statistical plan in research proposal

How to Create a Data Analysis Plan: A Detailed Guide

by Barche Blaise | Aug 12, 2020 | Writing

If a good research question equates to a story then, a roadmap will be very vita l for good storytelling. We advise every student/researcher to personally write his/her data analysis plan before seeking any advice. In this blog article, we will explore how to create a data analysis plan: the content and structure.

This data analysis plan serves as a roadmap to how data collected will be organised and analysed. It includes the following aspects:

• Clearly states the research objectives and hypothesis
• Identifies the dataset to be used
• Inclusion and exclusion criteria
• Clearly states the research variables
• States statistical test hypotheses and the software for statistical analysis
• Creating shell tables

1. Stating research question(s), objectives and hypotheses:

All research objectives or goals must be clearly stated. They must be Specific, Measurable, Attainable, Realistic and Time-bound (SMART). Hypotheses are theories obtained from personal experience or previous literature and they lay a foundation for the statistical methods that will be applied to extrapolate results to the entire population.

2. The dataset:

The dataset that will be used for statistical analysis must be described and important aspects of the dataset outlined. These include; owner of the dataset, how to get access to the dataset, how the dataset was checked for quality control and in what program is the dataset stored (Excel, Epi Info, SQL, Microsoft access etc.).

3. The inclusion and exclusion criteria :

They guide the aspects of the dataset that will be used for data analysis. These criteria will also guide the choice of variables included in the main analysis.

4. Variables:

Every variable collected in the study should be clearly stated. They should be presented based on the level of measurement (ordinal/nominal or ratio/interval levels), or the role the variable plays in the study (independent/predictors or dependent/outcome variables). The variable types should also be outlined.  The variable type in conjunction with the research hypothesis forms the basis for selecting the appropriate statistical tests for inferential statistics. A good data analysis plan should summarize the variables as demonstrated in Figure 1 below.

5. Statistical software

There are tons of software packages for data analysis, some common examples are SPSS, Epi Info, SAS, STATA, Microsoft Excel. Include the version number,  year of release and author/manufacturer. Beginners have the tendency to try different software and finally not master any. It is rather good to select one and master it because almost all statistical software have the same performance for basic and the majority of advance analysis needed for a student thesis. This is what we recommend to all our students at CRENC before they begin writing their results section .

6. Selecting the appropriate statistical method to test hypotheses

Depending on the research question, hypothesis and type of variable, several statistical methods can be used to answer the research question appropriately. This aspect of the data analysis plan outlines clearly why each statistical method will be used to test hypotheses. The level of statistical significance (p-value) which is often but not always <0.05 should also be written.  Presented in figures 2a and 2b are decision trees for some common statistical tests based on the variable type and research question

A good analysis plan should clearly describe how missing data will be analysed.

7. Creating shell tables

Data analysis involves three levels of analysis; univariable, bivariable and multivariable analysis with increasing order of complexity. Shell tables should be created in anticipation for the results that will be obtained from these different levels of analysis. Read our blog article on how to present tables and figures for more details. Suppose you carry out a study to investigate the prevalence and associated factors of a certain disease “X” in a population, then the shell tables can be represented as in Tables 1, Table 2 and Table 3 below.

Table 1: Example of a shell table from univariate analysis

Table 2: Example of a shell table from bivariate analysis

Table 3: Example of a shell table from multivariate analysis

aOR = adjusted odds ratio

Now that you have learned how to create a data analysis plan, these are the takeaway points. It should clearly state the:

• Research question, objectives, and hypotheses
• Dataset to be used
• Variable types and their role
• Statistical software and statistical methods
• Shell tables for univariate, bivariate and multivariate analysis

Further readings

Creating a Data Analysis Plan: What to Consider When Choosing Statistics for a Study https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4552232/pdf/cjhp-68-311.pdf

Creating an Analysis Plan: https://www.cdc.gov/globalhealth/healthprotection/fetp/training_modules/9/creating-analysis-plan_pw_final_09242013.pdf

Data Analysis Plan: https://www.statisticssolutions.com/dissertation-consulting-services/data-analysis-plan-2/

Photo created by freepik – www.freepik.com

Dr Barche is a physician and holds a Masters in Public Health. He is a senior fellow at CRENC with interests in Data Science and Data Analysis.

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16 comments.

Thanks. Quite informative.

Educative write-up. Thanks.

Easy to understand. Thanks Dr

Very explicit Dr. Thanks

I will always remember how you help me conceptualize and understand data science in a simple way. I can only hope that someday I’ll be in a position to repay you, my dear friend.

Plan d’analyse

This is interesting, Thanks

Very understandable and informative. Thank you..

love the figures.

Nice, and informative

This is so much educative and good for beginners, I would love to recommend that you create and share a video because some people are able to grasp when there is an instructor. Lots of love

Thank you Doctor very helpful.

Educative and clearly written. Thanks

Well said doctor,thank you.But when do you present in tables ,bars,pie chart etc?

Very informative guide!

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Guide to the statistical analysis plan

Affiliations.

• 1 Department of Anesthesiology and Critical Care Medicine, The Children's Hospital of Philadelphia, University of Pennsylvania, Perelman School of Medicine, Philadelphia, Pennsylvania.
• 2 Department of Epidemiology and Biostatistics, Dornsife School of Public Health, Drexel University, Philadelphia, Pennsylvania.
• PMID: 30609103
• DOI: 10.1111/pan.13576

Biomedical research has been struck with the problem of study findings that are not reproducible. With the advent of large databases and powerful statistical software, it has become easier to find associations and form conclusions from data without forming an a-priori hypothesis. This approach may yield associations without clinical relevance, false positive findings, or biased results due to "fishing" for the desired results. To improve reproducibility, transparency, and validity among clinical trials, the National Institute of Health recently updated its grant application requirements, which mandates registration of clinical trials and submission of the original statistical analysis plan (SAP) along with the research protocol. Many leading journals also require the SAP as part of the submission package. The goal of this article and the companion article detailing the SAP of an actual research study is to provide a practical guide on writing an effective SAP. We describe the what, why, when, where, and who of a SAP, and highlight the key contents of the SAP.

