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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 ([email protected]) , 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?)

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Methods for the development of NICE public health guidance (third edition)

NICE process and methods [PMG4] Published: 26 September 2012

• 1 Introduction
• 2 Topic selection and scoping the guidance
• 3 Determining the evidence for review and consideration
• 4 Identifying the evidence
• 5 Reviewing the scientific evidence
• 6 Incorporating health economics

7 Developing recommendations

• Appendix A Conceptual framework for the work of the Centre for Public Health Excellence (CPHE)
• Appendix B Electronic resources
• Appendix C Example of audit information to accompany search strategies
• Appendix D Glossary of study designs
• Appendix E Algorithm for classifying quantitative (experimental and observational) study designs
• Appendix F Quality appraisal checklist – quantitative intervention studies
• Appendix G Quality appraisal checklist – quantitative studies reporting correlations and associations
• Appendix H Quality appraisal checklist – qualitative studies
• Appendix I Quality appraisal checklist – economic evaluations
• Appendix J Process for using review-level material in exceptional circumstances
• Appendix K Examples of evidence tables
• Appendix L NICE review format
• Appendix M Fieldwork
• Appendix N Expert testimony summary template
• Update information
• About this document

NICE process and methods

7.1 introduction, 7.2 considering the evidence and other issues, 7.3 format and wording of recommendations, 7.4 nice documents, 7.5 revisions following stakeholder consultation, 7.6 considerations, 7.7 equality and diversity, 7.8 formulating research recommendations, 7.9 further reading.

Developing recommendations is at the heart of the work of the public health advisory committees (PHAC). It is not a straightforward task and it may not always be easy to reach agreement.

It is vital to ensure the recommendations:

are informed by the most appropriate and available evidence (both scientific and other evidence)

are set in a framework that acknowledges a range of social value judgements (see Social value judgements: principles for the development of NICE guidance )

take account of relevant theories of public health and behaviour change

reflect the views and experiences of both those being advised to take action (for example, healthcare professionals or teachers) and the people who might be affected by that action (the target population and their families or carers)

are practical (that is, they can be implemented).

This chapter describes the stages involved in creating clear, practical recommendations:

Considering the evidence and other factors.

Creating the format and wording of recommendations.

Revising the recommendations following consultation with stakeholders.

Developing considerations.

Equality and diversity.

Formulating research recommendations.

Recommendations are developed using a range of scientific evidence ( section 3.2.1 ) and other evidence – such as expert testimony, stakeholder and practitioner views, committee discussions and debate (see chapter 3 ).

The evidence may be assessed for validity, reliability and bias, however evidence is not the sole determinant of the content of recommendations. It requires interpretation, especially an assessment of its implicit and explicit value base. It also needs to be assessed in light of the conceptual framework for public health ( appendix A ) and theories relating to individual and organisational behaviour change.

The PHAC's 'considered judgement' should take account of a range of issues (including any ethical issues and social value judgements) and policy imperatives, as well as equality and diversity legislation (see chapter 1 ) to ensure the recommendations are ethical, practical and specific. There are no hard-and-fast rules or mechanisms for doing this: the PHAC should make conscious and explicit use of its members' skills and expertise to make assumptions and apply inductive and deductive reasoning.

The PHAC, with support from the Centre for Public Health Excellence (CPHE) project team, should examine the evidence and related documents and discuss whether or not they address the issues under consideration. They should focus on the decision which needs to be made and the recommendations that are required, as well as the research questions used for the NICE evidence reviews and the key questions from the scope. (This should be documented in the minutes – see The NICE public health guidance development process [third edition, 2012]. for more detail on minutes.) To do this, it should consider the following issues (these are not in hierarchal order).

7.2.1 Strength (type, quality, quantity and consistency) of the evidence

Statistical and methodological issues and the study types available should all be taken into account, along with the degree of bias in the findings. The evidence statements (see section 5.5 ) will describe the number, type and quality of studies and summarise the strength of evidence. The PHAC should agree that this is a fair summary of the evidence and be mindful that the strongest available evidence does not necessarily translate to important areas for action. The impact of the potential benefits and harms also needs to be taken into account (see sections 7.2.4 to 7.2.6 ).

Members may want to discuss any inconsistencies in the findings (different studies may relate to slightly different interventions, populations or settings). In addition, they should use the NICE conceptual framework for public health ( appendix A ) to identify which causation vectors are applicable. (See also chapter 2 .)

7.2.2 Applicability of evidence to the target populations and settings

The evidence statements conclude with a summary statement on applicability. This describes the evidence as 'directly', 'partially' or 'not applicable' and why. The PHAC should reach its own conclusion, based on its members knowledge, experience and understanding of the target populations. They may choose to make a recommendation on evidence that is weaker, but more applicable, than stronger evidence from another context. (For more detail on the assessing applicability, see section 5.6 .)

7.2.3 Availability of evidence to support implementation – including evidence from practice

The PHAC should assess the extent to which the available evidence is about efficacy, effectiveness or both. Often the distinction between the 2 is not made clear in reports of public health interventions, not least because the number of efficacy studies is relatively small (compared to clinical studies).

The PHAC should also judge whether or not it will be possible to put the recommendations into practice. They can decide by using expert testimony, by drawing on their own experience or on information from fieldwork in cases where this has been conducted. They may also be able to draw on qualitative studies from the reviews or other forms of evidence relating to organisational and political processes.

In addition, the PHAC should assess the degree of change in practice required, staff training needs, policy levers and funding streams.

7.2.4 Relative value of the outcomes (including impact on inequalities)

The PHAC should assess the extent to which the recommendations may impact on health inequalities. This needs to be made clear, regardless of whether the recommendation is aimed at the whole population, specific subgroups or a combination of both. As discussed in chapter 1 , NICE has a duty to comply with current Equality legislation ( Equality Act 2010 ) in addition to taking into account additional important considerations about health inequalities.

7.2.5 Trade-off between harms and benefits

Where possible, the PHAC should assess any potentially negative effects and whether these are offset by the anticipated benefits.

7.2.6 Size of effect and potential impact on individual and population health (if applicable)

The PHAC should consider whether it is possible to anticipate effect sizes at individual or population-level. If this is the case, it will be important to consider effect sizes along the whole causal chain, not just at the end points. This may be difficult because of the current state of the evidence.

7.2.7 Cost effectiveness

The PHAC should consider cost-effectiveness evidence and the economic models (see chapter 6 ).

7.2.8 Target groups

The PHAC should assess the particular characteristics of the target group, paying attention to social differences (including class, gender, ethnicity, disability, culture and sexual orientation). It should assess how these characteristics will impact on the effectiveness of interventions. For specific information relating to equality and diversity, see section 7.7 .

7.2.9 Philosophical basis for making recommendations

All evidence requires interpretation, based on past experience, as evidence alone cannot determine the content of a recommendation. The development of evidence-based recommendations involves inferential, inductive or deductive reasoning:

inferential because it involves moving from what is known (the evidence) to uncertainty about what is reasonably expected to happen as a consequence of implementing a recommendation

inductive when it is derived from evidence

deductive when it is drawn from theory or methodological principles.

NICE's Social value judgements: principles for the development of NICE guidance explicitly acknowledges that non-scientific values are brought to bear and all of NICE's advisory committees are encouraged to take account of (and to make explicit) the value judgments they make. The advisory committee may also draw upon the principles outlined in the Nuffield Committee on Bioethics report on public health when making its judgements [ 9 ] .

The PHAC should make explicit the kinds of interpretive methods they are using during the inferential and judgemental process (Kelly and Moore 2012).

7.2.10 Conceptual framework and logic model

When the advisory committee is developing its recommendations, it should keep in mind the overarching conceptual framework, how it applies to the topic in question and the resulting logic models. This will help to clarify the practical issues involved in bridging the gaps between the evidence and producing a recommendation. It will also demonstrate how evidence drawn from the organisational and political sciences can help inform the decision- making process. (See appendix A .)

7.2.11 Additional documents

In addition to the NICE evidence review(s) (particularly the evidence statements and the economic analysis and modelling), other documents available to help the PHAC develop recommendations include (not in hierarchical order):

related NICE guidance

policy reports and guidance produced by other organisations

position papers on current practice

evidence (including practice-based evidence) and opinions from expert witnesses

evidence reviews about the views and experiences of the target population

stakeholder comments on the draft recommendations

fieldwork report (if fieldwork was required)

any documents that help members consider equity issues – such as NICE's documentation on its responsibilities in relation to equalities legislation ( The equality scheme and action plan for implementation ).

