66 , 70–72 , 81 , 98 , 124 , 144 PGA and audit and feedback have consistently shown a positive effect on changing practice behavior of providers. 65 , 66 , 70 , 72 , 81 , 98 , 124 , 144 , 145 PGA (baseline practice performance) informs members, at the beginning of change, about a practice performance and opportunities for improvement. Specific practice indicators selected for PGA are related to the practices that are the focus of evidence-based practice change, such as every-4-hour pain assessment for acute pain management. 15 , 66 , 81

Auditing and feedback are ongoing processes of using and assessing performance indicators (e.g., every-4-hour pain assessment), aggregating data into reports, and discussing the findings with practitioners during the practice change. 22 , 49 , 66 , 70 , 72 , 81 , 98 , 145 This strategy helps staff know and see how their efforts to improve care and patient outcomes are progressing throughout the implementation process. Although there is no clear empirical evidence for how to provide audit and feedback, 70 , 146 effects may be larger when clinicians are active participants in implementing change and discuss the data rather than being passive recipients of feedback reports. 67 , 70 Qualitative studies provide some insight into use of audit and feedback. 60 , 67 One study on use of data feedback for improving treatment of acute myocardial infarction found that (1) feedback data must be perceived by physicians as important and valid, (2) the data source and timeliness of data feedback are critical to perceived validity, (3) time is required to establish credibility of data within a hospital, (4) benchmarking improves the validity of the data feedback, and (5) physician leaders can enhance the effectiveness of data feedback. Data feedback that profiles an individual physician’s practices can be effective but may be perceived as punitive; data feedback must persist to sustain improved performance; and effectiveness of data feedback is intertwined with the organizational context, including physician leadership and organizational culture. 60 Hysong and colleagues 67 found that high-performing institutions provided timely, individualized, nonpunitive feedback to providers, whereas low performers were more variable in their timeliness and nonpunitiveness and relied more on standardized, facility-level reports. The concept of useful feedback emerged as the core concept around which timeliness, individualization, nonpunitiveness, and customizability are important.

Users of an innovation usually try it for a period of time before adopting it in their practice. 22 , 39 , 147 When “trying an EBP” (piloting the change) is incorporated as part of the implementation process, users have an opportunity to use it for a period of time, provide feedback to those in charge of implementation, and modify the practice if necessary. 148 Piloting the EBP as part of implementation has a positive influence on the extent of adoption of the new practice. 22 , 39 , 148

Characteristics of users such as educational preparation, practice specialty, and views on innovativeness may influence adoption of an EBP, although findings are equivocal. 27 , 39 , 130 , 149–153 Nurses’ disposition to critical thinking is, however, positively correlated with research use, 154 and those in clinical educator roles are more likely to use research than staff nurses or nurse managers. 155

Social System

Clearly, the social system or context of care delivery matters when implementing EBPs. 2 , 30 , 33 , 39 , 60 , 84 , 85 , 91 , 92 , 101 , 156–163 For example, investigators demonstrated the effectiveness of a prompted voiding intervention for urinary incontinence in nursing homes, but sustaining the intervention in day-to-day practice was limited when the responsibility of carrying out the intervention was shifted to nursing home staff (rather than the investigative team) and required staffing levels in excess of a majority of nursing home settings. 164 This illustrates the importance of embedding interventions into ongoing processes of care.

Several organizational factors affect adoption of EBPs. 22 , 39 , 79 , 134 , 165–167 Vaughn and colleagues 101 demonstrated that organizational resources, physician full-time employees (FTEs) per 1,000 patient visits, organizational size, and whether the facility was located in or near a city affected use of evidence in the health care system of the Department of Veterans Affairs (VA). Large, mature, functionally differentiated organizations (e.g., divided into semiautonomous departments and units) that are specialized, with a focus of professional knowledge, slack resources to channel into new projects, decentralized decisionmaking, and low levels of formalization will more readily adopt innovations such as new practices based on evidence. Larger organizations are generally more innovative because size increases the likelihood that other predictors of innovation adoption—such as slack financial and human resources and differentiation—will be present. However, these organizational determinants account for only about 15 percent of the variation in innovation adoption between comparable organizations. 22 Adler and colleagues 168 hypothesize that while more structurally complex organizations may be more innovative and hence adopt EBPs relatively early, less structurally complex organizations may be able to diffuse EBPs more effectively. Establishing semiautonomous teams is associated with successful implementation of EBPs, and thus should be considered in managing organizational units. 168–170

As part of the work of implementing EBPs, it is important that the social system—unit, service line, or clinic—ensures that policies, procedures, standards, clinical pathways, and documentation systems support the use of the EBPs. 49 , 68 , 72 , 73 , 103 , 140 , 171 Documentation forms or clinical information systems may need revision to support changes in practice; documentation systems that fail to readily support the new practice thwart change. 82

Absorptive capacity for new knowledge is another social system factor that affects adoption of EBPs. Absorptive capacity is the knowledge and skills to enact the EBPs; the strength of evidence alone will not promote adoption. An organization that is able to systematically identify, capture, interpret, share, reframe, and recodify new knowledge, and put it to appropriate use, will be better able to assimilate EBPs. 82 , 103 , 172 , 173 A learning organizational culture and proactive leadership that promotes knowledge sharing are important components of building absorptive capacity for new knowledge. 66 , 139 , 142 , 174 Components of a receptive context for EBP include strong leadership, clear strategic vision, good managerial relations, visionary staff in key positions, a climate conducive to experimentation and risk taking, and effective data capture systems. Leadership is critical in encouraging organizational members to break out of the convergent thinking and routines that are the norm in large, well-established organizations. 4 , 22 , 39 , 122 , 148 , 163 , 175

An organization may be generally amenable to innovations but not ready or willing to assimilate a particular EBP. Elements of system readiness include tension for change, EBP-system fit, assessment of implications, support and advocacy for the EBP, dedicated time and resources, and capacity to evaluate the impact of the EBP during and following implementation. If there is tension around specific work or clinical issues and staff perceive that the situation is intolerable, a potential EBP is likely to be assimilated if it can successfully address the issues, and thereby reduce the tension. 22 , 175

Assessing and structuring workflow to fit with a potential EBP is an important component of fostering adoption. If implications of the EBP are fully assessed, anticipated, and planned for, the practice is more likely to be adopted. 148 , 162 , 176 If supporters for a specific EBP outnumber and are more strategically placed within the organizational power base than opponents, the EBP is more likely to be adopted by the organization. 60 , 175 Organizations that have the capacity to evaluate the impact of the EBP change are more likely to assimilate it. Effective implementation needs both a receptive climate and a good fit between the EBP and intended adopters’ needs and values. 22 , 60 , 140 , 175 , 177

Leadership support is critical for promoting use of EBPs. 33 , 59 , 72 , 85 , 98 , 122 , 178–181 This support, which is expressed verbally, provides necessary resources, materials, and time to fulfill assigned responsibilities. 148 , 171 , 182 , 183 Senior leaders need to create an organizational mission, vision, and strategic plan that incorporate EBP; implement performance expectations for staff that include EBP work; integrate the work of EBP into the governance structure of the health care system; demonstrate the value of EBPs through administrative behaviors; and establish explicit expectations that nurse leaders will create microsystems that value and support clinical inquiry. 122 , 183 , 184

A recent review of organizational interventions to implement EBPs for improving patient care examined five major aspects of patient care. The review suggests that revision of professional roles (changing responsibilities and work of health professionals such as expanding roles of nurses and pharmacists) improved processes of care, but it was less clear about the effect on improvement of patient outcomes. Multidisciplinary teams (collaborative practice teams of physicians, nurses, and allied health professionals) treating mostly patients with prevalent chronic diseases resulted in improved patient outcomes. Integrated care services (e.g., disease management and case management) resulted in improved patient outcomes and cost savings. Interventions aimed at knowledge management (principally via use of technology to support patient care) resulted in improved adherence to EBPs and patient outcomes. The last aspect, quality management, had the fewest reviews available, with the results uncertain. A number of organizational interventions were not included in this review (e.g., leadership, process redesign, organizational learning), and the authors note that the lack of a widely accepted taxonomy of organizational interventions is a problem in examining effectiveness across studies. 82

An organizational intervention that is receiving increasing attention is tailored interventions to overcome barriers to change. 162 , 175 , 185 This type of intervention focuses on first assessing needs in terms of what is causing the gap between current practice and EBP for a specified topic, what behaviors and/or mechanism need to change, what organizational units and persons should be involved, and identification of ways to facilitate the changes. This information is then used in tailoring an intervention for the setting that will promote use of the specified EBP. Based on a recent systematic review, effectiveness of tailored implementation interventions remains uncertain. 185

In summary, making an evidence-based change in practice involves a series of action steps and a complex, nonlinear process. Implementing the change will take several weeks to months, depending on the nature of the practice change. Increasing staff knowledge about a specific EBP and passive dissemination strategies are not likely to work, particularly in complex health care settings. Strategies that seem to have a positive effect on promoting use of EBPs include audit and feedback, use of clinical reminders and practice prompts, opinion leaders, change champions, interactive education, mass media, educational outreach/academic detailing, and characteristics of the context of care delivery (e.g., leadership, learning, questioning). It is important that senior leadership and those leading EBP improvements are aware of change as a process and continue to encourage and teach peers about the change in practice. The new practice must be continually reinforced and sustained or the practice change will be intermittent and soon fade, allowing more traditional methods of care to return. 15

• Practice Implications From Translation Science

Principles of Evidence-Based Practice for Patient Safety

Several translation science principles are informative for implementing patient safety initiatives:

• First, consider the context and engage health care personnel who are at the point of care in selecting and prioritizing patient safety initiatives, clearly communicating the evidence base (strength and type) for the patient safety practice topic(s) and the conditions or setting to which it applies. These communication messages need to be carefully designed and targeted to each stakeholder user group.
• Second, illustrate, through qualitative or quantitative data (e.g., near misses, sentinel events, adverse events, injuries from adverse events), the reason the organization and individuals within the organization should commit to an evidence-based safety practice topic. Clinicians tend to be more engaged in adopting patient safety initiatives when they understand the evidence base of the practice, in contrast to administrators saying, “We must do this because it is an external regulatory requirement.” For example, it is critical to converse with busy clinicians about the evidence-based rationale for doing fall-risk assessment, and to help them understand that fall-risk assessment is an external regulatory agency expectation because the strength of the evidence supports this patient safety practice.
• Third, didactic education alone is never enough to change practice; one-time education on a specific safety initiative is not enough. Simply improving knowledge does not necessarily improve practice. Rather, organizations must invest in the tools and skills needed to create a culture of evidence-based patient safety practices where questions are encouraged and systems are created to make it easy to do the right thing.
• Fourth, the context of EBP improvements in patient safety need to be addressed at each step of the implementation process; piloting the change in practice is essential to determine the fit between the EBP patient safety information/innovation and the setting of care delivery. There is no one way to implement, and what works in one agency may need modification to fit the organizational culture of another context.
• Finally, it is important to evaluate the processes and outcomes of implementation. Users and stakeholders need to know that the efforts to improve patient safety have a positive impact on quality of care. For example, if a new barcoding system is being used to administer blood products, it is imperative to know that the steps in the process are being followed (process indicators) and that the change in practice is resulting in fewer blood product transfusion errors (outcome indicators).

Research Implications

Translation science is young, and although there is a growing body of knowledge in this area, we have, to date, many unanswered questions. These include the type of audit and feedback (e.g., frequency, content, format) strategies that are most effective, the characteristics of opinion leaders that are critical for success, the role of specific context variables, and the combination of strategies that are most effective. We also know very little about use of tailored implementation interventions, or the key context attributes to assess and use in developing and testing tailored interventions. The types of clinical reminders that are most effective for making EBP knowledge available at the point of care require further empirical explanation. We also know very little about the intensity and intervention dose of single and multifaceted strategies that are effective for promoting and sustaining use of EBPs or how the effectiveness differs by type of topic (e.g., simple versus complex). Only recently has the context of care delivery been acknowledged as affecting use of evidence, and further empirical work is needed in this area to understand how complex adaptive systems of practice incorporate knowledge acquisition and use. Lastly, we do not know what strategies or combination of strategies work for whom, in what context, why they work in some settings or cases and not others, and what is the mechanism by which these strategies or combination of strategies work.