Keywords: SAP; reproducibility; research methodology; statistical analysis; transparency; validity.

© 2019 John Wiley & Sons Ltd.

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• Biomedical Research / standards*
• Data Interpretation, Statistical
• Databases, Factual
• Reproducibility of Results
• Research Design
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Tips for Writing a Statistical Analysis Plan

This column highlights research activities that may be of interest to asa members. this article includes information about new research solicitations and the federal budget for statistics. comments or suggestions for future articles may be sent to the amstat news managing editor at [email protected] ..

Contributing Editor Amy Herring is professor and associate chair of biostatistics at The University of North Carolina at Chapel Hill. She is PI of an R01 for developing statistical methods with applications in birth defects epidemiology and co-investigator on numerous projects in public health and medicine.

In the first of a series of articles commissioned by the ASA Committee on Funded Research , Jeremy Taylor provided an overview of the review process for statistical methodology grants in last month’s issue. This month, we consider important facets of writing statistical sections for NIH grants not primarily focused on development of new statistical methods. We assume readers are familiar with last month’s overview, particularly the description of the NIH, its review process, scoring of proposals, and other important issues.

In particular, we present tips for writing an excellent statistical analysis plan or biostatistical core for a biomedical or public health research grant with a primary focus outside of biostatistics. We will focus our attention on R01 research project grants and multi-project awards (e.g., P01, P30, P50, U19).

Multi-project awards support a multidisciplinary research team or group of investigators that focuses on a common research topic. They generally fund shared resources and facilities across multiple smaller research projects, and a biostatistics, data management, and/or bioinformatics core facility is often part of these proposals.

In an R01 proposal that does not involve statistics as a primary focus, the statistical portions of the grant usually contribute to the scores under the categories “Investigators,” “Approach,” and “Overall.” In the investigator category, reviewers are looking for evidence that the statistician or statistical analysis team has the skill and experience to evaluate the hypotheses in the specific aims. Your relevant skills and experience are judged based primarily on the information you provide in your biosketch(es) and the quality of the study design and analysis plan in the grant.

If you are a new researcher with relatively few publications, you should consider engaging a more senior biostatistician as a consultant or investigator on the grant to ensure the reviewers are comfortable with the level of statistical support. Reviewers will express their comfort with the planned study design and statistical analysis in the approach section of the grant, and both your credentials and statistical analysis plan/study design may affect the grant’s overall score.

For multi-project awards, a biostatistics, data management, and/or bioinformatics core facility is often scored as either acceptable or unacceptable, rather than using the typical 1–9 scale described in last month’s article. Unacceptable scores are not rare, so this scoring scheme does not mean the statistical section is less important than in other proposals.

Tips for Meeting Scientific Goals

Match the specific aims of the grant to your statistical analysis plan. Every hypothesis laid out in the specific aims should have a corresponding section in the analysis plan clearly describing how the hypothesis will be tested or otherwise evaluated. It is critical that the analysis plan is specific about how the investigator’s aims will be translated into hypotheses that you will then evaluate. It will be helpful if you use the same numbering system in the analysis plan that is used for the specific aims.

Know your audience. It is important to learn about the NIH review group that will score your application. Expectations for a statistical analysis section or biostatistics core may vary greatly across fields. Suppose the grant examines interactions between individuals’ genetic profiles and their diet in predicting cancer. Your grant may be reviewed by epidemiologists, bench scientists, or clinicians, and each type of reviewer would have different expectations of an excellent analysis plan. Current and previous review group rosters can be found at http://era.nih.gov/roster and can provide valuable information about the expertise (and expectations) of the review group. Previous grant reviews from the same review group can help you learn more about the review group’s expectations, even if they are from different proposals.

Provide something for everyone, explaining statistical concepts in clear, concise language that is accessible to nonstatisticians as well as to statisticians. As Taylor mentioned in last month’s issue, it is critical to “keep in mind the goal of making the application as easy as possible for reviewers to understand and appreciate.” Maybe you do really need that complex structural equations model or new methodology for dynamic treatment regimes to evaluate the specific aims; if so, it needs to be in the grant. However, in a nonstatistical review group, you may get three grant reviewers who have relatively little statistical knowledge. The reviewers may criticize an analysis plan if it comes across as overly involved or too ambitious. Be sure to take a little space or add a figure to explain the basic ideas of any complicated methods so a reviewer with a minimal background in statistics will get the big picture and understand why something more than a t -test is required.

Be specific. Don’t use boilerplate or a standard template for every grant you write. Reviewers will be looking to see how your analysis plan addresses the specific aims of the proposal. Have you addressed pertinent issues in the study at hand (e.g., a particular missing data problem, measurement error, or potential biases)? In a multi-project grant, reviewers will look to see whether the biostatistics core has the specific expertise to achieve all aims in the component grants. In some multi-project grants, you will need to show broad statistical expertise across biostatistics core members, as many multi-project grants devote considerable resources to helping new researchers get new projects off the ground.

Trust your collaborators. If they have a concern about the analysis plan, it is likely to be shared by the reviewers. Incorporating their feedback to improve the analysis plan will generally lead to a superior final product. But not always. Stick to your guns if you really think your collaborators are going in the wrong direction (e.g., using medical students for data cleaning is not acceptable, even if this has worked well for them in the past).

A second pair of eyes is often helpful. Offer to look over a colleague’s grants in exchange for having your colleague review yours.

Data cleaning and reproducibility are two critical concerns. Be sure you have addressed these issues in your proposal and have budgeted appropriately.

