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Research Recommendations – Examples and Writing Guide

Table of Contents

Research Recommendations

Definition:

Research recommendations refer to suggestions or advice given to someone who is looking to conduct research on a specific topic or area. These recommendations may include suggestions for research methods, data collection techniques, sources of information, and other factors that can help to ensure that the research is conducted in a rigorous and effective manner. Research recommendations may be provided by experts in the field, such as professors, researchers, or consultants, and are intended to help guide the researcher towards the most appropriate and effective approach to their research project.

Parts of Research Recommendations

Research recommendations can vary depending on the specific project or area of research, but typically they will include some or all of the following parts:

• Research question or objective : This is the overarching goal or purpose of the research project.
• Research methods : This includes the specific techniques and strategies that will be used to collect and analyze data. The methods will depend on the research question and the type of data being collected.
• Data collection: This refers to the process of gathering information or data that will be used to answer the research question. This can involve a range of different methods, including surveys, interviews, observations, or experiments.
• Data analysis : This involves the process of examining and interpreting the data that has been collected. This can involve statistical analysis, qualitative analysis, or a combination of both.
• Results and conclusions: This section summarizes the findings of the research and presents any conclusions or recommendations based on those findings.
• Limitations and future research: This section discusses any limitations of the study and suggests areas for future research that could build on the findings of the current project.

How to Write Research Recommendations

Writing research recommendations involves providing specific suggestions or advice to a researcher on how to conduct their study. Here are some steps to consider when writing research recommendations:

• Understand the research question: Before writing research recommendations, it is important to have a clear understanding of the research question and the objectives of the study. This will help to ensure that the recommendations are relevant and appropriate.
• Consider the research methods: Consider the most appropriate research methods that could be used to collect and analyze data that will address the research question. Identify the strengths and weaknesses of the different methods and how they might apply to the specific research question.
• Provide specific recommendations: Provide specific and actionable recommendations that the researcher can implement in their study. This can include recommendations related to sample size, data collection techniques, research instruments, data analysis methods, or other relevant factors.
• Justify recommendations : Justify why each recommendation is being made and how it will help to address the research question or objective. It is important to provide a clear rationale for each recommendation to help the researcher understand why it is important.
• Consider limitations and ethical considerations : Consider any limitations or potential ethical considerations that may arise in conducting the research. Provide recommendations for addressing these issues or mitigating their impact.
• Summarize recommendations: Provide a summary of the recommendations at the end of the report or document, highlighting the most important points and emphasizing how the recommendations will contribute to the overall success of the research project.

Example of Research Recommendations

Example of Research Recommendations sample for students:

• Further investigate the effects of X on Y by conducting a larger-scale randomized controlled trial with a diverse population.
• Explore the relationship between A and B by conducting qualitative interviews with individuals who have experience with both.
• Investigate the long-term effects of intervention C by conducting a follow-up study with participants one year after completion.
• Examine the effectiveness of intervention D in a real-world setting by conducting a field study in a naturalistic environment.
• Compare and contrast the results of this study with those of previous research on the same topic to identify any discrepancies or inconsistencies in the findings.
• Expand upon the limitations of this study by addressing potential confounding variables and conducting further analyses to control for them.
• Investigate the relationship between E and F by conducting a meta-analysis of existing literature on the topic.
• Explore the potential moderating effects of variable G on the relationship between H and I by conducting subgroup analyses.
• Identify potential areas for future research based on the gaps in current literature and the findings of this study.
• Conduct a replication study to validate the results of this study and further establish the generalizability of the findings.

Applications of Research Recommendations

Research recommendations are important as they provide guidance on how to improve or solve a problem. The applications of research recommendations are numerous and can be used in various fields. Some of the applications of research recommendations include:

• Policy-making: Research recommendations can be used to develop policies that address specific issues. For example, recommendations from research on climate change can be used to develop policies that reduce carbon emissions and promote sustainability.
• Program development: Research recommendations can guide the development of programs that address specific issues. For example, recommendations from research on education can be used to develop programs that improve student achievement.
• Product development : Research recommendations can guide the development of products that meet specific needs. For example, recommendations from research on consumer behavior can be used to develop products that appeal to consumers.
• Marketing strategies: Research recommendations can be used to develop effective marketing strategies. For example, recommendations from research on target audiences can be used to develop marketing strategies that effectively reach specific demographic groups.
• Medical practice : Research recommendations can guide medical practitioners in providing the best possible care to patients. For example, recommendations from research on treatments for specific conditions can be used to improve patient outcomes.
• Scientific research: Research recommendations can guide future research in a specific field. For example, recommendations from research on a specific disease can be used to guide future research on treatments and cures for that disease.

Purpose of Research Recommendations

The purpose of research recommendations is to provide guidance on how to improve or solve a problem based on the findings of research. Research recommendations are typically made at the end of a research study and are based on the conclusions drawn from the research data. The purpose of research recommendations is to provide actionable advice to individuals or organizations that can help them make informed decisions, develop effective strategies, or implement changes that address the issues identified in the research.

The main purpose of research recommendations is to facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings. Recommendations can help bridge the gap between research and practice by providing specific actions that can be taken based on the research results. By providing clear and actionable recommendations, researchers can help ensure that their findings are put into practice, leading to improvements in various fields, such as healthcare, education, business, and public policy.

Characteristics of Research Recommendations

Research recommendations are a key component of research studies and are intended to provide practical guidance on how to apply research findings to real-world problems. The following are some of the key characteristics of research recommendations:

• Actionable : Research recommendations should be specific and actionable, providing clear guidance on what actions should be taken to address the problem identified in the research.
• Evidence-based: Research recommendations should be based on the findings of the research study, supported by the data collected and analyzed.
• Contextual: Research recommendations should be tailored to the specific context in which they will be implemented, taking into account the unique circumstances and constraints of the situation.
• Feasible : Research recommendations should be realistic and feasible, taking into account the available resources, time constraints, and other factors that may impact their implementation.
• Prioritized: Research recommendations should be prioritized based on their potential impact and feasibility, with the most important recommendations given the highest priority.
• Communicated effectively: Research recommendations should be communicated clearly and effectively, using language that is understandable to the target audience.
• Evaluated : Research recommendations should be evaluated to determine their effectiveness in addressing the problem identified in the research, and to identify opportunities for improvement.

Advantages of Research Recommendations

Research recommendations have several advantages, including:

• Providing practical guidance: Research recommendations provide practical guidance on how to apply research findings to real-world problems, helping to bridge the gap between research and practice.
• Improving decision-making: Research recommendations help decision-makers make informed decisions based on the findings of research, leading to better outcomes and improved performance.
• Enhancing accountability : Research recommendations can help enhance accountability by providing clear guidance on what actions should be taken, and by providing a basis for evaluating progress and outcomes.
• Informing policy development : Research recommendations can inform the development of policies that are evidence-based and tailored to the specific needs of a given situation.
• Enhancing knowledge transfer: Research recommendations help facilitate the transfer of knowledge from researchers to practitioners, policymakers, or other stakeholders who can benefit from the research findings.
• Encouraging further research : Research recommendations can help identify gaps in knowledge and areas for further research, encouraging continued exploration and discovery.
• Promoting innovation: Research recommendations can help identify innovative solutions to complex problems, leading to new ideas and approaches.

Limitations of Research Recommendations

While research recommendations have several advantages, there are also some limitations to consider. These limitations include:

• Context-specific: Research recommendations may be context-specific and may not be applicable in all situations. Recommendations developed in one context may not be suitable for another context, requiring adaptation or modification.
• I mplementation challenges: Implementation of research recommendations may face challenges, such as lack of resources, resistance to change, or lack of buy-in from stakeholders.
• Limited scope: Research recommendations may be limited in scope, focusing only on a specific issue or aspect of a problem, while other important factors may be overlooked.
• Uncertainty : Research recommendations may be uncertain, particularly when the research findings are inconclusive or when the recommendations are based on limited data.
• Bias : Research recommendations may be influenced by researcher bias or conflicts of interest, leading to recommendations that are not in the best interests of stakeholders.
• Timing : Research recommendations may be time-sensitive, requiring timely action to be effective. Delayed action may result in missed opportunities or reduced effectiveness.
• Lack of evaluation: Research recommendations may not be evaluated to determine their effectiveness or impact, making it difficult to assess whether they are successful or not.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Recommendations – Guiding policy-makers for evidence-based decision making

Research recommendations play a crucial role in guiding scholars and researchers toward fruitful avenues of exploration. In an era marked by rapid technological advancements and an ever-expanding knowledge base, refining the process of generating research recommendations becomes imperative.

But, what is a research recommendation?

Research recommendations are suggestions or advice provided to researchers to guide their study on a specific topic . They are typically given by experts in the field. Research recommendations are more action-oriented and provide specific guidance for decision-makers, unlike implications that are broader and focus on the broader significance and consequences of the research findings. However, both are crucial components of a research study.

Difference Between Research Recommendations and Implication

Although research recommendations and implications are distinct components of a research study, they are closely related. The differences between them are as follows:

Types of Research Recommendations

Recommendations in research can take various forms, which are as follows:

These recommendations aim to assist researchers in navigating the vast landscape of academic knowledge.

Let us dive deeper to know about its key components and the steps to write an impactful research recommendation.

Key Components of Research Recommendations

The key components of research recommendations include defining the research question or objective, specifying research methods, outlining data collection and analysis processes, presenting results and conclusions, addressing limitations, and suggesting areas for future research. Here are some characteristics of research recommendations:

Research recommendations offer various advantages and play a crucial role in ensuring that research findings contribute to positive outcomes in various fields. However, they also have few limitations which highlights the significance of a well-crafted research recommendation in offering the promised advantages.

The importance of research recommendations ranges in various fields, influencing policy-making, program development, product development, marketing strategies, medical practice, and scientific research. Their purpose is to transfer knowledge from researchers to practitioners, policymakers, or stakeholders, facilitating informed decision-making and improving outcomes in different domains.

How to Write Research Recommendations?

Research recommendations can be generated through various means, including algorithmic approaches, expert opinions, or collaborative filtering techniques. Here is a step-wise guide to build your understanding on the development of research recommendations.

1. Understand the Research Question:

Understand the research question and objectives before writing recommendations. Also, ensure that your recommendations are relevant and directly address the goals of the study.

2. Review Existing Literature:

Familiarize yourself with relevant existing literature to help you identify gaps , and offer informed recommendations that contribute to the existing body of research.

3. Consider Research Methods:

Evaluate the appropriateness of different research methods in addressing the research question. Also, consider the nature of the data, the study design, and the specific objectives.

4. Identify Data Collection Techniques:

Gather dataset from diverse authentic sources. Include information such as keywords, abstracts, authors, publication dates, and citation metrics to provide a rich foundation for analysis.

5. Propose Data Analysis Methods:

Suggest appropriate data analysis methods based on the type of data collected. Consider whether statistical analysis, qualitative analysis, or a mixed-methods approach is most suitable.

6. Consider Limitations and Ethical Considerations:

Acknowledge any limitations and potential ethical considerations of the study. Furthermore, address these limitations or mitigate ethical concerns to ensure responsible research.

7. Justify Recommendations:

Explain how your recommendation contributes to addressing the research question or objective. Provide a strong rationale to help researchers understand the importance of following your suggestions.

8. Summarize Recommendations:

Provide a concise summary at the end of the report to emphasize how following these recommendations will contribute to the overall success of the research project.

By following these steps, you can create research recommendations that are actionable and contribute meaningfully to the success of the research project.

Download now to unlock some tips to improve your journey of writing research recommendations.

Example of a Research Recommendation

Here is an example of a research recommendation based on a hypothetical research to improve your understanding.

Research Recommendation: Enhancing Student Learning through Integrated Learning Platforms

Background:

The research study investigated the impact of an integrated learning platform on student learning outcomes in high school mathematics classes. The findings revealed a statistically significant improvement in student performance and engagement when compared to traditional teaching methods.

Recommendation:

In light of the research findings, it is recommended that educational institutions consider adopting and integrating the identified learning platform into their mathematics curriculum. The following specific recommendations are provided:

• Implementation of the Integrated Learning Platform:

Schools are encouraged to adopt the integrated learning platform in mathematics classrooms, ensuring proper training for teachers on its effective utilization.

• Professional Development for Educators:

Develop and implement professional programs to train educators in the effective use of the integrated learning platform to address any challenges teachers may face during the transition.

• Monitoring and Evaluation:

Establish a monitoring and evaluation system to track the impact of the integrated learning platform on student performance over time.

• Resource Allocation:

Allocate sufficient resources, both financial and technical, to support the widespread implementation of the integrated learning platform.

By implementing these recommendations, educational institutions can harness the potential of the integrated learning platform and enhance student learning experiences and academic achievements in mathematics.

This example covers the components of a research recommendation, providing specific actions based on the research findings, identifying the target audience, and outlining practical steps for implementation.

Using AI in Research Recommendation Writing

Enhancing research recommendations is an ongoing endeavor that requires the integration of cutting-edge technologies, collaborative efforts, and ethical considerations. By embracing data-driven approaches and leveraging advanced technologies, the research community can create more effective and personalized recommendation systems. However, it is accompanied by several limitations. Therefore, it is essential to approach the use of AI in research with a critical mindset, and complement its capabilities with human expertise and judgment.

Here are some limitations of integrating AI in writing research recommendation and some ways on how to counter them.

1. Data Bias

AI systems rely heavily on data for training. If the training data is biased or incomplete, the AI model may produce biased results or recommendations.

How to tackle: Audit regularly the model’s performance to identify any discrepancies and adjust the training data and algorithms accordingly.

2. Lack of Understanding of Context:

AI models may struggle to understand the nuanced context of a particular research problem. They may misinterpret information, leading to inaccurate recommendations.

How to tackle: Use AI to characterize research articles and topics. Employ them to extract features like keywords, authorship patterns and content-based details.

3. Ethical Considerations:

AI models might stereotype certain concepts or generate recommendations that could have negative consequences for certain individuals or groups.

How to tackle: Incorporate user feedback mechanisms to reduce redundancies. Establish an ethics review process for AI models in research recommendation writing.

4. Lack of Creativity and Intuition:

AI may struggle with tasks that require a deep understanding of the underlying principles or the ability to think outside the box.

How to tackle: Hybrid approaches can be employed by integrating AI in data analysis and identifying patterns for accelerating the data interpretation process.

5. Interpretability:

Many AI models, especially complex deep learning models, lack transparency on how the model arrived at a particular recommendation.

How to tackle: Implement models like decision trees or linear models. Provide clear explanation of the model architecture, training process, and decision-making criteria.

6. Dynamic Nature of Research:

Research fields are dynamic, and new information is constantly emerging. AI models may struggle to keep up with the rapidly changing landscape and may not be able to adapt to new developments.

How to tackle: Establish a feedback loop for continuous improvement. Regularly update the recommendation system based on user feedback and emerging research trends.

The integration of AI in research recommendation writing holds great promise for advancing knowledge and streamlining the research process. However, navigating these concerns is pivotal in ensuring the responsible deployment of these technologies. Researchers need to understand the use of responsible use of AI in research and must be aware of the ethical considerations.

Exploring research recommendations plays a critical role in shaping the trajectory of scientific inquiry. It serves as a compass, guiding researchers toward more robust methodologies, collaborative endeavors, and innovative approaches. Embracing these suggestions not only enhances the quality of individual studies but also contributes to the collective advancement of human understanding.

Frequently Asked Questions

The purpose of recommendations in research is to provide practical and actionable suggestions based on the study's findings, guiding future actions, policies, or interventions in a specific field or context. Recommendations bridges the gap between research outcomes and their real-world application.