This is an exciting area of investigation that has a direct impact on implementing patient safety practices. In planning investigations, researchers must use a conceptual model to guide the research and add to the empirical and theoretical understanding of this field of inquiry. Additionally, funding is needed for implementation studies that focus on evidence-based patient safety practices as the topic of concern. To generalize empirical findings from patient safety implementation studies, we must have a better understanding of what implementation strategies work, with whom, and in what types of settings, and we must investigate the underlying mechanisms of these strategies. This is likely to require mixed methods, a better understanding of complexity science, and greater appreciation for nontraditional methods and realistic inquiry. 87

Although the science of translating research into practice is fairly new, there is some guiding evidence of what implementation interventions to use in promoting patient safety practices. However, there is no magic bullet for translating what is known from research into practice. To move evidence-based interventions into practice, several strategies may be needed. Additionally, what works in one context of care may or may not work in another setting, thereby suggesting that context variables matter in implementation. 80

• Search Strategy

Several electronic databases were searched (MEDLINE ® , CINAHL ® , PubMed ® ) using terms of evidence-based practice research, implementation research, and patient safety. (The terms “quality improvement” or “quality improvement intervention research” were not used.) The Cochrane Collaboration–Cochrane Reviews was also searched to look for systematic reviews of specific implementation strategies, and the Journal of Implementation Science was also reviewed. I also requested the final reports of the TRIP I and TRIP II studies funded by AHRQ. Classic articles known to the author were also included in this chapter (e.g.,Locock et al. 123 ).

*Principal Investigator: Keela Herr (R01 grant no. CA115363-01; National Cancer Institute (NCI))Background

• Cite this Page Titler MG. The Evidence for Evidence-Based Practice Implementation. In: Hughes RG, editor. Patient Safety and Quality: An Evidence-Based Handbook for Nurses. Rockville (MD): Agency for Healthcare Research and Quality (US); 2008 Apr. Chapter 7.
• PDF version of this page (470K)

In this Page

Other titles in this collection.

• Advances in Patient Safety

Related information

• PMC PubMed Central citations
• PubMed Links to PubMed

Similar articles in PubMed

• It Is Not That Simple nor Compelling! Comment on "Translating Evidence Into Healthcare Policy and Practice: Single Versus Multi-faceted Implementation Strategies - Is There a Simple Answer to a Complex Question?". [Int J Health Policy Manag. 2015] It Is Not That Simple nor Compelling! Comment on "Translating Evidence Into Healthcare Policy and Practice: Single Versus Multi-faceted Implementation Strategies - Is There a Simple Answer to a Complex Question?". Bucknall T, Fossum M. Int J Health Policy Manag. 2015 Jul 28; 4(11):787-8. Epub 2015 Jul 28.
• Nursing implementation science: how evidence-based nursing requires evidence-based implementation. [J Nurs Scholarsh. 2008] Nursing implementation science: how evidence-based nursing requires evidence-based implementation. van Achterberg T, Schoonhoven L, Grol R. J Nurs Scholarsh. 2008; 40(4):302-10.
• Review The science of implementation: changing the practice of critical care. [Curr Opin Crit Care. 2008] Review The science of implementation: changing the practice of critical care. Weinert CR, Mann HJ. Curr Opin Crit Care. 2008 Aug; 14(4):460-5.
• Review Integrative review of implementation strategies for translation of research-based evidence by nurses. [Clin Nurse Spec. 2014] Review Integrative review of implementation strategies for translation of research-based evidence by nurses. Wuchner SS. Clin Nurse Spec. 2014 Jul-Aug; 28(4):214-23.
• Review A realist analysis of hospital patient safety in Wales: applied learning for alternative contexts from a multisite case study [ 2015] Review A realist analysis of hospital patient safety in Wales: applied learning for alternative contexts from a multisite case study Herepath A, Kitchener M, Waring J. 2015 Sep

Recent Activity

• The Evidence for Evidence-Based Practice Implementation - Patient Safety and Qua... The Evidence for Evidence-Based Practice Implementation - Patient Safety and Quality

Your browsing activity is empty.

Activity recording is turned off.

Turn recording back on

Connect with NLM

National Library of Medicine 8600 Rockville Pike Bethesda, MD 20894

Web Policies FOIA HHS Vulnerability Disclosure

Help Accessibility Careers

The Dyslexia Initiative

Advocating. educating. inspiring. empowering., join the #dyslexiarevolution.

• Apr 5, 2021

Research v Evidence, What does it all really mean?

by Ashley Roberts

A question that comes up a great deal within our community is what is the difference between evidence-based and research-based programs? This is a fair question that deserves a proper answer. Alyssa Ciarlante defines them as:

" Evidence-Based Practices or Evidence-Based Programs refer to individual practices (for example, single lessons or in-class activities) or programs (for example, year-long curricula) that are considered effective based on scientific evidence. To deem a program or practice “evidence-based,” researchers will typically study the impact of the resource(s) in a controlled setting, for example, they may study differences in skill growth between students whose educators used the resources and students whose educators did not. If sufficient research suggests that the program or practice is effective, it may be deemed “evidence-based.”

Evidence-Informed, also known as Research-Based, Practices are practices which were developed based on the best research available in the field. This means that users can feel confident that the strategies and activities included in the program or practice have a strong scientific basis for their use. Unlike Evidence-Based Practices or Programs, Research-Based Practices have not been researched in a controlled setting.

Terms like “evidence-based” or “research-based” are useful indicators of the type of evidence that exists behind programs, practices, or assessments, however, they can only tell us so much about the specific research behind each tool. For situations where more information on a resource’s evidence base would be beneficial, it may be helpful to request research summaries or articles from the resource’s publisher for further review, but regardless, evidence-based is the preferred method, not researched-based."

That might be clear as mud so let' try this approach from the Child Welfare Information Gateway:

"Evidence-based practices are approaches to prevention or treatment that are validated by some form of documented scientific evidence. This includes findings established through controlled clinical studies , but other methods of establishing evidence are valid as well.

Evidence-based programs use a defined curriculum or set of services that, when implemented with fidelity as a whole, has been validated by some form of scientific evidence. Evidence-based practices and programs may be described as "supported" or "well-supported", depending on the strength of the research design.

Evidence-informed practices use the best available research and practice knowledge to guide program design and implementation. This informed practice allows for innovation while incorporating the lessons learned from the existing research literature. Ideally, evidence-based and evidence-informed programs and practices should be individualized."

And then let's put it through this lens:

Science-based - Parts or components of the program or method are based on Science.

Research-based - Parts or components of the program or method are based on practices demonstrated effective through Research.

Evidence-based - The entire program or method has been demonstrated through Research to be effective.

What this boils down to is that evidence-based is PREFERRED over research-based. Think of it this way, evidence-based means significant studies were performed, with control groups, meeting the criteria of scientific research, and that the results were repeatable numerous times with minimal variation. Research-based means someone stands on the shoulders of the giants who did the work for the evidence to create something based off of the evidence, but it isn't put through the same rigors. It can be though and that's a very clear distinction. Research-based programs can be studied until they become evidence-based, but not all are.

Now the challenge is, when being presented with remediation plans for your child, in differentiating between the two, and knowing which one is being used with your child. We at DI are keen advocates in knowing exactly what program(s) schools are using with your child, and while you should ask if it's evidence or research-based, it is also up to you to find that data yourself by calling the

publisher. The publisher should be willing to turn over the data, if not, let that be a sign that something is amiss.

Now, in Overcoming Dyslexia, Dr. Sally Shaywitz refers to the What Works Clearinghouse for referencing which programs are evidence v research based.

(https://ies.ed.gov/ncee/wwc/) "The What Works Clearinghouse is an investment of the Institute of Education Sciences (IES) within the U.S. Department of Education that was established in 2002. The work of the WWC is managed by a team of staff at IES and conducted under a set of contracts held by several leading firms with expertise in education, research methodology, and the dissemination of education research. Follow the links to find more information about the key staff from American Institutes for Research, Mathematica Policy Research, Abt Associates, and Development Services Group, Inc who contribute to the WWC investment."

The issue here is that too many question the validity of WWC. Programs like Fountas and Pinnell and other balanced literacy programs are given high marks, while some well known dyslexia programs are not, if they're even included at all.

So then what is a parent to do?

As stated, get the evidence or research, whichever is available, from the publisher and with an understanding of scientific principles and methodologies, review the evidence with a discerning eye. Ask questions like how many children were in the trials? If it's 5 then the findings can't be very legitimate. If enough children were used to make up a large enough statistical pool then the findings are more valid. This is just an example, but a key one within educational data that must always be at the forefront. Why? Too many papers exist calling programs / data "research-based" when in fact scientific principles and statistical modeling were not followed correctly therefore the data upon which the programs are based is in essence invalid. As you start to look at the data, you will start to see what to look for, i.e. what questions to ask.

But, this brings up an important point that we've had to repeat a few times lately, at DI we do not recommend or back any programs. We are parent advocates, not researchers and we do not possess the expertise we believe is necessary to do so. We defer to the list of approved programs that The International Dyslexia Association has already defined.

References :

“Evidence-Based” vs. “Research-Based”: Understanding the Differences, https://apertureed.com/evidence-based-vs-research-based-understanding-differences/

Child Welfare Information Gateway, https://www.childwelfare.gov/topics/management/practice-improvement/evidence/ebp/definitions/

Evidence Based Assessment, https://pubmed.ncbi.nlm.nih.gov/17716047/

ESSA, https://www2.ed.gov/policy/elsec/leg/essa/guidanceuseseinvestment.pdf

Science-based, Research-based, Evidence-based: What's the difference?, https://www.dynaread.com/science-based-research-based-evidence-based

Recent Posts

A Response to Lucy's Rebranding Following Columbia University's Retreat From Her Curriculum

Lucy’s Misguided Desire For An Apology

Phoenix Rising

Donate (opens in a new window)

Curriculum and Instruction

12 Components of Research-Based Reading Programs

As children learn to read, they learn how spoken and written language relate to each other. For this to happen, the components of the reading program, including the instructional materials selected for classroom use, must relate to one another and be orchestrated into sequences of instruction that engage all children and meet their needs. The following are twelve of the essential components of research-based programs.

1. Children have opportunities to expand their use and appreciation of oral language

Children’s comprehension of written language depends in large part upon their effective use and understanding of oral language . Language experiences are a central component of good reading instruction. Children learn a great deal about the world, about themselves, and about each other from spoken language. Kindergarten and first-grade language instruction that focuses on listening, speaking, and understanding includes the following:

• Discussions that focus on a variety of topics, including problem solving
• Activities that help children understand the world, in and out of the classroom
• Songs, chants, and poems that are fun to sing and say
• Concept development and vocabulary -building lessons
• Games and other activities that involve talking, listening and, in particular, following directions

2. Children have opportunities to expand their use and appreciation of printed language

Children’s appreciation and understanding of the purposes and functions of written language are essential to their motivation for learning to read. Children must become aware that printed language is all around them on signs, billboards, and labels, and in books, magazines, and newspapers and that print serves many different purposes. Reading and writing instruction that focuses on the use and appreciation of written language includes the following:

• Activities that help children to understand that print represents spoken language
• Activities that highlight the meanings, uses, and production of print found in classroom signs, labels, notes, posters, calendars, and directions
• Activities that teach print conventions, such as directionality
• Activities in which children practice how to handle a book-how to turn pages, how to find the tops and bottoms of pages, and how to tell the front and back covers
• Lessons in word awareness that help children become conscious of individual words, for example, their boundaries, their appearance and their length
• Activities in which children practice with predictable and patterned language stories

3. Children have opportunities to hear good stories and informational books read aloud daily

Listening to and talking about books on a regular basis provides children with demonstrations of the benefits and pleasures of reading. Story reading introduces children to new words, new sentences, new places, and new ideas. They also hear the kinds of vocabulary , sentences, and text structures they will find in their school books and be expected to read and understand. Reading aloud to children every day, and talking about books and stories, supports and extends oral language development and helps students connect oral to written language.