7.2.12 Challenges

Table 7.1 summarises the challenges that may arise during the deliberations – and possible approaches to overcoming them. In each case, the PHAC will need to make its approach explicit in the 'Considerations' section of the guidance, stating the basis for its decision(s) and the assumption(s) made (see section 7.6 ).

Table 7.1 Interpreting the evidence: possible challenges and ways to overcome them

7.2.13 The process in practice

As soon as members have discussed the findings of a NICE evidence review (or any expert testimony), the PHAC should start drafting recommendations. This is an iterative process; the recommendations are likely to be revised on a number of occasions before the wording is finalised.

First, the PHAC should decide what they want to recommend and which sectors (including which professionals in those sectors) should act on the recommendations. (As an example, the recommendations could be aimed at practitioners in the NHS, schools, workplaces or local authorities.)

In the early stages, it can be helpful to work in small groups, supported by the CPHE project team and using sample templates. It may also help if the CPHE project team develops a first draft of the recommendations as a starting point for discussion, based on the PHAC's initial deliberations as a group. However they are developed, the CPHE project team should ensure the draft recommendations are clearly linked to evidence statements.

Between the PHAC meetings, the nuances of the words can be refined via email discussions among members (again, supported by the CPHE project team). (See The NICE public health guidance development process third edition 2012, for terms of reference and the standing orders that define quoracy for advisory committees.)

The recommendations may be prioritised (see section 7.8 ).

Where evidence on effectiveness or cost effectiveness is lacking or conflicting, the PHAC may decide that further research should be a condition for implementation.

Decisions can be made using a variety of approaches: discussion, informal or formal consensus or formal voting (for example, in instances when members disagree). The proceedings should be recorded and a clear statement made about the factors that have been considered and the methods used to achieve consensus. This ensures the process is as transparent as possible.

A summary of the generic and specific issues considered and the key deliberations should be given in the 'Considerations' section of the guidance (see section 7.5 ).

Writing the recommendations is 1 of the most important and difficult steps in developing guidance. Great care should be taken. Each recommendation should answer the reader's main question: 'What does this mean for me?' In addition, it should clearly specify the intervention or action to be taken (what, how often and for how long?) and the context or circumstances (where and when?).

The wording must be concise and unambiguous so that the target audience knows what to do in practice and the public know what is being recommended. The CPHE project team should ensure that the PHAC is supplied with a copy of the booklet 'Writing for NICE'.

7.3.1 Format of recommendations

The recommendations are grouped under 3 main headings as below.

Whose health will benefit from the recommendation?

All those who will be affected by the recommendation. This may include:

individuals

communities or families

larger population groups defined by a range of factors (for example, by age, gender, ethnicity, setting).

Who should take action?

The professionals and others who should take action, these may be:

practitioners

commissioners

policy makers

researchers.

They may be subdivided by sector and setting, for example:

other public sector bodies (government, government agencies and local government, arm's length bodies, armed forces, prisons, police service, education)

private and voluntary organisations (large, medium and small).

They may refer to specific job titles, such as:

Ensure those taking action are listed by type of organisation or by job title, do not mix organisations and job titles in the same list.

What action should they take?

Actions should be as specific as possible, although how prescriptive they are will be decided on a case-by-case basis and will depend on the evidence available. They may cover:

strategy, policy and planning

service management and delivery

individual practice

research priorities.

7.3.2 Wording of recommendations

Each recommendation should:

Stand alone and be understood without reference to supporting material (supporting information can be included in the 'Considerations' section of the guidance or as part of the implementation materials).

Be as specific as possible about the action and who should take it (the ability to check whether it is being implemented properly [audit] should be considered when finalising the wording).

Only contain 1 main action in each bullet point.

Provide a clear link to the supporting evidence statements and evidence reviews, preferably with a numeric reference (to review number and evidence statement number).

Avoid, wherever possible, terminology and jargon – where this is not possible, it needs to be clearly defined and unambiguous (NICE can advise on this and also give you a copy of the 'Writing for NICE' guide).

Avoid trade names. Any reference to products (for example, pedometers) and services (for example, slimming clubs) should be made in general terms to avoid giving the impression that NICE endorses a particular brand.

Avoid implying that interventions or actions should be 'done' to people (that is, use 'offer' and 'discuss' rather than 'prescribe' or 'give', also avoid 'subjects' and 'cases' – use 'people', 'patients', 'clients' or 'service users' instead).

Avoid labelling people (that is, don't describe someone as a 'drug user' or 'smoker', use instead, 'someone who takes drugs or who smokes').

Acknowledge the role of individuals, service users, clients or members of the public who are directly affected by the recommendations or the organisations that represent them in any decision-making.

Include cross-references to recommendations from other NICE guidance to avoid the need to repeat information. It should be clear where the recommendations come from (refer to the guidance template for instructions). Recommendations from other NICE guidance or NHS policy can be quoted verbatim (as appropriate).

Recommendations from other (non-NICE) guidance should not be quoted verbatim.

7.3.3 Reflecting the strength of recommendations

NICE's public health recommendations are not graded, but the PHAC's view of how important they are should be clear from the wording (see table 7.2). The importance of a recommendation should not necessarily reflect the strength of the evidence available to support it. Other important factors (for example, ethics, principles, potential outcomes and equality issues) all need to be considered by the PHAC (see sections 7.2.1 to 7.2.11 ).

Table 7.2 Reflecting the strength of recommendations

The public health guidance issued by NICE will take 2 formats:

The guidance: Available online in web format, the NICE guidance lists all the recommendations, with details of how they were developed and evidence statements. It contains clear links to other NICE products to support the implementation of the recommendation.

NICE pathways: NICE pathways are a practical online resource for healthcare professionals to use on a day-to-day basis. A pathway presents recommendations from the guidance in a set of interactive topic-based diagrams. It contains all the recommendations from and links to related NICE guidance and other NICE products (for example, relevant quality standards and implementation tools).

Once the PHAC has agreed the content of the guidance and draft recommendations have been constructed, a stakeholder consultation is conducted on the full draft guidance. The stakeholder consultation is described in The NICE public health guidance development process (third edition 2012). Stakeholders will review and comment on the full draft guidance. Registered stakeholders normally include professional organisations and statutory agencies representing practitioners, as well as voluntary organisations run by, or representing the interests of, the target populations. The CPHE project team should prepare a summary of stakeholder responses.

7.5.1 Fieldwork

In addition to stakeholder consultation, draft recommendations may, on occasion, be tested with key groups of practitioners or policy makers. This activity is referred to as 'Fieldwork'. Fieldwork is carried out on an exceptional basis for public health guidance in new or sensitive areas, and not as a matter of routine on all guidance. Often, particularly for those topics where there is already related NICE guidance, fieldwork findings add little to the insights generated by the consultation with stakeholders. Nevertheless, for areas where NICE does not yet have good links with key practitioners and stakeholder groups it can be a valuable part of the process. Exceptions would include occasions when NICE develops public health guidance in a new or (scientifically or politically) controversial topic area. For an exception to be made, the CPHE team will need to put the case for fieldwork to the centre director for approval.

During fieldwork, the draft recommendations are tested with policy makers, commissioners and practitioners to see how easy they are to implement. Appendix M provides a detailed overview of the fieldwork methods and process.

If fieldwork has been conducted (see appendix M ), a summary of the information collected and any key implications for the recommendations are presented as a fieldwork report to the PHAC.

The CPHE project team may also occasionally commission separate work to test out the draft recommendations directly with the target population (see chapter 3 for details).

7.5.2 PHAC meeting following stakeholder consultation

The PHAC meets to review the evidence in light of stakeholder responses, revise the recommendations (if necessary) and finalise the guidance. It uses the following documents:

summary of stakeholder responses to the guidance consultation

summary of how stakeholder responses impact on the draft recommendations (as necessary)

report on direct consultation with the target population (if conducted)

equality and diversity assessment carried out on the draft recommendations (see section 7.7 )

fieldwork report (if applicable) including any impact on the draft recommendations.

