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

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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:

Difference between research recommendation and implication

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:

Characteristics of research recommendation

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.

Advantages and limitations of a research recommendation

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


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.


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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The Ultimate Guide to Crafting Impactful Recommendations in Research

Harish M

Are you ready to take your research to the next level? Crafting impactful recommendations is the key to unlocking the full potential of your study. By providing clear, actionable suggestions based on your findings, you can bridge the gap between research and real-world application.

In this ultimate guide, we'll show you how to write recommendations that make a difference in your research report or paper.

You'll learn how to craft specific, actionable recommendations that connect seamlessly with your research findings. Whether you're a student, writer, teacher, or journalist, this guide will help you master the art of writing recommendations in research. Let's get started and make your research count!

Understanding the Purpose of Recommendations

Recommendations in research serve as a vital bridge between your findings and their real-world applications. They provide specific, action-oriented suggestions to guide future studies and decision-making processes. Let's dive into the key purposes of crafting effective recommendations:

Guiding Future Research

Research recommendations play a crucial role in steering scholars and researchers towards promising avenues of exploration. By highlighting gaps in current knowledge and proposing new research questions, recommendations help advance the field and drive innovation.

Influencing Decision-Making

Well-crafted recommendations have the power to shape policies, programs, and strategies across various domains, such as:

  • Policy-making
  • Product development
  • Marketing strategies
  • Medical practice

By providing clear, evidence-based suggestions, recommendations facilitate informed decision-making and improve outcomes.

Connecting Research to Practice

Recommendations act as a conduit for transferring knowledge from researchers to practitioners, policymakers, and stakeholders. They bridge the gap between academic findings and their practical applications, ensuring that research insights are effectively translated into real-world solutions.

Enhancing Research Impact

By crafting impactful recommendations, you can amplify the reach and influence of your research, attracting attention from peers, funding agencies, and decision-makers.

Addressing Limitations

Recommendations provide an opportunity to acknowledge and address the limitations of your study. By suggesting concrete and actionable possibilities for future research, you demonstrate a thorough understanding of your work's scope and potential areas for improvement.

Identifying Areas for Future Research

Discovering research gaps is a crucial step in crafting impactful recommendations. It involves reviewing existing studies and identifying unanswered questions or problems that warrant further investigation. Here are some strategies to help you identify areas for future research:

Explore Research Limitations

Take a close look at the limitations section of relevant studies. These limitations often provide valuable insights into potential areas for future research. Consider how addressing these limitations could enhance our understanding of the topic at hand.

Critically Analyze Discussion and Future Research Sections

When reading articles, pay special attention to the discussion and future research sections. These sections often highlight gaps in the current knowledge base and propose avenues for further exploration. Take note of any recurring themes or unanswered questions that emerge across multiple studies.

Utilize Targeted Search Terms

To streamline your search for research gaps, use targeted search terms such as "literature gap" or "future research" in combination with your subject keywords. This approach can help you quickly identify articles that explicitly discuss areas for future investigation.

Seek Guidance from Experts

Don't hesitate to reach out to your research advisor or other experts in your field. Their wealth of knowledge and experience can provide valuable insights into potential research gaps and emerging trends.

By employing these strategies, you'll be well-equipped to identify research gaps and craft recommendations that push the boundaries of current knowledge. Remember, the goal is to refine your research questions and focus your efforts on areas where more understanding is needed.

Structuring Your Recommendations

When it comes to structuring your recommendations, it's essential to keep them concise, organized, and tailored to your audience. Here are some key tips to help you craft impactful recommendations:

Prioritize and Organize

  • Limit your recommendations to the most relevant and targeted suggestions for your peers or colleagues in the field.
  • Place your recommendations at the end of the report, as they are often top of mind for readers.
  • Write your recommendations in order of priority, with the most important ones for decision-makers coming first.

Use a Clear and Actionable Format

  • Write recommendations in a clear, concise manner using actionable words derived from the data analyzed in your research.
  • Use bullet points instead of long paragraphs for clarity and readability.
  • Ensure that your recommendations are specific, measurable, attainable, relevant, and timely (SMART).

Connect Recommendations to Research

By following this simple formula, you can ensure that your recommendations are directly connected to your research and supported by a clear rationale.

Tailor to Your Audience

  • Consider the needs and interests of your target audience when crafting your recommendations.
  • Explain how your recommendations can solve the issues explored in your research.
  • Acknowledge any limitations or constraints of your study that may impact the implementation of your recommendations.

Avoid Common Pitfalls

  • Don't undermine your own work by suggesting incomplete or unnecessary recommendations.
  • Avoid using recommendations as a place for self-criticism or introducing new information not covered in your research.
  • Ensure that your recommendations are achievable and comprehensive, offering practical solutions for the issues considered in your paper.

By structuring your recommendations effectively, you can enhance the reliability and validity of your research findings, provide valuable strategies and suggestions for future research, and deliver impactful solutions to real-world problems.

Crafting Actionable and Specific Recommendations

Crafting actionable and specific recommendations is the key to ensuring your research findings have a real-world impact. Here are some essential tips to keep in mind:

Embrace Flexibility and Feasibility

Your recommendations should be open to discussion and new information, rather than being set in stone. Consider the following:

  • Be realistic and considerate of your team's capabilities when making recommendations.
  • Prioritize recommendations based on impact and reach, but be prepared to adjust based on team effort levels.
  • Focus on solutions that require the fewest changes first, adopting an MVP (Minimum Viable Product) approach.

Provide Detailed and Justified Recommendations

To avoid vagueness and misinterpretation, ensure your recommendations are:

  • Detailed, including photos, videos, or screenshots whenever possible.
  • Justified based on research findings, providing alternatives when findings don't align with expectations or business goals.

Use this formula when writing recommendations:

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

Adopt a Solution-Oriented Approach

Foster collaboration and participation.

  • Promote staff education on current research and create strategies to encourage adoption of promising clinical protocols.
  • Include representatives from the treatment community in the development of the research initiative and the review of proposals.
  • Require active, early, and permanent participation of treatment staff in the development, implementation, and interpretation of the study.

Tailor Recommendations to the Opportunity

When writing recommendations for a specific opportunity or program:

  • Highlight the strengths and qualifications of the researcher.
  • Provide specific examples of their work and accomplishments.
  • Explain how their research has contributed to the field.
  • Emphasize the researcher's potential for future success and their unique contributions.

By following these guidelines, you'll craft actionable and specific recommendations that drive meaningful change and showcase the value of your research.

Connecting Recommendations with Research Findings

Connecting your recommendations with research findings is crucial for ensuring the credibility and impact of your suggestions. Here's how you can seamlessly link your recommendations to the evidence uncovered in your study:

Grounding Recommendations in Research

Your recommendations should be firmly rooted in the data and insights gathered during your research process. Avoid including measures or suggestions that were not discussed or supported by your study findings. This approach ensures that your recommendations are evidence-based and directly relevant to the research at hand.

Highlighting the Significance of Collaboration

Research collaborations offer a wealth of benefits that can enhance an agency's competitive position. Consider the following factors when discussing the importance of collaboration in your recommendations:

  • Organizational Development: Participation in research collaborations depends on an agency's stage of development, compatibility with its mission and culture, and financial stability.
  • Trust-Building: Long-term collaboration success often hinges on a history of increasing involvement and trust between partners.
  • Infrastructure: A permanent infrastructure that facilitates long-term development is key to successful collaborative programs.

Emphasizing Commitment and Participation

Fostering quality improvement and organizational learning.

In your recommendations, highlight the importance of enhancing quality improvement strategies and fostering organizational learning. Show sensitivity to the needs and constraints of community-based programs, as this understanding is crucial for effective collaboration and implementation.

Addressing Limitations and Implications

If not already addressed in the discussion section, your recommendations should mention the limitations of the study and their implications. Examples of limitations include:

  • Sample size or composition
  • Participant attrition
  • Study duration

By acknowledging these limitations, you demonstrate a comprehensive understanding of your research and its potential impact.

By connecting your recommendations with research findings, you provide a solid foundation for your suggestions, emphasize the significance of collaboration, and showcase the potential for future research and practical applications.

Crafting impactful recommendations is a vital skill for any researcher looking to bridge the gap between their findings and real-world applications. By understanding the purpose of recommendations, identifying areas for future research, structuring your suggestions effectively, and connecting them to your research findings, you can unlock the full potential of your study. Remember to prioritize actionable, specific, and evidence-based recommendations that foster collaboration and drive meaningful change.

As you embark on your research journey, embrace the power of well-crafted recommendations to amplify the impact of your work. By following the guidelines outlined in this ultimate guide, you'll be well-equipped to write recommendations that resonate with your audience, inspire further investigation, and contribute to the advancement of your field. So go forth, make your research count, and let your recommendations be the catalyst for positive change.

Q: What are the steps to formulating recommendations in research? A: To formulate recommendations in research, you should first gain a thorough understanding of the research question. Review the existing literature to inform your recommendations and consider the research methods that were used. Identify which data collection techniques were employed and propose suitable data analysis methods. It's also essential to consider any limitations and ethical considerations of your research. Justify your recommendations clearly and finally, provide a summary of your recommendations.

Q: Why are recommendations significant in research studies? A: Recommendations play a crucial role in research as they form a key part of the analysis phase. They provide specific suggestions for interventions or strategies that address the problems and limitations discovered during the study. Recommendations are a direct response to the main findings derived from data collection and analysis, and they can guide future actions or research.

Q: Can you outline the seven steps involved in writing a research paper? A: Certainly. The seven steps to writing an excellent research paper include:

  • Allowing yourself sufficient time to complete the paper.
  • Defining the scope of your essay and crafting a clear thesis statement.
  • Conducting a thorough yet focused search for relevant research materials.
  • Reading the research materials carefully and taking detailed notes.
  • Writing your paper based on the information you've gathered and analyzed.
  • Editing your paper to ensure clarity, coherence, and correctness.
  • Submitting your paper following the guidelines provided.

Q: What tips can help make a research paper more effective? A: To enhance the effectiveness of a research paper, plan for the extensive process ahead and understand your audience. Decide on the structure your research writing will take and describe your methodology clearly. Write in a straightforward and clear manner, avoiding the use of clichés or overly complex language.