4. Children have opportunities to understand and manipulate the building blocks of spoken language

Children’s ability to think about individual words as a sequence of sounds ( phonemes ) is important to their learning how to read an alphabetic language. Toward that understanding, children learn that sentences are made up of groups of separate words, and that words are made up of separate sounds. Indeed, research has shown conclusively that children’s phonemic awareness , their understanding that spoken words can be divided into separate sounds, is one of the best predictors of their success in learning to read. Instruction that promotes children’s understanding and use of the building blocks of spoken language includes the following:

• Language games that teach children to identify rhyming words and to create rhymes on their own
• Activities that help children understand that spoken sentences are made up of groups of separate words, that words are made up of syllables, and that words can be broken down into separate sounds
• Auditory activities in which children manipulate the sounds of words, separate or segment the sounds of words, blend sounds, delete sounds, or substitute new sounds for those deleted

5. Children have opportunities to learn about and manipulate the building blocks of written language

Children must also become expert users of the building blocks of written language. Knowledge of letters (graphonemes) leads to success with learning to read. This includes the use, purpose, and function of letters. Instruction that helps children learn about the essential building blocks of written language includes the following:

• Alphabetic knowledge activities in which children learn the names of letters and learn to identify them rapidly and accurately
• A variety of writing activities in which children learn to print the letters that they are learning to identify
• Writing activities in which children have the opportunity to experiment with and manipulate letters to make words and messages

6. Children have opportunities to learn the relationship between the sounds of spoken language and the letters of written language

Increasing children’s awareness of the sounds of spoken language and their familiarity with the letters of written language prepares them to understand the alphabetic principle -that written words are composed of patterns of letters that represent the sounds of spoken words.

Effective instruction provides children with explicit and systematic teaching of sound-letter relationships in a sequence that permits the children to assimilate and apply what they are learning. Instruction that helps children understand the alphabetic principle and learn the most common relationships between sounds and letters includes the following:

• Alphabetic awareness activities in which children learn that printed words are made up of patterns of letters
• Lessons in sound-letter relationships that are organized systematically and that provide as much practice and review as is needed
• Activities in which children combine and manipulate letters to change words and spelling patterns

7. Children have opportunities to learn decoding strategies

Efficient decoding strategies permit readers to quickly and automatically translate the letters or spelling patterns of written words into speech sounds so that they can identify words and gain rapid access to their meanings. Children must learn to identify words quickly and effortlessly, so that they can focus on the meaning of what they are reading.

Research indicates that good readers rely primarily on print rather than on pictures or context to help them identify familiar words, and also to figure out words they have not seen before. For this reason, it is important that children learn effective sounding-out strategies that will allow them to decode words they have never seen in print.

Some strategies of decoding instruction focus primarily on the relationships between sounds and letters; others combine letter-sound practice with word families, with word parts (for example, onsets and rimes), and with blending activities. More advanced decoding strategies focus on structural analysis, the identification of root words , and prefixes and suffixes.

Instruction should introduce “irregular” words in a reasonable sequence and use these words in the program’s reading materials. It is important to realize, however, that essentially all words must become “ sight words ” - words children identify quickly, accurately, and effortlessly.

Effective decoding instruction is explicit and systematic and can include the following:

• Practice in decoding and identifying words that contain the letter-sound relationships children are learning to read and need for reading and writing
• Practice activities that involve word families and rhyming patterns
• Practice activities that involve blending together the components of sounded-out words
• “Word play” activities in which children change beginning, middle, or ending letters of related words, thus changing the words they decode and spell
• Introduction of phonetically “irregular” words in practice activities and stories

8. Children have opportunities to write and relate their writing to spelling and reading

As children learn to read and write words, they become aware of how these words are spelled. Increasing children’s awareness of spelling patterns hastens their progress in both reading and writing. In the early grades, spelling instruction must be coordinated with the program of reading instruction. As children progress, well organized, systematic lessons in spelling will be beneficial. Activities for effective spelling instruction should include the following:

• Activities that are related to the words that children are reading and writing
• Proofreading activities
• An emphasis on pride in correct spelling
• Lessons that help children attend to spelling conventions in a systematic way
• Activities that surround children in words and make reading and writing purpose-filled

9. Children have opportunities to practice accurate and fluent reading in decodable stories

The words in decodable stories do emphasize the sound-letter relation ships the children are learning. While many predictable and patterned books provide children with engaging language and print experiences, these books may not be based on the sound-letter relationships the children are learning.

Decodable stories provide children with the opportunity to practice what they are learning about letters and sounds. As children learn to read words, sentences, and stories fluently, accurately, and automatically, they no longer have to struggle to identify words and are free to pay closer attention to the meaning.

Research asserts that most children benefit from direct instruction in decoding , complemented by practice with simply written decodable stories. Further, for some children this sort of systematic approach is critical. Stories should “fit” the child’s reading level. Beginning readers should be able to read easily 90 percent or more of the words in a story, and after practice should be able to do so quickly, accurately, and effortlessly.

10. Children have opportunities to read and comprehend a wide assortment of books and other texts

As children develop effective decoding strategies and become fluent readers, they must read books and other texts that are less controlled in their vocabulary and sentence structure. They learn to use word order ( syntax ) and context to interpret words and understand their meanings. Soon, they become enthusiastic, independent readers of all kinds of written material including books, magazines, newspapers, computer screens, and more!

Providing children with a great many books, both narrative and informational, is of primary importance. Classroom and campus libraries must offer children a variety of reading materials, some that are easy to read and others that are more challenging and of increasing difficulty and complexity. Children need access to many books that travel home for reading with family members. Classrooms that ensure wide reading provide the following:

• Daily time for self-selected reading
• Access to books children want to read in their classrooms and school libraries
• Access to books that can be taken home to be read independently or to family members

11. Children have opportunities to develop and comprehend new vocabulary through wide reading and direct vocabulary instruction

Written language places greater demands on children’s vocabulary knowledge than does their everyday spoken language.

In fact, many of the new words children learn in a year are learned from concrete and meaningful experiences from being read to and as they read on their own.

It is obvious that the number of new words children learn from reading depends upon how much they read and that the amount children read varies enormously. Therefore, it is important that teachers read aloud to children and encourage them to do a great deal of voluntary and independent reading. In addition, during reading instruction, children should be encouraged to attend to the meanings of new words. Activities that promote the acquisition of vocabulary include the following:

• Wide reading of a variety of genres, both narrative and informational
• Instruction that provides explicit information both about the meanings of words and about how they are used in the stories the children are reading
• Activities that involve children in analyzing context to figure out the meaning of unfamiliar words in a reading passage
• Discussions of new words that occur during the course of the day, for example in books that have been read aloud by the teacher, in content area studies and in textbooks
• Activities that encourage children both to use words they are learning in their own writing, and to keep records of interesting and related words

12. Children have opportunities to learn and apply comprehension strategies as they reflect upon and think critically about what they read

Written language is not just speech written down. Instead, written language offers new vocabulary , new language patterns, new thoughts, and new ways of thinking. Comprehension depends on the ability to identify familiar works quickly and automatically, which includes fluent reading, as well as the ability to figure out new words. But this is not enough.

Comprehension also depends upon the understanding of word meanings, on the development of meaningful ideas from groups of words (phrases, clauses, and sentences) and the drawing of inferences. It also depends upon the demands of the text (its concepts, its density), and the knowledge the reader brings to the text. The discussion of good books with their friends and classmates is one avenue for making these connections.

Such discussions will help children to appreciate and reflect on new aspects of written language and on the wide, wonderful world of print. For children to receive the greatest benefit and enjoyment from their reading, they must receive comprehension strategy instruction that builds on their knowledge of the world and of language. Comprehension strategy instruction can include the following:

• Activities that help children learn to preview selections, anticipate content, and make connections between what they will read and what they already know
• Instruction that provides options when understanding breaks down (for example, rereading, asking for expert help, and looking up words)
• Guidance in helping children compare characters, events, and themes of different stories
• Activities that encourage discussion about what is being read and how ideas can be linked (for example, to draw conclusions and make predictions)
• Activities that help children extend their reading experiences though the reading of more difficult texts with the teacher

As these components are translated into classroom experiences, children will have opportunities to talk, read, and write in the many ways they use language both inside and out of the classroom. Because the language arts (reading, writing, listening and speaking) are so interrelated, children must be given the opportunity to practice the strands of language arts in connected and purposeful ways.

Classroom experiences that offer children opportunities to write for real life reasons include having children write letters of invitation to parents and other community members to visit their classrooms, or writing letters of thanks to individuals and organizations that have contributed to their school. Children write to record newly acquired information, to reflect on what they are learning and to organize their ideas. They also work in groups to write reports on special topics.

Classroom experiences that offer children opportunities to read, listen and speak for real life purposes include the reading of “everyday” notes, news, messages, lists, labels, and the reading of compositions and reports written in the classroom. In such classrooms, reading, writing, listening, and speaking become important and meaningful to every child.

Liked it? Share it!

Adams, M. J. (1990). Beginning to Read: Thinking and Learning About Print. Cambridge, MA: MIT Press.

Anderson, R. C., Hiebert, E. H., Scott, J. A., & Wilkinson, I. A. G. (1985). Becoming a Nation of Readers: The Report of the Commission on Reading. Champaign, IL: Center for the Study of Reading. Washington, DC: National Institute of Education.

Baker, S. K., Kame’enui, E. J., Simmons, D. C., Stahl, S. (1994). Beginning reading: Educational tools for diverse learners. School Psychology Review, 23, pp. 372-391.

Bear, D. R., Invernizzi, M., Templeton, S., & Johnston, F. (1996). Words their way: Word study for phonics, vocabulary, and spelling. Englewood Cliffs, NJ: Prentice-Hall.

Beck, I. L., & Juel C. (1992). The role of decoding in learning to read. In S. J. Samuels & A. E. Farstrup (Eds.), What research has to say about reading instruction (2nd ed., pp. 101-123). Newark, DE: International Reading Association

Beck, I. L., & McKeown, M. G. (1991). Conditions of vocabulary acquisition. In R. Barr, M. L. Kamil, P. Mosenthal, & P. D. Pearson (eds.), Handbook of reading research (Vol. 2, pp. 789-814). New York: Longman.

Biemiller, A. (1994). Some observations on beginning reading instruction. Educational Psychologist, 29, pp. 203-209.

Blachman, B. A. (1984). Relationship of rapid naming ability and language analysis skills to kindergarten and first-grade reading achievement. Journal of Education Psychology, 76, pp. 610-622.

Blachman, B. A. (1991). Getting ready to read: Learning how print maps to speech. In J. Kavanagh (Ed.), The language continuum: From infancy to literacy (pp. 1-22). Washington, DC: U. S. Dept. of Health and Human Services.

Brophy, J. and Good, T. (1986). Teacher behavior and student achievement. Handbook on Research on Teaching (3rd ed.). M. Wittrock (Ed.). New York: Macmillan.

Calfee, R. C., & Moran, C. (1993). Comprehending orthography, social construction of letter-sound in monolingual and bilingual programs. Reading and Writing: An Interdisciplinary Journal, 5, pp.205-225.

Calfee, R. C. A. (1995). Behind-the-scenes look at reading acquisition. Issues in Education, 1, pp. 77-82.

California Reading Task Force. (1995). Every child a reader. Sacramento, CA: Department of Education.

Chall, J. S. (1967). Learning to read: the great debate. New York: McGraw-Hill.

Clay, M. M. (1972). Reading: The patterning of complex behavior. Auckland, New Zealand: Heinemann.

Clay, M. M. (1991). The early detection of reading difficulties (3rd ed.). Portsmouth, NH: Heinemann.

Clay, M. M. (1991). Becoming literate: The construction of inner control. Portsmouth, NH: Heinemann.

Cummins, J. (1981). The role of primary language development in promoting educational success for language of minority students. Sacramento, CA: Department of Education.

Cunningham, P. (1991). Phonics they use: Words for reading and writing. New York: Teachers College Press.

Ehri, L. C. (1986). Sources of difficulty in learning to spell and read words. In M. L. Wolraich & D. Routh (Eds.), Advances in developmental and behavioral pediatrics (Vol. 7, pp. 121-195). Greenwich, CT: JAI Press.

Ehri, L. C. (1987). Learning to read and spell words. Journal of Reading Behavior, 19, pp. 5-31

Ehri, L. C. (1991). Development of the ability to read words. In R. Barr, M. L. Karmil, P. B. Mosenthal, & P. D. Pearson (Eds.), Handbook of Reading Research (Vol. 2, pp. 383-417). New York: Longman.

Ehri, L. C., & Wilce, L. S. (1985). Movement into reading: is the first stage of printed word learning visual or phonetic? Reading Research Quarterly, 20, 163-179.

Foorman, B. R. (in press). The case for early reading intervention. Foundations of reading acquisition. Mahwah, NJ: Lawrence Erlbaum.