If it appears from the consultation (or fieldwork) that professionals or others (as appropriate) do not endorse a recommendation, the PHAC should consider:

the possible reason(s) (for example, they may have concerns over training issues or capacity)

whether to amend the recommendation or associated recommendations to support implementation.

The 'Considerations' section of the guidance should clearly illustrate the range of issues the PHAC has considered in developing the recommendations. It should also make explicit the criteria used to create and prioritise them.

This section can be developed using the issues and documents listed in section 7.2 , the minutes from all its meetings (including any subgroup meetings) and records of any email discussions.

The following information may be included:

How the evidence statements were developed into recommendations – such as detail on the decisions made in relation to issues raised in table 7.1 and, in particular, on the strength and applicability of the evidence available.

How recommendations were prioritised – for example, whether this was based on the strength of evidence or policy imperatives.

The rationale for making recommendations that do not answer the key questions in the scope.

The rationale for making recommendations where there is a lack of evidence of effectiveness or cost effectiveness.

How evidence from practice was defined and the relative weight it was given compared to the evidence of effectiveness or cost effectiveness.

Testimony from expert witnesses.

Key facts for example, in relation to legislation, policy, funding and organisational issues.

Issues outside the remit of the guidance – to re-emphasise them.

Evidence not considered due to its quality or focus or due to time constraints.

The 'Considerations' section should not include any text that could be construed as a recommendation. However, where considerations correspond directly with groups of recommendations they should be grouped under the same sub-headings.

The considerations section may be used to point out where the PHAC would have liked to have made a recommendation but felt that there was insufficient evidence or lack of a rationale for doing so.

The following 2 questions should be addressed when assessing recommendations in line with equality and diversity legislation:

Does the guidance avoid unlawful discrimination?

Are there ways in which the guidance could better promote equality?

Equality issues are considered during the standard guidance development process and it is likely that many will have been considered during the PHAC's deliberations. These should be recorded in the 'Considerations' section of the guidance. (Where a direction suggested by the evidence has been altered in the interests of promoting equality, this should also be recorded.)

Recommendations should be formally assessed against equality considerations after the draft guidance has been issued for consultation. The findings should be considered by the PHAC when it reviews stakeholder comments (and fieldwork, if applicable).

To avoid unlawful discrimination, 4 issues are considered:

whether specific groups may be denied access to an intervention

whether specific groups will, in practice, find it more or less easy to access the intervention

whether any assessment needed to access the intervention will make it more or less difficult for specific groups to gain access

whether any features make it impossible or unreasonably difficult for people with disabilities to access the intervention.

In addition, the assessment checks whether stakeholders have raised any areas of possible discrimination.

Ideally, opportunities to promote equality will have been maximised during development of the draft recommendations. Nevertheless, after the consultation the PHAC should reconsider the draft recommendations specifically from this perspective: would, for example, changes to the wording (or the deletion of a recommendation or inclusion of new ones) further promote equality?

Suggested changes to the recommendations should be recorded and presented to the PHAC, together with stakeholder comments (and fieldwork, if applicable) on the draft guidance. Decisions on these issues should also be noted in the minutes of the relevant committee meetings.

Research recommendations provide an opportunity to highlight how the public health evidence base can be improved. More important, they ensure CPHE has access to the best possible evidence when the relevant guidance is being revised.

Research recommendations are a set of specific questions that aim to gather new evidence or strengthen the existing knowledge base, where it was previously equivocal. Recommendations to conduct both primary and secondary research (for example, systematic reviews) can be made.

The recommendations are drawn from a broader set of 'gaps in the evidence' and areas for further research that should be listed in an appendix to the guidance. These may have arisen out of the original evidence reviews or through general discussion at meetings.

This section provides a framework for formulating and prioritising research recommendations.

7.8.1 Principles for formulating research recommendations

There will probably be a large number of potential research recommendations. They can cover questions about effectiveness, implementation, acceptability, feasibility and costs.

Each research recommendation should be formulated as 1 question or as a set of closely related questions. It should consider the importance of issues relating to equality and diversity (for example, gender, ethnicity and people with special needs) and take into account the criteria set out in table 7.3 Each recommendation should also be evaluated and prioritised according to these criteria and this information should be presented as an appendix.

Only high-priority research recommendations should be included in the guidance.

7.8.2 Developing research recommendations

The PHACs should develop a maximum of 6 research recommendations. A research recommendation has 2 components: a well-formulated, answerable question (see below) and where appropriate a statement about the importance of the recommended research (see table 7.4).

It should comprise a question with explanatory text of not more than 150 words. See below for an example from Brief interventions and referral for smoking cessation in primary care and other settings (Public health guidance, NICE 2006).

Which brief interventions are most cost effective for increasing smoking cessation among lower socioeconomic and vulnerable groups?

Explanatory paragraph:

Smoking remains the leading cause of preventable morbidity and premature mortality in England, causing an estimated annual average of 86,500 deaths between 1998 and 2002. There is a clear social class gradient in smoking and it accounts for over half of the difference in risk of premature death between social classes. Smoking prevalence remains worryingly high in some groups. Vulnerable groups in society are the groups most likely to bear the burden of ill health and have the fewest resources with which to cope.

Alternatively, it may take the form of a recommendation that will answer: 'who should take action?' (for example, the Medical Research Council); and 'what action should they take?' (for example, to ensure certain outcome measures are used in studies that it funds in a particular topic area). If more than 1 organisation and 1 action is involved, these should be broken down into a bullet point list under the 2 headings.

Table 7.3 Checklist for formulating research questions about interventions using the population, intervention, comparison and outcome (PICO) model

PICO is a widely used mnemonic summarising the 4 major components of a research question about an intervention: patient (population), intervention, comparison and outcome (see table below).

7.8.3 Why is this question important?

The PHAC should draft a paragraph explaining the need for research following the process set out in the NICE research recommendations process and methods guide to ensure that: the process of developing the research recommendations is robust, transparent and that significant research priorities are identified

The NICE research recommendations process and methods guide describes a step-by-step approach to identifying uncertainties, formulating research recommendations and research questions, prioritising them and communicating them to the NICE Research and Development (R&D) team and researchers and funders.

Table 7.4 Research priorities

All proposed research recommendations should be included in the draft guidance that is made available for stakeholder consultation. Following consultation, the PHAC will take account of any concerns raised by stakeholders and they will be amended as appropriate. Draft research recommendations will be finalised post consultation. All the research recommendations contained in the guidance are added to a database on the NICE website. Those classed as high priority are highlighted. High-priority research recommendations are put through a second prioritisation process at NICE.

7.8.4 Other research recommendations

Important research recommendations that fall outside the scope of the guidance are communicated to research and development funders such as:

National Institute for Health Research (NIHR) Public Health Research Programme

NIHR Health Technology Assessment Programme

NIHR Health Services and Delivery Research Programme

Medical Research Council (MRC)

Economic and Social Research Council (ESRC)

DH Policy Research Programme (PRP).

7.8.5 Equality and diversity

Research recommendations should aim to address any gaps in the evidence base in relation to the groups identified in the Equality Act (2010 ) (or groups who are particularly disadvantaged with respect to the topic under consideration).

Glasziou P, Del Mar C, Salisbury J (2003) Evidence-based medicine workbook. London: British Medical Journal Books

Kelly MP,Moore TA (2012) The judgement process in evidence-based medicine and health technology assessment . Social Theory and Health, 10:1-19.i:10.1057/sth.2011.21

Michie S, Johnston M (2004) Changing clinical behaviour by making guidelines specific. British Medical Journal 328: 343–5

Research recommendation manual , National Institute for Clinical Excellence (2011)

Public health: ethical issues . London: Nuffield Council on Bioethics (2007)

Sackett DL, Straus SE, Richardson WS (2000) Evidence-based medicine: how to practice and teach EBM. Edinburgh: Churchill Livingstone

Schunemann HJ, Best D, Vist G et al. (2003) Letters, numbers, symbols and words: how to communicate grades of evidence and recommendations. Canadian Medical Association Journal 169: 677–80

Scottish Intercollegiate Guidelines Network (2002) SIGN 50. A guideline developer's handbook. Edinburgh: Scottish Intercollegiate Guidelines Network

Tannahill A (2008) Beyond evidence – to ethics: a decision making framework for health promotion, public health and health improvement. Health Promotion International 23 (4): 380–90

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[ 9 ] Nuffield Council on Bioethics (2007) Public health: ethical issues . London: Nuffield Council on Bioethics.

