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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples

Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.

To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.

After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.

This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.

Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results, other interesting articles.

To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.

Writing statistical hypotheses

The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.

A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.

While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.

Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
Null hypothesis: Parental income and GPA have no relationship with each other in college students.
Alternative hypothesis: Parental income and GPA are positively correlated in college students.

Planning your research design

A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.

First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.

In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.

Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.

In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
Experimental
Correlational

First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.

In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.

Measuring variables

When planning a research design, you should operationalize your variables and decide exactly how you will measure them.

For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:

Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).

Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.

Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.

In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.

Variable	Type of data
Age	Quantitative (ratio)
Gender	Categorical (nominal)
Race or ethnicity	Categorical (nominal)
Baseline test scores	Quantitative (interval)
Final test scores	Quantitative (interval)


Parental income	Quantitative (ratio)
GPA	Quantitative (interval)

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In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.

Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.

Sampling for statistical analysis

There are two main approaches to selecting a sample.

Probability sampling: every member of the population has a chance of being selected for the study through random selection.
Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.

In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.

But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.

If you want to use parametric tests for non-probability samples, you have to make the case that:

your sample is representative of the population you’re generalizing your findings to.
your sample lacks systematic bias.

Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.

If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .

Create an appropriate sampling procedure

Based on the resources available for your research, decide on how you’ll recruit participants.

Will you have resources to advertise your study widely, including outside of your university setting?
Will you have the means to recruit a diverse sample that represents a broad population?
Do you have time to contact and follow up with members of hard-to-reach groups?

Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.

Calculate sufficient sample size

Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.

There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.

To use these calculators, you have to understand and input these key components:

Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.

Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.

Inspect your data

There are various ways to inspect your data, including the following:

Organizing data from each variable in frequency distribution tables .
Displaying data from a key variable in a bar chart to view the distribution of responses.
Visualizing the relationship between two variables using a scatter plot .

By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.

A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.

Mean, median, mode, and standard deviation in a normal distribution

In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.

Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.

Calculate measures of central tendency

Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:

Mode : the most popular response or value in the data set.
Median : the value in the exact middle of the data set when ordered from low to high.
Mean : the sum of all values divided by the number of values.

However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.

Calculate measures of variability

Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:

Range : the highest value minus the lowest value of the data set.
Interquartile range : the range of the middle half of the data set.
Standard deviation : the average distance between each value in your data set and the mean.
Variance : the square of the standard deviation.

Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.

Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.

	Pretest scores	Posttest scores
Mean	68.44	75.25
Standard deviation	9.43	9.88
Variance	88.96	97.96
Range	36.25	45.12
	30

From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.

It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.

	Parental income (USD)	GPA
Mean	62,100	3.12
Standard deviation	15,000	0.45
Variance	225,000,000	0.16
Range	8,000–378,000	2.64–4.00
	653

A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.

Researchers often use two main methods (simultaneously) to make inferences in statistics.

Estimation: calculating population parameters based on sample statistics.
Hypothesis testing: a formal process for testing research predictions about the population using samples.

You can make two types of estimates of population parameters from sample statistics:

A point estimate : a value that represents your best guess of the exact parameter.
An interval estimate : a range of values that represent your best guess of where the parameter lies.

If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.

You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).

There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.

A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.

Hypothesis testing

Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.

Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:

A test statistic tells you how much your data differs from the null hypothesis of the test.
A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.

Statistical tests come in three main varieties:

Comparison tests assess group differences in outcomes.
Regression tests assess cause-and-effect relationships between variables.
Correlation tests assess relationships between variables without assuming causation.

Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.

Parametric tests

Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.

A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).

A simple linear regression includes one predictor variable and one outcome variable.
A multiple linear regression includes two or more predictor variables and one outcome variable.

Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.

A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
A z test is for exactly 1 or 2 groups when the sample is large.
An ANOVA is for 3 or more groups.

The z and t tests have subtypes based on the number and types of samples and the hypotheses:

If you have only one sample that you want to compare to a population mean, use a one-sample test .
If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
If you expect a difference between groups in a specific direction, use a one-tailed test .
If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .

The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.

However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.

You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:

a t value (test statistic) of 3.00
a p value of 0.0028

Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.

A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:

a t value of 3.08
a p value of 0.001

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The final step of statistical analysis is interpreting your results.

Statistical significance

In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.

Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.

This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.

Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.

Effect size

A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.

In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .

With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.

Decision errors

Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.

You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.

Frequentist versus Bayesian statistics

Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.

However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.

Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.

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

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval

Methodology

Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Likert scale

Research bias

Implicit bias
Framing effect
Cognitive bias
Placebo effect
Hawthorne effect
Hostile attribution bias
Affect heuristic

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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 .

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Let's take a look at our PPT Templates.

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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.

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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.

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Template 9: Why Our Statistical Analysis Company Template

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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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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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Writing a Rsearch Proposal

A research proposal describes what you will investigate, why it’s important, and how you will conduct your research. Your paper should include the topic, research question and hypothesis, methods, predictions, and results (if not actual, then projected).

Research Proposal Aims

Show your reader why your project is interesting, original, and important.

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

Introduction

Literature review

Research design

Reference list

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

Proposal Format

The proposal will usually have a title page that includes:

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

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

Introduce your topic
Give necessary background and context
Outline your problem statement and research questions To guide your introduction , include information about:
Who could have an interest in the topic (e.g., scientists, policymakers)
How much is already known about the topic
What is missing from this current knowledge
What new insights will your research contribute
Why you believe this research is worth doing

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 done or said, but rather using existing research as a jumping-off point for your own.

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

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

Research design and methods

Following the literature review, restate your main objectives . This brings the focus back to your 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. Write up your projected, if not actual, results.

Contribution to knowledge

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

For example, your results might have implications for:

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

Lastly, 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 free APA citation generators like BibGuru. Databases have a citation button you can click on to see your citation. Sometimes you have to re-format it as the citations may have mistakes.

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17 Research Proposal Examples

Chris Drew (PhD)

Dr. Chris Drew is the founder of the Helpful Professor. He holds a PhD in education and has published over 20 articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education. [Image Descriptor: Photo of Chris]

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research proposal example sections definition and purpose, explained below

A research proposal systematically and transparently outlines a proposed research project.

The purpose of a research proposal is to demonstrate a project’s viability and the researcher’s preparedness to conduct an academic study. It serves as a roadmap for the researcher.

The process holds value both externally (for accountability purposes and often as a requirement for a grant application) and intrinsic value (for helping the researcher to clarify the mechanics, purpose, and potential signficance of the study).

Key sections of a research proposal include: the title, abstract, introduction, literature review, research design and methods, timeline, budget, outcomes and implications, references, and appendix. Each is briefly explained below.

Watch my Guide: How to Write a Research Proposal

Get your Template for Writing your Research Proposal Here (With AI Prompts!)

Research Proposal Sample Structure

Title: The title should present a concise and descriptive statement that clearly conveys the core idea of the research projects. Make it as specific as possible. The reader should immediately be able to grasp the core idea of the intended research project. Often, the title is left too vague and does not help give an understanding of what exactly the study looks at.

Abstract: Abstracts are usually around 250-300 words and provide an overview of what is to follow – including the research problem , objectives, methods, expected outcomes, and significance of the study. Use it as a roadmap and ensure that, if the abstract is the only thing someone reads, they’ll get a good fly-by of what will be discussed in the peice.

Introduction: Introductions are all about contextualization. They often set the background information with a statement of the problem. At the end of the introduction, the reader should understand what the rationale for the study truly is. I like to see the research questions or hypotheses included in the introduction and I like to get a good understanding of what the significance of the research will be. It’s often easiest to write the introduction last

Literature Review: The literature review dives deep into the existing literature on the topic, demosntrating your thorough understanding of the existing literature including themes, strengths, weaknesses, and gaps in the literature. It serves both to demonstrate your knowledge of the field and, to demonstrate how the proposed study will fit alongside the literature on the topic. A good literature review concludes by clearly demonstrating how your research will contribute something new and innovative to the conversation in the literature.

Research Design and Methods: This section needs to clearly demonstrate how the data will be gathered and analyzed in a systematic and academically sound manner. Here, you need to demonstrate that the conclusions of your research will be both valid and reliable. Common points discussed in the research design and methods section include highlighting the research paradigm, methodologies, intended population or sample to be studied, data collection techniques, and data analysis procedures . Toward the end of this section, you are encouraged to also address ethical considerations and limitations of the research process , but also to explain why you chose your research design and how you are mitigating the identified risks and limitations.

Timeline: Provide an outline of the anticipated timeline for the study. Break it down into its various stages (including data collection, data analysis, and report writing). The goal of this section is firstly to establish a reasonable breakdown of steps for you to follow and secondly to demonstrate to the assessors that your project is practicable and feasible.

Budget: Estimate the costs associated with the research project and include evidence for your estimations. Typical costs include staffing costs, equipment, travel, and data collection tools. When applying for a scholarship, the budget should demonstrate that you are being responsible with your expensive and that your funding application is reasonable.

Expected Outcomes and Implications: A discussion of the anticipated findings or results of the research, as well as the potential contributions to the existing knowledge, theory, or practice in the field. This section should also address the potential impact of the research on relevant stakeholders and any broader implications for policy or practice.

References: A complete list of all the sources cited in the research proposal, formatted according to the required citation style. This demonstrates the researcher’s familiarity with the relevant literature and ensures proper attribution of ideas and information.

Appendices (if applicable): Any additional materials, such as questionnaires, interview guides, or consent forms, that provide further information or support for the research proposal. These materials should be included as appendices at the end of the document.

Research Proposal Examples

Research proposals often extend anywhere between 2,000 and 15,000 words in length. The following snippets are samples designed to briefly demonstrate what might be discussed in each section.

1. Education Studies Research Proposals

See some real sample pieces:

Assessment of the perceptions of teachers towards a new grading system
Does ICT use in secondary classrooms help or hinder student learning?
Digital technologies in focus project
Urban Middle School Teachers’ Experiences of the Implementation of
Restorative Justice Practices
Experiences of students of color in service learning

Consider this hypothetical education research proposal:

The Impact of Game-Based Learning on Student Engagement and Academic Performance in Middle School Mathematics

Abstract: The proposed study will explore multiplayer game-based learning techniques in middle school mathematics curricula and their effects on student engagement. The study aims to contribute to the current literature on game-based learning by examining the effects of multiplayer gaming in learning.

Introduction: Digital game-based learning has long been shunned within mathematics education for fears that it may distract students or lower the academic integrity of the classrooms. However, there is emerging evidence that digital games in math have emerging benefits not only for engagement but also academic skill development. Contributing to this discourse, this study seeks to explore the potential benefits of multiplayer digital game-based learning by examining its impact on middle school students’ engagement and academic performance in a mathematics class.

Literature Review: The literature review has identified gaps in the current knowledge, namely, while game-based learning has been extensively explored, the role of multiplayer games in supporting learning has not been studied.

Research Design and Methods: This study will employ a mixed-methods research design based upon action research in the classroom. A quasi-experimental pre-test/post-test control group design will first be used to compare the academic performance and engagement of middle school students exposed to game-based learning techniques with those in a control group receiving instruction without the aid of technology. Students will also be observed and interviewed in regard to the effect of communication and collaboration during gameplay on their learning.

Timeline: The study will take place across the second term of the school year with a pre-test taking place on the first day of the term and the post-test taking place on Wednesday in Week 10.

Budget: The key budgetary requirements will be the technologies required, including the subscription cost for the identified games and computers.

Expected Outcomes and Implications: It is expected that the findings will contribute to the current literature on game-based learning and inform educational practices, providing educators and policymakers with insights into how to better support student achievement in mathematics.