To make a research recommendation, analyze your findings, identify key insights, and propose specific, evidence-based actions. Include the relevance of the recommendations to the study's objectives and provide practical steps for implementation.

Begin a recommendation by succinctly summarizing the key findings of the research. Clearly state the purpose of the recommendation and its intended impact. Use a direct and actionable language to convey the suggested course of action.

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What are Implications and Recommendations in Research? How to Write It, with Examples

Highly cited research articles often contain both implications and recommendations , but there is often some confusion around the difference between implications and recommendations in research. Implications of a study are the impact your research makes in your chosen area; they discuss how the findings of the study may be important to justify further exploration of your research topic. Research recommendations suggest future actions or subsequent steps supported by your research findings. It helps to improve your field of research or cross-disciplinary fields through future research or provides frameworks for decision-makers or policymakers. Recommendations are the action plan you propose based on the outcome.

In this article, we aim to simplify these concepts for researchers by providing key insights on the following:  

• what are implications in research 
• what is recommendation in research 
• differences between implications and recommendations 
• how to write implications in research 
• how to write recommendation in research 
• sample recommendation in research 

Table of Contents

What are implications in research

The implications in research explain what the findings of the study mean to researchers or to certain subgroups or populations beyond the basic interpretation of results. Even if your findings fail to bring radical or disruptive changes to existing ways of doing things, they might have important implications for future research studies. For example, your proposed method for operating remote-controlled robots could be more precise, efficient, or cheaper than existing methods, or the remote-controlled robot could be used in other application areas. This could enable more researchers to study a specific problem or open up new research opportunities.   

Implications in research inform how the findings, drawn from your results, may be important for and impact policy, practice, theory, and subsequent research. Implications may be theoretical or practical. 1  

• Practical implications are potential values of the study with practical or real outcomes . Determining the practical implications of several solutions can aid in identifying optimal solution results. For example, clinical research or research on classroom learning mostly has practical implications in research . If you developed a new teaching method, the implication would be how teachers can use that method based on your findings.  
• Theoretical implications in research constitute additions to existing theories or establish new theories. These types of implications in research characterize the ability of research to influence society in apparent ways. It is, at most, an educated guess (theoretical) about the possible implication of action and need not be as absolute as practical implications in research . If your study supported the tested theory, the theoretical implication would be that the theory can explain the investigated phenomenon. Else, your study may serve as a basis for modifying the theory. Theories may be partially supported as well, implying further study of the theory or necessary modifications are required.  

What are recommendations in research?

Recommendations in research can be considered an important segment of the analysis phase. Recommendations allow you to suggest specific interventions or strategies to address the issues and constraints identified through your study. It responds to key findings arrived at through data collection and analysis. A process of prioritization can help you narrow down important findings for which recommendations are developed.  

Recommendations in research examples

Recommendations in research may vary depending on the purpose or beneficiary as seen in the table below.  

Table: Recommendations in research examples based on purpose and beneficiary  

If you’re wondering how to make recommendations in research . You can use the simple  recommendation in research example below as a handy template.  

Table: Sample recommendation in research template  

Basic differences between implications and recommendations in research

Implications and recommendations in research are two important aspects of a research paper or your thesis or dissertation. Implications discuss the importance of the research findings, while recommendations offer specific actions to solve a problem. So, the basic difference between the two is in their function and the questions asked to achieve it. The following table highlights the main differences between implications and recommendations in research .  

Table: Differences between implications and recommendations in research  

Where do implications go in your research paper.

Because the implications and recommendations of the research are based on study findings, both are usually written after the completion of a study. There is no specific section dedicated to implications in research ; they are usually integrated into the discussion section adding evidence as to why the results are meaningful and what they add to the field. Implications can be written after summarizing your main findings and before the recommendations and conclusion.   

Implications can also be presented in the conclusion section after a short summary of the study results.   

How to write implications in research

Implication means something that is inferred. The implications of your research are derived from the importance of your work and how it will impact future research. It is based on how previous studies have advanced your field and how your study can add to that.   

When figuring out how to write implications in research , a good strategy is to separate it into the different types of implications in research , such as social, political, technological, policy-related, or others. As mentioned earlier, the most frequently used are the theoretical and practical implications.   

Next, you need to ask, “Who will benefit the most from reading my paper?” Is it policymakers, physicians, the public, or other researchers? Once you know your target population, explain how your findings can help them.  

The implication section can include a paragraph or two that asserts the practical or managerial implications and links it to the study findings. A discussion can then follow, demonstrating that the findings can be practically implemented or how they will benefit a specific audience. The writer is given a specific degree of freedom when writing research implications , depending on the type of implication in research you want to discuss: practical or theoretical. Each is discussed differently, using different words or in separate sections. The implications can be based on how the findings in your study are similar or dissimilar to that in previous studies. Your study may reaffirm or disprove the results of other studies, which has important implications in research . You can also suggest future research directions in the light of your findings or require further research to confirm your findings, which are all crucial implications. Most importantly, ensure the implications in research are specific and that your tone reflects the strength of your findings without exaggerating your results.   

Implications in research can begin with the following specific sentence structures:  

• These findings suggest that…
• These results build on existing body of evidence of…
• These results should be considered when…
• While previous research focused on x, our results show that y…

What should recommendations in research look like?

Recommendations for future research should be:  

• Directly related to your research question or findings  
• Concrete and specific  
• Supported by a clear reasoning  

The recommendations in research can be based on the following factors:  

1. Beneficiary: A paper’s research contribution may be aimed at single or multiple beneficiaries, based on which recommendations can vary. For instance, if your research is about the quality of care in hospitals, the research recommendation to different beneficiaries might be as follows:  

• Nursing staff: Staff should undergo training to enhance their understanding of what quality of care entails.  
• Health science educators: Educators must design training modules that address quality-related issues in the hospital.  
• Hospital management: Develop policies that will increase staff participation in training related to health science.  

2. Limitations: The best way to figure out what to include in your research recommendations is to understand the limitations of your study. It could be based on factors that you have overlooked or could not consider in your present study. Accordingly, the researcher can recommend that other researchers approach the problem from a different perspective, dimension, or methodology. For example, research into the quality of care in hospitals can be based on quantitative data. The researcher can then recommend a qualitative study of factors influencing the quality of care, or they can suggest investigating the problem from the perspective of patients rather than the healthcare providers.   

3. Theory or Practice: Your recommendations in research could be implementation-oriented or further research-oriented.   

4. Your research: Research recommendations can be based on your topic, research objectives, literature review, and analysis, or evidence collected. For example, if your data points to the role of faculty involvement in developing effective programs, recommendations in research can include developing policies to increase faculty participation. Take a look at the evidence-based recommendation in research example s provided below.   

Table: Example of evidence-based research recommendation  

Avoid making the following mistakes when writing research recommendations :  

• Don’t undermine your own work: Recommendations in research should offer suggestions on how future studies can be built upon the current study as a natural extension of your work and not as an entirely new field of research.  
• Support your study arguments: Ensure that your research findings stand alone on their own merits to showcase the strength of your research paper.   

How to write recommendations in research

When writing research recommendations , your focus should be on highlighting what additional work can be done in that field. It gives direction to researchers, industries, or governments about changes or developments possible in this field. For example, recommendations in research can include practical and obtainable strategies offering suggestions to academia to address problems. It can also be a framework that helps government agencies in developing strategic or long-term plans for timely actions against disasters or aid nation-building.  

There are a few SMART 2 things to remember when writing recommendations in research. Your recommendations must be: 

• S pecific: Clearly state how challenges can be addressed for better outcomes and include an action plan that shows what can be achieved. 
• M easurable: Use verbs denoting measurable outcomes, such as identify, analyze, design, compute, assess, evaluate, revise, plan, etc., to strengthen recommendations in research .   
• A ttainable: Recommendations should offer a solution-oriented approach to problem-solving and must be written in a way that is easy to follow.  
• R elevant: Research recommendations should be reasonable, realistic, and result-based. Make sure to suggest future possibilities for your research field.  
• T imely: Time-based or time-sensitive recommendations in research help divide the action plan into long-term or short-term (immediate) goals. A timeline can also inform potential readers of what developments should occur over time.  

If you are wondering how many words to include in your research recommendation , a general rule of thumb would be to set aside 5% of the total word count for writing research recommendations . Finally, when writing the research implications and recommendations , stick to the facts and avoid overstating or over-generalizing the study findings. Both should be supported by evidence gathered through your data analysis.  

References:  

• Schmidt, F. L., & Hunter, J. E. (1998). The validity and utility of selection methods in personnel psychology: Practical and theoretical implications of 85 years of research findings.  Psychological bulletin ,  124 (2), 262.
• Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives.  Manag Rev ,  70 (11), 35-36.

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How to formulate research recommendations

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• Peer review
• Polly Brown ( pbrown{at}bmjgroup.com ) , publishing manager 1 ,
• Klara Brunnhuber , clinical editor 1 ,
• Kalipso Chalkidou , associate director, research and development 2 ,
• Iain Chalmers , director 3 ,
• Mike Clarke , director 4 ,
• Mark Fenton , editor 3 ,
• Carol Forbes , reviews manager 5 ,
• Julie Glanville , associate director/information service manager 5 ,
• Nicholas J Hicks , consultant in public health medicine 6 ,
• Janet Moody , identification and prioritisation manager 6 ,
• Sara Twaddle , director 7 ,
• Hazim Timimi , systems developer 8 ,
• Pamela Young , senior programme manager 6
• 1 BMJ Publishing Group, London WC1H 9JR,
• 2 National Institute for Health and Clinical Excellence, London WC1V 6NA,
• 3 Database of Uncertainties about the Effects of Treatments, James Lind Alliance Secretariat, James Lind Initiative, Oxford OX2 7LG,
• 4 UK Cochrane Centre, Oxford OX2 7LG,
• 5 Centre for Reviews and Dissemination, University of York, York YO10 5DD,
• 6 National Coordinating Centre for Health Technology Assessment, University of Southampton, Southampton SO16 7PX,
• 7 Scottish Intercollegiate Guidelines Network, Edinburgh EH2 1EN,
• 8 Update Software, Oxford OX2 7LG
• Correspondence to: PBrown
• Accepted 22 September 2006

“More research is needed” is a conclusion that fits most systematic reviews. But authors need to be more specific about what exactly is required

Long awaited reports of new research, systematic reviews, and clinical guidelines are too often a disappointing anticlimax for those wishing to use them to direct future research. After many months or years of effort and intellectual energy put into these projects, authors miss the opportunity to identify unanswered questions and outstanding gaps in the evidence. Most reports contain only a less than helpful, general research recommendation. This means that the potential value of these recommendations is lost.

Current recommendations

In 2005, representatives of organisations commissioning and summarising research, including the BMJ Publishing Group, the Centre for Reviews and Dissemination, the National Coordinating Centre for Health Technology Assessment, the National Institute for Health and Clinical Excellence, the Scottish Intercollegiate Guidelines Network, and the UK Cochrane Centre, met as members of the development group for the Database of Uncertainties about the Effects of Treatments (see bmj.com for details on all participating organisations). Our aim was to discuss the state of research recommendations within our organisations and to develop guidelines for improving the presentation of proposals for further research. All organisations had found weaknesses in the way researchers and authors of systematic reviews and clinical guidelines stated the need for further research. As part of the project, a member of the Centre for Reviews and Dissemination under-took a rapid literature search to identify information on research recommendation models, which found some individual methods but no group initiatives to attempt to standardise recommendations.

Suggested format for research recommendations on the effects of treatments

Core elements.

E Evidence (What is the current state of the evidence?)

P Population (What is the population of interest?)

I Intervention (What are the interventions of interest?)

C Comparison (What are the comparisons of interest?)

O Outcome (What are the outcomes of interest?)

T Time stamp (Date of recommendation)

Optional elements

d Disease burden or relevance

t Time aspect of core elements of EPICOT

s Appropriate study type according to local need

In January 2006, the National Coordinating Centre for Health Technology Assessment presented the findings of an initial comparative analysis of how different organisations currently structure their research recommendations. The National Institute for Health and Clinical Excellence and the National Coordinating Centre for Health Technology Assessment request authors to present recommendations in a four component format for formulating well built clinical questions around treatments: population, intervention, comparison, and outcomes (PICO). 1 In addition, the research recommendation is dated and authors are asked to provide the current state of the evidence to support the proposal.

Clinical Evidence , although not directly standardising its sections for research recommendations, presents gaps in the evidence using a slightly extended version of the PICO format: evidence, population, intervention, comparison, outcomes, and time (EPICOT). Clinical Evidence has used this inherent structure to feed research recommendations on interventions categorised as “unknown effectiveness” back to the National Coordinating Centre for Health Technology Assessment and for inclusion in the Database of Uncertainties about the Effects of Treatments ( http://www.duets.nhs.uk/ ).

We decided to propose the EPICOT format as the basis for its statement on formulating research recommendations and tested this proposal through discussion and example. We agreed that this set of components provided enough context for formulating research recommendations without limiting researchers. In order for the proposed framework to be flexible and more widely applicable, the group discussed using several optional components when they seemed relevant or were proposed by one or more of the group members. The final outcome of discussions resulted in the proposed EPICOT+ format (box).

A recent BMJ article highlighted how lack of research hinders the applicability of existing guidelines to patients in primary care who have had a stroke or transient ischaemic attack. 2 Most research in the area had been conducted in younger patients with a recent episode and in a hospital setting. The authors concluded that “further evidence should be collected on the efficacy and adverse effects of intensive blood pressure lowering in representative populations before we implement this guidance [from national and international guidelines] in primary care.” Table 1 outlines how their recommendations could be formulated using the EPICOT+ format. The decision on whether additional research is indeed clinically and ethically warranted will still lie with the organisation considering commissioning the research.

Research recommendation based on gap in the evidence identified by a cross sectional study of clinical guidelines for management of patients who have had a stroke

• View inline

Table 2 shows the use of EPICOT+ for an unanswered question on the effectiveness of compliance therapy in people with schizophrenia, identified by the Database of Uncertainties about the Effects of Treatments.

Research recommendation based on a gap in the evidence on treatment of schizophrenia identified by the Database of Uncertainties about the Effects of Treatments

Discussions around optional elements

Although the group agreed that the PICO elements should be core requirements for a research recommendation, intense discussion centred on the inclusion of factors defining a more detailed context, such as current state of evidence (E), appropriate study type (s), disease burden and relevance (d), and timeliness (t).

Initially, group members interpreted E differently. Some viewed it as the supporting evidence for a research recommendation and others as the suggested study type for a research recommendation. After discussion, we agreed that E should be used to refer to the amount and quality of research supporting the recommendation. However, the issue remained contentious as some of us thought that if a systematic review was available, its reference would sufficiently identify the strength of the existing evidence. Others thought that adding evidence to the set of core elements was important as it provided a summary of the supporting evidence, particularly as the recommendation was likely to be abstracted and used separately from the review or research that led to its formulation. In contrast, the suggested study type (s) was left as an optional element.

A research recommendation will rarely have an absolute value in itself. Its relative priority will be influenced by the burden of ill health (d), which is itself dependent on factors such as local prevalence, disease severity, relevant risk factors, and the priorities of the organisation considering commissioning the research.

Similarly, the issue of time (t) could be seen to be relevant to each of the core elements in varying ways—for example, duration of treatment, length of follow-up. The group therefore agreed that time had a subsidiary role within each core item; however, T as the date of the recommendation served to define its shelf life and therefore retained individual importance.

Applicability and usability

The proposed statement on research recommendations applies to uncertainties of the effects of any form of health intervention or treatment and is intended for research in humans rather than basic scientific research. Further investigation is required to assess the applicability of the format for questions around diagnosis, signs and symptoms, prognosis, investigations, and patient preference.