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Open Access

Ten Simple Rules for Writing Research Papers

* E-mail: [email protected]

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

  • Weixiong Zhang


Published: January 30, 2014

  • https://doi.org/10.1371/journal.pcbi.1003453
  • Reader Comments

Citation: Zhang W (2014) Ten Simple Rules for Writing Research Papers. PLoS Comput Biol 10(1): e1003453. https://doi.org/10.1371/journal.pcbi.1003453

Editor: Philip E. Bourne, University of California San Diego, United States of America

Copyright: © 2014 Weixiong Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License ., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: The author received no specific funding for this article.

Competing interests: The author has declared that no competing interests exist.

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.


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

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  • 10. Wikiquote page on Leonardo Da Vinci. Available: http://en.wikiquote.org/wiki/Leonardo_da_Vinci#Quotes_about_Leonardo . Accessed 13 December 2013.

The guidelines manual

NICE process and methods [PMG6] Published: 30 November 2012

  • Tools and resources
  • 1 Introduction
  • 2 The scope
  • 3 The Guideline Development Group
  • 4 Developing review questions and planning the systematic review
  • 5 Identifying the evidence: literature searching and evidence submission
  • 6 Reviewing the evidence
  • 7 Assessing cost effectiveness
  • 8 Linking clinical guidelines to other NICE guidance

9 Developing and wording guideline recommendations

  • 10 Writing the clinical guideline and the role of the NICE editors
  • 11 The consultation process and dealing with stakeholder comments
  • 12 Finalising and publishing the guideline
  • 13 Implementation support for clinical guidelines
  • 14 Updating published clinical guidelines and correcting errors
  • Summary of main changes from the 2009 guidelines manual
  • Update information
  • About this manual

NICE process and methods

9.1 interpreting the evidence to make recommendations.

  • 9.2 'Only in research' recommendations

9.3 Wording the guideline recommendations

9.4 prioritising recommendations, 9.5 formulating research recommendations, 9.6 further reading.

Many users of clinical guidelines do not have time to read the full document, and may want to focus only on the recommendations. It is therefore vital that recommendations are clear, can be understood by people who have not read the full guideline, and are based on the best available evidence of clinical and cost effectiveness. This chapter addresses key areas in developing guideline recommendations:

interpreting the evidence to make recommendations

wording the recommendations

prioritising recommendations for implementation

formulating research recommendations.

These processes are at the heart of the work of the Guideline Development Group (GDG). However, they are not straightforward and it may not be easy for the GDG to reach agreement. Consensus techniques may need to be used within the GDG (see section 3.5 ).

The GDG must decide what the evidence means in the context of the review questions and economic questions posed, and decide what recommendations can usefully be made to healthcare and other professionals.

In the full guideline, the aim should be to show clearly how the GDG moved from the evidence to the recommendation. This is done in a section called 'evidence to recommendations' so that it can be easily identified. This section may also be a useful way to integrate the findings from several evidence reviews that are related to the same recommendation(s).

Underpinning this section is the concept of the 'strength' of a recommendation (Schünemann et al. 2003). This takes into account the quality of the evidence but is conceptually different. Some recommendations are 'strong' in that the GDG believes that the vast majority of healthcare and other professionals and patients would choose a particular intervention if they considered the evidence in the same way that the GDG has. This is generally the case if the benefits clearly outweigh the harms for most people and the intervention is likely to be cost effective. However, there is often a closer balance between benefits and harms, and some patients would not choose an intervention whereas others would. This may happen, for example, if some patients are particularly averse to some side effect and others are not. In these circumstances the recommendation is generally weaker, although it may be possible to make stronger recommendations about specific groups of patients.

For all recommendations, a general principle of NICE clinical guidelines is that patients should be informed of their choices and be involved in decisions about their care. Patients may choose not to accept the advice to have the most cost-effective intervention, or they may opt for a treatment that has the same or lower long-term health and personal social service costs if, for example, they feel that its side effects are more tolerable. There might be little evidence of differences in cost effectiveness between drugs within a class, and the clinician and patient might choose between these drugs on the basis of side-effect profile. However, it is not usually possible to offer patients interventions that are above NICE's threshold for cost effectiveness (see section 7.3 ) because the opportunity cost of that course of action has been judged to be too great (see section 7.1.1 ).

The GRADE system (see section ) allocates labels or symbols to represent the strength of a recommendation. NICE has chosen not to do this, but instead to reflect the concept of strength in the wording of the recommendation (see section 9.3.3 ). The GDG's view of the strength of a recommendation should be clear from its discussions, as reported in the full guideline.

The following points will need to be covered in the discussions and can also be used as a framework for reporting those discussions.

9.1.1 Relative value placed on the outcomes considered

Often more outcome data are available than are actually used in decision-making. It is therefore important to have explicit discussion of which outcomes are considered important for decision-making (including consideration of the perspective of the decision-makers) when developing review protocols (see section 4.4 ), and of what relative importance was given to them. This might be done informally (for example, 'death was considered the most important outcome') or formally (for example, by the use of utility weights).

This discussion should be clearly separated from discussion of how this will play out when the evidence is reviewed, because there is a potential to introduce bias if outcomes are selected on the basis of the results. An example of this would be choosing only outcomes for which there were statistically significant results.

It may be important to note outcomes that were not considered to be important for decision-making, and why (such as surrogate outcomes if longer-term, more relevant outcomes are available). If the same set of outcomes is used for a number of review questions, it might be more efficient to record this information once and then refer back to it.

9.1.2 Trade-off between clinical benefits and harms

A key stage in moving from evidence to recommendations is weighing up the magnitude and importance of the benefits and harms of an intervention. This may be done qualitatively (for example, 'the evidence of a reduction in mortality outweighed a small increase in side effects'), or quantitatively using a decision model.

9.1.3 Trade-off between net health benefits and resource use

If there are net health benefits from an intervention, there should be an explanation of how the implications of resource use were considered in determining cost effectiveness. Again, this may be informal, or may be more formal and include the use of economic modelling. If there is no clear evidence of net health benefit, cost and resource use could be discussed here.

9.1.4 Quality of the evidence

There should be discussion of how the presence, likely magnitude and direction of potential biases and uncertainty in the clinical and economic evidence have influenced the recommendation, and why. This should reflect the judgement on the quality of the evidence as described in the GRADE profile and the NICE economic profile. Lower-quality evidence makes it more difficult to justify a strong recommendation in general, although there may be exceptions to this. For example, evidence on the frequency of adverse effects is often of low quality, but a strong recommendation might be made not to use a particular drug thought to have teratogenic effects in women of child-bearing potential.

The discussion of uncertainty may include consideration of whether the uncertainty is sufficient to justify delaying making a recommendation to await further research, taking into account the potential harm of failing to make a clear recommendation.

9.1.5 Other considerations

If the 'evidence to recommendations' section combines consideration of several possible interventions, it may include discussion of the position of an intervention within a pathway of care.

This is also the appropriate place to note how the GDG's responsibilities under equalities legislation and NICE's equality scheme have been discharged in reaching the recommendation(s). This covers inequalities related to age, disability, gender reassignment, marriage and civil partnership, race, religion or belief, sex and sexual orientation and socioeconomic status. The GDG will need to consider whether:

the evidence review has addressed areas identified in the scope as needing specific attention with regard to equalities issues

criteria for access to an intervention might be discriminatory, for example through membership of a particular group, or by using a test that might discriminate unlawfully

people with disabilities might find it impossible or unreasonably difficult to receive an intervention

guidance can be formulated so as to promote equalities, for example by making access more likely for certain groups, or by tailoring the intervention to specific groups.

Before the guideline is signed off, an equality impact assessment (EIA) form is completed by the National Collaborating Centre (NCC) or the NICE Internal Clinical Guidelines Programme [ 14 ] and the GDG to demonstrate how equality issues have been identified and considered during development. The EIA form is signed by the NCC Director and GDG Chair, and countersigned by the Centre for Clinical Practice (CCP) lead for the guideline, before being posted on the NICE website. Further guidance on how to complete the EIA form is outlined in the document Positively equal: a guide to addressing equality issues in developing NICE clinical guidelines .

It may be useful to briefly discuss the extent of change in practice that will be needed to implement a recommendation, and the possible need for carefully controlled implementation with, for example, training programmes or demonstration projects.

9.1.6 Challenges in formulating recommendations

There are many reasons why it can be difficult for a GDG to reach a decision about a recommendation. The evidence base is always imperfect, and so there is always a degree of judgement by the GDG. There may be very little, or no, good-quality evidence that directly addresses the review question the GDG has posed. In this situation, there are several options to consider:

The GDG may wish to look at evidence that is likely to be more at risk of bias than the evidence they had hoped to find. For example, if the GDG had set out to collect only randomised trials for a question of effectiveness, but found none, they might consider looking for good-quality non-randomised studies. However, there is a risk that considerable time and effort is spent finding and reviewing studies that are likely to be biased and so are hard to interpret. This approach should be pursued only if there is reason to believe that it will help the GDG to formulate a recommendation.

The GDG may wish to extrapolate from high-quality evidence in a related area, for example in a largely similar patient group or for a closely related intervention. The GDG will need to make its approach explicit, stating the basis it has used for extrapolating from the data and the assumptions that have been made. This will need to include consideration of the plausibility of the assumptions. This approach is unlikely to be helpful if the evidence is derived from a question that is too different from the review question, or if the evidence is not of the highest quality.

The GDG may consider basing a recommendation on its view of current most cost-effective practice. Formal consensus techniques may be used to elicit opinions from the GDG, although NICE does not recommend a particular approach. Importantly, it is not usually appropriate to involve stakeholders from outside the GDG in this process, as they will be offering opinions on recommendations without having seen the evidence considered by the GDG; in addition, stakeholders will not have agreed to adhere to the principles underlying NICE's decisions on recommendations. This approach would also allow some stakeholders input to the decision-making process that other stakeholders will not have. GDGs should therefore be particularly cautious about using and interpreting the results of such exercises involving stakeholders outside the GDG, and should discuss any proposed use with NICE. The final decision on whether such work with external stakeholders is warranted will be made by NICE.