Keep your eyes open for methodological opportunities. Many statisticians are successful at getting their own grants (as PI) based on interesting methodological issues that arise in collaboration.

Operational Aspects Critical to Meeting Scientific Goals

Set expectations early so there are no unpleasant surprises at the time of submission. Will you be a co-investigator (this is standard) or dual PI (uncommon but appropriate if there is a large statistical component)? Often, the roles “biostatistician” or “statistician” are used, and these generally indicate more basic support, rather than doctoral-level scientific leadership, with a few notable exceptions (one that comes to mind is the biostatistics core of a multi-project grant, in which multiple researchers may be listed with minimal support just in case their expertise is needed). What percent effort will be required of the statistical team? Do you need graduate student support and computing resources? Who will be responsible for data entry, data management, and archiving code? What is the time frame for grant writing?

Be realistic. Don’t promise too much work for too little time. Nobody is happy if you cannot meet the goals you set. When the analysis is extensive or involves some new methodological territory, be sure your percent effort is substantial. For many projects, 1.5 months of effort plus a graduate student research assistant will be appropriate in analysis years, with adjustment of the effort required if early years of the grant do not involve any data analysis (however, you would generally still want around 0.6–1.0 months of funding yourself if you expect to provide input on the study design and other important issues that may arise early in the study).

Along these lines, be aware that sometimes grants will face cuts, either by the PI right before submission (to get the budget within pre-specified limits) or by NIH at the time of funding, and the PI generally has wide latitude in how to apply the cuts. You should not be afraid to put your foot down if your 10% effort plus a graduate student is cut to only 4% of your own time with no graduate student. In this case, you would explain how much of your time is available on that limited basis (e.g., 4% may be only enough to support your participation in a single 1.5 hour meeting per week, with no statistical analysis included, and, in that case, you may prefer to spend your time on projects that will provide you with more interesting work) and negotiate to obtain enough effort to support the work needed. Your department chair (or a senior faculty member, if you are a junior faculty member) can be helpful in such negotiations. Don’t be afraid to refuse to work on a project if the % effort is truly inadequate (though you should check with colleagues to be sure your version of inadequate is not out of line).

Read the review criteria before writing your sections of the grant. For some grants, the review criteria specifically address statistical analysis plans. Responsiveness to these criteria can greatly enhance your chances of success in the peer review process.

Cores in multi-project awards can be tricky to write. Sometimes, a reviewer may be assigned to review only your core, and sometimes a reviewer may review the entire grant. Thus, the core should be responsive to the research projects in the grant while also standing alone for review. Biostatistics cores have special requirements beyond statistical analysis plans of R01s. A core needs a specific leader who will be responsible for all core activities. The application must explain the organization of the core and clearly describe how it operates, including how researcher requests to use the core will be prioritized. Core services will vary based on the goals of the multi-project award but typically provide expertise for the planning, conduct, analysis, and reporting of studies; scientific computing; data management; manuscript preparation; and training of core users (reviewers often look favorably on cores that incorporate a training component by providing relevant workshops and seminars). Often, a strong case can be made to include time for methodological research by core biostatisticians when the multi-project aims would benefit from enhanced statistical methodology. It is always a good idea to provide specific names for all personnel (including programmers and graduate students), rather than budgeting for unnamed individuals in these applications.

Be committed. Carefully tailor the personal statement on your biosketch and the accompanying list of publications to the grant application at hand. For example, you will want to include papers that are co-authored with your collaborators on the current application and other publications that show you have already worked in areas relevant to the grant. If the grant requires statistical assistance in an area in which you have no expertise, you may want to bring another statistician onto the team as co-investigator or a consultant to the grant. If you do not show you have the skills to carry out the proposed analysis, or if you do not look fully engaged with the grant, the grant may get less favorable scores for the investigators, approach, and overall components.

Block off time for last-minute changes well in advance. Your colleagues may have others inside the university review the grant before submission, and an aim may be replaced at the last minute. This could require a new analysis plan, new power calculations, etc. While major last-minute changes should not be a regular occurrence, this happens periodically, even with outstanding collaborators, and you should not be surprised to have requests for 11th-hour edits.

Meetings such as ENAR and JSM often offer roundtable discussions on writing statistical components of non-statistical grants. These discussions are a great way to share good (and bad!) experiences with colleagues to increase the probability of success in the future. Good luck!

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Writing the Data Analysis Plan

• First Online: 01 January 2010

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You and your project statistician have one major goal for your data analysis plan: You need to convince all the reviewers reading your proposal that you would know what to do with your data once your project is funded and your data are in hand. The data analytic plan is a signal to the reviewers about your ability to score, describe, and thoughtfully synthesize a large number of variables into appropriately-selected quantitative models once the data are collected. Reviewers respond very well to plans with a clear elucidation of the data analysis steps – in an appropriate order, with an appropriate level of detail and reference to relevant literatures, and with statistical models and methods for that map well into your proposed aims. A successful data analysis plan produces reviews that either include no comments about the data analysis plan or better yet, compliments it for being comprehensive and logical given your aims. This chapter offers practical advice about developing and writing a compelling, “bullet-proof” data analytic plan for your grant application.

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Panter, A.T. (2010). Writing the Data Analysis Plan. In: Pequegnat, W., Stover, E., Boyce, C. (eds) How to Write a Successful Research Grant Application. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1454-5_22

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Grant Writing and Statistics

Writing a statistical section that supports a successful grant proposal.  

SDBC statisticians can support you in the grant writing process by writing the statistical analysis sections including the power/sample size section of your grant proposal.  To write the statistical analysis section, we need to know the following: 1) the population under study, 2) the study design (RCT, prospective/retrospective cohort study, cross-sectional, etc) 3) the intervention/prognostic factor/exposure, 4) the comparison/control group (if applicable), and 5) the outcome of interest and how it will be measured.  We can also help with deciding the above factors.  For example, an alternative study design or outcome measure might be more effective at answering your research question(s) than what you had planned.