2. Psychology Research Proposals

See some real examples:

A situational analysis of shared leadership in a self-managing team
The effect of musical preference on running performance
Relationship between self-esteem and disordered eating amongst adolescent females

Consider this hypothetical psychology research proposal:

The Effects of Mindfulness-Based Interventions on Stress Reduction in College Students

Abstract: This research proposal examines the impact of mindfulness-based interventions on stress reduction among college students, using a pre-test/post-test experimental design with both quantitative and qualitative data collection methods .

Introduction: College students face heightened stress levels during exam weeks. This can affect both mental health and test performance. This study explores the potential benefits of mindfulness-based interventions such as meditation as a way to mediate stress levels in the weeks leading up to exam time.

Literature Review: Existing research on mindfulness-based meditation has shown the ability for mindfulness to increase metacognition, decrease anxiety levels, and decrease stress. Existing literature has looked at workplace, high school and general college-level applications. This study will contribute to the corpus of literature by exploring the effects of mindfulness directly in the context of exam weeks.

Research Design and Methods: Participants ( n= 234 ) will be randomly assigned to either an experimental group, receiving 5 days per week of 10-minute mindfulness-based interventions, or a control group, receiving no intervention. Data will be collected through self-report questionnaires, measuring stress levels, semi-structured interviews exploring participants’ experiences, and students’ test scores.

Timeline: The study will begin three weeks before the students’ exam week and conclude after each student’s final exam. Data collection will occur at the beginning (pre-test of self-reported stress levels) and end (post-test) of the three weeks.

Expected Outcomes and Implications: The study aims to provide evidence supporting the effectiveness of mindfulness-based interventions in reducing stress among college students in the lead up to exams, with potential implications for mental health support and stress management programs on college campuses.

3. Sociology Research Proposals

Understanding emerging social movements: A case study of ‘Jersey in Transition’
The interaction of health, education and employment in Western China
Can we preserve lower-income affordable neighbourhoods in the face of rising costs?

Consider this hypothetical sociology research proposal:

The Impact of Social Media Usage on Interpersonal Relationships among Young Adults

Abstract: This research proposal investigates the effects of social media usage on interpersonal relationships among young adults, using a longitudinal mixed-methods approach with ongoing semi-structured interviews to collect qualitative data.

Introduction: Social media platforms have become a key medium for the development of interpersonal relationships, particularly for young adults. This study examines the potential positive and negative effects of social media usage on young adults’ relationships and development over time.

Literature Review: A preliminary review of relevant literature has demonstrated that social media usage is central to development of a personal identity and relationships with others with similar subcultural interests. However, it has also been accompanied by data on mental health deline and deteriorating off-screen relationships. The literature is to-date lacking important longitudinal data on these topics.

Research Design and Methods: Participants ( n = 454 ) will be young adults aged 18-24. Ongoing self-report surveys will assess participants’ social media usage, relationship satisfaction, and communication patterns. A subset of participants will be selected for longitudinal in-depth interviews starting at age 18 and continuing for 5 years.

Timeline: The study will be conducted over a period of five years, including recruitment, data collection, analysis, and report writing.

Expected Outcomes and Implications: This study aims to provide insights into the complex relationship between social media usage and interpersonal relationships among young adults, potentially informing social policies and mental health support related to social media use.

4. Nursing Research Proposals

Does Orthopaedic Pre-assessment clinic prepare the patient for admission to hospital?
Nurses’ perceptions and experiences of providing psychological care to burns patients
Registered psychiatric nurse’s practice with mentally ill parents and their children

Consider this hypothetical nursing research proposal:

The Influence of Nurse-Patient Communication on Patient Satisfaction and Health Outcomes following Emergency Cesarians

Abstract: This research will examines the impact of effective nurse-patient communication on patient satisfaction and health outcomes for women following c-sections, utilizing a mixed-methods approach with patient surveys and semi-structured interviews.

Introduction: It has long been known that effective communication between nurses and patients is crucial for quality care. However, additional complications arise following emergency c-sections due to the interaction between new mother’s changing roles and recovery from surgery.

Literature Review: A review of the literature demonstrates the importance of nurse-patient communication, its impact on patient satisfaction, and potential links to health outcomes. However, communication between nurses and new mothers is less examined, and the specific experiences of those who have given birth via emergency c-section are to date unexamined.

Research Design and Methods: Participants will be patients in a hospital setting who have recently had an emergency c-section. A self-report survey will assess their satisfaction with nurse-patient communication and perceived health outcomes. A subset of participants will be selected for in-depth interviews to explore their experiences and perceptions of the communication with their nurses.

Timeline: The study will be conducted over a period of six months, including rolling recruitment, data collection, analysis, and report writing within the hospital.

Expected Outcomes and Implications: This study aims to provide evidence for the significance of nurse-patient communication in supporting new mothers who have had an emergency c-section. Recommendations will be presented for supporting nurses and midwives in improving outcomes for new mothers who had complications during birth.

5. Social Work Research Proposals

Experiences of negotiating employment and caring responsibilities of fathers post-divorce
Exploring kinship care in the north region of British Columbia

Consider this hypothetical social work research proposal:

The Role of a Family-Centered Intervention in Preventing Homelessness Among At-Risk Youthin a working-class town in Northern England

Abstract: This research proposal investigates the effectiveness of a family-centered intervention provided by a local council area in preventing homelessness among at-risk youth. This case study will use a mixed-methods approach with program evaluation data and semi-structured interviews to collect quantitative and qualitative data .

Introduction: Homelessness among youth remains a significant social issue. This study aims to assess the effectiveness of family-centered interventions in addressing this problem and identify factors that contribute to successful prevention strategies.

Literature Review: A review of the literature has demonstrated several key factors contributing to youth homelessness including lack of parental support, lack of social support, and low levels of family involvement. It also demonstrates the important role of family-centered interventions in addressing this issue. Drawing on current evidence, this study explores the effectiveness of one such intervention in preventing homelessness among at-risk youth in a working-class town in Northern England.

Research Design and Methods: The study will evaluate a new family-centered intervention program targeting at-risk youth and their families. Quantitative data on program outcomes, including housing stability and family functioning, will be collected through program records and evaluation reports. Semi-structured interviews with program staff, participants, and relevant stakeholders will provide qualitative insights into the factors contributing to program success or failure.

Timeline: The study will be conducted over a period of six months, including recruitment, data collection, analysis, and report writing.

Budget: Expenses include access to program evaluation data, interview materials, data analysis software, and any related travel costs for in-person interviews.

Expected Outcomes and Implications: This study aims to provide evidence for the effectiveness of family-centered interventions in preventing youth homelessness, potentially informing the expansion of or necessary changes to social work practices in Northern England.

Research Proposal Template

Get your Detailed Template for Writing your Research Proposal Here (With AI Prompts!)

This is a template for a 2500-word research proposal. You may find it difficult to squeeze everything into this wordcount, but it’s a common wordcount for Honors and MA-level dissertations.

Section	Checklist
Title	– Ensure the single-sentence title clearly states the study’s focus
Abstract (Words: 200)	– Briefly describe the research topicSummarize the research problem or question – Outline the research design and methods – Mention the expected outcomes and implications
Introduction (Words: 300)	– Introduce the research topic and its significance – Clearly state the research problem or question – Explain the purpose and objectives of the study – Provide a brief overview of
Literature Review (Words: 800)	– Gather the existing literature into themes and ket ideas – the themes and key ideas in the literature – Identify gaps or inconsistencies in the literature – Explain how the current study will contribute to the literature
Research Design and Methods (Words; 800)	– Describe the research paradigm (generally: positivism and interpretivism) – Describe the research design (e.g., qualitative, quantitative, or mixed-methods) – Explain the data collection methods (e.g., surveys, interviews, observations) – Detail the sampling strategy and target population – Outline the data analysis techniques (e.g., statistical analysis, thematic analysis) – Outline your validity and reliability procedures – Outline your intended ethics procedures – Explain the study design’s limitations and justify your decisions
Timeline (Single page table)	– Provide an overview of the research timeline – Break down the study into stages with specific timeframes (e.g., data collection, analysis, report writing) – Include any relevant deadlines or milestones
Budget (200 words)	– Estimate the costs associated with the research project – Detail specific expenses (e.g., materials, participant incentives, travel costs) – Include any necessary justifications for the budget items – Mention any funding sources or grant applications
Expected Outcomes and Implications (200 words)	– Summarize the anticipated findings or results of the study – Discuss the potential implications of the findings for theory, practice, or policy – Describe any possible limitations of the study

Your research proposal is where you really get going with your study. I’d strongly recommend working closely with your teacher in developing a research proposal that’s consistent with the requirements and culture of your institution, as in my experience it varies considerably. The above template is from my own courses that walk students through research proposals in a British School of Education.

Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 10 Reasons you’re Perpetually Single
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 20 Montessori Toddler Bedrooms (Design Inspiration)
Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 21 Montessori Homeschool Setups
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8 thoughts on “17 Research Proposal Examples”

Very excellent research proposals

very helpful

Very helpful

Dear Sir, I need some help to write an educational research proposal. Thank you.

Hi Levi, use the site search bar to ask a question and I’ll likely have a guide already written for your specific question. Thanks for reading!

very good research proposal

Thank you so much sir! ❤️

Very helpful 👌

75+ Realistic Statistics Project Ideas For Students To Score A+

Statistics is one of the major subjects for every student, even in high school or college. These days almost every student is searching for the best, and more practical statistics project ideas. Even if you are a humanities, science or commerce student, you should have a good command of it.

Statistics has many sub-topics such as normal curves, regression, correlation, statistical inference, and many more. But keep in mind that the difficulty level of statistics varies from your study level. It means that statistics concepts can be more difficult for college students than for school students. It implies that statistical project topics would be different for college students and school students. On the other hand, if you are looking for statistics assignment help , then you can get the best assignment help from us.

But before we unveil these good statistics project ideas. Let’s understand what a statistical project is.

What is a Statistical Project?

Table of Contents

A statistical project is the best process of answering the research questions using statistical terminologies and techniques. It also helps us to present the work written in the given report. In statistical projects, the research could be on scientific or generic fields such as advertising, nutrition, and lots more. Therefore the difficulty level of statistical projects varies with research topics. And the statistics concepts also differ from one case to another. You can also visit statanalytica blogs to get assistance for statistical projects assignment idea.

What are Statistics Topics?

There are tons of topics in statistics. The most common statistics topics are normal curves, binomials, regression, correlation, permutation and combinations, statistical inference, and more. And all the statics topics are applicable in our daily life. Whether it is the tech or entertainment industry, everyone uses statistics topics.

Tips for finding easy statistics project ideas

Finding the best and easiest statistics project is not an easy task. But here are some of the best tips that will help you to find easy statistics project ideas:-

Deeply analyze the data presented by the research
Do you have an affirmative statement of the problems that have initiated the research?
Study summary based on your research
Have a deep discussion of the students’ design to clarify the problem.

All these steps will help you to find the best statistics project ideas. The next step is to write down the essential component of the statistics paper, i.e.:-

Data analysis (by understanding the importance of data analytics projects )
Statement of the problem
Summary and conclusion
Research design

Although if you follow these steps precisely, you will surely find the best project on statistics. But we are here to make it easy for you; let’s have a look at

Statistics Project Ideas for High School

Let’s find out the best statistics project ideas for high school that will help you to score good grades and showcase your skills:-

Categorize the researched raw data into qualitative or quantitative
Evaluate the published reports and graphs based on the analyzed data and conclude.
Use dice to evaluate the bias and effect of completing data.
Discuss the factors that can affect the result of the given survey data.
Increasing use of plastic.
Are e-books better than conventional books?
Do extra-curricular activities help transform personalities?
Should stereotypical social issues be highlighted or not?
Should mobile phones be allowed in high schools or not?
The Significance of Medication in Class Performance.
Does the effect of a teacher who is a fresher at university influence the student’s performance?
Influence of Distinct Subjects on Students’ Performance.
Caffeine consumption among students as well as its effect on performance.
Are online classes helpful?
Influence of better students in class.
The significance of the front seats in the class on success rates. Does an online brochure creator reduce marketing costs?

