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5 ways that collaboration can further your research and your career

Author: guest contributor.

Collaborative research has become more prevalent globally over the past 50 years and researchers are increasingly required to work across disciplines, institutions and borders. With the goal of helping researchers make the most of these collaborations,  Nature Masterclasses  has launched a new online course called " Effective Collaboration in Research ." Read on for more information about the course and to learn how collaborating efficiently can help advance your research and your career.

Collaborative projects are inevitably associated with challenges that you might not experience with an individual research project. However, they can also offer many benefits. Here are just some of the ways in which collaborations can benefit your research and your career. 

• Maximise outputs. By combining expertise and resources you can answer bigger and more complex scientific questions and expand the breadth of your research. 
• Maximise impact. Research shows a positive correlation between collaborative papers and a high level of citations. For example, in one analysis of 28 million papers from the humanities and the natural, medical, and social sciences published between 1900 and 2011, papers with more authors received more citations, particularly if the authors were from different institutions 1 .
• Attract funding. Generating outputs that have an impact on policy, practice, industry, or the general public can increase your chances of getting funded. In addition, some funding bodies now give priority to international and industry-academia collaborations. For example, the EU Commission’s Horizon 2020 program, which offered nearly 80 billion Euros of funding between 2014 and 2020 for research projects tackling societal challenges, prioritized collaborative projects.
• Expand your network. Working collaboratively can help you meet potential future employers, mentors, and collaborators.
• Embrace the new. Collaborations are opportunities to learn new skills, make new friends, gain a new perspective, and join stimulating discussions and with experts in your field or complementary fields.

To help researchers maximise the benefits of collaborating, Nature Masterclasses has launched a new online course called " Effective Collaboration in Research ." The 8-hour course was developed with a panel of experts of researchers from across the international scientific community (academic and industry), Nature Research Editors, funding bodies and creators of collaborative tools for researchers—all of whom have extensive experience and expertise in conducting, publishing and funding collaborative research. 

The course includes a 1-hour free sample , enabling researchers to try the course for free after registering on Nature Masterclasses . Access to the full course requires a subscription; subscriptions are available to labs, departments and institutions. 

Nature Masterclasses is a Springer Nature product providing professional development training to researchers, via online courses and face-to-face workshops.

Claire Hodge is a Senior Marketing Manager at Nature Research. She is a member of the Researcher Services team, which provides services such as Nature Research Academies , Nature Research Editing Service and Nature Masterclasses to build the skills, confidence and careers of researchers.

Guest Contributors include Springer Nature staff and authors, industry experts, society partners, and many others. If you are interested in being a Guest Contributor, please contact us via email: [email protected] .

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How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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FUTURE RESEARCH

Types of future research suggestion.

The Future Research section of your dissertation is often combined with the Research Limitations section of your final, Conclusions chapter. This is because your future research suggestions generally arise out of the research limitations you have identified in your own dissertation. In this article, we discuss six types of future research suggestion. These include: (1) building on a particular finding in your research; (2) addressing a flaw in your research; examining (or testing) a theory (framework or model) either (3) for the first time or (4) in a new context, location and/or culture; (5) re-evaluating and (6) expanding a theory (framework or model). The goal of the article is to help you think about the potential types of future research suggestion that you may want to include in your dissertation.

Before we discuss each of these types of future research suggestion, we should explain why we use the word examining and then put or testing in brackets. This is simply because the word examining may be considered more appropriate when students use a qualitative research design; whereas the word testing fits better with dissertations drawing on a quantitative research design. We also put the words framework or model in brackets after the word theory . We do this because a theory , framework and model are not the same things. In the sections that follow, we discuss six types of future research suggestion.

Addressing research limitations in your dissertation

Building on a particular finding or aspect of your research, examining a conceptual framework (or testing a theoretical model) for the first time, examining a conceptual framework (or testing a theoretical model) in a new context, location and/or culture.

• Expanding a conceptual framework (or testing a theoretical model)

Re-evaluating a conceptual framework (or theoretical model)

In the Research Limitations section of your Conclusions chapter, you will have inevitably detailed the potential flaws (i.e., research limitations) of your dissertation. These may include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Identifying what these research limitations were and proposing future research suggestions that address them is arguably the easiest and quickest ways to complete the Future Research section of your Conclusions chapter.

Often, the findings from your dissertation research will highlight a number of new avenues that could be explored in future studies. These can be grouped into two categories:

Your dissertation will inevitably lead to findings that you did not anticipate from the start. These are useful when making future research suggestions because they can lead to entirely new avenues to explore in future studies. If this was the case, it is worth (a) briefly describing what these unanticipated findings were and (b) suggesting a research strategy that could be used to explore such findings in future.

Sometimes, dissertations manage to address all aspects of the research questions that were set. However, this is seldom the case. Typically, there will be aspects of your research questions that could not be answered. This is not necessarily a flaw in your research strategy, but may simply reflect that fact that the findings did not provide all the answers you hoped for. If this was the case, it is worth (a) briefly describing what aspects of your research questions were not answered and (b) suggesting a research strategy that could be used to explore such aspects in future.

You may want to recommend that future research examines the conceptual framework (or tests the theoretical model) that you developed. This is based on the assumption that the primary goal of your dissertation was to set out a conceptual framework (or build a theoretical model). It is also based on the assumption that whilst such a conceptual framework (or theoretical model) was presented, your dissertation did not attempt to examine (or test) it in the field . The focus of your dissertations was most likely a review of the literature rather than something that involved you conducting primary research.

Whilst it is quite rare for dissertations at the undergraduate and master's level to be primarily theoretical in nature like this, it is not unknown. If this was the case, you should think about how the conceptual framework (or theoretical model) that you have presented could be best examined (or tested) in the field . In understanding the how , you should think about two factors in particular:

What is the context, location and/or culture that would best lend itself to my conceptual framework (or theoretical model) if it were to be examined (or tested) in the field?