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Ten Simple Rules for Writing Research Papers

Weixiong zhang.

Department of Computer Science and Engineering, Department of Genetics, Washington University in St. Louis, St. Louis, Missouri, United States of America

The importance of writing well can never be overstated for a successful professional career, and the ability to write solid papers is an essential trait of a productive researcher. Writing and publishing a paper has its own life cycle; properly following a course of action and avoiding missteps can be vital to the overall success not only of a paper but of the underlying research as well. Here, we offer ten simple rules for writing and publishing research papers.

As a caveat, this essay is not about the mechanics of composing a paper, much of which has been covered elsewhere, e.g., [1] , [2] . Rather, it is about the principles and attitude that can help guide the process of writing in particular and research in general. In this regard, some of the discussion will complement, extend, and refine some advice given in early articles of this Ten Simple Rules series of PLOS Computational Biology [3] – [8] .

Rule 1: Make It a Driving Force

Never separate writing a paper from the underlying research. After all, writing and research are integral parts of the overall enterprise. Therefore, design a project with an ultimate paper firmly in mind. Include an outline of the paper in the initial project design documents to help form the research objectives, determine the logical flow of the experiments, and organize the materials and data to be used. Furthermore, use writing as a tool to reassess the overall project, reevaluate the logic of the experiments, and examine the validity of the results during the research. As a result, the overall research may need to be adjusted, the project design may be revised, new methods may be devised, and new data may be collected. The process of research and writing may be repeated if necessary.

Rule 2: Less Is More

It is often the case that more than one hypothesis or objective may be tackled in one project. It is also not uncommon that the data and results gathered for one objective can serve additional purposes. A decision on having one or more papers needs to be made, and the decision will be affected by various factors. Regardless of the validity of these factors, the overriding consideration must be the potential impact that the paper may have on the research subject and field. Therefore, the significance, completeness, and coherence of the results presented as a whole should be the principal guide for selecting the story to tell, the hypothesis to focus upon, and materials to include in the paper, as well as the yardstick for measuring the quality of the paper. By this metric, less is more , i.e., fewer but more significant papers serve both the research community and one's career better than more papers of less significance.

Rule 3: Pick the Right Audience

Deciding on an angle of the story to focus upon is the next hurdle to jump at the initial stage of the writing. The results from a computational study of a biological problem can often be presented to biologists, computational scientists, or both; deciding what story to tell and from what angle to pitch the main idea is important. This issue translates to choosing a target audience, as well as an appropriate journal, to cast the main messages to. This is critical for determining the organization of the paper and the level of detail of the story, so as to write the paper with the audience in mind. Indeed, writing a paper for biologists in general is different from writing for specialists in computational biology.

Rule 4: Be Logical

The foundation of “lively” writing for smooth reading is a sound and clear logic underlying the story of the paper. Although experiments may be carried out independently, the result from one experiment may form premises and/or provide supporting data for the next experiment. The experiments and results, therefore, must be presented in a logical order. In order to make the writing an easy process to follow, this logical flow should be determined before any other writing strategy or tactic is exercised. This logical order can also help you avoid discussing the same issue or presenting the same argument in multiple places in the paper, which may dilute the readers' attention.

An effective tactic to help develop a sound logical flow is to imaginatively create a set of figures and tables, which will ultimately be developed from experimental results, and order them in a logical way based on the information flow through the experiments. In other words, the figures and tables alone can tell the story without consulting additional material. If all or some of these figures and tables are included in the final manuscript, make every effort to make them self-contained (see Rule 5 below), a favorable feature for the paper to have. In addition, these figures and tables, as well as the threading logical flow, may be used to direct or organize research activities, reinforcing Rule 1.

Rule 5: Be Thorough and Make It Complete

Completeness is a cornerstone for a research paper, following Rule 2. This cornerstone needs to be set in both content and presentation. First, important and relevant aspects of a hypothesis pursued in the research should be discussed with detailed supporting data. If the page limit is an issue, focus on one or two main aspects with sufficient details in the main text and leave the rest to online supporting materials. As a reminder, be sure to keep the details of all experiments (e.g., parameters of the experiments and versions of software) for revision, post-publication correspondence, or importantly, reproducibility of the results. Second, don't simply state what results are presented in figures and tables, which makes the writing repetitive because they are self-contained (see below), but rather, interpret them with insights to the underlying story to be told (typically in the results section) and discuss their implication (typically in the discussion section).

Third, make the whole paper self-contained. Introduce an adequate amount of background and introductory material for the right audience (following Rule 3). A statistical test, e.g., hypergeometric tests for enrichment of a subset of objects, may be obvious to statisticians or computational biologists but may be foreign to others, so providing a sufficient amount of background is the key for delivery of the material. When an uncommon term is used, give a definition besides a reference to it. Fourth, try to avoid “making your readers do the arithmetic” [9] , i.e., be clear enough so that the readers don't have to make any inference from the presented data. If such results need to be discussed, make them explicit even though they may be readily derived from other data. Fifth, figures and tables are essential components of a paper, each of which must be included for a good reason; make each of them self-contained with all required information clearly specified in the legend to guide interpretation of the data presented.

Rule 6: Be Concise

This is a caveat to Rule 5 and is singled out to emphasize its importance. Being thorough is not a license to writing that is unnecessarily descriptive, repetitive, or lengthy. Rather, on the contrary, “simplicity is the ultimate sophistication” [10] . Overly elaborate writing is distracting and boring and places a burden on the readers. In contrast, the delivery of a message is more rigorous if the writing is precise and concise. One excellent example is Watson and Crick's Nobel-Prize-winning paper on the DNA double helix structure [11] —it is only two pages long!

Rule 7: Be Artistic

A complete draft of a paper requires a lot of work, so it pays to go the extra mile to polish it to facilitate enjoyable reading. A paper presented as a piece of art will give referees a positive initial impression of your passion toward the research and the quality of the work, which will work in your favor in the reviewing process. Therefore, concentrate on spelling, grammar, usage, and a “lively” writing style that avoids successions of simple, boring, declarative sentences. Have an authoritative dictionary with a thesaurus and a style manual, e.g., [1] , handy and use them relentlessly. Also pay attention to small details in presentation, such as paragraph indentation, page margins, and fonts. If you are not a native speaker of the language the paper is written in, make sure to have a native speaker go over the final draft to ensure correctness and accuracy of the language used.

Rule 8: Be Your Own Judge

A complete manuscript typically requires many rounds of revision. Taking a correct attitude during revision is critical to the resolution of most problems in the writing. Be objective and honest about your work and do not exaggerate or belittle the significance of the results and the elegance of the methods developed. After working long and hard, you are an expert on the problem you studied, and you are the best referee of your own work, after all . Therefore, inspect the research and the paper in the context of the state of the art.

When revising a draft, purge yourself out of the picture and leave your passion for your work aside. To be concrete, put yourself completely in the shoes of a referee and scrutinize all the pieces—the significance of the work, the logic of the story, the correctness of the results and conclusions, the organization of the paper, and the presentation of the materials. In practice, you may put a draft aside for a day or two—try to forget about it completely—and then come back to it fresh, consider it as if it were someone else's writing, and read it through while trying to poke holes in the story and writing. In this process, extract the meaning literally from the language as written and do not try to use your own view to interpret or extrapolate from what was written. Don't be afraid to throw away pieces of your writing and start over from scratch if they do not pass this “not-yourself” test. This can be painful, but the final manuscript will be more logically sound and better organized.

Rule 9: Test the Water in Your Own Backyard

It is wise to anticipate the possible questions and critiques the referees may raise and preemptively address their concerns before submission. To do so, collect feedback and critiques from others, e.g., colleagues and collaborators. Discuss your work with them and get their opinions, suggestions, and comments. A talk at a lab meeting or a departmental seminar will also help rectify potential issues that need to be addressed. If you are a graduate student, running the paper and results through the thesis committee may be effective to iron out possible problems.