When formulating recommendations, there are likely to be instances when members of the GDG disagree about the content of the final guideline. Formal consensus methods can be used for agreeing the final recommendations (see section 3.5 ). Whatever the approach used, there should be a clear record of the proceedings and how areas of disagreement have been handled. This may be summarised in the full guideline.

9.2 'Only in research' recommendations

If evidence of effectiveness is either lacking or too weak for reasonable conclusions to be reached, the GDG may recommend that particular interventions are used within the NHS only in the context of research. Factors that will be considered before issuing such recommendations include the following:

The intervention should have a reasonable prospect of providing benefits to patients in a cost-effective way.

The necessary research can realistically be set up or is already planned, or patients are already being recruited.

There is a real prospect that the research will inform future NICE guidance.

Writing the recommendations is one of the most important steps in developing a clinical guideline. Many people read only the recommendations, so the wording must be concise, unambiguous and easy to translate into clinical practice. Each recommendation, or bullet point within a recommendation, should contain only one main action.

The wording of recommendations should be agreed by the GDG, and should:

focus on the action that needs to be taken

include what readers need to know

reflect the strength of the recommendation

emphasise the involvement of the patient (and/or their carers if needed) in decisions on treatment and care

use plain English where possible and avoid vague language

follow NICE's standard advice on recommendations about drugs, waiting times and ineffective interventions.

The rest of this section explains these points in more detail. The lead editor for the guideline from NICE can advise on the wording of recommendations.

9.3.1 Focus on the action

Recommendations should begin with what needs to be done. When writing recommendations, keep in mind a reader who is saying, 'What does this mean for me?'. Recommendations should be as specific as possible about the exact intervention being recommended and the group of people for whom it is recommended (see also section 9.3.2).

Use direct instructions because they are clearer and easier to follow. Most recommendations should be worded in this way. Assume you are talking to the healthcare professional who is working with the patient at the time.

Record the person's blood pressure every 6 months.

Ask people in high-risk groups whether they have symptoms.

Carry out and record a focused baseline assessment for people with faecal incontinence to identify the contributory factors.

Recommendations about service organisation, or if the audience is not the healthcare professional. For example:'Care should be provided by a multidisciplinary team.'

Recommendations that a specific type of healthcare professional should carry out an intervention. For example: 'An occupational therapist should assess the patient's needs.'

Recommendations that use 'must' or 'must not' (see section ).

Start with a verb describing what the reader should do, such as 'offer', 'measure', 'advise', 'discuss', 'ask about' (see sections 9.3.3 and 9.3.4 for advice on the choice of verb).

Advise pregnant women to limit their intake of oily fish to two portions a week.

Perform surgery within 48 hours of symptom onset.

Offer relaxation techniques for managing pain, sleep problems and comorbid stress or anxiety.

Sometimes it is clearer to start with details of the patient group or other details, particularly if recommending different actions for slightly different circumstances or to make the sentence structure simpler. For example: 'If surgery is an option, refer the patient to a specialist surgeon to discuss the risks and benefits.'

9.3.2 Include what readers need to know

Recommendations should contain enough information to be understood without reference to the evidence or other supporting material. But do not add unnecessary details, because recommendations are more likely to be followed if they are clear and concise.

Define any specialised terminology that is used in the recommendations. Avoid using abbreviations unless your audience is likely to be more familiar with the abbreviation than with the term in full. If abbreviations are essential, define them at first mention and in a glossary.

Define the target population if it is not obvious from the context. Often it is necessary to define the population only in the first of a group of recommendations, if it is clear that the subsequent recommendations in that section relate to the same population.

Include cross-references to other recommendations in the guideline if necessary to avoid the need to repeat information such as treatment regimens.

Do not include reasons justifying the recommendation unless this will increase the likelihood that it will be followed – for example, if it involves a change in usual practice or needs particular emphasis.

Include only one main action in each recommendation or bullet point.

9.3.3 Reflect the strength of the recommendation

The description of the process of moving from evidence to recommendations in section 9.1 indicates that some recommendations can be made with more certainty than others. This concept of the 'strength' of a recommendation should be reflected in the consistent wording of recommendations within and across clinical guidelines. There are three levels of certainty:

recommendations for interventions that must (or must not) be used

recommendations for interventions that should (or should not) be used

recommendations for interventions that could be used.

The NICE guideline includes a standard section about how wording reflects the strength of recommendations. Recommendations for interventions that must or must not be used

Recommendations that an intervention must or must not be used are usually included only if there is a legal duty to apply the recommendation, for example to comply with health and safety regulations. In these instances, give a reference to supporting documents. These recommendations apply to all patients.

However, occasionally the consequences of not following a recommendation are so serious (for example, there is a high risk that the patient could die) that using 'must' (or 'must not') is justified. Discuss this with the Guidelines Commissioning Manager at NICE, and explain in the recommendation the reason for the use of 'must'.

If using 'must', word the recommendation in the passive voice ('an intervention must be used') because the distinction between 'should' and 'must' is lost when the recommendation is turned into a direct instruction.

Ultra-rapid detoxification under general anaesthesia or heavy sedation (where the airway needs to be supported) must not be used. This is because of the risk of serious adverse events, including death.

Gloves used for direct patient care:

must conform to current EU legislation (CE marked as medical gloves for single use) and

should be appropriate for the task. Recommendations for interventions that should or should not be used – 'strong' recommendations

For recommendations on interventions that 'should' be used, the GDG is confident that, for the vast majority of people, the intervention (or interventions) will do more good than harm, and will be cost effective.

Use direct instructions for recommendations of this type where possible (see section 9.3.1), rather than using the word 'should'. Use verbs such as 'offer', 'refer', 'advise' and 'discuss'.

Offer bariatric surgery as a first-line option (instead of lifestyle interventions or drug treatment) to adults with a BMI of more than 50 kg/m 2 .

Use similar forms of words (for example, 'Do not offer…') for recommendations on interventions that should not be used because the GDG is confident that they will not be of sufficient benefit for most patients.

Do not offer antibiotic prophylaxis against infective endocarditis to people at risk undergoing dental procedures.

If an intervention is strongly recommended but there are two or more options with similar cost effectiveness, and the choice will depend on the patient's values and preferences, a 'should' recommendation can be:

combined with a 'could' recommendation (see section, for example by using wording such as 'Offer a choice of drug A or drug B' or

followed by a 'could' recommendation, for example 'Offer drug treatment. Consider drug A or drug B.' Recommendations for interventions that could be used

For recommendations on interventions that 'could' be used, the GDG is confident that the intervention will do more good than harm for most patients, and will be cost effective. However, other options may be similarly cost effective, or some patients may opt for a less effective but cheaper intervention. The choice of intervention, and whether to have the intervention at all, is therefore more likely to vary depending on a person's values and preferences, and so the healthcare professional should spend more time considering and discussing the options with the patient. It may be possible to make 'strong' recommendations for subgroups of people with different values and preferences. NICE's report Social value judgements: principles for the development of NICE guidance (2nd edition; 2008) states the following:

Use direct instructions for recommendations of this type where possible (see section 9.3.1), rather than using the word 'could'.

Use 'consider' to indicate that the recommendation is less strong than a 'should' recommendation.

Consider combination chemotherapy to treat patients with advanced breast cancer for whom a greater probability of response is important and who understand and are likely to tolerate the additional toxicity.

Consider carbamazepine and oxcarbazepine but be aware of the risk of exacerbating myoclonic or absence seizures.

Do not use 'consider offering', because of potential confusion with the wording of strong recommendations. Also, it might be misinterpreted to mean that a healthcare professional may consider offering an intervention without discussing it with the patient.

To minimise confusion, only use 'consider' to indicate the strength of a recommendation. Avoid other possible uses of 'consider'. For example, if a particular clinical sign or symptom should make a healthcare professional think about a diagnosis, use 'be aware of the possible diagnosis…', 'explore a diagnosis of…' or similar, rather than 'consider a diagnosis of'. Use 'take other factors into account' or similar, instead of 'consider other factors'. 'Assess' and 'think about' are other possible alternatives to 'consider'.

9.3.4 Emphasise the patient's involvement

To emphasise the patient's role in decision-making and the need for them to consent to treatment, generally use verbs such as 'offer', 'consider' and 'discuss' in recommendations, rather than 'prescribe' or 'give'. As described above, 'consider' is used for recommendations on interventions that could be used, and implies that more discussion will be needed.

Use 'people' or 'patients' rather than 'individuals', 'cases' or 'subjects'. Where possible, use 'people' rather than 'patients' for people with mental health problems or chronic conditions. 'Service users' can be used for people with mental health problems if 'patients' is the only alternative. Do not use 'patients' in relation to healthy pregnant women. Recommendations about patient-centred care

The NICE guideline includes a standard section on patient-centred care that covers informed consent and taking into account the patient's individual needs. This section also cross-refers to NICE guidance on patient experience in adult NHS services , which covers subjects such as treating the person as an individual, communication, information and shared decision-making. NICE has also produced guidance on service user experience in adult mental health , which is cross-referred to in guidelines on mental health. The patient experience and service user experience guidance can be cross-referred to in recommendations, but specific recommendations should not be made on issues covered in that guidance unless there are particular reasons to do so that relate to the guideline topic. Examples include:

if there are issues relating to provision of information to patients, or to patients' support needs, that are specific to the condition covered by the guideline

if certain drugs are prescribed 'off-label' (see section ) and more detailed forms of consent than usual are required from patients.

9.3.5 Use plain English

In general, follow the principles of effective writing as described in the 'Writing for NICE' booklet, which is available on the NICE webboard for NCCs.

Avoid vague words and phrases, such as 'may' and 'can', or general statements such as 'is recommended', 'is useful/helpful', 'is needed' and 'treatment options include'. Instead, use an active verb that tells readers what they should do, and indicates the strength of the recommendation.

Instead of 'an intervention may be offered', say 'consider the intervention'.

Instead of 'an intervention is recommended', say 'offer the intervention'.

Instead of 'an intervention is helpful', say 'offer the intervention' or 'consider the intervention' (see section 9.3.3).