When the proposed format is applied to a specific research recommendation, the emphasis placed on the relevant part(s) of the EPICOT+ format may vary by author, audience, and intended purpose. For example, a recommendation for research into treatments for transient ischaemic attack may or may not define valid outcome measures to assess quality of life or gather data on adverse effects. Among many other factors, its implementation will also depend on the strength of current findings—that is, strong evidence may support a tightly focused recommendation whereas a lack of evidence would result in a more general recommendation.

The controversy within the group, especially around the optional components, reflects the different perspectives of the participating organisations—whether they were involved in commissioning, undertaking, or summarising research. Further issues will arise during the implementation of the proposed format, and we welcome feedback and discussion.

Summary points

No common guidelines exist for the formulation of recommendations for research on the effects of treatments

Major organisations involved in commissioning or summarising research compared their approaches and agreed on core questions

The essential items can be summarised as EPICOT+ (evidence, population, intervention, comparison, outcome, and time)

Further details, such as disease burden and appropriate study type, should be considered as required

We thank Patricia Atkinson and Jeremy Wyatt.

Contributors and sources All authors contributed to manuscript preparation and approved the final draft. NJH is the guarantor.

Competing interests None declared.

• Richardson WS ,
• Wilson MC ,
• Nishikawa J ,
• Hayward RSA
• McManus RJ ,
• Leonardi-Bee J ,
• PROGRESS Collaborative Group
• Warburton E
• Rothwell P ,
• McIntosh AM ,
• Lawrie SM ,
• Stanfield AC
• O'Donnell C ,
• Donohoe G ,
• Sharkey L ,
• Jablensky A ,
• Sartorius N ,
• Ernberg G ,

Implications or Recommendations in Research: What's the Difference?

• Peer Review

High-quality research articles that get many citations contain both implications and recommendations. Implications are the impact your research makes, whereas recommendations are specific actions that can then be taken based on your findings, such as for more research or for policymaking.

Updated on August 23, 2022

That seems clear enough, but the two are commonly confused.

This confusion is especially true if you come from a so-called high-context culture in which information is often implied based on the situation, as in many Asian cultures. High-context cultures are different from low-context cultures where information is more direct and explicit (as in North America and many European cultures).

Let's set these two straight in a low-context way; i.e., we'll be specific and direct! This is the best way to be in English academic writing because you're writing for the world.

Implications and recommendations in a research article

The standard format of STEM research articles is what's called IMRaD:

• Introduction
• Discussion/conclusions

Some journals call for a separate conclusions section, while others have the conclusions as the last part of the discussion. You'll write these four (or five) sections in the same sequence, though, no matter the journal.

The discussion section is typically where you restate your results and how well they confirmed your hypotheses. Give readers the answer to the questions for which they're looking to you for an answer.

At this point, many researchers assume their paper is finished. After all, aren't the results the most important part? As you might have guessed, no, you're not quite done yet.

The discussion/conclusions section is where to say what happened and what should now happen

The discussion/conclusions section of every good scientific article should contain the implications and recommendations.

The implications, first of all, are the impact your results have on your specific field. A high-impact, highly cited article will also broaden the scope here and provide implications to other fields. This is what makes research cross-disciplinary.

Recommendations, however, are suggestions to improve your field based on your results.

These two aspects help the reader understand your broader content: How and why your work is important to the world. They also tell the reader what can be changed in the future based on your results.

These aspects are what editors are looking for when selecting papers for peer review.

Implications and recommendations are, thus, written at the end of the discussion section, and before the concluding paragraph. They help to “wrap up” your paper. Once your reader understands what you found, the next logical step is what those results mean and what should come next.

Then they can take the baton, in the form of your work, and run with it. That gets you cited and extends your impact!

The order of implications and recommendations also matters. Both are written after you've summarized your main findings in the discussion section. Then, those results are interpreted based on ongoing work in the field. After this, the implications are stated, followed by the recommendations.

Writing an academic research paper is a bit like running a race. Finish strong, with your most important conclusion (recommendation) at the end. Leave readers with an understanding of your work's importance. Avoid generic, obvious phrases like "more research is needed to fully address this issue." Be specific.

The main differences between implications and recommendations (table)

Now let's dig a bit deeper into actually how to write these parts.

What are implications?

Research implications tell us how and why your results are important for the field at large. They help answer the question of “what does it mean?” Implications tell us how your work contributes to your field and what it adds to it. They're used when you want to tell your peers why your research is important for ongoing theory, practice, policymaking, and for future research.

Crucially, your implications must be evidence-based. This means they must be derived from the results in the paper.

Implications are written after you've summarized your main findings in the discussion section. They come before the recommendations and before the concluding paragraph. There is no specific section dedicated to implications. They must be integrated into your discussion so that the reader understands why the results are meaningful and what they add to the field.

A good strategy is to separate your implications into types. Implications can be social, political, technological, related to policies, or others, depending on your topic. The most frequently used types are theoretical and practical. Theoretical implications relate to how your findings connect to other theories or ideas in your field, while practical implications are related to what we can do with the results.

Key features of implications

• State the impact your research makes
• Helps us understand why your results are important
• Must be evidence-based
• Written in the discussion, before recommendations
• Can be theoretical, practical, or other (social, political, etc.)

Examples of implications

Let's take a look at some examples of research results below with their implications.

The result : one study found that learning items over time improves memory more than cramming material in a bunch of information at once .

The implications : This result suggests memory is better when studying is spread out over time, which could be due to memory consolidation processes.

The result : an intervention study found that mindfulness helps improve mental health if you have anxiety.

The implications : This result has implications for the role of executive functions on anxiety.

The result : a study found that musical learning helps language learning in children .

The implications : these findings suggest that language and music may work together to aid development.

What are recommendations?

As noted above, explaining how your results contribute to the real world is an important part of a successful article.

Likewise, stating how your findings can be used to improve something in future research is equally important. This brings us to the recommendations.

Research recommendations are suggestions and solutions you give for certain situations based on your results. Once the reader understands what your results mean with the implications, the next question they need to know is "what's next?"

Recommendations are calls to action on ways certain things in the field can be improved in the future based on your results. Recommendations are used when you want to convey that something different should be done based on what your analyses revealed.

Similar to implications, recommendations are also evidence-based. This means that your recommendations to the field must be drawn directly from your results.

The goal of the recommendations is to make clear, specific, and realistic suggestions to future researchers before they conduct a similar experiment. No matter what area your research is in, there will always be further research to do. Try to think about what would be helpful for other researchers to know before starting their work.

Recommendations are also written in the discussion section. They come after the implications and before the concluding paragraphs. Similar to the implications, there is usually no specific section dedicated to the recommendations. However, depending on how many solutions you want to suggest to the field, they may be written as a subsection.

Key features of recommendations

• Statements about what can be done differently in the field based on your findings
• Must be realistic and specific
• Written in the discussion, after implications and before conclusions
• Related to both your field and, preferably, a wider context to the research

Examples of recommendations

Here are some research results and their recommendations.

A meta-analysis found that actively recalling material from your memory is better than simply re-reading it .

• The recommendation: Based on these findings, teachers and other educators should encourage students to practice active recall strategies.

A medical intervention found that daily exercise helps prevent cardiovascular disease .

• The recommendation: Based on these results, physicians are recommended to encourage patients to exercise and walk regularly. Also recommended is to encourage more walking through public health offices in communities.

A study found that many research articles do not contain the sample sizes needed to statistically confirm their findings .

The recommendation: To improve the current state of the field, researchers should consider doing power analysis based on their experiment's design.

What else is important about implications and recommendations?

When writing recommendations and implications, be careful not to overstate the impact of your results. It can be tempting for researchers to inflate the importance of their findings and make grandiose statements about what their work means.

Remember that implications and recommendations must be coming directly from your results. Therefore, they must be straightforward, realistic, and plausible.

Another good thing to remember is to make sure the implications and recommendations are stated clearly and separately. Do not attach them to the endings of other paragraphs just to add them in. Use similar example phrases as those listed in the table when starting your sentences to clearly indicate when it's an implication and when it's a recommendation.

When your peers, or brand-new readers, read your paper, they shouldn't have to hunt through your discussion to find the implications and recommendations. They should be clear, visible, and understandable on their own.

That'll get you cited more, and you'll make a greater contribution to your area of science while extending the life and impact of your work.

The AJE Team

See our "Privacy Policy"

Turn your research insights into actionable recommendations

At the end of one presentation, my colleague approached me and asked what I recommended based on the research. I was a bit puzzled. I didn’t expect anyone to ask me this kind of question. By that point in my career, I wasn’t aware that I had to make recommendations based on the research insights. I could talk about the next steps regarding what other research we had to conduct. I could also relay the information that something wasn’t working in a prototype, but I had no idea what to suggest. 

How to move from qualitative data to actionable insights

Over time, more and more colleagues asked for these recommendations. Finally, I realized that one of the key pieces I was missing in my reports was the “so what?” The prototype isn’t working, so what do we do next? Because I didn’t include suggestions, my colleagues had a difficult time marrying actions to my insights. Sure, the team could see the noticeable changes, but the next steps were a struggle, especially for generative research. 

Without these suggestions, my insights started to fall flat. My colleagues were excited about them and loved seeing the video clips, but they weren’t working with the findings. With this, I set out to experiment on how to write recommendations within a user research report. 

.css-1nrevy2{position:relative;display:inline-block;} How to write recommendations 

For a while, I wasn’t sure how to write recommendations. And, even now, I believe there is no  one right way . When I first started looking into this, I started with two main questions:

What do recommendations mean to stakeholders?

How prescriptive should recommendations be?

When people asked me for recommendations, I had no idea what they were looking for. I was nervous I would step on people’s toes and give the impression I thought I knew more than I did. I wasn’t a designer and didn’t want to make whacky design recommendations or impractical suggestions that would get developers rolling their eyes. 

When in doubt, I dusted off my internal research cap and sat with stakeholders to understand what they meant by recommendations. I asked them for examples of what they expected and what made a suggestion “helpful” or “actionable.” I walked away with a list of “must-haves” for my recommendations. They had to be:

Flexible. Just because I made an initial recommendation did not mean it was the only path forward. Once I presented the recommendations, we could talk through other ideas and consider new information. There were a few times when I revised my recommendations based on conversations I had with colleagues.

Feasible.  At first, I started presenting my recommendations without any prior feedback. My worst nightmare came true. The designer and developer sat back, arms crossed, and said, “A lot of this is impossible.” I quickly learned to review some of my recommendations I was uncertain about with them beforehand. Alternatively, I came up with several recommendations for one solution to help combat this problem.

Prioritized (to my best abilities).  Since I am not entirely sure of the recommendation’s effort, I use a chart of impact and reach to prioritize suggestions. Then, once I present this list, it may get reprioritized depending on effort levels from the team (hey, flexibility!).

Detailed.  This point helped me a lot with my second question regarding how in-depth I should make my recommendations. Some of the best detail comes from photos, videos, or screenshots, and colleagues appreciated when I linked recommendations with this media. They also told me to put in as much detail as possible to avoid vagueness, misinterpretation, and endless debate. 

Think MVP. Think about the solution with the fewest changes instead of recommending complex changes to a feature or product. What are some minor changes that the team can make to improve the experience or product?

Justified.  This part was the hardest for me. When my research findings didn’t align with expectations or business goals, I had no idea what to say. When I receive results that highlight we are going in the wrong direction, my recommendations become even more critical. Instead of telling the team that the new product or feature sucks and we should stop working on it, I offer alternatives. I follow the concept of “no, but...” So, “no, this isn’t working, but we found that users value X and Y, which could lead to increased retention” (or whatever metric we were looking at.

Let’s look at some examples

Although this list was beneficial in guiding my recommendations, I still wasn’t well-versed in how to write them. So, after some time, I created a formula for writing recommendations:

Observed problem/pain point/unmet need + consequence + potential solution

Evaluative research

Let’s imagine we are testing a check-out page, and we found that users were having a hard time filling out the shipping and billing forms, especially when there were two different addresses.

A non-specific and unhelpful recommendation might look like :

Users get frustrated when filling out the shipping and billing form.

The reasons this recommendation is not ideal are :

It provides no context or detail of the problem 

There is no proposed solution 

It sounds a bit judgemental (focus on the problem!) 

There is no immediate movement forward with this

A redesign recommendation about the same problem might look like this :

Users overlook the mandatory fields in the shipping and billing form, causing them to go back and fill out the form again. With this, they become frustrated. Include markers of required fields and avoid deleting information when users submit if they haven’t filled out all required fields.

Let’s take another example :

We tested an entirely new concept for our travel company, allowing people to pay to become “prime” travel members. In our user base, no one found any value in having or paying for a membership. However, they did find value in several of the features, such as sharing trips with family members or splitting costs but could not justify paying for them.

A suboptimal recommendation could look like this :

Users would not sign-up or pay for a prime membership.

Again, there is a considerable lack of context and understanding here, as well as action. Instead, we could try something like:

Users do not find enough value in the prime membership to sign-up or pay for it. Therefore, they do not see themselves using the feature. However, they did find value in two features: sharing trips with friends and splitting the trip costs. Focusing, instead, on these features could bring more people to our platform and increase retention. 

Generative research

Generative research can look a bit trickier because there isn’t always an inherent problem you are solving. For example, you might not be able to point to a usability issue, so you have to look more broadly at pain points or unmet needs. 

For example, in our generative research, we found that people often forget to buy gifts for loved ones, making them feel guilty and rushed at the last minute to find something meaningful but quickly.

This finding is extremely broad and could go in so many directions. With suggestions, we don’t necessarily want to lead our teams down only one path (flexibility!), but we also don’t want to leave the recommendation too vague (detailed). I use  How Might We statements  to help me build generative research recommendations. 

Just reporting the above wouldn’t entirely be enough for a recommendation, so let’s try to put it in a more actionable format:

People struggled to remember to buy gifts for loved one’s birthdays or special days. By the time their calendar notified them, it was too late to get a gift, leaving them filled with guilt and rushing to purchase a meaningful gift to arrive on time. How might we help people remember birthdays early enough to find meaningful gifts for their loved ones?

A great follow-up to generative research recommendations can be  running an ideation workshop !

Researching the right thing versus researching the thing right

How to format recommendations in your report.

I always end with recommendations because people leave a presentation with their minds buzzing and next steps top of mind (hopefully!). My favorite way to format suggestions is in a chart. That way, I can link the recommendation back to the insight and priority. My recommendations look like this:

Overall, play around with the recommendations that you give to your teams. The best thing you can do is ask for what they expect and then ask for feedback. By catering and iterating to your colleagues’ needs, you will help them make better decisions based on your research insights!

Written by Nikki Anderson, User Research Lead & Instructor. Nikki is a User Research Lead and Instructor with over eight years of experience. She has worked in all different sizes of companies, ranging from a tiny start-up called ALICE to large corporation Zalando, and also as a freelancer. During this time, she has led a diverse range of end-to-end research projects across the world, specializing in generative user research. Nikki also owns her own company, User Research Academy, a community and education platform designed to help people get into the field of user research, or learn more about how user research impacts their current role. User Research Academy hosts online classes, content, as well as personalized mentorship opportunities with Nikki. She is extremely passionate about teaching and supporting others throughout their journey in user research. To spread the word of research and help others transition and grow in the field, she writes as a writer at dscout and Dovetail. Outside of the world of user research, you can find Nikki (happily) surrounded by animals, including her dog and two cats, reading on her Kindle, playing old-school video games like Pokemon and World of Warcraft, and writing fiction novels.

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• Knowledge Base
• Dissertation
• How to Write Recommendations in Research | Examples & Tips

How to Write Recommendations in Research | Examples & Tips

Published on 15 September 2022 by Tegan George .

