examples of research output

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​​Outputs from Research 

A research output is the product of research .  It can take many different forms or types.  See here for a full glossary of output types.

The tables below sets out the generic criteria for assessing outputs and the definitions of the starred levels, as used during the REF2021 exercise.

Definitions 

'World-leading', 'internationally' and 'nationally' in this context refer to quality standards. They do not refer to the nature or geographical scope of particular subjects, nor to the locus of research, nor its place of dissemination.

Definitions of Originality, Rigour and Significance

Supplementary Output criteria – Understanding the thresholds:

The 'Panel criteria' explains in more detail how the sub-panels apply the assessment criteria and interpret the thresholds:

Main Panel A: Medicine, health and life sciences  Main Panel B: Physical sciences, engineering and mathematics  Main Panel C: Social sciences  Main Panel D: Arts and humanities ​

Definition of Research for the REF

1. For the purposes of the REF, research is defined as a process of investigation leading to new insights, effectively shared.

2. It  includes  work of direct relevance to the needs of commerce, industry, culture, society, and to the public and voluntary sectors; scholarship; the invention and generation of ideas, images, performances, artefacts including design, where these lead to new or substantially improved insights; and the use of existing knowledge in experimental development to produce new or substantially improved materials, devices, products and processes, including design and construction. It excludes routine testing and routine analysis of materials, components and processes such as for the maintenance of national standards, as distinct from the development of new analytical techniques. 

It also  excludes  the development of teaching materials that do not embody original research.

3. It  includes  research that is published, disseminated or made publicly available in the form of assessable research outputs, and confidential reports 

​Output FAQs

Q.  what is a research output.

A research output is the product of research.  An underpinning principle of the REF is that all forms of research output will be assessed on a fair and equal basis.  Sub-panels will not regard any particular form of output as of greater or lesser quality than another per se.  You can access the full list of eligible output types her​e.

Q.  When is the next Research Excellence Framework?

The next exercise will be REF 2029, with results published in 2029.  It is therefore likely that we will make our submission towards the end of 2028, but the actual timetable hasn't been confirmed yet.

A sector-wide consultation is currently occurring to help refine the detail of the next exercise.  You can learn more about the emerging REF 2029 here.

Q.  Why am I being contacted now, if we don't know the final details for a future assessment?

Although we don't know all of the detail, we know that some of the core components of the previous exercise will be retained.  This will include the assessment of research outputs. 

To make the internal process more manageable and avoid a rush at the end of the REF cycle, we will be conducting an output review process on an annual basis, in some shape and form to spread the workload.

Furthermore, regardless of any external assessment frameworks, it is also important for us to understand the quality of research being produced at Edinburgh Napier University and to introduce support mechanisms that will enhance the quality of the research conducted.  This is of benefit to the University and to you and your career development.

Q. I haven't produced any REF-eligible outputs as yet, what should I do?

We recognise that not everyone contacted this year will have produced a REF-eligible output so early on in a new REF cycle.  If this is the case, you can respond with a nil return and you may be contacted again in a future annual review.

If you need additional support to help you deliver on your research objectives, please contact your line manager and/or Head of Research to discuss.

Q.  I was contacted last year to identify an output, but I have not received a notification for the 2024 annual cycle, why not?

Due to administrative capacity in RIE and the lack of detail on the REF 2029 rules relating to staff and outputs, we are restricting this years' scoring activity to a manageable volume based on a set of pre-defined, targeted criteria.

An output review process will be repeated annually.  If an output is not reviewed in the current year, we anticipate that it will be included in a future review process if it remains in your top selection.

Once we know more about the shape of future REF, we will adapt the annual process to meet the new eligibility criteria and aim to increase the volume of outputs being reviewed.

Q. I am unfamiliar with the REF criteria, and I do not feel well-enough equipped to provide a score or qualitative statement for my output/s, what should I do?

The output self-scoring field is optional.  We appreciate that some staff may not be familiar with the criteria and are therefore unable to provide a reliable score. 

The REF team has been working with Schools to develop a programme of REF awareness and output quality enhancement which aims to promote understanding of REF criteria and enable staff to score their work in future.  We aim to deliver quality enhancement training in all Schools by the end of the 2023-24 academic cycle.

Please look out for further communications on this.

For those staff who do wish to provide a score and commentary, please refer specifically to the REF main panel output criteria: Main Panel A: Medicine, health and life sciences  Main Panel B: Physical sciences, engineering and mathematics  Main Panel C: Social sciences  Main Panel D: Arts and humanities 

Q. Can I refer to Journal impact factors or other metrics as a basis of Output quality?

An underpinning principle of REF is that journal impact factors or any hierarchy of journals, journal-based metrics (this includes ABS rating, journal ranking and total citations) should not be used in the assessment o​f outputs. No output is privileged or disadvantaged on the basis of the publisher, where it is published or the medium of its publication. 

An output should be assessed on its content and contribution to advancing knowledge in its own right and in the context of the REF quality threshold criteria, irrespective of the ranking of the journal or publication outlet in which it appears.

You should refer only to the REF output quality criteria (please see definitions above) if you are adding the optional self-score and commentary field and you should not refer to any journal ranking sources.

Q. What is Open Access Policy and how does it affect my outputs?

Under current rules, to be eligible for future research assessment exercises, higher education institutions (HEIs) are required to implement processes and procedures to comply with the REF Open Access policy. 

It is a requirement for all journal articles and conference proceedings with an International Standard Serial Number (ISSN), accepted for publication after 1 April 2016, to be made open access.  This can be achieved by either publishing the output in an open access journal outlet or by depositing an author accepted manuscript version in the University's repository within three months of the acceptance date.

Although the current Open Access policy applies only to journal and conference proceedings with an ISSN, Edinburgh Napier University expects staff to deposit all forms of research output in the University research management system, subject to any publishers' restrictions.

You can read the University's Open Access Policy here .

Q. My Output is likely to form part of a portfolio of work (multi-component output), how do I collate and present this type of output for assessment?

The REF team will be working with relevant School research leadership teams to develop platforms to present multicomponent / portfolio submissions.  In the meantime, please use the commentary section to describe how your output could form part of a multicomponent submission and provide any useful contextual information about the research question your work is addressing.

Q. How will the information I provide about my outputs be used and for what purpose?

In the 2024 output cycle, a minimum of one output identified by each identified author will be reviewed by a panel of internal and external subject experts.

The information provided will be used to enable us to report on research quality measures as identified in the University R&I strategy.

Output quality data will be recorded centrally on the University's REF module in Worktribe.  Access to this data is restricted to a core team of REF staff based with the Research, Innovation and Enterprise Office and key senior leaders in the School.

The data will not be used for any other purpose, other than for monitoring REF-related preparations.

Q. Who else will be involved in reviewing my output/s?

Outputs will be reviewed by an expert panel of internal and external independent reviewers.

Q. Will I receive feedback on my Output/s?

The REF team encourages open and transparent communication relating to output review and feedback.  We will be working with senior research leaders within the School to promote this.

Q.  I have identified more than one Output, will all of my identified outputs be reviewed this year?

In the 2024 cycle, we are committed to reviewing at least one output from each contacted author via an internal, external and moderation review process in the 2024 cycle.

​Once we know more about the shape of a future REF, we will adapt the annual process to meet the new criteria / eligibility.

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Research Outputs

Scholars circulate and share research in a variety of ways and in numerous genres. Below you'll find a few common examples. Keep in mind there are many other ways to circulate knowledge: factsheets, software, code, government publications, clinical guidelines, and exhibitions, just to name a few.

Outputs Defined

Original research article.

An article published in an academic journal can go by several names: original research, an article, a scholarly article, or a peer reviewed article. This format is an important output for many fields and disciplines. Original research articles are written by one or a number of authors who typically advance a new argument or idea to their field.

Conference Presentations or Proceedings

Conferences are organized events, usually centered on one field or topic, where researchers gather to present and discuss their work. Typically, presenters submit abstracts, or short summaries of their work, before a conference, and a group of organizers select a number of researchers who will present. Conference presentations are frequently transcribed and published in written form after they are given.

Books are often composed of a collection of chapters, each written by a unique author. Usually, these kinds of books are organized by theme, with each author's chapter presenting a unique argument or perspective. Books with uniquely authored chapters are often curated and organized by one or more editors, who may contribute a chapter or foreward themselves.

Often, when researchers perform their work, they will produce or work with large amounts of data, which they compile into datasets. Datasets can contain information about a wide variety of topics, from genetic code to demographic information. These datasets can then be published either independently, or as an accompaniment to another scholarly output, such as an article. Many scientific grants and journals now require researchers to publish datasets.

For some scholars, artwork is a primary research output. Scholars’ artwork can come in diverse forms and media, such as paintings, sculptures, musical performances, choreography, or literary works like poems. s.

Reports can come in many forms and may serve many functions. They can be authored by one or a number of people, and are frequently commissioned by government or private agencies. Some examples of reports are market reports, which analyze and predict a sector of an economy, technical reports, which can explain to researchers or clients how to complete a complex task, or white papers, which can inform or persuade an audience about a wide range of complex issues.

Digital Scholarship

Digital scholarship is a research output that significantly incorporates or relies on digital methodologies, authoring, presentation, and presentation. Digital scholarship often complements and adds to more traditional research outputs, and may be presented in a multimedia format. Some examples include mapping projects; multimodal projects that may be composed of text, visual, and audio elements; or digital, interactive archives.

Researchers from every field and discipline produce books as a research output. Because of this, books can vary widely in content, length, form, and style, but often provide a broad overview of a topic compared to research outputs that are more limited in length, such as articles or conference proceedings. Books may be written by one or many authors, and researchers may contribute to a book in a number of ways: they could author an entire book, write a forward, or collect and organize existing works in an anthology, among others.

Scholars may be called upon by media outlets to share their knowledge about the topic they study. Interviews can provide an opportunity for researchers to teach a more general audience about the work that they perform.

Article in a Newspaper or Magazine

While a significant amount of researchers’ work is intended for a scholarly audience, occasionally researchers will publish in popular newspapers or magazines. Articles in these popular genres can be intended to inform a general audience of an issue in which the researcher is an expert, or they may be intended to persuade an audience about an issue.

In addition to other scholarly outputs, many researchers also compose blogs about the work they do. Unlike books or articles, blogs are often shorter, more general, and more conversational, which makes them accessible to a wider audience. Blogs, again unlike other formats, can be published almost in real time, which can allow scholars to share current developments of their work.

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Research Impact : Outputs and Activities

• Outputs and Activities
• Establishing Your Author Name and Presence
• Enhancing Your Impact
• Tracking Your Work
• Telling Your Story
• Impact Frameworks

What are Scholarly Outputs and Activities?

Scholarly/research outputs and activities represent the various outputs and activities created or executed by scholars and investigators in the course of their academic and/or research efforts.

One common output is in the form of scholarly publications which are defined by Washington University as:

". . . articles, abstracts, presentations at professional meetings and grant applications, [that] provide the main vehicle to disseminate findings, thoughts, and analysis to the scientific, academic, and lay communities. For academic activities to contribute to the advancement of knowledge, they must be published in sufficient detail and accuracy to enable others to understand and elaborate the results. For the authors of such work, successful publication improves opportunities for academic funding and promotion while enhancing scientific and scholarly achievement and repute."

Examples of activities include: editorial board memberships, leadership in professional societies, meeting organizer, consultative efforts, contributions to successful grant applications, invited talks and presentations, admininstrative roles, contribution of service to a clinical laboratory program, to name a few. For more examples of activities, see Washington University School of Medicine Appointments & Promotions Guidelines and Requirements or the "Examples of Outputs and Activities" box below. Also of interest is Table 1 in the " Research impact: We need negative metrics too " work.

Tracking your research outputs and activities is key to being able to document the impact of your research. One starting point for telling a story about your research impact is your publications. Advances in digital technology afford numerous avenues for scholars to not only disseminate research findings but also to document the diffusion of their research. The capacity to measure and report tangible outcomes can be used for a variety of purposes and tailored for various audiences ranging from the layperson, physicians, investigators, organizations, and funding agencies. Publication data can be used to craft a compelling narrative about your impact. See Quantifying the Impact of My Publications for examples of how to tell a story using publication data.

Another tip is to utilize various means of disseminating your research. See Strategies for Enhancing Research Impact for more information.

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Outputs Versus Outcomes

• First Online: 02 October 2020

Cite this chapter

• Jacqui Ewart 3 &
• Kate Ames 4  

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This chapter explores what we mean by research project deliverables—particularly the difference between outputs and outcomes. This is an increasingly important distinction to funding bodies. Research outputs, which are key performance indicators for academics, are not always the same as project outcomes. Setting expectations amongst team members and between researchers and funders is critical in the early stages of research project management, and can make the difference between whether a team is willing to work together, and/or able to be funded in an ongoing capacity. We also examine issues we can encounter when reporting for industry and government.

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Juniper, E. F. (2009). Validated questionnaires should not be modified. European Respiratory Journal , 34, 1015–1017. https://doi.org/10.1183/09031936.00110209 .

Mills-Scofield, D. (2012, November 26). It’s Not Just Semantics: Managing Outcomes Vs. Outputs. Harvard Business Review. Retrieved from https://hbr.org/2012/11/its-not-just-semantics-managing-outcomes .

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Ewart, J., Ames, K. (2020). Outputs Versus Outcomes. In: Managing Your Academic Research Project. Springer, Singapore. https://doi.org/10.1007/978-981-15-9192-1_7

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Home » Research Report – Example, Writing Guide and Types

Research Report – Example, Writing Guide and Types

Table of Contents

Research Report

Definition:

Research Report is a written document that presents the results of a research project or study, including the research question, methodology, results, and conclusions, in a clear and objective manner.

The purpose of a research report is to communicate the findings of the research to the intended audience, which could be other researchers, stakeholders, or the general public.

Components of Research Report

Components of Research Report are as follows:

Introduction

The introduction sets the stage for the research report and provides a brief overview of the research question or problem being investigated. It should include a clear statement of the purpose of the study and its significance or relevance to the field of research. It may also provide background information or a literature review to help contextualize the research.

Literature Review

The literature review provides a critical analysis and synthesis of the existing research and scholarship relevant to the research question or problem. It should identify the gaps, inconsistencies, and contradictions in the literature and show how the current study addresses these issues. The literature review also establishes the theoretical framework or conceptual model that guides the research.

Methodology

The methodology section describes the research design, methods, and procedures used to collect and analyze data. It should include information on the sample or participants, data collection instruments, data collection procedures, and data analysis techniques. The methodology should be clear and detailed enough to allow other researchers to replicate the study.

The results section presents the findings of the study in a clear and objective manner. It should provide a detailed description of the data and statistics used to answer the research question or test the hypothesis. Tables, graphs, and figures may be included to help visualize the data and illustrate the key findings.

The discussion section interprets the results of the study and explains their significance or relevance to the research question or problem. It should also compare the current findings with those of previous studies and identify the implications for future research or practice. The discussion should be based on the results presented in the previous section and should avoid speculation or unfounded conclusions.

The conclusion summarizes the key findings of the study and restates the main argument or thesis presented in the introduction. It should also provide a brief overview of the contributions of the study to the field of research and the implications for practice or policy.

The references section lists all the sources cited in the research report, following a specific citation style, such as APA or MLA.

The appendices section includes any additional material, such as data tables, figures, or instruments used in the study, that could not be included in the main text due to space limitations.

Types of Research Report

Types of Research Report are as follows:

Thesis is a type of research report. A thesis is a long-form research document that presents the findings and conclusions of an original research study conducted by a student as part of a graduate or postgraduate program. It is typically written by a student pursuing a higher degree, such as a Master’s or Doctoral degree, although it can also be written by researchers or scholars in other fields.

Research Paper

Research paper is a type of research report. A research paper is a document that presents the results of a research study or investigation. Research papers can be written in a variety of fields, including science, social science, humanities, and business. They typically follow a standard format that includes an introduction, literature review, methodology, results, discussion, and conclusion sections.

Technical Report

A technical report is a detailed report that provides information about a specific technical or scientific problem or project. Technical reports are often used in engineering, science, and other technical fields to document research and development work.

Progress Report

A progress report provides an update on the progress of a research project or program over a specific period of time. Progress reports are typically used to communicate the status of a project to stakeholders, funders, or project managers.

Feasibility Report

A feasibility report assesses the feasibility of a proposed project or plan, providing an analysis of the potential risks, benefits, and costs associated with the project. Feasibility reports are often used in business, engineering, and other fields to determine the viability of a project before it is undertaken.

Field Report

A field report documents observations and findings from fieldwork, which is research conducted in the natural environment or setting. Field reports are often used in anthropology, ecology, and other social and natural sciences.

Experimental Report

An experimental report documents the results of a scientific experiment, including the hypothesis, methods, results, and conclusions. Experimental reports are often used in biology, chemistry, and other sciences to communicate the results of laboratory experiments.

Case Study Report

A case study report provides an in-depth analysis of a specific case or situation, often used in psychology, social work, and other fields to document and understand complex cases or phenomena.

Literature Review Report

A literature review report synthesizes and summarizes existing research on a specific topic, providing an overview of the current state of knowledge on the subject. Literature review reports are often used in social sciences, education, and other fields to identify gaps in the literature and guide future research.