'Appropriate' is often redundant: for example 'give appropriate advice', because we would never recommend giving inappropriate advice.

9.3.6 Recommendations on drugs, including off-label use

Guideline developers should follow NICE's standard procedure when referring to drugs. This includes using standard wording when off-label use of drugs is recommended. Use generic names

Give the recommended international non-proprietary name (rINN), as listed in the British national formulary (BNF). Usually, only the generic name is needed. Occasionally (for example, if referring to a specific preparation or device), the proprietary name may be given in parentheses at first mention. Do not give the manufacturer's name. Do not give dosages

Readers are expected to refer to the summary of product characteristics (SPC) for details of dosages. Include dosage information only if there is evidence that a particular drug is often prescribed at the wrong dosage, or there is clear evidence about the effectiveness of different dose levels. If off-label use is being recommended and there is no relevant dosage information in the BNF, include details of the dosage regimen in the full guideline. SPCs can be found in the Electronic Medicines Compendium . Off-label use

Make it clear if the recommended use is outside the drug's licensed indication ('off label').

Recommendations are usually about the uses of drugs (often referred to as the licensed indications) for which the drug regulatory authority has granted a marketing authorisation, either in the UK or under the European centralised authorisation procedure. However, there are clinical situations when the use of a drug off-label may be judged by the prescriber to be in the best clinical interests of the patient. Off-label use may be recommended if the clinical need cannot be met by a licensed product and there is a sufficient evidence base and/or experience of using the drug to demonstrate its safety and efficacy to support this. Off-label prescribing is particularly common in pregnant women and in children and young people (see below), as these groups have often been excluded from clinical trials during drug development. When prescribing a drug off-label, the prescriber should follow relevant professional guidance (for example, the General Medical Council's Good practice in prescribing medicines – guidance for doctors ) and make a clinical judgement, taking full responsibility for the decision for the patient under his or her direct care. In addition, the patient (or those with authority to give consent on their behalf) should be made fully aware of these factors and provide informed consent, which should be documented by the prescriber.

A licensed drug is accompanied by an SPC, which describes the indications, cautions and contraindications for a drug based on an assessment of safety, quality and efficacy by the regulatory authority. The NCC and GDG should check recommended uses against the licensed indications listed in the SPC, and include a footnote if the drug does not have a UK marketing authorisation for the use being recommended. The footnote should make it clear that the drug is not licensed for the stated use.

This standard wording for the footnote captures the above points:

At the time of publication ([month year]), [name of drug] did not have a UK marketing authorisation for this indication. The prescriber should follow relevant professional guidance, taking full responsibility for the decision. Informed consent should be obtained and documented. See the General Medical Council's Good practice in prescribing medicines – guidance for doctors for further information.

Additional information can be added as needed – for example, if off-label use is recommended and the drug is commonly used in UK clinical practice, a phrase such as 'Although this use is common in UK clinical practice' can be added. Other examples of footnote wording are shown in box 9.1. In cases where the SPC for a drug specifically mentions a caution or contraindication for its use but the GDG wishes to recommend the drug, this should be stated clearly in the recommendation or footnote. The evidence that the GDG has considered in reaching the conclusion that use in these circumstances can be justified should be clearly set out in the full guideline.

If a guideline includes recommendations for off-label use of drugs, the introduction to the NICE version should include standard wording (as in the NICE guideline template) about the responsibilities of the prescriber and the need to follow relevant professional guidance (for example, the General Medical Council's Good practice in prescribing medicines – guidance for doctors ).

If there is no information on dosage regimens available in a recognised source (such as the BNF), the NCC should document dosage information in the full guideline and alert the NICE implementation team to ensure that this is disseminated to prescribers.

Prescribing drugs outside their licensed indications to children and young people

In certain circumstances drugs are prescribed to children and young people outside their licensed indications (off-label use) because the clinical need cannot be met by licensed drugs; for example, for an indication not specified in the marketing authorisation, or administration of a different dose. The Standing Committee on Medicines (a joint committee of the Royal College of Paediatrics and Child Health and the Neonatal and Paediatric Pharmacists Group) has issued a policy statement on the use of unlicensed drugs and the use of licensed drugs for unlicensed applications in children and young people. This states clearly that such use is necessary in paediatric practice and that doctors are legally allowed to prescribe drugs outside their licensed indications where there are no suitable alternatives and where use is justified by a responsible body of professional opinion (Joint Royal College of Paediatrics and Child Health/Neonatal and Paediatric Pharmacists Group Standing Committee on Medicines 2010).

Therefore, where there is no alternative treatment and only where there is a sufficient evidence base and/or experience of using the drug to demonstrate its safety and efficacy, a clinical guideline may recommend use of a drug outside its licensed indications for treating a child or young person. It is expected that prescribers will use the SPC to inform their prescribing decisions for individual patients, and they should be able to justify using a drug outside its licensed indications. Informed consent should be obtained from the child and/or their parent or guardian as appropriate and documented.

Footnotes for recommendations addressing off label-use of drugs in children and young people should follow the format described above and in box 9.1.

Box 9.1 Examples of footnotes to guideline recommendations about the off-label use of drugs

9.3.7 recommendations on waiting times and ineffective interventions.

Guideline developers should follow NICE's standard advice for recommendations on waiting times. It is also acceptable to make recommendations that advise stopping the use of an ineffective intervention. Waiting times and other policies set by other bodies

Avoid giving targets for waiting and referral times: refer to relevant targets set by the Department of Health or the Welsh Government, and where possible direct readers to the relevant document rather than including the target in the recommendation. This is because policy can change, making a guideline that includes such targets out of date. If no target exists, recommendations may include a maximum time if the GDG considers this to be essential.

Sometimes a recommendation will need to specify a waiting time, referral time or time of intervention because this relates to the safety and/or effectiveness of a clinical intervention. In this case, check that the recommendation does not conflict with relevant targets set by the Department of Health or the Welsh Government and ensure that the clinical reason for specifying the time is made clear. Ineffective interventions

Recommend stopping ineffective interventions: state explicitly if particular treatments or activities should not be carried out or should be stopped (see box 9.2).

Box 9.2 Example of a recommendation about stopping ineffective practice

9.3.8 using tables in recommendations.

Do not use tables to summarise several actions in one recommendation. Such summaries make it more difficult to link the recommended actions to the evidence summaries. A recommendation may include a small table to improve clarity; for example, to present information that should be shared with patients, or if the information is most easily understood when tabulated. An example is shown in box 9.3.

Box 9.3 Example of a table within a recommendation

[From: Unstable angina and NSTEMI: the early management of unstable angina and non-ST-segment-elevation myocardial infarction . NICE clinical guideline 94 (2010).]

NICE's standard clinical guidelines can cover large clinical areas and, as a result, often contain a considerable number of recommendations relevant to the many review questions. The GDG will need to identify a subset of these recommendations as key priorities for implementation. These may be used to guide implementation activities (see chapter 13 ) and may be useful in the subsequent development of NICE quality standards. The number of recommendations prioritised in this way will vary depending on the guideline, but is normally between 5 and 10. There is no 'ranking' within this set of recommendations.

Key priorities for implementation are usually those that are likely to do at least one of the following:

have a high impact on outcomes that are important to patients

have a high impact on reducing variation in care and outcomes

set challenging but achievable expectations of health services

focus on key infrastructural and clinical requirements for high-quality care

include actions that are measurable

lead to more efficient use of NHS resources

promote patient choice

promote equality.

In addition, the GDG should attempt to identify recommendations that are particularly likely to benefit from support from NICE's implementation programme. Criteria overlap with those above, but include whether a recommendation:

relates to an intervention that is not part of routine care

requires changes in service delivery

requires retraining of staff or the development of new skills and competencies

highlights the need for practice to change

affects and needs to be implemented across a number of agencies or settings (complex interactions)

may be viewed as potentially contentious, or difficult to implement for other reasons.

There should be a clear record of which criteria were considered particularly important by the GDG for each prioritised recommendation. This should be reported in a short paragraph in the full guideline.

The GDG is likely to identify areas in which there are uncertainties or where robust evidence is lacking. NICE has published a Research recommendations process and methods guide , which details the approach to be used across NICE's guidance-producing programmes to identify key uncertainties and associated research recommendations.

For standard clinical guidelines where there may be many hundreds of uncertainties, it will not be possible to document every uncertainty in detail. Similarly, although GDGs could write research recommendations for dealing with each uncertainty, this is not likely to be feasible. Therefore the GDG should select up to five key research recommendations for inclusion in the NICE version of the guideline; more research recommendations may be listed in the full guideline. Further information about how these should be derived can be found in the research recommendation process and methods guide.

Brown P, Brunnhuber K, Chalkidou K et al. (2006) How to formulate research recommendations. British Medical Journal 333: 804–6

Claxton K, Sculpher MJ (2006) Using value of information analysis to prioritise health research: some lessons from recent UK experience. Pharmacoeconomics 24: 1055–68

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

Guideline Implementability Appraisal (GLIA) [online]

Joint Royal College of Paediatrics and Child Health/Neonatal and Paediatric Pharmacists Group Standing Committee on Medicines (2010) The use of unlicensed medicines or licensed medicines for unlicensed applications in paediatric practice [online]

Lord SJ, Irwig L, Simes RJ (2006) When is measuring sensitivity and specificity sufficient to evaluate a diagnostic test, and when do we need randomized trials? Annals of Internal Medicine 144: 850–5

Sackett DL, Straus SE, Richardson WS (2000) Evidence-based medicine: how to practice and teach EBM, 2nd edition. Edinburgh: Churchill Livingstone

Schünemann HJ, Best D, Vist G et al. for the GRADE Working Group (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

[ 14 ] Information throughout this manual relating to the role of the National Collaborating Centres in guideline development also applies to the Internal Clinical Guidelines Programme at NICE.

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Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.)

Cover of Public involvement in research: assessing impact through a realist evaluation

Public involvement in research: assessing impact through a realist evaluation.

Chapter 9 conclusions and recommendations for future research.

  • How well have we achieved our original aim and objectives?

