Show that you understand the current state of research on your topic.
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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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.
Like your dissertation or thesis, the proposal will usually have a title page that includes:
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:
To guide your introduction , include information about:
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:
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.
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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:
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
Research phase | Objectives | Deadline |
---|---|---|
1. Background research and literature review | 20th January | |
2. Research design planning | and data analysis methods | 13th February |
3. Data collection and preparation | with selected participants and code interviews | 24th March |
4. Data analysis | of interview transcripts | 22nd April |
5. Writing | 17th June | |
6. Revision | final work | 28th July |
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:
To determine your budget, think about:
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
Statistics
Research 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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The problems we face today such as climate change are a product of the society’s current outlook on the environment. Therefore, finding and implementing a solution requires a different outlook. One approach can be a systematic change in schooling children on these concepts. This paper presents the preliminary results of a project that focuses on creating awareness on the concepts of natural and built environment and their interaction with each other. The project involved 130 students, who have different socio-economic backgrounds, academic and art achievements, from six secondary schools. One part of the project involved the determination of the most important natural and built environmental problems and solution proposals according to participants in groups consisting of four–five participants in a group setting. During this study, they discussed their problems and proposals within a wider setting including other participants and supervising academicians. This chapter groups and discusses these problems and solution proposals. The results indicate that most of the children are aware of many problems such as environmental pollution, which was the most discussed topic. Yet, some important problems were not mentioned, and energy management was the least detailed and understood issue in the discussions.
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This study was part of the Scientific and Technological Research Council of Turkey (TÜBİTAK) 117B154 «Nature and Architecture for Little Designers» Project. The authors would also like to thank the principles and educators in the schools the workshops were conducted in and the students who participated in the workshops, our little designers….
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Department of Archtecture, Dokuz Eylul University, Izmir, Turkey
Ebru Güller, Ayça Tokuç & Kutluğ Savaşır
Department of Electrics and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey
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Ibrahim Dincer
Dokuz Eylul University, Buca, Izmir, Turkey
Can Ozgur Colpan
Faculty of Engineering, Department of Mechanical Engineering, Dokuz Eylul University, Buca, Izmir, Turkey
Mehmet Akif Ezan
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Güller, E., Tokuç, A., Köktürk, G., Savaşır, K. (2020). Environmental Problems and Solution Proposals from the Perspective of Secondary School Students. In: Dincer, I., Colpan, C., Ezan, M. (eds) Environmentally-Benign Energy Solutions. Green Energy and Technology. Springer, Cham. https://doi.org/10.1007/978-3-030-20637-6_1
DOI : https://doi.org/10.1007/978-3-030-20637-6_1
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With hundreds of billions of euros currently available through the Horizon Europe program, the importance of creating a good environmental project proposal cannot be overstated. Funding is available for sustainable technologies that show promise in moving the world forward without the carbon emissions that have been increasing since the Industrial Revolution.
Below are the five key components that every project proposal needs to consider when applying for Horizon Europe funding.
This goes without saying: the base point for receiving any program funding rests on a thorough and well-written grant proposal.
Project scope thoroughness is usually attainable from researchers because they know their project inside and out. However, thoroughness regrading proposal requirements and guidelines can easily be lacking, especially in areas that do not pertain directly to the project.
When it comes to the written quality of a proposal, this can vary quite a bit. Many researchers are excellent writers, and this can often translate to well-written proposals. However, the depth and extended scope of specific proposal parameters might not be met if researchers are not familiar with them - especially for those that refuse to collaborate with industry experts or outside writers.
Funders like to see their projects succeed - they don’t like to waste valuable money, especially when there is only a finite amount involved and it could be used on other deserving projects. Grantees need to show a clear management plan for how they will use the funding to carry out the successful completion of their project - simply having a promising idea or even a possible breakthrough technology is not enough.
Outlining a proper methodology is crucial for any proposed project. It must cover project duration, project budget, relevant project activities, milestones, and other key aspects within the proposal. For environmental project proposals in particular, it is essential to include relevant information on biodiversity effects, land use effects (including watersheds, national parks, fisheries, and other ecosystems), utilization of natural resources, environmental health, environmental protection and any other environmental issues that have an impact on sustainable development.
The steps that will be taken to achieve the envisioned impacts – environmental, but also commercial – are a key aspect of the new Horizon Europe Impact section, discussed in more detail in our recent blog here .
This is a critical component that is often lacking in unsuccessful project proposals - collaboration, especially with industry, is crucial for project success. There are several reasons behind this: 1) Industry leaders have insights and can share valuable real-world experiences with researchers that have often not brought their ideas out of the lab, 2) Industry leaders are proficient at getting products to market and implementing them in tangible, applicable ways, which is the goal of any project that receives funding, and 3) Collaboration with industry allows for a diverse means of thinking about problems and solutions, which will make project success more likely.
Successful proposals almost always showcase and highlight collaborative efforts with industry experts, community members, and other stakeholders. Project objectives are often tailored around at least some input from these leaders during the writing and decision-making process.
Simply put, covering all aspects of grant application requires following meticulous details. Researchers that are familiar with grant proposals might find this straightforward, but those with little experience or little interest in this area may find it difficult - or frustrating - to follow painstaking rules.
Even with the most well-deserving projects and ground-breaking ideas, grant funding programs, including Horizon Europe, cannot allocate funds to those that do not meet the specific requirements of an application. It is imperative for researchers to cover all areas of the proposal and to ensure that they are thoroughly familiar with the writing process. In this area, it might be worth considering bringing on a dedicated project manager to oversee the successful implementation of the grant application.