What research strategy is most appropriate to examine my conceptual framework (or test my theoretical model)?

If the future research suggestion that you want to make is based on examining your conceptual framework (or testing your theoretical model) in the field , you need to suggest the best scenario for doing so.

More often than not, you will not only have set out a conceptual framework (or theoretical model), as described in the previous section, but you will also have examined (or tested) it in the field . When you do this, focus is typically placed on a specific context, location and/or culture.

If this is the case, the obvious future research suggestion that you could propose would be to examine your conceptual framework (or test the theoretical model) in a new context, location and/or culture. For example, perhaps you focused on consumers (rather than businesses), or Canada (rather than the United Kingdom), or a more individualistic culture like the United States (rather than a more collectivist culture like China).

When you propose a new context, location and/or culture as your future research suggestion, make sure you justify the choice that you make. For example, there may be little value in future studies looking at different cultures if culture is not an important component underlying your conceptual framework (or theoretical model). If you are not sure whether a new context, location or culture is more appropriate, or what new context, location or culture you should select, a review the literature will often help clarify where you focus should be.

Expanding a conceptual framework (or theoretical model)

Assuming that you have set out a conceptual framework (or theoretical model) and examined (or tested) it in the field , another series of future research suggestions comes out of expanding that conceptual framework (or theoretical model).

We talk about a series of future research suggestions because there are so many ways that you can expand on your conceptual framework (or theoretical model). For example, you can do this by:

Examining constructs (or variables) that were included in your conceptual framework (or theoretical model) but were not focused.

Looking at a particular relationship aspect of your conceptual framework (or theoretical model) further.

Adding new constructs (or variables) to the conceptual framework (or theoretical model) you set out (if justified by the literature).

It would be possible to include one or a number of these as future research suggestions. Again, make sure that any suggestions you make have are justified , either by your findings or the literature.

With the dissertation process at the undergraduate and master's level lasting between 3 and 9 months, a lot a can happen in between. For example, a specific event (e.g., 9/11, the economic crisis) or some new theory or evidence that undermines (or questions) the literature (theory) and assumptions underpinning your conceptual framework (or theoretical model). Clearly, there is little you can do about this. However, if this happens, reflecting on it and re-evaluating your conceptual framework (or theoretical model), as well as your findings, is an obvious source of future research suggestions.

What Is Research, and Why Do People Do It?

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Abstractspiepr Abs1

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

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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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.)

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?

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• 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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Research statement, what is a research statement.

The research statement (or statement of research interests) is a common component of academic job applications. It is a summary of your research accomplishments, current work, and future direction and potential of your work.

The statement can discuss specific issues such as:

• funding history and potential
• requirements for laboratory equipment and space and other resources
• potential research and industrial collaborations
• how your research contributes to your field
• future direction of your research

The research statement should be technical, but should be intelligible to all members of the department, including those outside your subdiscipline. So keep the “big picture” in mind. The strongest research statements present a readable, compelling, and realistic research agenda that fits well with the needs, facilities, and goals of the department.

Research statements can be weakened by:

• overly ambitious proposals
• lack of clear direction
• lack of big-picture focus
• inadequate attention to the needs and facilities of the department or position

Why a Research Statement?

• It conveys to search committees the pieces of your professional identity and charts the course of your scholarly journey.
• It communicates a sense that your research will follow logically from what you have done and that it will be different, important, and innovative.
• It gives a context for your research interests—Why does your research matter? The so what?
• It combines your achievements and current work with the proposal for upcoming research.
• areas of specialty and expertise
• potential to get funding
• academic strengths and abilities
• compatibility with the department or school
• ability to think and communicate like a serious scholar and/or scientist

Formatting of Research Statements

The goal of the research statement is to introduce yourself to a search committee, which will probably contain scientists both in and outside your field, and get them excited about your research. To encourage people to read it:

• make it one or two pages, three at most
• use informative section headings and subheadings
• use bullets
• use an easily readable font size
• make the margins a reasonable size

Organization of Research Statements

Think of the overarching theme guiding your main research subject area. Write an essay that lays out:

• The main theme(s) and why it is important and what specific skills you use to attack the problem.
• A few specific examples of problems you have already solved with success to build credibility and inform people outside your field about what you do.
• A discussion of the future direction of your research. This section should be really exciting to people both in and outside your field. Don’t sell yourself short; if you think your research could lead to answers for big important questions, say so!
• A final paragraph that gives a good overall impression of your research.

Writing Research Statements

• Avoid jargon. Make sure that you describe your research in language that many people outside your specific subject area can understand. Ask people both in and outside your field to read it before you send your application. A search committee won’t get excited about something they can’t understand.
• Write as clearly, concisely, and concretely as you can.
• Keep it at a summary level; give more detail in the job talk.
• Ask others to proofread it. Be sure there are no spelling errors.
• Convince the search committee not only that you are knowledgeable, but that you are the right person to carry out the research.
• Include information that sets you apart (e.g., publication in  Science, Nature,  or a prestigious journal in your field).
• What excites you about your research? Sound fresh.
• Include preliminary results and how to build on results.
• Point out how current faculty may become future partners.
• Acknowledge the work of others.
• Use language that shows you are an independent researcher.
• BUT focus on your research work, not yourself.
• Include potential funding partners and industrial collaborations. Be creative!
• Provide a summary of your research.
• Put in background material to give the context/relevance/significance of your research.
• List major findings, outcomes, and implications.
• Describe both current and planned (future) research.
• Communicate a sense that your research will follow logically from what you have done and that it will be unique, significant, and innovative (and easy to fund).

Describe Your Future Goals or Research Plans

• Major problem(s) you want to focus on in your research.
• The problem’s relevance and significance to the field.
• Your specific goals for the next three to five years, including potential impact and outcomes.
• If you know what a particular agency funds, you can name the agency and briefly outline a proposal.
• Give broad enough goals so that if one area doesn’t get funded, you can pursue other research goals and funding.