Recommendations in research are a crucial component of your discussion section and the conclusion of your thesis , dissertation , or research paper .

As you conduct your research and analyse the data you collected , perhaps there are ideas or results that don’t quite fit the scope of your research topic . Or, maybe your results suggest that there are further implications of your results or the causal relationships between previously-studied variables than covered in extant research.

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Table of contents

What should recommendations look like, building your research recommendation, how should your recommendations be written, recommendation in research example, frequently asked questions about recommendations.

Recommendations for future research should be:

• Concrete and specific
• Supported with a clear rationale
• Directly connected to your research

Overall, strive to highlight ways other researchers can reproduce or replicate your results to draw further conclusions, and suggest different directions that future research can take, if applicable.

Relatedly, when making these recommendations, avoid:

• Undermining your own work, but rather offer suggestions on how future studies can build upon it
• Suggesting recommendations actually needed to complete your argument, but rather ensure that your research stands alone on its own merits
• Using recommendations as a place for self-criticism, but rather as a natural extension point for your work

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There are many different ways to frame recommendations, but the easiest is perhaps to follow the formula of research question   conclusion  recommendation. Here’s an example.

Conclusion An important condition for controlling many social skills is mastering language. If children have a better command of language, they can express themselves better and are better able to understand their peers. Opportunities to practice social skills are thus dependent on the development of language skills.

As a rule of thumb, try to limit yourself to only the most relevant future recommendations: ones that stem directly from your work. While you can have multiple recommendations for each research conclusion, it is also acceptable to have one recommendation that is connected to more than one conclusion.

These recommendations should be targeted at your audience, specifically toward peers or colleagues in your field that work on similar topics to yours. They can flow directly from any limitations you found while conducting your work, offering concrete and actionable possibilities for how future research can build on anything that your own work was unable to address at the time of your writing.

See below for a full research recommendation example that you can use as a template to write your own.

The current study can be interpreted as a first step in the research on COPD speech characteristics. However, the results of this study should be treated with caution due to the small sample size and the lack of details regarding the participants’ characteristics.

Future research could further examine the differences in speech characteristics between exacerbated COPD patients, stable COPD patients, and healthy controls. It could also contribute to a deeper understanding of the acoustic measurements suitable for e-health measurements.

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While it may be tempting to present new arguments or evidence in your thesis or disseration conclusion , especially if you have a particularly striking argument you’d like to finish your analysis with, you shouldn’t. Theses and dissertations follow a more formal structure than this.

All your findings and arguments should be presented in the body of the text (more specifically in the discussion section and results section .) The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

The conclusion of your thesis or dissertation should include the following:

• A restatement of your research question
• A summary of your key arguments and/or results
• A short discussion of the implications of your research

For a stronger dissertation conclusion , avoid including:

• Generic concluding phrases (e.g. “In conclusion…”)
• Weak statements that undermine your argument (e.g. “There are good points on both sides of this issue.”)

Your conclusion should leave the reader with a strong, decisive impression of your work.

In a thesis or dissertation, the discussion is an in-depth exploration of the results, going into detail about the meaning of your findings and citing relevant sources to put them in context.

The conclusion is more shorter and more general: it concisely answers your main research question and makes recommendations based on your overall findings.

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Undergraduate Research Experiences for STEM Students: Successes, Challenges, and Opportunities (2017)

Chapter: 9 conclusions and recommendations, 9 conclusions and recommendations.

Practitioners designing or improving undergraduate research experiences (UREs) can build on the experiences of colleagues and learn from the increasingly robust literature about UREs and the considerable body of evidence about how students learn. The questions practitioners ask themselves during the design process should include questions about the goals of the campus, program, faculty, and students. Other factors to consider when designing a URE include the issues raised in the conceptual framework for learning and instruction, the available resources, how the program or experience will be evaluated or studied, and how to design the program from the outset to incorporate these considerations, as well as how to build in opportunities to improve the experience over time in light of new evidence. (Some of these topics are addressed in Chapter 8 .)

Colleges and universities that offer or wish to offer UREs to their students should undertake baseline evaluations of their current offerings and create plans to develop a culture of improvement in which faculty are supported in their efforts to continuously refine UREs based on the evidence currently available and evidence that they and others generate in the future. While much of the evidence to date is descriptive, it forms a body of knowledge that can be used to identify research questions about UREs, both those designed around the apprenticeship model and those designed using the more recent course-based undergraduate research experience (CURE) model. Internships and other avenues by which undergraduates do research provide many of the same sorts of experiences but are not well studied. In any case, it is clear that students value these experiences; that many faculty do as well; and that they contribute to broadening participation in science,

technology, engineering, and mathematics (STEM) education and careers. The findings from the research literature reported in Chapter 4 provide guidance to those designing both opportunities to improve practical and academic skills and opportunities for students to “try out” a professional role of interest.

Little research has been done that provides answers to mechanistic questions about how UREs work. Additional studies are needed to know which features of UREs are most important for positive outcomes with which students and to gain information about other questions of this type. This additional research is needed to better understand and compare different strategies for UREs designed for a diversity of students, mentors, and institutions. Therefore, the committee recommends steps that could increase the quantity and quality of evidence available in the future and makes recommendations for how faculty, departments, and institutions might approach decisions about UREs using currently available information. Multiple detailed recommendations about the kinds of research that might be useful are provided in the research agenda in Chapter 7 .

In addition to the specific research recommended in Chapter 7 , in this chapter the committee provides a series of interrelated conclusions and recommendations related to UREs for the STEM disciplines and intended to highlight the issues of primary importance to administrators, URE program designers, mentors to URE students, funders of UREs, those leading the departments and institutions offering UREs, and those conducting research about UREs. These conclusions and recommendations are based on the expert views of the committee and informed by their review of the available research, the papers commissioned for this report, and input from presenters during committee meetings. Table 9-1 defines categories of these URE “actors,” gives examples of specific roles included in each category, specifies key URE actions for which that category is responsible, and lists the conclusions and recommendations the committee views as most relevant to that actor category.

RESEARCH ON URES

Conclusion 1: The current and emerging landscape of what constitutes UREs is diverse and complex. Students can engage in STEM-based undergraduate research in many different ways, across a variety of settings, and along a continuum that extends and expands upon learning opportunities in other educational settings. The following characteristics define UREs. Due to the variation in the types of UREs, not all experiences include all of the following characteristics in the same way; experiences vary in how much a particular characteristic is emphasized.

TABLE 9-1 Audiences for Committee’s Conclusions and Recommendations

• They engage students in research practices including the ability to argue from evidence.
• They aim to generate novel information with an emphasis on discovery and innovation or to determine whether recent preliminary results can be replicated.
• They focus on significant, relevant problems of interest to STEM researchers and, in some cases, a broader community (e.g., civic engagement).
• They emphasize and expect collaboration and teamwork.
• They involve iterative refinement of experimental design, experimental questions, or data obtained.
• They allow students to master specific research techniques.
• They help students engage in reflection about the problems being investigated and the work being undertaken to address those problems.
• They require communication of results, either through publication or presentations in various STEM venues.
• They are structured and guided by a mentor, with students assuming increasing ownership of some aspects of the project over time.

UREs are generally designed to add value to STEM offerings by promoting an understanding of the ways that knowledge is generated in STEM fields and to extend student learning beyond what happens in the small group work of an inquiry-based course. UREs add value by enabling students to understand and contribute to the research questions that are driving the field for one or more STEM topics or to grapple with design challenges of interest to professionals. They help students understand what it means to be a STEM researcher in a way that would be difficult to convey in a lecture course or even in an inquiry-based learning setting. As participants in a URE, students can learn by engaging in planning, experimentation, evaluation, interpretation, and communication of data and other results in light of what is already known about the question of interest. They can pose relevant questions that can be solved only through investigative or design efforts—individually or in teams—and attempt to answer these questions despite the challenges, setbacks, and ambiguity of the process and the results obtained.

The diversity of UREs reflects the reality that different STEM disciplines operate from varying traditions, expectations, and constraints (e.g., lab safety issues) in providing opportunities for undergraduates to engage in research. In addition, individual institutions and departments have cultures that promote research participation to various degrees and at different stages in students’ academic careers. Some programs emphasize design and problem solving in addition to discovery. UREs in different disciplines can

take many forms (e.g., apprentice-style, course-based, internships, project-based), but the definitional characteristics described above are similar across different STEM fields.

Furthermore, students in today’s university landscape may have opportunities to engage with many different types of UREs throughout their education, including involvement in a formal program (which could include mentoring, tutoring, research, and seminars about research), an apprentice-style URE under the guidance of an individual or team of faculty members, an internship, or enrolling in one or more CUREs or in a consortium- or project-based program.

Conclusion 2: Research on the efficacy of UREs is still in the early stages of development compared with other interventions to improve undergraduate STEM education.

• The types of UREs are diverse, and their goals are even more diverse. Questions and methodologies used to investigate the roles and effectiveness of UREs in achieving those goals are similarly diverse.
• Most of the studies of UREs to date are descriptive case studies or use correlational designs. Many of these studies report positive outcomes from engagement in a URE.
• Only a small number of studies have employed research designs that can support inferences about causation. Most of these studies find evidence for a causal relationship between URE participation and subsequent persistence in STEM. More studies are needed to provide evidence that participation in UREs is a causal factor in a range of desired student outcomes.

Taking the entire body of evidence into account, the committee concludes that the published peer-reviewed literature to date suggests that participation in a URE is beneficial for students .

As discussed in the report’s Introduction (see Chapter 1 ) and in the research agenda (see Chapter 7 ), the committee considered descriptive, causal, and mechanistic questions in our reading of the literature on UREs. Scientific approaches to answering descriptive, causal, and mechanistic questions require deciding what to look for, determining how to examine it, and knowing appropriate ways to score or quantify the effect.

Descriptive questions ask what is happening without making claims as to why it is happening—that is, without making claims as to whether the research experience caused these changes. A descriptive statement about UREs only claims that certain changes occurred during or after the time the students were engaged in undergraduate research. Descriptive studies

cannot determine whether any benefits observed were caused by participation in the URE.

Causal questions seek to discover whether a specific intervention leads to a specific outcome, other things being equal. To address such questions, causal evidence can be generated from a comparison of carefully selected groups that do and do not experience UREs. The groups can be made roughly equivalent by random assignment (ensuring that URE and non-URE groups are the same on average as the sample size increases) or by controlling for an exhaustive set of characteristics and experiences that might render the groups different prior to the URE. Other quasi-experimental strategies can also be used. Simply comparing students who enroll in a URE with students who do not is not adequate for determining causality because there may be selection bias. For example, students already interested in STEM are more likely to seek out such opportunities and more likely to be selected for such programs. Instead the investigator would have to compare future enrollment patterns (or other measures) between closely matched students, some of whom enrolled in a URE and some of whom did not. Controlling for selection bias to enable an inference about causation can pose significant challenges.

Questions of mechanism or of process also can be explored to understand why a causal intervention leads to the observed effect. Perhaps the URE enhances a student’s confidence in her ability to succeed in her chosen field or deepens her commitment to the field by exposing her to the joy of discovery. Through these pathways that act on the participant’s purposive behavior, the URE enhances the likelihood that she persists in STEM. The question for the researcher then becomes what research design would provide support for this hypothesis of mechanism over other candidate explanations for why the URE is a causal factor in STEM persistence.

The committee has examined the literature and finds a rich descriptive foundation for testable hypotheses about the effects of UREs on student outcomes. These studies are encouraging; a few of them have generated evidence that a URE can be a positive causal factor in the progression and persistence of STEM students. The weight of the evidence has been descriptive; it relies primarily on self-reports of short-term gains by students who chose to participate in UREs and does not include direct measures of changes in the students’ knowledge, skills, or other measures of success across comparable groups of students who did and did not participate in UREs.

While acknowledging the scarcity of strong causal evidence on the benefits of UREs, the committee takes seriously the weight of the descriptive evidence. Many of the published studies of UREs show that students who participate report a range of benefits, such as increased understanding of the research process, encouragement to persist in STEM, and support that helps them sustain their identity as researchers and continue with their

plans to enroll in a graduate program in STEM (see Chapter 4 ). These are effective starting points for causal studies.

Conclusion 3: Studies focused on students from historically underrepresented groups indicate that participation in UREs improves their persistence in STEM and helps to validate their disciplinary identity.

Various UREs have been specifically designed to increase the number of historically underrepresented students who go on to become STEM majors and ultimately STEM professionals. While many UREs offer one or more supplemental opportunities to support students’ academic or social success, such as mentoring, tutoring, summer bridge programs, career or graduate school workshops, and research-oriented seminars, those designed for underrepresented students appear to emphasize such features as integral and integrated components of the program. In particular, studies of undergraduate research programs targeting underrepresented minority students have begun to document positive outcomes such as degree completion and persistence in interest in STEM careers ( Byars-Winston et al., 2015 ; Chemers et al., 2011 ; Jones et al., 2010 ; Nagda et al., 1998 ; Schultz et al., 2011 ). Most of these studies collected data on apprentice-style UREs, in which the undergraduate becomes a functioning member of a research group along with the graduate students, postdoctoral fellows, and mentor.

Recommendation 1: Researchers with expertise in education research should conduct well-designed studies in collaboration with URE program directors to improve the evidence base about the processes and effects of UREs. This research should address how the various components of UREs may benefit students. It should also include additional causal evidence for the individual and additive effects of outcomes from student participation in different types of UREs. Not all UREs need be designed to undertake this type of research, but it would be very useful to have some UREs that are designed to facilitate these efforts to improve the evidence base .

As the focus on UREs has grown, so have questions about their implementation. Many articles have been published describing specific UREs (see Chapter 2 ). Large amounts of research have also been undertaken to explore more generally how students learn, and the resulting body of evidence has led to the development and adoption of “active learning” strategies and experiences. If a student in a URE has an opportunity to, for example, analyze new data or to reformulate a hypothesis in light of the student’s analysis, this activity fits into the category that is described as active learning. Surveys of student participants and unpublished evaluations pro-

vide additional information about UREs but do not establish causation or determine the mechanism(s). Consequently, little is currently known about the mechanisms of precisely how UREs work and which aspects of UREs are most powerful. Important components that have been reported include student ownership of the URE project, time to tackle a question iteratively, and opportunities to report and defend one’s conclusions ( Hanauer and Dolan, 2014 ; Thiry et al., 2011 ).

There are many unanswered questions and opportunities for further research into the role and mechanism of UREs. Attention to research design as UREs are planned is important; more carefully designed studies are needed to understand the ways that UREs influence a student’s education and to evaluate the outcomes that have been reported for URE participants. Appropriate studies, which include matched samples or similar controls, would facilitate research on the ways that UREs benefit students, enabling both education researchers and implementers of UREs to determine optimal features for program design and giving the community a more robust understanding of how UREs work.

See the research agenda ( Chapter 7 ) for specific recommendations about research topics and approaches.

Recommendation 2: Funders should provide appropriate resources to support the design, implementation, and analysis of some URE programs that are specifically designed to enable detailed research establishing the effects on participant outcomes and on other variables of interest such as the consequences for mentors or institutions.

Not all UREs need to be the subject of extensive study. In many cases, a straightforward evaluation is adequate to determine whether the URE is meeting its goals. However, to achieve more widespread improvement in both the types and quality of the UREs offered in the future, additional evidence about the possible causal effects and mechanisms of action of UREs needs to be systematically collected and disseminated. This includes a better understanding of the implementation differences for a variety of institutions (e.g., community colleges, primarily undergraduate institutions, research universities) to ensure that the desired outcomes can translate across settings. Increasing the evidence about precisely how UREs work and which aspects of UREs are most powerful will require careful attention to study design during planning for the UREs.