Rule 10: Build a Virtual Team of Collaborators

When a submission is rejected or poorly reviewed, don't be offended and don't take it personally. Be aware that the referees spent their time on the paper, which they might have otherwise devoted to their own research, so they are doing you a favor and helping you shape the paper to be more accessible to the targeted audience. Therefore, consider the referees as your collaborators and treat the reviews with respect. This attitude can improve the quality of your paper and research.

Read and examine the reviews objectively—the principles set in Rule 8 apply here as well. Often a criticism was raised because one of the aspects of a hypothesis was not adequately studied, or an important result from previous research was not mentioned or not consistent with yours. If a critique is about the robustness of a method used or the validity of a result, often the research needs to be redone or more data need to be collected. If you believe the referee has misunderstood a particular point, check the writing. It is often the case that improper wording or presentation misled the referee. If that's the case, revise the writing thoroughly. Don't argue without supporting data. Don't submit the paper elsewhere without additional work. This can only temporally mitigate the issue, you will not be happy with the paper in the long run, and this may hurt your reputation.

Finally, keep in mind that writing is personal, and it takes a lot of practice to find one's style. What works and what does not work vary from person to person. Undoubtedly, dedicated practice will help produce stronger papers with long-lasting impact.

Acknowledgments

Thanks to Sharlee Climer, Richard Korf, and Kevin Zhang for critical reading of the manuscript.

Funding Statement

The author received no specific funding for this article.

Steps to Take in Writing a Recommendation Report

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How to Write a Needs Assessment Report

How to write an executive summary on ethics in the workplace, how to properly format for an interoffice memo.

• What Are the Duties of a Call Center Human Resources Manager?
• How to Write a Business Requirements Document

Writing a recommendation report usually involves describing a situation, evaluating possible alternatives and proposing a solution to a problem. The final report should include clear, precise and evidence-based fndings and recommendations. Whether you’re writing a project, performance or risk assessment, clearly stating the facts makes it easier for others to reach a decision based on your research.

Selecting a Format

A comprehensive recommendation report typically includes a table of contents, executive summary, data acquisition methodology, options and conclusions. You can download a business recommendation template or create your own document or presentation format. Additionally, you can provide attachments with details or include links to websites with relevant information. Select a format that is best suited to the type of report you are writing.

Describing the Situation

The goal of the recommendation report is to outline helpful recommendations that can fulfill a need or resolve a vexing problem, according to the University of Arkansas . When writing a recommendation report, start by clearly stating what you’re evaluating. This sets the tone. For example, if you need to create a report on absenteeism at your company, start by listing statistics about the problem. Then add commentary about its impact. For example, absenteeism might lead to decreased productivity, missed deadlines and lowered customer satisfaction.

Conducting Research

In your recommendation, you need to define the methodology used to collect data. For example, you might create an online survey, conduct focus groups, complete interviews or read literature on the subject. This activity helps you find out what other people think about the topic and what actions they may be taking based on their own experiences. For example, you might discover that employees at your company may miss work repeatedly due to caregiver obligations, poor health or transportation problems. Analyze the situation thoroughly before drawing any conclusions. For instance, you can use analysis techniques such as drawing a fishbone diagram to determine the root cause of the problem.

Qualifying Alternatives

After you lay out the problem, you may go on to suggest viable solutions, as explained by professor T. Miles at West Virginia University. In writing research recommendations, recall what factors should be considered For example, when writing a recommendation report about employee development needs, list options for developing professional skills, such as effective communication, negotiation and decision making. Your recommendation report may also include details about past interventions and results.

Summarizing Findings

Summarize your findings from your research using concise charts, lists and diagrams. This makes it easier for your superiors to interpret your recommendation and draw their own conclusions. For example, you may find that employees fail to recognize the impact of their absenteeism. Promoting awareness can increase attendance and maintain appropriate coverage.

Your recommendations should have specific, measurable and achievable actions defined. They should also be realistic and time constrained. A recommendation might suggest design, distribution and display by the end of the month of posters in the workplace that highlight incentives and punishments, for instance.

• West Virginia University: Recommendation Report
• Microsoft Office Templates: Business Report (Elegant)
• University of Arkansas: Recommendation Report

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2 Considerations in Designing Your Research Approach

Once you’ve identified your area of interest, sorted through and analyzed the literature to identify the problem you’d like to address, and developed both a purpose and a question; the next step is to design your study. This chapter will provide a basic overview of the considerations any researcher must think about as they design a research study.

Chapter 2: Learning Objectives

As you work to identify the best approach to identify an answer to your research question, you will be able to:

• Compare the conceptualization and operational activities of the process
• Discuss the difference between an independent and dependent variable
• Discuss the importance of sampling
• Contrast research approaches
• Demonstrate a systematic approach to selecting a research design

Understanding the Language of Research

As you work to determine which approach you will consider in order to best answer your question, you’ll need to consider how to address both the conceptual and operational components of your inquiry. As we discussed in Chapter 1; theory often informs practice (deductive approaches). Theory is often discussed in terms of abstract, or immeasurable, constructs. Because of the ambiguous nature of theory, it is important to conceptualize the parameters of your investigation. Conceptualizing is the process of defining what is or is not included in your description of a specific construct.

Understanding Theoretical and Contextual Framework

You may consider the theoretical or contextual framework for your study as the ‘lens’ through which you want your reader to view the work from. That is, this is your opportunity frame their experience with this information through your educated perspective on the material.

How Will You Determine the Subjective Aspects of Your Work?

Consider exploring one’s motivation to advance their education:

• That is if you’re determining whether clinicians who have advanced credentials are more motivated at work; you’ll need to create a clear delineation between motivation and effort and work out how to measure each of these independently

Operationalization is the process of defining concepts or constructs in a measurable way. As you dive into the ‘HOW’ you will go about your research, you will need to understand the terminology related to study design

As we discussed in Chapter 1, there are several kinds of Variables. As a reminder, a variable is an objective and measurable representation of a theoretical construct. An independent variable is a variable which causes an effect on the dependent, or outcome variable. Note that there may be more than one independent variable in your study. Therefore, the dependent variable is the variable which you are measuring as an effect of an intervention or influence; you can think of this as the outcome variable. Identifying at least these two variables is an essential first step in designing your study. This is because how you explore the relationship between your effect (independent variable) and outcome (dependent variable) with help guide your methodology. Other variables to consider include mediating variables , which are variables that are explained by both the independent and dependent variables. Moderating variables influence the relationship between the independent and dependent variables and control variables which may have an impact on the dependent variable but does not help to explain the dependent variable.

Assigning Dependent and Independent Variables

You would like to determine the relationship between weight and tidal volume:

• Dependent Variable : Which variable DEPENDS on the other? Or, which variable will define the OUTCOME? ( Tidal volume)
• Independent Variable : Does the variable INFLUENCE, HELP EXPLAIN, or have an IMPACT on the dependent variable? (Weight)

You would like to determine whether the number of hours spent in clinical training influences post training test scores :

• Dependent Variable : Score on post training test
• Independent Variable : Number of hours in clinical training

Identifying and assigning the dependent and independent variable(s) is one of the most important research activities as this will help guide you toward the type of information you’ll be collecting and what you will do with that information. However, as you consider both the outcome (dependent) variable and the impact (independent) variable, it is also important to consider what other variables may influence the relationship between these two primary variables.

There are very few instances wherein you can control EVERY variable. However, it is your job as a researcher to plan for, acknowledge, and attempt to address anything that may influence the results you present.

levels of measurement can be thought of as values within each variable. For example, traditionally, the variable ‘Gender’ had two values: male or female. The modern variable of ‘Gender’ may have several values which are used to delineate each potential designation within the variable. Each value represents a specific designation of measure.

Values of measures may be considered quantitative (numeric); in our example of traditional gender you may assign a numeric (quantitative) value to male and female as either ‘1’ and ‘2’, respectively. Values may also be assigned non-numerically; meaning they are qualitative. It is important to note that if you want to analyze non-numeric data, it must be coded first.

Understanding and Assigning Value

You may create a question asking respondents to rank their agreement with a statement on a scale ranging from strongly disagree to strongly agree. Although qualitative in nature, we can assign a numeric value to each level of measurement as a ‘code’.

• 1= Strongly Disagree
• 2= Somewhat Disagree
• 3= Neither Disagree nor Agree
• 4= Somewhat Agree
• 5= Strongly Agree

By doing this, we can explore relationships between the attributes and variables using quantitative statistical methods.