Additional statistics project examples:

The use of mobile phones in the classroom is always a debatable topic. Therefore, it is always a good statistics project idea to write statistics about how many students and teachers are in favor of using mobile phones in the classroom.

Small Business Statistics Project Topics

The impact of the pandemic on small business survival rates.
Analysis of the most profitable industries for small businesses.
Small business failure rates by region and industry.
The relationship between access to funding and small business success rates.
The impact of social media marketing (SMM) on small business growth.
The role of e-commerce in small business growth.
The impact of government regulations on small business success rates.
The gender gap in small business ownership and success rates.
The impact of employee retention on small business growth and success rates.
The relationship between small business growth and community development.
The impact of the gig economy on small business growth.
Analysis of the most common reasons for small business failure.
The role of technology in small business growth and success rates.
The impact of competition on small business survival rates.
The relationship between small business ownership and educational attainment.

Statistics Project Ideas on Socio-Economics

Income versus explanation analysis in society.
Peak traffic times in your city.
The significance of agricultural loans for farmers.
Food habits in low-income families.
Malpractices of low-income groups.
Analysis of road accidents in the suburb and the town area.
The effect of smoking on medical costs.
Regression analysis on national income.
Income vs Consumption Explanation Study in Society.
A Study of the Worldwide Economic Growth
The Influence of the Pandemic on Health in the UK
Influence of Advertisement on Health Costs
The effect of poverty on crime rates.
Do federal elections affect stock prices?

Statistics Project Ideas for University Students (2023)

Analyzing the impact of COVID-19 on a particular industry or economic sector.
Examining the relationship between income and health outcomes in a particular population or geographic area.
Investigate the factors influencing student success in a particular course or academic program.
Analyzing the effectiveness of a specific marketing campaign or promotional strategy.
Evaluating the relationship between social media usage and mental health outcomes.
Examining the impact of climate change on a particular ecosystem or species.
Investigating the factors influencing voter turnout in a particular election or geographic area.
Analyzing the relationship between exercise and mental health outcomes.
Evaluating the effectiveness of a particular intervention or program in addressing a specific social issue, such as poverty or homelessness.
Examining the relationship between crime rates and economic conditions in a particular area.

Statistics Survey Project Ideas

Let’s find out some of the best statistics survey project ideas. Here we go:-

Have a deep statistics analysis on the pollution level across various cities worldwide.
Find out the most selling smartphones globally and used by college students.
Do the behavioral survey of Omicron variant patients across the world.
Conduct a survey about the global warming world.

Sometimes conducting a survey is itself a headache for you. That is why it is better to get easy statistics to project ideas. A survey report on E-books vs Textbooks is a good idea for students to conduct a survey and write down all useful insights collected from the survey report.

Statistics Project Ideas Hypothesis Testing

Statistics project ideas for hypothesis testing are not for everyone. But have a look at some of the best statistic project examples for hypothesis testing:-

Peppermint essential oil affects the pangs of anxiety
Immunity during winter for students who take more vitamin C than those who don’t.
The productivity level of young boys as compared with the young girls.
Obesity level of children whose parents are obese.

Hypothesis testing plays an important role in concluding the most estimated result of the experiment. That is why we always suggest students conduct the hypothesis test for the present situation. Like you consider the students’ choice regarding the subjects. And write the statistical factors, like whether students select their subject based on the industry’s stability or as per their liking.

AP Statistics Project Ideas

Let’s have a look at some of the AP statistics project ideas. If statistics are your primary subject, these projects will impact your grades.

Find out the impact of school jobs and activities on the student’s overall grades.
Who influences the children more on religious views, either the month or the father?
Are age and sleeping related to each other, i.e., adult people tend to sleep less than kids and old-age citizens?
Does plastic surgery change the perspective towards you the people?

To show the study of AP statistics project ideas, you need to offer arguments based on the evidence, perform research, and analyze the issues. You can write a statistics project based on alcohol advertisements and their effect on younger people of these ads.

Statistics Final Project Ideas

A massive number of students look for statistics and final project ideas. Have a look at some of the best final projects in statistics:-

Do high heel sandals harm the body posture of the lady?
Does the patient’s intelligence also affect the brilliance of the child?
Is there any relation to eating hotdogs while watching a baseball match in the stadium?
Does an opinion poll change the initially perceived election results?

If you are a final-year student looking for exciting project ideas, write a statistical report on the regression analysis. The analysis can be done on the national income, and you can put all the ins-outs on this topic with a detailed report.

Two variable statistics project Ideas

Have a look at the two-variable statistics project where one variable affects the other one:-

Are electric cars a good choice to have control over global warming?
Investing in FDIs can help the country to grow its GDP.
Is lockdown the best solution to stop the spread of Coronavirus?
Investing in cryptocurrency can have a significant impact on your future.

Statistics Project Ideas for College Students

There are tons of college statistics project examples. But we will share the best ideas for statistics projects for the college. As we have already discussed, college statistics project ideas are pretty complex compared with school-level projects. Let’s have a look at the best statistics project ideas for college:-

Excessive use of the internet reduces the creativity and innovation skills of the students.
The use of social media has bypassed studying in the students’ free time.
Can college students develop drug habits if given a chance?
Does a college freshman’s experience with their roommate affect their overall experience at the institution?
A comparative study on the pricing of different clothing stores in your town.
College students’ Web browsing habits.
Comparison between male and female students in college.
Statistical analysis of the highway accidents in your local neighborhood.
Students in college choose common subjects.
Choosing aspects of a subject in college.
Course price differentiation in colleges.
There is less interest in the students in humanities subjects as compared with science and technology.
Relationship between birth order as well as academic success.
Is being headstrong difficult, or does it make things easier?
Popular movie genre among students in college.
What kinds of music do college students like the most?
Difference between the male and female population in a city based on their age.
The Significance of Analytics in Studying Statistics
Influence of backbenchers on their performance in class.

Fun Statistics Project Ideas

Have a look at some of the statistics projects examples:-

Most of the volleyball players are tall compared with a few short ones.
Men tend to have more interest in cricket as compared with females.
Shorter and chubby girls are more friendly than tall and skinny girls.
Aggression between students is based on the environment where they grew up.
Students involved in co-curricular activities tend to have lower grades than those who don’t.
Highly pressured employees consume more alcohol than those who do repetitive tasks jobs.

The Point With Statistics Projects Ideas

To write an impressive statistical project, you need to follow some points. Let’s have a look at these points:-

Always work with organized information. If you get unorganized data, try to organize it first and then start working.
Start with an outline, and it will help you to organize the final data of your statistics project. For this, you can also look at previous statistics project examples.
Always write for the beginner’s audience. Don’t expect that your audience already knows everything. For this, be brief, simple, and to the point.
Don’t miss the citation because it always helps showcase your projects’ authenticity. And keep the citation in the given format.
The outcome of your statistical test should refer to the hypothesis being tested.
If you have spent lots of time researching your project, you can take the help of statistics project writing services. For this, you can approach statistics homework help experts, and they will offer you the best statistics projects on your researched idea.
Don’t get anxious while doing your statistics projects. Because most of the time, the professors give the research questions to the students. And the students need to collect, analyze, and interpret the information to provide the most suitable answer or conclusion to the question using statistical methods and techniques.

There are plenty of tons or even thousands of statistics project ideas to work on. But in this blog, I have mentioned some of the best and more realistic statistics project ideas. If you work on any of these ideas, you will not just get good grades but will also enjoy your project while working on it. As the quote said, “Do what you love, love what you do.”

Also, follow the steps mentioned at the end of the blog to finish up with the best-in-class statistics project. We have covered these ideas for almost every student. But still, if you are not able to find the best project for you, you should get in touch with our experts. Our team of experts will instantly get in touch with you and help you find the most suitable statistics project ideas for you.

Q1. What is meant by statistical project?

Statistics projects are a paper used to present the comprehension analysis of gathering statistical data. It contains the statistical data for the collected statistical data. In other words, it brings the significant results of a specific research question.

Q2. What are some practical uses for statistics in everyday life?

Many people use statistics to make decisions in budgeting and financial planning. On the other hand, most banks use statistics to lower the risk of lending operations, predict the impact of economic crises, and analyze activity in the financial market.

How to Find the Best Online Statistics Homework Help

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An research proposal examples on statistics is a prosaic composition of a small volume and free composition, expressing individual impressions and thoughts on a specific occasion or issue and obviously not claiming a definitive or exhaustive interpretation of the subject.

Some signs of statistics research proposal:

the presence of a specific topic or question. A work devoted to the analysis of a wide range of problems in biology, by definition, cannot be performed in the genre of statistics research proposal topic.
The research proposal expresses individual impressions and thoughts on a specific occasion or issue, in this case, on statistics and does not knowingly pretend to a definitive or exhaustive interpretation of the subject.
As a rule, an essay suggests a new, subjectively colored word about something, such a work may have a philosophical, historical, biographical, journalistic, literary, critical, popular scientific or purely fiction character.
in the content of an research proposal samples on statistics , first of all, the author’s personality is assessed - his worldview, thoughts and feelings.

The goal of an research proposal in statistics is to develop such skills as independent creative thinking and writing out your own thoughts.

Writing an research proposal is extremely useful, because it allows the author to learn to clearly and correctly formulate thoughts, structure information, use basic concepts, highlight causal relationships, illustrate experience with relevant examples, and substantiate his conclusions.

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Research Proposal Example/Sample

Full Walkthrough + Free Proposal Template

In this video, we walk you through two successful (approved) research proposals , one for a Master’s-level project, and one for a PhD-level dissertation. We also start off by unpacking our free research proposal template and discussing the four core sections of a research proposal, so that you have a clear understanding of the basics before diving into the actual proposals.

Research proposal example/sample – Master’s-level (PDF/Word)
Research proposal example/sample – PhD-level (PDF/Word)
Proposal template (Fully editable)

If you’re working on a research proposal for a dissertation or thesis, you may also find the following useful:

Research Proposal Bootcamp : Learn how to write a research proposal as efficiently and effectively as possible
1:1 Proposal Coaching : Get hands-on help with your research proposal

Free Webinar: How To Write A Research Proposal

Research Proposal Example: Frequently Asked Questions

Are the sample proposals real.

Yes. The proposals are real and were approved by the respective universities.

Can I copy one of these proposals for my own research?

As we discuss in the video, every research proposal will be slightly different, depending on the university’s unique requirements, as well as the nature of the research itself. Therefore, you’ll need to tailor your research proposal to suit your specific context.

You can learn more about the basics of writing a research proposal here .

How do I get the research proposal template?

You can access our free proposal template here .

Is the proposal template really free?

Yes. There is no cost for the proposal template and you are free to use it as a foundation for your research proposal.

Where can I learn more about proposal writing?

For self-directed learners, our Research Proposal Bootcamp is a great starting point.

For students that want hands-on guidance, our private coaching service is recommended.

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Home » Research Proposal – Types, Template and Example

Research Proposal – Types, Template and Example

Table of Contents

Research Proposal

Research proposal is a document that outlines a proposed research project . It is typically written by researchers, scholars, or students who intend to conduct research to address a specific research question or problem.