Research Report Example

Following is a Research Report Example sample for Students:

Title: The Impact of Social Media on Academic Performance among High School Students

This study aims to investigate the relationship between social media use and academic performance among high school students. The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The findings indicate that there is a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students. The results of this study have important implications for educators, parents, and policymakers, as they highlight the need for strategies that can help students balance their social media use and academic responsibilities.

Introduction:

Social media has become an integral part of the lives of high school students. With the widespread use of social media platforms such as Facebook, Twitter, Instagram, and Snapchat, students can connect with friends, share photos and videos, and engage in discussions on a range of topics. While social media offers many benefits, concerns have been raised about its impact on academic performance. Many studies have found a negative correlation between social media use and academic performance among high school students (Kirschner & Karpinski, 2010; Paul, Baker, & Cochran, 2012).

Given the growing importance of social media in the lives of high school students, it is important to investigate its impact on academic performance. This study aims to address this gap by examining the relationship between social media use and academic performance among high school students.

Methodology:

The study utilized a quantitative research design, which involved a survey questionnaire administered to a sample of 200 high school students. The questionnaire was developed based on previous studies and was designed to measure the frequency and duration of social media use, as well as academic performance.

The participants were selected using a convenience sampling technique, and the survey questionnaire was distributed in the classroom during regular school hours. The data collected were analyzed using descriptive statistics and correlation analysis.

The findings indicate that the majority of high school students use social media platforms on a daily basis, with Facebook being the most popular platform. The results also show a negative correlation between social media use and academic performance, suggesting that excessive social media use can lead to poor academic performance among high school students.

Discussion:

The results of this study have important implications for educators, parents, and policymakers. The negative correlation between social media use and academic performance suggests that strategies should be put in place to help students balance their social media use and academic responsibilities. For example, educators could incorporate social media into their teaching strategies to engage students and enhance learning. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. Policymakers could develop guidelines and policies to regulate social media use among high school students.

Conclusion:

In conclusion, this study provides evidence of the negative impact of social media on academic performance among high school students. The findings highlight the need for strategies that can help students balance their social media use and academic responsibilities. Further research is needed to explore the specific mechanisms by which social media use affects academic performance and to develop effective strategies for addressing this issue.

Limitations:

One limitation of this study is the use of convenience sampling, which limits the generalizability of the findings to other populations. Future studies should use random sampling techniques to increase the representativeness of the sample. Another limitation is the use of self-reported measures, which may be subject to social desirability bias. Future studies could use objective measures of social media use and academic performance, such as tracking software and school records.

Implications:

The findings of this study have important implications for educators, parents, and policymakers. Educators could incorporate social media into their teaching strategies to engage students and enhance learning. For example, teachers could use social media platforms to share relevant educational resources and facilitate online discussions. Parents could limit their children’s social media use and encourage them to prioritize their academic responsibilities. They could also engage in open communication with their children to understand their social media use and its impact on their academic performance. Policymakers could develop guidelines and policies to regulate social media use among high school students. For example, schools could implement social media policies that restrict access during class time and encourage responsible use.

References:

• Kirschner, P. A., & Karpinski, A. C. (2010). Facebook® and academic performance. Computers in Human Behavior, 26(6), 1237-1245.
• Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Journal of the Research Center for Educational Technology, 8(1), 1-19.
• Pantic, I. (2014). Online social networking and mental health. Cyberpsychology, Behavior, and Social Networking, 17(10), 652-657.
• Rosen, L. D., Carrier, L. M., & Cheever, N. A. (2013). Facebook and texting made me do it: Media-induced task-switching while studying. Computers in Human Behavior, 29(3), 948-958.

Note*: Above mention, Example is just a sample for the students’ guide. Do not directly copy and paste as your College or University assignment. Kindly do some research and Write your own.

Applications of Research Report

Research reports have many applications, including:

• Communicating research findings: The primary application of a research report is to communicate the results of a study to other researchers, stakeholders, or the general public. The report serves as a way to share new knowledge, insights, and discoveries with others in the field.
• Informing policy and practice : Research reports can inform policy and practice by providing evidence-based recommendations for decision-makers. For example, a research report on the effectiveness of a new drug could inform regulatory agencies in their decision-making process.
• Supporting further research: Research reports can provide a foundation for further research in a particular area. Other researchers may use the findings and methodology of a report to develop new research questions or to build on existing research.
• Evaluating programs and interventions : Research reports can be used to evaluate the effectiveness of programs and interventions in achieving their intended outcomes. For example, a research report on a new educational program could provide evidence of its impact on student performance.
• Demonstrating impact : Research reports can be used to demonstrate the impact of research funding or to evaluate the success of research projects. By presenting the findings and outcomes of a study, research reports can show the value of research to funders and stakeholders.
• Enhancing professional development : Research reports can be used to enhance professional development by providing a source of information and learning for researchers and practitioners in a particular field. For example, a research report on a new teaching methodology could provide insights and ideas for educators to incorporate into their own practice.

How to write Research Report

Here are some steps you can follow to write a research report:

• Identify the research question: The first step in writing a research report is to identify your research question. This will help you focus your research and organize your findings.
• Conduct research : Once you have identified your research question, you will need to conduct research to gather relevant data and information. This can involve conducting experiments, reviewing literature, or analyzing data.
• Organize your findings: Once you have gathered all of your data, you will need to organize your findings in a way that is clear and understandable. This can involve creating tables, graphs, or charts to illustrate your results.
• Write the report: Once you have organized your findings, you can begin writing the report. Start with an introduction that provides background information and explains the purpose of your research. Next, provide a detailed description of your research methods and findings. Finally, summarize your results and draw conclusions based on your findings.
• Proofread and edit: After you have written your report, be sure to proofread and edit it carefully. Check for grammar and spelling errors, and make sure that your report is well-organized and easy to read.
• Include a reference list: Be sure to include a list of references that you used in your research. This will give credit to your sources and allow readers to further explore the topic if they choose.
• Format your report: Finally, format your report according to the guidelines provided by your instructor or organization. This may include formatting requirements for headings, margins, fonts, and spacing.

Purpose of Research Report

The purpose of a research report is to communicate the results of a research study to a specific audience, such as peers in the same field, stakeholders, or the general public. The report provides a detailed description of the research methods, findings, and conclusions.

Some common purposes of a research report include:

• Sharing knowledge: A research report allows researchers to share their findings and knowledge with others in their field. This helps to advance the field and improve the understanding of a particular topic.
• Identifying trends: A research report can identify trends and patterns in data, which can help guide future research and inform decision-making.
• Addressing problems: A research report can provide insights into problems or issues and suggest solutions or recommendations for addressing them.
• Evaluating programs or interventions : A research report can evaluate the effectiveness of programs or interventions, which can inform decision-making about whether to continue, modify, or discontinue them.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies.

When to Write Research Report

A research report should be written after completing the research study. This includes collecting data, analyzing the results, and drawing conclusions based on the findings. Once the research is complete, the report should be written in a timely manner while the information is still fresh in the researcher’s mind.

In academic settings, research reports are often required as part of coursework or as part of a thesis or dissertation. In this case, the report should be written according to the guidelines provided by the instructor or institution.

In other settings, such as in industry or government, research reports may be required to inform decision-making or to comply with regulatory requirements. In these cases, the report should be written as soon as possible after the research is completed in order to inform decision-making in a timely manner.

Overall, the timing of when to write a research report depends on the purpose of the research, the expectations of the audience, and any regulatory requirements that need to be met. However, it is important to complete the report in a timely manner while the information is still fresh in the researcher’s mind.

Characteristics of Research Report

There are several characteristics of a research report that distinguish it from other types of writing. These characteristics include:

• Objective: A research report should be written in an objective and unbiased manner. It should present the facts and findings of the research study without any personal opinions or biases.
• Systematic: A research report should be written in a systematic manner. It should follow a clear and logical structure, and the information should be presented in a way that is easy to understand and follow.
• Detailed: A research report should be detailed and comprehensive. It should provide a thorough description of the research methods, results, and conclusions.
• Accurate : A research report should be accurate and based on sound research methods. The findings and conclusions should be supported by data and evidence.
• Organized: A research report should be well-organized. It should include headings and subheadings to help the reader navigate the report and understand the main points.
• Clear and concise: A research report should be written in clear and concise language. The information should be presented in a way that is easy to understand, and unnecessary jargon should be avoided.
• Citations and references: A research report should include citations and references to support the findings and conclusions. This helps to give credit to other researchers and to provide readers with the opportunity to further explore the topic.

Advantages of Research Report

Research reports have several advantages, including:

• Communicating research findings: Research reports allow researchers to communicate their findings to a wider audience, including other researchers, stakeholders, and the general public. This helps to disseminate knowledge and advance the understanding of a particular topic.
• Providing evidence for decision-making : Research reports can provide evidence to inform decision-making, such as in the case of policy-making, program planning, or product development. The findings and conclusions can help guide decisions and improve outcomes.
• Supporting further research: Research reports can provide a foundation for further research on a particular topic. Other researchers can build on the findings and conclusions of the report, which can lead to further discoveries and advancements in the field.
• Demonstrating expertise: Research reports can demonstrate the expertise of the researchers and their ability to conduct rigorous and high-quality research. This can be important for securing funding, promotions, and other professional opportunities.
• Meeting regulatory requirements: In some fields, research reports are required to meet regulatory requirements, such as in the case of drug trials or environmental impact studies. Producing a high-quality research report can help ensure compliance with these requirements.

Limitations of Research Report

Despite their advantages, research reports also have some limitations, including:

• Time-consuming: Conducting research and writing a report can be a time-consuming process, particularly for large-scale studies. This can limit the frequency and speed of producing research reports.
• Expensive: Conducting research and producing a report can be expensive, particularly for studies that require specialized equipment, personnel, or data. This can limit the scope and feasibility of some research studies.
• Limited generalizability: Research studies often focus on a specific population or context, which can limit the generalizability of the findings to other populations or contexts.
• Potential bias : Researchers may have biases or conflicts of interest that can influence the findings and conclusions of the research study. Additionally, participants may also have biases or may not be representative of the larger population, which can limit the validity and reliability of the findings.
• Accessibility: Research reports may be written in technical or academic language, which can limit their accessibility to a wider audience. Additionally, some research may be behind paywalls or require specialized access, which can limit the ability of others to read and use the findings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Article Contents

1. introduction, 2. limitations of previous research, goals, and research questions, 3. the austrian science fund, 6. discussion, 7. conclusions.

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Types of research output profiles: A multilevel latent class analysis of the Austrian Science Fund’s final project report data

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Rüdiger Mutz, Lutz Bornmann, Hans-Dieter Daniel, Types of research output profiles: A multilevel latent class analysis of the Austrian Science Fund’s final project report data, Research Evaluation , Volume 22, Issue 2, June 2013, Pages 118–133, https://doi.org/10.1093/reseval/rvs038

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Starting out from a broad concept of research output, this article looks at the question as to what research outputs can typically be expected from certain disciplines. Based on a secondary analysis of data from final project reports (ex post research evaluation) at the Austrian Science Fund (FWF), Austria’s central funding organization for basic research, the goals are (1) to find, across all scientific disciplines, types of funded research projects with similar research output profiles; and (2) to classify the scientific disciplines in homogeneous segments bottom-up according to the frequency distribution of these research output profiles. The data comprised 1,742 completed, FWF-funded research projects across 22 scientific disciplines. The multilevel latent class (LC) analysis produced four LCs or types of research output profiles: ‘Not Book’, ‘Book and Non-Reviewed Journal Article’, ‘Multiple Outputs’, and ‘Journal Article, Conference Contribution, and Career Development’. The class membership can be predicted by three covariates: project duration, requested grant sum, and project head’s age. In addition, five segments of disciplines can be distinguished: ‘Life Sciences and Medicine’, ‘Social Sciences/Arts and Humanities’, ‘Formal Sciences’, ‘Technical Sciences’, and ‘Physical Sciences’. In ‘Social Sciences/Arts and Humanities’ almost all projects are of the type ‘Book and Non-Reviewed Journal Article’, but, vice versa, not all projects of the ‘Book and Non-reviewed Journal Article’ type are in the ‘Social Sciences/Arts and Humanities’ segment. The research projects differ not only qualitatively in their output profile; they also differ quantitatively, so that projects can be ranked according to amount of output.

Research funding organizations have shown increasing interest in ex post research evaluation of the funded projects ( European Science Foundation 2011a ). For instance, the Austrian Science Fund (FWF), Austria’s central funding organization for the promotion of basic research and the subject of this article, has conducted ex post research evaluations for some years now ( Dinges 2005 ). By collecting and analysing information on the ‘progress, productivity, and quality’ ( European Science Foundation 2011b : 3) of funded projects, research funding organizations hope ‘to be able to identify gaps and opportunities, avoid duplication, encourage collaboration, and strengthen the case for research’ ( European Science Foundation 2011b : 3). As stated succinctly in the title of a 2011 working document by the European Science Foundation (ESF), a central topic in this connection is ‘The Capture and Analysis of Research Outputs’ ( European Science Foundation 2011a ). This involves the issues of what research outputs are actually important for ex post research evaluation, how they can be classified (typology) and how the data can be analysed. The ESF document provides the following definition of outputs: ‘Research outputs, as the products generated from research, include the means of evidencing, interpreting, and disseminating the findings of a research study’ ( European Science Foundation 2011a : 5).

But opinions differ on what research output categories should be included in ex post research evaluation. Without doubt, publication in a scientific journal is viewed in all scientific disciplines as the primary communication form ( European Commission 2010 ). For assessing the merits of a publication, bibliometric analyses are favoured. In the humanities and social sciences, however, the use of classical bibliometric analysis ( Glänzel 1996 ; Nederhof et al. 1989 ; Nederhof 2006 ; Van Leeuwen 2006 ) is viewed critically in the face of different forms of research outputs (e.g. monographs) and limitations of the databases ( Cronin and La Barre 2004 ; Hicks 2004 ; Archambault et al. 2006 ). For these disciplines, other forms of quantitative evaluation are under discussion ( Kousha and Thelwell 2009 ; White et al. 2009 ).

A number of authors have made a plea for extending classical biblio analysis and for broadening the concept of ‘research output’ generally ( Bourke and Butler 1996 ; Lewison 2003 ; Butler 2008 ; Huang and Chang 2008 ; Linmans 2010 ; Sarli et al. 2010 ): ‘A fair and just research evaluation should take into account the diversity of research output across disciplines and include all major forms of research publications’ ( Huang and Chang 2008 : 2018). Huang and Chang (2008) looked at an empirical analysis conducted of the publication types of all publications in the year 1998–9 across all disciplines at the University of Hong Kong and found that journal articles accounted for 90% and 99% of the total publications produced only in the disciplines medicine and physics. The other disciplines produced output in the form of very different types of written communication, such as books, book chapters, and conference and working papers. Huang and Chang’s (2008) comprehensive review of the literature on the characteristics of research output showed that especially in the humanities and social sciences, books, monographs, and book chapters are important forms of written communication.

The German Research Foundation (DFG), Germany’s central funding organization for basic research, carried out a survey in the year 2004 on the publishing strategies of researchers with regard to open access ( Deutsche Forschungsgemeinschaft 2005 ), and 1,083 DFG-funded researchers responded (response rate of 67.7%). When the researchers were asked to name their preferred form of traditional publication of their own work, they mentioned articles in scientific journals (on the average about 20 articles in 5 years). Life scientists published the largest number of journal articles (23.6 articles in 5 years) and humanities scholars and social scientists the fewest (12.7 articles in 5 years). Papers in proceedings were published far more often by engineering scholars than by researchers in other disciplines. Social scientists and humanities scholars had a greater preference for publishing their work in edited volumes and monographs than researchers in other disciplines. However, big differences in the numbers reported (e.g. number of books, number of journal articles) were found within disciplines. This study and the Huang and Chang study made it clear that not only the sciences and humanities differ greatly from other disciplines in their preferred form of written communication. There are great differences also within the natural sciences and humanities. The Expert Group on Assessment of University-Based Research set up by the European Commission came to similar conclusions ( European Commission 2010 : 26). In the opinion of the expert group, the peer-reviewed journal article is used as the primary form of written communication in all scientific disciplines. In addition, engineering scientists primarily publish in conference proceedings, whereas social scientists and humanists show a wide range of research outputs, with monographs and books as the most important forms of written communications.

The broadest concept of research output is used by the Research Council UK (RCUK) (see www.rcuk.ac.uk ), the United Kingdom’s (UK) central funding organization, and the Research Assessment Exercise (RAE) ( www.rae.ac.uk ), which in 2014 will be replaced by the new system, Research Excellence Framework (REF) (ww.ref.ac.uk). RAE and REF have the task of assessing the quality of research in higher education institutions in the UK. Whereas the RAE focuses on scientific impact, the performance measurement by the REF in addition includes societal impact—that is, any social, economic or cultural impact, or benefit beyond academia. As research output, the RAE and REF include different forms of research products (journal article, book, conference contribution, patent, software, Internet publication, and so on). The Research Outcome System (ROS) of RCUK distinguishes a total of nine categories of research outputs: publication, other research output, collaboration, communication, exploitation, recognition, staff development, further funding, and impact. The new REF is planned to extend the currently peer-supported RAE with a quantitative, indicator-based evaluation system that includes bibliometric and other quantitative methods. Butler and McAllister ( Butler and McAllister 2009 , 2011 ) spoke generally of a metric as opposed to peer review that would capture more than the classical bibliometric analysis based on journal articles does. RAE and REF are based on a research production model ( Bence and Oppenheim 2005 ) that differentiates between inputs (personnel, equipment, overheads), research generation processes, outputs (paper, articles, and so on), and utilization of research (scientific and societal impact). This kind of structuring in input, process, output, outcome/impact is also found in other frameworks for research evaluation, such as in the payback approach ( Buxton and Haney 1998 ; European Commission 2010 ; Banzi et al. 2011 ) and other national and international evaluation systems ( European Commission 2010 ).