Levels of measurement

One of the most important aspects of operationalizing a theoretical construct is to determine the level(s) of measurement. This is done by assessing the types of variables and values:

• Nominal : also called categorical. This level of measurement is used to describe a variable with two or more values BUT there is no intrinsic ordering to the categories

Example of a Nominal Variable

You would like to collect information about the gender (variable) of individuals participating in your study. Your level of measures may be:

You may then assign these measures a numeric value:

• Non-Binary=3
• Ordinal : This level of measurement is used to describe variable values that have a specific rank order. BUT that order does not indicate a specificity between ranks.

Example of an Ordinal Variable

You provide a scale of agreement for respondents to indicate their level of agreement with the use of a current policy within the hospital:

• Strongly Agree
• Strongly Disagree

Note: Those who strongly disagree with the use of this policy disapprove MORE than do those who disagree; however, there is no quantifiable value for how much more.

• Interval : You’ll use this level of measurement for variable values which are rank ordered AND have specified intervals between ranks and can tell you ‘how much more’.

Example of an Interval Variable

You classify the ages of the participants in your study:

• 18-24 years old
• 25-30 years old
• 31-35 years old
• >35 years old

NOTE: 35 is 5 more than 30. The quantifiable ‘how much more’ is what distinguishes age as an interval variable.

• Ratio : Ratio values have all of the qualities of a nominal, ordinal, and/or interval scale BUT ALSO have a ‘true zero’. In this case true zero indicates a lack of the underlying construct (i.e. it does not exist). Additionally, there is a ratio between points on this particular scale. That is, in this case, 10 IS twice that of 5.

Example of a Ratio Variable

You are doing a pre and post bronchodilator treatment trial for a new drug. You must establish baseline heart rate in your treatment group:

• Pulse rate is a ratio variable because the scale has an absolute zero (asystole) and there is a ratio between the number of times the heart beats (i.e. a change in heart rate of 10 beats per minute)

Identification of variable and values is essential to a successful project. Not only will doing this early in the process allow you to predict factors that may affect your research question, but it will also guide you toward the type of data you will collect and determine what kind of statistical analyses you will likely be performing in order to understand and present the results of your work.

Scales are used to glean insight into a situation or phenomenon and can be used to help quantify information that would otherwise be difficult to understand or convey. Although there are several types of scales used by researchers, we’ll focus on the two of the most common:

• Binary scale : Nominal scale that offers two possible outcomes, or values. Questions that force a respondent to answer either ‘yes’ or ‘no’ utilize a binary scale. IF you offer more than two options, your scale is no longer binary, but is still a nominal scaled item

• Likert scales : Likert scales are popular for measuring ordinal data and include indications from respondents. Data can be quantified using codes assigned to responses and an overall summation for each attribute can be associated with each respondent

Sampling is the statistical process of selecting a subset (called a “sample”) of a population of interest for purposes of making observations and statistical inferences about that population. We cannot study entire populations because of feasibility and cost constraints, and hence, we must select a representative sample from the population of interest for observation and analysis. It is extremely important to choose a sample that is truly representative of the population so that the inferences derived from the sample can be generalized back to the population of interest. Probability sampling is a technique in which every unit in the population has a chance (non-zero probability) of being selected in the sample, and this chance can be accurately determined. An example of probability sampling is simple random sampling wherein you include ALL possible participants in a population and utilize a method to randomly select a sample that is representative of that population. Nonprobability Sampling is a sampling technique in which some units of the population have zero chance of selection or where the probability of selection cannot be accurately determined. Typically, units are selected based on certain non-random criteria, such as quota or convenience. Because selection is non-random, nonprobability sampling does not allow the estimation of sampling errors, and may be subjected to a sampling bias. Therefore, information from a sample cannot be generalized back to the population. An example of nonprobability sampling is utilizing a convenience sample of participants due to your close proximity or access to them.

Why does sampling matter?

When you measure a certain observation from a given unit, such as a person’s response to a Likert-scaled item, that observation is called a response. In other words, a response is a measurement value provided by a sampled unit. Each respondent will give you different responses to different items in an instrument. Responses from different respondents to the same item or observation can be graphed into a frequency distribution based on their frequency of occurrences. For a large number of responses in a sample, this frequency distribution tends to resemble a bell-shaped curve called a normal distribution, which can be used to estimate overall characteristics of the entire sample, such as sample mean (average of all observations in a sample) or standard deviation (variability or spread of observations in a sample). These sample estimates are called sample statistics (a “statistic” is a value that is estimated from observed data). Populations also have means and standard deviations that could be obtained if we could sample the entire population. However, since the entire population can never be sampled, population characteristics are always unknown, and are called population parameters (and not “statistic” because they are not statistically estimated from data). Sample statistics may differ from population parameters if the sample is not perfectly representative of the population; the difference between the two is called sampling error. Theoretically, if we could gradually increase the sample size so that the sample approaches closer and closer to the population, then sampling error will decrease and a sample statistic will increasingly approximate the corresponding population parameter.

If a sample is truly representative of the population, then the estimated sample statistics should be identical to corresponding theoretical population parameters. How do we know if the sample statistics are at least reasonably close to the population parameters? Here, we need to understand the concept of a sampling distribution . Imagine that you took three different random samples from a given population, as shown below, and for each sample, you derived sample statistics such as sample mean and standard deviation. If each random sample was truly representative of the population, then your three sample means from the three random samples will be identical (and equal to the population parameter), and the variability in sample means will be zero. But this is extremely unlikely, given that each random sample will likely constitute a different subset of the population, and hence, their means may be slightly different from each other. However, you can take these three sample means and plot a frequency histogram of sample means. If the number of such samples increases from three to 10 to 100, the frequency histogram becomes a sampling distribution. Hence, a sampling distribution is a frequency distribution of a sample statistic (like sample mean) from a set of samples, while the commonly referenced frequency distribution is the distribution of a response (observation) from a single sample. Just like a frequency distribution, the sampling distribution will also tend to have more sample statistics clustered around the mean (which presumably is an estimate of a population parameter), with fewer values scattered around the mean. With an infinitely large number of samples, this distribution will approach a normal distribution. The variability or spread of a sample statistic in a sampling distribution (i.e., the standard deviation of a sampling statistic) is called its standard error. In contrast, the term standard deviation is reserved for variability of an observed response from a single sample.

The mean value of a sample statistic in a sampling distribution is presumed to be an estimate of the unknown population parameter. Based on the spread of this sampling distribution (i.e., based on standard error), it is also possible to estimate confidence intervals for that prediction population parameter. Confidence interval is the estimated probability that a population parameter lies within a specific interval of sample statistic values. All normal distributions tend to follow a 68-95-99 percent rule (see below), which says that over 68% of the cases in the distribution lie within one standard deviation of the mean value (μ 1σ), over 95% of the cases in the distribution lie within two standard deviations of the mean (μ 2σ), and over 99% of the cases in the distribution lie within three standard deviations of the mean value (μ 3σ). Since a sampling distribution with an infinite number of samples will approach a normal distribution, the same 68-95-99 rule applies, and it can be said that:

• (Sample statistic one standard error) represents a 68% confidence interval for the population parameter.
• (Sample statistic two standard errors) represents a 95% confidence interval for the population parameter.
• (Sample statistic three standard errors) represents a 99% confidence interval for the population parameter.

A sample is “biased” (i.e., not representative of the population) if its sampling distribution cannot be estimated or if the sampling distribution violates the 68-95-99 percent rule. As an aside, note that in most regression analysis where we examine the significance of regression coefficients with p<0.05, we are attempting to see if the sampling statistic (regression coefficient) predicts the corresponding population parameter (true effect size) with a 95% confidence interval. Interestingly, the “six sigma” standard attempts to identify manufacturing defects outside the 99% confidence interval or six standard deviations (standard deviation is represented using the Greek letter sigma), representing significance testing at p<0.01.

Types of Research Designs

There are many different approaches to research. The list provided here is not exhaustive by any means; rather, this is a brief list of the most common approaches you may identify as you review the literature related to your interest.