Types of Research Proposal

Research proposals can vary depending on the nature of the research project and the specific requirements of the funding agency, academic institution, or research program. Here are some common types of research proposals:

Academic Research Proposal

This is the most common type of research proposal, which is prepared by students, scholars, or researchers to seek approval and funding for an academic research project. It includes all the essential components mentioned earlier, such as the introduction, literature review , methodology , and expected outcomes.

Grant Proposal

A grant proposal is specifically designed to secure funding from external sources, such as government agencies, foundations, or private organizations. It typically includes additional sections, such as a detailed budget, project timeline, evaluation plan, and a description of the project’s alignment with the funding agency’s priorities and objectives.

Dissertation or Thesis Proposal

Students pursuing a master’s or doctoral degree often need to submit a proposal outlining their intended research for their dissertation or thesis. These proposals are usually more extensive and comprehensive, including an in-depth literature review, theoretical framework, research questions or hypotheses, and a detailed methodology.

Research Project Proposal

This type of proposal is often prepared by researchers or research teams within an organization or institution. It outlines a specific research project that aims to address a particular problem, explore a specific area of interest, or provide insights for decision-making. Research project proposals may include sections on project management, collaboration, and dissemination of results.

Research Fellowship Proposal

Researchers or scholars applying for research fellowships may be required to submit a proposal outlining their proposed research project. These proposals often emphasize the novelty and significance of the research and its alignment with the goals and objectives of the fellowship program.

Collaborative Research Proposal

In cases where researchers from multiple institutions or disciplines collaborate on a research project, a collaborative research proposal is prepared. This proposal highlights the objectives, responsibilities, and contributions of each collaborator, as well as the overall research plan and coordination mechanisms.

Research Proposal Outline

A research proposal typically follows a standard outline that helps structure the document and ensure all essential components are included. While the specific headings and subheadings may vary slightly depending on the requirements of your institution or funding agency, the following outline provides a general structure for a research proposal:

Title of the research proposal
Name of the researcher(s) or principal investigator(s)
Affiliation or institution
Date of submission
A concise summary of the research proposal, typically limited to 200-300 words.
Briefly introduce the research problem or question, state the objectives, summarize the methodology, and highlight the expected outcomes or significance of the research.
Provide an overview of the subject area and the specific research problem or question.
Present relevant background information, theories, or concepts to establish the need for the research.
Clearly state the research objectives or research questions that the study aims to address.
Indicate the significance or potential contributions of the research.
Summarize and analyze relevant studies, theories, or scholarly works.
Identify research gaps or unresolved issues that your study intends to address.
Highlight the novelty or uniqueness of your research.
Describe the overall approach or research design that will be used (e.g., experimental, qualitative, quantitative).
Justify the chosen approach based on the research objectives and question.
Explain how data will be collected (e.g., surveys, interviews, experiments).
Describe the sampling strategy and sample size, if applicable.
Address any ethical considerations related to data collection.
Outline the data analysis techniques or statistical methods that will be applied.
Explain how the data will be interpreted and analyzed to answer the research question(s).
Provide a detailed schedule or timeline that outlines the various stages of the research project.
Specify the estimated duration for each stage, including data collection, analysis, and report writing.
State the potential outcomes or results of the research.
Discuss the potential significance or contributions of the study to the field.
Address any potential limitations or challenges that may be encountered.
Identify the resources required to conduct the research, such as funding, equipment, or access to data.
Specify any collaborations or partnerships necessary for the successful completion of the study.
Include a list of cited references in the appropriate citation style (e.g., APA, MLA).

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Research Proposal Example Template

Here’s an example of a research proposal to give you an idea of how it can be structured:

Title: The Impact of Social Media on Adolescent Well-being: A Mixed-Methods Study

This research proposal aims to investigate the impact of social media on the well-being of adolescents. The study will employ a mixed-methods approach, combining quantitative surveys and qualitative interviews to gather comprehensive data. The research objectives include examining the relationship between social media use and mental health, exploring the role of peer influence in shaping online behaviors, and identifying strategies for promoting healthy social media use among adolescents. The findings of this study will contribute to the understanding of the effects of social media on adolescent well-being and inform the development of targeted interventions.

1. Introduction

1.1 Background and Context:

Adolescents today are immersed in social media platforms, which have become integral to their daily lives. However, concerns have been raised about the potential negative impact of social media on their well-being, including increased rates of depression, anxiety, and body dissatisfaction. It is crucial to investigate this phenomenon further and understand the underlying mechanisms to develop effective strategies for promoting healthy social media use among adolescents.

1.2 Research Objectives:

The main objectives of this study are:

To examine the association between social media use and mental health outcomes among adolescents.
To explore the influence of peer relationships and social comparison on online behaviors.
To identify strategies and interventions to foster positive social media use and enhance adolescent well-being.

2. Literature Review

Extensive research has been conducted on the impact of social media on adolescents. Existing literature suggests that excessive social media use can contribute to negative outcomes, such as low self-esteem, cyberbullying, and addictive behaviors. However, some studies have also highlighted the positive aspects of social media, such as providing opportunities for self-expression and social support. This study will build upon this literature by incorporating both quantitative and qualitative approaches to gain a more nuanced understanding of the relationship between social media and adolescent well-being.

3. Methodology

3.1 Research Design:

This study will adopt a mixed-methods approach, combining quantitative surveys and qualitative interviews. The quantitative phase will involve administering standardized questionnaires to a representative sample of adolescents to assess their social media use, mental health indicators, and perceived social support. The qualitative phase will include in-depth interviews with a subset of participants to explore their experiences, motivations, and perceptions related to social media use.

3.2 Data Collection Methods:

Quantitative data will be collected through an online survey distributed to schools in the target region. The survey will include validated scales to measure social media use, mental health outcomes, and perceived social support. Qualitative data will be collected through semi-structured interviews with a purposive sample of participants. The interviews will be audio-recorded and transcribed for thematic analysis.

3.3 Data Analysis:

Quantitative data will be analyzed using descriptive statistics and regression analysis to examine the relationships between variables. Qualitative data will be analyzed thematically to identify common themes and patterns within participants’ narratives. Integration of quantitative and qualitative findings will provide a comprehensive understanding of the research questions.

4. Timeline

The research project will be conducted over a period of 12 months, divided into specific phases, including literature review, study design, data collection, analysis, and report writing. A detailed timeline outlining the key milestones and activities is provided in Appendix A.

5. Expected Outcomes and Significance

This study aims to contribute to the existing literature on the impact of social media on adolescent well-being by employing a mixed-methods approach. The findings will inform the development of evidence-based interventions and guidelines to promote healthy social media use among adolescents. This research has the potential to benefit adolescents, parents, educators, and policymakers by providing insights into the complex relationship between social media and well-being and offering strategies for fostering positive online experiences.

6. Resources

The resources required for this research include access to a representative sample of adolescents, research assistants for data collection, statistical software for data analysis, and funding to cover survey administration and participant incentives. Ethical considerations will be taken into account, ensuring participant confidentiality and obtaining informed consent.

7. References

Research Proposal Writing Guide

Writing a research proposal can be a complex task, but with proper guidance and organization, you can create a compelling and well-structured proposal. Here’s a step-by-step guide to help you through the process:

Understand the requirements: Familiarize yourself with the guidelines and requirements provided by your institution, funding agency, or program. Pay attention to formatting, page limits, specific sections or headings, and any other instructions.
Identify your research topic: Choose a research topic that aligns with your interests, expertise, and the goals of your program or funding opportunity. Ensure that your topic is specific, focused, and relevant to the field of study.
Conduct a literature review : Review existing literature and research relevant to your topic. Identify key theories, concepts, methodologies, and findings related to your research question. This will help you establish the context, identify research gaps, and demonstrate the significance of your proposed study.
Define your research objectives and research question(s): Clearly state the objectives you aim to achieve with your research. Formulate research questions that address the gaps identified in the literature review. Your research objectives and questions should be specific, measurable, achievable, relevant, and time-bound (SMART).
Develop a research methodology: Determine the most appropriate research design and methodology for your study. Consider whether quantitative, qualitative, or mixed-methods approaches will best address your research question(s). Describe the data collection methods, sampling strategy, data analysis techniques, and any ethical considerations associated with your research.
Create a research plan and timeline: Outline the various stages of your research project, including tasks, milestones, and deadlines. Develop a realistic timeline that considers factors such as data collection, analysis, and report writing. This plan will help you stay organized and manage your time effectively throughout the research process.
A. Introduction: Provide background information on the research problem, highlight its significance, and introduce your research objectives and questions.
B. Literature review: Summarize relevant literature, identify gaps, and justify the need for your proposed research.
C . Methodology: Describe your research design, data collection methods, sampling strategy, data analysis techniques, and any ethical considerations.
D . Expected outcomes and significance: Explain the potential outcomes, contributions, and implications of your research.
E. Resources: Identify the resources required to conduct your research, such as funding, equipment, or access to data.
F . References: Include a list of cited references in the appropriate citation style.
Revise and proofread: Review your proposal for clarity, coherence, and logical flow. Check for grammar and spelling errors. Seek feedback from mentors, colleagues, or advisors to refine and improve your proposal.
Finalize and submit: Make any necessary revisions based on feedback and finalize your research proposal. Ensure that you have met all the requirements and formatting guidelines. Submit your proposal within the specified deadline.

Research Proposal Length

The length of a research proposal can vary depending on the specific guidelines provided by your institution or funding agency. However, research proposals typically range from 1,500 to 3,000 words, excluding references and any additional supporting documents.

Purpose of Research Proposal

The purpose of a research proposal is to outline and communicate your research project to others, such as academic institutions, funding agencies, or potential collaborators. It serves several important purposes:

Demonstrate the significance of the research: A research proposal explains the importance and relevance of your research project. It outlines the research problem or question, highlights the gaps in existing knowledge, and explains how your study will contribute to the field. By clearly articulating the significance of your research, you can convince others of its value and potential impact.
Provide a clear research plan: A research proposal outlines the methodology, design, and approach you will use to conduct your study. It describes the research objectives, data collection methods, data analysis techniques, and potential outcomes. By presenting a clear research plan, you demonstrate that your study is well-thought-out, feasible, and likely to produce meaningful results.
Secure funding or support: For researchers seeking funding or support for their projects, a research proposal is essential. It allows you to make a persuasive case for why your research is deserving of financial resources or institutional backing. The proposal explains the budgetary requirements, resources needed, and potential benefits of the research, helping you secure the necessary funding or support.
Seek feedback and guidance: Presenting a research proposal provides an opportunity to receive feedback and guidance from experts in your field. It allows you to engage in discussions and receive suggestions for refining your research plan, improving the methodology, or addressing any potential limitations. This feedback can enhance the quality of your study and increase its chances of success.
Establish ethical considerations: A research proposal also addresses ethical considerations associated with your study. It outlines how you will ensure participant confidentiality, obtain informed consent, and adhere to ethical guidelines and regulations. By demonstrating your awareness and commitment to ethical research practices, you build trust and credibility in your proposed study.