Previous research on research outputs has had the following limitations:

As the databases for the empirical analysis, studies up to now used mainly literature databases ( Glänzel 1996 ; Nederhof et al. 1989 ) and (survey) data from researchers ( Deutsche Forschungsgemeinschaft 2005 ; Huang and Chang 2008 ). Therefore, the unit of analysis was people and not projects (European Science Foundation 2011). But the different research outputs and also inputs (e.g. human resources, funding) are tied with the research projects.

For the individual disciplines, the frequencies of certain research outputs were presented mostly in totals and separately without any closer examination of the combination of different research outputs in the form of a core profile. For example, some disciplines focus more on monographs and conference contributions and not so much on journal articles, whereas for other disciplines it is just the opposite. Beyond that, the variability of research output within a discipline, such as that found in a study conducted by the DFG ( Deutsche Forschungsgemeinschaft 2005 ), was hardly considered.

The studies often did not describe the research output comprehensively, as the RAE, REF, and RCUK do, for instance, and instead restricted the study to a specific research output category, such as journal articles. This can lead to an inadequate treatment of some disciplines. Technical sciences can be at a disadvantage, for instance, if patents are not included in the study. Moreover, mostly only selected disciplines were included in the analyses, such as social sciences and humanities, so that comparative analysis of various disciplines was not possible. But research projects in different disciplines can be very similar in the profiles of research output categories (abbreviated in the following as ‘research output profiles’).

The studies did not distinguish between quality and quantity of research outputs. For example, life sciences are similar to natural sciences in research output profiles, but life sciences have a higher volume of journal articles than the natural sciences do ( Deutsche Forschungsgemeinschaft 2005 ).

The goals of our study are:

Based on a secondary analysis of data in final project reports ( Glass 1976 ) at the FWF, Austria’s central funding organization for basic research, the goals of this study were (1) to find, across all scientific disciplines, types of funded research projects with similar research output profiles; and (2) to classify the scientific disciplines in homogeneous segments (e.g. humanities, natural sciences, engineering sciences) bottom-up according to the frequency distribution of these research output profiles. We aimed to establish the types of funded research projects using multilevel latent class analysis (MLLCA) ( Vermunt 2003 ; Kimberly and Muthén 2010 ; Mutz and Seeling 2010 ; Mutz and Daniel 2012 ).

The research questions are:

Are there any types of FWF-funded projects that have different core profiles of research outputs?

Do types of research output profiles vary across scientific disciplines? Can disciplines be clustered into segments according to the different proportions of certain types of research output profiles?

How does the probability of being in a particular type of research output profile depend on a set of project-related covariates (e.g. requested grant sum)?

Is there any additional variability within types of research output profiles that allows for a quantitative ranking of projects according to higher or lower research productivity?

The FWF is Austria’s central funding organization for the promotion of basic research. It is equally committed to all scientific disciplines. The body responsible for funding decisions at the FWF is the board of trustees, made up of 26 elected reporters and 26 alternates ( Bornmann 2012 ; Fischer and Reckling 2010 ; Mutz, Bornmann and Daniel 2012a , 2012b ; Sturn and Novak 2012 ). For each grant application, the FWF obtains at least two international expert reviews (ex ante evaluation). The number of reviewers depends on the amount of funding requested. The expert review consists (among other things) of an extensive written comment and a rating providing an overall numerical assessment of the application. At the FWF board’s decision meetings, the reporters present the written reviews and ratings of each grant application. In the period from 1999 to 2009 the approval rate of proposals was 44.2%. Since 2003, all funded projects are evaluated after completion ( Dinges 2005 ) (see www.fwf.ac.at/de/projects/evaluation-fwf.html ). The FWF surveys the FWF-funded researchers, asking them to report the outputs of their research projects using a category system that is akin to the research output system of RCUK. Additionally, referees are requested to provide a brief review giving their opinions on aspects of the final project report. They are also requested to assign a numerical rating to each aspect. The final reports were used for accountability purposes and to improve the quality of FWF’s decision procedure ( Dinges 2005 ).

The data for this study comprised 1,742 FWF-funded research projects called ‘Stand-Alone Projects’ across all fields of science (22 scientific disciplines classified into six research areas), which contributed to 60% of all FWF grants (‘Stand-Alone Projects’, ‘Special Research Programs’, ‘Awards and Prizes’, ‘Transnational Funding Activities’) and finished within a period of 9 years (2002–10). The labelling of the scientific disciplines and the research areas was adopted from the FWF ( Fischer and Reckling 2010 ). Each project head was requested to report the results of his or her research project by completing a form (final project report) containing several sections (summary for public relations; brief project report; information on project participants; attachments; collaboration with FWF).

Of the 1,742 completed FWF-funded research projects ( Table 1 ), most were in the natural sciences (31.6%), and the fewest were in the social sciences (6.0%) and technical sciences (4.5%). The finished projects (end of funding) were approved for funding in the period 1999–2010, one-third of them in 2003–4 alone. Due to still ongoing research projects, projects approved for funding in 2007–8 make up only 3.9% of the total database of 1,742 FWF-funded research projects. The average duration of the research projects was 39 months. In 84.5% of the projects, the project heads were men. The average age of the project heads was 47.

Sample description ( N = 1,742 completed FWF-funded research projects)

Note : N = frequency, per cent = column per cent, M = mean, SD = standard deviation, range = minimum and maximum.

The following six research output categories were captured in quantity and number (count data) and served as the basis for the analysis: publication (peer-reviewed journal article; non-peer-reviewed journal article, monograph, anthology, mass communication, i.e. any kind of publication in mass media, e.g. newspaper article), conference contribution (invited paper, paper, poster), award, patent, career development (diploma/degree, PhD dissertation, habilitation thesis) follow-up project (FWF funded or not). It was not differentiated between different sub-categories of the mentioned research output categories. For example, hybrid, open access and standard peer-reviewed journal articles or ongoing or terminated PhD dissertations were summarized under the respective research output category. In order to avoid problems with different publication lags, the FWF treated equally manuscripts, already published, and manuscripts, accepted for publication. The ex post evaluation approach of the FWF does not distinguish between project publications written in English and written in any other language.

Because of strongly skewed distributions, the count variables were transformed in 2-point to 5-point ordinal scale variables with at most equally sized ordinal classes, to avoid sparse classes or cells in a multivariate statistical analysis. To draw up a typology, actually, binary variables might be sufficient in which it was coded whether the particular research output category (e.g. monograph) existed (= 1) for a research project or not (= 0). However, because we wanted to differentiate a qualitative dimension (types) and a quantitative dimension (amount of output), we chose an ordinal scale with a sparse number of ordinal classes that in addition allow a quantitative assessment.

The research output variables ( Table 2 ) show a large share of zeros. The most frequently produced types of publication were reviewed journal articles (an average of five per project) and conference papers (on average nine), with a large variance across the research projects. For publication of research results, monographs are used the least (0.2 monographs per project).

Data description ( N = 1,742 FWF-funded research projects)

Note : Per cent = row per cent, M = mean of the raw data, SD = standard deviation of the raw data, Max = maximum, R 2 indicates how well an indicator is explained by the final LC model.

In a review of the literature Gonzalez-Brambila and Velosos (2007) discuss age, sex, education, and cohort effects as empirically investigated determinants of research outputs. In our study, we included the following covariates to predict research profile type membership ( Table 1 ): time period of the approval decision, time period of the project end, project duration; overall rating of the proposal, requested grant sum; gender and age of the project head. This information was taken from an ex ante evaluation of the project proposals. In the ex ante evaluation, two to three reviewers rated each proposal on a scale from 1 to 100 (ascending from poor to excellent). The mean of the overall ratings of a proposal averaged across reviewers was 89.7 (minimum: 61.7, maximum: 100).

4.2 Statistical procedure

Latent Class Analysis (LCA) in its basic structure can be defined as a statistical procedure that extracts clusters of units (latent classes (LCs)) that are homogenous with respect to the observed nominal or ordinal scale variables ( McCutcheon 1987 ). Similar to factor analysis, LCs are extracted in such a way that the correlations between the observed variables should vanish completely within each LC (local stochastic independence). LCA is favoured towards cluster analysis due to the fact that fewer pre-decisions are required than in common cluster analysis procedures (e.g. similarity measure, aggregation algorithm). Efficient algorithms for parameter estimation (maximum likelihood) are used, and a broad range of different models (LCA, IRT models, multilevel models, and more) are offered ( Magidson and Vermunt 2004 ; Vermunt and Magidson 2005a ). In a more advanced version of LCA, MLLCA, the nested data structure is additionally considered. In our study, research projects are nested within certain scientific disciplines; LCs or project types might vary between scientific disciplines. In MLLCA, not only are projects grouped according to their output profiles but also scientific disciplines will be segmented according to their different proportions of types of output profiles. In the technical framework of MLLCA, LCs represent the types of research output profile, and latent clusters (GClass) indicate the segments of disciplines. It will be presumed that a project in a certain LC behaves the same way (same research output profile) irrespective of the latent cluster to which the project belongs.

In secondary analysis the problem frequently arises that the assumption of local stochastic independence does not fully hold. For instance, career development output categories like diploma/degree and PhD dissertation are more strongly correlated with one another than with the other research output categories, so that a LCA cannot completely clarify the association between the two career development outputs. There are three possible ways to handle this problem ( Magidson and Vermunt 2004 ): First, one or more direct effects can be added that account for the residual correlations between the observed research output variables that are responsible for the violation of the local stochastic independence assumption. Second, one or more variables that are responsible for high residual correlations can be eliminated. Third, the number of latent variables (LCs, continuous latent variables) is increased. In this study we used all three strategies. After a first model run, the residuals were inspected, and a few direct effects were included in the MLLCA model. Additionally, two variables that were responsible for high residual correlations were eliminated—non-peer-reviewed journal articles and diplomas/degrees. Last but not least a MLLCA model was tested that incorporates a continuous latent variable comparable to a factor analysis. With this C-factor not only can residual correlations among the output variables be explained but also additional quantitative differences between research projects (amount of research output) can be assessed and can be taken for a ranking of projects, respectively. If, over and above, a model fits the data with the same structure (i.e. loadings of the research output variables on the factor) for all LCs as well as or better than a model with different structures in terms of different loadings of the variables in each LC, all research projects can be compared or ranked on the same scale of the latent variable.

For statistical analysis of the data we used MLLCA as implemented in the software program Latent GOLD 4.5 ( Vermunt and Magidson 2005b ). Following Bijmolt, Paas, and Vermunt (2004) , Lukočienė, Varriale, and Vermunt (2010) and Rindskopf (2006) , in a first step we calculated a simple LCA of the research outputs to obtain types of research projects with a similar research output profile. To determine the optimal number of classes (project types, segments of disciplines), information criteria were used, such as the Bayesian information criterion (BIC) or Akaike information criterion (AIC). The lower BIC or AIC the better the model fits. These information criteria penalize models for complexity (number of parameters), making it possible to make direct comparisons among models of different numbers of parameters. Results of a simulation study conducted by Lukočienė and Vermunt (2010) for MLLCA models showed that in all simulation conditions, the more advanced criteria AIC3 ( Bozdagon 1993 ) and the BIC(k) outperformed the usual BIC to identify the true number of higher-level LCs (Lukočienė, Varriale and Vermunt 2010 ). Unlike BIC, BIC(k) uses the number of groups, here the number of disciplines, in the formula for sample size n : BIC(k) =−2 * LL – df * ln(k); AIC3 = −2 * LL −3 * df, where df denotes the degrees of freedom, LL denotes the loglikelihood. In the second step , we took the hierarchical structure of data into account, calculating an MLLCA to obtain latent clusters of scientific disciplines, or segments. In a third step we fixed the number of latent clusters of the second step and again determined the number of LCs. However, Lukočienė and Vermunt’s (2010) simulation study showed that the third step results in very small improvement of 1%. We therefore abstained from applying this step.

In the last step we included covariates in the model to explain the LC membership ( Vermunt 2010 ). However, this one-step procedure has the disadvantage that by including the covariates, the model and its parameters, respectively, could change. Therefore, a three-step procedure was suggested. First, we estimated a LC model. Second, we assigned the subjects to the LCs according to their highest posterior class membership probability. Third, the LCs were regressed on a set of covariates using a multinomial regression model. However, this procedure does not take into account the uncertainty of class membership. Bolck, Croon, and Hagenaars (2004) showed that such a modelling strategy underestimates the true relationships between LCs and covariates. Recently, Vermunt (2010) developed a procedure that takes into account the uncertainty of class membership by including the classification table that cross-tabulates modal and probabilistic class assignment ( Vermunt and Magidson (2005b) as weighting matrix into the multinomial regression model. We followed this improved three-step approach. The covariates mentioned above were included for prediction of class membership ( Table 1 ).

5.1 Latent structure of research output profiles

In the first step the nested data structure (projects are nested within scientific disciplines) was ignored, and simple LC models were explored. Table 3 shows the results of fitting the models containing one to 11 LCs with and without a continuous latent C-factor, respectively. For model comparison we used the AIC3. Out of all 22 models, Model 15 with four LCs, 107 parameters, and one C-factor shows the smallest AIC3. We therefore decided on this model. With regard to our research questions, there were four types of projects with different research output profiles (qualitative dimension). Additionally, the projects differed in their productivity, i.e. the amount of outputs, represented by the continuous latent C-factor (quantitative dimension).

Fit statistics for exploratory LC models (project types)

Note : MNR = model number, NCL = number of latent classes, LL = loglikelihood, NPAR = number of parameter, AIC3 = Akaike information criterion 3. Final model grey coloured.

Figure 1 shows the four LCs or project types with different research output profiles. The 2-point to 5-point ordinal scales were re-scaled such that the numerical values varied within the range of 0–1.0 ( Vermunt and Magidson 2005b : 117). We obtained this scaling by subtracting the lowest observed value from the class-specific mean and dividing the results by the range, where the range was nothing but the difference between highest and lowest value. The advantage of this scaling is that all variables can be depicted on the same scale as the class-specific probabilities for nominal variables. It must be noted that the LC results depicted in Fig. 1 were the results of the final MLLCA model (introduced in Section 5.2 ) and not the non-nested LC model in Table 3 . However, this does not matter, because the LC models with and without nesting do not differ.

LCs of research output profiles (* = not used in the MLLCA).

The four LCs or project types with different research output profiles can be described as follows (class sizes in per cent of the total number of projects in parentheses):

Latent Class 1 ‘ Not Book ’ (37.0%): The research output profile of this research project type is quite similar to the average profile across all projects but with fewer non-reviewed journal articles, anthologies, and monographs than the average.

Latent Class 2 ‘ Book and Non-Reviewed Journal Article ’ (35.8%): this project type uses anthologies and monographs but also non-reviewed journal articles and mass communication as primary forms of written communication. Career development—such as diploma/degree, PhD dissertation and habilitation thesis—reviewed journal articles and follow-up projects score quite below the average.

Latent Class 3 ‘ Multiple Outputs ’ (17.9%): This project type generates research outputs in multiple ways with above-average outputs as peer-reviewed journal articles, non-reviewed journal articles, anthologies, monographs, conference papers, habilitation theses, PhD dissertations, diplomas/degrees, follow-up projects, but with fewer other conference contributions.

Latent Class 4 ‘ Journal Article, Conference Contribution, and Career Development ’ (9.3%): this most productive project type focuses strongly on peer-reviewed journal articles, with many published papers in combination with conference contributions (papers or other products), career development (diploma/degree, PhD dissertation, habilitation thesis), and follow-up projects, but this type uses fewer monographs as a form of written communication.

Of all the output variables, peer-reviewed journal articles and conference contributions discriminate the best between the LCs, with a discrimination index of about 0.60 ( Table 2 , last column, R 2 ).

5.2 Multilevel latent structure of research output profiles

In a multilevel latent structure model it is presumed that there is variation among the 22 scientific disciplines in the unconditional probabilities (the probabilities belonging to each LC). In an MLLCA the 22 scientific disciplines are grouped into latent clusters or segments according to their different proportion of types of research output profiles, as obtained in Section 5.1 .