The rising need for environmental solutions and the massive increase in grant funding for these projects opens new opportunities for academic projects and businesses alike.
Although there is a wealth of grant funding available, the grant application process is often difficult and includes many specific requirements, as mentioned above.
Linq Consulting provides technical assistance and grants management services to help researchers throughout the grant writing process. We specialize in all areas of creating and managing successful proposals, with experience crafting all three main sections of typical Horizon Europe proposals, as well as proposals for other funding sources. Our team of expert researchers, writers, and project managers can help with all aspects of obtaining grant funding and can help get environmental projects to market no matter what stage of development you are in.
Click here for more information on how we connect grant makers and funders to grantees.
If you or your organisation would like assistance in taking advantage of some of the available funding set aside for sustainability research and development, get in touch with us at [email protected]
Maximizing Impact: Strategies for Optimizing the Impact Section in Horizon Europe Proposals
Tips for Applying to the EIC Pathfinder: Understanding High Risk and Game-Changing Opportunities
Sustainable Tech: Use of fish waste for fertilizer and other circular approaches
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You’ve successfully completed the comprehensive process and are now ready to finalize your proposal. What does that involve? The academic calendar provides important context for this process and outlines timelines and committee structure .
Normally, PhD candidates submit a dissertation proposal to the committee in time to ‘defend’ it by the end of the second year (sixth term) in the program. There is no formal defense but there is an expectation that PhD candidates will present their proposal to the advisory committee, and to revise the proposal to address all concerns and issues raised. Once the committee is satisfied with the proposal it can be approved and the milestone form completed.
Expectations regarding proposal content and structure vary. You are strongly recommended to discuss with your supervisor and committee the final content and structure of your proposal. However, there are some common proposal elements that can be used to prepare a draft and that can be modified as needed. A general proposal structure (and approximate single-space length estimates) is outlined below:
SERS PhD students may in consultation with their supervisor and committee decide to follow a dissertation by manuscript format. In the manuscript option, the thesis will comprise the following:
The entire thesis must be formatted according to the requirements of the GSPA. All chapters, including those presenting previously published work, must use a consistent format, and must be continuously paginated.
The following are other requirements for the manuscript thesis option:
* Adopted from the Waterloo-Laurier Joint Program in Geography .
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Terry Irwin , cameron tonkinwise , Gideon Kossoff
A 32 page overview of Transition Design, its origins and influences. The document also contains an expanded bibliography and teaching materials to share.
Terry Irwin , Gideon Kossoff
The 2017 Transition Design Seminar is required for all masters and doctoral students at Carnegie Mellon University. This year's syllabus is a website intended to share with educators and researchers outside CMU. It contains readings, videos, exercises and assignments as well as a discussion forum in which externals students, faculty and researchers are invited to participate.
Heather Burns
In response to the hitherto unchallenged assumptions supporting a globalized economy, the Leadership for Sustainability Education (LSE) program, formerly Leadership in Ecology, Culture, and Learning, was developed as part of an emerging sustainability movement. This article highlights the favorable conditions that provided the context for the evolution of the LSE program, including organizational policies and practices at Portland State University, and a commitment to community-university partnerships that conveyed the University’s motto, “Let Knowledge Serve the City.” We discuss the potential that higher education has to transform practices and ways of thinking necessary for ecological sustainability and social justice. Following this overview, we outline the main elements of the LSE Master’s degree program, including the four key learning areas: self-understanding and commitment, systemic view of the world, bio-cultural relationships, and tools for sustainable change. Additionally, we describe the types of learning experiences and assessment strategies employed throughout the program. We conclude by sharing the key authors and thinkers who influence the program and coursework.
David R Young
Terry Irwin
John Hardman
Joseph Tursi
This thesis explores the problems with the current state of education as it pertains to ecoliteracy and sustainability, and the methods of transitioning from an outdated teaching model to an holistic and student centered curriculum. The first part of this thesis addresses sustainability current role in the US education system on a federal, state, and grassroots level. It then transitions into what could happen if education continues on the same path. The last part of the paper provides a holistic solution to introducing and creating ecoliterate students, schools, and communities through a network of supported systems. Those solutions are a lesson template titled DIPR, which focuses on inquiry and project based learning, and a professional development workshop based on a design charrette model.
A newly expanded bibliography for Transition Design, organized into the categories of the Transition Design Framework: Vision; Theories of Change; Mindset & Posture; New Ways of Designing
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Numbers, Facts and Trends Shaping Your World
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A majority of Americans say climate change is having at least some impact on their local community, and half say their area has experienced extreme weather over the past year, particularly those living in South Central states such as Texas and Alabama. On a related policy question, a large majority of Americans favor the idea of revising building standards so new construction can better withstand extreme weather events.
At the local level, experience with environmental problems – such as air and water pollution – varies across groups. Black and Hispanic adults are particularly likely to say they experience environmental problems in their local community, as are those with lower family incomes.
And when it comes to climate policy considerations, large majorities of Black and Hispanic adults – across income levels – say it’s very important to ensure that lower-income communities benefit from proposals aimed at reducing the effects of climate change.
Overall, 57% of U.S. adults say climate change is affecting their own community either a great deal (17%) or some (40%). Smaller shares say climate change is affecting their community not too much (27%) or not at all (15%).
Most Americans (77%) say human activity contributes either a great deal (44%) or some (33%) to global climate change. Far fewer (22%) say human activities such as the burning of fossil fuels contribute not too much or not at all to climate change.
Republicans continue to be less likely to believe that human activity plays at least some part in global climate change. Still, 59% of this group says human activity contributes at least some, while 40% say human activity has not too much of a role or no role in climate change.