The following documents provide more information on the elements of study design that are helpful for developing an effective statistical analysis section. 

•   WRITING AN EFFECTIVE RESEARCH PROPOSAL Marja J. Verhoef, PhD  Robert J. Hilsden, MD MSc FRCPC 
• Bridging Clinical Investigators and Statisticians: Writing the Statistical Methodology for a Research Proposal Beverley Adams-Huet, MS and Chul Ahn, PhD Journal of Investigative Medicine  Volume 57, Number 8, December 2009 Writing the Statistical Analysis Plan                                                       

The following table summarizes the role of statisticians in developing the statistical plan for a grant proposal. 

• Overview of grant writing process and getting started
• Writing concise research aims that are specific and measurable
• Resources for power and sample size calculation
• Useful online resources

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Acknowledging the SDBC

Please use the following text to acknowledge the CTSI Study Design and Biostatistics Center:

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"This investigation was supported by the Study Design and Biostatistics Center (SDBC), with funding in part from the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UM1TR004409 . The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health."

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Top 10 Statistical Analysis Research Proposal Templates with Samples and Examples

Densil Nazimudeen

In the dynamic realm of scientific inquiry, statistical analysis is the bedrock upon which informed decisions are built. A well-defined statistical analysis research proposal delineates the scope of work and serves as a roadmap for acquiring and extracting invaluable insights from data. As data classification and decision mapping weave intricately into this process, the significance of a meticulously structured research proposal cannot be overstated.

In the pursuit of effective communication and streamlined comprehension, the integration of visual aids is paramount. This is precisely where SlideTeam’s Top 10 Statistical Analysis Research Proposal Templates come into play. These PPT Themes, carefully curated to cater to diverse needs, bridge the gap between complexity and clarity.

Here is an engaging blog post about the Top 7 Market Analysis Report Templates with Examples and Samples. Click here to read.

These PPT Designs encompass various elements, harmonizing an enterprise analytics solution with a user-friendly design. As organizations seek cooperation to surmount intricate statistical analysis cost structure s, these PPT Templates offer an unparalleled advantage. Each PPT Template encapsulates the essence of data-driven research, infusing creativity into the otherwise technical aspects. These PPT Slides facilitate a flawless narrative flow with strategically embedded keywords like acquisition and extraction , data classification , and decision mapping .  

Since each PPT Slide was painstakingly created to be 100% editable, they represent the height of usability and creativity. The content can be changed to suit your needs and effectively deliver your message. To produce memorable and significant presentations, these PPT Themes purposefully lure viewers in with appealing, content-ready layouts, attention-grabbing imagery, and stunning typography.

Let's take a look at our PPT Templates.

Template 1: Project Context and Objectives of Statistical Analysis of Research Findings Template

With the help of this PPT Preset, you can demonstrate the project context and objective of data analytics, along with the details of the benefits and advantages of choosing their company’s services. It helps you present a comprehensive overview of how data analytics aligns with the project's goals. It also highlights the unique selling points of the company in the field of data analytics. Furthermore, this PPT Theme provides a structured framework for discussing the company's expertise and capabilities in data analytics.

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Template 2: Scope of Work for Statistical Analysis of Research Findings Template

With the help of this PPT Layout, you can showcase the scope of work for research data analysis projects. It highlights specific focus areas, such as data acquisition and extraction , examination, and cleaning. This PPT Theme provides a visual roadmap for the research data analysis journey. It also illustrates the methodologies and techniques that will be employed in each stage of analysis. Furthermore, this PPT Theme enables clients or stakeholders to understand the depth and breadth of the analysis process. 

Template 3: Plan of Action for Statistical Analysis of Research Findings Template

Use this PPT Slide to deliver a structured, organized action plan for research data analysis projects. It helps you to demonstrate the different phases of the data analysis journey: data collection, data pre-processing, data analysis, and data classification . This PPT Theme highlights the significance of data pre-processing in preparing raw data for analysis. It communicates the strategic importance of data classification in deriving meaningful insights. Also, it enables stakeholders to comprehend the project's timeline and resource allocation for each phase. 

Template 4: Timeline for Statistical Analysis of Research Findings Template

With the help of this PPT Theme, you can showcase the timeline for a research data analysis project that focuses on business issue understanding, data understanding, data preparation, etc. It offers stakeholders a comprehensive view of the project's progress and projected duration. It demonstrates the company's expertise in managing the various stages of research data analysis. It also facilitates project planning and resource allocation by separating the process into distinct phases. Also, this PPT Preset presents a cohesive and logical flow of how the project will unfold, from issue identification to actionable insights. 

Template 5: Key Deliverables for Statistical Analysis of Research Findings Template

With the help of this PPT Template, you can demonstrate the critical deliverables for research data analysis, which cover problem/ decision mapping , analysis and design, implementation, ongoing, etc. It helps you showcase the company's expertise in managing the various phases of research data analysis. It facilitates client understanding by showcasing tangible and intangible outcomes at each stage. It enhances project planning and stakeholder alignment by clearly defining what each phase produces. Also, this PPT Theme reflects the company's commitment to delivering comprehensive and impactful solutions through a structured approach. 

Template 6: Statistical Analysis Cost Structure 1/2 Template

This PPT Slide focuses on the data analytics cost structure, covering phases like architecture design, hardware and software configuration, system development and integration, etc. It also covers costs incurred by each team member. This PPT Slide emphasizes the financial commitment required for system development and integration. It also demonstrates a comprehensive view of the project's financial allocation across various phases. It facilitates informed decision-making by visually representing the financial considerations at each stage. Furthermore, it enables stakeholders to understand the project's distribution of resources and budget. 