Table 4 shows the results of fitting models containing one to eight latent clusters (M 1 –M 8 ), each with four LCs and with one continuous latent C-factor, respectively. With respect to BIC(k) and AIC3, a 5-GClass model will be favoured, i.e. there are five different segments of scientific disciplines with different proportions of the project types or LCs. Additionally, using the option of ‘cluster-independent C-factor’, we tested (M 9 ) whether the same loading structure can be held in all four LCs. The BIC(k) and the AIC3 improved slightly from model M 5 to the more restricted model M 9 with 122 − 89 = 33 fewer parameters than M 5 . Therefore, the assumption of a cluster-independent C-factor held, which made it possible to compare and rank all projects on the same scale. Including direct effects, such as the association between habilitation thesis and PhD dissertation, further improved the model. Only one residual (res = 3.88) was somewhat larger than the criterion of 3.84 ( Magidson and Vermunt 2004 ). To fulfil the basic model assumption of local stochastic independence, we chose model M 10 as the final model.

Fit statistics of models for variation among scientific disciplines (GClass) with four LCs and one C-factor

Note : MNR = model number, LL = loglikelihood, NPAR = number of parameters, BIC(k) = Bayesian information criterion for k clusters, AIC3 = Akaike information criterion 3.

To assess the separation between LCs, we calculated entropy-based measures, which varied between 0 and 1.0. They show how well the observed variables were able to predict the class membership (Lukočienė, Varriale and Vermunt 2010 ). For LC, the R 2 entropy amounted to 0.78, for latent clusters R 2 entropy amounted to 0.98. The separation of both the LCs and the latent clusters is therefore very large. Another model validity index is the proportion of classification error. For each project and each LC or latent cluster a posterior probability that a project belongs to the respective class can be estimated. Out of this set of probabilities the highest one indicates the LC to which a project or discipline should be assigned (modal assignment). Overall, the modal assignments can deviate from the expected assignments according to the sum of the posterior probabilities. The classification error indicates the amount of misclassification. For model M 10 the classification error was comparatively low, with 11.0% at the level of projects and 0.7% at the level of disciplines.

Based on Fig. 1 it could be supposed that the LCs do not represent a qualitative configuration but rather a quantitative dimension, in that the individual profiles run largely parallel and differ only in the level, that is, the quantity of research output. To prove this assumption the LCs were order-restricted (model M 11 ). However, the BIC(k) as well as the AIC3 of M 11 strongly increased in comparison to all other models, with the result that the assumption of a quantitative dimension behind the LCs was not very plausible.

To illustrate the meaning of these segments of scientific disciplines, Table 5 shows the distribution of the projects among the four LCs ( Fig. 1 ) of each of the five segments of disciplines (latent clusters). The last column of numbers in Table 5 indicates the size of the LCs or types of research output profiles. The last row of numbers in Table 5 indicates the proportion of disciplines that were in each discipline segment. The latent clusters or segments of scientific disciplines can be described according to the disciplines that belong to them (cluster sizes in per cent of the total number of disciplines in parentheses):

Latent Cluster 1 ‘ Life Sciences and Medicine ’ (31.6%): biology; botany; zoology; geosciences; preclinical medicine; clinical medicine; agricultural, forestry and veterinary sciences.

Latent Cluster 2 ‘ Social Sciences / Arts and Humanities ’ (31.4%): social sciences; jurisprudence; philosophy/theology; history; linguistics and literary studies; art history; other humanities fields.

Latent Cluster 3 ‘ Formal Sciences ’ (13.9%): mathematics; computer sciences; economic sciences.

Latent Cluster 4 ‘ Technical Sciences ’ (13.5%): Other natural sciences; technical sciences; psychology.

Latent Cluster 5 ‘ Physical Sciences ’ (9.6%): physics, astronomy and mechanics; chemistry.

Relative class sizes and distribution of projects among LCs (project output types) within each latent clusters (discipline segments) for M 10 (column per cent)

Note : LC size = size of the latent class, GClass size = size of the latent clusters, proportions over 0.30 (except for class sizes) are in bold face .

The remaining columns in Table 5 show the distribution of projects in each discipline segment or the probability of a project showing a specific profile type given its latent cluster membership. For instance, of all projects falling into the first GClass 84% are in LC 1 (‘Not Book’), 0% are in LC 2 (‘Book and Non-Reviewed Journal Article’), 6% are in LC 3 (‘Multiple Outputs’), and 10% are in LC 4 (‘Journal Article, Conference Contribution, and Career Development’). High proportions in a cell indicate a strong association of the corresponding segment of disciplines in the column with the corresponding type of research output profile in the row. In this respect the segment ‘Life Sciences and Medicine’ (GClass 1) was strongly associated with the ‘Not Book’ project type (LC 1) (84% of projects of this segment), but 10% of this cluster fell also in the most productive type, ‘Journal Article, Conference Contribution, and Career Development’ (LC 4). In the segment ‘Social Sciences/Arts and Humanities’ (GClass 2) almost all projects (97%) are of the second ‘Book and Non-Reviewed Journal Article’ type (LC 2). Projects of the third segment ‘Formal Sciences’ are classified about 80% in the ‘Multiple Outputs’ type, 14% also in the ‘Not Book’ type. The fourth segment, ‘Technical Sciences’, is rather heterogeneous, with over 95% of the projects of this segment in the first three project types and 37% even in the ‘Book and Non-Reviewed Journal Article’ type (LC 2). The projects of the last segment, ‘Physical Sciences’, can be divided mainly into two groups: 38% in the first project type ‘Not Book’ and 56% in the most productive project type, ‘Journal Article, Conference Contribution, and Career Development’. Overall, except for ‘Humanities’, there is no one-to-one assignment of a segment of disciplines to a special type of research output profile. Disciplines show great heterogeneity in their research output profiles.

Figure 2 shows the LC proportions for each single discipline, structured according to the latent cluster (segments of disciplines). This finding also replicated the basic findings in Table 5 at the level of single disciplines. It is of interest that the ‘Book and Non-reviewed Journal Article’ type (LC 2) played an important role not only in ‘Social Sciences/Arts and Humanities’ but also in ‘Technical Sciences’.

Estimated proportions of the four LCs of projects for each scientific discipline (stacked bars plot), classified into one of five latent clusters (1–5, separated by dashed lines).

5.3 Explaining LC membership

To explain the LC membership we conducted a modified multilevel multinomial regression model with the latent-class membership as categorical variable and the set of covariates as predictors ( Vermunt 2010 ). Beforehand, the continuous covariates time, age, duration, overall rating of a proposal (ex ante evaluation), and requested grant sum were z -transformed ( M = 0, S = 1) to facilitate the interpretation of the regression results independently of the units of the covariates ( Table 6 ).

Wald statistics are used to assess the statistical significance of a set of parameter estimates. Using Wald statistics, the restriction is tested that each estimate in a set of parameters associated with a given covariate equals zero ( Vermunt and Magidson 2005b ). A non-significant Wald statistic indicates that the respective covariate does not differ between the LCs. Additionally, we calculated a z -test for each single parameter. There are three covariates that explained the class membership with statistically significant Wald tests: project duration, requested grant sum, and the project head’s age. The overall rating of the proposal (ex ante evaluation), for instance, had no impact on the class membership. Research projects with a duration longer than the average of 39 months were more often in LC 4 (‘Journal Article, Conference Contribution, and Career Development’) than research projects with a shorter than average duration were. The higher the requested grant sum of a project, the less probable it was for the project to be in LC 2 (‘Book and Non-Reviewed Journal Article’), but the more probable it was for it to be in LC 4 (‘Journal Article, Conference Contribution, and Career Development’). Projects where the project head was older than the average age of 47 were more frequently in LC 2 (‘Book and Non-Reviewed Journal Article’), whereas projects where the project head was younger than 47 tended to be in LC 3 (‘Multiple Outputs’). Additionally, the percentage of projects in LC 4 (‘Journal Article, Conference Contribution, and Career Development’) decreased from project end year 2002 to project end 2010.

In sum, projects that belong to the ‘Book and Non-Reviewed Journal Article’ type (LC 2) tended to have rather low requested grant sums and project heads who were older than the average, whereas the most productive ‘Journal Article, Conference Contribution, and Career Development’ type was characterized by above-average requested grant sums and above-average project durations. Further, the percentage of this most productive type decreased over time (time of project end). The third type, ‘Multiple Outputs’, tended to have younger project heads.

5.4 Ranking of projects

Until now it was assumed that output profiles of research projects can be fully explained by the LC or types of output profiles into which the projects were classified. However, as Table 3 shows, projects differed not only with respect to LCs or latent cluster but also with respect to an additional quantitative dimension, a latent C-factor, referring to classical concepts of factor analysis. Unlike LCs, all output variables have positive loadings on this dimension—namely, with the same correlation or loading structure within each LC. Thus, the higher the value in any of the output variable, the higher the value of the C-factor is. Positive values in the C-factor represent productivity above average of the projects in this LC, and negative values indicate projects with less productivity with respect to projects in the same LC. In sum, the C-factor represents productivity differences of projects within each LC, similar to a Mixed-Rasch model in psychometrics ( Mutz, Borchers and Becker 2002 ; Mutz and Daniel 2007 ). This type of ranking can be used by the FWF (and other funding organizations) for comparative evaluation of the output of different projects within a certain time period.

According to the C-factor, the projects within each LC or project type could be ranked ( Fig. 3 ) from left (projects with the highest productivity) to right (projects with the lowest productivity). Additionally, Goldstein-adjusted confidence intervals are shown which makes it possible to interpret non-overlapping intervals of two projects as statistical significant differences at the 5% probability level ( Mutz and Daniel 2007 ). Roughly speaking, only the first and the last 100 projects in each LC actually showed statistically significant differences in their C-factor values.

Rankings of projects within LCs from left (largest amount of research output) to right (smallest amount of research output) and Goldstein-adjusted confidence intervals.

The aim of this study was to conduct a secondary analysis of final report data from the FWF (ex post evaluation) for the years 2002–10 (project end) and—using multilevel LCA—to build bottom-up a typology of research projects and, further, to classify scientific disciplines according to the different proportions of the types of research output profiles found. Referring to our four research questions, the results can be summarized as follows:

The 1,742 completed FWF-funded research projects available for a final report can be classified according to the research output profiles in the following four types with relatively high discrimination: 37% of all projects are in the ‘Not Book’ type, 35.8% in the ‘Book and Non-Reviewed Journal’ type, 17.9% in the ‘Multiple Outputs’ type, and 9.3% in the ‘Journal Article, Conference Contribution, and Career Development’ type, which is the most productive type in terms of number of journal articles and career-related activities. These project types represent primarily a qualitative configuration and not a quantitative dimension according to which projects can be ranked.

The 22 scientific disciplines can be divided into five segments of disciplines based on different proportions of the types of research output profiles: 31.6% of all projects can be classified in the segment ‘Life Science and Medicine’, 31.4% in ‘Social Sciences/Arts and Humanities’, 13.9% in ‘Formal Sciences’, 13.5% in ‘Technical Sciences’ and 9.6% in ‘Physical Sciences’, such as chemistry and physics. Only the ‘Social Sciences/Arts and Humanities’ segment is almost fully associated with one research output profile (‘Book and Non-Reviewed Journal Article’ type); all other segments show different proportions of the four research output profiles. Psychology and economic sciences are usually subsumed under humanities and social sciences. But the MLLCA showed that these two scientific disciplines do not belong to the segment ‘Social Sciences/Arts and Humanities’. Additionally, the fourth and most productive type of research output profile is highly represented (56%) in the fifth segment of disciplines, ‘Physical Sciences’, and with only 10% in ‘Life Science and Medicine’, contrary to the findings of the DFG ( Deutsche Forschungsgemeinschaft 2005 ) mentioned above in the introduction. ‘Life Sciences and Medicine’ is strongly associated (84%) with the ‘Not Book’ type. Projects of the third segment, ‘Formal Sciences’, are classified about 80% in the ‘Multiple Outputs’ type and 14% also in the ‘Not Book’ type. The fourth segment, ‘Technical Sciences’, is rather heterogeneous, with over 90% of the projects in this segment in the first three project types and 37% even in the ‘Book and Non-Reviewed Journal Article’ type. In the end, the findings of the Expert Group on Assessment of University-Based Research set up by the European Commission ( European Commission 2010 ) on the disciplines’ preferred forms of communication are too simple. To sum up, there are not only differences between scientific disciplines in the research output profiles; there is also great heterogeneity of research output profiles within disciplines and segments of disciplines, respectively.

Membership in a particular project type can essentially be explained by three covariates—project duration, requested grant sum, and the project head’s age. Projects that belong to the ‘Book and Non-Reviewed Journal Article’ type tend to be characterized by small requested grant sums and project heads who are older than the average, whereas the most productive type, ‘Journal Article, Conference Contribution, and Career Development’, tends to be characterized by high requested grant sums and longer than average project durations, but whose proportion decreases the more the date of the project termination approximates the year 2010. Reviewers’ overall rating of the proposal (ex ante evaluation) had no influence on latent-class membership.

Projects differ not only in the qualitative configuration of research outputs, their research output profiles, but also with respect to a quantitative dimension that makes productivity rankings of projects possible. The higher the output of a project in each of the research output variables, the higher its value on the quantitative (latent) dimension is. Only the first and the last 100 projects within each project type differed statistically significantly on this dimension.

However, there are also some limitations of our study that have to be discussed: first, the findings represent a specific picture of the research situation in one country, namely, Austria, in a 10-year period situation, and they may not necessarily apply in other countries. The quality of the research was not considered, such as through using international reference values for bibliographic indicators ( Opthof and Leydesdorff 2010 ; Bornmann and Mutz 2011 ) or through using discipline-specific quality criteria. Second, the study included only projects (in particular, ‘Stand-Alone Projects’) that were funded by the FWF. Research projects in Austria that were funded by other research funding organizations, that were not Stand-Alone Projects (40%) or that were funded by higher education institutions themselves could not be included. Further, research projects are mostly financed by mixed funding—that is, in part by grants from various research funding organizations and in part by matching funds from the relevant higher education institution (e.g. human resources), so that research output profiles cannot necessarily be explained by covariates of a single research funding organization. Third, the persons responsible for preparing a report (here, the project heads) always have a certain leeway to mention or not mention certain results of their research as results of the FWF-funded research projects in the final report (e.g. journal articles, career development). In social psychology terms, this phenomenon can be subsumed under the concept of ‘social desirability’ ( Nederhof 1985 ). Social desirability is a psychological tendency to respond in a manner that conforms to consensual standards and general expectancies in a culture. The findings of this study could thus also in part reflect different report policies in the different scientific disciplines.

Despite these limitations, we draw the following conclusions from the results:

Concept of ‘ research output ’ : If the aim is to include all disciplines in the ex post research evaluation, it is necessary to define the term ‘research output’ more broadly, as do the RCUK and the FWF, and to include—in addition to journal articles—also other output categories, such as monographs, anthologies, conference contributions, and patents, in order to treat all disciplines fairly with regard to research output.

Arts and Humanities : As has been repeatedly demanded, the arts and humanities really should be treated as an independent and relatively uniform area ( Nederhof et al. 1989 ; Nederhof 2006 ). Instead of counting only journal articles and their citations, however, it is important to include also monographs and anthologies ( Kousha and Thelwell 2009 ). Psychology and economic sciences do not belong to the segment ‘Social Sciences/Arts and Humanities’. Therefore, it is rather problematic to subsume psychology, economic sciences, social sciences, sociology, and humanities in one unique concept, ‘Social Sciences and Humanities’, as is often the case ( Archambault et al. 2006 ; Nederhof 2006 ).

Hierarchy of the sciences : A most familiar and widespread belief is that scientific disciplines can be classified as ‘hard’ sciences and ‘soft’ sciences, with physics at the top of the hierarchy, social sciences at the bottom and biology somewhere in between ( Smith et al. 2000 ). The strategy followed here made it possible to work out, bottom-up from the research outputs of funded research projects, an empirically based typology of scientific disciplines that at its heart is not hierarchically structured. The typology found reflects much more strongly the real structure of science than the top-down classification systems of sciences allow. However, the identified research output profiles do not unambiguously indicate the segment of the discipline. For instance, almost all projects in the segment ‘Social Sciences/Arts and Humanities’ are of the ‘Book and Non-Reviewed Journal Article’ type, but not all projects of the ‘Book and Non-Reviewed Journal Article’ type are in the segment ‘Social sciences/Arts and Humanities’; there is also a high proportion of ‘Book and Non-Reviewed Journal Article’ type projects in the segment ‘Technical Sciences’.

Research output profiles: Using MLLCA, research projects are not examined with regard to few arbitrarily selected project outputs; instead, the profile or combination of multiple research outputs is analysed. This should receive more attention also in ex post research evaluations of projects.

Ranking of projects : In addition, with MLLCA a qualitative dimension of different types of projects and segments of disciplines can be distinguished from a quantitative dimension that captures research productivity. In this way, projects and possibly also scientific disciplines can be ranked according to their productivity.

Selected model parameters of the regression from LCs on covariates

Note : LC = latent class, Par = parameter estimate, SE = standard error, Wald = Wald test, df = degrees of freedom.

*p < 0.05 ( z -test) **p < 0.05 (Wald test, df = 3).

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The Research Problem & Statement

What they are & how to write them (with examples)

By: Derek Jansen (MBA) | Expert Reviewed By: Eunice Rautenbach (DTech) | March 2023

If you’re new to academic research, you’re bound to encounter the concept of a “ research problem ” or “ problem statement ” fairly early in your learning journey. Having a good research problem is essential, as it provides a foundation for developing high-quality research, from relatively small research papers to a full-length PhD dissertations and theses.