The initially stated overarching aim of this research was to identify the contextual factors and mechanisms that are regularly associated with effective and cost-effective public involvement in research. While recognising the limitations of our analysis, we believe we have largely achieved this in our revised theory of public involvement in research set out in Chapter 8 . We have developed and tested this theory of public involvement in research in eight diverse case studies; this has highlighted important contextual factors, in particular PI leadership, which had not previously been prominent in the literature. We have identified how this critical contextual factor shapes key mechanisms of public involvement, including the identification of a senior lead for involvement, resource allocation for involvement and facilitation of research partners. These mechanisms then lead to specific outcomes in improving the quality of research, notably recruitment strategies and materials and data collection tools and methods. We have identified a ‘virtuous circle’ of feedback to research partners on their contribution leading to their improved confidence and motivation, which facilitates their continued contribution. Following feedback from the HS&DR Board on our original application we did not seek to assess the cost-effectiveness of different mechanisms of public involvement but we did cost the different types of public involvement as discussed in Chapter 7 . A key finding is that many research projects undercost public involvement.

In our original proposal we emphasised our desire to include case studies involving young people and families with children in the research process. We recruited two studies involving parents of young children aged under 5 years, and two projects involving ‘older’ young people in the 18- to 25-years age group. We recognise that in doing this we missed studies involving children and young people aged under 18 years; in principle we would have liked to have included studies involving such children and young people, but, given the resources at our disposal and the additional resource, ethical and governance issues this would have entailed, we regretfully concluded that this would not be feasible for our study. In terms of the four studies with parental and young persons’ involvement that we did include, we have not done a separate analysis of their data, but the themes emerging from those case studies were consistent with our other case studies and contributed to our overall analysis.

In terms of the initial objectives, we successfully recruited the sample of eight diverse case studies and collected and analysed data from them (objective 1). As intended, we identified the outcomes of involvement from multiple stakeholders‘ perspectives, although we did not get as many research partners‘ perspectives as we would have liked – see limitations below (objective 2). It was more difficult than expected to track the impact of public involvement from project inception through to completion (objective 3), as all of our projects turned out to have longer time scales than our own. Even to track involvement over a stage of a case study research project proved difficult, as the research usually did not fall into neatly staged time periods and one study had no involvement activity over the study period.

Nevertheless, we were able to track seven of the eight case studies prospectively and in real time over time periods of up to 9 months, giving us an unusual window on involvement processes that have previously mainly been observed retrospectively. We were successful in comparing the contextual factors, mechanisms and outcomes associated with public involvement from different stakeholders‘ perspectives and costing the different mechanisms for public involvement (objective 4). We only partly achieved our final objective of undertaking a consensus exercise among stakeholders to assess the merits of the realist evaluation approach and our approach to the measurement and valuation of economic costs of public involvement in research (objective 5). A final consensus event was held, where very useful discussion and amendment of our theory of public involvement took place, and the economic approach was discussed and helpfully critiqued by participants. However, as our earlier discussions developed more fully than expected, we decided to let them continue rather than interrupt them in order to run the final exercise to assess the merits of the realist evaluation approach. We did, however, test our analysis with all our case study participants by sending a draft of this final report for comment. We received a number of helpful comments and corrections but no disagreement with our overall analysis.

  • What were the limitations of our study?

Realist evaluation is a relatively new approach and we recognise that there were a number of limitations to our study. We sought to follow the approach recommended by Pawson, but we acknowledge that we were not always able to do so. In particular, our theory of public involvement in research evolved over time and initially was not as tightly framed in terms of a testable hypothesis as Pawson recommends. In his latest book Pawson strongly recommends that outcomes should be measured with quantitative data, 17 but we did not do so; we were not aware of the existence of quantitative data or tools that would enable us to collect such data to answer our research questions. Even in terms of qualitative data, we did not capture as much information on outcomes as we initially envisaged. There were several reasons for this. The most important was that capturing outcomes in public involvement is easier the more operational the focus of involvement, and more difficult the more strategic the involvement. Thus, it was relatively easy to see the impact of a patient panel on the redesign of a recruitment leaflet but harder to capture the impact of research partners in a multidisciplinary team discussion of research design.

We also found it was sometimes more difficult to engage research partners as participants in our research than researchers or research managers. On reflection this is not surprising. Research partners are generally motivated to take part in research relevant to their lived experience of a health condition or situation, whereas our research was quite detached from their lived experience; in addition people had many constraints on their time, so getting involved in our research as well as their own was likely to be a burden too far for some. Researchers clearly also face significant time pressures but they had a more direct interest in our research, as they are obliged to engage with public involvement to satisfy research funders such as the NIHR. Moreover, researchers were being paid by their employers for their time during interviews with us, while research partners were not paid by us and usually not paid by their research teams. Whatever the reasons, we had less response from research partners than researchers or research managers, particularly for the third round of data collection; thus we have fewer data on outcomes from research partners‘ perspectives and we need to be aware of a possible selection bias towards more engaged research partners. Such a bias could have implications for our findings; for example payment might have been a more important motivating factor for less engaged advisory group members.

There were a number of practical difficulties we encountered. One challenge was when to recruit the case studies. We recruited four of our eight case studies prior to the full application, but this was more than 1 year before our project started and 15 months or more before data collection began. In this intervening period, we found that the time scales of some of the case studies were no longer ideal for our project and we faced the choice of whether to continue with them, although this timing was not ideal, or seek at a late moment to recruit alternative ones. One of our case studies ultimately undertook no involvement activity over the study period, so we obtained fewer data from it, and it contributed relatively little to our analysis. Similarly, one of the four case studies we recruited later experienced some delays itself in beginning and so we had a more limited period for data collection than initially envisaged. Research governance approvals took much longer than expected, particularly as we had to take three of our research partners, who were going to collect data within NHS projects, through the research passport process, which essentially truncated our data collection period from 1 year to 9 months. Even if we had had the full year initially envisaged for data collection, our conclusion with hindsight was that this was insufficiently long. To compare initial plans and intentions for involvement with the reality of what actually happened required a longer time period than a year for most of our case studies.

In the light of the importance we have placed on the commitment of PIs, there is an issue of potential selection bias in the recruitment of our sample. As our sampling strategy explicitly involved a networking approach to PIs of projects where we thought some significant public involvement was taking place, we were likely (as we did) to recruit enthusiasts and, at worst, those non-committed who were at least open to the potential value of public involvement. There were, unsurprisingly, no highly sceptical PIs in our sample. We have no data therefore on how public involvement may work in research where the PI is sceptical but may feel compelled to undertake involvement because of funder requirements or other factors.

  • What would we do differently next time?

If we were to design this study again, there are a number of changes we would make. Most importantly we would go for a longer time period to be able to capture involvement through the whole research process from initial design through to dissemination. We would seek to recruit far more potential case studies in principle, so that we had greater choice of which to proceed with once our study began in earnest. We would include case studies from the application stage to capture the important early involvement of research partners in the initial design period. It might be preferable to research a smaller number of case studies, allowing a more in-depth ethnographic approach. Although challenging, it would be very informative to seek to sample sceptical PIs. This might require a brief screening exercise of a larger group of PIs on their attitudes to and experience of public involvement.

The economic evaluation was challenging in a number of ways, particularly in seeking to obtain completed resource logs from case study research partners. Having a 2-week data collection period was also problematic in a field such as public involvement, where activity may be very episodic and infrequent. Thus, collecting economic data alongside other case study data in a more integrated way, and particularly with interviews and more ethnographic observation of case study activities, might be advantageous. The new budgeting tool developed by INVOLVE and the MHRN may provide a useful resource for future economic evaluations. 23

We have learned much from the involvement of research partners in our research team and, although many aspects of our approach worked well, there are some things we would do differently in future. Even though we included substantial resources for research partner involvement in all aspects of our study, we underestimated how time-consuming such full involvement would be. We were perhaps overambitious in trying to ensure such full involvement with the number of research partners and the number and complexity of the case studies. We were also perhaps naive in expecting all the research partners to play the same role in the team; different research partners came with different experiences and skills, and, like most of our case studies, we might have been better to be less prescriptive and allow the roles to develop more organically within the project.

  • Implications for research practice and funding

If one of the objectives of R&D policy is to increase the extent and effectiveness of public involvement in research, then a key implication of this research is the importance of influencing PIs to value public involvement in research or to delegate to other senior colleagues in leading on involvement in their research. Training is unlikely to be the key mechanism here; senior researchers are much more likely to be influenced by peers or by their personal experience of the benefits of public involvement. Early career researchers may be shaped by training but again peer learning and culture may be more influential. For those researchers sceptical or agnostic about public involvement, the requirement of funders is a key factor that is likely to make them engage with the involvement agenda. Therefore, funders need to scrutinise the track record of research teams on public involvement to ascertain whether there is any evidence of commitment or leadership on involvement.

One of the findings of the economic analysis was that PIs have consistently underestimated the costs of public involvement in their grant applications. Clearly the field will benefit from the guidance and budgeting tool recently disseminated by MHRN and INVOLVE. It was also notable that there was a degree of variation in the real costs of public involvement and that effective involvement is not necessarily costly. Different models of involvement incur different costs and researchers need to be made aware of the costs and benefits of these different options.

One methodological lesson we learned was the impact that conducting this research had on some participants’ reflection on the impact of public involvement. Particularly for research staff, the questions we asked sometimes made them reflect upon what they were doing and change aspects of their approach to involvement. Thus, the more the NIHR and other funders can build reporting, audit and other forms of evaluation on the impact of public involvement directly into their processes with PIs, the more likely such questioning might stimulate similar reflection.