Identify Potential Funding Sources

• Almost every institution wants to know whether you’ll be able to get external funding for research.
• Try to provide some possible sources of funding for the research, such as NIH, NSF, foundations, private agencies.
• Mention past funding, if appropriate.

Be Realistic

There is a delicate balance between a realistic research statement where you promise to work on problems you really think you can solve and over-reaching or dabbling in too many subject areas. Select an over-arching theme for your research statement and leave miscellaneous ideas or projects out. Everyone knows that you will work on more than what you mention in this statement.

Consider Also Preparing a Longer Version

• A longer version (five–15 pages) can be brought to your interview. (Check with your advisor to see if this is necessary.)
• You may be asked to describe research plans and budget in detail at the campus interview. Be prepared.
• Include laboratory needs (how much budget you need for equipment, how many grad assistants, etc.) to start up the research.

Samples of Research Statements

To find sample research statements with content specific to your discipline, search on the internet for your discipline + “Research Statement.”

• University of Pennsylvania Sample Research Statement
• Advice on writing a Research Statement (Plan) from the journal  Science

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How to Write an “Implications of Research” Section

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• 24th October 2022

When writing research papers , theses, journal articles, or dissertations, one cannot ignore the importance of research. You’re not only the writer of your paper but also the researcher ! Moreover, it’s not just about researching your topic, filling your paper with abundant citations, and topping it off with a reference list. You need to dig deep into your research and provide related literature on your topic. You must also discuss the implications of your research.

Interested in learning more about implications of research? Read on! This post will define these implications, why they’re essential, and most importantly, how to write them. If you’re a visual learner, you might enjoy this video .

What Are Implications of Research?

Implications are potential questions from your research that justify further exploration. They state how your research findings could affect policies, theories, and/or practices.

Implications can either be practical or theoretical. The former is the direct impact of your findings on related practices, whereas the latter is the impact on the theories you have chosen in your study.

Example of a practical implication: If you’re researching a teaching method, the implication would be how teachers can use that method based on your findings.

Example of a theoretical implication: You added a new variable to Theory A so that it could cover a broader perspective.

Finally, implications aren’t the same as recommendations, and it’s important to know the difference between them .

Questions you should consider when developing the implications section:

●  What is the significance of your findings?

●  How do the findings of your study fit with or contradict existing research on this topic?

●  Do your results support or challenge existing theories? If they support them, what new information do they contribute? If they challenge them, why do you think that is?

Why Are Implications Important?

You need implications for the following reasons:

● To reflect on what you set out to accomplish in the first place

● To see if there’s a change to the initial perspective, now that you’ve collected the data

● To inform your audience, who might be curious about the impact of your research

How to Write an Implications Section

Usually, you write your research implications in the discussion section of your paper. This is the section before the conclusion when you discuss all the hard work you did. Additionally, you’ll write the implications section before making recommendations for future research.

Implications should begin with what you discovered in your study, which differs from what previous studies found, and then you can discuss the implications of your findings.

Your implications need to be specific, meaning you should show the exact contributions of your research and why they’re essential. They should also begin with a specific sentence structure.

Examples of starting implication sentences:

●  These results build on existing evidence of…

●  These findings suggest that…

●  These results should be considered when…

●  While previous research has focused on x , these results show that y …

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You should write your implications after you’ve stated the results of your research. In other words, summarize your findings and put them into context.

The result : One study found that young learners enjoy short activities when learning a foreign language.

The implications : This result suggests that foreign language teachers use short activities when teaching young learners, as they positively affect learning.

 Example 2

The result : One study found that people who listen to calming music just before going to bed sleep better than those who watch TV.

The implications : These findings suggest that listening to calming music aids sleep quality, whereas watching TV does not.

To summarize, remember these key pointers:

●  Implications are the impact of your findings on the field of study.

●  They serve as a reflection of the research you’ve conducted.              

●  They show the specific contributions of your findings and why the audience should care.

●  They can be practical or theoretical.

●  They aren’t the same as recommendations.

●  You write them in the discussion section of the paper.

●  State the results first, and then state their implications.

Are you currently working on a thesis or dissertation? Once you’ve finished your paper (implications included), our proofreading team can help ensure that your spelling, punctuation, and grammar are perfect. Consider submitting a 500-word document for free.

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Educational resources and simple solutions for your research journey

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

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

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

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

Table of Contents

What are implications in research

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

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

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

What are recommendations in research?

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

Recommendations in research examples

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

Table: Recommendations in research examples based on purpose and beneficiary  

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

Table: Sample recommendation in research template  

Basic differences between implications and recommendations in research

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

Table: Differences between implications and recommendations in research  

Where do implications go in your research paper

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

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

How to write implications in research

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

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

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

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

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

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

What should recommendations in research look like?

Recommendations for future research should be:  

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

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

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

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

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

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

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

Table: Example of evidence-based research recommendation  

Avoid making the following mistakes when writing research recommendations :  

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

How to write recommendations in research

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

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

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

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

References:  

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

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Implications in Research – Types, Examples and Writing Guide

Table of Contents

Implications in Research

Implications in research refer to the potential consequences, applications, or outcomes of the findings and conclusions of a research study. These can include both theoretical and practical implications that extend beyond the immediate scope of the study and may impact various stakeholders, such as policymakers, practitioners, researchers , or the general public.

Structure of Implications

The format of implications in research typically follows the structure below:

• Restate the main findings: Begin by restating the main findings of the study in a brief summary .
• Link to the research question/hypothesis : Clearly articulate how the findings are related to the research question /hypothesis.
• Discuss the practical implications: Discuss the practical implications of the findings, including their potential impact on the field or industry.
• Discuss the theoretical implications : Discuss the theoretical implications of the findings, including their potential impact on existing theories or the development of new ones.
• Identify limitations: Identify the limitations of the study and how they may affect the generalizability of the findings.
• Suggest directions for future research: Suggest areas for future research that could build on the current study’s findings and address any limitations.