In this post, we’ll unpack what a research problem is and how it’s related to a problem statement . We’ll also share some examples and provide a step-by-step process you can follow to identify and evaluate study-worthy research problems for your own project.

Overview: Research Problem 101

What is a research problem.

• What is a problem statement?

Where do research problems come from?

• How to find a suitable research problem
• Key takeaways

A research problem is, at the simplest level, the core issue that a study will try to solve or (at least) examine. In other words, it’s an explicit declaration about the problem that your dissertation, thesis or research paper will address. More technically, it identifies the research gap that the study will attempt to fill (more on that later).

Let’s look at an example to make the research problem a little more tangible.

To justify a hypothetical study, you might argue that there’s currently a lack of research regarding the challenges experienced by first-generation college students when writing their dissertations [ PROBLEM ] . As a result, these students struggle to successfully complete their dissertations, leading to higher-than-average dropout rates [ CONSEQUENCE ]. Therefore, your study will aim to address this lack of research – i.e., this research problem [ SOLUTION ].

A research problem can be theoretical in nature, focusing on an area of academic research that is lacking in some way. Alternatively, a research problem can be more applied in nature, focused on finding a practical solution to an established problem within an industry or an organisation. In other words, theoretical research problems are motivated by the desire to grow the overall body of knowledge , while applied research problems are motivated by the need to find practical solutions to current real-world problems (such as the one in the example above).

As you can probably see, the research problem acts as the driving force behind any study , as it directly shapes the research aims, objectives and research questions , as well as the research approach. Therefore, it’s really important to develop a very clearly articulated research problem before you even start your research proposal . A vague research problem will lead to unfocused, potentially conflicting research aims, objectives and research questions .

What is a research problem statement?

As the name suggests, a problem statement (within a research context, at least) is an explicit statement that clearly and concisely articulates the specific research problem your study will address. While your research problem can span over multiple paragraphs, your problem statement should be brief , ideally no longer than one paragraph . Importantly, it must clearly state what the problem is (whether theoretical or practical in nature) and how the study will address it.

Here’s an example of a statement of the problem in a research context:

Rural communities across Ghana lack access to clean water, leading to high rates of waterborne illnesses and infant mortality. Despite this, there is little research investigating the effectiveness of community-led water supply projects within the Ghanaian context. Therefore, this study aims to investigate the effectiveness of such projects in improving access to clean water and reducing rates of waterborne illnesses in these communities.

As you can see, this problem statement clearly and concisely identifies the issue that needs to be addressed (i.e., a lack of research regarding the effectiveness of community-led water supply projects) and the research question that the study aims to answer (i.e., are community-led water supply projects effective in reducing waterborne illnesses?), all within one short paragraph.

Need a helping hand?

Wherever there is a lack of well-established and agreed-upon academic literature , there is an opportunity for research problems to arise, since there is a paucity of (credible) knowledge. In other words, research problems are derived from research gaps . These gaps can arise from various sources, including the emergence of new frontiers or new contexts, as well as disagreements within the existing research.

Let’s look at each of these scenarios:

New frontiers – new technologies, discoveries or breakthroughs can open up entirely new frontiers where there is very little existing research, thereby creating fresh research gaps. For example, as generative AI technology became accessible to the general public in 2023, the full implications and knock-on effects of this were (or perhaps, still are) largely unknown and therefore present multiple avenues for researchers to explore.

New contexts – very often, existing research tends to be concentrated on specific contexts and geographies. Therefore, even within well-studied fields, there is often a lack of research within niche contexts. For example, just because a study finds certain results within a western context doesn’t mean that it would necessarily find the same within an eastern context. If there’s reason to believe that results may vary across these geographies, a potential research gap emerges.

Disagreements – within many areas of existing research, there are (quite naturally) conflicting views between researchers, where each side presents strong points that pull in opposing directions. In such cases, it’s still somewhat uncertain as to which viewpoint (if any) is more accurate. As a result, there is room for further research in an attempt to “settle” the debate.

Of course, many other potential scenarios can give rise to research gaps, and consequently, research problems, but these common ones are a useful starting point. If you’re interested in research gaps, you can learn more here .

How to find a research problem

Given that research problems flow from research gaps , finding a strong research problem for your research project means that you’ll need to first identify a clear research gap. Below, we’ll present a four-step process to help you find and evaluate potential research problems.

If you’ve read our other articles about finding a research topic , you’ll find the process below very familiar as the research problem is the foundation of any study . In other words, finding a research problem is much the same as finding a research topic.

Step 1 – Identify your area of interest

Naturally, the starting point is to first identify a general area of interest . Chances are you already have something in mind, but if not, have a look at past dissertations and theses within your institution to get some inspiration. These present a goldmine of information as they’ll not only give you ideas for your own research, but they’ll also help you see exactly what the norms and expectations are for these types of projects.

At this stage, you don’t need to get super specific. The objective is simply to identify a couple of potential research areas that interest you. For example, if you’re undertaking research as part of a business degree, you may be interested in social media marketing strategies for small businesses, leadership strategies for multinational companies, etc.

Depending on the type of project you’re undertaking, there may also be restrictions or requirements regarding what topic areas you’re allowed to investigate, what type of methodology you can utilise, etc. So, be sure to first familiarise yourself with your institution’s specific requirements and keep these front of mind as you explore potential research ideas.

Step 2 – Review the literature and develop a shortlist

Once you’ve decided on an area that interests you, it’s time to sink your teeth into the literature . In other words, you’ll need to familiarise yourself with the existing research regarding your interest area. Google Scholar is a good starting point for this, as you can simply enter a few keywords and quickly get a feel for what’s out there. Keep an eye out for recent literature reviews and systematic review-type journal articles, as these will provide a good overview of the current state of research.

At this stage, you don’t need to read every journal article from start to finish . A good strategy is to pay attention to the abstract, intro and conclusion , as together these provide a snapshot of the key takeaways. As you work your way through the literature, keep an eye out for what’s missing – in other words, what questions does the current research not answer adequately (or at all)? Importantly, pay attention to the section titled “ further research is needed ”, typically found towards the very end of each journal article. This section will specifically outline potential research gaps that you can explore, based on the current state of knowledge (provided the article you’re looking at is recent).

Take the time to engage with the literature and develop a big-picture understanding of the current state of knowledge. Reviewing the literature takes time and is an iterative process , but it’s an essential part of the research process, so don’t cut corners at this stage.

As you work through the review process, take note of any potential research gaps that are of interest to you. From there, develop a shortlist of potential research gaps (and resultant research problems) – ideally 3 – 5 options that interest you.

Step 3 – Evaluate your potential options

Once you’ve developed your shortlist, you’ll need to evaluate your options to identify a winner. There are many potential evaluation criteria that you can use, but we’ll outline three common ones here: value, practicality and personal appeal.

Value – a good research problem needs to create value when successfully addressed. Ask yourself:

• Who will this study benefit (e.g., practitioners, researchers, academia)?
• How will it benefit them specifically?
• How much will it benefit them?

Practicality – a good research problem needs to be manageable in light of your resources. Ask yourself:

• What data will I need access to?
• What knowledge and skills will I need to undertake the analysis?
• What equipment or software will I need to process and/or analyse the data?
• How much time will I need?
• What costs might I incur?

Personal appeal – a research project is a commitment, so the research problem that you choose needs to be genuinely attractive and interesting to you. Ask yourself:

• How appealing is the prospect of solving this research problem (on a scale of 1 – 10)?
• Why, specifically, is it attractive (or unattractive) to me?
• Does the research align with my longer-term goals (e.g., career goals, educational path, etc)?

Depending on how many potential options you have, you may want to consider creating a spreadsheet where you numerically rate each of the options in terms of these criteria. Remember to also include any criteria specified by your institution . From there, tally up the numbers and pick a winner.

Step 4 – Craft your problem statement

Once you’ve selected your research problem, the final step is to craft a problem statement. Remember, your problem statement needs to be a concise outline of what the core issue is and how your study will address it. Aim to fit this within one paragraph – don’t waffle on. Have a look at the problem statement example we mentioned earlier if you need some inspiration.

Key Takeaways

We’ve covered a lot of ground. Let’s do a quick recap of the key takeaways:

• A research problem is an explanation of the issue that your study will try to solve. This explanation needs to highlight the problem , the consequence and the solution or response.
• A problem statement is a clear and concise summary of the research problem , typically contained within one paragraph.
• Research problems emerge from research gaps , which themselves can emerge from multiple potential sources, including new frontiers, new contexts or disagreements within the existing literature.
• To find a research problem, you need to first identify your area of interest , then review the literature and develop a shortlist, after which you’ll evaluate your options, select a winner and craft a problem statement .

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

I APPRECIATE YOUR CONCISE AND MIND-CAPTIVATING INSIGHTS ON THE STATEMENT OF PROBLEMS. PLEASE I STILL NEED SOME SAMPLES RELATED TO SUICIDES.

Very pleased and appreciate clear information.

Your videos and information have been a life saver for me throughout my dissertation journey. I wish I’d discovered them sooner. Thank you!

Very interesting. Thank you. Please I need a PhD topic in climate change in relation to health.

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

Published on October 12, 2022 by Shona McCombes and Tegan George. Revised on November 21, 2023.

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research.

The format of a research proposal varies between fields, but most proposals will contain at least these elements:

Introduction

Literature review.

• Research design

Reference list

While the sections may vary, the overall objective is always the same. A research proposal serves as a blueprint and guide for your research plan, helping you get organized and feel confident in the path forward you choose to take.

Table of contents

Research proposal purpose, research proposal examples, research design and methods, contribution to knowledge, research schedule, other interesting articles, frequently asked questions about research proposals.

Academics often have to write research proposals to get funding for their projects. As a student, you might have to write a research proposal as part of a grad school application , or prior to starting your thesis or dissertation .

In addition to helping you figure out what your research can look like, a proposal can also serve to demonstrate why your project is worth pursuing to a funder, educational institution, or supervisor.

Research proposal length

The length of a research proposal can vary quite a bit. A bachelor’s or master’s thesis proposal can be just a few pages, while proposals for PhD dissertations or research funding are usually much longer and more detailed. Your supervisor can help you determine the best length for your work.

One trick to get started is to think of your proposal’s structure as a shorter version of your thesis or dissertation , only without the results , conclusion and discussion sections.

Download our research proposal template

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See an example

Writing a research proposal can be quite challenging, but a good starting point could be to look at some examples. We’ve included a few for you below.

• Example research proposal #1: “A Conceptual Framework for Scheduling Constraint Management”
• Example research proposal #2: “Medical Students as Mediators of Change in Tobacco Use”

Like your dissertation or thesis, the proposal will usually have a title page that includes:

• The proposed title of your project
• Your supervisor’s name
• Your institution and department

The first part of your proposal is the initial pitch for your project. Make sure it succinctly explains what you want to do and why.

Your introduction should:

• Introduce your topic
• Give necessary background and context
• Outline your  problem statement  and research questions

To guide your introduction , include information about:

• Who could have an interest in the topic (e.g., scientists, policymakers)
• How much is already known about the topic
• What is missing from this current knowledge
• What new insights your research will contribute
• Why you believe this research is worth doing

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As you get started, it’s important to demonstrate that you’re familiar with the most important research on your topic. A strong literature review  shows your reader that your project has a solid foundation in existing knowledge or theory. It also shows that you’re not simply repeating what other people have already done or said, but rather using existing research as a jumping-off point for your own.

In this section, share exactly how your project will contribute to ongoing conversations in the field by:

• Comparing and contrasting the main theories, methods, and debates
• Examining the strengths and weaknesses of different approaches
• Explaining how will you build on, challenge, or synthesize prior scholarship

Following the literature review, restate your main  objectives . This brings the focus back to your own project. Next, your research design or methodology section will describe your overall approach, and the practical steps you will take to answer your research questions.

To finish your proposal on a strong note, explore the potential implications of your research for your field. Emphasize again what you aim to contribute and why it matters.

For example, your results might have implications for:

• Improving best practices
• Informing policymaking decisions
• Strengthening a theory or model
• Challenging popular or scientific beliefs
• Creating a basis for future research

Last but not least, your research proposal must include correct citations for every source you have used, compiled in a reference list . To create citations quickly and easily, you can use our free APA citation generator .

Some institutions or funders require a detailed timeline of the project, asking you to forecast what you will do at each stage and how long it may take. While not always required, be sure to check the requirements of your project.

Here’s an example schedule to help you get started. You can also download a template at the button below.

Download our research schedule template

If you are applying for research funding, chances are you will have to include a detailed budget. This shows your estimates of how much each part of your project will cost.

Make sure to check what type of costs the funding body will agree to cover. For each item, include:

• Cost : exactly how much money do you need?
• Justification : why is this cost necessary to complete the research?
• Source : how did you calculate the amount?

To determine your budget, think about:

• Travel costs : do you need to go somewhere to collect your data? How will you get there, and how much time will you need? What will you do there (e.g., interviews, archival research)?
• Materials : do you need access to any tools or technologies?
• Help : do you need to hire any research assistants for the project? What will they do, and how much will you pay them?

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.

Methodology

• Sampling methods
• 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

Once you’ve decided on your research objectives , you need to explain them in your paper, at the end of your problem statement .

Keep your research objectives clear and concise, and use appropriate verbs to accurately convey the work that you will carry out for each one.

I will compare …

A research aim is a broad statement indicating the general purpose of your research project. It should appear in your introduction at the end of your problem statement , before your research objectives.

Research objectives are more specific than your research aim. They indicate the specific ways you’ll address the overarching aim.

A PhD, which is short for philosophiae doctor (doctor of philosophy in Latin), is the highest university degree that can be obtained. In a PhD, students spend 3–5 years writing a dissertation , which aims to make a significant, original contribution to current knowledge.

A PhD is intended to prepare students for a career as a researcher, whether that be in academia, the public sector, or the private sector.

A master’s is a 1- or 2-year graduate degree that can prepare you for a variety of careers.

All master’s involve graduate-level coursework. Some are research-intensive and intend to prepare students for further study in a PhD; these usually require their students to write a master’s thesis . Others focus on professional training for a specific career.

Critical thinking refers to the ability to evaluate information and to be aware of biases or assumptions, including your own.

Like information literacy , it involves evaluating arguments, identifying and solving problems in an objective and systematic way, and clearly communicating your ideas.

The best way to remember the difference between a research plan and a research proposal is that they have fundamentally different audiences. A research plan helps you, the researcher, organize your thoughts. On the other hand, a dissertation proposal or research proposal aims to convince others (e.g., a supervisor, a funding body, or a dissertation committee) that your research topic is relevant and worthy of being conducted.

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Research Paradigms: Explanation and Examples

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• 1st March 2022

Are you planning a research project? If so, you’ll need a research paradigm. But what exactly is a research paradigm, and why is it important? This blog post will cover the following:

●  The definition of a research paradigm

●  Why research paradigms are important

●  Common examples of research paradigms

●  Merging research paradigms

●  Expert editing and proofreading

Read on to find out more or learn about research paradigms in the video below!

The Definition of a Research Paradigm

A research paradigm is a philosophical framework that your research is based on. It offers a pattern of beliefs and understandings from which the theories and practices of your research project operate.

A research paradigm consists of ontology, epistemology, and research methodology .

●  Ontology answers the question: “What is reality?” That is, does a single reality exist within your research? An example of an ontological question would be: “Does God exist?” There are two possible realities (or ontologies) in response to this question: “Yes, God exists,” or “No, God does not exist.”

●  Epistemology is the study of knowledge. It answers the question: “How is it possible to know reality?” Epistemology incorporates the validity, parameters, and methods of acquiring knowledge. An example of an epistemological question would be: “How is it possible to know whether God exists or not?”

●  Research Methodology answers the question: “How do we go about discovering the answer or reality?” This includes the process of data collection and analysis. Research methodology should outline how you conduct your research and demonstrate that the findings are valid.

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Together, ontology and epistemology comprise research philosophy.

Research philosophy combined with research methodology comprises a research paradigm.

Why Are Research Paradigms Important?

Research paradigms are important because they form the philosophical basis of a research project. Research paradigms influence how different schools of learning (such as the sciences versus the humanities) undertake their research. Once a research philosophy has been determined, an appropriate methodology can be chosen.

Furthermore, a knowledge of the philosophical foundation of your research will increase its quality and improve your performance in any analysis you may have to undergo!

Common Examples of Research Paradigms

1. Positivism

Positivists believe that there’s a single reality that’s possible to measure and understand. Because of this, they’re most likely to use quantitative methods in their research. Typically, positivists propose a hypothesis that can be proved or disproved using statistical data analysis. Positivism tends to investigate the existence of a relationship between two variables rather than the reason behind it.

2. Constructivism

Constructivists believe that there’s no single reality or truth, but rather multiple realities. They devote themselves to understanding and interpreting the meaning attached to an action. For this reason, constructivists tend to use qualitative research methods , such as interviews or case studies, which focus on providing different perspectives. Constructivism aims to provide the answer to “why.” For example, asking “Why do 25% of the employees of an organization regularly arrive late to work?” rather than merely establishing the relationship between two variables (e.g., time of arrival at work and availability of nearby parking).