Democrats across generations are in broad agreement that human activity has at least some effect on climate change. Among Republicans, Gen Zers and Millennials are more likely than Gen X and Baby Boomer and older adults to see human activity as playing a role in global climate change. See the Appendix for details.
The overall share of Americans who say their area is affected a great deal by climate change is down 7 percentage points, from 24% a year ago to 17% today.
Americans’ beliefs about local impact of climate change are more closely linked to their partisanship than to where they live. Perceptions of local climate impact vary modestly across census regions. The regions that are relatively likely to say climate change is impacting their communities, such as New England and the Pacific, tend to be places that lean Democratic in their political affiliation. There are also modest differences by generation in beliefs about its local impact.
A separate question in the survey finds that half of Americans say their local area experienced an extreme weather event in the past 12 months.
A large majority (84%) in the West South Central region say they have experienced extreme weather in the last 12 months. The region was impacted by a severe winter storm in February that led to a power crisis in Texas. In contrast to the overall partisan differences seen on this question, comparable majorities of Republicans and Democrats in the West South Central region report their communities have experienced extreme weather in the past year.
Climate change is thought to be a key factor in the occurrence of more frequent and intense or extreme weather events. When asked about a federal government proposal to change building standards so that new construction will better withstand extreme weather events, 75% of U.S. adults responded in favor of this proposal, while 23% said it is a bad idea because it could increase costs and cause delays in important projects.
There is near consensus among Democrats and Democratic-leaning independents (90%) that revising building standards so construction better withstands extreme weather is a good idea. A 57% majority of Republicans and GOP leaners agree, although support is considerably higher among moderate and liberal Republicans (71%) than conservative Republicans (50%).
People who report direct experience with extreme weather in the past year are particularly likely to consider this a good idea (81% vs. 69% of those who do not report recent experience with extreme weather).
Overall, about six-in-ten Americans say they see at least moderate problems where they live when it comes to an excess of garbage (62%) and water pollution in lakes, rivers and streams (60%). About half (52%) say the same about local air pollution, and about four-in-ten say safe drinking water (41%) or a lack of greenspace (39%) are at least moderate problems.
Past research has found that Black, Hispanic and Asian American communities are more likely to be exposed to air pollution and other environmental hazards in their local area.
The Center survey finds Black and Hispanic adults particularly likely to say their local communities are having problems across this set of five environmental issues, and they stand out for the large share who consider these to be “big problems” where they live. About four-in-ten Black (41%) and Hispanic (37%) adults say the amount of garbage, waste and landfills in their community is a big problem. Black and Hispanic adults are also more likely than White adults to report that their community has big problems with air and water pollution, drinking water safety and a lack of greenspace and parks. A majority of Black (57%) and about half of Hispanic adults (53%) consider at least one of these five issues a big problem in their local area.
Lower-income Americans are also more likely to report that their area has big problems with these environmental issues. For example, about three-in-ten lower-income adults say their local community has a big problem with air pollution. About half as many upper-income adults (16%) say the same about their community. Half of those with lower family incomes say their local communities are having a big problem with at least one of these five environmental issues.
The Biden administration has brought a new focus to environmental justice concerns underlying climate and energy policy. Biden has called for $1.4 billion in his recent budget proposal for initiatives aimed at helping communities address racial, ethnic and income inequalities in pollution and other environmental hazards.
As Americans think about proposals to address climate change, Black (68%) and Hispanic adults (55%) stand out for the high shares who say it is very important to them that such proposals help lower-income communities.
More than half of lower-income Americans (54%) say this is very important to them, compared with 36% of upper-income adults.
Middle- and upper-income Black adults (70%) are about as likely as lower-income Black adults (66%) to say this is very important to them, however. Similarly, there are no differences on this question between middle/upper income Hispanic adults and those with lower incomes (54% vs. 57%, respectively).
A majority of Democrats and independents who lean toward the Democratic Party (59%) say it is very important to them that climate change proposals help lower-income communities; far fewer Republicans and Republican leaners (27%) say this.
A large majority of Americans (86%) say they try to live in ways that help protect the environment all the time (22%) or some of the time (64%). Just 14% say they never or rarely make such an effort. These findings are largely unchanged since the question was last asked in October 2019 .
In contrast to views and behaviors related to climate change, Baby Boomer and older adults are more likely than those in younger generations to say they try to live in environmentally conscious ways all the time (29%, vs. 21% in Gen X, 16% of Millennials and 15% in Gen Z).
And, unlike views on many policy issues related to the environment, similar shares of Democrats (88%) and Republicans (84%) say they make an effort to do this at least some of the time.
Majorities of U.S. adults say they take some everyday actions in order to help protect the environment, including reducing their food waste (81%), using fewer plastics that cannot be reused such as plastic bags, straws or cups (72%) or reducing the amount of water they use (67%). More than half of Americans (54%) say they drive less or carpool to help the environment, and 40% say they eat less meat.
About one-in-five adults (18%) say they do all five of these activities to help the environment, a similar share to when these questions were last asked in October 2019. On average, Americans do 3.3 of these activities.
People who say they try to be environmentally conscious all the time are much more likely to say they are doing specific things to protect the environment. For instance, a large majority (89%) of people who make an effort to live in ways that help protect the environment all the time say they use fewer single-use plastics such as bags and straws in order to protect the environment. This compares with 35% of those who say they do not or don’t often make an effort to protect the environment.
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The average direct, indirect and total effects of environmental concern on pro-environmental behavior.