  • Recommendations for further research

There are a number of gaps in our knowledge around public involvement in research that follow from our findings, and would benefit from further research, including realist evaluation to extend and further test the theory we have developed here:

  • In-depth exploration of how PIs become committed to public involvement and how to influence agnostic or sceptical PIs would be very helpful. Further research might compare, for example, training with peer-influencing strategies in engendering PI commitment. Research could explore the leadership role of other research team members, including research partners, and how collective leadership might support effective public involvement.
  • More methodological work is needed on how to robustly capture the impact and outcomes of public involvement in research (building as well on the PiiAF work of Popay et al. 51 ), including further economic analysis and exploration of impact when research partners are integral to research teams.
  • Research to develop approaches and carry out a full cost–benefit analysis of public involvement in research would be beneficial. Although methodologically challenging, it would be very useful to conduct some longer-term studies which sought to quantify the impact of public involvement on such key indicators as participant recruitment and retention in clinical trials.
  • It would also be helpful to capture qualitatively the experiences and perspectives of research partners who have had mixed or negative experiences, since they may be less likely than enthusiasts to volunteer to participate in studies of involvement in research such as ours. Similarly, further research might explore the (relatively rare) experiences of marginalised and seldom-heard groups involved in research.
  • Payment for public involvement in research remains a contested issue with strongly held positions for and against; it would be helpful to further explore the value research partners and researchers place on payment and its effectiveness for enhancing involvement in and impact on research.
  • A final relatively narrow but important question that we identified after data collection had finished is: what is the impact of the long periods of relative non-involvement following initial periods of more intense involvement for research partners in some types of research, particularly clinical trials?

Included under terms of UK Non-commercial Government License .

  • Cite this Page Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.) Chapter 9, Conclusions and recommendations for future research.
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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.

Representing the relationship among variables which impact the association of intelligence and earning potential. Intelligence is the independent variable and earning potential, the dependent variable. However, something like effort, which would impact the relationship between intelligence and earning potential, is considered a moderating variable. Academic achievement is considered a mediating variable as it can be explained by both the independent variable (intelligence) as well as the dependent variable (earning potential).

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.

Table differentiating the types of variable classifications as well as describing the types of statistical analyses inherent to the classifications.

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

Table illustrating binary scale wherein questions are asked and respondents are given two options to answer. In this case, 'yes' or 'no'.

  • 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

Likert Scale indicating scaled responses between 1 and 5 to questions. A selection of 1 indicates strongly disagree and a selection of 5 indicates strongly agree

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.

Representation of sample statistics for a data set of responses. Graphic indicates item names, individual responses, missing data, and mean for a specific set of responses.

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.

Describes the frequency distribution for random sampling

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.

Deliniates the 68-95-99 percent rule for confidence intervals. The bell curve indicates the percentage of chance that exists that the researcher made an error

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 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?”.

Table presenting the question, "What am I trying do do?". The logic is then branched for the reader to decide either the purpose is to understand more about the relationships either among or between OR to describe a process, phenomenon, or practice.

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.

Table continuing the logic from step 1 to step two in identifying general approach. General approaches are usually classified as either experimental, in that they are manipulating an independent variable to measure an outcome, or non-experimental wherein they are not directly manipulating an independent variable.

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.

A continuation of the stepwise approach to identifying the best study approach. In step 3, you are asked to consider what it is you are trying to determine by exploring the interactions between or among variables. Most people either want to investigate causality, compare groups, find associations, or describe a process, phenomenon, or practice.

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

Final step in the process of identifying the most appropriate approach is added to the figure. Depending on how you answered the question in step 3, your approach would either be experimental, quasi-experimental, comparative, associational, or descriptive.

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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  • USC Libraries
  • Research Guides

Organizing Your Social Sciences Research Paper

  • Reading Research Effectively
  • Purpose of Guide
  • Design Flaws to Avoid
  • Independent and Dependent Variables
  • Glossary of Research Terms
  • Narrowing a Topic Idea
  • Broadening a Topic Idea
  • Extending the Timeliness of a Topic Idea
  • Academic Writing Style
  • Applying Critical Thinking
  • Choosing a Title
  • Making an Outline
  • Paragraph Development
  • Research Process Video Series
  • Executive Summary
  • The C.A.R.S. Model
  • Background Information
  • The Research Problem/Question
  • Theoretical Framework
  • Citation Tracking
  • Content Alert Services
  • Evaluating Sources
  • Primary Sources
  • Secondary Sources
  • Tiertiary Sources
  • Scholarly vs. Popular Publications
  • Qualitative Methods
  • Quantitative Methods
  • Insiderness
  • Using Non-Textual Elements
  • Limitations of the Study
  • Common Grammar Mistakes
  • Writing Concisely
  • Avoiding Plagiarism
  • Footnotes or Endnotes?
  • Further Readings
  • Generative AI and Writing
  • USC Libraries Tutorials and Other Guides
  • Bibliography

Reading a Scholarly Article or Research Paper

Identifying a research problem to investigate usually requires a preliminary search for and critical review of the literature in order to gain an understanding about how scholars have examined a topic. Scholars rarely structure research studies in a way that can be followed like a story; they are complex and detail-intensive and often written in a descriptive and conclusive narrative form. However, in the social and behavioral sciences, journal articles and stand-alone research reports are generally organized in a consistent format that makes it easier to compare and contrast studies and to interpret their contents.

General Reading Strategies

W hen you first read an article or research paper, focus on asking specific questions about each section. This strategy can help with overall comprehension and with understanding how the content relates [or does not relate] to the problem you want to investigate. As you review more and more studies, the process of understanding and critically evaluating the research will become easier because the content of what you review will begin to coalescence around common themes and patterns of analysis. Below are recommendations on how to read each section of a research paper effectively. Note that the sections to read are out of order from how you will find them organized in a journal article or research paper.

1.  Abstract

The abstract summarizes the background, methods, results, discussion, and conclusions of a scholarly article or research paper. Use the abstract to filter out sources that may have appeared useful when you began searching for information but, in reality, are not relevant. Questions to consider when reading the abstract are:

  • Is this study related to my question or area of research?
  • What is this study about and why is it being done ?
  • What is the working hypothesis or underlying thesis?
  • What is the primary finding of the study?
  • Are there words or terminology that I can use to either narrow or broaden the parameters of my search for more information?

2.  Introduction

If, after reading the abstract, you believe the paper may be useful, focus on examining the research problem and identifying the questions the author is trying to address. This information is usually located within the first few paragraphs of the introduction or in the concluding paragraph. Look for information about how and in what way this relates to what you are investigating. In addition to the research problem, the introduction should provide the main argument and theoretical framework of the study and, in the last paragraphs of the introduction, describe what the author(s) intend to accomplish. Questions to consider when reading the introduction include:

  • What is this study trying to prove or disprove?
  • What is the author(s) trying to test or demonstrate?
  • What do we already know about this topic and what gaps does this study try to fill or contribute a new understanding to the research problem?
  • Why should I care about what is being investigated?
  • Will this study tell me anything new related to the research problem I am investigating?

3.  Literature Review

The literature review describes and critically evaluates what is already known about a topic. Read the literature review to obtain a big picture perspective about how the topic has been studied and to begin the process of seeing where your potential study fits within the domain of prior research. Questions to consider when reading the literature review include:

  • W hat other research has been conducted about this topic and what are the main themes that have emerged?
  • What does prior research reveal about what is already known about the topic and what remains to be discovered?
  • What have been the most important past findings about the research problem?
  • How has prior research led the author(s) to conduct this particular study?
  • Is there any prior research that is unique or groundbreaking?
  • Are there any studies I could use as a model for designing and organizing my own study?

4.  Discussion/Conclusion

The discussion and conclusion are usually the last two sections of text in a scholarly article or research report. They reveal how the author(s) interpreted the findings of their research and presented recommendations or courses of action based on those findings. Often in the conclusion, the author(s) highlight recommendations for further research that can be used to develop your own study. Questions to consider when reading the discussion and conclusion sections include:

  • What is the overall meaning of the study and why is this important? [i.e., how have the author(s) addressed the " So What? " question].
  • What do you find to be the most important ways that the findings have been interpreted?
  • What are the weaknesses in their argument?
  • Do you believe conclusions about the significance of the study and its findings are valid?
  • What limitations of the study do the author(s) describe and how might this help formulate my own research?
  • Does the conclusion contain any recommendations for future research?

5.  Methods/Methodology

The methods section describes the materials, techniques, and procedures for gathering information used to examine the research problem. If what you have read so far closely supports your understanding of the topic, then move on to examining how the author(s) gathered information during the research process. Questions to consider when reading the methods section include:

  • Did the study use qualitative [based on interviews, observations, content analysis], quantitative [based on statistical analysis], or a mixed-methods approach to examining the research problem?
  • What was the type of information or data used?
  • Could this method of analysis be repeated and can I adopt the same approach?
  • Is enough information available to repeat the study or should new data be found to expand or improve understanding of the research problem?

6.  Results

After reading the above sections, you should have a clear understanding of the general findings of the study. Therefore, read the results section to identify how key findings were discussed in relation to the research problem. If any non-textual elements [e.g., graphs, charts, tables, etc.] are confusing, focus on the explanations about them in the text. Questions to consider when reading the results section include:

  • W hat did the author(s) find and how did they find it?
  • Does the author(s) highlight any findings as most significant?
  • Are the results presented in a factual and unbiased way?
  • Does the analysis of results in the discussion section agree with how the results are presented?
  • Is all the data present and did the author(s) adequately address gaps?
  • What conclusions do you formulate from this data and does it match with the author's conclusions?

7.  References

The references list the sources used by the author(s) to document what prior research and information was used when conducting the study. After reviewing the article or research paper, use the references to identify additional sources of information on the topic and to examine critically how these sources supported the overall research agenda. Questions to consider when reading the references include:

  • Do the sources cited by the author(s) reflect a diversity of disciplinary viewpoints, i.e., are the sources all from a particular field of study or do the sources reflect multiple areas of study?
  • Are there any unique or interesting sources that could be incorporated into my study?
  • What other authors are respected in this field, i.e., who has multiple works cited or is cited most often by others?
  • What other research should I review to clarify any remaining issues or that I need more information about?

NOTE :  A final strategy in reviewing research is to copy and paste the title of the source [journal article, book, research report] into Google Scholar . If it appears, look for a "cited by" followed by a hyperlinked number [e.g., Cited by 45]. This number indicates how many times the study has been subsequently cited in other, more recently published works. This strategy, known as citation tracking, can be an effective means of expanding your review of pertinent literature based on a study you have found useful and how scholars have cited it. The same strategies described above can be applied to reading articles you find in the list of cited by references.