3. Pragmatists

Pragmatists believe that reality is continually interpreted and renegotiated against the backdrop of new and unpredictable situations. Because of this, the philosophy they apply in research depends on the research question itself. Pragmatists often combine positivist and constructivist principles in the same research project, using both qualitative and quantitative methods to investigate different components of a research problem. They believe that the optimal research methods are those that most successfully answer the research question.

Merging Research Paradigms

While most social science research operates from either a positivist (experimental) or constructivist paradigm, it’s possible to combine both, as the field of psychology often does. Quantitative and qualitative methodology are frequently used together in psychology, illustrating the subject’s footing in multiple research paradigms (positivist and constructivist).

Expert Editing and Proofreading

If you’re writing a research proposal or paper , you’ll want to ensure that your writing is error-free, fluent, and precise. Although re-reading your own work is valuable, it can be very helpful to get another opinion on your writing. We offer a free trial of proofreading and editing services when you submit your first document. Find out more today!

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Enhancing Program Performance with Logic Models

Division of Extension

Home » Enhancing Program Performance with Logic Models » Section 2: More about Outcomes » 2.4: Examples of Outputs vs. Outcomes

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2.4: Examples of Outputs vs. Outcomes

Try not to confuse outcomes with outputs. Outputs are the activities we do or accomplish that help achieve outcomes. Outcomes are the results of those activities for individuals, families, groups, or communities. Look at the following examples.

Another way to look at the difference between outputs and outcomes (Hatry, 1999):

• Outputs: Is the client served?
• Outcomes: Has the client’s situation improved?

Hints About What Are and Are Not Outcomes

Recruiting and training staff and volunteers.

In most cases, recruitment and training refer to internal program functions intended to support or improve program activities. The number of staff and/or volunteers recruited, the number trained, the resources committed to their development, etc. indicate the volume of these internal functions. These aspects help our programs accomplish outcomes; they are not outcomes. They do not represent benefits or changes for program participants or beneficiaries.

If, however, the program is addressing a situation of low volunteer involvement in community affairs and the purpose of the program is to increase volunteering among community residents as a part of a larger community development initiative, then increased numbers of residents volunteering in community life would be an outcome.

Number or type of participants who attend; number of clients served.

This information relates to “participation” or “reach” in our logic model that are part of Outputs. It indicates the volume or extent to which we reached the target audience. It does not indicate whether the participants or clients benefited or are doing anything differently as a result of the program, so it is not an outcome.

If, however, the purpose of the program is to increase use of a service by an underserved group, then numbers using the service would be an outcome. Notice, the outcome is not numbers attending or served; the outcome is expressed as use that indicates behavioral change.

Surveys conducted; curriculum developed; research generated.

These items refer to activities we undertake and accomplish. They may be classified as “what we do”. These are Outputs. They may be essential aspects that are necessary and make it possible for a group or community to change. But, they do not represent benefits or changes in participants and so are not outcomes.

Participant Satisfaction

For our purposes in education and outreach programming, client satisfaction may be necessary but is not sufficient. A participant may be satisfied with various aspects of the program (professionalism of staff, location, facility, timeliness, responsiveness of service, etc) but this does not mean that the person learned, benefited or his/her condition improved. If a participant is pleased and satisfied with the program, it may mean that s/he will fully participate and complete a program. As such, satisfaction can be an important step along the way to outcomes. It, however, is generally not an outcome.

In some cases, we may have to settle for participant satisfaction. In programs where individuals are extremely mobile or it is difficult to track people beyond the immediate program service, satisfaction measures may be the best we can do.

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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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How to write a phd research proposal.

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Research output

From peer-reviewed papers to book chapters, monographs and conference proceedings

From peer-reviewed papers to book chapters, monographs, conference proceedings and practice-based outputs. 

There are three ways to add your research outputs to Pure. This section refers to creating records from the pre-built templates that are available in Pure. 

You can also import your research outputs from an online source or using a BibTeX or RIS file .

There are a variety of research outputs and Pure has 47 sub-type templates that can be used. The most common types are listed in the table at the bottom of this section. Please refer to the guide for each type when adding research output records. 

A short video has been created demonstrating how to add a publication using the Contribution to Journal > Article template. The principles in this video apply to all the other research output templates. Click on the ≡ icon in the top left hand corner of the video to skip to different sections. 

There are a variety of research outputs and Pure has 47 sub-type templates that can be used. These are listed in the table below. Please refer to the guide for each type when adding research output records.

If you are not sure which template to use, please ask your local Pure contact for guidance. They will also be able to advise you on Open Access and/or REF-related requirements.

Please note that the research output records that you add will be validated by your College or School research administrator. Only validated research output records will be displayed on Edinburgh Research Explorer . 

External Persons Affiliations

The external persons affiliations on research output records in Pure populate the research network map on the Pure Portal. If there are no external persons affiliations on a research output record, the network map on profile pages will not include that research output record and may appear empty.

Please follow the steps below to add external persons affiliations to the research output records.

Claim content

It is also possible that there is already a research output record in Pure for your research output. You can ask to be added to this existing record by claiming the record. Please use the guide below to claim content.

21 Research Objectives Examples (Copy and Paste)

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Research objectives refer to the definitive statements made by researchers at the beginning of a research project detailing exactly what a research project aims to achieve.

These objectives are explicit goals clearly and concisely projected by the researcher to present a clear intention or course of action for his or her qualitative or quantitative study. 

Research objectives are typically nested under one overarching research aim. The objectives are the steps you’ll need to take in order to achieve the aim (see the examples below, for example, which demonstrate an aim followed by 3 objectives, which is what I recommend to my research students).

Research Objectives vs Research Aims

Research aim and research objectives are fundamental constituents of any study, fitting together like two pieces of the same puzzle.

The ‘research aim’ describes the overarching goal or purpose of the study (Kumar, 2019). This is usually a broad, high-level purpose statement, summing up the central question that the research intends to answer.

Example of an Overarching Research Aim:

“The aim of this study is to explore the impact of climate change on crop productivity.” 

Comparatively, ‘research objectives’ are concrete goals that underpin the research aim, providing stepwise actions to achieve the aim.

Objectives break the primary aim into manageable, focused pieces, and are usually characterized as being more specific, measurable, achievable, relevant, and time-bound (SMART).

Examples of Specific Research Objectives:

1. “To examine the effects of rising temperatures on the yield of rice crops during the upcoming growth season.” 2. “To assess changes in rainfall patterns in major agricultural regions over the first decade of the twenty-first century (2000-2010).” 3. “To analyze the impact of changing weather patterns on crop diseases within the same timeframe.”

The distinction between these two terms, though subtle, is significant for successfully conducting a study. The research aim provides the study with direction, while the research objectives set the path to achieving this aim, thereby ensuring the study’s efficiency and effectiveness.

How to Write Research Objectives

I usually recommend to my students that they use the SMART framework to create their research objectives.

SMART is an acronym standing for Specific, Measurable, Achievable, Relevant, and Time-bound. It provides a clear method of defining solid research objectives and helps students know where to start in writing their objectives (Locke & Latham, 2013).

Each element of this acronym adds a distinct dimension to the framework, aiding in the creation of comprehensive, well-delineated objectives.

Here is each step:

• Specific : We need to avoid ambiguity in our objectives. They need to be clear and precise (Doran, 1981). For instance, rather than stating the objective as “to study the effects of social media,” a more focused detail would be “to examine the effects of social media use (Facebook, Instagram, and Twitter) on the academic performance of college students.”
• Measurable: The measurable attribute provides a clear criterion to determine if the objective has been met (Locke & Latham, 2013). A quantifiable element, such as a percentage or a number, adds a measurable quality. For example, “to increase response rate to the annual customer survey by 10%,” makes it easier to ascertain achievement.
• Achievable: The achievable aspect encourages researchers to craft realistic objectives, resembling a self-check mechanism to ensure the objectives align with the scope and resources at disposal (Doran, 1981). For example, “to interview 25 participants selected randomly from a population of 100” is an attainable objective as long as the researcher has access to these participants.
• Relevance : Relevance, the fourth element, compels the researcher to tailor the objectives in alignment with overarching goals of the study (Locke & Latham, 2013). This is extremely important – each objective must help you meet your overall one-sentence ‘aim’ in your study.
• Time-Bound: Lastly, the time-bound element fosters a sense of urgency and prioritization, preventing procrastination and enhancing productivity (Doran, 1981). “To analyze the effect of laptop use in lectures on student engagement over the course of two semesters this year” expresses a clear deadline, thus serving as a motivator for timely completion.

You’re not expected to fit every single element of the SMART framework in one objective, but across your objectives, try to touch on each of the five components.

Research Objectives Examples

1. Field: Psychology

Aim: To explore the impact of sleep deprivation on cognitive performance in college students.

• Objective 1: To compare cognitive test scores of students with less than six hours of sleep and those with 8 or more hours of sleep.
• Objective 2: To investigate the relationship between class grades and reported sleep duration.
• Objective 3: To survey student perceptions and experiences on how sleep deprivation affects their cognitive capabilities.

2. Field: Environmental Science

Aim: To understand the effects of urban green spaces on human well-being in a metropolitan city.

• Objective 1: To assess the physical and mental health benefits of regular exposure to urban green spaces.
• Objective 2: To evaluate the social impacts of urban green spaces on community interactions.
• Objective 3: To examine patterns of use for different types of urban green spaces. 

3. Field: Technology

Aim: To investigate the influence of using social media on productivity in the workplace.

• Objective 1: To measure the amount of time spent on social media during work hours.
• Objective 2: To evaluate the perceived impact of social media use on task completion and work efficiency.
• Objective 3: To explore whether company policies on social media usage correlate with different patterns of productivity.

4. Field: Education

Aim: To examine the effectiveness of online vs traditional face-to-face learning on student engagement and achievement.

• Objective 1: To compare student grades between the groups exposed to online and traditional face-to-face learning.
• Objective 2: To assess student engagement levels in both learning environments.
• Objective 3: To collate student perceptions and preferences regarding both learning methods.

5. Field: Health

Aim: To determine the impact of a Mediterranean diet on cardiac health among adults over 50.

• Objective 1: To assess changes in cardiovascular health metrics after following a Mediterranean diet for six months.
• Objective 2: To compare these health metrics with a similar group who follow their regular diet.
• Objective 3: To document participants’ experiences and adherence to the Mediterranean diet.

6. Field: Environmental Science

Aim: To analyze the impact of urban farming on community sustainability.

• Objective 1: To document the types and quantity of food produced through urban farming initiatives.
• Objective 2: To assess the effect of urban farming on local communities’ access to fresh produce.
• Objective 3: To examine the social dynamics and cooperative relationships in the creating and maintaining of urban farms.

7. Field: Sociology

Aim: To investigate the influence of home offices on work-life balance during remote work.

• Objective 1: To survey remote workers on their perceptions of work-life balance since setting up home offices.
• Objective 2: To conduct an observational study of daily work routines and family interactions in a home office setting.
• Objective 3: To assess the correlation, if any, between physical boundaries of workspaces and mental boundaries for work in the home setting.

8. Field: Economics

Aim: To evaluate the effects of minimum wage increases on small businesses.

• Objective 1: To analyze cost structures, pricing changes, and profitability of small businesses before and after minimum wage increases.
• Objective 2: To survey small business owners on the strategies they employ to navigate minimum wage increases.
• Objective 3: To examine employment trends in small businesses in response to wage increase legislation.

9. Field: Education

Aim: To explore the role of extracurricular activities in promoting soft skills among high school students.

• Objective 1: To assess the variety of soft skills developed through different types of extracurricular activities.
• Objective 2: To compare self-reported soft skills between students who participate in extracurricular activities and those who do not.
• Objective 3: To investigate the teachers’ perspectives on the contribution of extracurricular activities to students’ skill development.

10. Field: Technology

Aim: To assess the impact of virtual reality (VR) technology on the tourism industry.

• Objective 1: To document the types and popularity of VR experiences available in the tourism market.
• Objective 2: To survey tourists on their interest levels and satisfaction rates with VR tourism experiences.
• Objective 3: To determine whether VR tourism experiences correlate with increased interest in real-life travel to the simulated destinations.

11. Field: Biochemistry

Aim: To examine the role of antioxidants in preventing cellular damage.

• Objective 1: To identify the types and quantities of antioxidants in common fruits and vegetables.
• Objective 2: To determine the effects of various antioxidants on free radical neutralization in controlled lab tests.
• Objective 3: To investigate potential beneficial impacts of antioxidant-rich diets on long-term cellular health.

12. Field: Linguistics

Aim: To determine the influence of early exposure to multiple languages on cognitive development in children.

• Objective 1: To assess cognitive development milestones in monolingual and multilingual children.
• Objective 2: To document the number and intensity of language exposures for each group in the study.
• Objective 3: To investigate the specific cognitive advantages, if any, enjoyed by multilingual children.

13. Field: Art History

Aim: To explore the impact of the Renaissance period on modern-day art trends.

• Objective 1: To identify key characteristics and styles of Renaissance art.
• Objective 2: To analyze modern art pieces for the influence of the Renaissance style.
• Objective 3: To survey modern-day artists for their inspirations and the influence of historical art movements on their work.

14. Field: Cybersecurity

Aim: To assess the effectiveness of two-factor authentication (2FA) in preventing unauthorized system access.

• Objective 1: To measure the frequency of unauthorized access attempts before and after the introduction of 2FA.
• Objective 2: To survey users about their experiences and challenges with 2FA implementation.
• Objective 3: To evaluate the efficacy of different types of 2FA (SMS-based, authenticator apps, biometrics, etc.).

15. Field: Cultural Studies

Aim: To analyze the role of music in cultural identity formation among ethnic minorities.

• Objective 1: To document the types and frequency of traditional music practices within selected ethnic minority communities.
• Objective 2: To survey community members on the role of music in their personal and communal identity.
• Objective 3: To explore the resilience and transmission of traditional music practices in contemporary society.

16. Field: Astronomy

Aim: To explore the impact of solar activity on satellite communication.

• Objective 1: To categorize different types of solar activities and their frequencies of occurrence.
• Objective 2: To ascertain how variations in solar activity may influence satellite communication.
• Objective 3: To investigate preventative and damage-control measures currently in place during periods of high solar activity.

17. Field: Literature

Aim: To examine narrative techniques in contemporary graphic novels.

• Objective 1: To identify a range of narrative techniques employed in this genre.
• Objective 2: To analyze the ways in which these narrative techniques engage readers and affect story interpretation.
• Objective 3: To compare narrative techniques in graphic novels to those found in traditional printed novels.

18. Field: Renewable Energy

Aim: To investigate the feasibility of solar energy as a primary renewable resource within urban areas.

• Objective 1: To quantify the average sunlight hours across urban areas in different climatic zones. 
• Objective 2: To calculate the potential solar energy that could be harnessed within these areas.
• Objective 3: To identify barriers or challenges to widespread solar energy implementation in urban settings and potential solutions.

19. Field: Sports Science

Aim: To evaluate the role of pre-game rituals in athlete performance.

• Objective 1: To identify the variety and frequency of pre-game rituals among professional athletes in several sports.
• Objective 2: To measure the impact of pre-game rituals on individual athletes’ performance metrics.
• Objective 3: To examine the psychological mechanisms that might explain the effects (if any) of pre-game ritual on performance.

20. Field: Ecology

Aim: To investigate the effects of urban noise pollution on bird populations.

• Objective 1: To record and quantify urban noise levels in various bird habitats.
• Objective 2: To measure bird population densities in relation to noise levels.
• Objective 3: To determine any changes in bird behavior or vocalization linked to noise levels.

21. Field: Food Science

Aim: To examine the influence of cooking methods on the nutritional value of vegetables.

• Objective 1: To identify the nutrient content of various vegetables both raw and after different cooking processes.
• Objective 2: To compare the effect of various cooking methods on the nutrient retention of these vegetables.
• Objective 3: To propose cooking strategies that optimize nutrient retention.

The Importance of Research Objectives

The importance of research objectives cannot be overstated. In essence, these guideposts articulate what the researcher aims to discover, understand, or examine (Kothari, 2014).

When drafting research objectives, it’s essential to make them simple and comprehensible, specific to the point of being quantifiable where possible, achievable in a practical sense, relevant to the chosen research question, and time-constrained to ensure efficient progress (Kumar, 2019). 

Remember that a good research objective is integral to the success of your project, offering a clear path forward for setting out a research design , and serving as the bedrock of your study plan. Each objective must distinctly address a different dimension of your research question or problem (Kothari, 2014). Always bear in mind that the ultimate purpose of your research objectives is to succinctly encapsulate your aims in the clearest way possible, facilitating a coherent, comprehensive and rational approach to your planned study, and furnishing a scientific roadmap for your journey into the depths of knowledge and research (Kumar, 2019). 

Kothari, C.R (2014). Research Methodology: Methods and Techniques . New Delhi: New Age International.

Kumar, R. (2019). Research Methodology: A Step-by-Step Guide for Beginners .New York: SAGE Publications.

Doran, G. T. (1981). There’s a S.M.A.R.T. way to write management’s goals and objectives. Management review, 70 (11), 35-36.