Not all UREs need to be designed to achieve this goal; many can provide opportunities to students by relying on pre-existing knowledge and iterative improvement as that knowledge base grows. However, for the knowledge base to grow, funders must provide resources for some URE designers and social science researchers to undertake thoughtful and well-planned studies

on causal and mechanistic issues. This will maximize the chances for the creation and dissemination of information that can lead to the development of sustainable and effective UREs. These studies can result from a partnership formed as the URE is designed and funded, or evaluators and social scientists could identify promising and/or effective existing programs and then raise funds on their own to support the study of those programs to answer the questions of interest. In deciding upon the UREs that are chosen for these extensive studies, it will be important to consider whether, collectively, they are representative of UREs in general. For example, large and small UREs at large and small schools targeted at both introductory and advanced students and topics should be studied.

CONSTRUCTION OF URES

Conclusion 4: The committee was unable to find evidence that URE designers are taking full advantage of the information available in the education literature on strategies for designing, implementing, and evaluating learning experiences. STEM faculty members do not generally receive training in interpreting or conducting education research. Partnerships between those with expertise in education research and those with expertise in implementing UREs are one way to strengthen the application of evidence on what works in planning and implementing UREs.

As discussed in Chapters 3 and 4 , there is an extensive body of literature on pedagogy and how people learn; helping STEM faculty to access the existing literature and incorporate those concepts as they design UREs could improve student experiences. New studies that specifically focus on UREs may provide more targeted information that could be used to design, implement, sustain, or scale up UREs and facilitate iterative improvements. Information about the features of UREs that elicit particular outcomes or best serve certain populations of students should be considered when implementing a new instantiation of an existing model of a URE or improving upon an existing URE model.

Conclusion 5: Evaluations of UREs are often conducted to inform program providers and funders; however, they may not be accessible to others. While these evaluations are not designed to be research studies and often have small sample sizes, they may contain information that could be useful to those initiating new URE programs and those refining UREs. Increasing access to these evaluations and to the accumulated experience of the program providers may enable URE designers and implementers to build upon knowledge gained from earlier UREs.

As discussed in Chapter 1 , the committee searched for evaluations of URE programs in several different ways but was not able to locate many published evaluations to study. Although some evaluations were found in the literature, the committee could not determine a way to systematically examine the program evaluations that have been prepared. The National Science Foundation and other funders generally require grant recipients to submit evaluation data, but that information is not currently aggregated and shared publicly, even for programs that are using a common evaluation tool. 1

Therefore, while program evaluation likely serves a useful role in providing descriptive data about a program for the institutions and funders supporting the program, much of the summative evaluation work that has been done to date adds relatively little to the broader knowledge base and overall conversations around undergraduate research. Some of the challenges of evaluation include budget and sample size constraints.

Similarly, it is difficult for designers of UREs to benefit systematically from the work of others who have designed and run UREs in the past because of the lack of an easy and consistent mechanism for collecting, analyzing, and sharing data. If these evaluations were more accessible they might be beneficial to others designing and evaluating UREs by helping them to gather ideas and inspiration from the experiences of others. A few such stories are provided in this report, and others can be found among the many resources offered by the Council on Undergraduate Research 2 and on other websites such as CUREnet. 3

Recommendation 3: Designers of UREs should base their design decisions on sound evidence. Consultations with education and social science researchers may be helpful as designers analyze the literature and make decisions on the creation or improvement of UREs. Professional development materials should be created and made available to faculty. Educational and disciplinary societies should consider how they can provide resources and connections to those working on UREs.

Faculty and other organizers of UREs can use the expanding body of scholarship as they design or improve the programs and experiences offered to their students. URE designers will need to make decisions about how to adapt approaches reported in the literature to make the programs they develop more suitable to their own expertise, student population(s), and available resources. Disciplinary societies and other national groups, such as those focused on improving pedagogy, can play important roles in

___________________

1 Personal knowledge of Janet Branchaw, member of the Committee on Strengthening Research Experiences for Undergraduate STEM Students.

2 See www.cur.org [November 2016].

3 See ( curenet.cns.utexas.edu ) [November 2016].

bringing these issues to the forefront through events at their national and regional meetings and through publications in their journals and newsletters. They can develop repositories for various kinds of resources appropriate for their members who are designing and implementing UREs. The ability to travel to conferences and to access and discuss resources created by other individuals and groups is a crucial aspect of support (see Recommendations 7 and 8 for further discussion).

See Chapter 8 for specific questions to consider when one is designing or implementing UREs.

CURRENT OFFERINGS

Conclusion 6: Data at the institutional, state, or national levels on the number and type of UREs offered, or who participates in UREs overall or at specific types of institutions, have not been collected systematically. Although the committee found that some individual institutions track at least some of this type of information, we were unable to determine how common it is to do so or what specific information is most often gathered.

There is no one central database or repository that catalogs UREs at institutions of higher education, the nature of the research experiences they provide, or the relevant demographics (student, departmental, and institutional). The lack of comprehensive data makes it difficult to know how many students participate in UREs; where UREs are offered; and if there are gaps in access to UREs across different institutional types, disciplines, or groups of students. One of the challenges of describing the undergraduate research landscape is that students do not have to be enrolled in a formal program to have a research experience. Informal experiences, for example a work-study job, are typically not well documented. Another challenge is that some students participate in CUREs or other research experiences (such as internships) that are not necessarily labeled as such. Institutional administrators may be unaware of CUREs that are already part of their curriculum. (For example, establishment of CUREs may be under the purview of a faculty curriculum committee and may not be recognized as a distinct program.) Student participation in UREs may occur at their home institution or elsewhere during the summer. Therefore, it is very difficult for a science department, and likely any other STEM department, to know what percentage of their graduating majors have had a research experience, let alone to gather such information on students who left the major. 4

4 This point was made by Marco Molinaro, University of California, Davis, in a presentation to the Committee on Strengthening Research Experience for Undergraduate STEM Students, September 16, 2015.

Conclusion 7: While data are lacking on the precise number of students engaged in UREs, there is some evidence of a recent growth in course-based undergraduate research experiences (CUREs), which engage a cohort of students in a research project as part of a formal academic experience.

There has been an increase in the number of grants and the dollar amount spent on CUREs over the past decade (see Chapter 3 ). CUREs can be particularly useful in scaling UREs to reach a much larger population of students ( Bangera and Brownell, 2014 ). By using a familiar mechanism—enrollment in a course—a CURE can provide a more comfortable route for students unfamiliar with research to gain their first experience. CUREs also can provide such experiences to students with diverse backgrounds, especially if an institution or department mandates participation sometime during a student’s matriculation. Establishing CUREs may be more cost-effective at schools with little on-site research activity. However, designing a CURE is a new and time-consuming challenge for many faculty members. Connecting to nationally organized research networks can provide faculty with helpful resources for the development of a CURE based around their own research or a local community need, or these networks can link interested faculty to an ongoing collaborative project. Collaborative projects can provide shared curriculum, faculty professional development and community, and other advantages when starting or expanding a URE program. See the discussion in the report from a convocation on Integrating Discovery-based Research into the Undergraduate Curriculum ( National Academies of Sciences, Engineering, and Medicine, 2015 ).

Recommendation 4: Institutions should collect data on student participation in UREs to inform their planning and to look for opportunities to improve quality and access.

Better tracking of student participation could lead to better assessment of outcomes and improved quality of experience. Such metrics could be useful for both prospective students and campus planners. An integrated institutional system for research opportunities could facilitate the creation of tiered research experiences that allow students to progress in skills and responsibility and create support structures for students, providing, for example, seminars in communications, safety, and ethics for undergraduate researchers. Institutions could also use these data to measure the impact of UREs on student outcomes, such as student success rates in introductory courses, retention in STEM degree programs, and completion of STEM degrees.

While individual institutions may choose to collect additional information depending on their goals and resources, relevant student demographics

and the following design elements would provide baseline data. At a minimum, such data should include

• Type of URE;
• Each student’s discipline;
• Duration of the experience;
• Hours spent per week;
• When the student began the URE (e.g., first year, capstone);
• Compensation status (e.g., paid, unpaid, credit); and
• Location and format (e.g., on home campus, on another campus, internship, co-op).

National aggregation of some of the student participation variables collected by various campuses might be considered by funders. The existing Integrated Postsecondary Education Data System database, organized by the National Center for Education Statistics at the U.S. Department of Education, may be a suitable repository for certain aspects of this information.

Recommendation 5: Administrators and faculty at all types of colleges and universities should continually and holistically evaluate the range of UREs that they offer. As part of this process, institutions should:

• Consider how best to leverage available resources (including off-campus experiences available to students and current or potential networks or partnerships that the institution may form) when offering UREs so that they align with their institution’s mission and priorities;
• Consider whether current UREs are both accessible and welcoming to students from various subpopulations across campus (e.g., historically underrepresented students, first generation college students, those with disabilities, non-STEM majors, prospective kindergarten-through-12th-grade teachers); and
• Gather and analyze data on the types of UREs offered and the students who participate, making this information widely available to the campus community and using it to make evidence-based decisions about improving opportunities for URE participation. This may entail devising or implementing systems for tracking relevant data (see Conclusion 4 ).

Resources available for starting, maintaining, and expanding UREs vary from campus to campus. At some campuses, UREs are a central focus and many resources are devoted to them. At other institutions—for example, many community colleges—UREs are seen as extra, and new resources may be required to ensure availability of courses and facilities. Resource-

constrained institutions may need to focus more on ensuring that students are aware of potential UREs that already exist on campus and elsewhere in near proximity to campus. All institutional discussions about UREs must consider both the financial resources and physical resources (e.g., laboratories, field stations, engineering design studios) required, while remembering that faculty time is a crucial resource. The incentives and disincentives for faculty to spend time on UREs are significant. Those institutions with an explicit mission to promote undergraduate research may provide more recognition and rewards to departments and faculty than those with another focus. The culture of the institution with respect to innovation in pedagogy and support for faculty development also can have a major influence on the extent to which UREs are introduced or improved.

Access to UREs may vary across campus and by department, and participation in UREs may vary across student groups. It is important for campuses to consider the factors that may facilitate or discourage students from participation in UREs. Inconsistent procedures or a faculty preference for students with high grades or previous research experience may limit options for some student populations.

UREs often grow based on the initiative of individual faculty members and other personnel, and an institution may not have complete or even rudimentary knowledge of all of the opportunities available or whether there are gaps or inconsistencies in its offerings. A uniform method for tracking the UREs available on a given campus would be useful to students and would provide a starting point for analyzing the options. Tracking might consist of notations in course listings and, where feasible, on student transcripts. Analysis might consider the types of UREs offered, the resources available to each type of URE, and variations within or between various disciplines and programs. Attention to whether all students or groups of students have appropriate access to UREs would foster consideration of how to best allocate resources and programming on individual campuses, in order to focus resources and opportunities where they are most needed.

Conclusion 8: The quality of mentoring can make a substantial difference in a student’s experiences with research. However, professional development in how to be a good mentor is not available to many faculty or other prospective mentors (e.g., graduate students, postdoctoral fellows).

Engagement in quality mentored research experiences has been linked to self-reported gains in research skills and productivity as well as retention in STEM (see Chapter 5 ). Quality mentoring in UREs has been shown

to increase persistence in STEM for historically underrepresented students ( Hernandez et al., 2016 ). In addition, poor mentoring during UREs has been shown to decrease retention of students ( Hernandez et al., 2016 ).

More general research on good mentoring in the STEM environment has been positively associated with self-reported gains in identity as a STEM researcher, a sense of belonging, and confidence to function as a STEM researcher ( Byars-Winston et al., 2015 ; Chemers et al., 2011 ; Pfund et al., 2016 ; Thiry et al., 2011 ). The frequency and quality of mentee-mentor interactions has been associated with students’ reports of persistence in STEM, with mentoring directly or indirectly improving both grades and persistence in college. For students from historically underrepresented ethnic/racial groups, quality mentoring has been associated with self-reported enhanced recruitment into graduate school and research-related career pathways ( Byars-Winston et al., 2015 ). Therefore, it is important to ensure that faculty and mentors receive the proper development of mentoring skills.

Recommendation 6: Administrators and faculty at colleges and universities should ensure that all who mentor undergraduates in research experiences (this includes faculty, instructors, postdoctoral fellows, graduate students, and undergraduates serving as peer mentors) have access to appropriate professional development opportunities to help them grow and succeed in this role.

Although many organizations recognize effective mentors (e.g., the National Science Foundation’s Presidential Awards for Excellence in Science, Mathematics, and Engineering Mentoring), there currently are no standard criteria for selecting, evaluating, or recognizing mentors specifically for UREs. In addition, there are no requirements that mentors meet some minimum level of competency before engaging in mentoring or participate in professional development to obtain a baseline of knowledge and skills in mentoring, including cultural competence in mentoring diverse groups of students. Traditionally, the only experience required for being a mentor is having been mentored, regardless of whether the experience was negative or positive ( Handelsman et al., 2005 ; Pfund et al., 2015 ). Explicit consideration of how the relationships are formed, supported, and evaluated can improve mentor-mentee relationships. To ensure that the mentors associated with a URE are prepared appropriately, thereby increasing the chances of a positive experience for both mentors and mentees, all prospective mentors should prepare for their role. Available resources include the Entering Mentoring course (see Pfund et al., 2015 ) and the book Successful STEM Mentoring Initiative for Underrepresented Students ( Packard, 2016 ).

A person who is an ineffective mentor for one student might be inspiring for another, and the setting in which the mentoring takes place (e.g., a CURE or apprentice-style URE, a laboratory or field-research environment) may also influence mentor effectiveness. Thus, there should be some mechanism for monitoring such relationships during the URE, or there should be opportunity for a student who is unhappy with the relationship to seek other mentors. Indeed, cultivating a team of mentors with different experiences and expertise may be the best strategy for any student. A parallel volume to the Entering Mentoring curriculum mentioned above, Entering Research Facilitator’s Manual ( Branchaw et al., 2010 ), is designed to help students with their research mentor-mentee relationships and to coach them on building teams of mentors to guide them. As mentioned in Chapter 5 , the Entering Research curriculum also contains information designed to support a group of students as they go through their first apprentice-style research experience, each working in separate research groups and also meeting together as a cohort focused on learning about research.

PRIORITIES FOR THE FUTURE

Conclusion 9: The unique assets, resources, priorities, and constraints of the department and institution, in addition to those of individual mentors, impact the goals and structures of UREs. Schools across the country are showing considerable creativity in using unique resources, repurposing current assets, and leveraging student enthusiasm to increase research opportunities for their students.

Given current calls for UREs and the growing conversation about their benefits, an increasing number of two- and four-year colleges and universities are increasing their efforts to support undergraduate research. Departments, institutions, and individual faculty members influence the precise nature of UREs in multiple ways and at multiple levels. The physical resources available, including laboratories, field stations, and engineering design studios and testing facilities, make a difference, as does the ability to access resources in the surrounding community (including other parts of the campus). Institutions with an explicit mission to promote undergraduate research may provide more time, resources (e.g., financial, support personnel, space, equipment), and recognition and rewards to departments and faculty in support of UREs than do institutions without that mission. The culture of the institution with respect to innovation in pedagogy and support for faculty development also affects the extent to which UREs are introduced or improved.

Development of UREs requires significant time and effort. Whether or not faculty attempt to implement UREs can depend on whether departmental

or institutional reward and recognition systems compensate for or even recognize the time required to initiate and implement them. The availability of national consortia can help to alleviate many of the time and logistical problems but not those obstacles associated with recognition and resources.