Experimental

Experimental research is typically performed in a controlled environment so that the researcher can manipulate an independent variable and measure the outcome (dependent variable) between a group of subjects who received the manipulated variable (intervention) and a group of subjects who did not receive the intervention. This type of design typically adheres to the scientific method in order to test a hypothesis. A hypothesis is a proposed explanation for a phenomenon and serves as the starting point for the investigation.  You may see a hypothesis indicated as (H O ), also called the null hypothesis. This is to differentiate it from an alternative hypothesis (H 1 or H A ), which is any hypothesis other than the null.

Development of the Hypothesis

There are two types of hypotheses, the null (HO) and an alternative (H 1 or H A )

• H O = There is no significant difference between length of stay for patients diagnosed with COPD and those diagnosed with CHF.
• H 1 or H A = There is a significant difference between length of stay for patients diagnosed with COPD and those diagnosed with CHF

NOTE: Accepting the null hypothesis would mean that your data confirm that there is no difference. Rejecting the null would mean that your data indicated that there is a significant difference in patient outcomes for these groups; therefore, rejecting the null means accepting an alternative hypothesis.

Randomized Experimental : Participants are randomly assigned to either a treatment (intervention) or a control group. Typically, the treatment group receives an intervention (independent variable) and the outcome of each group is considered dependent variables and compared for effect. Independent variables in this case are considered active in that this variable can be manipulated.

Example of Randomized Experimental Approach

You would like to assess outcomes as they relate to the post delivery resuscitation of  very low birthweight infants in the delivery room. You have decided that one group will receive direct intubation and surfactant (intervention group) in the delivery room and the other will receive the standard care of CPAP (control group). Participants will be randomly assigned to groups and as a bonus, the assignment to groups will be blinded. You will then compare the difference between participants in each group regarding need for oxygen at 36 weeks adjusted gestational age.

• Dependent Variable: Need for oxygen at 36 weeks adjusted gestational age
• Independent Variable (Active) : Administration of surfactant

Quasi Experimental : Similar to the randomized experimental approach aside from the random assignment. In quasi-experimental approaches, participants are NOT randomly assigned; however, one group does receive an intervention while the control group does not and outcomes are still compared. The independent variable is also active.

Example of Quasi Experimental Approach

You would like to assess outcomes as they relate to the post delivery resuscitation of  very low birthweight infants in the delivery room. You have decided that one group will receive direct intubation and surfactant (intervention group) in the delivery room and the other will receive the standard care of CPAP (control group). Participants will be assigned to groups based on administration of maternal steroids. You will then compare the difference between participants in each group regarding need for oxygen at 36 weeks adjusted gestational age.

Non Experimental

Non-experimental approaches include a wide variety of approaches; therefore, it is difficult to list them all in a succinct way here. However, it is safe to say that a study approach is considered non-experimental when there lacks intentional manipulation of the independent variable.

Comparative approach : Groups are compared to reveal differences in outcome (dependent variable). Groups are typically formed based on independent variables that cannot be manipulated but are important to the study. This type of independent variable is known as an attribute independent variable. In this approach there are a few categories (2-4 levels) of attribute independent variables that are then compared.

Example of Comparative Approach

You would like to investigate the perceptions of first and second year student-instructor engagement and student learning and instructor motivation in the clinical environment.

• Dependent Variable : Student perception of experience (2 levels: First and second year)
• Independent Variable : Student-instructor engagement in learning and motivation

Associational or Correlational approach : Two or more variables for the same group of participants are explored for relationships. Independent variables are also attributive in this approach; meaning, they can be manipulated to impact the dependent variable. Variables included in this approach are typically continuous or have at least five ordered categories.

Example of Associational or Correlational Approach

You would like to conduct a study to better understand practitioner attitudes about the future of the profession.

• Dependent Variable: Attitude about the future of the profession
• Independent Variable(s): Age, gender, autonomy

Descriptive research : Projects which only gather data which can be described, not inferred. That is, results and data collected cannot be inferred back to the population nor can comparisons or associations be made. Many qualitative studies are considered descriptive. This is done by considering only one variable at a time and there is no independent variable.

Example of Descriptive Research

You would like to describe the development of a protocol to implement high flow nasal cannula as an intermediate therapy for acute respiratory failure to be used in the Emergency Department at your institution. You plan to compare rates of intubation before and after implementation of the protocol.

• You are DESCRIBING a process
• You may collect and compare data using descriptive statistics

It is important to note that it is possible to have more than one approach in one research project. This is because the approach selected is specific to the question that has been asked. If there is more than one question asked, it is reasonable to assume that more than one approach may be used.

There are a few areas of research that although fit under the category of non-experimental; do not quite fit within the classifications presented here. Two of these areas are quality improvement (QI) projects and protocol development.

Quality improvement (QI) projects: The purpose of a QI project is to evaluate the performance of systems, processes, or practices to determine whether either function or operational improvements are needed. Using tools such as the SQUIRE explanation and elaboration guidelines , is extremely helpful in developing, conducting, and analyzing a thorough and impactful QI project.

The SQUIRE guidelines focus on the following four questions:

• Why did you start?
• What did you do?
• What did you find?
• What does it mean?

These four questions are then expanded upon to help develop the systematic approach to your inquiry and presentation of your findings. An extended investigation of this method is covered in Chapter 6.

Protocol Development

Before we dig too deep into the development of protocols, a clarification needs to be made regarding vocabulary relating to projects of this nature. Although frequently used interchangeably, the terms protocol and guideline are not synonymous. A protocol is described as an official procedure or system of rules governing a process. A guideline is a suggested course of action, policy, or conduct. In healthcare, this is an important distinction; a protocol is a course of action to which treatment must follow without deviation whereas a guideline, although firmly rooted in evidence, allows for deviation based on best judgment of a clinician or presentation of a specific case. Through a research lens, this distinction is important because the process by which these two objectives are realized are very different. The complete process for the development of guidelines which are generalizable beyond a specific situation is best outlined by the World Health Organization Handbook

The development of both guidelines often involves a team of people who are charged with first evaluating the existing evidence and then contributing an interpretation of that evidence toward the consensus of best practice. This is why guidelines are typically issued by federal or state agencies or professional organization. Protocols are generally less generalizable due to contextual constraints. However, even organizational protocols are not developed by a single individual. This does not mean, however, that you cannot begin the process of developing a guideline or protocol for your organization on you own; rather, it is important to frame the work you contribute as the foundation upon which a group can work toward the consensus of best practice. Typically, this initial work is referred to as a narrative review. A narrative review can be described as a broad perspective on a topic which may or may not be impacted by bias. This type of review differs from a systematic review in that it is understood that a narrative review may not encompass all relevant literature on a relevant topic as might a systematic review. Another note; the development of both guidelines and protocols is often an iterative process requiring several cycles of evaluation and revision.  A systematic review is described as exhaustive review of the literature relevant to a specific topic. In addition to being exhaustive, a systematic review includes methodology which is both explicit and reproducible to select, evaluate, and synthesize ALL available evidence. A meta-analysis is a systematic approach to evaluating the data from independent studies of the same subject to evaluate overall trends. Often, a meta-analysis is part of a systematic review.

Selecting your approach

As we’ve discussed, there are several factors which will guide your approach selection. Emphasis should be placed on the development of your purpose and problem statements as well as your research question. Ambiguity in these areas may cause some confusion as you begin to consider what approach you will take to answer your question.  Here we will work to narrow the scope of your approach using a systematic process and answering a few specific questions:

Step 1: Outlining your general purpose

Understanding the overarching goal of your study will help direct the rest of your approach. Here, you will ask yourself “What am I trying to do?”.

Step 2: Identifying your general approach

Earlier we discussed the difference between experimental and non-experimental approaches. As we mentioned, these are two broad categories of approaches. Your general purpose will determine which of these two general approaches you take. The determination here will point you toward a more focused, or specific, approach.

• Experimental: Experimental research is typically performed in a controlled environment so that the researcher can manipulate an independent variable and measure the outcome (dependent variable) between a group of subjects who received the manipulated variable (intervention) and a group of subjects who did not receive the intervention. A true experimental approach means that you have random selection or assignment of participants. All other elements aside, if you do NOT have randomization incorporated into your approach, your approach becomes quasi-experimental.
• Non-experimental: Nonexperimental research is an extremely broad category of approaches. Therefore, the simplest way to explain non-experimental research is to simply state that this approach lacks the manipulation of an independent variable. That is, you are not imposing an intervention on one group and comparing the outcome with a control group. Rather, you may have attribute independent variables which influence, or impact, the dependent variable, but the purpose of the research is not the direct manipulation of that variable. There are several different types of non-experimental research approaches, as we will soon see; however, it is important to understand that descriptive research is always classified as nonexperimental.