Fox, B., & Routh, D. K. (1975). Analyzing spoken language into words, syllables, and phonemes: Adevelopmental study. Journal of Psycholinguistic Research, 4, pp. 331-412.

Goldfield, B. A., & Snow, C. A. (1984). Reading books with children: The mechanics of parental influences on children’s reading achievement. In J. Flood (Ed.), Understanding reading comprehension. Newark, DE: International Reading Association (pp. 204-215).

Graves, D. (1994). A fresh look at writing. Portsmouth, NH: Heinemann.

Hodgkinson, J. L. (1992). A demographic look at tomorrow (Report No. ISBN-0-937846-57-0). Washington, DC: Institute for Educational Leadership (ERIC Document Reproduction Service No.ED 359 087).

Hodgkinson, H. L. (1993). American education: The good, the bad, and the task. Phi Delta Kappan 74, pp. 619-623.

Johnston, F., Juel, C., & Invernizzi, M. (1995). Guidelines for volunteer tutors of emergent and early readers. Charlottesville, VA: University of Virginia Bookstore.

Juel, C. (1994). Learning to read and write in one elementary school. New York: Springer-Verlag.

Kame’enui, E. J., & Carnine, D. W. (Eds.). (in press). Effective teaching strategies that accommodate diverse learners. Columbus, OH: Merrill Education, Prentice Hall.

Kirk, C. (1079). Patterns of word segmentation in preschool children. Child Study Journal, 9, pp. 37-49.

Liberman, I. Y., Shankweiler, D., & Liberman, A. M. (1991). The alphabetic principle and learning to read. In Phonology and reading disability: Solving the reading puzzle. Washington, DC: International Academy for Research in Learning Disabilities, Monograph Series, U. S. Dept. of Health and Human Services, Public Health Service; National Institutes of Health.

Lindamood, C., Bell, N., & Lindamood, P. (1992). Issues in phonological awareness assessment. Annals of Dyslexia, 42, pp. 242-259.

Lindamood C., Lindamood, P. (1975). The A. D. D. program: auditory discrimination in depth (2nd ed.). Columbus, OH: SRA Division, Macmillan/McGraw-Hill.

Lundberg, I., Grost, J., & Peterson, O. P. (1988). Effects of an extensive program for stimulating phonological awareness in preschool children. Reading Research Quarterly, 23, pp. 264-284.

Lyon, G. R. (1994). Research in learning disabilities at the NICHD. Bethesda, MD: NICHD Technical Document/Human Learning and Behavior Branch.

Mason, J. M. (1980). When do children begin to read: An exploration of four-year-old children’s letter and word reading competencies. Reading Research Quarterly 15, pp. 203-227.

McKeown, M. G. (1985). The acquisition of word meaning from context by children of high and low ability. Reading Research Quarterly, 20, pp. 482-496.

Meltzer, N. S., & Herse, R. (1969). The boundaries of written words as seen by first graders. Journal of Reading Behavior, 1, pp. 3-14.

Miller, C., & Winick, D. M. (1996, November). Public school accountability: The Texas story. Texas Monthly, pp. 112-118.

Moats, L. C. (1995). Spelling: Development, Disabilities, and instruction. Timonium, MD: York Press.

Nagy, W. E. (1988). Teaching vocabulary to Improve reading comprehension. Urbanna, IL: ERIC Clearinghouse on Reading and Communication Skills. Urbana, IL: National Council of Teachers of English.

Nagy, W. E., & Anderson, R. C. (1984). How many words are there in printed school English? Reading Research Quarterly, 19, pp. 304-330.

Nagy, W. E., Anderson, R. C., & Herman, P. A. (1987). Learning word meanings from context during normal reading. American Educational Research Journal, 24, pp. 2370270.

Perfetti, C. A. (1985). Reading ability. New York: Oxford University Press.

Perfetti, C. A., & Lesgold, A. M. (1979). Coding and comprehension in skilled reading and implications for reading instruction. In L. B. Resnick & P. A. Weaver (Eds.), Theory and practice of early reading (Vol. 1, pp. 57-840) Hillsdale, NJ: Lawrence Erlbaum.

Pearson, D. (1993). Focus on research: Teaching and learning reading: A research perspective. Language Arts, 70, pp. 502-511.

Pearson, D. (in press). Reclaiming the center. To appear in a volume by M. Graves and B. Taylor. New York: Teachers in College Press.

Pressley, M., & Rankin, J. (f1994). More about whole language methods of reading instruction for students at risk for early reading failure. Learning Disabilities Research and Practice, 9, pp. 157-168.

Pressley, M., & Woloshyn, V. (Eds.) (1995). Cognitive strategy instruction that really improves children’s academic performance (2nd ed.). Cambridge, MA: Brookline Books.

Reid, J. F. (1966). Learning to think about reading. Educational Research, 9, pp. 56-62.

Rozin, P., Bressman, B., & Taft, M. (1974). Do children understand the basic relationship between speech and writing? The mow-motorcycle test. Journal of Reading Behavior, 6, pp.327-334.

Share, D. L., & Stanovich, K. E. (1995). Cognitive processes in early reading development: Accommodation individual differences into a mode of acquisition. Issues in Education: Contributions from Educational Psychology, 1, pp.1-57.

Shefelbine, J. (Fall 1995). Learning and using phonics in beginning reading. Thrust for Educational Leadership, 25, pp. 8-9.

Speer, O. B., & Lamb, G. S. (1976). First-grade reading ability and fluency in naming verbal symbols. The Reading Teacher, 26, pp. 572-576.

Stahl, S. A. (1992). Saying the “p” word: Nine guidelines for exemplary phonics instruction. The Reading Teacher, 43, pp. 618-625.

Stanovich, K. E. (1980). Toward an interactive-compensatory model of individual differences in the development of reading fluency. Reading Research Quarterly, 16, pp. 32-71.

Stanovich, K. E. (1986). Matthew effects in reading: Some Consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, pp.360-407.

Stanovich, K. E., Cunningham, A. E., & Cramer, B. B. (1984). Assessing phonological awareness in kindergarten children: Issues of task comparability. Journal of Experimental Child Psychology, 38, pp. 175-190.

Sulzby, E. (1983). A commentary on Ehri’s critique of five studies related to letter-name knowledge and learning to read: “Broadening the question.” In L. M. Gentile, M. L. Kamil, & J. S. Blanchard (Eds.), Reading research revisited. Columbus, OH: Charles E. Merrill.

Tummer, W. E., Herriman, M. L., & Nesdale, A. R. (1988). Metalinguistic abilities and beginning reading. Reading Research Quarterly, 23, pp. 134-158.

Vellutino, F. R. (1991). Introduction to three studies on reading acquisition: Convergent findings on theoretical foundations of code-oriented versus whole-language approaches to reading instruction. Journal of Educational Psychology, 83, pp. 427-443.

Williams, J. P. (1991). The meaning of a phonics base for reading instruction. In All language and the creation of literacy (pp. 9-19). Baltimore, MD: Orton Dyslexia Society.

Yopp, H. K. (1988). The validity and reliability of phonemic awareness tests. Reading Research Quarterly, 23, pp. 159-177.

Excerpted from: Beginning Reading Instruction: Components and Features of a Research-Based Reading Program. (1996). Texas Education Agency.

Related Topics

Have a language expert improve your writing

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

• Knowledge Base

Methodology

• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

A research design is a strategy for answering your   research question  using empirical data. Creating a research design means making decisions about:

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research objectives and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, other interesting articles, frequently asked questions about research design.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities—start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed-methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analyzing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study—plants, animals, organizations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

• Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalize your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study , your aim is to deeply understand a specific context, not to generalize to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question .

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what kinds of data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected—for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

Prevent plagiarism. Run a free check.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in—for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced, while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method , you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample—by mail, online, by phone, or in person?

If you’re using a probability sampling method , it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method , how will you avoid research bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organizing and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymize and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

On its own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyze the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarize your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarize your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analyzing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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

• Simple random sampling
• Stratified sampling
• Cluster sampling
• Likert scales
• Reproducibility

 Statistics

• Null hypothesis
• Statistical power
• Probability distribution
• Effect size
• Poisson distribution

Research bias

• Optimism bias
• Cognitive bias
• Implicit bias
• Hawthorne effect
• Anchoring bias
• Explicit bias

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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.

Operationalization means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalize the variables that you want to measure.

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

Cite this Scribbr article

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

McCombes, S. (2023, November 20). What Is a Research Design | Types, Guide & Examples. Scribbr. Retrieved June 18, 2024, from https://www.scribbr.com/methodology/research-design/

Is this article helpful?

Shona McCombes

Other students also liked, guide to experimental design | overview, steps, & examples, how to write a research proposal | examples & templates, ethical considerations in research | types & examples, get unlimited documents corrected.

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

• CRLT Consultation Services
• Consultation
• Midterm Student Feedback
• Classroom Observation
• Teaching Philosophy
• Upcoming Events and Seminars
• CRLT Calendar
• Orientations
• Teaching Academies
• Provost's Seminars
• Past Events
• For Faculty
• For Grad Students & Postdocs
• For Chairs, Deans & Directors
• Customized Workshops & Retreats
• Assessment, Curriculum, & Learning Analytics Services
• CRLT in Engineering
• CRLT Players
• Foundational Course Initiative
• CRLT Grants
• Other U-M Grants
• Provost's Teaching Innovation Prize
• U-M Teaching Awards
• Retired Grants
• Staff Directory
• Faculty Advisory Board
• Annual Report
• Equity-Focused Teaching
• Preparing to Teach
• Teaching Strategies
• Testing and Grading
• Teaching with Technology
• Teaching Philosophy & Statements
• Training GSIs
• Evaluation of Teaching
• Occasional Papers

Research-based Principles of Learning & Teaching Strategies

Eberly center for teaching excellence, carnegie mellon university.

• Administer a diagnostic assessment or have students assess their own prior knowledge ( See “Selected Classroom Assessment Techniques (CATs) for Getting Feedback on Student Learning” ).
• Use brainstorming to reveal prior knowledge.
• Identify discipline-specific conventions explicitly.
• Ask students to make and test predictions (See “ Teaching for Retention in Science, Engineering & Mathematics ”). 
• Connect the material to students’ interests.
• Provide authentic, real-world tasks (See “ Teaching for Retention in Science, Engineering & Mathematics ").
• Show relevance to students’ current academic lives.
• Provide rubrics ( See “Best Practices for Designing & Grading Exams” , and “Sample Laboratory Report Rubrics” ). 
• Provide students with the organizational structure of the course.
• Share the organization of each lecture, lab, or discussion explicitly ( See “Teaching Strategies" ).
• Make connections among concepts explicit.
• Ask students to draw a concept map to expose their understanding of how course material is organized. 
• Provide isolated practice of weak or missing skills.
• Give students opportunities to practice skills including low-stakes, ungraded assignments ( See “Selected Classroom Assessment Techniques (CATs) for Getting Feedback on Student Learning” ).
• Give students opportunities to apply skills or knowledge in diverse contexts.
• Specify skills or knowledge and ask students to Identify contexts in which they apply. 
• Be explicit about your goals in your course materials ( See “Strategies for Effective Lesson Planning” ).
• Stage assignments by breaking tasks into smaller assignments.
• Look for patterns of errors in student work.
• Prioritize your feedback ( See “Responding to Student Writing – A Sample Commenting Protocol” ).
• Incorporate peer feedback. 
• Make uncertainty safe.
• Examine your assumptions about students.
• Model inclusive language, behavior, and attitudes ( See “Diversity and Inclusion in the Classroom” ).
• Establish and reinforce ground rules for interaction ( See “Guidelines for Class Participation” ).
• Use the syllabus and first day of class to establish the course climate ( See “Creating your Syllabus” ). 
• Check students’ understanding of the task.
• Have students do guided self-assessments.
• Require students to reflect on and annotate their own work.
• Prompt students to analyze the effectiveness of their study skills.
• Have students engage in peer feedback. 

Contact CRLT

location_on University of Michigan 1071 Palmer Commons 100 Washtenaw Ave. Ann Arbor, MI 48109-2218

phone Phone: (734) 764-0505

description Fax: (734) 647-3600

email Email: [email protected]

Connect with CRLT

directions Directions to CRLT

group Staff Directory

markunread_mailbox Subscribe to our Blog

• SUGGESTED TOPICS
• The Magazine
• Newsletters
• Managing Yourself
• Managing Teams
• Work-life Balance
• The Big Idea
• Data & Visuals
• Reading Lists
• Case Selections
• HBR Learning
• Topic Feeds
• Account Settings
• Email Preferences

Build a Corporate Culture That Works

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their culture in such a way that the words become an organizational reality that molds employee behavior as intended.