Importance of Research Proposal

The research proposal holds significant importance in the research process. Here are some key reasons why research proposals are important:

Planning and organization: A research proposal requires careful planning and organization of your research project. It forces you to think through the research objectives, research questions, methodology, and potential outcomes before embarking on the actual study. This planning phase helps you establish a clear direction and framework for your research, ensuring that your efforts are focused and purposeful.
Demonstrating the significance of the research: A research proposal allows you to articulate the significance and relevance of your study. By providing a thorough literature review and clearly defining the research problem or question, you can showcase the gaps in existing knowledge that your research aims to address. This demonstrates to others, such as funding agencies or academic institutions, why your research is important and deserving of support.
Obtaining funding and resources: Research proposals are often required to secure funding for your research project. Funding agencies and organizations need to evaluate the feasibility and potential impact of the proposed research before allocating resources. A well-crafted research proposal helps convince funders of the value of your research and increases the likelihood of securing financial support, grants, or scholarships.
Receiving feedback and guidance: Presenting a research proposal provides an opportunity to seek feedback and guidance from experts in your field. By sharing your research plan and objectives with others, you can benefit from their insights and suggestions. This feedback can help refine your research design, strengthen your methodology, and ensure that your study is rigorous and well-informed.
Ethical considerations: A research proposal addresses ethical considerations associated with your study. It outlines how you will protect the rights and welfare of participants, maintain confidentiality, obtain informed consent, and adhere to ethical guidelines and regulations. This emphasis on ethical practices ensures that your research is conducted responsibly and with integrity.
Enhancing collaboration and partnerships: A research proposal can facilitate collaborations and partnerships with other researchers, institutions, or organizations. When presenting your research plan, you may attract the interest of potential collaborators who share similar research interests or possess complementary expertise. Collaborative partnerships can enrich your study, expand your resources, and foster knowledge exchange.
Establishing a research trajectory: A research proposal serves as a foundation for your research project. Once approved, it becomes a roadmap that guides your study’s implementation, data collection, analysis, and reporting. It helps maintain focus and ensures that your research stays on track and aligned with the initial objectives.

When to Write Research Proposal

The timing of when to write a research proposal can vary depending on the specific requirements and circumstances. However, here are a few common situations when it is appropriate to write a research proposal:

Academic research: If you are a student pursuing a research degree, such as a Ph.D. or Master’s by research, you will typically be required to write a research proposal as part of the application process. This is usually done before starting the research program to outline your proposed study and seek approval from the academic institution.
Funding applications: When applying for research grants, scholarships, or funding from organizations or institutions, you will often need to submit a research proposal. Funding agencies require a detailed description of your research project, including its objectives, methodology, and expected outcomes. Writing a research proposal in this context is necessary to secure financial support for your study.
Research collaborations: When collaborating with other researchers, institutions, or organizations on a research project, it is common to prepare a research proposal. This helps outline the research objectives, roles and responsibilities, and expected contributions from each party. Writing a research proposal in this case allows all collaborators to align their efforts and ensure a shared understanding of the project.
Research project within an organization: If you are conducting research within an organization, such as a company or government agency, you may be required to write a research proposal to gain approval and support for your study. This proposal outlines the research objectives, methodology, resources needed, and expected outcomes, ensuring that the project aligns with the organization’s goals and objectives.
Independent research projects: Even if you are not required to write a research proposal, it can still be beneficial to develop one for your independent research projects. Writing a research proposal helps you plan and structure your study, clarify your research objectives, and anticipate potential challenges or limitations. It also allows you to communicate your research plans effectively to supervisors, mentors, or collaborators.

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Research Proposal Examples for Every Science Field

Looking for research funding can be a daunting task, especially when you are starting out. A great way to improve grant-writing skills is to get inspired by winning research proposal examples.

To assist you in writing a competitive proposal, I have curated a collection of real-life research proposal examples from various scientific disciplines. These examples will allow you to gain inspiration about the way research proposals are structured and written.

Structure of a Research Proposal

A research proposal serves as a road-map for a project, outlining the objectives, methodology, resources, and expected outcomes. The main goal of writing a research proposal is to convince funding agency of the value and feasibility of a research project. But a proposal also helps scientists themselves to clarify their planned approach.

While the exact structure may vary depending on the science field and institutional guidelines, a research proposal typically includes the following sections: Problem, Objectives, Methodology, Resources, Participants, Results&Impact, Dissemination, Timeline, and Budget. I will use this structure for the example research proposals in this article.

Research Proposal Example Structure including the description of a project outline:Problem: The knowledge gap that should be filledObjectives: The objectives that will help solve the identified problemMethodology: The approach that leads to reaching the objectivesResources: The resources needed to accomplish the objectivesParticipants: The research team’s qualification for implementing the research methodology and their complementary valueResults & Impact: The new knowledge that will be created and its real-world impactDissemination: The proper target audience and how you will reach themTimeline: The time required for performing each part of the research projectBudget: The cost items and the distribution of funding between participantsOn the side a PhD student is carrying a money bag.

Here is a brief description of what each of the nine proposal sections should hold.

A concise and informative title that captures the essence of the research proposal. Sometimes an abstract is required that briefly summarizes the proposed project.

Clearly define the research problem or gap in knowledge that the study aims to address. Present relevant background information and cite existing literature to support the need for further investigation.

State the specific objectives and research questions that the study seeks to answer. These objectives should be clear, measurable, and aligned with the problem statement.

Methodology

Describe the research design, methodology, and techniques that will be employed to collect and analyze data. Justify your chosen approach and discuss its strengths and limitations.

Outline the resources required for the successful execution of the research project, such as equipment, facilities, software, and access to specific datasets or archives.

Participants

Describe the research team’s qualification for implementing the research methodology and their complementary value

Results and Impact

Describe the expected results, outcomes, and potential impact of the research. Discuss how the findings will contribute to the field and address the research gap identified earlier.

Dissemination

Explain how the research results will be disseminated to the academic community and wider audiences. This may include publications, conference presentations, workshops, data sharing or collaborations with industry partners.

Develop a realistic timeline that outlines the major milestones and activities of the research project. Consider potential challenges or delays and incorporate contingency plans.

Provide a detailed budget estimate, including anticipated expenses for research materials, equipment, participant compensation, travel, and other relevant costs. Justify the budget based on the project’s scope and requirements.

Consider that the above-mentioned proposal headings can be called differently depending on the funder’s requirements. However, you can be sure in one proposal’s section or another each of the mentioned sections will be included. Whenever provided, always use the proposal structure as required by the funding agency.

Research Proposal template download

This research proposal template includes the nine headings that we just discussed. For each heading, a key sentence skeleton is provided to help you to kick-start the proposal writing process.

Real-Life Research Proposal Examples

Proposals can vary from field to field so I will provide you with research proposal examples proposals in four main branches of science: social sciences, life sciences, physical sciences, and engineering and technology. For each science field, you will be able to download real-life winning research proposal examples.

To illustrate the principle of writing a scientific proposal while adhering to the nine sections I outlined earlier, for each discipline I will also provide you with a sample hypothetical research proposal. These examples are formulated using the key sentence structure that is included in the download template .

In case the research proposal examples I provide do not hold exactly what you are looking for, use the Open Grants database. It holds approved research proposals from various funding agencies in many countries. When looking for research proposals examples in the database, use the filer to search for specific keywords and organize the results to view proposals that have been funded.

Research Proposals Examples in Social Sciences

Here are real-life research proposal examples of funded projects in social sciences.



(Cultural Anthropology)

Here is an outline of a hypothetical Social Sciences research proposal that is structured using the nine proposal sections we discussed earlier. This proposal example is produced using the key sentence skeleton that you will access in the proposal template .

The Influence of Social Media on Political Participation among Young Adults

Social media platforms have become prominent spaces for political discussions and information sharing. However, the impact of social media on political participation among young adults remains a topic of debate.

Research Proposal Objectives description symbol

With the project, we aim to establish the relationship between social media usage and political engagement among young adults. To achieve this aim, we have three specific objectives:

Examine the association between social media usage patterns and various forms of political participation, such as voting, attending political rallies, and engaging in political discussions.
Investigate the role of social media in shaping political attitudes, opinions, and behaviors among young adults.
Provide evidence-based recommendations for utilizing social media platforms to enhance youth political participation.

Research Proposal Methodology description symbol

During the project, a mixed methods approach, combining quantitative surveys and qualitative interviews will be used to determine the impact of social media use on youth political engagement. In particular, surveys will collect data on social media usage, political participation, and attitudes. Interviews will provide in-depth insights into participants’ experiences and perceptions.

Research Proposal Resources description symbol

The project will use survey software, transcription tools, and statistical analysis software to statistically evaluate the gathered results. The project will also use project funding for participant compensation.

Research Proposal Participant description symbol

Principal investigator, Jane Goodrich will lead a multidisciplinary research team comprising social scientists, political scientists, and communication experts with expertise in political science and social media research.

Research Proposal Results and Impact description symbol

The project will contribute to a better understanding of the influence of social media on political participation among young adults, including:

inform about the association between social media usage and political participation among youth.
determine the relationship between social media content and political preferences among youth.
provide guidelines for enhancing youth engagement in democratic processes through social media use.

Research Proposal Dissemination description symbol

We will disseminate the research results within policymakers and NGOs through academic publications in peer-reviewed journals, presentations at relevant conferences, and policy briefs.

Research Proposal Timeline description symbol

The project will start will be completed within two years and for the first two objectives a periodic report will be submitted in months 12 and 18.

The total eligible project costs are 58,800 USD, where 15% covers participant recruitment and compensation, 5% covers survey software licenses, 55% are dedicated for salaries, and 25% are intended for dissemination activities.

Research Proposal Examples in Life Sciences

Here are real-life research project examples in life sciences.




(postdoctoral fellowship)
(National Institutes of Environmental Health Sciences)

Here is a hypothetical research proposal example in Life Sciences. Just like the previous example, it consists of the nine discussed proposal sections and it is structured using the key sentence skeleton that you will access in the proposal template .

Investigating the Role of Gut Microbiota in Obesity and Metabolic Syndrome (GUT-MET)

Obesity and metabolic syndrome pose significant health challenges worldwide, leading to numerous chronic diseases and increasing healthcare costs. Despite extensive research, the precise mechanisms underlying these conditions remain incompletely understood. A critical knowledge gap exists regarding the role of gut microbiota in the development and progression of obesity and metabolic syndrome.

With the GUT-MET project, we aim to unravel the complex interactions between gut microbiota and obesity/metabolic syndrome. To achieve this aim, we have the following specific objectives:

Investigate the composition and diversity of gut microbiota in individuals with obesity and metabolic syndrome.
Determine the functional role of specific gut microbial species and their metabolites in the pathogenesis of obesity and metabolic syndrome.

During the project, we will employ the following key methodologies:

Perform comprehensive metagenomic and metabolomic analyses to characterize the gut microbiota and associated metabolic pathways.
Conduct animal studies to investigate the causal relationship between gut microbiota alterations and the development of obesity and metabolic syndrome.

The project will benefit from state-of-the-art laboratory facilities, including advanced sequencing and analytical equipment, as well as access to a well-established cohort of participants with obesity and metabolic syndrome.

Research Proposal Participants description symbol

Dr. Emma Johnson, a renowned expert in gut microbiota research and Professor of Molecular Biology at the University of PeerRecognized, will lead the project. Dr. Johnson has published extensively in high-impact journals and has received multiple research grants focused on the gut microbiota and metabolic health.

The project will deliver crucial insights into the role of gut microbiota in obesity and metabolic syndrome. Specifically, it will:

Identify microbial signatures associated with obesity and metabolic syndrome for potential diagnostic and therapeutic applications.
Uncover key microbial metabolites and pathways implicated in disease development, enabling the development of targeted interventions.

We will actively disseminate the project results within the scientific community, healthcare professionals, and relevant stakeholders through publications in peer-reviewed journals, presentations at international conferences, and engagement with patient advocacy groups.

The project will be executed over a period of 36 months. Key milestones include data collection and analysis, animal studies, manuscript preparation, and knowledge transfer activities.

The total eligible project costs are $1,500,000, with the budget allocated for 55% personnel, 25% laboratory supplies, 5% data analysis, and 15% knowledge dissemination activities as specified in the research call guidelines.