2.1. background, designing observational study as experimental, 2.2. data section, 2.3. technical point and description of the method, 2.4. sensitivity analysis.
Click here to enlarge figure
4. discussion, author contributions, data availability statement, conflicts of interest.
Item Definitions | Categories | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
I Affective dimension (worry) (treatment): Are you concerned about the environmental situation? * | nk/na | nothing | little | much | ||||||
Freq. | % | Freq. | % | Freq. | % | Freq. | % | |||
327 | 1.33 | 836 | 3.40 | 4384 | 17.84 | 19,024 | 77.42 | |||
Conative dimension (M) (mediator) (tolerance, attitudes and intentions): the sum of 2 to 9 | ||||||||||
Would you be in favor of the following environmental protection measures? | nk/na | yes | no | |||||||
Freq. | % | Freq. | % | Freq. | % | |||||
I | Mandatory, subject to a fine, the separation of household waste | 3 | 0.01 | 12,719 | 51.76 | 11,849 | 48.22 | |||
I | Regulate or restrict the abusive water consumption of each dwelling | 2 | 0.01 | 19,532 | 79.49 | 5037 | 20.50 | |||
I | Establishing an environmental tax on the most polluting fuels | 1 | 0.00 | 15,611 | 63.53 | 8959 | 36.46 | |||
I | Restrictive measures on the use of private transport | 2 | 0.01 | 11,363 | 46.25 | 13,206 | 53.75 | |||
I | Introduce an eco-tax on tourism | 3 | 0.01 | 8055 | 32.78 | 16,513 | 67.21 | |||
I | Installation of a renewable energy park (wind, solar) in your municipality despite the effect on the landscape | - | - | 17,576 | 71.53 | 6995 | 28.47 | |||
I | Pay more for the use of alternative energies | 3 | 0.01 | 5587 | 22.74 | 18,981 | 77.25 | |||
I | Reduce noise on main roads (anti-noise panels, sound-reducing paving) | 1 | 0.00 | 20,930 | 85.18 | 3640 | 14.81 | |||
Cognitive dimension (C ) (confounder): the sum of 10 and 11 | ||||||||||
I Environmental campaign: In the last year, have you been aware of any awareness-raising campaigns concerning environmental protection (water, energy, recycling, etc.)? | nk/na | yes | no | |||||||
Freq. | % | Freq. | % | Freq. | % | |||||
704 | 2.87 | 14,762 | 60.08 | 9105 | 37.06 | |||||
I Environmental problem detection: During 2007, have you detected any environmental problems in your environment? | nk/na | yes | no | |||||||
Freq. | % | Freq. | % | Freq. | % | |||||
- | - | 6436 | 26.19 | 18,135 | 73.81 | |||||
Active dimension or pro-environmental behavior (Y) (PEB, outcome): the sum of 12 to 17 | ||||||||||
Do you use any of the following products? | Never % | Sometimes % | Somewhat often % | Whenever possible % | nk/na % | |||||
I Recycled paper? | 66.22 | 18.67 | 7.85 | 5.02 | 2.24 | |||||
I Returnable packaging? | 72.02 | 14.34 | 7.45 | 4.59 | 1.60 | |||||
I Rechargeable batteries? | 49.20 | 20.93 | 16.83 | 11.62 | 1.42 | |||||
Rate the importance they attach to the following elements when buying a new product (household appliance, food product, cleaning product, etc.): | No importance % | Little importance % | Quite importance % | Very importance % | ||||||
I Energy consumption/efficiency | 6.74 | 15.31 | 48.15 | 29.80 | ||||||
I Eco-label/eco-guarantee (organic food) | 18.96 | 29.98 | 35.70 | 15.35 | ||||||
I Local product/proximity of products | 18.71 | 27.01 | 34.65 | 19.63 |
Variables | Categories | Frequency | Cluster_1 | Cluster_2 | Cluster_3 | Cluster_4 |
---|---|---|---|---|---|---|
Are you concerned about the environmental situation? * | Much | 77.42 | 81.91 | 59.58 | 89.40 | 76.27 |
Little | 17.84 | 16.39 | 29.43 | 8.66 | 19.24 | |
nothing, nk/na | 4.73 | 1.70 | 10.99 | 1.94 | 4.48 | |
Household income [net, monthly] | 1. Less than €1100 | 26.33 | 13.58 | 34.57 | 23.98 | 29.48 |
2. From €1101 to €1800 | 27.53 | 27.72 | 26.16 | 28.86 | 27.12 | |
3. From 1801 to 2700 €. | 15.55 | 19.86 | 13.23 | 16.50 | 13.95 | |
4. More than 2700 € | 9.52 | 12.86 | 6.56 | 11.28 | 8.15 | |
5. na/nk | 21.08 | 25.97 | 19.48 | 19.37 | 21.30 | |