Template 7: Statistical Analysis Cost Structure 2/2 Template

With the help of this PPT Layout, you can demonstrate the data analytics cost structure, which covers various services offered like research design, questionnaire design, sample size identification, etc., along with their corresponding prices. This PPT Theme helps you demonstrate the financial commitment required for each distinct service in the data analytics journey. It enables clients or stakeholders to understand the financial distribution across various services. Furthermore, it facilitates decision-making by visually representing the cost breakdown of each service. 

Template 8: Statistical Analysis Team Cost Structure Template

With the help of this PPT Theme, you can showcase the packages offered by call centre service providers, such as essential, business plus, enterprise, and premium. It illustrates the hourly cost rates for each specialist role in the package context. This PPT Theme enables clients to make informed decisions by understanding the offerings and costs of each package. It highlights specialists' specific skills and expertise at different hourly cost rates. Furthermore, it enhances transparency by showcasing the hourly cost rates of each specialist role. 

Template 9: Why Our Statistical Analysis Company Template

This PPT Layout effectively communicates why customers choose the company for their data analytics needs using a visually impactful template. It highlights the company's strengths, such as the amount of data, cleanliness, complexity, etc. This PPT Theme enables clients to understand the strategic advantages of choosing the company for their data analytics requirements. Furthermore, it facilitates a comprehensive overview of the company's unique selling points in the data analytics domain. 

Template 10: About Our Statistical Analysis of Research Findings Template

This PPT Preset articulates why customers select the company for their data analytics needs. It introduces the company and its identity, encompassing aspects like who we are, vision, and mission. This PPT Theme presents the company's mission statement, outlining its purpose and commitment to clients. It offers clients an understanding of the company's ethos and long-term goals. Furthermore, effective communication helps reflect the company's commitment to transparency and client understanding. 

Embark on an exploration of these statistical analysis research proposal templates today!

The curated collection of the Top 10 Statistical Analysis Research Proposal Templates offers a valuable resource for researchers and scholars. These templates, real-world samples, and examples provide a solid foundation for crafting compelling research proposals. By harnessing these tools, researchers can streamline proposal creation, ensuring clarity, structure, and methodological rigor. Our research proposal presentation templates cater to diverse research avenues, whether delving into quantitative data, experimental design, or survey analysis. Embracing these templates saves time and enhances the quality of proposals, fostering effective communication of research intentions. As we conclude, this repository is a pivotal asset, empowering researchers to embark on their academic pursuits confidently.

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Unlock insights with a compelling blog that explores the Top 10 Research Project Proposal Templates with Samples and Examples. Click here to learn more.

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FAQs on Statistical Analysis Research Proposal

What is statistical analysis, and what are its types.

Statistical analysis involves interpreting data to uncover patterns, relationships, and insights. Its types include descriptive (summarizing data), inferential (drawing conclusions from samples), and exploratory (finding new trends). Regression analyzes dependencies, ANOVA compares groups, and hypothesis testing validates assumptions. Each type aids decision-making across various fields.

What is the purpose of statistical analysis in research?

Statistical analysis in research reveals patterns, relationships, and trends within data. It validates hypotheses, aids in drawing accurate conclusions, and supports evidence-based decision-making. Providing objective insights enhances the reliability and credibility of research findings across diverse fields.

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How to prepare a Research Proposal

Health research, medical education and clinical practice form the three pillars of modern day medical practice. As one authority rightly put it: ‘Health research is not a luxury, but an essential need that no nation can afford to ignore’. Health research can and should be pursued by a broad range of people. Even if they do not conduct research themselves, they need to grasp the principles of the scientific method to understand the value and limitations of science and to be able to assess and evaluate results of research before applying them. This review paper aims to highlight the essential concepts to the students and beginning researchers and sensitize and motivate the readers to access the vast literature available on research methodologies.

Most students and beginning researchers do not fully understand what a research proposal means, nor do they understand its importance. 1 A research proposal is a detailed description of a proposed study designed to investigate a given problem. 2

A research proposal is intended to convince others that you have a worthwhile research project and that you have the competence and the work-plan to complete it. Broadly the research proposal must address the following questions regardless of your research area and the methodology you choose: What you plan to accomplish, why do you want to do it and how are you going to do it. 1 The aim of this article is to highlight the essential concepts and not to provide extensive details about this topic.

The elements of a research proposal are highlighted below:

1. Title: It should be concise and descriptive. It must be informative and catchy. An effective title not only prick’s the readers interest, but also predisposes him/her favorably towards the proposal. Often titles are stated in terms of a functional relationship, because such titles clearly indicate the independent and dependent variables. 1 The title may need to be revised after completion of writing of the protocol to reflect more closely the sense of the study. 3

2. Abstract: It is a brief summary of approximately 300 words. It should include the main research question, the rationale for the study, the hypothesis (if any) and the method. Descriptions of the method may include the design, procedures, the sample and any instruments that will be used. 1 It should stand on its own, and not refer the reader to points in the project description. 3

3. Introduction: The introduction provides the readers with the background information. Its purpose is to establish a framework for the research, so that readers can understand how it relates to other research. 4 It should answer the question of why the research needs to be done and what will be its relevance. It puts the proposal in context. 3

The introduction typically begins with a statement of the research problem in precise and clear terms. 1

The importance of the statement of the research problem 5 : The statement of the problem is the essential basis for the construction of a research proposal (research objectives, hypotheses, methodology, work plan and budget etc). It is an integral part of selecting a research topic. It will guide and put into sharper focus the research design being considered for solving the problem. It allows the investigator to describe the problem systematically, to reflect on its importance, its priority in the country and region and to point out why the proposed research on the problem should be undertaken. It also facilitates peer review of the research proposal by the funding agencies.