Reading Tip

Specific Reading Strategies

Effectively reading scholarly research is an acquired skill that involves attention to detail and an ability to comprehend complex ideas, data, and theoretical concepts in a way that applies logically to the research problem you are investigating. Here are some specific reading strategies to consider.

As You are Reading

  • Focus on information that is most relevant to the research problem; skim over the other parts.
  • As noted above, read content out of order! This isn't a novel; you want to start with the spoiler to quickly assess the relevance of the study.
  • Think critically about what you read and seek to build your own arguments; not everything may be entirely valid, examined effectively, or thoroughly investigated.
  • Look up the definitions of unfamiliar words, concepts, or terminology. A good scholarly source is Credo Reference .

Taking notes as you read will save time when you go back to examine your sources. Here are some suggestions:

  • Mark or highlight important text as you read [e.g., you can use the highlight text  feature in a PDF document]
  • Take notes in the margins [e.g., Adobe Reader offers pop-up sticky notes].
  • Highlight important quotations; consider using different colors to differentiate between quotes and other types of important text.
  • Summarize key points about the study at the end of the paper. To save time, these can be in the form of a concise bulleted list of statements [e.g., intro has provides historical background; lit review has important sources; good conclusions].

Write down thoughts that come to mind that may help clarify your understanding of the research problem. Here are some examples of questions to ask yourself:

  • Do I understand all of the terminology and key concepts?
  • Do I understand the parts of this study most relevant to my topic?
  • What specific problem does the research address and why is it important?
  • Are there any issues or perspectives the author(s) did not consider?
  • Do I have any reason to question the validity or reliability of this research?
  • How do the findings relate to my research interests and to other works which I have read?

Adapted from text originally created by Holly Burt, Behavioral Sciences Librarian, USC Libraries, April 2018.

Another Reading Tip

When is it Important to Read the Entire Article or Research Paper

Laubepin argues, "Very few articles in a field are so important that every word needs to be read carefully." However, this implies that some studies are worth reading carefully. As painful and time-consuming as it may seem, there are valid reasons for reading a study in its entirety from beginning to end. Here are some examples:

  • Studies Published Very Recently .  The author(s) of a recent, well written study will provide a survey of the most important or impactful prior research in the literature review section. This can establish an understanding of how scholars in the past addressed the research problem. In addition, the most recently published sources will highlight what is currently known and what gaps in understanding currently exist about a topic, usually in the form of the need for further research in the conclusion .
  • Surveys of the Research Problem .  Some papers provide a comprehensive analytical overview of the research problem. Reading this type of study can help you understand underlying issues and discover why scholars have chosen to investigate the topic. This is particularly important if the study was published very recently because the author(s) should cite all or most of the key prior research on the topic. Note that, if it is a long-standing problem, there may be studies that specifically review the literature to identify gaps that remain. These studies often include the word review in their title [e.g., Hügel, Stephan, and Anna R. Davies. "Public Participation, Engagement, and Climate Change Adaptation: A Review of the Research Literature." Wiley Interdisciplinary Reviews: Climate Change 11 (July-August 2020): https://doi.org/10.1002/ wcc.645].
  • Highly Cited .  If you keep coming across the same citation to a study while you are reviewing the literature, this implies it was foundational in establishing an understanding of the research problem or the study had a significant impact within the literature [positive or negative]. Carefully reading a highly cited source can help you understand how the topic emerged and motivated scholars to further investigate the problem. It also could be a study you need to cite as foundational in your own paper to demonstrate to the reader that you understand the roots of the problem.
  • Historical Overview .  Knowing the historical background of a research problem may not be the focus of your analysis. Nevertheless, carefully reading a study that provides a thorough description and analysis of the history behind an event, issue, or phenomenon can add important context to understanding the topic and what aspect of the problem you may want to examine further.
  • Innovative Methodological Design .  Some studies are significant and worth reading in their entirety because the author(s) designed a unique or innovative approach to researching the problem. This may justify reading the entire study because it can motivate you to think creatively about pursuing an alternative or non-traditional approach to examining your topic of interest. These types of studies are generally easy to identify because they are often cited in others works because of their unique approach to studying the research problem.
  • Cross-disciplinary Approach .  R eviewing studies produced outside of your discipline is an essential component of investigating research problems in the social and behavioral sciences. Consider reading a study that was conducted by author(s) based in a different discipline [e.g., an anthropologist studying political cultures; a study of hiring practices in companies published in a sociology journal]. This approach can generate a new understanding or a unique perspective about the topic . If you are not sure how to search for studies published in a discipline outside of your major or of the course you are taking, contact a librarian for assistance.

Laubepin, Frederique. How to Read (and Understand) a Social Science Journal Article . Inter-University Consortium for Political and Social Research (ISPSR), 2013; Shon, Phillip Chong Ho. How to Read Journal Articles in the Social Sciences: A Very Practical Guide for Students . 2nd edition. Thousand Oaks, CA: Sage, 2015; Lockhart, Tara, and Mary Soliday. "The Critical Place of Reading in Writing Transfer (and Beyond): A Report of Student Experiences." Pedagogy 16 (2016): 23-37; Maguire, Moira, Ann Everitt Reynolds, and Brid Delahunt. "Reading to Be: The Role of Academic Reading in Emergent Academic and Professional Student Identities." Journal of University Teaching and Learning Practice 17 (2020): 5-12.

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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
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  • University of Arkansas: Recommendation Report

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Home » Research Paper – Structure, Examples and Writing Guide

Research Paper – Structure, Examples and Writing Guide

Table of Contents

Research Paper

Research Paper


Research Paper is a written document that presents the author’s original research, analysis, and interpretation of a specific topic or issue.

It is typically based on Empirical Evidence, and may involve qualitative or quantitative research methods, or a combination of both. The purpose of a research paper is to contribute new knowledge or insights to a particular field of study, and to demonstrate the author’s understanding of the existing literature and theories related to the topic.

Structure of Research Paper

The structure of a research paper typically follows a standard format, consisting of several sections that convey specific information about the research study. The following is a detailed explanation of the structure of a research paper:

The title page contains the title of the paper, the name(s) of the author(s), and the affiliation(s) of the author(s). It also includes the date of submission and possibly, the name of the journal or conference where the paper is to be published.

The abstract is a brief summary of the research paper, typically ranging from 100 to 250 words. It should include the research question, the methods used, the key findings, and the implications of the results. The abstract should be written in a concise and clear manner to allow readers to quickly grasp the essence of the research.


The introduction section of a research paper provides background information about the research problem, the research question, and the research objectives. It also outlines the significance of the research, the research gap that it aims to fill, and the approach taken to address the research question. Finally, the introduction section ends with a clear statement of the research hypothesis or research question.

Literature Review

The literature review section of a research paper provides an overview of the existing literature on the topic of study. It includes a critical analysis and synthesis of the literature, highlighting the key concepts, themes, and debates. The literature review should also demonstrate the research gap and how the current study seeks to address it.

The methods section of a research paper describes the research design, the sample selection, the data collection and analysis procedures, and the statistical methods used to analyze the data. This section should provide sufficient detail for other researchers to replicate the study.

The results section presents the findings of the research, using tables, graphs, and figures to illustrate the data. The findings should be presented in a clear and concise manner, with reference to the research question and hypothesis.

The discussion section of a research paper interprets the findings and discusses their implications for the research question, the literature review, and the field of study. It should also address the limitations of the study and suggest future research directions.

The conclusion section summarizes the main findings of the study, restates the research question and hypothesis, and provides a final reflection on the significance of the research.

The references section provides a list of all the sources cited in the paper, following a specific citation style such as APA, MLA or Chicago.

How to Write Research Paper

You can write Research Paper by the following guide:

  • Choose a Topic: The first step is to select a topic that interests you and is relevant to your field of study. Brainstorm ideas and narrow down to a research question that is specific and researchable.
  • Conduct a Literature Review: The literature review helps you identify the gap in the existing research and provides a basis for your research question. It also helps you to develop a theoretical framework and research hypothesis.
  • Develop a Thesis Statement : The thesis statement is the main argument of your research paper. It should be clear, concise and specific to your research question.
  • Plan your Research: Develop a research plan that outlines the methods, data sources, and data analysis procedures. This will help you to collect and analyze data effectively.
  • Collect and Analyze Data: Collect data using various methods such as surveys, interviews, observations, or experiments. Analyze data using statistical tools or other qualitative methods.
  • Organize your Paper : Organize your paper into sections such as Introduction, Literature Review, Methods, Results, Discussion, and Conclusion. Ensure that each section is coherent and follows a logical flow.
  • Write your Paper : Start by writing the introduction, followed by the literature review, methods, results, discussion, and conclusion. Ensure that your writing is clear, concise, and follows the required formatting and citation styles.
  • Edit and Proofread your Paper: Review your paper for grammar and spelling errors, and ensure that it is well-structured and easy to read. Ask someone else to review your paper to get feedback and suggestions for improvement.
  • Cite your Sources: Ensure that you properly cite all sources used in your research paper. This is essential for giving credit to the original authors and avoiding plagiarism.

Research Paper Example

Note : The below example research paper is for illustrative purposes only and is not an actual research paper. Actual research papers may have different structures, contents, and formats depending on the field of study, research question, data collection and analysis methods, and other factors. Students should always consult with their professors or supervisors for specific guidelines and expectations for their research papers.

Research Paper Example sample for Students:

Title: The Impact of Social Media on Mental Health among Young Adults

Abstract: This study aims to investigate the impact of social media use on the mental health of young adults. A literature review was conducted to examine the existing research on the topic. A survey was then administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO (Fear of Missing Out) are significant predictors of mental health problems among young adults.

Introduction: Social media has become an integral part of modern life, particularly among young adults. While social media has many benefits, including increased communication and social connectivity, it has also been associated with negative outcomes, such as addiction, cyberbullying, and mental health problems. This study aims to investigate the impact of social media use on the mental health of young adults.

Literature Review: The literature review highlights the existing research on the impact of social media use on mental health. The review shows that social media use is associated with depression, anxiety, stress, and other mental health problems. The review also identifies the factors that contribute to the negative impact of social media, including social comparison, cyberbullying, and FOMO.