Household type | 1. One-person household | 18.30 | 12.86 | 23.00 | 15.65 | 20.40 |
2. Single couple | 23.54 | 19.86 | 23.19 | 23.87 | 25.55 | |
3. Parent-child household | 47.11 | 57.15 | 41.44 | 49.64 | 43.29 | |
4. Household with others | 11.05 | 10.13 | 12.37 | 10.84 | 10.77 | |
Education * | 1. University studies | 15.21 | 21.97 | 11.17 | 18.19 | 12.53 |
2. Baccalaureate and vocational education | 26.87 | 36.34 | 22.81 | 28.73 | 22.93 | |
3. Illiterate up to 1st stage secondary education | 57.62 | 41.69 | 66.02 | 53.08 | 64.54 | |
Municipality size (number of inhabitants) | 1. Provincial capitals and municipalities > 100,000 | 38.65 | 43.61 | 36.74 | 40.61 | 35.35 |
2. From 50,001 to 100,000 | 8.30 | 9.14 | 8.44 | 8.35 | 7.67 | |
3. 20,001 to 50,000 | 15.56 | 16.85 | 15.09 | 15.46 | 15.32 | |
4. 10,000 to 20,000 | 12.33 | 11.50 | 12.74 | 12.14 | 12.67 | |
5. Less than 10,000 | 25.15 | 18.89 | 26.99 | 23.44 | 28.99 | |
N | 26,689 | 4462 | 6170 | 8119 | 7938 |
Stats. | Affective | Conative | Cognitive | PEB | Income | Household Type | Education | Age | Municipality Size |
---|---|---|---|---|---|---|---|---|---|
mean | 2 | 3.81 | 0.60 | 17.83 | 1.89 | 3.08 | 2.57 | 60.12 | 2.94 |
T = 1 | - | 2.89 | 0.34 | 15.74 | 1.60 | 3.07 | 2.76 | 66.54 | 3.15 |
T = 2 | - | 3.69 | 0.50 | 17.70 | 1.94 | 3.06 | 2.58 | 58.71 | 2.94 |
T = 3 | - | 4.85 | 0.97 | 20.06 | 2.10 | 3.10 | 2.38 | 55.12 | 2.74 |
sd | 0.81 | 2.26 | 0.69 | 5.77 | 0.96 | 2.08 | 0.67 | 17.95 | 1.67 |
T = 1 | - | 2.30 | 0.55 | 6.03 | 0.90 | 2.31 | 0.53 | 17.58 | 1.72 |
T = 2 | - | 2.12 | 0.65 | 5.49 | 0.95 | 2.03 | 0.66 | 17.79 | 1.64 |
T = 3 | - | 1.91 | 0.71 | 4.89 | 0.97 | 1.88 | 0.75 | 16.55 | 1.64 |
p50 | 2 | 4 | 0 | 18 | 2 | 3 | 3 | 61 | 3 |
T = 1 | - | 3 | 0 | 16 | 1 | 2 | 3 | 72 | 3 |
T = 2 | - | 4 | 0 | 18 | 2 | 3 | 3 | 58 | 3 |
T = 3 | - | 5 | 1 | 20 | 2 | 3 | 3 | 54 | 3 |
p25 | 1 | 2 | 0 | 14 | 1 | 1 | 2 | 45 | 1 |
T = 1 | - | 1 | 0 | 11 | 1 | 1 | 3 | 53 | 1 |
T = 2 | - | 2 | 0 | 13 | 1 | 2 | 2 | 44 | 1 |
T = 3 | - | 4 | 0 | 17 | 1 | 2 | 2 | 42 | 1 |
p75 | 3 | 5 | 1 | 22 | 3 | 4 | 3 | 75 | 5 |
T = 1 | - | 5 | 1 | 20 | 2 | 4 | 3 | 80 | 5 |
T = 2 | - | 5 | 1 | 22 | 3 | 4 | 3 | 75 | 5 |
T = 3 | - | 6 | 1 | 23 | 3 | 4 | 3 | 69 | 4 |
min | 1 | 0 | 0 | 3 | 1 | 1 | 1 | 19 | 1 |
max | 3 | 8 | 2 | 38 | 4 | 8 | 3 | 98 | 5 |
N | 3300 obs., [1100 per level of environmental concern (T)] |
Variables | Gaussian Identity (OLS) | Gaussian Identity (OLS) | Ordered_logit | |||
---|---|---|---|---|---|---|
Ec. (1) | Ec. (2) | Ec. (3) | ||||
PEB (Y) | (95% Conf. Interval) | Cn (M) | (95% Conf. Interval) | Affective (T) | ( 95% Conf. Interval) | |
Conative dim. (C ) | 0.54 *** | [0.45; 0.64] | ||||
(11.22) | ||||||
Affective dim. | 1.11 *** | [0.82; 1.39] | 0.88 *** | [0.78; 0.97] | ||
(7.60) | (18.11) | |||||
Cognitive dim. (C ) | 0.64 *** | [0.32; 0.97] | 0.31 *** | [0.20; 0.42] | 1.06 *** | [0.96; 1.16] |
(3.90) | (5.75) | (20.63) | ||||
Income | 0.78 *** | [0.53; 1.02] | ||||
(6.18) | ||||||
Household_type | 0.03 | [−0.07; 0.13] | ||||
(0.57) | ||||||
Education | −0.60 *** | [−0.93; −0.27] | ||||
(−3.59) | ||||||
Age | −0.003 | [−0.01; 0.009] | ||||
(−0.53) | ||||||
Municipality size | −0.031 | [−0.15; −0.091] | ||||
(−0.50) | ||||||
_cons | 13.43 *** | [11.89; 14.97] | 1.85 *** | [1.65; 2.05] | ||
(17.08) | (18.26) | |||||
/cut1_ec.(3) | −0.16 *** | [−2.24; −0.07] | ||||
(−3.70) | ||||||
/cut2_ec.(3) | 1.39 *** | [1.30; 1.49] | ||||
(28.29) | ||||||
var(e.peb)_ec.(1) | 27.37 *** | [26.01; 29.02] | ||||
(35.80) | ||||||
var(e.cn)_ec(2) | 4.45 *** | [4.28; 4.63] | ||||
(49.24) | ||||||
N | 2638 | 3300 | 3300 |
1 | (accessed on 20 March 2024). |
2 | |
3 | |
4 | (accessed on 18 June 2018). |
5 |
Pro-Environmental Behavior | Environmental Concern as the Treatment or Exposure Variable | |||||
---|---|---|---|---|---|---|
Nothing | Little | Much | ||||
Mean | std. dev. | Mean | std. dev. | Mean | std. dev. | |
PEB [min = 3; max = 38] | 15.74 | 6.03 | 17.70 | 5.49 | 20.06 | 4.89 |