Step 3: Narrowing down your specific purpose

Now that you’ve decided what the general purpose and approach, you can begin to really narrow down the ‘how’ of your research. I find that this is best done by again asking yourself what you are really trying to do. Now that you understand the boundaries of your purpose and approach, you can work to understand the fine points about what types of interactions between variables you’re looking to explore and determine.

Step 4 : Selecting your specific approach

As you can see, there are specific words you should pay attention to when you’re describing your purpose. Given these key words, like ‘determine causality’, or ‘compare groups’, you’ll have a bit more direction as to what approach is most appropriate to identify the best answer to your question. Once we know what it is we really want to do with the information we’re planning to gather (variables), we can select an approach. Selecting your specific approach

Key Takeaways

There are several important concepts presented in this chapter:

• The theoretical/conceptual framework is the frame, or lens, that YOU build for your reader. It is the perspective through which you would like them to view your work.
• Constructs represent abstract theory
• Variables are the concrete measures of constructs
• There are several different types of variables; however, understanding the relationship between the independent variable (impact variable) and the dependent variable (outcome variable) is extremely important
• Attributes are levels within variables
• Attributes and variables must be classified in terms of measurement: Nominal, ordinal, interval, and ratio variables each represent different information and must be assessed correctly to have meaning
• Sampling is very important because whether your sample represents the larger population is an important factor in how your research is presented and interpreted
• There are A LOT of different approaches to research. Systematically approaching the selection of your approach by first defining your problem and purpose statements and your research question will be helpful as you narrow your focus on the which approach best captures the interaction between or among variables

Crawford, L.M., Burkholder, G.J., Cox, K.A. (2020). Writing the Research Proposal. In G.J. Burkholder, K.A Cox, L.M. Crawford, and J.H. Hitchcock (Eds.), Research design and methods: An applied guide for the scholar-practitioner (pp. 309-334). Sage Publications

Gliner, J.A., Morgan, G.A., & Leech, N.L. (2017). Research methods in applied settings: An integrated approach to design and analysis. Routledge

• This section can be attributed to Bhattacherjee, A. (2012) published under Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License ↵

Defining a construct through your lens as a researcher. How you are choosing to describe the boundaries associated with your work

A measurable representation of an abstract construct

A variable that can explain another variable. A variable which may be manipulated (active) or describes (attribute) to affect an outcome

The variable which is measured as an outcome and is affected by the independent variable(s)

Variables that are explained by both the independent and dependent variables

Influence the relationship between the independent and dependent variable

A variable which has an impact on the dependent variable, but does not explain the outcome (dependent variable)

values within each variable.

The assignment of a number to an attribute to describe a variable

Variable with two or more layers but without a specific order

A variable which has a specific rank order but no specificity between the ranks

Rank ordered variable with specified intervals between ranks

Has a true zero within the scale against which it is measured

A tool, or measure, used to quantify material that may be difficult to do so otherwise

Nominal scale with two potential outcomes

Used to measure ordinal data with a ranking system

Method of selecting a subset of the population to study.

Method of sampling wherein potential for sampling is equally likely for the entire population

Method of sampling where in the likelihood of being selected into a sample is not equal across the population

Visual representation of how a sample falls around a mean

A proposed explanation for the observed phenomenon

A form of experimental study design were participants are randomly assigned to either an intervention or control group

A form of experimental design involving both intervention and control groups but lacks randomization

Groups of participants are compared to identify differences in outcome

Two or more variables for the SAME group of participants are explored for relationships

Research projects wherein data gathered and described, but no relationships are inferred

A subset of nonexperimental research wherein the performance of systems, processes, or practices are evaluated for either efficiency or effectiveness

An official procedure or system

A suggested course of action

A broad perspective on a topic, typically from the perspective of a single author

An exhaustive review of literature relevant to a specific topic; typically performed by a group of people

Systematic approach to evaluating data from independent studies on a topic to evaluate or identify trends

Research performed in a controlled environment in which a researcher can manipulate an independent variable and measure a dependent variable (outcome)

Broad category of research approaches which lack the manipulation of an independent variable

Practical Research: A Basic Guide to Planning, Doing, and Writing Copyright © by megankoster. All Rights Reserved.

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Literature Review How To

• Things To Consider
• Synthesizing Sources
• Video Tutorials
• Books On Literature Reviews

Consider This

• What is the specific thesis, problem, or research question that my literature review aims to define?
• What type of literature review am I conducting? Am I looking at issues of theory, methodology, policy, quantitative research (e.g. interviews, observations), qualitative research (e.g., studies, surveys, statistics)?
• What is the scope of my literature review? What types of publications am I using (e.g., journals, books, government documents)?
• What discipline am I working in (e.g. Public Health, Nursing, Kinesiology etc.)?
• Has my search for sources been wide enough to ensure that I have found all the relevant material?
• Has it been narrow enough to exclude irrelevant material?
• Is the number of sources I've used appropriate for the length of my paper (i.e. if your literature review is part of a larger paper or assignment)?
• Have I critically analyzed the resources I found?
• How will I avoid just listing and summarizing resources? Do I assess them, discussing strengths and weaknesses?
• Have I cited and discussed studies contrary to my perspective?
• Will the reader find my literature review relevant, appropriate, and or useful?

FIRST, ASK YOUR PROFESSOR!

The format of a literature review may vary from discipline to discipline and from assignment to assignment. However, a literature review must do these things:

• Be organized around and related directly to the thesis or research question you are developing
• Synthesize results into a summary of what is and is not known
• Identify problematic areas or areas of controversy in the literature
• Formulate questions or issues that need further research

Remember! A literature review is not a list describing or summarizing one piece of literature after another.

Try and avoid starting every paragraph with the name of a researcher or the title of the work. Rather, try organizing the literature review into sections that present themes or identify trends, including relevant theories. You are not trying to list all the material published on a topic, but to synthesize and evaluate it according to the guiding concept of your thesis or research question.

Consider These For Each Source

• Has the author formulated a problem/issue?
• Is the problem/issue clearly defined and is its significance (scope, severity, and relevance) clearly established?
• Could the problem/issue have been approached more effectively and or from another perspective?
• What is the author's research orientation (e.g., interpretive, critical science, combination)?
• What is the author's theoretical framework (e.g., psychological, developmental, feminist)?
• What is the relationship between #4 and #5?
• Has the author evaluated the literature relevant to the topic (i.e. does the author include a literature review and or provide sources that take positions she/he does not agree with)?
• How accurate and valid are the measurements, statistics or data the author has provided?
• Is the analysis of the measurements, statistics or data accurate and relevant to the research question?
• Are the conclusions validly based upon the data and analysis?
• · How does the author structure the argument? Can you retrace the steps he/she takes and analyze the flow of the argument to see if it progresses logically?
• In what ways does this piece contribute to our understanding of the topic, and in what ways is it useful? What are the strengths and limitations?
• How does this book or article relate to my thesis or research question?
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• Last Updated: Nov 30, 2018 4:51 PM
• URL: https://libguides.csun.edu/literature-review

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

Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.

• Restate the problem statement addressed in the paper
• Summarize your overall arguments or findings
• Suggest the key takeaways from your paper

The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .

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Table of contents

Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.

The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.

While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.

For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:

Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:

“In conclusion …”

Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.

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Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.

Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.

Argumentative paper: Restate your thesis and arguments

In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.

Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.

Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.

Empirical paper: Summarize your findings

In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.

Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.

Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.

Argumentative paper: Strong closing statement

An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?

If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.

Empirical paper: Future research directions

In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.

Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .

Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.

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

• Argumentative paper
• Empirical paper

While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.

As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.

If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.

• Do they understand from your conclusion what your research was about?
• Are they able to summarize the implications of your findings?
• Can they answer your research question based on your conclusion?

You can also get an expert to proofread and feedback your paper with a paper editing service .

Prevent plagiarism. Run a free check.

The conclusion of a research paper has several key elements you should make sure to include:

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

No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers 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 results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.

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