Methods : A survey was administered to 200 university students to collect data on their social media use, mental health status, and perceived impact of social media on their mental health. The survey included questions on social media use, mental health status (measured using the DASS-21), and perceived impact of social media on their mental health. Data were analyzed using descriptive statistics and regression analysis.

Results : The results showed that social media use is positively associated with depression, anxiety, and stress. The study also found that social comparison, cyberbullying, and FOMO are significant predictors of mental health problems among young adults.

Discussion : The study’s findings suggest that social media use has a negative impact on the mental health of young adults. The study highlights the need for interventions that address the factors contributing to the negative impact of social media, such as social comparison, cyberbullying, and FOMO.

Conclusion : In conclusion, social media use has a significant impact on the mental health of young adults. The study’s findings underscore the need for interventions that promote healthy social media use and address the negative outcomes associated with social media use. Future research can explore the effectiveness of interventions aimed at reducing the negative impact of social media on mental health. Additionally, longitudinal studies can investigate the long-term effects of social media use on mental health.

Limitations : The study has some limitations, including the use of self-report measures and a cross-sectional design. The use of self-report measures may result in biased responses, and a cross-sectional design limits the ability to establish causality.

Implications: The study’s findings have implications for mental health professionals, educators, and policymakers. Mental health professionals can use the findings to develop interventions that address the negative impact of social media use on mental health. Educators can incorporate social media literacy into their curriculum to promote healthy social media use among young adults. Policymakers can use the findings to develop policies that protect young adults from the negative outcomes associated with social media use.

References :

  • Twenge, J. M., & Campbell, W. K. (2019). Associations between screen time and lower psychological well-being among children and adolescents: Evidence from a population-based study. Preventive medicine reports, 15, 100918.
  • Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., … & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among US young adults. Computers in Human Behavior, 69, 1-9.
  • Van der Meer, T. G., & Verhoeven, J. W. (2017). Social media and its impact on academic performance of students. Journal of Information Technology Education: Research, 16, 383-398.

Appendix : The survey used in this study is provided below.

Social Media and Mental Health Survey

  • How often do you use social media per day?
  • Less than 30 minutes
  • 30 minutes to 1 hour
  • 1 to 2 hours
  • 2 to 4 hours
  • More than 4 hours
  • Which social media platforms do you use?
  • Others (Please specify)
  • How often do you experience the following on social media?
  • Social comparison (comparing yourself to others)
  • Cyberbullying
  • Fear of Missing Out (FOMO)
  • Have you ever experienced any of the following mental health problems in the past month?
  • Do you think social media use has a positive or negative impact on your mental health?
  • Very positive
  • Somewhat positive
  • Somewhat negative
  • Very negative
  • In your opinion, which factors contribute to the negative impact of social media on mental health?
  • Social comparison
  • In your opinion, what interventions could be effective in reducing the negative impact of social media on mental health?
  • Education on healthy social media use
  • Counseling for mental health problems caused by social media
  • Social media detox programs
  • Regulation of social media use

Thank you for your participation!

Applications of Research Paper

Research papers have several applications in various fields, including:

  • Advancing knowledge: Research papers contribute to the advancement of knowledge by generating new insights, theories, and findings that can inform future research and practice. They help to answer important questions, clarify existing knowledge, and identify areas that require further investigation.
  • Informing policy: Research papers can inform policy decisions by providing evidence-based recommendations for policymakers. They can help to identify gaps in current policies, evaluate the effectiveness of interventions, and inform the development of new policies and regulations.
  • Improving practice: Research papers can improve practice by providing evidence-based guidance for professionals in various fields, including medicine, education, business, and psychology. They can inform the development of best practices, guidelines, and standards of care that can improve outcomes for individuals and organizations.
  • Educating students : Research papers are often used as teaching tools in universities and colleges to educate students about research methods, data analysis, and academic writing. They help students to develop critical thinking skills, research skills, and communication skills that are essential for success in many careers.
  • Fostering collaboration: Research papers can foster collaboration among researchers, practitioners, and policymakers by providing a platform for sharing knowledge and ideas. They can facilitate interdisciplinary collaborations and partnerships that can lead to innovative solutions to complex problems.

When to Write Research Paper

Research papers are typically written when a person has completed a research project or when they have conducted a study and have obtained data or findings that they want to share with the academic or professional community. Research papers are usually written in academic settings, such as universities, but they can also be written in professional settings, such as research organizations, government agencies, or private companies.

Here are some common situations where a person might need to write a research paper:

  • For academic purposes: Students in universities and colleges are often required to write research papers as part of their coursework, particularly in the social sciences, natural sciences, and humanities. Writing research papers helps students to develop research skills, critical thinking skills, and academic writing skills.
  • For publication: Researchers often write research papers to publish their findings in academic journals or to present their work at academic conferences. Publishing research papers is an important way to disseminate research findings to the academic community and to establish oneself as an expert in a particular field.
  • To inform policy or practice : Researchers may write research papers to inform policy decisions or to improve practice in various fields. Research findings can be used to inform the development of policies, guidelines, and best practices that can improve outcomes for individuals and organizations.
  • To share new insights or ideas: Researchers may write research papers to share new insights or ideas with the academic or professional community. They may present new theories, propose new research methods, or challenge existing paradigms in their field.

Purpose of Research Paper

The purpose of a research paper is to present the results of a study or investigation in a clear, concise, and structured manner. Research papers are written to communicate new knowledge, ideas, or findings to a specific audience, such as researchers, scholars, practitioners, or policymakers. The primary purposes of a research paper are:

  • To contribute to the body of knowledge : Research papers aim to add new knowledge or insights to a particular field or discipline. They do this by reporting the results of empirical studies, reviewing and synthesizing existing literature, proposing new theories, or providing new perspectives on a topic.
  • To inform or persuade: Research papers are written to inform or persuade the reader about a particular issue, topic, or phenomenon. They present evidence and arguments to support their claims and seek to persuade the reader of the validity of their findings or recommendations.
  • To advance the field: Research papers seek to advance the field or discipline by identifying gaps in knowledge, proposing new research questions or approaches, or challenging existing assumptions or paradigms. They aim to contribute to ongoing debates and discussions within a field and to stimulate further research and inquiry.
  • To demonstrate research skills: Research papers demonstrate the author’s research skills, including their ability to design and conduct a study, collect and analyze data, and interpret and communicate findings. They also demonstrate the author’s ability to critically evaluate existing literature, synthesize information from multiple sources, and write in a clear and structured manner.

Characteristics of Research Paper

Research papers have several characteristics that distinguish them from other forms of academic or professional writing. Here are some common characteristics of research papers:

  • Evidence-based: Research papers are based on empirical evidence, which is collected through rigorous research methods such as experiments, surveys, observations, or interviews. They rely on objective data and facts to support their claims and conclusions.
  • Structured and organized: Research papers have a clear and logical structure, with sections such as introduction, literature review, methods, results, discussion, and conclusion. They are organized in a way that helps the reader to follow the argument and understand the findings.
  • Formal and objective: Research papers are written in a formal and objective tone, with an emphasis on clarity, precision, and accuracy. They avoid subjective language or personal opinions and instead rely on objective data and analysis to support their arguments.
  • Citations and references: Research papers include citations and references to acknowledge the sources of information and ideas used in the paper. They use a specific citation style, such as APA, MLA, or Chicago, to ensure consistency and accuracy.
  • Peer-reviewed: Research papers are often peer-reviewed, which means they are evaluated by other experts in the field before they are published. Peer-review ensures that the research is of high quality, meets ethical standards, and contributes to the advancement of knowledge in the field.
  • Objective and unbiased: Research papers strive to be objective and unbiased in their presentation of the findings. They avoid personal biases or preconceptions and instead rely on the data and analysis to draw conclusions.

Advantages of Research Paper

Research papers have many advantages, both for the individual researcher and for the broader academic and professional community. Here are some advantages of research papers:

  • Contribution to knowledge: Research papers contribute to the body of knowledge in a particular field or discipline. They add new information, insights, and perspectives to existing literature and help advance the understanding of a particular phenomenon or issue.
  • Opportunity for intellectual growth: Research papers provide an opportunity for intellectual growth for the researcher. They require critical thinking, problem-solving, and creativity, which can help develop the researcher’s skills and knowledge.
  • Career advancement: Research papers can help advance the researcher’s career by demonstrating their expertise and contributions to the field. They can also lead to new research opportunities, collaborations, and funding.
  • Academic recognition: Research papers can lead to academic recognition in the form of awards, grants, or invitations to speak at conferences or events. They can also contribute to the researcher’s reputation and standing in the field.
  • Impact on policy and practice: Research papers can have a significant impact on policy and practice. They can inform policy decisions, guide practice, and lead to changes in laws, regulations, or procedures.
  • Advancement of society: Research papers can contribute to the advancement of society by addressing important issues, identifying solutions to problems, and promoting social justice and equality.

Limitations of Research Paper

Research papers also have some limitations that should be considered when interpreting their findings or implications. Here are some common limitations of research papers:

  • Limited generalizability: Research findings may not be generalizable to other populations, settings, or contexts. Studies often use specific samples or conditions that may not reflect the broader population or real-world situations.
  • Potential for bias : Research papers may be biased due to factors such as sample selection, measurement errors, or researcher biases. It is important to evaluate the quality of the research design and methods used to ensure that the findings are valid and reliable.
  • Ethical concerns: Research papers may raise ethical concerns, such as the use of vulnerable populations or invasive procedures. Researchers must adhere to ethical guidelines and obtain informed consent from participants to ensure that the research is conducted in a responsible and respectful manner.
  • Limitations of methodology: Research papers may be limited by the methodology used to collect and analyze data. For example, certain research methods may not capture the complexity or nuance of a particular phenomenon, or may not be appropriate for certain research questions.
  • Publication bias: Research papers may be subject to publication bias, where positive or significant findings are more likely to be published than negative or non-significant findings. This can skew the overall findings of a particular area of research.
  • Time and resource constraints: Research papers may be limited by time and resource constraints, which can affect the quality and scope of the research. Researchers may not have access to certain data or resources, or may be unable to conduct long-term studies due to practical limitations.

About the author

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Muhammad Hassan

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