Locke, E. A., & Latham, G. P. (2013). New Developments in Goal Setting and Task Performance . New York: Routledge.

• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Number Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Word Games for Kids (Free and Easy)
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 25 Outdoor Games for Kids
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 50 Incentives to Give to Students

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9 Best Marketing Research Methods to Know Your Buyer Better [+ Examples]

Published: August 08, 2024

One of the most underrated skills you can have as a marketer is marketing research — which is great news for this unapologetic cyber sleuth.

From brand design and product development to buyer personas and competitive analysis, I’ve researched a number of initiatives in my decade-long marketing career.

And let me tell you: having the right marketing research methods in your toolbox is a must.

Market research is the secret to crafting a strategy that will truly help you accomplish your goals. The good news is there is no shortage of options.

How to Choose a Marketing Research Method

Thanks to the Internet, we have more marketing research (or market research) methods at our fingertips than ever, but they’re not all created equal. Let’s quickly go over how to choose the right one.

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1. Identify your objective.

What are you researching? Do you need to understand your audience better? How about your competition? Or maybe you want to know more about your customer’s feelings about a specific product.

Before starting your research, take some time to identify precisely what you’re looking for. This could be a goal you want to reach, a problem you need to solve, or a question you need to answer.

For example, an objective may be as foundational as understanding your ideal customer better to create new buyer personas for your marketing agency (pause for flashbacks to my former life).

Or if you’re an organic sode company, it could be trying to learn what flavors people are craving.

2. Determine what type of data and research you need.

Next, determine what data type will best answer the problems or questions you identified. There are primarily two types: qualitative and quantitative. (Sound familiar, right?)

• Qualitative Data is non-numerical information, like subjective characteristics, opinions, and feelings. It’s pretty open to interpretation and descriptive, but it’s also harder to measure. This type of data can be collected through interviews, observations, and open-ended questions.
• Quantitative Data , on the other hand, is numerical information, such as quantities, sizes, amounts, or percentages. It’s measurable and usually pretty hard to argue with, coming from a reputable source. It can be derived through surveys, experiments, or statistical analysis.

Understanding the differences between qualitative and quantitative data will help you pinpoint which research methods will yield the desired results.

For instance, thinking of our earlier examples, qualitative data would usually be best suited for buyer personas, while quantitative data is more useful for the soda flavors.

However, truth be told, the two really work together.

Qualitative conclusions are usually drawn from quantitative, numerical data. So, you’ll likely need both to get the complete picture of your subject.

For example, if your quantitative data says 70% of people are Team Black and only 30% are Team Green — Shout out to my fellow House of the Dragon fans — your qualitative data will say people support Black more than Green.

(As they should.)

Primary Research vs Secondary Research

You’ll also want to understand the difference between primary and secondary research.

Primary research involves collecting new, original data directly from the source (say, your target market). In other words, it’s information gathered first-hand that wasn’t found elsewhere.

Some examples include conducting experiments, surveys, interviews, observations, or focus groups.

Meanwhile, secondary research is the analysis and interpretation of existing data collected from others. Think of this like what we used to do for school projects: We would read a book, scour the internet, or pull insights from others to work from.

So, which is better?

Personally, I say any research is good research, but if you have the time and resources, primary research is hard to top. With it, you don’t have to worry about your source's credibility or how relevant it is to your specific objective.

You are in full control and best equipped to get the reliable information you need.

3. Put it all together.

Once you know your objective and what kind of data you want, you’re ready to select your marketing research method.

For instance, let’s say you’re a restaurant trying to see how attendees felt about the Speed Dating event you hosted last week.

You shouldn’t run a field experiment or download a third-party report on speed dating events; those would be useless to you. You need to conduct a survey that allows you to ask pointed questions about the event.

This would yield both qualitative and quantitative data you can use to improve and bring together more love birds next time around.

Best Market Research Methods for 2024

Now that you know what you’re looking for in a marketing research method, let’s dive into the best options.

Note: According to HubSpot’s 2024 State of Marketing report, understanding customers and their needs is one of the biggest challenges facing marketers today. The options we discuss are great consumer research methodologies , but they can also be used for other areas.

Primary Research

1. interviews.

Interviews are a form of primary research where you ask people specific questions about a topic or theme. They typically deliver qualitative information.

I’ve conducted many interviews for marketing purposes, but I’ve also done many for journalistic purposes, like this profile on comedian Zarna Garg . There’s no better way to gather candid, open-ended insights in my book, but that doesn’t mean they’re a cure-all.

What I like: Real-time conversations allow you to ask different questions if you’re not getting the information you need. They also push interviewees to respond quickly, which can result in more authentic answers.

What I dislike: They can be time-consuming and harder to measure (read: get quantitative data) unless you ask pointed yes or no questions.

Best for: Creating buyer personas or getting feedback on customer experience, a product, or content.

2. Focus Groups

Focus groups are similar to conducting interviews but on a larger scale.

In marketing and business, this typically means getting a small group together in a room (or Zoom), asking them questions about various topics you are researching. You record and/or observe their responses to then take action.

They are ideal for collecting long-form, open-ended feedback, and subjective opinions.

One well-known focus group you may remember was run by Domino’s Pizza in 2009 .

After poor ratings and dropping over $100 million in revenue, the brand conducted focus groups with real customers to learn where they could have done better.

It was met with comments like “worst excuse for pizza I’ve ever had” and “the crust tastes like cardboard.” But rather than running from the tough love, it took the hit and completely overhauled its recipes.

The team admitted their missteps and returned to the market with better food and a campaign detailing their “Pizza Turn Around.”

The result? The brand won a ton of praise for its willingness to take feedback, efforts to do right by its consumers, and clever campaign. But, most importantly, revenue for Domino’s rose by 14.3% over the previous year.

The brand continues to conduct focus groups and share real footage from them in its promotion:

What I like: Similar to interviewing, you can dig deeper and pivot as needed due to the real-time nature. They’re personal and detailed.

What I dislike: Once again, they can be time-consuming and make it difficult to get quantitative data. There is also a chance some participants may overshadow others.

Best for: Product research or development

Pro tip: Need help planning your focus group? Our free Market Research Kit includes a handy template to start organizing your thoughts in addition to a SWOT Analysis Template, Survey Template, Focus Group Template, Presentation Template, Five Forces Industry Analysis Template, and an instructional guide for all of them. Download yours here now.

3. Surveys or Polls

Surveys are a form of primary research where individuals are asked a collection of questions. It can take many different forms.

They could be in person, over the phone or video call, by email, via an online form, or even on social media. Questions can be also open-ended or closed to deliver qualitative or quantitative information.

A great example of a close-ended survey is HubSpot’s annual State of Marketing .

In the State of Marketing, HubSpot asks marketing professionals from around the world a series of multiple-choice questions to gather data on the state of the marketing industry and to identify trends.

The survey covers various topics related to marketing strategies, tactics, tools, and challenges that marketers face. It aims to provide benchmarks to help you make informed decisions about your marketing.

It also helps us understand where our customers’ heads are so we can better evolve our products to meet their needs.

Apple is no stranger to surveys, either.

In 2011, the tech giant launched Apple Customer Pulse , which it described as “an online community of Apple product users who provide input on a variety of subjects and issues concerning Apple.”

"For example, we did a large voluntary survey of email subscribers and top readers a few years back."

While these readers gave us a long list of topics, formats, or content types they wanted to see, they sometimes engaged more with content types they didn’t select or favor as much on the surveys when we ran follow-up ‘in the wild’ tests, like A/B testing.”  

Pepsi saw similar results when it ran its iconic field experiment, “The Pepsi Challenge” for the first time in 1975.

The beverage brand set up tables at malls, beaches, and other public locations and ran a blindfolded taste test. Shoppers were given two cups of soda, one containing Pepsi, the other Coca-Cola (Pepsi’s biggest competitor). They were then asked to taste both and report which they preferred.

People overwhelmingly preferred Pepsi, and the brand has repeated the experiment multiple times over the years to the same results.

What I like: It yields qualitative and quantitative data and can make for engaging marketing content, especially in the digital age.

What I dislike: It can be very time-consuming. And, if you’re not careful, there is a high risk for scientific error.

Best for: Product testing and competitive analysis

Pro tip:  " Don’t make critical business decisions off of just one data set," advises Pamela Bump. "Use the survey, competitive intelligence, external data, or even a focus group to give you one layer of ideas or a short-list for improvements or solutions to test. Then gather your own fresh data to test in an experiment or trial and better refine your data-backed strategy."

Secondary Research

8. public domain or third-party research.

While original data is always a plus, there are plenty of external resources you can access online and even at a library when you’re limited on time or resources.

Some reputable resources you can use include:

• Pew Research Center
• McKinley Global Institute
• Relevant Global or Government Organizations (i.e United Nations or NASA)

It’s also smart to turn to reputable organizations that are specific to your industry or field. For instance, if you’re a gardening or landscaping company, you may want to pull statistics from the Environmental Protection Agency (EPA).

If you’re a digital marketing agency, you could look to Google Research or HubSpot Research . (Hey, I know them!)

What I like: You can save time on gathering data and spend more time on analyzing. You can also rest assured the data is from a source you trust.

What I dislike: You may not find data specific to your needs.

Best for: Companies under a time or resource crunch, adding factual support to content

Pro tip: Fellow HubSpotter Iskiev suggests using third-party data to inspire your original research. “Sometimes, I use public third-party data for ideas and inspiration. Once I have written my survey and gotten all my ideas out, I read similar reports from other sources and usually end up with useful additions for my own research.”

9. Buy Research

If the data you need isn’t available publicly and you can’t do your own market research, you can also buy some. There are many reputable analytics companies that offer subscriptions to access their data. Statista is one of my favorites, but there’s also Euromonitor , Mintel , and BCC Research .

What I like: Same as public domain research

What I dislike: You may not find data specific to your needs. It also adds to your expenses.

Best for: Companies under a time or resource crunch or adding factual support to content

Which marketing research method should you use?

You’re not going to like my answer, but “it depends.” The best marketing research method for you will depend on your objective and data needs, but also your budget and timeline.

My advice? Aim for a mix of quantitative and qualitative data. If you can do your own original research, awesome. But if not, don’t beat yourself up. Lean into free or low-cost tools . You could do primary research for qualitative data, then tap public sources for quantitative data. Or perhaps the reverse is best for you.

Whatever your marketing research method mix, take the time to think it through and ensure you’re left with information that will truly help you achieve your goals.

Don't forget to share this post!

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Research on evaluation of city–industry integration in industrial parks, 1. introduction, 2. evaluation indicator system of city–industry integration in industrial parks, 2.1. the original meaning of industry-city integration, 2.2. indicators of land–industry integration in industrial park, 2.3. indicators of residence–industry integration in industrial park, 3. data collection and processing of city–industry integration in industrial parks, 3.1. data collection methods and procedures, 3.1.1. select the evaluation objects of city–industry integration in etdzs, 3.1.2. determine the scope of the sample etdzs on the map, 3.1.3. pick up the polygon vertex coordinates of factories, green space water area and unbuilt area in etdzs and calculate their area, 3.1.4. obtain the data of enterprises in etdzs, 3.1.5. get data on the land area of residential areas in etdzs, 3.1.6. get air quality index (aqi) data, 3.1.7. obtain the data of rail transit stations, 3.2. calculation process and data characteristics of the complex index—matching degree between residence and environment, 3.2.1. supportive residential area score in industrial parks, 3.2.2. the inverse matching relationship between the air quality composite index and supportive residential area, 3.2.3. matching degree score between residence and environment in industrial parks, 4. evaluation process and results of city–industry integration in industrial parks, 4.1. standardization of basic indicators, 4.2. determine the weight of indicators based on the analytic hierarchy process of expert scores, 4.2.1. modelling the hierarchy, 4.2.2. constructing the comparison discriminant matrix, 4.2.3. hierarchical single sorting with individual expert weights for indicators, 4.2.4. maximum eigenvalues of judgement matrices and consistency tests, 4.2.5. determine the average weight of experts for the indicator, 4.3. evaluation results of city–industry integration in sample industrial parks, 4.4. evaluation verification based on entropy weight method.

• From judgment matrix to normalization matrix P = (p ij ) 23×4

5. Discussion

5.1. the enlightenment for practice from the benchmark industrial park of city–industry integration: from the chengdu model to the beijing model, 5.2. academic contributions, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

Name of ETDZ	Vertex Number	Longitude	Latitude
Beijing ETDZ	2\1	116.593694686889	39.7775400221545
Beijing ETDZ	2\2	116.566790061568	39.8058337127532
Beijing ETDZ	2\3	116.563310623168	39.8012829059164
Beijing ETDZ	2\4	116.552753448486	39.7962052557995
Beijing ETDZ	2\5	116.532583236694	39.8201394667626
Beijing ETDZ	2\6	116.512670516967	39.8113711500501
Beijing ETDZ	2\7	116.502628326416	39.8184254477015
Beijing ETDZ	2\8	116.476020812988	39.8052392565111
Beijing ETDZ	2\9	116.478853225708	39.8024698349971
Beijing ETDZ	2\10	116.470527648925	39.7974582172923
Beijing ETDZ	2\12	116.492757797241	39.7848616184177
Beijing ETDZ	2\13	116.483144760131	39.7802444862761
Beijing ETDZ	2\14	116.495332717895	39.7737140133473
Beijing ETDZ	2\15	116.493530273437	39.7645439197561
Beijing ETDZ	2\16	116.481084823608	39.7655995323052
Beijing ETDZ	2\17	116.486663818359	39.7322739560859
Beijing ETDZ	2\26	116.503314971923	39.7116766334180
Beijing ETDZ	2\27	116.532339864756	39.7147385924770
Beijing ETDZ	2\29	116.530622866242	39.7422610182087
Beijing ETDZ	2\30	116.547775268554	39.7675787622142
Beijing ETDZ	2\32	116.575155258178	39.7733182071942

Digital Scale	Implication
1	Equally important
3	One factor is slightly more important than the other
5	One factor is significantly more important than the other
7	One factor is more strongly important than the other
9	One factor is extremely more important than the other
2, 4, 6, 8	The median of the two adjacent judgments above

First-Level Indicator (Intermediate Layer Element)	Second-Level Indicator (Intermediate Layer Element)	Three-Level Indicator (Factor Layer)	Index Calculation Formula
Intensive degree of production function areas (Land–industry integration)	Industrial land efficiency	Investment intensity	0.5 × Registered capital of unit industrial land + 0.5 × paid-in capital of unit industrial land
		Employment density	Number of people paying social security in industrial enterprises/Industrial land area
		Density of invention patents on industrial land	Authorized patents of inventions for industrial enterprises /industrial land area
		Industrial output intensity	Industrial added value/industrial land area
	Service industry land efficiency	Service output intensity	Added value of service sector/land use of service sector(built-up area—factory area—green space and water area—residential area)
		Density of invention patents on services land	Authorized patents of inventions for services enterprises/service sector land area
		Service employment density	Number of people paying social security in services enterprises/services land area
Integration degree of production functional area and life service functional area (Residence–industry integration)	Match degree between residence and environment	-	match degree between residence and environment calculation formula: 1 − |z + z − 1|. z refers to industrial park residential area support, positive indicator. z refers to the composite air quality index of the industrial park, inverse indicator.
	Supportive Rail transit facilities	-	Standardization of rail traffic numbers

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Click here to enlarge figure

First-Level Indicator (Criterion Layer B)	Second-Level Indicator (Sub-Criterion Layer C)	Three-Level Indicator (Elements Layer D)	Index Calculation Formula
Land–industry integration (Coordination degree and balance between internal industries and carriers of production function zones and ser-vice function zones, B1)	Industrial land efficiency (C11)	Investment intensity (D111)	0.5 × Registered capital of unit industrial land + 0.5 × paid-in capital of unit industrial land
		Employment density (D112)	Number of people paying social security in industrial enterprises/Industrial land area (factory area)
		Density of invention patents on industrial land (D113)	Authorized patents of inventions for industrial enterprises/industrial land area
		Industrial output intensity (D114)	Industrial added value/industrial land area
	Service industry land efficiency (C12)	Output intensity of the service sector (D121)	Added value of service sector/land use of service sector, where land area of service sector = built-up area—factory area—green space and water area—residential area
		Density of invention patents on services land (D122)	Authorized patents of inventions for services enterprises/service sector land area
		Services employment density (D123)	Service employment density = Number of people paying social security in service sector enterprises/service sector land area
Residence–industry integration (Coordination and integration of production functional areas and residential service functional areas, B2)	Matching degree be-tween residence and environment (C21)	Residential area supporting scale (z )	The standardized value of per capita residential area × 0.5 + the standardized value of the proportion of residential area to built-up area × 0.5	match degree be-tween residence and environment calculation formula: |z + z − 1|
		Air Quality Composite Index (z )	AQI standardized value of industrial park× 0.5+ standardized value of (AQI of industrial park ÷ AQI of the mother city of industrial park) × 0.5
	Rail transit supporting facilities (C22)		The range standardization of “number of rail transit stations/built-up area of ETDZs”