Then it is necessary to provide the context and set the stage for the research question in such a way as to show its necessity and importance. 1 This step is necessary for the investigators to familiarize themselves with existing knowledge about the research problem and to find out whether or not others have investigated the same or similar problems. This step is accomplished by a thorough and critical review of the literature and by personal communication with experts. 5 It helps further understanding of the problem proposed for research and may lead to refining the statement of the problem, to identify the study variables and conceptualize their relationships, and in formulation and selection of a research hypothesis. 5 It ensures that you are not "re-inventing the wheel" and demonstrates your understanding of the research problem. It gives due credit to those who have laid the groundwork for your proposed research. 1 In a proposal, the literature review is generally brief and to the point. The literature selected should be pertinent and relevant. 6

Against this background, you then present the rationale of the proposed study and clearly indicate why it is worth doing.

4. Objectives: Research objectives are the goals to be achieved by conducting the research. 5 They may be stated as ‘general’ and ‘specific’.

The general objective of the research is what is to be accomplished by the research project, for example, to determine whether or not a new vaccine should be incorporated in a public health program.

The specific objectives relate to the specific research questions the investigator wants to answer through the proposed study and may be presented as primary and secondary objectives, for example, primary: To determine the degree of protection that is attributable to the new vaccine in a study population by comparing the vaccinated and unvaccinated groups. 5 Secondary: To study the cost-effectiveness of this programme.

Young investigators are advised to resist the temptation to put too many objectives or over-ambitious objectives that cannot be adequately achieved by the implementation of the protocol. 3

5. Variables: During the planning stage, it is necessary to identify the key variables of the study and their method of measurement and unit of measurement must be clearly indicated. Four types of variables are important in research 5 :

a. Independent variables: variables that are manipulated or treated in a study in order to see what effect differences in them will have on those variables proposed as being dependent on them. The different synonyms for the term ‘independent variable’ which are used in literature are: cause, input, predisposing factor, risk factor, determinant, antecedent, characteristic and attribute.

b. Dependent variables: variables in which changes are results of the level or amount of the independent variable or variables.

Synonyms: effect, outcome, consequence, result, condition, disease.

c. Confounding or intervening variables: variables that should be studied because they may influence or ‘mix’ the effect of the independent variables. For instance, in a study of the effect of measles (independent variable) on child mortality (dependent variable), the nutritional status of the child may play an intervening (confounding) role.

d. Background variables: variables that are so often of relevance in investigations of groups or populations that they should be considered for possible inclusion in the study. For example sex, age, ethnic origin, education, marital status, social status etc.

The objective of research is usually to determine the effect of changes in one or more independent variables on one or more dependent variables. For example, a study may ask "Will alcohol intake (independent variable) have an effect on development of gastric ulcer (dependent variable)?"

Certain variables may not be easy to identify. The characteristics that define these variables must be clearly identified for the purpose of the study.

6. Questions and/ or hypotheses: If you as a researcher know enough to make prediction concerning what you are studying, then the hypothesis may be formulated. A hypothesis can be defined as a tentative prediction or explanation of the relationship between two or more variables. In other words, the hypothesis translates the problem statement into a precise, unambiguous prediction of expected outcomes. Hypotheses are not meant to be haphazard guesses, but should reflect the depth of knowledge, imagination and experience of the investigator. 5 In the process of formulating the hypotheses, all variables relevant to the study must be identified. For example: "Health education involving active participation by mothers will produce more positive changes in child feeding than health education based on lectures". Here the independent variable is types of health education and the dependent variable is changes in child feeding.

A research question poses a relationship between two or more variables but phrases the relationship as a question; a hypothesis represents a declarative statement of the relations between two or more variables. 7

For exploratory or phenomenological research, you may not have any hypothesis (please do not confuse the hypothesis with the statistical null hypothesis). 1 Questions are relevant to normative or census type research (How many of them are there? Is there a relationship between them?). Deciding whether to use questions or hypotheses depends on factors such as the purpose of the study, the nature of the design and methodology, and the audience of the research (at times even the outlook and preference of the committee members, particularly the Chair). 6

7. Methodology: The method section is very important because it tells your research Committee how you plan to tackle your research problem. The guiding principle for writing the Methods section is that it should contain sufficient information for the reader to determine whether the methodology is sound. Some even argue that a good proposal should contain sufficient details for another qualified researcher to implement the study. 1 Indicate the methodological steps you will take to answer every question or to test every hypothesis illustrated in the Questions/hypotheses section. 6 It is vital that you consult a biostatistician during the planning stage of your study, 8 to resolve the methodological issues before submitting the proposal.

This section should include:

Research design: The selection of the research strategy is the core of research design and is probably the single most important decision the investigator has to make. The choice of the strategy, whether descriptive, analytical, experimental, operational or a combination of these depend on a number of considerations, 5 but this choice must be explained in relation to the study objectives. 3

Research subjects or participants: Depending on the type of your study, the following questions should be answered 3 , 5

• - What are the criteria for inclusion or selection?
• - What are the criteria for exclusion?
• - What is the sampling procedure you will use so as to ensure representativeness and reliability of the sample and to minimize sampling errors? The key reason for being concerned with sampling is the issue of validity-both internal and external of the study results. 9
• - Will there be use of controls in your study? Controls or comparison groups are used in scientific research in order to increase the validity of the conclusions. Control groups are necessary in all analytical epidemiological studies, in experimental studies of drug trials, in research on effects of intervention programmes and disease control measures and in many other investigations. Some descriptive studies (studies of existing data, surveys) may not require control groups.
• - What are the criteria for discontinuation?

Sample size: The proposal should provide information and justification (basis on which the sample size is calculated) about sample size in the methodology section. 3 A larger sample size than needed to test the research hypothesis increases the cost and duration of the study and will be unethical if it exposes human subjects to any potential unnecessary risk without additional benefit. A smaller sample size than needed can also be unethical as it exposes human subjects to risk with no benefit to scientific knowledge. Calculation of sample size has been made easy by computer software programmes, but the principles underlying the estimation should be well understood.