It will be harder for faculty to find the time to develop UREs at institutions where they are required to teach many courses per semester, although in some circumstances faculty can teach CUREs that also advance their own research ( Shortlidge et al., 2016 ). Faculty at community colleges generally have the heaviest teaching expectations, little or no expectations or incentives to maintain a research program, limited access to lab or design space or to scientific and engineering journals, and few resources to undertake any kind of a research program. These constraints may limit the extent to which UREs can be offered to the approximately 40 percent of U.S. undergraduates who are enrolled in the nation’s community colleges (which collectively also serve the highest percentage of the nation’s underrepresented students). 5

Recommendation 7: Administrators and faculty at all types of colleges and universities should work together within and, where feasible, across institutions to create a culture that supports the development of evidence-based, iterative, and continuous refinement of UREs, in an effort to improve student learning outcomes and overall academic success. This should include the development, evaluation, and revision of policies and practices designed to create a culture supportive of the participation of faculty and other mentors in effective UREs. Policies should consider pedagogy, professional development, cross-cultural awareness, hiring practices, compensation, promotion (incentives, rewards), and the tenure process.

Colleges and universities that would like to expand or improve the UREs offered to their students should consider the campus culture and climate and the incentives that affect faculty choices. Those campuses that cultivate an environment supportive of the iterative and continuous refinement of UREs and that offer incentives for evaluation and evidence-based improvement of UREs seem more likely to sustain successful programs. Faculty and others who develop and implement UREs need support to be able to evaluate their courses or programs and to analyze evidence to make decisions about URE design. This kind of support may be fostered by expanding the mission of on-campus centers for learning and teaching to focus more on UREs or by providing incentives for URE developers from the natural sciences and engineering to collaborate with colleagues in the social sciences or colleges of education with expertise in designing studies

5 See http://nces.ed.gov/programs/coe/indicator_cha.asp [November 2016].

involving human subjects. Supporting closer communication between URE developers and the members of the campus Institutional Review Board may help projects to move forward more seamlessly. Interdepartmental and intercampus connections (especially those between two- and four-year institutions) can be valuable for linking faculty with the appropriate resources, colleagues, and diverse student populations. Faculty who have been active in professional development on how students learn in the classroom may have valuable experiences and expertise to share.

The refinement or expansion of UREs should build on evidence from data on student participation, pedagogy, and outcomes, which are integral components of the original design. As UREs are validated and refined, institutions should make efforts to facilitate connections among different departments and disciplines, including the creation of multidisciplinary UREs. Student engagement in learning in general, and with UREs more specifically, depends largely on the culture of the department and the institution and on whether students see their surroundings as inclusive and energetic places to learn and thrive. A study that examined the relationship between campus missions and the five benchmarks for effective educational practice (measured by the National Survey of Student Engagement) showed that different programs, policies, and approaches may work better, depending on the institution’s mission ( Kezar and Kinzie, 2006 ).

The Council on Undergraduate Research (2012) document Characteristics of Excellence in Undergraduate Research outlines several best practices for UREs based on the apprenticeship model (see Chapter 8 ). That document is not the result of a detailed analysis of the evidence but is based on the extensive experiences and expertise of the council’s members. It suggests that undergraduate research should be a normal part of the undergraduate experience regardless of the type of institution. It also identifies changes necessary to include UREs as part of the curriculum and culture changes necessary to support curricular reform, co-curricular activities, and modifications to the incentives and rewards for faculty to engage with undergraduate research. In addition, professional development opportunities specifically designed to help improve the pedagogical and mentoring skills of instructional staff in using evidence-based practices can be important for a supportive learning culture.

Recommendation 8: Administrators and faculty at all types of colleges and universities should work to develop strong and sustainable partnerships within and between institutions and with educational and professional societies for the purpose of sharing resources to facilitate the creation of sustainable URE programs.

Networks of faculty, institutions, regionally and nationally coordinated URE initiatives, professional societies, and funders should be strengthened

to facilitate the exchange of evidence and experience related to UREs. These networks could build on the existing work of professional societies that assist faculty with pedagogy. They can help provide a venue for considering the policy context and larger implications of increasing the number, size, and scope of UREs. Such networks also can provide a more robust infrastructure, to improve the sustainability and expansion of URE opportunities. The sharing of human, financial, scientific, and technical resources can strengthen the broad implementation of effective, high-quality, and more cost-efficient UREs. It may be especially important for community colleges and minority-serving institutions to engage in partnerships in order to expand the opportunities for undergraduates (both transfer and technical students) to participate in diverse UREs (see discussion in National Academies of Sciences, Engineering, and Medicine, 2015 , and Elgin et al., 2016 ). Consortia can facilitate the sharing of resources across disciplines and departments within the same institution or at different institutions, organizations, and agencies. Consortia that employ research methodologies in common can share curriculum, research data collected, and common assessment tools, lessening the time burden for individual faculty and providing a large pool of students from which to assess the efficacy of individual programs.

Changes in the funding climate can have substantial impacts on the types of programs that exist, iterative refinement of programs, and whether and how programs might be expanded to broaden participation by more undergraduates. For those institutions that have not yet established URE programs or are at the beginning phases of establishing one, mechanisms for achieving success and sustainability may include increased institutional ownership of programs of undergraduate research, development of a broad range of programs of different types and funding structures, formation of undergraduate research offices or repurposing some of the responsibilities and activities of those which already exist, and engagement in community promotion and dissemination of student accomplishments (e.g., student symposia, support for undergraduate student travel to give presentations at professional meetings).

Over time, institutions must develop robust plans for ensuring the long-term sustained funding of high-quality UREs. Those plans should include assuming that more fiscal responsibility for sustaining such efforts will be borne by the home institution as external support for such efforts decreases and ultimately ends. Building UREs into the curriculum and structure of a department’s courses and other programs, and thus its funding model, can help with sustainability. Partnerships with nonprofit organizations and industry, as well as seeking funding from diverse agencies, can also facilitate programmatic sustainability, especially if the UREs they fund can also support the mission and programs of the funders (e.g., through research internships or through CUREs that focus on community-

based research questions and challenges). Partnerships among institutions also may have greater potential to study and evaluate student outcomes from URE participation across broader demographic groups and to reduce overall costs through the sharing of administrative or other resources (such as libraries, microscopes, etc.).

Bangera, G., and Brownell, S.E. (2014). Course-based undergraduate research experiences can make scientific research more inclusive. CBE–Life Sciences Education , 13 (4), 602-606.

Branchaw, J.L., Pfund, C., and Rediske, R. (2010) Entering Research Facilitator’s Manual: Workshops for Students Beginning Research in Science . New York: Freeman & Company.

Byars-Winston, A.M., Branchaw, J., Pfund, C., Leverett, P., and Newton, J. (2015). Culturally diverse undergraduate researchers’ academic outcomes and perceptions of their research mentoring relationships. International Journal of Science Education , 37 (15), 2,533-2,554.

Chemers, M.M., Zurbriggen, E.L., Syed, M., Goza, B.K., and Bearman, S. (2011). The role of efficacy and identity in science career commitment among underrepresented minority students. Journal of Social Issues , 67 (3), 469-491.

Council on Undergraduate Research. (2012). Characteristics of Excellence in Undergraduate Research . Washington, DC: Council on Undergraduate Research.

Elgin, S.C.R., Bangera, G., Decatur, S.M., Dolan, E.L., Guertin, L., Newstetter, W.C., San Juan, E.F., Smith, M.A., Weaver, G.C., Wessler, S.R., Brenner, K.A., and Labov, J.B. 2016. Insights from a convocation: Integrating discovery-based research into the undergraduate curriculum. CBE–Life Sciences Education, 15 , 1-7.

Hanauer, D., and Dolan, E. (2014) The Project Ownership Survey: Measuring differences in scientific inquiry experiences, CBE–Life Sciences Education , 13 , 149-158.

Handelsman, J., Pfund, C., Lauffer, S.M., and Pribbenow, C.M. (2005). Entering Mentoring . Madison, WI: The Wisconsin Program for Scientific Teaching.

Hernandez, P.R., Estrada, M., Woodcock, A., and Schultz, P.W. (2016). Protégé perceptions of high mentorship quality depend on shared values more than on demographic match. Journal of Experimental Education. Available: http://www.tandfonline.com/doi/full/10.1080/00220973.2016.1246405 [November 2016].

Jones, P., Selby, D., and Sterling, S.R. (2010). Sustainability Education: Perspectives and Practice Across Higher Education . New York: Earthscan.

Kezar, A.J., and Kinzie, J. (2006). Examining the ways institutions create student engagement: The role of mission. Journal of College Student Development , 47 (2), 149-172.

National Academies of Sciences, Engineering, and Medicine. (2015). Integrating Discovery-Based Research into the Undergraduate Curriculum: Report of a Convocation . Washington, DC: National Academies Press.

Nagda, B.A., Gregerman, S.R., Jonides, J., von Hippel, W., and Lerner, J.S. (1998). Undergraduate student-faculty research partnerships affect student retention. Review of Higher Education, 22 , 55-72. Available: http://scholar.harvard.edu/files/jenniferlerner/files/nagda_1998_paper.pdf [February 2017].

Packard, P. (2016). Successful STEM Mentoring Initiatives for Underrepresented Students: A Research-Based Guide for Faculty and Administrators . Sterling, VA: Stylus.

Pfund, C., Branchaw, J.L., and Handelsman, J. (2015). Entering Mentoring: A Seminar to Train a New Generation of Scientists (2nd ed). New York: Macmillan Learning.

Pfund, C., Byars-Winston, A., Branchaw, J.L., Hurtado, S., and Eagan, M.K. (2016). Defining attributes and metrics of effective research mentoring relationships. AIDS and Behavior, 20 , 238-248.

Schultz, P.W., Hernandez, P.R., Woodcock, A., Estrada, M., Chance, R.C., Aguilar, M., and Serpe, R.T. (2011). Patching the pipeline reducing educational disparities in the sciences through minority training programs. Educational Evaluation and Policy Analysis , 33 (1), 95-114.

Shortlidge, E.E., Bangera, G., and Brownell, S.E. (2016). Faculty perspectives on developing and teaching course-based undergraduate research experiences. BioScience, 66 (1), 54-62.

Thiry, H., Laursen, S.L., and Hunter, A.B. (2011). What experiences help students become scientists? A comparative study of research and other sources of personal and professional gains for STEM undergraduates. Journal of Higher Education, 82 (4), 358-389.

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Undergraduate research has a rich history, and many practicing researchers point to undergraduate research experiences (UREs) as crucial to their own career success. There are many ongoing efforts to improve undergraduate science, technology, engineering, and mathematics (STEM) education that focus on increasing the active engagement of students and decreasing traditional lecture-based teaching, and UREs have been proposed as a solution to these efforts and may be a key strategy for broadening participation in STEM. In light of the proposals questions have been asked about what is known about student participation in UREs, best practices in UREs design, and evidence of beneficial outcomes from UREs.

Undergraduate Research Experiences for STEM Students provides a comprehensive overview of and insights about the current and rapidly evolving types of UREs, in an effort to improve understanding of the complexity of UREs in terms of their content, their surrounding context, the diversity of the student participants, and the opportunities for learning provided by a research experience. This study analyzes UREs by considering them as part of a learning system that is shaped by forces related to national policy, institutional leadership, and departmental culture, as well as by the interactions among faculty, other mentors, and students. The report provides a set of questions to be considered by those implementing UREs as well as an agenda for future research that can help answer questions about how UREs work and which aspects of the experiences are most powerful.

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How to write recommendations in a research paper

Many students put in a lot of effort and write a good report however they are not able to give proper recommendations. Recommendations in the research paper should be included in your research. As a researcher, you display a deep understanding of the topic of research. Therefore you should be able to give recommendations. Here are a few tips that will help you to give appropriate recommendations. 

Recommendations in the research paper should be the objective of the research. Therefore at least one of your objectives of the paper is to provide recommendations to the parties associated or the parties that will benefit from your research. For example, to encourage higher employee engagement HR department should make strategies that invest in the well-being of employees. Additionally, the HR department should also collect regular feedback through online surveys.

Recommendations in the research paper should come from your review and analysis For example It was observed that coaches interviewed were associated with the club were working with the club from the past 2-3 years only. This shows that the attrition rate of coaches is high and therefore clubs should work on reducing the turnover of coaches.

Recommendations in the research paper should also come from the data you have analysed. For example, the research found that people over 65 years of age are at greater risk of social isolation. Therefore, it is recommended that policies that are made for combating social isolation should target this specific group.

Recommendations in the research paper should also come from observation. For example, it is observed that Lenovo’s income is stable and gross revenue has displayed a negative turn. Therefore the company should analyse its marketing and branding strategy.

Recommendations in the research paper should be written in the order of priority. The most important recommendations for decision-makers should come first. However, if the recommendations are of equal importance then it should come in the sequence in which the topic is approached in the research. 

Recommendations in a research paper if associated with different categories then you should categorize them. For example, you have separate recommendations for policymakers, educators, and administrators then you can categorize the recommendations. 

Recommendations in the research paper should come purely from your research. For example, you have written research on the impact on HR strategies on motivation. However, nowhere you have discussed Reward and recognition. Then you should not give recommendations for using rewards and recognition measures to boost employee motivation.

The use of bullet points offers better clarity rather than using long paragraphs. For example this paragraph “ It is recommended  that Britannia Biscuit should launch and promote sugar-free options apart from the existing product range. Promotion efforts should be directed at creating a fresh and healthy image. A campaign that conveys a sense of health and vitality to the consumer while enjoying biscuit  is recommended” can be written as:

• The company should launch and promote sugar-free options
• The company should work towards creating s fresh and healthy image
• The company should run a campaign to convey its healthy image

The inclusion of an action plan along with recommendation adds more weightage to your recommendation. Recommendations should be clear and conscience and written using actionable words. Recommendations should display a solution-oriented approach and in some cases should highlight the scope for further research. 

When you choose to publish with PLOS, your research makes an impact. Make your work accessible to all, without restrictions, and accelerate scientific discovery with options like preprints and published peer review that make your work more Open.

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• How to Write Discussions and Conclusions

The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your experiment and provides context for the results.

What makes an effective discussion?

When you’re ready to write your discussion, you’ve already introduced the purpose of your study and provided an in-depth description of the methodology. The discussion informs readers about the larger implications of your study based on the results. Highlighting these implications while not overstating the findings can be challenging, especially when you’re submitting to a journal that selects articles based on novelty or potential impact. Regardless of what journal you are submitting to, the discussion section always serves the same purpose: concluding what your study results actually mean.

A successful discussion section puts your findings in context. It should include:

• the results of your research,
• a discussion of related research, and
• a comparison between your results and initial hypothesis.

Tip: Not all journals share the same naming conventions.

You can apply the advice in this article to the conclusion, results or discussion sections of your manuscript.

Our Early Career Researcher community tells us that the conclusion is often considered the most difficult aspect of a manuscript to write. To help, this guide provides questions to ask yourself, a basic structure to model your discussion off of and examples from published manuscripts. 

Questions to ask yourself:

• Was my hypothesis correct?
• If my hypothesis is partially correct or entirely different, what can be learned from the results? 
• How do the conclusions reshape or add onto the existing knowledge in the field? What does previous research say about the topic? 
• Why are the results important or relevant to your audience? Do they add further evidence to a scientific consensus or disprove prior studies? 
• How can future research build on these observations? What are the key experiments that must be done? 
• What is the “take-home” message you want your reader to leave with?