Types of Implications in Research

Types of Implications in Research are as follows:

Theoretical Implications

These are the implications that a study has for advancing theoretical understanding in a particular field. For example, a study that finds a new relationship between two variables can have implications for the development of theories and models in that field.

Practical Implications

These are the implications that a study has for solving practical problems or improving real-world outcomes. For example, a study that finds a new treatment for a disease can have implications for improving the health of patients.

Methodological Implications

These are the implications that a study has for advancing research methods and techniques. For example, a study that introduces a new method for data analysis can have implications for how future research in that field is conducted.

Ethical Implications

These are the implications that a study has for ethical considerations in research. For example, a study that involves human participants must consider the ethical implications of the research on the participants and take steps to protect their rights and welfare.

Policy Implications

These are the implications that a study has for informing policy decisions. For example, a study that examines the effectiveness of a particular policy can have implications for policymakers who are considering whether to implement or change that policy.

Societal Implications

These are the implications that a study has for society as a whole. For example, a study that examines the impact of a social issue such as poverty or inequality can have implications for how society addresses that issue.

Forms of Implications In Research

Forms of Implications are as follows:

Positive Implications

These refer to the positive outcomes or benefits that may result from a study’s findings. For example, a study that finds a new treatment for a disease can have positive implications for patients, healthcare providers, and the wider society.

Negative Implications

These refer to the negative outcomes or risks that may result from a study’s findings. For example, a study that finds a harmful side effect of a medication can have negative implications for patients, healthcare providers, and the wider society.

Direct Implications

These refer to the immediate consequences of a study’s findings. For example, a study that finds a new method for reducing greenhouse gas emissions can have direct implications for policymakers and businesses.

Indirect Implications

These refer to the broader or long-term consequences of a study’s findings. For example, a study that finds a link between childhood trauma and mental health issues can have indirect implications for social welfare policies, education, and public health.

Importance of Implications in Research

The following are some of the reasons why implications are important in research:

• To inform policy and practice: Research implications can inform policy and practice decisions by providing evidence-based recommendations for actions that can be taken to address the issues identified in the research. This can lead to more effective policies and practices that are grounded in empirical evidence.
• To guide future research: Implications can also guide future research by identifying areas that need further investigation, highlighting gaps in current knowledge, and suggesting new directions for research.
• To increase the impact of research : By communicating the practical and theoretical implications of their research, researchers can increase the impact of their work by demonstrating its relevance and importance to a wider audience.
• To enhance the credibility of research : Implications can help to enhance the credibility of research by demonstrating that the findings have practical and theoretical significance and are not just abstract or academic exercises.
• To foster collaboration and engagement : Implications can also foster collaboration and engagement between researchers, practitioners, policymakers, and other stakeholders by providing a common language and understanding of the practical and theoretical implications of the research.

Example of Implications in Research

Here are some examples of implications in research:

• Medical research: A study on the efficacy of a new drug for a specific disease can have significant implications for medical practitioners, patients, and pharmaceutical companies. If the drug is found to be effective, it can be used to treat patients with the disease, improve their health outcomes, and generate revenue for the pharmaceutical company.
• Educational research: A study on the impact of technology on student learning can have implications for educators and policymakers. If the study finds that technology improves student learning outcomes, educators can incorporate technology into their teaching methods, and policymakers can allocate more resources to technology in schools.
• Social work research: A study on the effectiveness of a new intervention program for individuals with mental health issues can have implications for social workers, mental health professionals, and policymakers. If the program is found to be effective, social workers and mental health professionals can incorporate it into their practice, and policymakers can allocate more resources to the program.
• Environmental research: A study on the impact of climate change on a particular ecosystem can have implications for environmentalists, policymakers, and industries. If the study finds that the ecosystem is at risk, environmentalists can advocate for policy changes to protect the ecosystem, policymakers can allocate resources to mitigate the impact of climate change, and industries can adjust their practices to reduce their carbon footprint.
• Economic research: A study on the impact of minimum wage on employment can have implications for policymakers and businesses. If the study finds that increasing the minimum wage does not lead to job losses, policymakers can implement policies to increase the minimum wage, and businesses can adjust their payroll practices.

How to Write Implications in Research

Writing implications in research involves discussing the potential outcomes or consequences of your findings and the practical applications of your study’s results. Here are some steps to follow when writing implications in research:

• Summarize your key findings: Before discussing the implications of your research, briefly summarize your key findings. This will provide context for your implications and help readers understand how your research relates to your conclusions.
• Identify the implications: Identify the potential implications of your research based on your key findings. Consider how your results might be applied in the real world, what further research might be necessary, and what other areas of study could be impacted by your research.
• Connect implications to research question: Make sure that your implications are directly related to your research question or hypotheses. This will help to ensure that your implications are relevant and meaningful.
• Consider limitations : Acknowledge any limitations or weaknesses of your research, and discuss how these might impact the implications of your research. This will help to provide a more balanced view of your findings.
• Discuss practical applications : Discuss the practical applications of your research and how your findings could be used in real-world situations. This might include recommendations for policy or practice changes, or suggestions for future research.
• Be clear and concise : When writing implications in research, be clear and concise. Use simple language and avoid jargon or technical terms that might be confusing to readers.
• Provide a strong conclusion: Provide a strong conclusion that summarizes your key implications and leaves readers with a clear understanding of the significance of your research.