N (3300 obs.) | 1100 | 1100 | 1100 | |||
χ Pearson’s test = 486.75; Pr. = 0.000; correlate = 0.30 * |
Variables | Statistics | Conative Dimension [Mediator (M)] | Cognitive Dimension [Confounder (C )] |
---|---|---|---|
Affective dimension (T) | Pearson’s test | 474.71 (pr. = 0.000) | 495.58 (pr. = 0.000) |
Correlate | 0.35 * | 0.36 * | |
PEB (Y) (outcome) (active dimension) | Pearson’s test | 760.42 (pr. = 0.000) | 290 (pr. = 0.000) |
Correlate | 0.29 * | 0.24 * | |
Conative dimension (M) | Pearson’s test | - | 219,22 (pr. = 0.000) |
Correlate | - | 0.21 * |
Causal Effects under Different Hypothesis | DAG_1 | DAG_2 | ||||
---|---|---|---|---|---|---|
no Confounder (no Cognitive), no Covariates | with Confounder (Cognitive), no Covariates | |||||
Estimate (1) | 95% CI (2) | p-Value (3) | Estimate (4) | 95% CI (5) | p-Value (6) | |
A. Estimations under the hypothesis of simple treatment and control conditions, i. e., the lowest level of affection (1) vs. the other two conditions (average) | ||||||
ACME | 0.54 | [0.47–0.63] | <2 × 10 *** | 0.44 | [0.34–0.52] | <2 × 10 *** |
ADE | 1.60 | [1.34–1.81] | <2 × 10 *** | 1.31 | [1.05–1.52] | <2 × 10 *** |
TE | 2.14 | [1.90–2.43] | <2 × 10 *** | 1.75 | [1.52–1.96] | <2 × 10 *** |
PM | 0.25 | [0.21–0.31] | <2 × 10 *** | 0.25 | [0.19–0.30] | <2 × 10 *** |
B. Estimations under the hypothesis of the lowest value of the treatment as control (=1 = the lowest level of concern) compares to the highest level of the exposure (treatment = 3 = level of concern) | ||||||
ACME | 1.07 | [0.89–1.31] | <2 × 10 *** | 0.88 | [0.71–1.10] | <2 × 10 *** |
ADE | 3.26 | [2.79–3.72] | <2 × 10 *** | 2.65 | [2.21–3.20] | <2 × 10 *** |
TE | 4.33 | [3.89–4.82] | <2 × 10 *** | 3.54 | [3.10–4.05] | <2 × 10 *** |
PM | 0.24 | [0.20–0.31] | <2 × 10 *** | 0.25 | [0.18–0.31] | <2 × 10 *** |
C. Estimations under the hypothesis of the intermediate value of the treatment as control (=2 = intermediate level of concern) compares to the highest level of the exposure (treatment = 3 = level of concern) | ||||||
ACME | 0.53 | [0.43–0.64] | <2 × 10 *** | 0.45 | [0.37–0.56] | <2 × 10 *** |
ADE | 1.61 | [1.33–1.84] | <2 × 10 *** | 1.33 | [1.12–1.58] | <2 × 10 *** |
TE | 2.14 | [1.91–2.38] | <2 × 10 *** | 1.78 | [1.57–2.02] | <2 × 10 *** |
PM | 0.24 | [0.20–0.30] | <2 × 10 *** | 0.25 | [0.20–0.32] | <2 × 10 *** |
Sample | 3300 obs. | |||||
Adjusted for covariates: income, household type, education, age, and size of the municipality: | ||||||
D. Estimations under the hypothesis of the lowest value of the treatment as control (=1 = the lowest level of concern) compares to the highest level of the exposure (treatment = 3 = level of concern) | ||||||
ACME | 1.04 | [0.82–1.29] | <2 × 10 *** | 0.96 | [0.76–1.15] | <2 × 10 *** |
ADE | 2.60 | [2.14–3.10] | <2 × 10 *** | 2.24 | [1.63–2.78] | <2 × 10 *** |
TE | 3.64 | [3.16–4.16] | <2 × 10 *** | 3.21 | [2.63–3.76] | <2 × 10 *** |
PM | 0.28 | [0.22–0.36] | <2 × 10 *** | 0.29 | [0.23–0.39] | <2 × 10 *** |
E. Estimations under the hypothesis of the intermediate value of the treatment as control (=2 = intermediate level of concern) compares to the highest level of the exposure (treatment = 3 = level of concern) | ||||||
ACME | 0.52 | [0.42–0.63] | <2 × 10 *** | 0.47 | [0.37–0.58] | <2 × 10 *** |
ADE | 1.29 | [1.00–1.59] | <2 × 10 *** | 1.10 | [0.79–1.40] | <2 × 10 *** |
TE | 1.81 | [1.57–2.13] | <2 × 10 *** | 1.57 | [1.26–1.92] | <2 × 10 *** |
PM | 0.28 | [0.23–0.38] | <2 × 10 *** | 0.30 | [0.24–0.37] | <2 × 10 *** |
Sample | 3300 |
Causal Effects | DAG_1 | DAG_2 | ||||
---|---|---|---|---|---|---|
Estimate (1) | 95% CI (2) | p-Value (3) | Estimate (4) | 95% CI (5) | p-Value (6) | |
ACME [control] | 0.71 | [0.48–0.93] | <2 × 10 *** | 0.65 | [0.42–0.88] | <2 × 10 *** |
ACME [treated] | 0.60 | [0.47–0.78] | <2 × 10 *** | 0.55 | [0.40–0.70] | <2 × 10 *** |
ADE [control] | 1.48 | [1.10–1.94] | <2 × 10 *** | 1.32 | [0.98–1.72] | <2 × 10 *** |