Research Proposals Examples in Natural Sciences

Here are real-life research proposal examples of funded projects in natural sciences.


(FNU)

(USGS) (Mendenhall Research Fellowship Program)
(Earth Venture Mission – 3 NNH21ZDA002O)

Here is a Natural Sciences research proposal example that is structured using the same nine sections. I created this proposal example using the key sentence skeleton that you will access in the proposal template .

Assessing the Impact of Climate Change on Biodiversity Dynamics in Fragile Ecosystems (CLIM-BIODIV)

Climate change poses a significant threat to global biodiversity, particularly in fragile ecosystems such as tropical rainforests and coral reefs. Understanding the specific impacts of climate change on biodiversity dynamics within these ecosystems is crucial for effective conservation and management strategies. However, there is a knowledge gap regarding the precise mechanisms through which climate change influences species composition, population dynamics, and ecosystem functioning in these vulnerable habitats.

With the CLIM-BIODIV project, we aim to assess the impact of climate change on biodiversity dynamics in fragile ecosystems. To achieve this aim, we have the following specific objectives:

Investigate how changes in temperature and precipitation patterns influence species distributions and community composition in tropical rainforests.
Assess the effects of ocean warming and acidification on coral reef ecosystems, including changes in coral bleaching events, species diversity, and ecosystem resilience.
Conduct field surveys and employ remote sensing techniques to assess changes in species distributions and community composition in tropical rainforests.
Utilize experimental approaches and long-term monitoring data to evaluate the response of coral reefs to varying temperature and pH conditions.

The project will benefit from access to field sites in ecologically sensitive regions, advanced remote sensing technology, and collaboration with local conservation organizations to facilitate data collection and knowledge sharing.

Dr. Alexander Chen, an established researcher in climate change and biodiversity conservation, will lead the project. Dr. Chen is a Professor of Ecology at the University of Peer Recognized, with a track record of three Nature publications and successful grant applications exceeding 25 million dollars.

The project will provide valuable insights into the impacts of climate change on biodiversity dynamics in fragile ecosystems. It will:

Enhance our understanding of how tropical rainforest communities respond to climate change, informing targeted conservation strategies.
Contribute to the identification of vulnerable coral reef ecosystems and guide management practices for their protection and resilience.

We will disseminate the project results to the scientific community, conservation practitioners, and policymakers through publications in reputable journals, participation in international conferences, and engagement with local communities and relevant stakeholders.

The project will commence on March 1, 2024, and will be implemented over a period of 48 months. Key milestones include data collection and analysis, modeling exercises, stakeholder engagement, and knowledge transfer activities. These are summarized in the Gantt chart.

The total eligible project costs are $2,000,000, with budget allocation for research personnel, fieldwork expenses, laboratory analyses, modeling software, data management, and dissemination activities.

Research Proposal Examples in Engineering and Technology

Here are real-life research proposal examples of funded research projects in the field of science and technology.


(USGS) (Mendenhall Postdoctoral Research Fellowship)


(ROSES E.7 (Support for Open Source Tools, Frameworks, and Libraries))

Here is a hypothetical Engineering and Technology research proposal example that is structured using the same nine proposal sections we discussed earlier. I used the key sentence skeleton available in the proposal template to produce this example.

Developing Sustainable Materials for Energy-Efficient Buildings (SUST-BUILD)

The construction industry is a major contributor to global energy consumption and greenhouse gas emissions. Addressing this issue requires the development of sustainable materials that promote energy efficiency in buildings. However, there is a need for innovative engineering solutions to overcome existing challenges related to the performance, cost-effectiveness, and scalability of such materials.

With the SUST-BUILD project, we aim to develop sustainable materials for energy-efficient buildings. Our specific objectives are as follows:

Design and optimize novel insulating materials with enhanced thermal properties and reduced environmental impact.
Develop advanced coatings and surface treatments to improve the energy efficiency and durability of building envelopes.
Conduct extensive material characterization and simulation studies to guide the design and optimization of insulating materials.
Utilize advanced coating techniques and perform full-scale testing to evaluate the performance and durability of building envelope treatments.

The project will benefit from access to state-of-the-art laboratory facilities, including material testing equipment, thermal analysis tools, and coating application setups. Collaboration with industry partners will facilitate the translation of research findings into practical applications.

Dr. Maria Rodriguez, an experienced researcher in sustainable materials and building technologies, will lead the project. Dr. Rodriguez holds a position as Associate Professor in the Department of Engineering at Peer Recognized University and has a strong publication record and expertise in the field.

The project will deliver tangible outcomes for energy-efficient buildings. It will:

Develop sustainable insulating materials with superior thermal performance, contributing to reduced energy consumption and greenhouse gas emissions in buildings.
Introduce advanced coatings and surface treatments developed from sustainable materials that enhance the durability and energy efficiency of building envelopes, thereby improving long-term building performance.

We will disseminate project results to relevant stakeholders, including industry professionals, architects, and policymakers. This will be accomplished through publications in scientific journals, presentations at conferences and seminars, and engagement with industry associations.

The project will commence on September 1, 2024, and will be implemented over a period of 36 months. Key milestones include material development and optimization, performance testing, prototype fabrication, and knowledge transfer activities. The milestones are summarized in the Gantt chart.

The total eligible project costs are $1,800,000. The budget will cover personnel salaries (60%), materials and equipment (10%), laboratory testing (5%), prototyping (15%), data analysis (5%), and dissemination activities (5%) as specified in the research call guidelines.

Final Tips for Writing an Winning Research Proposal

Come up with a good research idea.

Ideas are the currency of research world. I have prepared a 3 step approach that will help you to come up with a research idea that is worth turning into a proposal. You can download the Research Idea Generation Toolkit in this article.

Research project idea generation in three steps: 1. Generate many ideas 2. Refine the best ones 3. Rate and select the winner

Start with a strong research outline

Before even writing one sentence of the research proposal, I suggest you use the Research Project Canvas . It will help you to first come up with different research ideas and then choose the best one for writing a full research proposal.

Research Proposal Template in the middle between a Research Project Canvas and a Full Research Proposal

Tailor to the requirements of the project funder

Treat the submission guide like a Monk treats the Bible and follow its strict requirements to the last detail. The funder might set requirements for the topic, your experience, employment conditions, host institution, the research team, funding amount, and so forth.

What you would like to do in the research is irrelevant unless it falls within the boundaries defined by the funder.

Make the reviewer’s job of finding flaws in your proposal difficult by ensuring that you have addressed each requirement clearly. If applicable, you can even use a table with requirements versus your approach. This will make your proposed approach absolutely evident for the reviewers.

Before submitting, assess your proposal using the criteria reviewers have to follow.

Conduct thorough background research

Before writing your research proposal, conduct comprehensive background research to familiarize yourself with existing literature, theories, and methodologies related to your topic. This will help you identify research gaps and formulate research questions that address these gaps. You will also establish competence in the eyes of reviewers by citing relevant literature.

Be concise and clear

Define research questions that are specific, measurable, and aligned with the problem statement.

If you think the reviewers might be from a field outside your own, avoid unnecessary jargon or complex language to help them to understand the proposal better.

Be specific in describing the research methodology. For example, include a brief description of the experimental methods you will rely upon, add a summary of the materials that you are going to use, attach samples of questionnaires that you will use, and include any other proof that demonstrates the thoroughness you have put into developing the research plan. Adding a flowchart is a great way to present the methodology.

Create a realistic timeline and budget

Develop a realistic project timeline that includes key milestones and activities, allowing for potential challenges or delays. Similarly, create a detailed budget estimate that covers all anticipated expenses, ensuring that it aligns with the scope and requirements of your research project. Be transparent and justify your budget allocations.

Demonstrate the significance and potential impact of the research

Clearly articulate the significance of your research and its potential impact on the field. Discuss how your findings can contribute to theory development, practical applications, policy-making, or other relevant areas.

Pay attention to formatting and style guidelines

Follow the formatting and style guidelines provided by your institution or funding agency. Pay attention to details such as font size, margins, referencing style, and section headings. Adhering to these guidelines demonstrates professionalism and attention to detail.

Take a break before editing

After preparing the first draft, set it aside for at least a week. Then thoroughly check it for logic and revise, revise, revise. Use the proposal submission guide to review your proposal against the requirements. Remember to use grammar checking tools to check for errors.

Finally, read the proposal out loud. This will help to ensure good readability.

Seek feedback

Share your proposal with mentors, colleagues, or members of your research community to receive constructive feedback and suggestions for improvement. Take these seriously since they provide a third party view of what is written (instead of what you think you have written).

Reviewing good examples is one of the best ways to learn a new skill. I hope that the research proposal examples in this article will be useful for you to get going with writing your own research proposal.

Have fun with the writing process and I hope your project gets approved!

Learning from research proposal examples alone is not enough

The research proposal examples I provided will help you to improve your grant writing skills. But learning from example proposals alone will take you a rather long time to master writing winning proposals.

To write a winning research proposal, you have to know how to add that elusive X-Factor that convinces the reviewers to move your proposal from the category “good” to the category “support”. This includes creating self-explanatory figures, creating a budget, collaborating with co-authors, and presenting a convincing story.

To write a research proposal that maximizes your chances of receiving research funding, read my book “ Write a Winning Research Proposal “.

Book Cover for "Write a Winning Research Proposal: How to Generate Grant Ideas and Secure Funding Using Research Project Canvas" by Martins Zaumanis. Includes research project examples.

This isn’t just a book. It’s a complete research proposal writing toolkit that includes a project ideation canvas, budget spreadsheet, project rating scorecard, virtual collaboration whiteboard, proposal pitch formula, graphics creation cheat sheet, review checklist and other valuable resources that will help you succeed.

Hey! My name is Martins Zaumanis and I am a materials scientist in Switzerland ( Google Scholar ). As the first person in my family with a PhD, I have first-hand experience of the challenges starting scientists face in academia. With this blog, I want to help young researchers succeed in academia. I call the blog “Peer Recognized”, because peer recognition is what lifts academic careers and pushes science forward.

Besides this blog, I have written the Peer Recognized book series and created the Peer Recognized Academy offering interactive online courses.

One comment

Hi Martins, I’ve recently discovered your content and it is great. I will be implementing much of it into my workflow, as well as using it to teach some graduate courses! I noticed that a materials science-focused proposal could be a very helpful addition.

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Research: Writing effective statistical methodology for a research proposal

"Clinical research is judged to be valid not by the results but how it is designed and conducted. The cliché of ‘do it right or do it over’ is particularly apt in clinical research." This publication i s a great resource to help you organise and plan your research team.

The Journal of Investigative Medicine published an informative article titled "Bridging Clinical Investigators and Statisticians: Writing the Statistical Methodology for a Research Proposal."

The introduction : "Clinical research is judged to be valid not by the results but how it is designed and conducted. The cliché of ‘do it right or do it over’ is particularly apt in clinical research.

One of the questions a clinical investigator frequently asks in planning clinical research is “Do I need a statistician as part of my clinical research team?” The answer is “Yes!” since a statistician can help to optimize design, analysis and interpretation of results, and drawing conclusions. When developing a clinical research proposal, how early in the process should the clinical investigator contact the statistician? Answer - it is never too early. Statistics cannot rescue a poorly designed protocol after the study has begun. A statistician can be a valuable member of a clinical research team and often serves as a co-investigator. Large multicenter projects such as Phase III randomized clinical trials for drug approval by a regulatory agency nearly always have a statistician (or several) on their team. However, smaller, typically single center studies may also require rigorous statistical methodology in design and analysis. These studies are often devised by young clinical investigators launching their clinical research career who may have not collaborated with a statistician. Many clinical investigators are familiar with the statistical role in the analysis of research data1, but researchers may not be as aware of the role of a statistician in designing clinical research and developing the study protocol. In this paper we discuss topics and situations that clinical investigators and statisticians commonly encounter while planning a research study and writing the statistical methods section. We stress the importance of having the statistical methodology planned well in advance of conducting the clinical research study. Working in conjunction with a statistician can also be a key training opportunity for the clinical investigator beginning a clinical research career."