Interventions: If an intervention is introduced, a description must be given of the drugs or devices (proprietary names, manufacturer, chemical composition, dose, frequency of administration) if they are already commercially available. If they are in phases of experimentation or are already commercially available but used for other indications, information must be provided on available pre-clinical investigations in animals and/or results of studies already conducted in humans (in such cases, approval of the drug regulatory agency in the country is needed before the study). 3

Ethical issues 3 : Ethical considerations apply to all types of health research. Before the proposal is submitted to the Ethics Committee for approval, two important documents mentioned below (where appropriate) must be appended to the proposal. In additions, there is another vital issue of Conflict of Interest, wherein the researchers should furnish a statement regarding the same.

The Informed consent form (informed decision-making): A consent form, where appropriate, must be developed and attached to the proposal. It should be written in the prospective subjects’ mother tongue and in simple language which can be easily understood by the subject. The use of medical terminology should be avoided as far as possible. Special care is needed when subjects are illiterate. It should explain why the study is being done and why the subject has been asked to participate. It should describe, in sequence, what will happen in the course of the study, giving enough detail for the subject to gain a clear idea of what to expect. It should clarify whether or not the study procedures offer any benefits to the subject or to others, and explain the nature, likelihood and treatment of anticipated discomfort or adverse effects, including psychological and social risks, if any. Where relevant, a comparison with risks posed by standard drugs or treatment must be included. If the risks are unknown or a comparative risk cannot be given it should be so stated. It should indicate that the subject has the right to withdraw from the study at any time without, in any way, affecting his/her further medical care. It should assure the participant of confidentiality of the findings.

Ethics checklist: The proposal must describe the measures that will be undertaken to ensure that the proposed research is carried out in accordance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical research involving Human Subjects. 10 It must answer the following questions:

• • Is the research design adequate to provide answers to the research question? It is unethical to expose subjects to research that will have no value.
• • Is the method of selection of research subjects justified? The use of vulnerable subjects as research participants needs special justification. Vulnerable subjects include those in prison, minors and persons with mental disability. In international research it is important to mention that the population in which the study is conducted will benefit from any potential outcome of the research and the research is not being conducted solely for the benefit of some other population. Justification is needed for any inducement, financial or otherwise, for the participants to be enrolled in the study.
• • Are the interventions justified, in terms of risk/benefit ratio? Risks are not limited to physical harm. Psychological and social risks must also be considered.
• • For observations made, have measures been taken to ensure confidentiality?

Research setting 5 : The research setting includes all the pertinent facets of the study, such as the population to be studied (sampling frame), the place and time of study.

Study instruments 3 , 5 : Instruments are the tools by which the data are collected. For validated questionnaires/interview schedules, reference to published work should be given and the instrument appended to the proposal. For new a questionnaire which is being designed specifically for your study the details about preparing, precoding and pretesting of questionnaire should be furnished and the document appended to the proposal. Descriptions of other methods of observations like medical examination, laboratory tests and screening procedures is necessary- for established procedures, reference of published work cited but for new or modified procedure, an adequate description is necessary with justification for the same.

Collection of data: A short description of the protocol of data collection. For example, in a study on blood pressure measurement: time of participant arrival, rest for 5p. 10 minutes, which apparatus (standard calibrated) to be used, in which room to take measurement, measurement in sitting or lying down position, how many measurements, measurement in which arm first (whether this is going to be randomized), details of cuff and its placement, who will take the measurement. This minimizes the possibility of confusion, delays and errors.

Data analysis: The description should include the design of the analysis form, plans for processing and coding the data and the choice of the statistical method to be applied to each data. What will be the procedures for accounting for missing, unused or spurious data?

Monitoring, supervision and quality control: Detailed statement about the all logistical issues to satisfy the requirements of Good Clinical Practices (GCP), protocol procedures, responsibilities of each member of the research team, training of study investigators, steps taken to assure quality control (laboratory procedures, equipment calibration etc)

Gantt chart: A Gantt chart is an overview of tasks/proposed activities and a time frame for the same. You put weeks, days or months at one side, and the tasks at the other. You draw fat lines to indicate the period the task will be performed to give a timeline for your research study (take help of tutorial on youtube). 11

Significance of the study: Indicate how your research will refine, revise or extend existing knowledge in the area under investigation. How will it benefit the concerned stakeholders? What could be the larger implications of your research study?

Dissemination of the study results: How do you propose to share the findings of your study with professional peers, practitioners, participants and the funding agency?

Budget: A proposal budget with item wise/activity wise breakdown and justification for the same. Indicate how will the study be financed.

References: The proposal should end with relevant references on the subject. For web based search include the date of access for the cited website, for example: add the sentence "accessed on June 10, 2008".

Appendixes: Include the appropriate appendixes in the proposal. For example: Interview protocols, sample of informed consent forms, cover letters sent to appropriate stakeholders, official letters for permission to conduct research. Regarding original scales or questionnaires, if the instrument is copyrighted then permission in writing to reproduce the instrument from the copyright holder or proof of purchase of the instrument must be submitted.

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Home » How To Write A Research Proposal – Step-by-Step [Template]

How To Write A Research Proposal – Step-by-Step [Template]

Table of Contents

How To Write a Research Proposal

Writing a Research proposal involves several steps to ensure a well-structured and comprehensive document. Here is an explanation of each step:

1. Title and Abstract

• Choose a concise and descriptive title that reflects the essence of your research.
• Write an abstract summarizing your research question, objectives, methodology, and expected outcomes. It should provide a brief overview of your proposal.

2. Introduction:

• Provide an introduction to your research topic, highlighting its significance and relevance.
• Clearly state the research problem or question you aim to address.
• Discuss the background and context of the study, including previous research in the field.