Purpose of Implications in Research

The purposes of implications in research include:

• Informing practice: The implications of research can provide guidance for practitioners, policymakers, and other stakeholders about how to apply research findings in practical settings.
• Generating new research questions: Implications can also inspire new research questions that build upon the findings of the original study.
• Identifying gaps in knowledge: Implications can help to identify areas where more research is needed to fully understand a phenomenon.
• Promoting scientific literacy: Implications can also help to promote scientific literacy by communicating research findings in accessible and relevant ways.
• Facilitating decision-making : The implications of research can assist decision-makers in making informed decisions based on scientific evidence.
• Contributing to theory development : Implications can also contribute to the development of theories by expanding upon or challenging existing theories.

When to Write Implications in Research

Here are some specific situations of when to write implications in research:

• Research proposal : When writing a research proposal, it is important to include a section on the potential implications of the research. This section should discuss the potential impact of the research on the field and its potential applications.
• Literature review : The literature review is an important section of the research paper where the researcher summarizes existing knowledge on the topic. This is also a good place to discuss the potential implications of the research. The researcher can identify gaps in the literature and suggest areas for further research.
• Conclusion or discussion section : The conclusion or discussion section is where the researcher summarizes the findings of the study and interprets their meaning. This is a good place to discuss the implications of the research and its potential impact on the field.

Advantages of Implications in Research

Implications are an important part of research that can provide a range of advantages. Here are some of the key advantages of implications in research:

• Practical applications: Implications can help researchers to identify practical applications of their research findings, which can be useful for practitioners and policymakers who are interested in applying the research in real-world contexts.
• Improved decision-making: Implications can also help decision-makers to make more informed decisions based on the research findings. By clearly identifying the implications of the research, decision-makers can understand the potential outcomes of their decisions and make better choices.
• Future research directions : Implications can also guide future research directions by highlighting areas that require further investigation or by suggesting new research questions. This can help to build on existing knowledge and fill gaps in the current understanding of a topic.
• Increased relevance: By highlighting the implications of their research, researchers can increase the relevance of their work to real-world problems and challenges. This can help to increase the impact of their research and make it more meaningful to stakeholders.
• Enhanced communication : Implications can also help researchers to communicate their findings more effectively to a wider audience. By highlighting the practical applications and potential benefits of their research, researchers can engage with stakeholders and communicate the value of their work more clearly.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Interest Statement Samples That Worked

A good research interest statement sample can be hard to find. Still, it can also be a beneficial reference for writing one and preparing for a grad school application or post-graduate position . In many cases, admissions committees use it in lieu of a grad school interview, so it is important to write a strong statement. In this blog post we’ve included research interest statement samples and several tips that will help you write a strong statement to help improve your chances of getting into grad school . 

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Article Contents 12 min read

Research interest statement samples, research statement of interest #1.

As the child of an immigrant, I have always been fascinated by the relationship between identity, geographic territory, and economic development. With the rise of globalization, there is a broader effort in the social sciences to study the link between cultural identity, human mobility, and economic development in the contemporary world. I hope that my research will contribute to this as well. I am applying to the X University Global Anthropology program, as it is the best place for me to explore my research interests and channel them towards my long-term goals. I believe that my undergraduate education and the research experience it gave me have prepared me to undertake advanced research projects, thus making me an excellent candidate for this program.

I spent the first two years of undergraduate studies taking psychology courses. I went to university knowing that I wanted to learn about human behavior and culture. I was thirsty for information, but I did not know what kind of information just yet. It wasn’t until I took an elective anthropology class in my second year and started discussing identity in anthropology that something clicked. Unlike many other social sciences, anthropology explores the different ways that cultures affect human behavior and that connected right away with my experience as an immigrant. I have been passionate about the subject ever since, and I intend on spending my career exploring this topic further.

In the long run, I am interested in understanding how geography affects the construction of one’s cultural identity, especially when it comes to immigrants. Literature already exists on the topic, but most of it examines the upper levels of this process of social reproduction, concentrating on the roles of governments and associations in promoting ties between migrants and their homelands. Prof. Jane Doe Smith is one of the anthropologists researching the transnational migration experience, and I hope to have the opportunity to work with her at X University.

I was fortunate to be part of a summer research experience as an undergraduate, which took place in several west African countries, including Mali, Senegal, and Nigeria. Dr. Sam Smith was leading the research, and my time on his team allowed me to gain hands-on experience in research while living abroad. One of the things that I did almost daily was interview the subjects in a controlled environment, and sometimes I got to be a part of traditional ceremonies. I learnt how to observe without being intrusive and how to interact with clinical subjects. The experience only strengthened my curiosity and conviction that today more than ever, we need to understand what identity is and the different factors that can affect it.

I enrolled in several challenging research-oriented courses such as Applied Statistical Inference for the Behavioral Sciences, Principles of Measurement, and more throughout my degree. I was also able to work as a research lab assistant for one of my mentors, Mr. Jonathan Smith. I worked with him while he studied the relationship between identity, culture and “self.” My main duties were to assist in the creating of surveys and other assessment materials, administer written and verbal tests to participants, create literature reviews for potential resources, create summaries of findings for analysis and other office duties such as reserving testing rooms. This particular experience allowed me to get some hands-on experience with data collection, data analysis, report preparation and the creation of data summaries.

I know that there is a lot more that I can learn from the X University. I have seen the exemplary work in anthropology and other social studies done by the staff and alumni of this school. It has inspired and convinced me beyond the shadow of a doubt that pursuing my graduate studies in your program meets my personal, academic, and professional goals objectives.

My advanced research skills, passion for anthropology and clinical research, as well as my academic proficiency make me the ideal candidate for X University's Clinical Global Anthropology Master’s program. I believe that X University’s rigorous curriculum and facilities make it the perfect place for me, my long-term career goals and my research commitments. 