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Blog Business How to Write a Research Proposal: A Step-by-Step

How to Write a Research Proposal: A Step-by-Step

Written by: Danesh Ramuthi Nov 29, 2023

A research proposal is a structured outline for a planned study on a specific topic. It serves as a roadmap, guiding researchers through the process of converting their research idea into a feasible project.

The aim of a research proposal is multifold: it articulates the research problem, establishes a theoretical framework, outlines the research methodology and highlights the potential significance of the study. Importantly, it’s a critical tool for scholars seeking grant funding or approval for their research projects.

Crafting a good research proposal requires not only understanding your research topic and methodological approaches but also the ability to present your ideas clearly and persuasively. Explore Venngage’s Proposal Maker and Research Proposals Templates to begin your journey in writing a compelling research proposal.

What to include in a research proposal?

In a research proposal, include a clear statement of your research question or problem, along with an explanation of its significance. This should be followed by a literature review that situates your proposed study within the context of existing research.

Your proposal should also outline the research methodology, detailing how you plan to conduct your study, including data collection and analysis methods.

Additionally, include a theoretical framework that guides your research approach, a timeline or research schedule, and a budget if applicable. It’s important to also address the anticipated outcomes and potential implications of your study. A well-structured research proposal will clearly communicate your research objectives, methods and significance to the readers.

Light Blue Shape Semiotic Analysis Research Proposal

How to format a research proposal?

Formatting a research proposal involves adhering to a structured outline to ensure clarity and coherence. While specific requirements may vary, a standard research proposal typically includes the following elements:

Title Page: Must include the title of your research proposal, your name and affiliations. The title should be concise and descriptive of your proposed research.
Abstract: A brief summary of your proposal, usually not exceeding 250 words. It should highlight the research question, methodology and the potential impact of the study.
Introduction: Introduces your research question or problem, explains its significance, and states the objectives of your study.
Literature review: Here, you contextualize your research within existing scholarship, demonstrating your knowledge of the field and how your research will contribute to it.
Methodology: Outline your research methods, including how you will collect and analyze data. This section should be detailed enough to show the feasibility and thoughtfulness of your approach.
Timeline: Provide an estimated schedule for your research, breaking down the process into stages with a realistic timeline for each.
Budget (if applicable): If your research requires funding, include a detailed budget outlining expected cost.
References/Bibliography: List all sources referenced in your proposal in a consistent citation style.

Green And Orange Modern Research Proposal

How to write a research proposal in 11 steps?

Writing a research proposal template in structured steps ensures a comprehensive and coherent presentation of your research project. Let’s look at the explanation for each of the steps here:

Step 1: Title and Abstract Step 2: Introduction Step 3: Research objectives Step 4: Literature review Step 5: Methodology Step 6: Timeline Step 7: Resources Step 8: Ethical considerations Step 9: Expected outcomes and significance Step 10: References Step 11: Appendices

Step 1: title and abstract.

Select a concise, descriptive title and write an abstract summarizing your research question, objectives, methodology and expected outcomes. The abstract should include your research question, the objectives you aim to achieve, the methodology you plan to employ and the anticipated outcomes.

Step 2: Introduction

In this section, introduce the topic of your research, emphasizing its significance and relevance to the field. Articulate the research problem or question in clear terms and provide background context, which should include an overview of previous research in the field.

Step 3: Research objectives

Here, you’ll need to outline specific, clear and achievable objectives that align with your research problem. These objectives should be well-defined, focused and measurable, serving as the guiding pillars for your study. They help in establishing what you intend to accomplish through your research and provide a clear direction for your investigation.

Step 4: Literature review

In this part, conduct a thorough review of existing literature related to your research topic. This involves a detailed summary of key findings and major contributions from previous research. Identify existing gaps in the literature and articulate how your research aims to fill these gaps. The literature review not only shows your grasp of the subject matter but also how your research will contribute new insights or perspectives to the field.

Step 5: Methodology

Describe the design of your research and the methodologies you will employ. This should include detailed information on data collection methods, instruments to be used and analysis techniques. Justify the appropriateness of these methods for your research.

Step 6: Timeline

Construct a detailed timeline that maps out the major milestones and activities of your research project. Break the entire research process into smaller, manageable tasks and assign realistic time frames to each. This timeline should cover everything from the initial research phase to the final submission, including periods for data collection, analysis and report writing.

It helps in ensuring your project stays on track and demonstrates to reviewers that you have a well-thought-out plan for completing your research efficiently.

Step 7: Resources

Identify all the resources that will be required for your research, such as specific databases, laboratory equipment, software or funding. Provide details on how these resources will be accessed or acquired.

If your research requires funding, explain how it will be utilized effectively to support various aspects of the project.

Step 8: Ethical considerations

Address any ethical issues that may arise during your research. This is particularly important for research involving human subjects. Describe the measures you will take to ensure ethical standards are maintained, such as obtaining informed consent, ensuring participant privacy, and adhering to data protection regulations.

Here, in this section you should reassure reviewers that you are committed to conducting your research responsibly and ethically.

Step 9: Expected outcomes and significance

Articulate the expected outcomes or results of your research. Explain the potential impact and significance of these outcomes, whether in advancing academic knowledge, influencing policy or addressing specific societal or practical issues.

Step 10: References

Compile a comprehensive list of all the references cited in your proposal. Adhere to a consistent citation style (like APA or MLA) throughout your document. The reference section not only gives credit to the original authors of your sourced information but also strengthens the credibility of your proposal.

Step 11: Appendices

Include additional supporting materials that are pertinent to your research proposal. This can be survey questionnaires, interview guides, detailed data analysis plans or any supplementary information that supports the main text.

Appendices provide further depth to your proposal, showcasing the thoroughness of your preparation.

Beige And Dark Green Minimalist Research Proposal

Research proposal FAQs

1. how long should a research proposal be.

The length of a research proposal can vary depending on the requirements of the academic institution, funding body or specific guidelines provided. Generally, research proposals range from 500 to 1500 words or about one to a few pages long. It’s important to provide enough detail to clearly convey your research idea, objectives and methodology, while being concise. Always check

2. Why is the research plan pivotal to a research project?

The research plan is pivotal to a research project because it acts as a blueprint, guiding every phase of the study. It outlines the objectives, methodology, timeline and expected outcomes, providing a structured approach and ensuring that the research is systematically conducted.

A well-crafted plan helps in identifying potential challenges, allocating resources efficiently and maintaining focus on the research goals. It is also essential for communicating the project’s feasibility and importance to stakeholders, such as funding bodies or academic supervisors.

Simple Minimalist White Research Proposal

Mastering how to write a research proposal is an essential skill for any scholar, whether in social and behavioral sciences, academic writing or any field requiring scholarly research. From this article, you have learned key components, from the literature review to the research design, helping you develop a persuasive and well-structured proposal.

Remember, a good research proposal not only highlights your proposed research and methodology but also demonstrates its relevance and potential impact.

For additional support, consider utilizing Venngage’s Proposal Maker and Research Proposals Templates , valuable tools in crafting a compelling proposal that stands out.

Whether it’s for grant funding, a research paper or a dissertation proposal, these resources can assist in transforming your research idea into a successful submission.

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First page of “RESEARCH PROPOSAL ON STATISTICAL ANALYSIS ON FACTORS AFFECTING ACADEMIC ACHEIVMENT OF FEMALE STUDENTS IN AMBO UNIVERSTIY (IN THE CASE OF COLLEGE NATURAL AND COMPUTATIONAL SCIENCE).”

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RESEARCH PROPOSAL ON STATISTICAL ANALYSIS ON FACTORS AFFECTING ACADEMIC ACHEIVMENT OF FEMALE STUDENTS IN AMBO UNIVERSTIY (IN THE CASE OF COLLEGE NATURAL AND COMPUTATIONAL SCIENCE).

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Bridging Clinical Investigators and Statisticians: Writing the Statistical Methodology for a Research Proposal

Introduction.

Clinical research is judged to be valid not by the results but how it is designed and conducted. The cliché of ‘do it right or do it over’ is particularly apt in clinical research.

One of the questions a clinical investigator frequently asks in planning clinical research is “Do I need a statistician as part of my clinical research team?” The answer is “Yes!” since a statistician can help to optimize design, analysis and interpretation of results, and drawing conclusions. When developing a clinical research proposal, how early in the process should the clinical investigator contact the statistician? Answer - it is never too early. Statistics cannot rescue a poorly designed protocol after the study has begun. A statistician can be a valuable member of a clinical research team and often serves as a co-investigator. Large multicenter projects such as Phase III randomized clinical trials for drug approval by a regulatory agency nearly always have a statistician (or several) on their team. However, smaller, typically single center studies may also require rigorous statistical methodology in design and analysis. These studies are often devised by young clinical investigators launching their clinical research career who may have not collaborated with a statistician. Many clinical investigators are familiar with the statistical role in the analysis of research data 1 , but researchers may not be as aware of the role of a statistician in designing clinical research and developing the study protocol. In this paper we discuss topics and situations that clinical investigators and statisticians commonly encounter while planning a research study and writing the statistical methods section. We stress the importance of having the statistical methodology planned well in advance of conducting the clinical research study. Working in conjunction with a statistician can also be a key training opportunity for the clinical investigator beginning a clinical research career.

GETTING STARTED ON THE STATISTICAL ANALYSIS PLAN

Why work with a statistician.

The study design, sample size, and statistical analysis must be able to properly evaluate the research hypothesis set forth by the clinical investigator. Otherwise, the consequences of a poorly developed statistical approach may result in a failure to obtain extramural funding and result in a flawed clinical study that cannot adequately test the desired hypotheses. Statisticians provide design advice and develop the statistical methods that best correspond to the research hypothesis. For the planning of a clinical study, a statistician can provide valuable information on key design points as summarized in Table 1 . The statistician can discuss with the clinical investigators questions such as: Is the design valid? Overly ambitious? Will the data be analyzable?

The role of the statistician in developing the statistical plan

Very early in the planning stages, it is important to send the statistician a draft of the proposal. Any protocol changes may affect the required sample size and analysis plans so it is important to meet with the statistician throughout the planning stages and later if modifications have been made to the study design. Before the statistical section can be developed, what information does the statistician need? Questions from a statistician concerning design, power and sample size, and analysis may include:

What is the research hypothesis?
What is the type of study design?
What is the most important measurement (primary outcome variable)?
What is the type of variable and unit of measurement?
What is a clinically meaningful difference for the primary outcome?
How many subjects can be recruited or observed within a study period? How many groups or treatment arms are to be included in your design?
Will there be an equal number of participants or observations in each group? i.e., what is the allocation ratio?
How many total evaluations and measurements?
For repeated measurements, what is the measurement interval?

You are not expected to have all the answers at your first meeting and ongoing conversations with the statistician can serve to develop these ideas. Eventually, the answers to these questions comprise the justification for the design selected, provide the basis for the sample size estimate, and drive the choice of statistical analysis. A brief consultation with a statistician will not be adequate to address these issues. The interaction with a statistician to construct the statistical section is not usually one meeting, email, or phone call. It is a process that will help you think through the design of your study. This is also an excellent opportunity to ask questions and enhance your statistical education. Additionally, the exchange of ideas is beneficial to the statistician who will better appreciate the clinical research question. The discussions with a statistician could lead to changes in study design, such as proposing a smaller, more focused study design to collect preliminary data.