All too often a culture is described as a set of anodyne norms, principles, or values, which do not offer decision-makers guidance on how to make difficult choices when faced with conflicting but equally defensible courses of action.

The trick to making a desired culture come alive is to debate and articulate it using dilemmas. If you identify the tough dilemmas your employees routinely face and clearly state how they should be resolved—“In this company, when we come across this dilemma, we turn left”—then your desired culture will take root and influence the behavior of the team.

To develop a culture that works, follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value statement.

Start by thinking about the dilemmas your people will face.

Idea in Brief

The problem.

There’s a widespread understanding that managing corporate culture is key to business success. Yet few companies articulate their corporate culture in such a way that the words become an organizational reality that molds employee behavior as intended.

What Usually Happens

How to fix it.

Follow six rules: Ground your culture in the dilemmas you are likely to confront, dilemma-test your values, communicate your values in colorful terms, hire people who fit, let culture drive strategy, and know when to pull back from a value.

At the beginning of my career, I worked for the health-care-software specialist HBOC. One day, a woman from human resources came into the cafeteria with a roll of tape and began sticking posters on the walls. They proclaimed in royal blue the company’s values: “Transparency, Respect, Integrity, Honesty.” The next day we received wallet-sized plastic cards with the same words and were asked to memorize them so that we could incorporate them into our actions. The following year, when management was indicted on 17 counts of conspiracy and fraud, we learned what the company’s values really were.

• EM Erin Meyer is a professor at INSEAD, where she directs the executive education program Leading Across Borders and Cultures. She is the author of The Culture Map: Breaking Through the Invisible Boundaries of Global Business (PublicAffairs, 2014) and coauthor (with Reed Hastings) of No Rules Rules: Netflix and the Culture of Reinvention (Penguin, 2020). ErinMeyerINSEAD

Partner Center

Google DeepMind Shifts From Research Lab to AI Product Factory

By Julia Love and Mark Bergen

Over one week in mid-May, two companies announced artificial intelligence products built using one of Google’s seminal breakthroughs. On May 13, OpenAI Inc. introduced a new version of the model that underpins ChatGPT, its wildly popular chatbot that relies on a technology known as a transformer that Google first described in a research paper in 2017. The next day, Google announced AI Overviews, a product that offers responses to some searches with answers written by its own system based on the same technology.

The Overviews launch didn’t go well. The feature began offering embarrassing suggestions, such as advising people to ...

Learn more about Bloomberg Law or Log In to keep reading:

Learn about bloomberg law.

AI-powered legal analytics, workflow tools and premium legal & business news.

Already a subscriber?

Log in to keep reading or access research tools.

News Details

Sngx: valuation change based on reverse split….

By David Bautz, PhD

NASDAQ:SNGX

READ THE FULL SNGX RESEARCH REPORT

Business Update

Reaches Agreement with European Medicines Agency on Second Confirmatory Trial for HyBryte™

On April 3, 2024, Soligenix, Inc. (NASDAQ:SNGX) announced it has reached agreement with the European Medicines Agency (EMA) on the key design elements for a confirmatory Phase 3 trial of HyBryte (synthetic hypericin) for the treatment of cutaneous T cell lymphoma (CTCL).

The proposed Phase 3 FLASH 2 trial will be a randomized, double blind, placebo controlled, multicenter study that will enroll approximately 80 subjects with CTCL. HyBryte will be applied topically to CTCL lesions twice weekly for 18 weeks, with each application followed 21 (± 3) hours later by the administration of visible light at a wavelength of 500 to 650 nm. The light will be administered starting at 6 J/cm 2 and will be increased upwards by 2 J/cm 2  until: 1) the patient experiences a Grade 1 erythema; 2) the patient reaches a maximum dose of 30 J/cm 2 , or 3) the patient cannot tolerate the treatment time, whichever comes first. All of the subjects lesions that are readily available to the visible light source will be treated and 3 to 5 index lesions will be prospectively identified and indexed for the modified composite assessment of index lesions severity (mCAILS) evaluation prior to randomization. The primary endpoint will be assessed by the percent of patients in each treatment group (HyBryte or placebo) achieving a Partial or Complete Response of the treated lesions defined as a ≥50% reduction in the total mCAILS score for the 3-5 index lesions following 18 weeks of treatment compared to the total mCAILS score at baseline. Following treatment, subjects will be followed every 4 weeks for a total of 12 weeks (through Week 30). One interim analysis is planned after 60% of the total subjects have completed the primary endpoint evaluation. A sample size recalculation may be performed after examining the assumptions or the trial can be halted for futility, safety concerns, or overwhelming efficacy. The figure below gives a comparison between the proposed FLASH2 trial and the previously completed FLASH trial. The similarities between the two increases our confidence in a successful outcome for the FLASH2 trial.

We anticipate that the trial will begin enrollment prior to the end of 2024 and topline results are expected in the second half of 2026. The company is continuing discussions with the U.S. Food and Drug Administration (FDA) on an appropriate study design as the agency has expressed a preference for a longer duration comparative study over a placebo-controlled trial.

Orphan Drug Designation for Marburg Marburgvirus Vaccine

On April 15, 2024, Soligenix announced that the U.S. FDA has granted orphan drug designation (ODD) to the active ingredient in MarVax™, the subunit protein vaccine of recombinantly expressed Marburg marburgvirus (MARV) glycoprotein, for “the prevention and post-exposure prophylaxis against MARV infection”.

ODD is designed to assist companies that are developing therapies for rare diseases and disorders, defined as those that affect 200,000 people or fewer in the U.S. Drugs granted ODD receive a seven year term of market exclusivity upon final FDA approval. In addition, financial and regulatory benefits are available including government grants to conduct clinical trials, waiver of FDA user fees, and certain tax credits.

MarVax addresses the potentially lethal Marburg Virus Disease caused by MARV. While vaccines exist for Zaire ebolavirus , they are ineffective against MARV. MarVax is based on the company’s novel vaccine platform that consists of a robust protein manufacturing process, a nano-emulsion adjuvant that induces a strong immune response, and thermostabilization of the adjuvant and antigen in a single vial that is stable at elevated temperatures for extended timeframes.

Patent Protection Extended for Filovirus Vaccine Platform

On April 25, 2024, Soligenix announced it received notice of intent to grant additional patents based on its patent application titled “Compositions and Methods of Manufacturing Trivalent Filovirus Vaccines” in the United Kingdom and South Africa, with other jurisdictions pending. Multiple patents have previously been issued in the U.S. within the same patent family. The described vaccine platform was previously used to successfully produce mono-, bi-, and tri-valent candidates for Zaire ebolavirus , Sudan ebolavirus , and Marburg marburgvirus .

Financial Update

On May 10, 2024, Soligenix announced financial results for the first quarter of 2024. The company reported revenues of $0.1 million for the first quarter of 2024, compared to $0.3 million for the first quarter of 2023. The revenues are derived from government contracts and grants to support the development of SGX943 for treatment of emerging and/or antibiotic resistant infectious diseases, development of CiVax™, and evaluation of HyBryte for expanded treatment in patients with early-stage CTCL. R&D expenses for the first quarter of 2024 were $1.1 million, compared to $0.9 million for the first quarter of 2023. The increase was primarily due to an increase in preliminary costs associated with the anticipated initiation of the Phase 2 study in Behcet’s Disease and the second confirmatory Phase 3 CTCL trial. G&A expenses for the first quarter of 2024 were $1.0 million, compared to $1.2 million for the first quarter of 2023. The decrease was primarily due to a decrease in legal and professional fees.

Soligenix exited the first quarter of 2024 with approximately $4.3 million in cash and cash equivalents. Subsequent to the end of the quarter, the company raised gross proceeds of $4.75 million through the (pre-split) sale of 11.875 million shares at $0.40 per share along with 11.875 million warrants with an exercise price of $0.40 per share and a five-year expiration date. Following the reverse split the company has approximately 987,490 shares outstanding and, when factoring in stock options, warrants, and the potential convertible debt the fully diluted share count is approximately 2.9 million.

We look forward to the initiation of the second confirmatory Phase 3 clinical trial of HyBryte and we’re confident that the FDA will come to an agreement with the company soon on its preferences for the trial. Following the recent reverse split our valuation is now $32.00 per share.

SUBSCRIBE TO ZACKS SMALL CAP RESEARCH  to receive our articles and reports emailed directly to you each morning. Please visit our  website  for additional information on Zacks SCR. 

DISCLOSURE: Zacks SCR has received compensation from the issuer directly, from an investment manager, or from an investor relations consulting firm, engaged by the issuer, for providing research coverage for a period of no less than one year. Research articles, as seen here, are part of the service Zacks SCR provides and Zacks SCR receives quarterly payments totaling a maximum fee of up to $40,000 annually for these services provided to or regarding the issuer. Full Disclaimer HERE .

Multimedia Files:

Free Weekly Digest

Zacks SCR Premium

Get In Touch

[email protected]

• follow us on twitter

Subscribe to Zacks SCR PREMIUM

Zacks Full Access login

Invalid login, please try again. You have retries remaining before your account will be locked.

Your account is locked and it will be unlocked automatically in minutes.

Forgot your password? Email us at [email protected] for assistance.

• Privacy Policy

• See us on facebook
• See us on twitter
• See us on youtube
• See us on linkedin
• See us on instagram

Six distinct types of depression identified in Stanford Medicine-led study

Brain imaging, known as functional MRI, combined with machine learning can predict a treatment response based on one’s depression “biotype.”

June 17, 2024 - By Rachel Tompa

Researchers have identified six subtypes of depression, paving the way toward personalized treatment. Damerfie -   stock.adobe.com

In the not-too-distant future, a screening assessment for depression could include a quick brain scan to identify the best treatment.

Brain imaging combined with machine learning can reveal subtypes of depression and anxiety, according to a new study led by researchers at Stanford Medicine. The study , published June 17 in the journal Nature Medicine , sorts depression into six biological subtypes, or “biotypes,” and identifies treatments that are more likely or less likely to work for three of these subtypes.

Better methods for matching patients with treatments are desperately needed, said the study’s senior author,  Leanne Williams , PhD, the Vincent V.C. Woo Professor, a professor of psychiatry and behavioral sciences, and the director of Stanford Medicine’s Center for Precision Mental Health and Wellness . Williams, who lost her partner to depression in 2015, has focused her work on pioneering the field of precision psychiatry .

Around 30% of people with depression have what’s known as treatment-resistant depression , meaning multiple kinds of medication or therapy have failed to improve their symptoms. And for up to two-thirds of people with depression, treatment fails to fully reverse their symptoms to healthy levels.  

That’s in part because there’s no good way to know which antidepressant or type of therapy could help a given patient. Medications are prescribed through a trial-and-error method, so it can take months or years to land on a drug that works — if it ever happens. And spending so long trying treatment after treatment, only to experience no relief, can worsen depression symptoms.

“The goal of our work is figuring out how we can get it right the first time,” Williams said. “It’s very frustrating to be in the field of depression and not have a better alternative to this one-size-fits-all approach.”

Biotypes predict treatment response

To better understand the biology underlying depression and anxiety, Williams and her colleagues assessed 801 study participants who were previously diagnosed with depression or anxiety using the imaging technology known as functional MRI, or fMRI, to measure brain activity. They scanned the volunteers’ brains at rest and when they were engaged in different tasks designed to test their cognitive and emotional functioning. The scientists narrowed in on regions of the brain, and the connections between them, that were already known to play a role in depression.

Using a machine learning approach known as cluster analysis to group the patients’ brain images, they identified six distinct patterns of activity in the brain regions they studied.

Leanne Williams

The scientists also randomly assigned 250 of the study participants to receive one of three commonly used antidepressants or behavioral talk therapy. Patients with one subtype, which is characterized by overactivity in cognitive regions of the brain, experienced the best response to the antidepressant venlafaxine (commonly known as Effexor) compared with those who have other biotypes. Those with another subtype, whose brains at rest had higher levels of activity among three regions associated with depression and problem-solving, had better alleviation of symptoms with behavioral talk therapy. And those with a third subtype, who had lower levels of activity at rest in the brain circuit that controls attention, were less likely to see improvement of their symptoms with talk therapy than those with other biotypes.