I am applying to the brain and development master's program of X university because it is one of the few universities that not only has a program that combines the two disciplines that I majored in my undergraduate studies: Psychology and Linguistics; but also because it is a program that I know would allow me to grow as a researcher, contribute to my chosen fields and achieve my long-term career goals. My research is motivated by two of my favorite things: language and music. To be more specific, hip-hop music. In 20xx, Rollingstone magazine published an article stating that hip hop was now more popular than rock and roll. The rise in popularity of this initially very niche genre has sparked a conversation in specific academic fields such as psychology, sociology, linguistics, and English about the use of language within it but also the effects that it can have on those who listen to it. I hope to one day contribute to that conversation by studying the relationship between hip-hop music and vocabulary development, and I believe that pursuing this particular research interest at X university is the best way for me to do that.

There are many potential places this research may lead me and many potential topics I may explore. Furthermore, there are many things that it would allow us to learn about the effect that music has on our brains and society at large.

I was fortunate enough to work under Dr. Jane D. Smith at the University of X for two years while conducting her recently published study on vocabulary instruction for children with a developmental language disorder. During my time in her lab, I interviewed participants and put together evaluation materials for them. I was also responsible for data entry, analysis, and summarizing. This experience gave me the skills and the knowledge that allowed me to exceed expectations for my final research project in undergraduate school.

One of my undergraduate degree requirements was to complete a small independent study under the supervision of a professor. I chose to study music's effect on children's vocabulary development. Several studies look for ways to decrease the million-word gap, and I wanted to see if this thing that I am so passionate about, music, had any effect at all. I compiled multiple literature reviews and analyzed their results, and I found that there is indeed a correlation between the number of words that a child spoke and the amount of music that they were exposed to. 

This research is currently being explored on a larger scale by Prof. John Doe at X university and learning from him is one of the many reasons I have applied to this program. I took several research methodology courses throughout my degree, and I would love to enroll in the Applied Statistics for Psychology course he is currently teaching to build upon the foundational knowledge I already have. There are several other faculty members in the brain and language department with whom learning from would be a dream come true. In addition to that, working with them is a real possibility because the research they are currently doing and the research I hope to pursue are greatly matched.

I genuinely believe that X university has the curriculum and facilities that I need to meet my long-term goals and research commitments. I also believe that my academic achievements, eagerness to learn, and passion make me the perfect candidate for your program. 

A research interest statement is essential for most graduate school, post-graduate, and academic job applications . Sometimes, it may be referred to it as a " statement of intent " or "description of research interests." While they are similar, a research interest statement may require some additional information.

Generally, your statement will pride a brief overview of your research background, including your past research experience, the current state of your research, and the future research you'd like to complete.

Research interest statements are usually written in the form of a short essay. However, different graduate programs can have specific requirements , so make sure to check the program you are applying to and read their particular instructions.

The exact requirements of the research interest statement can vary depending on where you are applying and for what position. Most faculty positions will need you to produce a separate file for your statement, and most of the time, for an academic program, you can simply include your statement within your CV for graduate school .

What is the Point of a Research Interest Statement?

Your research statement plays a big role in the committee's decision of whether to accept you. Undergraduate programs are centered around classes and grades, but graduate and post-graduate programs are all about your research and what your research contributes to your program of choice.

Ultimately, they are trying to figure out if you, as a person, and your research, would be a good fit for their program. A strong statement will showcase your passion for research, the connection between your future interests and the program, and the extent of your writing skills.

Writing a strong statement can be helpful to you, as well. Having to explain your research and talk about your goals coherently will give you a chance to define your future research and career plans, as well as academic interests. Additionally, once you are accepted you may reuse parts of your research interest statement to apply for graduate school scholarships or grants . 

Looking for tips on getting into grad school? This infographic is for you:

What To Include in Your Research Interest Statement?

Unless otherwise stated by the program or faculty that you are applying to, your statement should be one to two pages long or between 500 and 1,000 words.

There is rarely a specific question or prompt but they might ask for a particular detail to be included in your interest statement. For example, a university’s requirements may look something like this:

“In your statement of interest, you should detail your study and/or research interests and reasons for seeking admission. You must identify a faculty member from the Anthropology Department with whom you are interested in being your advisor. The length of a statement of intent should be 2 pages in length (single-spaced, Times New Roman font size 12 point)”

Your research statement should be in an academic essay format. It needs to be concise, well-organized, and easy to read. For graduate school, PhD or post-doc positions , your research interest statement will usually be a part of your grad school resume . We recommend that you stick to the following things when it comes to the format:

Your statement should include a brief history of your past and/or current research such as your undergraduate research experience .

The statement should also address your proposed research in the program you are applying to. What questions do you want to solve and why are you applying to this specific institution to solve these problems?

Finally, you want to talk about the future of your research and how will your proposed research lead to bigger questions.

How to Write a Strong Research Interest Statement

Outline the content of your research interest statement.

• Introduction: This is a functional academic document, unlike college essays or personal statements, so you want to go straight to the point and focus on the key information that needs to be conveyed. You want to use this paragraph to tell the committee why you are writing this statement. In other words, you should clearly state what kind of research you are interested in pursuing at the institution in question and explain why you are drawn to the subject. 
• Body: This is your “why and how” paragraphs. In 2 or 3 paragraphs, you should expand on your interest, background, accomplishments, and plans in the field of research. Depending on your level of experience, you may use this time to talk about your previous or current research. If you do not have much experience, then you may use this paragraph to talk about any skills or academic achievements that could be relevant to your future research. 
• Conclusion: To conclude, you should restate your interest and tie it back to the research you intend to continue at the university. Be specific about the direction you’d like to take the research in, who you’d like to work with, and what the institution has that would help you. We also suggest including a concise statement that reiterates your unique suitability for the program, and what you can contribute to it and your chosen field.