How to structure a discussion

Trying to fit a complete discussion into a single paragraph can add unnecessary stress to the writing process. If possible, you’ll want to give yourself two or three paragraphs to give the reader a comprehensive understanding of your study as a whole. Here’s one way to structure an effective discussion:

Writing Tips

While the above sections can help you brainstorm and structure your discussion, there are many common mistakes that writers revert to when having difficulties with their paper. Writing a discussion can be a delicate balance between summarizing your results, providing proper context for your research and avoiding introducing new information. Remember that your paper should be both confident and honest about the results! 

• Read the journal’s guidelines on the discussion and conclusion sections. If possible, learn about the guidelines before writing the discussion to ensure you’re writing to meet their expectations. 
• Begin with a clear statement of the principal findings. This will reinforce the main take-away for the reader and set up the rest of the discussion. 
• Explain why the outcomes of your study are important to the reader. Discuss the implications of your findings realistically based on previous literature, highlighting both the strengths and limitations of the research. 
• State whether the results prove or disprove your hypothesis. If your hypothesis was disproved, what might be the reasons? 
• Introduce new or expanded ways to think about the research question. Indicate what next steps can be taken to further pursue any unresolved questions. 
• If dealing with a contemporary or ongoing problem, such as climate change, discuss possible consequences if the problem is avoided. 
• Be concise. Adding unnecessary detail can distract from the main findings. 

Don’t

• Rewrite your abstract. Statements with “we investigated” or “we studied” generally do not belong in the discussion. 
• Include new arguments or evidence not previously discussed. Necessary information and evidence should be introduced in the main body of the paper. 
• Apologize. Even if your research contains significant limitations, don’t undermine your authority by including statements that doubt your methodology or execution. 
• Shy away from speaking on limitations or negative results. Including limitations and negative results will give readers a complete understanding of the presented research. Potential limitations include sources of potential bias, threats to internal or external validity, barriers to implementing an intervention and other issues inherent to the study design. 
• Overstate the importance of your findings. Making grand statements about how a study will fully resolve large questions can lead readers to doubt the success of the research. 

Snippets of Effective Discussions:

Consumer-based actions to reduce plastic pollution in rivers: A multi-criteria decision analysis approach

Identifying reliable indicators of fitness in polar bears

• How to Write a Great Title
• How to Write an Abstract
• How to Write Your Methods
• How to Report Statistics
• How to Edit Your Work

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There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…

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• v.18(6); 2022 Jun

Ten simple rules for good research practice

Simon schwab.

1 Center for Reproducible Science, University of Zurich, Zurich, Switzerland

2 Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland

Perrine Janiaud

3 Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland

Michael Dayan

4 Human Neuroscience Platform, Fondation Campus Biotech Geneva, Geneva, Switzerland

Valentin Amrhein

5 Department of Environmental Sciences, Zoology, University of Basel, Basel, Switzerland

Radoslaw Panczak

6 Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland

Patricia M. Palagi

7 SIB Training Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland

Lars G. Hemkens

8 Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America

9 Meta-Research Innovation Center Berlin (METRIC-B), Berlin Institute of Health, Berlin, Germany

Meike Ramon

10 Applied Face Cognition Lab, University of Lausanne, Lausanne, Switzerland

Nicolas Rothen

11 Faculty of Psychology, UniDistance Suisse, Brig, Switzerland

Stephen Senn

12 Statistical Consultant, Edinburgh, United Kingdom

Leonhard Held

This is a PLOS Computational Biology Methods paper.

Introduction

The lack of research reproducibility has caused growing concern across various scientific fields [ 1 – 5 ]. Today, there is widespread agreement, within and outside academia, that scientific research is suffering from a reproducibility crisis [ 6 , 7 ]. Researchers reach different conclusions—even when the same data have been processed—simply due to varied analytical procedures [ 8 , 9 ]. As we continue to recognize this problematic situation, some major causes of irreproducible research have been identified. This, in turn, provides the foundation for improvement by identifying and advocating for good research practices (GRPs). Indeed, powerful solutions are available, for example, preregistration of study protocols and statistical analysis plans, sharing of data and analysis code, and adherence to reporting guidelines. Although these and other best practices may facilitate reproducible research and increase trust in science, it remains the responsibility of researchers themselves to actively integrate them into their everyday research practices.

Contrary to ubiquitous specialized training, cross-disciplinary courses focusing on best practices to enhance the quality of research are lacking at universities and are urgently needed. The intersections between disciplines offer a space for peer evaluation, mutual learning, and sharing of best practices. In medical research, interdisciplinary work is inevitable. For example, conducting clinical trials requires experts with diverse backgrounds, including clinical medicine, pharmacology, biostatistics, evidence synthesis, nursing, and implementation science. Bringing researchers with diverse backgrounds and levels of experience together to exchange knowledge and learn about problems and solutions adds value and improves the quality of research.

The present selection of rules was based on our experiences with teaching GRP courses at the University of Zurich, our course participants’ feedback, and the views of a cross-disciplinary group of experts from within the Swiss Reproducibility Network ( www.swissrn.org ). The list is neither exhaustive, nor does it aim to address and systematically summarize the wide spectrum of issues including research ethics and legal aspects (e.g., related to misconduct, conflicts of interests, and scientific integrity). Instead, we focused on practical advice at the different stages of everyday research: from planning and execution to reporting of research. For a more comprehensive overview on GRPs, we point to the United Kingdom’s Medical Research Council’s guidelines [ 10 ] and the Swedish Research Council’s report [ 11 ]. While the discussion of the rules may predominantly focus on clinical research, much applies, in principle, to basic biomedical research and research in other domains as well.

The 10 proposed rules can serve multiple purposes: an introduction for researchers to relevant concepts to improve research quality, a primer for early-career researchers who participate in our GRP courses, or a starting point for lecturers who plan a GRP course at their own institutions. The 10 rules are grouped according to planning (5 rules), execution (3 rules), and reporting of research (2 rules); see Fig 1 . These principles can (and should) be implemented as a habit in everyday research, just like toothbrushing.

GRP, good research practices.

Research planning

Rule 1: specify your research question.

Coming up with a research question is not always simple and may take time. A successful study requires a narrow and clear research question. In evidence-based research, prior studies are assessed in a systematic and transparent way to identify a research gap for a new study that answers a question that matters [ 12 ]. Papers that provide a comprehensive overview of the current state of research in the field are particularly helpful—for example, systematic reviews. Perspective papers may also be useful, for example, there is a paper with the title “SARS-CoV-2 and COVID-19: The most important research questions.” However, a systematic assessment of research gaps deserves more attention than opinion-based publications.

In the next step, a vague research question should be further developed and refined. In clinical research and evidence-based medicine, there is an approach called population, intervention, comparator, outcome, and time frame (PICOT) with a set of criteria that can help framing a research question [ 13 ]. From a well-developed research question, subsequent steps will follow, which may include the exact definition of the population, the outcome, the data to be collected, and the sample size that is required. It may be useful to find out if other researchers find the idea interesting as well and whether it might promise a valuable contribution to the field. However, actively involving the public or the patients can be a more effective way to determine what research questions matter.

The level of details in a research question also depends on whether the planned research is confirmatory or exploratory. In contrast to confirmatory research, exploratory research does not require a well-defined hypothesis from the start. Some examples of exploratory experiments are those based on omics and multi-omics experiments (genomics, bulk RNA-Seq, single-cell, etc.) in systems biology and connectomics and whole-brain analyses in brain imaging. Both exploration and confirmation are needed in science, and it is helpful to understand their strengths and limitations [ 14 , 15 ].

Rule 2: Write and register a study protocol

In clinical research, registration of clinical trials has become a standard since the late 1990 and is now a legal requirement in many countries. Such studies require a study protocol to be registered, for example, with ClinicalTrials.gov, the European Clinical Trials Register, or the World Health Organization’s International Clinical Trials Registry Platform. Similar effort has been implemented for registration of systematic reviews (PROSPERO). Study registration has also been proposed for observational studies [ 16 ] and more recently in preclinical animal research [ 17 ] and is now being advocated across disciplines under the term “preregistration” [ 18 , 19 ].

Study protocols typically document at minimum the research question and hypothesis, a description of the population, the targeted sample size, the inclusion/exclusion criteria, the study design, the data collection, the data processing and transformation, and the planned statistical analyses. The registration of study protocols reduces publication bias and hindsight bias and can safeguard honest research and minimize waste of research [ 20 – 22 ]. Registration ensures that studies can be scrutinized by comparing the reported research with what was actually planned and written in the protocol, and any discrepancies may indicate serious problems (e.g., outcome switching).

Note that registration does not mean that researchers have no flexibility to adapt the plan as needed. Indeed, new or more appropriate procedures may become available or known only after registration of a study. Therefore, a more detailed statistical analysis plan can be amended to the protocol before the data are observed or unblinded [ 23 , 24 ]. Likewise, registration does not exclude the possibility to conduct exploratory data analyses; however, they must be clearly reported as such.

To go even further, registered reports are a novel article type that incentivize high-quality research—irrespective of the ultimate study outcome [ 25 , 26 ]. With registered reports, peer-reviewers decide before anyone knows the results of the study, and they have a more active role in being able to influence the design and analysis of the study. Journals from various disciplines increasingly support registered reports [ 27 ].

Naturally, preregistration and registered reports also have their limitations and may not be appropriate in a purely hypothesis-generating (explorative) framework. Reports of exploratory studies should indeed not be molded into a confirmatory framework; appropriate rigorous reporting alternatives have been suggested and start to become implemented [ 28 , 29 ].

Rule 3: Justify your sample size

Early-career researchers in our GRP courses often identify sample size as an issue in their research. For example, they say that they work with a low number of samples due to slow growth of cells, or they have a limited number of patient tumor samples due to a rare disease. But if your sample size is too low, your study has a high risk of providing a false negative result (type II error). In other words, you are unlikely to find an effect even if there truly was an effect.

Unfortunately, there is more bad news with small studies. When an effect from a small study was selected for drawing conclusions because it was statistically significant, low power increases the probability that an effect size is overestimated [ 30 , 31 ]. The reason is that with low power, studies that due to sampling variation find larger (overestimated) effects are much more likely to be statistically significant than those that happen to find smaller (more realistic) effects [ 30 , 32 , 33 ]. Thus, in such situations, effect sizes are often overestimated. For the phenomenon that small studies often report more extreme results (in meta-analyses), the term “small-study effect” was introduced [ 34 ]. In any case, an underpowered study is a problematic study, no matter the outcome.

In conclusion, small sample sizes can undermine research, but when is a study too small? For one study, a total of 50 patients may be fine, but for another, 1,000 patients may be required. How large a study needs to be designed requires an appropriate sample size calculation. Appropriate sample size calculation ensures that enough data are collected to ensure sufficient statistical power (the probability to reject the null hypothesis when it is in fact false).

Low-powered studies can be avoided by performing a sample size calculation to find out the required sample size of the study. This requires specifying a primary outcome variable and the magnitude of effect you are interested in (among some other factors); in clinical research, this is often the minimal clinically relevant difference. The statistical power is often set at 80% or larger. A comprehensive list of packages for sample size calculation are available [ 35 ], among them the R package “pwr” [ 36 ]. There are also many online calculators available, for example, the University of Zurich’s “SampleSizeR” [ 37 ].

A worthwhile alternative for planning the sample size that puts less emphasis on null hypothesis testing is based on the desired precision of the study; for example, one can calculate the sample size that is necessary to obtain a desired width of a confidence interval for the targeted effect [ 38 – 40 ]. A general framework to sample size justification beyond a calculation-only approach has been proposed [ 41 ]. It is also worth mentioning that some study types have other requirements or need specific methods. In diagnostic testing, one would need to determine the anticipated minimal sensitivity or specificity; in prognostic research, the number of parameters that can be used to fit a prediction model given a fixed sample size should be specified. Designs can also be so complex that a simulation (Monte Carlo method) may be required.

Sample size calculations should be done under different assumptions, and the largest estimated sample size is often the safer bet than a best-case scenario. The calculated sample size should further be adjusted to allow for possible missing data. Due to the complexity of accurately calculating sample size, researchers should strongly consider consulting a statistician early in the study design process.

Rule 4: Write a data management plan

In 2020, 2 Coronavirus Disease 2019 (COVID-19) papers in leading medical journals were retracted after major concerns about the data were raised [ 42 ]. Today, raw data are more often recognized as a key outcome of research along with the paper. Therefore, it is important to develop a strategy for the life cycle of data, including suitable infrastructure for long-term storage.

The data life cycle is described in a data management plan: a document that describes what data will be collected and how the data will be organized, stored, handled, and protected during and after the end of the research project. Several funders require a data management plan in grant submissions, and publishers like PLOS encourage authors to do so as well. The Wellcome Trust provides guidance in the development of a data management plan, including real examples from neuroimaging, genomics, and social sciences [ 43 ]. However, projects do not always allocate funding and resources to the actual implementation of the data management plan.

The Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promote maximal use of data and enable machines to access and reuse data with minimal human intervention [ 44 ]. FAIR principles require the data to be retained, preserved, and shared preferably with an immutable unique identifier and a clear usage license. Appropriate metadata will help other researchers (or machines) to discover, process, and understand the data. However, requesting researchers to fully comply with the FAIR data principles in every detail is an ambitious goal.

Multidisciplinary data repositories that support FAIR are, for example, Dryad (datadryad.org https://datadryad.org/ ), EUDAT ( www.eudat.eu ), OSF (osf.io https://osf.io/ ), and Zenodo (zenodo.org https://zenodo.org/ ). A number of institutional and field-specific repositories may also be suitable. However, sometimes, authors may not be able to make their data publicly available for legal or ethical reasons. In such cases, a data user agreement can indicate the conditions required to access the data. Journals highlight what are acceptable and what are unacceptable data access restrictions and often require a data availability statement.

Organizing the study artifacts in a structured way greatly facilitates the reuse of data and code within and outside the lab, enhancing collaborations and maximizing the research investment. Support and courses for data management plans are sometimes available at universities. Another 10 simple rules paper for creating a good data management plan is dedicated to this topic [ 45 ].

Rule 5: Reduce bias

Bias is a distorted view in favor of or against a particular idea. In statistics, bias is a systematic deviation of a statistical estimate from the (true) quantity it estimates. Bias can invalidate our conclusions, and the more bias there is, the less valid they are. For example, in clinical studies, bias may mislead us into reaching a causal conclusion that the difference in the outcomes was due to the intervention or the exposure. This is a big concern, and, therefore, the risk of bias is assessed in clinical trials [ 46 ] as well as in observational studies [ 47 , 48 ].

There are many different forms of bias that can occur in a study, and they may overlap (e.g., allocation bias and confounding bias) [ 49 ]. Bias can occur at different stages, for example, immortal time bias in the design of the study, information bias in the execution of the study, and publication bias in the reporting of research. Understanding bias allows us researchers to remain vigilant of potential sources of bias when peer-reviewing and designing own studies. We summarized some common types of bias and some preventive steps in Table 1 , but many other forms of bias exist; for a comprehensive overview, see the Oxford University’s Catalogue of Bias [ 50 ].

For a comprehensive collection, see catalogofbias.org .

Here are some noteworthy examples of study bias from the literature: An example of information bias was observed when in 1998 an alleged association between the measles, mumps, and rubella (MMR) vaccine and autism was reported. Recall bias (a subtype of information bias) emerged when parents of autistic children recalled the onset of autism after an MMR vaccination more often than parents of similar children who were diagnosed prior to the media coverage of that controversial and meanwhile retracted study [ 51 ]. A study from 2001 showed better survival for academy award-winning actors, but this was due to immortal time bias that favors the treatment or exposure group [ 52 , 53 ]. A study systematically investigated self-reports about musculoskeletal symptoms and found the presence of information bias. The reason was that participants with little computer-time overestimated, and participants with a lot of computer-time spent underestimated their computer usage [ 54 ].