The biotypes and their response to behavioral therapy make sense based on what they know about these regions of the brain, said Jun Ma, MD, PhD, the Beth and George Vitoux Professor of Medicine at the University of Illinois Chicago and one of the authors of the study. The type of therapy used in their trial teaches patients skills to better address daily problems, so the high levels of activity in these brain regions may allow patients with that biotype to more readily adopt new skills. As for those with lower activity in the region associated with attention and engagement, Ma said it’s possible that pharmaceutical treatment to first address that lower activity could help those patients gain more from talk therapy.

“To our knowledge, this is the first time we’ve been able to demonstrate that depression can be explained by different disruptions to the functioning of the brain,” Williams said. “In essence, it’s a demonstration of a personalized medicine approach for mental health based on objective measures of brain function.”

In another recently published study , Williams and her team showed that using fMRI brain imaging improves their ability to identify individuals likely to respond to antidepressant treatment. In that study, the scientists focused on a subtype they call the cognitive biotype of depression, which affects more than a quarter of those with depression and is less likely to respond to standard antidepressants. By identifying those with the cognitive biotype using fMRI, the researchers accurately predicted the likelihood of remission in 63% of patients, compared with 36% accuracy without using brain imaging. That improved accuracy means that providers may be more likely to get the treatment right the first time. The scientists are now studying novel treatments for this biotype with the hope of finding more options for those who don’t respond to standard antidepressants.

Further explorations of depression

The different biotypes also correlate with differences in symptoms and task performance among the trial participants. Those with overactive cognitive regions of the brain, for example, had higher levels of anhedonia (inability to feel pleasure) than those with other biotypes; they also performed worse on executive function tasks. Those with the subtype that responded best to talk therapy also made errors on executive function tasks but performed well on cognitive tasks.

One of the six biotypes uncovered in the study showed no noticeable brain activity differences in the imaged regions from the activity of people without depression. Williams believes they likely haven’t explored the full range of brain biology underlying this disorder — their study focused on regions known to be involved in depression and anxiety, but there could be other types of dysfunction in this biotype that their imaging didn’t capture.

Williams and her team are expanding the imaging study to include more participants. She also wants to test more kinds of treatments in all six biotypes, including medicines that haven’t traditionally been used for depression.

Her colleague  Laura Hack , MD, PhD, an assistant professor of psychiatry and behavioral sciences, has begun using the imaging technique in her clinical practice at Stanford Medicine through an experimental protocol . The team also wants to establish easy-to-follow standards for the method so that other practicing psychiatrists can begin implementing it.

“To really move the field toward precision psychiatry, we need to identify treatments most likely to be effective for patients and get them on that treatment as soon as possible,” Ma said. “Having information on their brain function, in particular the validated signatures we evaluated in this study, would help inform more precise treatment and prescriptions for individuals.”

Researchers from Columbia University; Yale University School of Medicine; the University of California, Los Angeles; UC San Francisco; the University of Sydney; the University of Texas MD Anderson; and the University of Illinois Chicago also contributed to the study.

Datasets in the study were funded by the National Institutes of Health (grant numbers R01MH101496, UH2HL132368, U01MH109985 and U01MH136062) and by Brain Resource Ltd.

• Rachel Tompa Rachel Tompa is a freelance science writer.

About Stanford Medicine

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

Hope amid crisis

Psychiatry’s new frontiers

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

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

Widespread horse-based mobility arose around 2,200 BCE in Eurasia

• Pablo Librado   ORCID: orcid.org/0000-0001-5974-2189 1   nAff2 ,
• Gaetan Tressières   ORCID: orcid.org/0000-0001-5827-5398 1 ,
• Lorelei Chauvey   ORCID: orcid.org/0000-0002-5015-3924 1 ,
• Antoine Fages 1   nAff3 ,
• Naveed Khan 1   nAff4 ,
• Stéphanie Schiavinato   ORCID: orcid.org/0000-0001-9007-6305 1 ,
• Laure Calvière-Tonasso   ORCID: orcid.org/0000-0001-5140-074X 1 ,
• Mariya A. Kusliy 1   nAff5 ,
• Charleen Gaunitz 1   nAff6 ,
• Xuexue Liu 1 ,
• Stefanie Wagner 1 , 7 ,
• Clio Der Sarkissian 1 ,
• Andaine Seguin-Orlando   ORCID: orcid.org/0000-0002-8265-3229 1 ,
• Aude Perdereau   ORCID: orcid.org/0000-0001-7125-5308 8 ,
• Jean-Marc Aury   ORCID: orcid.org/0000-0003-1718-3010 9 ,
• John Southon 10 ,
• Beth Shapiro 11 ,
• Olivier Bouchez 12 ,
• Cécile Donnadieu 12 ,
• Yvette Running Horse Collin   ORCID: orcid.org/0000-0001-8565-9776 1   nAff13 ,
• Kristian M. Gregersen   ORCID: orcid.org/0000-0003-3318-1197 14 ,
• Mads Dengsø Jessen   ORCID: orcid.org/0000-0002-9310-3986 15 ,
• Kirsten Christensen 16 ,
• Lone Claudi-Hansen 16 ,
• Mélanie Pruvost   ORCID: orcid.org/0000-0001-7824-2155 17 ,
• Erich Pucher 18 ,
• Hrvoje Vulic 19 ,
• Mario Novak   ORCID: orcid.org/0000-0002-4567-8742 20 ,
• Andrea Rimpf   ORCID: orcid.org/0000-0003-2834-342X 21 ,
• Peter Turk   ORCID: orcid.org/0000-0003-1995-0113 22 ,
• Simone Reiter 23 ,
• Gottfried Brem 23 ,
• Christoph Schwall   ORCID: orcid.org/0000-0002-6310-4056 24 , 25 ,
• Éric Barrey 26 ,
• Céline Robert   ORCID: orcid.org/0000-0002-3899-1060 26 , 27 ,
• Christophe Degueurce   ORCID: orcid.org/0000-0003-1932-1341 27 ,
• Liora Kolska Horwitz 28 ,
• Lutz Klassen 29 ,
• Uffe Rasmussen 30 ,
• Jacob Kveiborg 31 ,
• Niels Nørkjær Johannsen   ORCID: orcid.org/0000-0003-3550-2548 32 ,
• Daniel Makowiecki 33 ,
• Przemysław Makarowicz   ORCID: orcid.org/0000-0003-4452-7704 34 ,
• Marcin Szeliga   ORCID: orcid.org/0000-0002-5185-073X 35 ,
• Vasyl Ilchyshyn 36 ,
• Vitalii Rud   ORCID: orcid.org/0000-0001-8087-2236 37 ,
• Jan Romaniszyn 34 ,
• Victoria E. Mullin 38 ,
• Marta Verdugo 38 ,
• Daniel G. Bradley   ORCID: orcid.org/0000-0001-7335-7092 38 ,
• João L. Cardoso 39 , 40 ,
• Maria J. Valente 41 ,
• Miguel Telles Antunes 42 ,
• Carly Ameen 43 ,
• Richard Thomas   ORCID: orcid.org/0000-0003-1207-793X 44 ,
• Arne Ludwig   ORCID: orcid.org/0000-0001-7249-9953 45 , 46 ,
• Matilde Marzullo 47 ,
• Ornella Prato 47 ,
• Giovanna Bagnasco Gianni 47 ,
• Umberto Tecchiati 47 ,
• José Granado 48 ,
• Angela Schlumbaum 48 ,
• Sabine Deschler-Erb 48 ,
• Monika Schernig Mráz 48 ,
• Nicolas Boulbes 49 ,
• Armelle Gardeisen 50 ,
• Christian Mayer 51 ,
• Hans-Jürgen Döhle 52 ,
• Magdolna Vicze 53 ,
• Pavel A. Kosintsev 54 , 55 ,
• René Kyselý   ORCID: orcid.org/0000-0002-0788-7287 56 ,
• Lubomír Peške 57 ,
• Terry O’Connor 58 ,
• Elina Ananyevskaya 59 ,
• Irina Shevnina 60 ,
• Andrey Logvin 60 ,
• Alexey A. Kovalev   ORCID: orcid.org/0000-0003-2637-3131 61 ,
• Tumur-Ochir Iderkhangai 62 ,
• Mikhail V. Sablin   ORCID: orcid.org/0000-0002-2773-7454 63 ,
• Petr K. Dashkovskiy 64 ,
• Alexander S. Graphodatsky 5 ,
• Ilia Merts   ORCID: orcid.org/0000-0001-9066-9629 65 , 66 ,
• Viktor Merts 65 ,
• Aleksei K. Kasparov 67 ,
• Vladimir V. Pitulko   ORCID: orcid.org/0000-0001-5672-2756 67 , 68 ,
• Vedat Onar 69 ,
• Aliye Öztan 70 ,
• Benjamin S. Arbuckle 71 ,
• Hugh McColl   ORCID: orcid.org/0000-0002-7568-4270 6 ,
• Gabriel Renaud 1   nAff72 ,
• Ruslan Khaskhanov 73 ,
• Sergey Demidenko 74 ,
• Anna Kadieva 75 ,
• Biyaslan Atabiev 76 ,
• Marie Sundqvist 77 ,
• Gabriella Lindgren   ORCID: orcid.org/0000-0001-6046-9669 78 , 79 ,
• F. Javier López-Cachero   ORCID: orcid.org/0000-0002-3121-4015 80 ,
• Silvia Albizuri   ORCID: orcid.org/0000-0001-6194-0475 80 ,
• Tajana Trbojević Vukičević 81 ,
• Anita Rapan Papeša   ORCID: orcid.org/0000-0002-0041-1892 19 ,
• Marcel Burić   ORCID: orcid.org/0000-0001-8025-3611 82 ,
• Petra Rajić Šikanjić   ORCID: orcid.org/0000-0002-7760-5193 83 ,
• Jaco Weinstock 84 ,
• David Asensio Vilaró 85 ,
• Ferran Codina 86 ,
• Cristina García Dalmau 87 ,
• Jordi Morer de Llorens 88 ,
• Josep Pou 89 ,
• Gabriel de Prado 90 ,
• Joan Sanmartí 91 , 92 ,
• Nabil Kallala 93 , 94 ,
• Joan Ramon Torres 95 ,
• Bouthéina Maraoui-Telmini 94 ,
• Maria-Carme Belarte Franco 91 , 96 , 97 ,
• Silvia Valenzuela-Lamas   ORCID: orcid.org/0000-0001-9886-0372 98 , 99 ,
• Antoine Zazzo 100 ,
• Sébastien Lepetz 100 ,
• Sylvie Duchesne   ORCID: orcid.org/0000-0003-0138-8409 1 ,
• Anatoly Alexeev 101 ,
• Jamsranjav Bayarsaikhan 102 , 103 ,
• Jean-Luc Houle 104 ,
• Noost Bayarkhuu 105 ,
• Tsagaan Turbat 105 ,
• Éric Crubézy   ORCID: orcid.org/0000-0001-9417-9053 1 ,
• Irina Shingiray 106 ,
• Marjan Mashkour   ORCID: orcid.org/0000-0003-3630-9459 100 , 107 ,
• Natalia Ya. Berezina   ORCID: orcid.org/0000-0001-5704-9153 108 ,
• Dmitriy S. Korobov   ORCID: orcid.org/0000-0002-9571-0405 74 ,
• Andrey Belinskiy 109 ,
• Alexey Kalmykov   ORCID: orcid.org/0000-0002-6939-5544 109 ,
• Jean-Paul Demoule   ORCID: orcid.org/0000-0003-4712-5110 110 ,
• Sabine Reinhold   ORCID: orcid.org/0000-0002-8107-6300 111 ,
• Svend Hansen 111 ,
• Barbara Wallner   ORCID: orcid.org/0000-0003-4159-0695 23 ,
• Natalia Roslyakova   ORCID: orcid.org/0000-0002-1888-2713 112 ,
• Pavel F. Kuznetsov   ORCID: orcid.org/0000-0002-4898-5350 112 ,
• Alexey A. Tishkin   ORCID: orcid.org/0000-0002-7769-136X 66 ,
• Patrick Wincker 9 ,
• Katherine Kanne   ORCID: orcid.org/0000-0003-0808-3501 43   nAff113 ,
• Alan Outram   ORCID: orcid.org/0000-0003-3360-089X 43 &
• Ludovic Orlando   ORCID: orcid.org/0000-0003-3936-1850 1  

Nature ( 2024 ) Cite this article

4013 Accesses

1791 Altmetric

Metrics details

We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

• Archaeology
• Evolutionary genetics
• Population genetics

Horses revolutionized human history with fast mobility 1 . However, the timeline between their domestication and widespread integration as a means of transportation remains contentious 2–4 . Here we assemble a large collection of 475 ancient horse genomes to assess the period when these animals were first reshaped by human agency in Eurasia. We find that reproductive control of the modern domestic lineage emerged ~2,200 BCE (Before Common Era), through close kin mating and shortened generation times. Reproductive control emerged following a severe domestication bottleneck starting no earlier than ~2,700 BCE, and coincided with a sudden expansion across Eurasia that ultimately resulted in the replacement of nearly every local horse lineage. This expansion marked the rise of widespread horse-based mobility in human history, which refutes the commonly-held narrative of large horse herds accompanying the massive migration of steppe peoples across Europe ~3,000 BCE and earlier 3,5 . Finally, we detect significantly shortened generation times at Botai ~3,500 BCE, a settlement from Central Asia associated with corrals and a subsistence economy centered on horses 6,7 . This supports local horse husbandry before the rise of modern domestic bloodlines.