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Tips for Writing Your Statement

• Give Yourself Ample Time: Much like with other components of your application, like your CV or a graduate school interview question , preparation is the key to success. You should give yourself enough time to thoroughly research the program or faculty you are applying to, gather all the information or documents that can aid you in writing, and then write and rewrite as many times as you need to. Give yourself at least 6 weeks to draft, redraft, and finalize your statement. You may also want to consider investing in a graduate school admissions consultant as they have more experience writing these types of essays and may see things that you can’t.
• Research the Program/Faculty: The purpose of your research interest statement is to tell the committee all about your research plans, how it will contribute to the field and convince them that not only is their institution is the best place for it, but that you will be an asset to them as a candidate. You should have a good idea of the research interests of the professors in the institutions you are applying to, any specialized equipment or laboratories that could aid in your research, and more broadly how your research goals fit in with their goals. 

Common Pitfalls to Avoid

• Being Too Personal: Often, students will confuse the grad school statement of purpose and the research interest statement or statement of intent. It is essential to understand the difference between these two documents because some programs will ask for both of these documents. There is quite a bit of overlap between the two essays, so they are very easy to mix up. Both documents ask applicants to focus on their research interests, relevant past academic and professional experiences, and their long-term goals in the field. However, a statement of purpose is more like a personal statement that describes your overall journey and suitability for a program. In contrast, a research interest statement is a more formal academic document about the research you intend to pursue in a program. It will include many details such as the faculty members you want to work with, the program facilities and resources you wish to use, etc.
• Not Following Guidelines: As mentioned earlier, these statements can vary depending on the discipline and the faculty. It is crucial that you review all the institution's guidelines and follow them. Some schools will have a specific word count, others may simply give you a maximum and minimum word count. Others may even have a specific prompt or question that you will need to answer with your essay. You want to make sure that you are following the instructions exactly as they are provided by the program.
• Using Too Much Academic Jargon: Your statement will be read by people who are most likely knowledgeable, but they might not be from your specific field or specialty. We understand that it may not be possible to be clear about your research without using a few niche words, but try to keep them at a minimum and avoid using acronyms that are not well known outside of your specialty.
• Having One Generic Statement: The requirements of your research statement are different from one school to another, and you should tailor your letter to the program you are writing to. We know that the research and experience you are talking about are still the same, but the qualities and aspects of that experience you play up should be tailored to the school you are applying to. You should be mentioning specific professors, specialized labs, or other unique aspects of the program you are applying to. 

It is essentially an essay that provides a brief overview of your research experience and goals. It is also sometimes referred to as a "statement of intent" or "description of research interests."

This statement tells the admissions committee more about you as an applicant. This includes your past research experience, the current state of your research, and the future research you'd like to complete.

No. Some graduate school programs might ask for a statement of purpose or a writing sample instead. You should always check the requirements of the specific program that you’re applying to.

Generally, your statement should be 400 to 1000 words or about two pages long. That said, most programs will give you guidelines so make sure you check those and follow them.

You certainly can but we do not recommend it. You should always tailor your statement to the program you are applying to. 

We recommend that you doublecheck the information provided by your chosen program as they often have specific instructions for the format of the letter. If none exist, stick to easily legible fonts, a decent font size, spacing, margins, etc.  

We recommend giving yourself at least 6 weeks to write your statement. This will give you ample time to brainstorm, write a strong letter, read it again and edit it as many times as necessary. It also gives you enough time to get expert eyes on your letter and work with them to improve it if you wish.

No. Research interest statements are often required for post-graduate school applications and for other positions in academic faculties.

Absolutely! You can always reach out to admissions professionals, such as graduate school admissions consultants or grad school essays tutors .

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Thank you for your excellent site

BeMo Academic Consulting

You are very welcome, Rasool!

Sadia Sultana

hello, thanks for providing guide line for Research Interest statement, the important aspect of scholarship application. Kindly guide me, What should be the title of the Research Statement. Thanks

Hi Sadia! Check the requirements of your school first. They might provide some info on whether a title is even needed. 

Sadia Tasnim Epa

I'm very pleased that you have mentioned every detail of research interest which helped me to clear all of my doubts.... Thank you very much.

Hi Sadia! Glad you found this helpful!

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What makes the Focus List even more helpful is that each selection is accompanied by a full Zacks Analyst Report, which explains the reasoning behind every stock's selection and why we believe it's a good pick for the long-term.

The portfolio's past performance only solidifies why investors should consider it as a starting point. For 2020, the Focus List gained 13.85% on an annualized basis compared to the S&P 500's return of 9.38%. Cumulatively, the portfolio has returned 2,519.23% while the S&P returned 854.95%. Returns are for the period of February 1, 1996 to March 31, 2021.

Focus List Methodology

When stocks are picked for the Focus List, it reflects our enduring reliance on the power of earnings estimate revisions.

Earnings estimates, or expectations of growth and profitability, come from brokerage analysts who track publicly traded companies; these analysts work together with company management to analyze every aspect that may affect future earnings, like interest rates, the economy, and sector and industry optimism.

Earnings estimate revisions are very important, since investors also need to take into consideration what a company will earn in the future.

Stocks that receive upward earnings estimate revisions are more likely to receive even more upward changes in the future. For example, if an analyst raised their estimates last month, they're more likely to do it again this month, and other analysts are likely to do the same.

Utilizing the power of earnings estimate revisions is when the Zacks Rank joins the party. A unique, proprietary stock-rating model, the Zacks Rank uses changes to quarterly earnings expectations to help investors create a winning portfolio.

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The Focus List is comprised of stocks hand-picked from a long list of #1 (Strong Buy) or #2 (Buy) ranked companies, meaning that each new addition boasts a bullish earnings consensus among analysts.

It can be very profitable to buy stocks with rising earnings estimates, as stock prices respond to revisions. By adding Focus List stocks, there's a great chance you'll be getting into companies whose future earnings estimates will be raised, which can lead to price momentum.

Focus List Spotlight: UnitedHealth Group ( UNH Quick Quote UNH - Free Report )

UnitedHealth Group, Inc. provides a wide range of health care products and services, such as health maintenance organizations (HMOs), point of service plans (POS), preferred provider organizations (PPOs), and managed fee-for-service programs.