ETDZs	Industrial Investment Intensity	Employment Density in Industrial Area	Patent Density of Industrial Inventions	Industrial Output Intensity	Service Output Intensity	Service Employment Density	Patent Density of Inventions in the Service Sector	Air Quality Composite Index	Match Degree between Residence and Environment	Rail Stations per Unit Area
Tianjin	0.147	0.329	0.166	0.615	0.743	0.249	0.099	0.519	0.287	0.44
Beijing	1.000	1.000	1.000	1.000	0.912	1.000	1.000	0.168	0.337	1.00
Nantong	0.056	0.052	0.090	0.171	0.084	0.025	0.007	0.391	0.384	0.00
Kunshan	0.024	0.059	0.132	0.554	0.405	0.056	0.043	0.493	0.679	0.000
Ningguo	0.030	0.127	0.053	0.088	0.535	0.463	0.017	0.141	0.511	0.000
Daya Bay	0.024	0.015	0.004	0.162	0.069	0.004	0.000	0.328	0.742	0.000
Kunming	0.055	0.064	0.034	0.182	0.076	0.062	0.029	0.370	1.000	0.180
Ningbo Daxie	0.066	0.064	0.128	0.300	0.167	0.034	0.003	0.417	0.001	0.000
Chengdu (Damian)	0.127	0.242	0.098	0.612	0.117	0.059	0.009	0.445	0.530	0.987
Rugao	0.095	0.174	0.136	0.866	0.331	0.011	0.008	0.644	0.299	0.000
Quanzhou	0.154	0.474	0.222	0.720	0.542	0.133	0.096	0.505	0.385	0.000
Jiashan	0.056	0.166	0.094	0.446	1.000	0.107	0.021	0.523	0.401	0.000
Zouping	0.212	0.158	0.014	0.207	0.088	0.009	0.001	0.679	0.128	0.000
Lianyungang	0.001	0.060	0.099	0.102	0.029	0.037	0.002	0.558	0.421	0.000
Hanzhong	0.019	0.030	0.005	0.580	0.229	0.033	0.000	0.677	0.574	0.000
Korla	0.053	0.145	0.000	0.629	0.000	0.003	0.000	0.839	0.058	0.000
Zhangjiagang	0.173	0.465	0.247	0.358	0.138	0.042	0.018	0.482	0.692	0.000
Linyi	0.033	0.047	0.028	0.061	0.008	0.000	0.001	0.625	0.693	0.000
Longyan	0.000	0.000	0.076	0.000	0.213	0.120	0.045	0.372	0.256	0.000
Hai’an	0.053	0.097	0.191	0.346	0.083	0.013	0.008	0.644	0.366	0.423
Wuhan	0.128	0.181	0.135	0.474	0.056	0.013	0.009	0.460	0.356	0.436
Zhengzhou	0.040	0.080	0.036	0.355	0.132	0.046	0.005	0.692	0.426	0.236
Changchun	0.060	0.126	0.010	0.522	0.304	0.009	0.002	0.417	0.870	0.539

First-Level Indicator (Guideline Layer B)	Weight (w)	Second-Level Indicator (Sub-Guideline Layer C)	Weight (w)	Three-Level Indicator or Definitions (Element Layer D)	Weight (w)
Land–industry integration (Coordination degree and balance between internal industries and carriers of production function zones and service function zones, B1)	0.417	Industrial land efficiency (C11)	0.597	Industrial investment intensity D111	0.211
				Industrial employment density D112	0.183
				density of invention patents on industrial land D113	0.098
				Industrial output intensity D114	0.508
		Service industry land efficiency (C12)	0.403	Service industry output intensity D121	0.512
				density of invention patents on services land D122	0.178
				Services employment density D1232	0.31
Residence–industry integration (Coordination and integration of production functional areas and residential service functional areas, B2)	0.583	Matching degree be-tween residence and environment (C21)	0.556	The degree of negative correlation between residential area size and AQI
		Rail transit supporting facilities (C22)	0.444	Rail transit station per unit area

ETDZs	Land–Industry Integration Weighted Score with Ranking		Industrial Land Efficiency Weighted Score and Ranking		Weighted Score and Ranking of Service Sector Land Use Efficiency
Beijing	0.982	1	1.000	1	0.955	1
Tianjin	0.442	2	0.420	4	0.475	3
Quanzhou	0.438	3	0.507	2	0.336	5
Jiashan	0.387	4	0.278	12	0.548	2
Rugao	0.372	5	0.505	3	0.174	7
Kunshan	0.279	6	0.310	9	0.232	6
Chengdu	0.266	7	0.392	5	0.080	14
Changchun	0.244	8	0.302	11	0.159	8
Hanzhong	0.233	9	0.304	10	0.128	10
Ningguo	0.217	10	0.079	20	0.420	4
Korla	0.214	11	0.357	6	0.001	23
Zhangjiagang	0.231	12	0.328	7	0.087	12
Wuhan	0.201	13	0.314	8	0.034	20
Zhengzhou	0.157	14	0.207	14	0.083	13
Ningbo Daxie	0.153	15	0.190	15	0.096	11
Hai’an	0.153	16	0.223	13	0.048	17
Zouping	0.127	17	0.180	16	0.048	18
Kunming	0.097	18	0.119	17	0.063	15
Nantong	0.090	19	0.117	18	0.052	16
Daya Bay	0.069	20	0.090	19	0.036	19
Longyan	0.067	21	0.007	23	0.154	9
Lianyungang	0.054	22	0.073	21	0.027	21
Linyi	0.031	23	0.049	22	0.005	22

ETDZs	Weighted Score and Ranking for Residence–Industry Integration		Match Degree between Residence and Environment and Ranking		Standardized Scores and Rankings of Rail Transit Stations per Unit Area
Chengdu	0.978	1	0.971	3	0.987	2
Wuhan	0.579	2	0.694	12	0.436	5
Tianjin	0.570	3	0.677	14	0.437	4
Hai’an	0.556	4	1.000	1	0	8
Zhengzhou	0.554	5	0.809	9	0.236	6
Lianyungang	0.543	6	0.978	2	0	8
Beijing	0.529	7	0.151	22	1.000	1
Changchun	0.525	8	0.514	17	0.539	3
Rugao	0.509	9	0.916	4	0	8
Daya Bay	0.497	10	0.894	5	0	8
Jiashan	0.491	11	0.884	6	0	8
Korla	0.465	12	0.837	7	0	8
Quanzhou	0.458	13	0.824	8	0	8
Kunshan	0.397	14	0.715	10	0	8
Zhangjiagang	0.396	15	0.712	11	0	8
Zouping	0.377	16	0.678	13	0	8
Nantong	0.346	17	0.622	15	0	8
Hanzhong	0.320	18	0.577	16	0	8
Kunming	0.285	19	0.370	20	0.180	7
Linyi	0.255	20	0.459	18	0	8
Ningguo	0.226	21	0.408	19	0	8
Longyan	0.204	22	0.366	21	0	8
Ningbo Daxie	0	23	0	23	0	8

First-Level Indicator	Entropy Weight (w)	Second-Level Indicator	Entropy Weight (w)	Three-Level Indicator or Interpretations	Entropy Weight (w)
Land–industry integration	0.577	Industrial land efficiency	0.373	Industrial investment intensity,	0.313
				Industrial employment density	0.248
				Density of invention patents on industrial land	0.329
				Industrial output intensity	0.111
		Service industry land efficiency	0.627	Service industry output intensity	0.142
				Density of invention patents on industrial land	0.549
				Services employment density	0.309
Residence–industry integration	0.423	Matching degree be-tween residence and environment	0.1	The degree of negative correlation between residential area size and AQI
		Rail transit supporting facilities	0.9	Rail transit station per unit area

	Industrial Technology Level	Green Manufacturing Maturity	Negative Externality	Service Sector Development	Residential Area Ratio	Residential Land Area per Capita	Density of Rail Transit Stations
Beijing	extremely high	extremely high	extremely low	high	medium to high	medium to low	high
Chengdu	medium	medium to high	medium to low	medium	medium to high	medium to high	high

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Xu, M.; Luo, Y.; Li, D. Research on Evaluation of City–Industry Integration in Industrial Parks. Sustainability 2024 , 16 , 6906. https://doi.org/10.3390/su16166906

Xu M, Luo Y, Li D. Research on Evaluation of City–Industry Integration in Industrial Parks. Sustainability . 2024; 16(16):6906. https://doi.org/10.3390/su16166906

Xu, Mingqiang, Yaoyao Luo, and Dingyao Li. 2024. "Research on Evaluation of City–Industry Integration in Industrial Parks" Sustainability 16, no. 16: 6906. https://doi.org/10.3390/su16166906

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How to cite ChatGPT

Use discount code STYLEBLOG15 for 15% off APA Style print products with free shipping in the United States.

We, the APA Style team, are not robots. We can all pass a CAPTCHA test , and we know our roles in a Turing test . And, like so many nonrobot human beings this year, we’ve spent a fair amount of time reading, learning, and thinking about issues related to large language models, artificial intelligence (AI), AI-generated text, and specifically ChatGPT . We’ve also been gathering opinions and feedback about the use and citation of ChatGPT. Thank you to everyone who has contributed and shared ideas, opinions, research, and feedback.

In this post, I discuss situations where students and researchers use ChatGPT to create text and to facilitate their research, not to write the full text of their paper or manuscript. We know instructors have differing opinions about how or even whether students should use ChatGPT, and we’ll be continuing to collect feedback about instructor and student questions. As always, defer to instructor guidelines when writing student papers. For more about guidelines and policies about student and author use of ChatGPT, see the last section of this post.

Quoting or reproducing the text created by ChatGPT in your paper

If you’ve used ChatGPT or other AI tools in your research, describe how you used the tool in your Method section or in a comparable section of your paper. For literature reviews or other types of essays or response or reaction papers, you might describe how you used the tool in your introduction. In your text, provide the prompt you used and then any portion of the relevant text that was generated in response.

Unfortunately, the results of a ChatGPT “chat” are not retrievable by other readers, and although nonretrievable data or quotations in APA Style papers are usually cited as personal communications , with ChatGPT-generated text there is no person communicating. Quoting ChatGPT’s text from a chat session is therefore more like sharing an algorithm’s output; thus, credit the author of the algorithm with a reference list entry and the corresponding in-text citation.

When prompted with “Is the left brain right brain divide real or a metaphor?” the ChatGPT-generated text indicated that although the two brain hemispheres are somewhat specialized, “the notation that people can be characterized as ‘left-brained’ or ‘right-brained’ is considered to be an oversimplification and a popular myth” (OpenAI, 2023).

OpenAI. (2023). ChatGPT (Mar 14 version) [Large language model]. https://chat.openai.com/chat

You may also put the full text of long responses from ChatGPT in an appendix of your paper or in online supplemental materials, so readers have access to the exact text that was generated. It is particularly important to document the exact text created because ChatGPT will generate a unique response in each chat session, even if given the same prompt. If you create appendices or supplemental materials, remember that each should be called out at least once in the body of your APA Style paper.

When given a follow-up prompt of “What is a more accurate representation?” the ChatGPT-generated text indicated that “different brain regions work together to support various cognitive processes” and “the functional specialization of different regions can change in response to experience and environmental factors” (OpenAI, 2023; see Appendix A for the full transcript).

Creating a reference to ChatGPT or other AI models and software

The in-text citations and references above are adapted from the reference template for software in Section 10.10 of the Publication Manual (American Psychological Association, 2020, Chapter 10). Although here we focus on ChatGPT, because these guidelines are based on the software template, they can be adapted to note the use of other large language models (e.g., Bard), algorithms, and similar software.

The reference and in-text citations for ChatGPT are formatted as follows:

• Parenthetical citation: (OpenAI, 2023)
• Narrative citation: OpenAI (2023)

Let’s break that reference down and look at the four elements (author, date, title, and source):

Author: The author of the model is OpenAI.

Date: The date is the year of the version you used. Following the template in Section 10.10, you need to include only the year, not the exact date. The version number provides the specific date information a reader might need.

Title: The name of the model is “ChatGPT,” so that serves as the title and is italicized in your reference, as shown in the template. Although OpenAI labels unique iterations (i.e., ChatGPT-3, ChatGPT-4), they are using “ChatGPT” as the general name of the model, with updates identified with version numbers.

The version number is included after the title in parentheses. The format for the version number in ChatGPT references includes the date because that is how OpenAI is labeling the versions. Different large language models or software might use different version numbering; use the version number in the format the author or publisher provides, which may be a numbering system (e.g., Version 2.0) or other methods.

Bracketed text is used in references for additional descriptions when they are needed to help a reader understand what’s being cited. References for a number of common sources, such as journal articles and books, do not include bracketed descriptions, but things outside of the typical peer-reviewed system often do. In the case of a reference for ChatGPT, provide the descriptor “Large language model” in square brackets. OpenAI describes ChatGPT-4 as a “large multimodal model,” so that description may be provided instead if you are using ChatGPT-4. Later versions and software or models from other companies may need different descriptions, based on how the publishers describe the model. The goal of the bracketed text is to briefly describe the kind of model to your reader.

Source: When the publisher name and the author name are the same, do not repeat the publisher name in the source element of the reference, and move directly to the URL. This is the case for ChatGPT. The URL for ChatGPT is https://chat.openai.com/chat . For other models or products for which you may create a reference, use the URL that links as directly as possible to the source (i.e., the page where you can access the model, not the publisher’s homepage).

Other questions about citing ChatGPT

You may have noticed the confidence with which ChatGPT described the ideas of brain lateralization and how the brain operates, without citing any sources. I asked for a list of sources to support those claims and ChatGPT provided five references—four of which I was able to find online. The fifth does not seem to be a real article; the digital object identifier given for that reference belongs to a different article, and I was not able to find any article with the authors, date, title, and source details that ChatGPT provided. Authors using ChatGPT or similar AI tools for research should consider making this scrutiny of the primary sources a standard process. If the sources are real, accurate, and relevant, it may be better to read those original sources to learn from that research and paraphrase or quote from those articles, as applicable, than to use the model’s interpretation of them.

We’ve also received a number of other questions about ChatGPT. Should students be allowed to use it? What guidelines should instructors create for students using AI? Does using AI-generated text constitute plagiarism? Should authors who use ChatGPT credit ChatGPT or OpenAI in their byline? What are the copyright implications ?

On these questions, researchers, editors, instructors, and others are actively debating and creating parameters and guidelines. Many of you have sent us feedback, and we encourage you to continue to do so in the comments below. We will also study the policies and procedures being established by instructors, publishers, and academic institutions, with a goal of creating guidelines that reflect the many real-world applications of AI-generated text.

For questions about manuscript byline credit, plagiarism, and related ChatGPT and AI topics, the APA Style team is seeking the recommendations of APA Journals editors. APA Style guidelines based on those recommendations will be posted on this blog and on the APA Style site later this year.

Update: APA Journals has published policies on the use of generative AI in scholarly materials .

We, the APA Style team humans, appreciate your patience as we navigate these unique challenges and new ways of thinking about how authors, researchers, and students learn, write, and work with new technologies.

American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.). https://doi.org/10.1037/0000165-000

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remarkRemark \newsiamremark hypothesisHypothesis \newsiamthm claimClaim \newsiamremark exampleExample \newsiamremark notationNotation \newsiamthm resultResult \newsiamthm assumptionAssumption \headers Operator Learning Using Random FeaturesNicholas H. Nelsen and Andrew M. Stuart

Operator Learning Using Random Features: A Tool for Scientific Computing † † thanks: Published electronically August 8, 2024 in the SIGEST section of SIAM Review . The corresponding SIGEST editorial commentary may be found at the following link: https://doi.org/10.1137/24N975943 . The present paper is an expanded version of an article that originally appeared in SIAM Journal on Scientific Computing , Volume 43, Number 5, 2021, pages A3212–A3243, under the title “The Random Feature Model for Input-Output Maps between Banach Spaces.” \funding The original work  [ 109 ] was supported by the National Science Foundation (NSF) Graduate Research Fellowship Program under award DGE-1745301, NSF award DMS-1818977, Office of Naval Research (ONR) award N00014-17-1-2079, NSF award AGS-1835860, and ONR award N00014-19-1-2408. This SIGEST article is supported by the Amazon/Caltech AI4Science Fellowship held by the first author and by the Department of Defense Vannevar Bush Faculty Fellowship, under ONR award N00014-22-1-2790, held by the second author.

Supervised operator learning centers on the use of training data, in the form of input-output pairs, to estimate maps between infinite-dimensional spaces. It is emerging as a powerful tool to complement traditional scientific computing, which may often be framed in terms of operators mapping between spaces of functions. Building on the classical random features methodology for scalar regression, this paper introduces the function-valued random features method. This leads to a supervised operator learning architecture that is practical for nonlinear problems yet is structured enough to facilitate efficient training through the optimization of a convex, quadratic cost. Due to the quadratic structure, the trained model is equipped with convergence guarantees and error and complexity bounds, properties that are not readily available for most other operator learning architectures. At its core, the proposed approach builds a linear combination of random operators. This turns out to be a low-rank approximation of an operator-valued kernel ridge regression algorithm, and hence the method also has strong connections to Gaussian process regression. The paper designs function-valued random features that are tailored to the structure of two nonlinear operator learning benchmark problems arising from parametric partial differential equations. Numerical results demonstrate the scalability, discretization invariance, and transferability of the function-valued random features method.