ADE [treated] | 1.38 | [1.05–1.75] | <2 × 10 *** | 1.22 | [0.94–1.57] | <2 × 10 *** |
TE | 2.09 | [1.61–2.59] | <2 × 10 *** | 1.87 | [1.49–2.33] | <2 × 10 *** |
PM [control] | 0.34 | [0.26–0.42] | <2 × 10 *** | 0.35 | [0.24–0.42] | <2 × 10 *** |
PM [treated] | 0.29 | [0.23–0.36] | <2 × 10 *** | 0.29 | [0.23–0.36] | <2 × 10 *** |
ACME [average] | 0.66 | [0.48–0.86] | <2 × 10 *** | 0.60 | [0.41–0.79] | <2 × 10 *** |
ADE [average] | 1.43 | [1.08–1.85] | <2 × 10 *** | 1.27 | [0.96–1.65] | <2 × 10 *** |
PM [average] | 0.31 | [0.25–0.39] | <2 × 10 *** | 0.32 | [0.24–0.38] | <2 × 10 *** |
TMint-test for the null hypothesis, [(ACME (3)) − (ACME (1)) = 0]; N = 2629 Obs. | ||||||
(ACME (3)) − (ACME (1)) = −0.10, p-value = 0.1; alternative hypothesis: true ACME (3) − ACME (1) is not equal to 95 percent confidence interval: [−0.20; 0.018] | (ACME (3)) − (ACME (1)) = −0.09, p-value = 0.04; alternative hypothesis: true ACME (3) − ACME (1) is not equal to 95 percent confidence interval: [−0.18; −0.003] |
Covariates/ Moderators | DAG_1 | DAG_2 | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
Average Effects for the Average Treatment (Worry) Level | Adjusting for Treatment (Worry) to the Highest Level (t = 3) | Modmed-Test on Average(5) | Average Effects for the Average Treatment (Worry) Level | Adjusting for Treatment (Worry) to the Highest Level (t = 3) | Modmed-Test on Average (10) | |||||
ACME (1) | ADE (2) | ACME (3) | ADE (4) | Diff Diff | ACME (6) | ADE (7) | ACME (8) | ADE (9) | Diff Diff | |
Income (mean) | 0.51 *** | 1.24 *** | 1.56 *** | 3.72 *** | Income: 0.60; [0.10] CI [0.95] = [−0.65; 0.81] ; [2.2 × 10 ] CI [0.95] = [−1.08; 3.13] | 0.46 *** | 1.10 *** | 1.40 *** | 3.27 *** | Income: 0.14; [0.24] CI [0.95] = [−0.10; 0.39] 0.99; [0.02] CI [0.95] = [−0.10; 0.39] |
1. Income_ | 0.56 *** | 1.53 *** | 1.73 *** | 4.56 *** | 0.51 *** | 1.40 *** | 1.53 *** | 4.17 *** | ||
4. Income_ | 0.39 *** | 0.60 | 1.13 *** | 1.71 | 0.34 *** | 0.38 | 1.06 *** | 1.14 *** | ||
Age (average) | 0.54 *** | 1.23 *** | 1.62 *** | 3.78 *** | Age: −0.07: [0.44] CI [0.95] = [−0.25; 0.13] 0.28; [0.30] CI [0.95] = [−0.14; 0.71] | 0.48 *** | 1.10 *** | 1.48 *** | 3.27 *** | Age: −0.06; [0.42] CI [0.95] = [−0.30; 0.14] ; [0.26] CI [0.95] = [−0.17; 0.75] |
Age | 0.54 *** | 1.14 *** | 1.58 *** | 3.58 *** | 0.47 *** | 0.93 *** | 1.43 *** | 2.96 *** | ||
Age | 0.45 *** | 1.42 *** | 1.37 *** | 4.32 *** | 0.41 *** | 1.21 | 1.20 *** | 3.71 *** | ||
Household (mean) | 0.51 *** | 1.28 *** | 1.57 *** | 3.75 *** | Household type: 0.021; [0.72] CI [0.95] = [−0.13; 0.15] 0.22; [0.32] CI [0.95] = [−0.15; 0.65] | 0.47 *** | 1.11 *** | 1.43 *** | 3.31 *** | Household type: 0.03; [0.66] CI [0.95] = [−0.13; 0.17] 0.259; [0.30] CI [0.95] = [−0.15; 0.73] |
1. Househ | 0.54 *** | 1.55 *** | 1.64 *** | 4.68 *** | 0.48 *** | 1.40 *** | 1.47 *** | 4.37 *** | ||
2. Househ | 0.52 *** | 1.31 *** | 1.57 *** | 3.94 *** | 0.48 *** | 1.17 *** | 1.45 *** | 3.56 *** | ||
3. Househ | 0.50 *** | 1.10 *** | 1.51 *** | 3.36 *** | 0.44 *** | 0.92 *** | 1.36 *** | 2.78 *** | ||
4. Househ | 0.49 *** | 0.92 *** | 1.44 *** | 2.70 *** | 0.43 *** | 0.66 * | 1.30 *** | 1.92 * | ||
Education (mean) | 0.51 *** | 1.25 *** | 1.55 *** | 3.75 *** | Education: −0.11; [0.48] CI [0.95] = [−0.38; 0.16] −0.57; [0.16] CI [0.95] = [−1.21; 0.12] −0.19; [0.10] CI [0.95] = [−0.39; 0.03] −1.22; [2.2 × 10 ] CI [0.95]= [−1.83; −0.49] −0.10; [0.24] CI [0.95] = [−0.27; 0.03] [0.06] CI [0.95] = [−0.99; 0.05] | 0.46 *** | 0.11 *** | 1.41 *** | 3.31 *** | Education: −0.07; [0.56] CI [0.95] = [−0.29; 0.13] −0.63; [0.10] CI [0.95] = [−1.39; 0.18] −0.19; [0.12] CI [0.95] = [−0.44; 0.08] −1.26; [2.2 × 10 ] CI [0.95] = [−2.02; −0.60] −0.11; [0.14] CI [0.95] = [−0.26; 0.03] −0.57; [0.02] CI [0.95] = [−1.09; −0.06] |
1. Edu_university | 0.36 *** | 0.26 | 1.06 *** | 0.91 | 0.33 *** | 0.12 | 0.99 *** | 0.31 | ||
2. Edu_Bacc. + voc. | 1.35 *** | 2.71 *** | 1.35 *** | 2.71 *** | 0.40 *** | 0.73 *** | 1.25 *** | 2.26 *** | ||