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

Access options

Access Nature and 54 other Nature Portfolio journals

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

$29.99 / 30 days

cancel any time

Subscribe to this journal

Receive 51 print issues and online access

$199.00 per year

only $3.90 per issue

Rent or buy this article

Prices vary by article type

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

Similar content being viewed by others

The origins and spread of domestic horses from the Western Eurasian steppes

Animal domestication in the era of ancient genomics

Refining the evolutionary tree of the horse Y chromosome

Author information.

Pablo Librado

Present address: Institut de Biologia Evolutiva (CSIC – Universitat Pompeu Fabra), Barcelona, Spain

Antoine Fages

Present address: Zoological institute, Department of Environmental Sciences, University of Basel, Vesalgasse 1, Basel, Switzerland

Naveed Khan

Present address: Department of Biotechnology, Abdul Wali Khan University, Mardan, Pakistan

Mariya A. Kusliy

Present address: Department of the Diversity and Evolution of Genomes, Institute of Molecular and Cellular Biology SB RAS, 8/2 Academician Lavrentiev Avenue, Novosibirsk, Russia

Charleen Gaunitz

Present address: Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Yvette Running Horse Collin

Present address: Taku Skan Skan Wasakliyapi: Global Institute for Traditional Sciences, 522 Seventh Street, Suite 202, Rapid City, South Dakota, USA

Gabriel Renaud

Present address: Department of Health Technology, Section for Bioinformatics, Technical University of Denmark (DTU), Copenhagen, Denmark

Katherine Kanne

Present address: School of Archaeology, University College Dublin, Dublin, Ireland

Authors and Affiliations

Centre d’Anthropobiologie et de Génomique de Toulouse, CNRS UMR 5288, Université Paul Sabatier, Faculté de Médecine Purpan, 37 Allées Jules Guesde, Toulouse, France

Pablo Librado, Gaetan Tressières, Lorelei Chauvey, Antoine Fages, Naveed Khan, Stéphanie Schiavinato, Laure Calvière-Tonasso, Mariya A. Kusliy, Charleen Gaunitz, Xuexue Liu, Stefanie Wagner, Clio Der Sarkissian, Andaine Seguin-Orlando, Yvette Running Horse Collin, Gabriel Renaud, Sylvie Duchesne, Éric Crubézy & Ludovic Orlando

Department of the Diversity and Evolution of Genomes, Institute of Molecular and Cellular Biology SB RAS, 8/2 Academician Lavrentiev Avenue, Novosibirsk, Russia

Alexander S. Graphodatsky

Lundbeck Foundation GeoGenetics Centre, Globe Institute, University of Copenhagen, Copenhagen, Denmark

Hugh McColl

INRAE Division Ecology and Biodiversity (ECODIV), Plant Genomic Resources Center (CNRGV), 24 Chemin de Borde Rouge – Auzeville, Castanet Tolosan Cedex, France

Stefanie Wagner

Genoscope, Institut de Biologie François Jacob, CEA, CNRS, Université d’Évry, Université Paris-Saclay, Évry, France

Aude Perdereau

Génomique Métabolique, Genoscope, Institut François Jacob, CEA, CNRS, Univ Évry, Université Paris-Saclay, Évry, France

Jean-Marc Aury & Patrick Wincker

Department of Earth System Science, University of California, Irvine, Irvine, CA, USA

John Southon

Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, CA, USA

Beth Shapiro

INRAE, GeT-PlaGe, Genotoul, Castanet-Tolosan, France

Olivier Bouchez & Cécile Donnadieu

The Royal Danish Academy, Institute of Conservation, Philip de Langes Allé 10, Copenhagen K, Denmark

Kristian M. Gregersen

National Museum of Denmark, Department for Prehistory, Middle Ages and Renaissance, Ny Vestergade 10, Copenhagen K, Denmark

Mads Dengsø Jessen

Museum Vestsjælland, Forten 10, Holbæk, Denmark

Kirsten Christensen & Lone Claudi-Hansen

UMR 5199 De la Préhistoire à l’Actuel: Culture, Environnement et Anthropologie (PACEA), CNRS, Université de Bordeaux, Pessac Cédex, France

Mélanie Pruvost

Museum of Natural History, Burgring 7, Vienna, Austria

Erich Pucher

Vinkovci Municipal Museum, Trg bana Josipa Šokčevića 16, Vinkovci, Croatia

Hrvoje Vulic & Anita Rapan Papeša

Centre for Applied Bioanthropology, Institute for Anthropological Research, Ljudevita Gaja 32, Zagreb, Croatia

Mario Novak

Ilok Town Museum, Šetalište o. Mladena Barbarića 5, Ilok, Croatia

Andrea Rimpf

Narodni muzej Slovenije, Prešernova 20, Ljubljana, Slovenia

Institute of Animal Breeding and Genetics, Department of Biomedical Sciences, University of Veterinary Medicine Vienna, Veterinärplatz 1, Vienna, Austria

Simone Reiter, Gottfried Brem & Barbara Wallner

Leibniz-Zentrum für Archäologie (LEIZA), Ludwig-Lindenschmit-Forum 1, Mainz, Germany

Christoph Schwall

Department of Prehistory & Western Asian/Northeast African Archaeology, Austrian Archaeological Institute (OeAI), Austrian Academy of Sciences (OeAW), Dominikanerbastei 16, Vienna, Austria

Université Paris-Saclay, AgroParisTech, INRAE, GABI UMR1313, Jouy-en-Josas, France

Éric Barrey & Céline Robert

Ecole Nationale Vétérinaire d’Alfort, 7 Av du Général De Gaulle, Maisons-Alfort, France

Céline Robert & Christophe Degueurce

National Natural History Collections, Edmond J. Safra Campus, Givat Ram, The Hebrew University, Jerusalem, Israel

Liora Kolska Horwitz

Museum Østjylland, Søndergade 1, Grenaa, Denmark

Lutz Klassen

Moesgaard Museum, Department of Archaeology, Moesgaard Allé 20, Højbjerg, Denmark

Uffe Rasmussen

Moesgaard Museum, Department of Archaeological Science and Conservation, Moesgaard Allé 15, Højbjerg, Denmark

Jacob Kveiborg

Department of Archaeology and Heritage Studies, Aarhus University, Højbjerg, Denmark

Niels Nørkjær Johannsen

Institute of Archaeology, Faculty of History, Nicolaus Copernicus University, Toruń, Poland

Daniel Makowiecki

Faculty of Archaeology, Adam Mickiewicz University, Poznań, Poland

Przemysław Makarowicz & Jan Romaniszyn

Institute of Archaeology, Maria Curie-Skłodowska University, Lublin, Poland

Marcin Szeliga

Kremenetsko-Pochaivskii Derzhavnyi Istoriko-arkhitekturnyi Zapovidnik, Kremenets, Kozubskogo 6, <City>, Ukraine

Vasyl Ilchyshyn

Institute of Archaeology, National Academy of Sciences of Ukraine, Volodymyr Ivasiuk Avenue 12, Kyiv, Ukraine

Vitalii Rud

Smurfit Institute of Genetics, Trinity College Dublin, Dublin, Ireland

Victoria E. Mullin, Marta Verdugo & Daniel G. Bradley

ICArEHB, Campus de Gambelas, University of Algarve, Faro, Portugal

João L. Cardoso

Universidade Aberta, Lisbon, Portugal

Faculdade de Ciências Humanas e Sociais, Centro de Estudos de Arqueologia, Artes e Ciências do Património, Universidade do Algarve, Faro, Portugal

Maria J. Valente

Centre for Research on Science and Geological Engineering, Universidade Nova de Lisboa, Lisbon, Portugal

Miguel Telles Antunes

Department of Archaeology and History, University of Exeter, Exeter, UK

Carly Ameen, Katherine Kanne & Alan Outram

School of Archaeology and Ancient History, University of Leicester, University Road, Leicester, UK

Richard Thomas

Department of Evolutionary Genetics, Leibniz-Institute for Zoo and Wildlife Research, Berlin, Germany

Arne Ludwig

Albrecht Daniel Thaer-Institute, Faculty of Life Sciences, Humboldt University Berlin, Berlin, Germany

Università degli Studi di Milano, Dipartimento di Beni Culturali e Ambientali, Milan, Italy

Matilde Marzullo, Ornella Prato, Giovanna Bagnasco Gianni & Umberto Tecchiati

Basel University, Department of Environmental Sciences, Integrative Prehistory and Archaeological Science, Basel, Switzerland

José Granado, Angela Schlumbaum, Sabine Deschler-Erb & Monika Schernig Mráz

Institut de Paléontologie Humaine, Fondation Albert Ier, Paris/UMR 7194 HNHP, MNHN-CNRS-UPVD/EPCC Centre Européen de Recherche Préhistorique, Tautavel, France

Nicolas Boulbes

Centre National de Recherche Scientifique, Archéologie des Sociétés Méditeranéennes, Archimède IA-ANR-11-LABX-0032-01, Université Paul Valéry, Montpellier, France

Armelle Gardeisen

Federal Monuments Authority Austria, Department for Digitalization and Knowledge Transfer, Vienna, Austria

Christian Mayer

Landesamt für Denkmalpflege und Archäologie Sachsen-Anhalt – Landesmuseum für Vorgeschichte, Halle (Saale), Germany

Hans-Jürgen Döhle

National Institute of Archaeology, Hungarian National Museum, Budapest, Hungary

Magdolna Vicze

Paleoecology Laboratory, Institute of Plant and Animal Ecology, Ural Branch of the Russian Academy of Sciences, Ekaterinburg, Russia

Pavel A. Kosintsev

Department of History of the Institute of Humanities, Ural Federal University, Ekaterinburg, Russia

Department of Natural Sciences and Archaeometry, Institute of Archaeology of the Czech Academy of Sciences, Letenská 4, Prague 1, Czechia

René Kyselý

Professional biological consultant, Moskevská 61, Prague 10, Czechia

Lubomír Peške

Department of Archaeology, University of York, c/o Kings Manor, York, UK

Terry O’Connor

Department of Archaeology, History Faculty, Vilnius University, Vilnius, Lithuania

Elina Ananyevskaya

Laboratory for Archaeological Research, Akhmet Baitursynuly Kostanay Regional University, Kostanay, Kazakhstan

Irina Shevnina & Andrey Logvin

Department of Archaeological Heritage Preservation, Institute of Archaeology of the Russian Academy of Sciences, Dm. Ulyanov Street, 19, Moscow, Russia

Alexey A. Kovalev

Department of Innovation and Technology, Ulaanbaatar Science and Technology Park, National University of Mongolia, Bayanzurkh district, Luvsantseveen street, 5th khoroo, Ulaanbaatar, Mongolia

Tumur-Ochir Iderkhangai

Zoological Institute, Russian Academy of Sciences, University Quay, 1, St Petersburg, Russia

Mikhail V. Sablin

Department of Russian Regional Studies, National and State-confessional Relations, Altai State University, Prospekt Lenina, 61, Barnaul, Russia