Information bias can be mitigated by using objective rather than subjective measurements. Standardized operating procedures (SOP) and electronic lab notebooks additionally help to follow well-designed protocols for data collection and handling [ 55 ]. Despite the failure to mitigate bias in studies, complete descriptions of data and methods can at least allow the assessment of risk of bias.

Research execution

Rule 6: avoid questionable research practices.

Questionable research practices (QRPs) can lead to exaggerated findings and false conclusions and thus lead to irreproducible research. Often, QRPs are used with no bad intentions. This becomes evident when methods sections explicitly describe such procedures, for example, to increase the number of samples until statistical significance is reached that supports the hypothesis. Therefore, it is important that researchers know about QRPs in order to recognize and avoid them.

Several questionable QRPs have been named [ 56 , 57 ]. Among them are low statistical power, pseudoreplication, repeated inspection of data, p -hacking [ 58 ], selective reporting, and hypothesizing after the results are known (HARKing).

The first 2 QRPs, low statistical power and pseudoreplication, can be prevented by proper planning and designing of studies, including sample size calculation and appropriate statistical methodology to avoid treating data as independent when in fact they are not. Statistical power is not equal to reproducibility, but statistical power is a precondition of reproducibility as the lack thereof can result in false negative as well as false positive findings (see Rule 3 ).

In fact, a lot of QRP can be avoided with a study protocol and statistical analysis plan. Preregistration, as described in Rule 2, is considered best practice for this purpose. However, many of these issues can additionally be rooted in institutional incentives and rewards. Both funding and promotion are often tied to the quantity rather than the quality of the research output. At universities, still only few or no rewards are given for writing and registering protocols, sharing data, publishing negative findings, and conducting replication studies. Thus, a wider “culture change” is needed.

Rule 7: Be cautious with interpretations of statistical significance

It would help if more researchers were familiar with correct interpretations and possible misinterpretations of statistical tests, p -values, confidence intervals, and statistical power [ 59 , 60 ]. A statistically significant p -value does not necessarily mean that there is a clinically or biologically relevant effect. Specifically, the traditional dichotomization into statistically significant ( p < 0.05) versus statistically nonsignificant ( p ≥ 0.05) results is seldom appropriate, can lead to cherry-picking of results and may eventually corrupt science [ 61 ]. We instead recommend reporting exact p -values and interpreting them in a graded way in terms of the compatibility of the null hypothesis with the data [ 62 , 63 ]. Moreover, a p -value around 0.05 (e.g., 0.047 or 0.055) provides only little information, as is best illustrated by the associated replication power: The probability that a hypothetical replication study of the same design will lead to a statistically significant result is only 50% [ 64 ] and is even lower in the presence of publication bias and regression to the mean (the phenomenon that effect estimates in replication studies are often smaller than the estimates in the original study) [ 65 ]. Claims of novel discoveries should therefore be based on a smaller p -value threshold (e.g., p < 0.005) [ 66 ], but this really depends on the discipline (genome-wide screenings or studies in particle physics often apply much lower thresholds).

Generally, there is often too much emphasis on p -values. A statistical index such as the p -value is just the final product of an analysis, the tip of the iceberg [ 67 ]. Statistical analyses often include many complex stages, from data processing, cleaning, transformation, addressing missing data, modeling, to statistical inference. Errors and pitfalls can creep in at any stage, and even a tiny error can have a big impact on the result [ 68 ]. Also, when many hypothesis tests are conducted (multiple testing), false positive rates may need to be controlled to protect against wrong conclusions, although adjustments for multiple testing are debated [ 69 – 71 ].

Thus, a p -value alone is not a measure of how credible a scientific finding is [ 72 ]. Instead, the quality of the research must be considered, including the study design, the quality of the measurement, and the validity of the assumptions that underlie the data analysis [ 60 , 73 ]. Frameworks exist that help to systematically and transparently assess the certainty in evidence; the most established and widely used one is Grading of Recommendations, Assessment, Development and Evaluations (GRADE; www.gradeworkinggroup.org ) [ 74 ].

Training in basic statistics, statistical programming, and reproducible analyses and better involvement of data professionals in academia is necessary. University departments sometimes have statisticians that can support researchers. Importantly, statisticians need to be involved early in the process and on an equal footing and not just at the end of a project to perform the final data analysis.

Rule 8: Make your research open

In reality, science often lacks transparency. Open science makes the process of producing evidence and claims transparent and accessible to others [ 75 ]. Several universities and research funders have already implemented open science roadmaps to advocate free and public science as well as open access to scientific knowledge, with the aim of further developing the credibility of research. Open research allows more eyes to see it and critique it, a principle similar to the “Linus’s law” in software development, which says that if there are enough people to test a software, most bugs will be discovered.

As science often progresses incrementally, writing and sharing a study protocol and making data and methods readily available is crucial to facilitate knowledge building. The Open Science Framework (osf.io) is a free and open-source project management tool that supports researchers throughout the entire project life cycle. OSF enables preregistration of study protocols and sharing of documents, data, analysis code, supplementary materials, and preprints.

To facilitate reproducibility, a research paper can link to data and analysis code deposited on OSF. Computational notebooks are now readily available that unite data processing, data transformations, statistical analyses, figures and tables in a single document (e.g., R Markdown, Jupyter); see also the 10 simple rules for reproducible computational research [ 76 ]. Making both data and code open thus minimizes waste of funding resources and accelerates science.

Open science can also advance researchers’ careers, especially for early-career researchers. The increased visibility, retrievability, and citations of datasets can all help with career building [ 77 ]. Therefore, institutions should provide necessary training, and hiring committees and journals should align their core values with open science, to attract researchers who aim for transparent and credible research [ 78 ].

Research reporting

Rule 9: report all findings.

Publication bias occurs when the outcome of a study influences the decision whether to publish it. Researchers, reviewers, and publishers often find nonsignificant study results not interesting or worth publishing. As a consequence, outcomes and analyses are only selectively reported in the literature [ 79 ], also known as the file drawer effect [ 80 ].

The extent of publication bias in the literature is illustrated by the overwhelming frequency of statistically significant findings [ 81 ]. A study extracted p -values from MEDLINE and PubMed Central and showed that 96% of the records reported at least 1 statistically significant p -value [ 82 ], which seems implausible in the real world. Another study plotted the distribution of more than 1 million z -values from Medline, revealing a huge gap from −2 to 2 [ 83 ]. Positive studies (i.e., statistically significant, perceived as striking or showing a beneficial effect) were 4 times more likely to get published than negative studies [ 84 ].

Often a statistically nonsignificant result is interpreted as a “null” finding. But a nonsignificant finding does not necessarily mean a null effect; absence of evidence is not evidence of absence [ 85 ]. An individual study may be underpowered, resulting in a nonsignificant finding, but the cumulative evidence from multiple studies may indeed provide sufficient evidence in a meta-analysis. Another argument is that a confidence interval that contains the null value often also contains non-null values that may be of high practical importance. Only if all the values inside the interval are deemed unimportant from a practical perspective, then it may be fair to describe a result as a null finding [ 61 ]. We should thus never report “no difference” or “no association” just because a p -value is larger than 0.05 or, equivalently, because a confidence interval includes the “null” [ 61 ].

On the other hand, studies sometimes report statistically nonsignificant results with “spin” to claim that the experimental treatment is beneficial, often by focusing their conclusions on statistically significant differences on secondary outcomes despite a statistically nonsignificant difference for the primary outcome [ 86 , 87 ].

Findings that are not being published have a tremendous impact on the research ecosystem, distorting our knowledge of the scientific landscape by perpetuating misconceptions, and jeopardizing judgment of researchers and the public trust in science. In clinical research, publication bias can mislead care decisions and harm patients, for example, when treatments appear useful despite only minimal or even absent benefits reported in studies that were not published and thus are unknown to physicians [ 88 ]. Moreover, publication bias also directly affects the formulation and proliferation of scientific theories, which are taught to students and early-career researchers, thereby perpetuating biased research from the core. It has been shown in modeling studies that unless a sufficient proportion of negative studies are published, a false claim can become an accepted fact [ 89 ] and the false positive rates influence trustworthiness in a given field [ 90 ].

In sum, negative findings are undervalued. They need to be more consistently reported at the study level or be systematically investigated at the systematic review level. Researchers have their share of responsibilities, but there is clearly a lack of incentives from promotion and tenure committees, journals, and funders.

Rule 10: Follow reporting guidelines

Study reports need to faithfully describe the aim of the study and what was done, including potential deviations from the original protocol, as well as what was found. Yet, there is ample evidence of discrepancies between protocols and research reports, and of insufficient quality of reporting [ 79 , 91 – 95 ]. Reporting deficiencies threaten our ability to clearly communicate findings, replicate studies, make informed decisions, and build on existing evidence, wasting time and resources invested in the research [ 96 ].

Reporting guidelines aim to provide the minimum information needed on key design features and analysis decisions, ensuring that findings can be adequately used and studies replicated. In 2008, the Enhancing the QUAlity and Transparency Of Health Research (EQUATOR) network was initiated to provide reporting guidelines for a variety of study designs along with guidelines for education and training on how to enhance quality and transparency of health research. Currently, there are 468 reporting guidelines listed in the network; see the most prominent guidelines in Table 2 . Furthermore, following the ICMJE recommendations, medical journals are increasingly endorsing reporting guidelines [ 97 ], in some cases making it mandatory to submit the appropriate reporting checklist along with the manuscript.

The EQUATOR Network is a library with more than 400 reporting guidelines in health research ( www.equator-network.org ).

The use of reporting guidelines and journal endorsement has led to a positive impact on the quality and transparency of research reporting, but improvement is still needed to maximize the value of research [ 98 , 99 ].

Conclusions

Originally, this paper targeted early-career researchers; however, throughout the development of the rules, it became clear that the present recommendations can serve all researchers irrespective of their seniority. We focused on practical guidelines for planning, conducting, and reporting of research. Others have aligned GRP with similar topics [ 100 , 101 ]. Even though we provide 10 simple rules, the word “simple” should not be taken lightly. Putting the rules into practice usually requires effort and time, especially at the beginning of a research project. However, time can also be redeemed, for example, when certain choices can be justified to reviewers by providing a study protocol or when data can be quickly reanalyzed by using computational notebooks and dynamic reports.

Researchers have field-specific research skills, but sometimes are not aware of best practices in other fields that can be useful. Universities should offer cross-disciplinary GRP courses across faculties to train the next generation of scientists. Such courses are an important building block to improve the reproducibility of science.

Acknowledgments

This article was written along the Good Research Practice (GRP) courses at the University of Zurich provided by the Center of Reproducible Science ( www.crs.uzh.ch ). All materials from the course are available at https://osf.io/t9rqm/ . We appreciated the discussion, development, and refinement of this article within the working group “training” of the SwissRN ( www.swissrn.org ). We are grateful to Philip Bourne for a lot of valuable comments on the earlier versions of the manuscript.

Funding Statement

S.S. received funding from SfwF (Stiftung für wissenschaftliche Forschung an der Universität Zürich; grant no. STWF-19-007). The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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FTC Announces Rule Banning Noncompetes

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Today, the Federal Trade Commission issued a final rule to promote competition by banning noncompetes nationwide, protecting the fundamental freedom of workers to change jobs, increasing innovation, and fostering new business formation.

“Noncompete clauses keep wages low, suppress new ideas, and rob the American economy of dynamism, including from the more than 8,500 new startups that would be created a year once noncompetes are banned,” said FTC Chair Lina M. Khan. “The FTC’s final rule to ban noncompetes will ensure Americans have the freedom to pursue a new job, start a new business, or bring a new idea to market.”

The FTC estimates that the final rule banning noncompetes will lead to new business formation growing by 2.7% per year, resulting in more than 8,500 additional new businesses created each year. The final rule is expected to result in higher earnings for workers, with estimated earnings increasing for the average worker by an additional $524 per year, and it is expected to lower health care costs by up to $194 billion over the next decade. In addition, the final rule is expected to help drive innovation, leading to an estimated average increase of 17,000 to 29,000 more patents each year for the next 10 years under the final rule.

Noncompetes are a widespread and often exploitative practice imposing contractual conditions that prevent workers from taking a new job or starting a new business. Noncompetes often force workers to either stay in a job they want to leave or bear other significant harms and costs, such as being forced to switch to a lower-paying field, being forced to relocate, being forced to leave the workforce altogether, or being forced to defend against expensive litigation. An estimated 30 million workers—nearly one in five Americans—are subject to a noncompete.

Under the FTC’s new rule, existing noncompetes for the vast majority of workers will no longer be enforceable after the rule’s effective date. Existing noncompetes for senior executives - who represent less than 0.75% of workers - can remain in force under the FTC’s final rule, but employers are banned from entering into or attempting to enforce any new noncompetes, even if they involve senior executives. Employers will be required to provide notice to workers other than senior executives who are bound by an existing noncompete that they will not be enforcing any noncompetes against them.

In January 2023, the FTC issued a  proposed rule which was subject to a 90-day public comment period. The FTC received more than 26,000 comments on the proposed rule, with over 25,000 comments in support of the FTC’s proposed ban on noncompetes. The comments informed the FTC’s final rulemaking process, with the FTC carefully reviewing each comment and making changes to the proposed rule in response to the public’s feedback.

In the final rule, the Commission has determined that it is an unfair method of competition, and therefore a violation of Section 5 of the FTC Act, for employers to enter into noncompetes with workers and to enforce certain noncompetes.

The Commission found that noncompetes tend to negatively affect competitive conditions in labor markets by inhibiting efficient matching between workers and employers. The Commission also found that noncompetes tend to negatively affect competitive conditions in product and service markets, inhibiting new business formation and innovation. There is also evidence that noncompetes lead to increased market concentration and higher prices for consumers.

Alternatives to Noncompetes

The Commission found that employers have several alternatives to noncompetes that still enable firms to protect their investments without having to enforce a noncompete.

Trade secret laws and non-disclosure agreements (NDAs) both provide employers with well-established means to protect proprietary and other sensitive information. Researchers estimate that over 95% of workers with a noncompete already have an NDA.

The Commission also finds that instead of using noncompetes to lock in workers, employers that wish to retain employees can compete on the merits for the worker’s labor services by improving wages and working conditions.

Changes from the NPRM

Under the final rule, existing noncompetes for senior executives can remain in force. Employers, however, are prohibited from entering into or enforcing new noncompetes with senior executives. The final rule defines senior executives as workers earning more than $151,164 annually and who are in policy-making positions.

Additionally, the Commission has eliminated a provision in the proposed rule that would have required employers to legally modify existing noncompetes by formally rescinding them. That change will help to streamline compliance.

Instead, under the final rule, employers will simply have to provide notice to workers bound to an existing noncompete that the noncompete agreement will not be enforced against them in the future. To aid employers’ compliance with this requirement, the Commission has included model language in the final rule that employers can use to communicate to workers. 

The Commission vote to approve the issuance of the final rule was 3-2 with Commissioners Melissa Holyoak and Andrew N. Ferguson voting no. Commissioners Rebecca Kelly Slaughter , Alvaro Bedoya , Melissa Holyoak and Andrew N. Ferguson each issued separate statements. Chair Lina M. Khan will issue a separate statement.

The final rule will become effective 120 days after publication in the Federal Register.

Once the rule is effective, market participants can report information about a suspected violation of the rule to the Bureau of Competition by emailing  [email protected] . 

The Federal Trade Commission develops policy initiatives on issues that affect competition, consumers, and the U.S. economy. The FTC will never demand money, make threats, tell you to transfer money, or promise you a prize. Follow the  FTC on social media , read  consumer alerts  and the  business blog , and  sign up to get the latest FTC news and alerts .

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