3. Research Objectives

• Outline the specific objectives or aims of your research. These objectives should be clear, achievable, and aligned with the research problem.

4. Literature Review:

• Conduct a comprehensive review of relevant literature and studies related to your research topic.
• Summarize key findings, identify gaps, and highlight how your research will contribute to the existing knowledge.

5. Methodology:

• Describe the research design and methodology you plan to employ to address your research objectives.
• Explain the data collection methods, instruments, and analysis techniques you will use.
• Justify why the chosen methods are appropriate and suitable for your research.

6. Timeline:

• Create a timeline or schedule that outlines the major milestones and activities of your research project.
• Break down the research process into smaller tasks and estimate the time required for each task.

7. Resources:

• Identify the resources needed for your research, such as access to specific databases, equipment, or funding.
• Explain how you will acquire or utilize these resources to carry out your research effectively.

8. Ethical Considerations:

• Discuss any ethical issues that may arise during your research and explain how you plan to address them.
• If your research involves human subjects, explain how you will ensure their informed consent and privacy.

9. Expected Outcomes and Significance:

• Clearly state the expected outcomes or results of your research.
• Highlight the potential impact and significance of your research in advancing knowledge or addressing practical issues.

10. References:

• Provide a list of all the references cited in your proposal, following a consistent citation style (e.g., APA, MLA).

11. Appendices:

• Include any additional supporting materials, such as survey questionnaires, interview guides, or data analysis plans.

Research Proposal Format

The format of a research proposal may vary depending on the specific requirements of the institution or funding agency. However, the following is a commonly used format for a research proposal:

1. Title Page:

• Include the title of your research proposal, your name, your affiliation or institution, and the date.

2. Abstract:

• Provide a brief summary of your research proposal, highlighting the research problem, objectives, methodology, and expected outcomes.

3. Introduction:

• Introduce the research topic and provide background information.
• State the research problem or question you aim to address.
• Explain the significance and relevance of the research.
• Review relevant literature and studies related to your research topic.
• Summarize key findings and identify gaps in the existing knowledge.
• Explain how your research will contribute to filling those gaps.

5. Research Objectives:

• Clearly state the specific objectives or aims of your research.
• Ensure that the objectives are clear, focused, and aligned with the research problem.

6. Methodology:

• Describe the research design and methodology you plan to use.
• Explain the data collection methods, instruments, and analysis techniques.
• Justify why the chosen methods are appropriate for your research.

7. Timeline:

8. Resources:

• Explain how you will acquire or utilize these resources effectively.

9. Ethical Considerations:

• If applicable, explain how you will ensure informed consent and protect the privacy of research participants.

10. Expected Outcomes and Significance:

11. References:

12. Appendices:

Research Proposal Template

Here’s a template for a research proposal:

1. Introduction:

2. Literature Review:

3. Research Objectives:

4. Methodology:

5. Timeline:

6. Resources:

7. Ethical Considerations:

8. Expected Outcomes and Significance:

9. References:

10. Appendices:

Research Proposal Sample

Title: The Impact of Online Education on Student Learning Outcomes: A Comparative Study

1. Introduction

Online education has gained significant prominence in recent years, especially due to the COVID-19 pandemic. This research proposal aims to investigate the impact of online education on student learning outcomes by comparing them with traditional face-to-face instruction. The study will explore various aspects of online education, such as instructional methods, student engagement, and academic performance, to provide insights into the effectiveness of online learning.

2. Objectives

The main objectives of this research are as follows:

• To compare student learning outcomes between online and traditional face-to-face education.
• To examine the factors influencing student engagement in online learning environments.
• To assess the effectiveness of different instructional methods employed in online education.
• To identify challenges and opportunities associated with online education and suggest recommendations for improvement.

3. Methodology

3.1 Study Design

This research will utilize a mixed-methods approach to gather both quantitative and qualitative data. The study will include the following components:

3.2 Participants

The research will involve undergraduate students from two universities, one offering online education and the other providing face-to-face instruction. A total of 500 students (250 from each university) will be selected randomly to participate in the study.

3.3 Data Collection

The research will employ the following data collection methods:

• Quantitative: Pre- and post-assessments will be conducted to measure students’ learning outcomes. Data on student demographics and academic performance will also be collected from university records.
• Qualitative: Focus group discussions and individual interviews will be conducted with students to gather their perceptions and experiences regarding online education.

3.4 Data Analysis

Quantitative data will be analyzed using statistical software, employing descriptive statistics, t-tests, and regression analysis. Qualitative data will be transcribed, coded, and analyzed thematically to identify recurring patterns and themes.

4. Ethical Considerations

The study will adhere to ethical guidelines, ensuring the privacy and confidentiality of participants. Informed consent will be obtained, and participants will have the right to withdraw from the study at any time.

5. Significance and Expected Outcomes

This research will contribute to the existing literature by providing empirical evidence on the impact of online education on student learning outcomes. The findings will help educational institutions and policymakers make informed decisions about incorporating online learning methods and improving the quality of online education. Moreover, the study will identify potential challenges and opportunities related to online education and offer recommendations for enhancing student engagement and overall learning outcomes.

6. Timeline

The proposed research will be conducted over a period of 12 months, including data collection, analysis, and report writing.

The estimated budget for this research includes expenses related to data collection, software licenses, participant compensation, and research assistance. A detailed budget breakdown will be provided in the final research plan.

8. Conclusion

This research proposal aims to investigate the impact of online education on student learning outcomes through a comparative study with traditional face-to-face instruction. By exploring various dimensions of online education, this research will provide valuable insights into the effectiveness and challenges associated with online learning. The findings will contribute to the ongoing discourse on educational practices and help shape future strategies for maximizing student learning outcomes in online education settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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