Petr K. Dashkovskiy

Toraighyrov University, Joint Research Center for Archeological Studies, Avenue Lomova 64, Pavlodar, Kazakhstan

Ilia Merts & Viktor Merts

Department of Archaeology, Ethnography and Museology, Altai State University, Prospekt Lenina, 61, Barnaul, Russia

Ilia Merts & Alexey A. Tishkin

Institute of the History of Material Culture, Russian Academy of Sciences, 18 Dvortsovaya Emb., St. Petersburg, Russian Federation

Aleksei K. Kasparov & Vladimir V. Pitulko

Peter the Great Museum of Anthropology and Ethnography (Kunstkamera), Russian Academy of Sciences, 3, Universitetskaya nab., St Petersburg, Russia

Vladimir V. Pitulko

Osteoarchaeology Practice and Research Center and Department of Anatomy, Faculty of Veterinary Medicine, Istanbul University-Cerrahpaşa, Istanbul, Türkiye

Archaeology Department, Ankara University, Ankara, Türkiye

Aliye Öztan

Department of Anthropology, Alumni Building, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

Benjamin S. Arbuckle

Kh. Ibragimov Complex Institute of the Russian Academy of Sciences (CI RAS), 21 a V. Alieva (Staropromyslovskoe highway), Grozny, Chechen Republic, Russia

Ruslan Khaskhanov

Institute of Archaeology, Russian Academy of Sciences, Dm. Uljanova, 19, Moscow, Russia

Sergey Demidenko & Dmitriy S. Korobov

State Historical Museum, Department of Archaeological Monuments, Moscow, Red Square 1, Moscow, Russian Federation

Anna Kadieva

Institute for Caucasus Archaeology, Ul. Katkhanova 30, Nalchik, Russian Federation

Biyaslan Atabiev

Östra Greda Research Group, Vialmvägen 5, Borgholm, Sweden

Marie Sundqvist

Department of Animal Breeding and Genetics, Swedish University of Agricultural Sciences, Uppsala, Sweden

Gabriella Lindgren

Center for Animal Breeding and Genetics, Department of Biosystems, KU Leuven, Leuven, Belgium

Institut d’Arqueologia de la Universitat de Barcelona (IAUB), Seminari d’Estudis i Recerques Prehistoriques (SERP-UB), Universitat de Barcelona (UB), Barcelona, Spain

F. Javier López-Cachero & Silvia Albizuri

Department of Anatomy, Histology and Embryology, Faculty of Veterinary Medicine, University of Zagreb, Heinzelova 55, Zagreb, Croatia

Tajana Trbojević Vukičević

Department of Archaeology, Faculty of Humanities and Social Sciences, University of Zagreb, I. Lučića 3, Zagreb, Croatia

Marcel Burić

Institute for Anthropological Research, Ljudevita Gaja 32, Zagreb, Croatia

Petra Rajić Šikanjić

Faculty of Arts and Humanities (Archaeology), University of Southampton, Southampton, UK

Jaco Weinstock

Secció de Prehistòria i Arqueologia, IAUB Institut d’Arqueologia de la Universitat de Barcelona, C/Montalegre 6-8, Barcelona, Spain

David Asensio Vilaró

C/Major, 20, La Tallada d’Empordà, <City>, Spain

Ferran Codina

Mosaïques Archéologie, Espace d’activités de la Barthe, Cournonterral, France

Cristina García Dalmau

Mon IberRocs SCL, Carrer Sta Anna 28, Vilanova i la Geltrú (Barcelona), Spain

Jordi Morer de Llorens

Ajuntament de Calafell, Plaça Catalunya, 1, Calafell (Tarragona), Spain

Museu d’Arqueologia de Catalunya (MAC-Ullastret), Afores s/n, Puig de Sant Andreu, Ullastret, Spain

Gabriel de Prado

IEC-Institut d’Estudis Catalans (Union Académique Internationale), Carrer Del Carme, 47, Barcelona, Spain

Joan Sanmartí & Maria-Carme Belarte Franco

Departament d’Història i Arqueologia, Facultat de Geografia i Història, Universitat de Barcelona, Carrer Montalegre 6-8, Barcelona, Spain

Joan Sanmartí

Ecole Tunisienne d’Histoire et d’Anthropologie, <City>, Tunisia

Nabil Kallala

University of Tunis, Institut National du Patrimoine, Tunis, Tunisia

Nabil Kallala & Bouthéina Maraoui-Telmini

Consell Insular d’Eivissa, Avenida de España 49, Eivissa, Illes Balears, Spain

Joan Ramon Torres

ICREA, Catalan Institution for Research and Advanced Studies, Passeig Lluís Companys 23, Barcelona, Spain

Maria-Carme Belarte Franco

ICAC (Catalan Institute of Classical Archaeology), Pl. del Rovellat, s/n., Tarragona, Spain

Archaeology of Social Dynamics (ASD, 2021SGR_00501), Institució Milà i Fontanals, Consejo Superior de Investigaciones Científicas (IMF-CSIC), C/Egipcíaques 15, Barcelona, Spain

Silvia Valenzuela-Lamas

UNIARQ - Unidade de Arqueologia, Universidade de Lisboa, Alameda da Universidade, Lisboa, Portugal

Centre National de Recherche Scientifique, Muséum national d’Histoire naturelle, Archéozoologie, Archéobotanique (AASPE), CP 56, Paris, France

Antoine Zazzo, Sébastien Lepetz & Marjan Mashkour

Institute for Humanities Research and Indigenous Studies of the North (IHRISN), Petrovskogo St. 1, Yakutsk, Russia

Anatoly Alexeev

Max Planck Institute of Geoanthropology, Kahlaische Str. 10, Jena, Germany

Jamsranjav Bayarsaikhan

Institute of Archaeology, Mongolian Academy of Science, Ulaanbaatar, Mongolia

Department of Folk Studies and Anthropology, Western Kentucky University, Bowling Green, USA

Jean-Luc Houle

Archaeological Research Center and Department of Anthropology and Archaeology, National University of Mongolia, Ulaanbaatar, Mongolia

Noost Bayarkhuu & Tsagaan Turbat

Faculty of History, University of Oxford, George Street, Oxford, UK

Irina Shingiray

University of Tehran, Central Laboratory, Bioarchaeology Laboratory, Archaeozoology section, Jalalieh street 6, Tehran, Iran

Marjan Mashkour

Research Institute and Museum of Anthropology, Lomonosov Moscow State University, Moscow, Russia

Natalia Ya. Berezina

Nasledie Cultural Heritage Unit, Stavropol, Russia

Andrey Belinskiy & Alexey Kalmykov

UMR du CNRS 8215 Trajectoires, Institut d’Art et Archéologie, 3 rue Michelet, Paris, France

Jean-Paul Demoule

Eurasia Department of the German Archaeological Institute, Berlin, Germany

Sabine Reinhold & Svend Hansen

Department of Russian History and Archaeology, Samara State University of Social Sciences and Education, Samara, Russia

Natalia Roslyakova & Pavel F. Kuznetsov

You can also search for this author in PubMed   Google Scholar

Corresponding authors

Correspondence to Pablo Librado or Ludovic Orlando .

Supplementary information

Supplementary information.

This Supplementary Information file contains the following sections: Section 1. Archaeological Contexts and Sample Information; Section 2. Radiocarbon Dating; Section 3. Genome Analyses; Section 4. Measuring temporal variations in the horse generation time; Supplementary References.

Reporting Summary

Peer review file, supplementary tables.

This file contains Supplementary Tables 1 and 2.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

Librado, P., Tressières, G., Chauvey, L. et al. Widespread horse-based mobility arose around 2,200 BCE in Eurasia. Nature (2024). https://doi.org/10.1038/s41586-024-07597-5

Download citation

Received : 17 July 2023

Accepted : 23 May 2024

Published : 06 June 2024

DOI : https://doi.org/10.1038/s41586-024-07597-5

Share this article

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

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

Provided by the Springer Nature SharedIt content-sharing initiative

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

Quick links

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

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

Help | Advanced Search

Computer Science > Computation and Language

Title: evaluating the retrieval component in llm-based question answering systems.

Abstract: Question answering systems (QA) utilizing Large Language Models (LLMs) heavily depend on the retrieval component to provide them with domain-specific information and reduce the risk of generating inaccurate responses or hallucinations. Although the evaluation of retrievers dates back to the early research in Information Retrieval, assessing their performance within LLM-based chatbots remains a challenge. This study proposes a straightforward baseline for evaluating retrievers in Retrieval-Augmented Generation (RAG)-based chatbots. Our findings demonstrate that this evaluation framework provides a better image of how the retriever performs and is more aligned with the overall performance of the QA system. Although conventional metrics such as precision, recall, and F1 score may not fully capture LLMs' capabilities - as they can yield accurate responses despite imperfect retrievers - our method considers LLMs' strengths to ignore irrelevant contexts, as well as potential errors and hallucinations in their responses.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .

Months after being diagnosed with COVID-19, one in five people are still suffering from symptoms, new research finds

One in five adults infected with COVID-19 may still be suffering its effects months after their diagnosis, according to new research out of the United States. 

An investigation by more than two dozen researchers found while the average time of recovery was 20 days, an estimated 22.5 per cent failed to recover 90 days after infection. 

The report, based out of the United States and published in the Journal of the American Medical Association, mirrored recent reporting by Australian researchers. 

The peer-reviewed study used data from the Collaborative Cohort of Cohorts for COVID-19 Research (C4R), a long-term collaboration of 14 different studies across the US.

Some of the studies have been following its own participants for up to 50 years, meaning they can now compare their health pre- and post-COVID-19 diagnosis.

A total of 4,708 participants were asked whether they were "completely recovered from COVID-19". 

Once they confirmed their recovery, they were asked how long it had taken.

"[We] found that one in five adults infected with SARS-CoV-2 did not fully recover by three months post-infection in a racially and ethnically diverse US population-based sample," the report said.

"Recovery by 90 days was less likely in women and participants with pre-pandemic clinical cardiovascular disease.

"Vaccination prior to infection and infection during the Omicron variant wave were associated with greater recovery … results were similar for reinfections."

The research team noted the results may have been limited by the self-reported recovery time and the "potential for measurement error, uncontrolled confounding and selection bias".

Dr Mulu Abraha Woldegiorgis, a researcher at the Australian National University (ANU), told the ABC it was "interesting" to see the findings classified by "before and after Omicron".

"The prevalence [of long COVID] during Omicron was the same as ours," she said. 

"They use slightly different definitions and methodology, but even with that the prevalence was high. It shows us that long COVID is still a public health concern globally." 

Long COVID 'not just cough, or tiredness' 

Four years after the beginning of the pandemic, much about "long COVID" remains a mystery for health officials.

Earlier this year Queensland's chief health officer called for the term  "long COVID" to be scrapped despite stating the symptoms were "real". 

"Using this term long COVID implies this virus has some unique, exceptional and sinister property that differentiates it form other viruses," Dr John Gerrard said. 

"I want to make it clear that the symptoms that some patients describe after having COVID-19 are real. We believe they are real." 

A study of more than 11,000 Australians who had tested positive for COVID-19 has had similar results — almost one in five were still experiencing symptoms three months after a 2022 diagnosis. 

The joint ANU and Western Australia Department of Health study, released in March, found 90 per cent of participants with long COVID were suffering multiple symptoms. 

Tiredness, fatigue, "brain fog", sleep problems, coughing, and changes in their menstrual cycle were frequently reported. 

"Among respondents with long COVID who had worked or studied prior to their infection, 15.2 per cent had reduced their number of hours, and 2.7 per cent had not returned to work at all," the report said. 

The researchers also noted long COVID was more prevalent in its sample than the levels reported by other studies in the United Kingdom and Canada. 

Dr Woldegiorgis was the lead researcher on the ANU report. She said Australia presented a "unique" cohort of highly vaccinated people. 

"You have multiple symptoms, it's not just cough, or tiredness, they have multiple symptoms and that affects them," she said. 

"A longer term assessment is important. What we saw was by 90 days, so a long term follow-up may provide additional information on how people are going in a year or two.  

"What's the recovery period? Are they recovering soon or is the term longer?" 

The report also found those who had been vaccinated were less at risk of developing long COVID. 

"I want to stress the importance of vaccination," Dr Woldegiorgis said.

"In Australia the vast majority were vaccinated ... at least one dose prevents long COVID compared to no vaccination." 

• X (formerly Twitter)
• Epidemics and Pandemics
• United States