UNH, a #3 (Hold) stock, was added to the Focus List on June 22, 2015 at $120.33 per share. Since then, shares have increased 381.68% to $579.61.

Six analysts revised their earnings estimate higher in the last 60 days for fiscal 2024, while the Zacks Consensus Estimate has increased $0.12 to $27.69. UNH also boasts an average earnings surprise of 3.3%.

Earnings for UNH are forecasted to see growth of 10.2% for the current fiscal year as well.

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• Knowledge Base

Methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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14% of PCs shipped globally in Q2 2024 were AI-capable

Tuesday, 13 August 2024

According to the latest Canalys data, 8.8 million AI-capable PCs were shipped in Q2 2024. These devices are defined as desktops and notebooks that include a chipset or block for dedicated AI workloads, such as an NPU. Shipments of AI-capable PCs represented 14% of all PCs shipped in the quarter. With all major processor vendors’ AI-capable PC roadmaps now well underway, the stage is set for a significant ramp-up in device availability and end-user adoption in the second half of 2024 and beyond. 

“The second quarter of 2024 added significant momentum to the expansion of AI-capable PCs,” said Ishan Dutt, Principal Analyst at Canalys. “June saw the launch of Copilot+ PCs incorporating Qualcomm’s Snapdragon X series of chips, based on Arm architecture. While shipment volumes in the quarter were relatively small due to the limited weeks and geographical coverage of availability, the broad commitment of Windows OEMs to adopt these products into their portfolios bodes well for the category’s outlook. In the x86 space, Intel ramped up its delivery of Core Ultra chipsets, reporting strong sequential performance for its AI PC products, while AMD announced its Ryzen AI 300 series of notebook processors in June, with product releases starting in mid-July.”   

“With a strong foundation now set, AI-capable PC shipments are poised to gain further traction in the second half of 2024,” added Dutt. “Processor vendors and OEMs are set to target a wider base of customers through new product category availability across more price points. Meanwhile, channel partners are signaling a preference for AI-related features in PCs, with close to 60% of respondents in a May poll indicating that they expect customers to favor devices with a Copilot key. The market performance of AI-capable PCs has largely aligned with expectations and the industry remains on track to ship around 44 million units in 2024 and 103 million units in 2025, according to Canalys forecasts.” 

Vendor and platform highlights 

With its entire Mac portfolio incorporating M-series chips with the Neural Engine, Apple boasts the highest AI-capable PC shipments and share of the portfolio currently. The announcement of Apple Intelligence, which is now in the developer beta stage in the US, brought greater clarity to AI use cases for Mac. When the features go live, they will be compatible with the vast majority of the existing Mac installed base, providing Apple with rapid scaling of users’ exposure to its AI experiences.   

In the Windows space, AI-capable PC shipments grew 127% sequentially. Lenovo, the highest shipping PC vendor globally, made its foray into Snapdragon X powered PCs with the Yoga Slim 7x and ThinkPad T14s, helping to boost the AI-capable share of its total Windows PC shipments to around 6% in the quarter. This represents a sequential growth of 228% for its AI-capable PC shipments. HP achieved around 8% AI-capable PC share within its Windows shipments, with the launch of the Omnibook X 14 and EliteBook Ultra G1 Snapdragon Copilot+ PCs on top of its broadening offerings of Core Ultra devices across various product lines. Dell had just below 7% of its Windows shipments represented by AI-capable PCs, backed by its strong commercial presence. It launched Copilot+ PCs across its XPS, Latitude and Inspiron lines but with staggered availability.   

“A key benefit from AI-capable PCs that has materialized for PC OEMs is the growth boost within their premium offerings,” added Dutt. “Windows PC shipments in the above US$800 range grew 9% sequentially in Q2 2024, with AI-capable PC shipments in those price bands up 126%. As the range of features from first- and third-party applications that leverage the NPU increase and the benefits to performance and efficiency become clearer, the value proposition for AI-capable PCs shall remain strong. This is especially important over the next 12 months as a significant portion of the installed base will be refreshed as part of the ongoing Windows upgrade cycle.”  

Canalys AI-capable PC definition:  

At a minimum, an AI-capable PC must be a desktop or notebook possessing a dedicated chipset or block to run on-device AI workloads. Examples of these dedicated chipsets include AMD’s XDNA, Apple’s Neural Engine, Intel’s AI Boost and Qualcomm’s Hexagon. 

Further reading: 

Canalys special report: Now and Next for AI-capable PC   

For more information, please contact:  

Ishan Dutt:  [email protected]  

About PC Analysis

Canalys’ PC Analysis service provides quarterly updated shipment data to help with accurate market sizing, competitive analysis and identifying growth opportunities in the market. Canalys PC shipment data is granular, guided by a strict methodology, and broken down by market, vendor and channel, with additional splits, such as GPU, CPU, storage and memory. In addition, Canalys also publishes quarterly forecasts to help better understand the future trajectory and changing landscape of the PC industry.  

About Canalys

Canalys is an independent analyst company that strives to guide clients on the future of the technology industry and to think beyond the business models of the past. We deliver smart market insights to IT, channel and service provider professionals around the world. We stake our reputation on the quality of our data, our innovative use of technology and our high level of customer service.

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Now and next for AI-capable PCs

Revolutionizing computing: ai pcs and the market outlook, you may be interested in.

Worldwide tablet shipments up 18% in Q2 2024 

Global PC market recovery continues with 3% growth in Q2 2024

US PC market set for 5% growth in 2024 amid a healthy recovery trajectory 

India's PC and tablet market poised for 11% growth in 2024 

To receive media alerts directly, or for more information about our events, services or custom research and consulting capabilities, please contact us. Alternatively, you can email [email protected].

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