3. Edu_Illit. + prim. | 0.55 *** | 1.49 *** | 1.68 *** | 4.50 *** | 0.51 *** | 1.35 *** | 1.52 *** | 4.00 *** | ||
Municipal size (mean) | 1.56 *** | 3.80 *** | 1.56 *** | 3.83 *** | Municipality size: −0.049; [0.64] CI [0.95] = [−0.26; 0.17] 0.049; [0.86] CI [0.95] = [−0.47; 0.57] 0.001; [0.98] CI [0.95] = [−0.14; 0.13] 0.017; [0.92] CI [0.95] = [−0.43; 0.36] −0.02; [0.78] CI [0.95] = [−0.27; 0.18] ; [0.84] CI [0.95] = [−0.50; 0.49] | 0.48 *** | 1.10 *** | 1.43 *** | 3.29 *** | Municipality size: −0.03; [0.74] CI [0.95] = [−0.24; 0.16] 0.08; [0.80] CI [0.95] = [−0.53; 0.64] −0.02; [0.66] CI [0.95] = [−0.16; 0.12] 0.05; [0.84] CI [0.95] = [−0.33; 0.44] −0.03; [0.76] CI [0.95] = [−0.26; 0.15] 0.02; [0.99] CI [0.95] = [−0.55; 0.59] |
1. >100,000 | 0.48 *** | 1.29 *** | 1.48 *** | 3.97 *** | 0.44 *** | 1.13 *** | 1.33 *** | 3.44 *** | ||
2. [50,000–100,000] | 0.50 *** | 1.29 *** | 1.51 *** | 3.82 *** | 0.45 *** | 1.11 *** | 1.40 *** | 3.35 *** | ||
3. [20,000–50,000] | 0.52 *** | 1.26 *** | 1.54 *** | 3.80 *** | 0.46 *** | 1.11 *** | 1.41 *** | 3.36 *** | ||
4. [10,000–20,000] | 0.53 *** | 1.25 *** | 1.60 *** | 3.77 *** | 0.49 *** | 1.05 *** | 1.45 *** | 3.29 *** | ||
5. <10,000 | 0.55 *** | 1.22 *** | 1.63 *** | 3.70 *** | 0.51 *** | 1.05 *** | 1.51 *** | 3.19 *** | ||
Sample | 2629 |
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Hernández-Alemán, A.; Cruz-Pérez, N.; Santamarta, J.C. The Average Direct, Indirect and Total Effects of Environmental Concern on Pro-Environmental Behavior. Land 2024 , 13 , 1229. https://doi.org/10.3390/land13081229
Hernández-Alemán A, Cruz-Pérez N, Santamarta JC. The Average Direct, Indirect and Total Effects of Environmental Concern on Pro-Environmental Behavior. Land . 2024; 13(8):1229. https://doi.org/10.3390/land13081229
Hernández-Alemán, Anastasia, Noelia Cruz-Pérez, and Juan C. Santamarta. 2024. "The Average Direct, Indirect and Total Effects of Environmental Concern on Pro-Environmental Behavior" Land 13, no. 8: 1229. https://doi.org/10.3390/land13081229
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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.
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.
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
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.
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.
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
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
Key Takeaways
Here’s a summary of the main points about research proposals discussed in the previous sections:
Q1. How is a research proposal evaluated?
A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6
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.
Arts programs | 1,000-1,500 | |
University of Birmingham | Law School programs | 2,500 |
PhD | 2,500 | |
2,000 | ||
Research degrees | 2,000-3,500 |
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
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
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How to write a phd research proposal.
The future of academia: how ai tools are changing the way we do research, you may also like, dissertation printing and binding | types & comparison , what is a dissertation preface definition and examples , how to write your research paper in apa..., how to choose a dissertation topic, how to write an academic paragraph (step-by-step guide), maintaining academic integrity with paperpal’s generative ai writing..., research funding basics: what should a grant proposal..., how to write an abstract in research papers..., how to write dissertation acknowledgements.
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Looking for Research Proposals on Environmental Issues and ideas? Get them here for free! We have collected dozens of previously unpublished examples in one place.
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