A general outline of the statistical methods section is shown in Table 2 . There may be deviations from this format depending on the particular study design. The statistical write-up is rarely less than one page and may total several pages. Although some clinical investigators trained in statistics do prepare this section, more commonly the statistician constructs and writes up the statistical methods section for grants and protocols in close collaboration with the investigators. However, it is important that clinical investigators develop a conceptual understanding of the proposed statistical methodology. Take advantage of any study design and biostatistics classes offered at your institutions to make statistical collaborations more fruitful.

Outline of the statistical methods section

STUDY DESIGN

Type of design.

Before the statistical section can progress, the study design must be known. Study designs that are commonly used in clinical research include case-control, cohort, randomized controlled design, crossover, and factorial designs. A randomized controlled trial has many features but most commonly incorporates what is called a parallel group design where individuals are randomly assigned to a particular treatment or intervention group. In a crossover study, the subject participates in more than one study intervention phase, ideally studied in a random sequence, such as comparing triglyceride responses within the same individual on a low fat versus a high fat diet.

How do we select participants for the study? There are many types of sampling procedures, the basis of which is to avoid or reduce bias. Bias can be defined as ”a systematic tendency to produce an outcome that differs from the underlying truth”. 2 Although true randomness is the goal of a sampling, it is generally not achievable. The study subjects are not usually selected at random to participate in clinical research. Instead, in most clinical trials, the “random” element in randomization is that the consented subjects are assigned by chance to a particular treatment or intervention. The clinical inclusion and exclusion criteria coupled with informed consent will determine who will be the study participants and, ultimately, to what population the study results will be generalizable.

Sample size

With the study design and the make-up of the study sample determined, the sample size estimates can be obtained. Fundamental to estimating sample size are the concepts of statistical hypothesis testing, type I error, type II error, and power ( Table 3 ). In planning clinical research it is necessary to determine the number of subjects to be required so that the study achieves sufficient statistical power to detect the hypothesized effect. If the reader is not familiar with the concept of statistical hypothesis testing, introductory biostatistics texts and many web sites cover this topic. Briefly, in trials to demonstrate improved efficacy of a new treatment over placebo/standard treatments, the null hypothesis is that there is no difference between treatments and the alternative hypothesis is that there is a treatment difference. The research hypothesis usually corresponds to the alternative hypothesis which represents a minimal meaningful difference in clinical outcomes. Statistically, we either 1) reject the null hypothesis in favor of the alternative hypothesis or 2) we fail to reject the null hypothesis.

Definitions for statistical hypothesis testing

Typically, the sample size is computed to provide a fixed level of power under a specified alternative hypothesis. Power is an important consideration for several reasons. Low power can cause a true difference in clinical outcomes between study groups to go undetected. However, too much power may yield statistically significant results that are not meaningfully different to clinicians. The probability of Type I error (α) of 0.05 (two-sided) and power of 0.80 and 0.90 have been widely used for the sample size estimation in clinical trials. The sample size estimate will also allow the estimation of the total cost of the proposed study.

A clinical trial that is conducted without attention to sample size or power information takes the risks of either failing to detect clinically meaningful differences (Type II error) due to not enough subjects or taking an unnecessarily excessive number of samples for a study. Both cases fail to adhere to the Ethical Guidelines of the American Statistical Association which says “Avoid the use of excessive or inadequate number of research subjects by making informed recommendations for study size”. 3

What information is needed to calculate power and sample size?

The components that most sample size programs require for input include:

Choose Type I error (alpha)
Choose Power
Choose clinical outcome variable and effect size (difference between means, proportions, survival times, regression parameters)
Variation estimate
Allocation ratio

Clinical outcome measures

Clearly describe the clinical outcomes that will be analyzed to the statistician. The variable type ( Table 4 ) and distribution of the primary outcome measurement must be defined before sample size and power calculations can proceed. The sample size estimates are mainly needed for the primary outcome. However, providing power estimates for secondary outcomes is often helpful to reviewers.

Variable types and derivations to be described in the statistical analysis plan

Describe each variable and type to be collected triglycerides (non-normally distributed, log transformed due to skewness)

Effect size

As an example, suppose a parallel group study is being designed to compare systolic blood pressure between two treatments and the investigators want to be able to detect a mean 10 mm Hg difference between groups. This 10 mm Hg difference is referred to as the effect size, detectable difference, or minimal expected difference.

How is the effect size determined?

Choose an effect size that is based on clinical knowledge of the primary endpoint. A sample size that ‘worked’ in a published paper is no guarantee of success in a different setting. The selected effect size is unique to your study intervention, the specific type of participants in your study sample, and perhaps an aspect of the outcome measurement that is unique to your clinic or laboratory. 4

The investigator and statistician examine the literature, the investigator’s own past research, or a combination of the above to determine a study effect size. To investigate the difference in mean blood pressure between two treatments, the effect size options might be 2 mm Hg, 6 mm Hg, 10 mm Hg or 20 mm Hg. Which of these differences do you need to have the ability to detect? This is a clinical question, not a statistical question. Effect size is a measure of the magnitude of the treatment effect and represents a clinically or biologically important difference. Choosing a 20 mm Hg effect size yields a smaller sample size than a 10 mm Hg effect size since it is easier to statistically detect the larger difference. However, an effect size of 10 mm Hg or smaller magnitude may be more a realistic treatment effect and less likely to result in a flawed or wasted study.

Variation estimates for sample size calculations

In addition to effect size, we may need to estimate how much the outcome varies from person to person. For continuous variables, the variation estimate is often in the form of a standard deviation. If the hypothesized difference in systolic blood pressure is an effect size of 10 mm Hg, a study with a blood pressure standard deviation of 22 mm Hg will have lower power than a study where the standard deviation is 14 mm Hg. For a continuous outcome such as blood pressure, a measure of the variation is another part of the formula needed to compute the sample size. An estimate of variation can be derived from a literature search or from the investigator’s preliminary data. Obtaining this information can be a challenge for both the clinical investigator and statistician.

Table 5 shows sample sizes scenarios for detecting differences in blood pressure when comparing two treatments based on a t-test. A standard deviation of 14 mm Hg is chosen to estimate the variation. Sample sizes are calculated for power of 0.80 and 0.90 at the two-sided 0.05 significance level. Notice that the smaller effect sizes require a larger sample size and that the sample size increases as the power increases from 0.80 to 0.90.

Scenarios for choosing sample size

Primary Outcome Variable	Effect size Mean detectable difference between groups	Estimated standard deviation	Sample size per group α = 0.05 Power = 0.80	Sample size per group α = 0.05 Power = 0.90

Systolic blood pressure, mm Hg	6	14	86	115
	8	14	49	65
	10	14	32	42
	20	14	9	11

Determining a reasonable and affordable sample size estimate is a team effort. There are practical issues such as budgets or recruitment limitations that may come into play. A too large sample size could preclude the ability to conduct the research. The research team will assess scenarios with varying detectable differences and power as seen in Table 5 (calculations performed using PS power 5 available at the website < http://biostat.mc.vanderbilt.edu/twiki/bin/view/Main/PowerSampleSize >). Typically a scenario can be worked out which is both clinically and statistically viable.

The elements of sample size calculations presented here pertain to relatively simple designs. Cluster samples or family data need special statistical adjustments. For a longitudinal or repeated measures design, the correlation between the repeated measurements is incorporated into the sample size calculations. 6 , 7

Do all studies need sample size and power estimates?

Pilot studies.

Pilot studies may not need a power analysis since they are more about testing the protocol than testing a hypothesis. 8 Sometimes there are no preliminary data and thus pilot data must be obtained to provide estimates for designing for a more definitive study. However, calling a study a pilot study to avoid power analyses and to keep the sample small is misrepresentation. 8

Sample size calculations are necessary when the study goal is precision instead of power. The goal may be to describe the precision of a proportion or mean or other statistic that is to be estimated from our sample. Precision in this context is based upon finding a suitably narrow confidence interval for the statistic of interest, such that the lower and upper limits of the confidence interval include a clinically meaningful range of values. We may want to know how many subjects are required to be 95% confident that an interval contains the true, but unknown, value. For example, how many subjects are needed for 10% precision if we expect a 30% allele prevalence in a genetic study? Instead of power, we estimate the sample size for the desired precision based on a single proportion of 0.30 and summarize by stating “With 80 subjects, the precision for a 30% allele prevalence rate is approximately 10% (95% confidence interval: 21% to 40%).” If greater precision is desirable then the sample size is increased accordingly.

Accounting for attrition

Withdrawal and dropout are unwelcome realities of clinical research. Missing data in clinical trials or repeated measurement studies are inevitable. Consider missing data issues when designing, planning and conducting studies to minimize missing data impact. Sample size estimates are finalized by adjusting for attrition based upon the anticipated number of dropouts.

Randomization plan

Random allocation of subjects to study groups is fundamental to the clinical trial design. Randomization, which is a way to reduce bias, involves random allocation of the participants to the treatment groups. If investigators compare a new treatment against a standard treatment, the study subjects are allocated to one of these treatments by a random process. A general description of the randomization approach may be introduced in the clinical methods section of the proposal, for example, “Treatment assignment will be determined using stratified, blocked randomization”. Specific randomization details will need to be elaborated upon in the statistical methods section, including how the allocation procedure will be implemented, e.g., via computer programs, web site, lists, or sealed envelopes. If stratification is deemed necessary, include in the proposal a description of each stratification variable and the number of levels for each stratum, for instance, gender (male, female), diabetes (type 1, type 2). However, keep the number of strata and stratum levels minimal. 9 Discuss the advantages and disadvantages of the various allocation approaches with the study statistician.

Knowledge of the treatment assignment might influence how much of a dosage change is made to a study treatment or how an adverse event is assessed. Blinding or masking is another component of study design used to try to eliminate such bias. 10 In a double-blind randomized trial, neither the study subjects nor the clinical investigators know the treatment assignment.

Describe the planned blinding scheme. For example, “This is a double-blind randomized study to investigate the effect of propranolol versus no propranolol on the incidences of total mortality and of total mortality plus nonfatal myocardial infarction in 158 older patients with CHF and prior myocardial infarction.” Specify who is to be blinded and the steps that will be taken to maintain the blind. It is important that evaluators such as a radiologists, pathologists, or lab personnel who have no direct contact with the study subjects remain blinded to treatment assignments.

It may be impossible or difficult to use the double-blind procedures in some clinical trials. For example, it is not feasible to design a double-blind clinical trial for the comparison of surgical and non-surgical interventions. Or, blinding might not be completely successful; study personnel may be inadvertently alerted as to the probable treatment assignment due to the occurrence of a specific adverse event. If blinding is not feasible, offer an explanation for lack of blinding procedures in the research proposal.

STATISTICAL ANALYSIS METHODOLOGY

The statistical analysis methods for analyzing the study outcomes are to be carefully detailed. Specifying these methods in advance is another way to minimize bias and maintain the integrity of the analysis. Any changes to the statistical methods must be justified and decided upon before the blind is broken. 11 In the statistical analysis plan not only must the statistical hypotheses to be tested be described and justified but we also detail which subjects and observations will be included or excluded in each analysis.

Analysis data sets

Intention-to-treat analysis

It is crucial to define the primary sample of subjects analyzed in the reporting of clinical trial results. Defined in Table 6 , intention-to-treat (ITT) and per-protocol analyses are commonly reported in medical literature result sections. For a randomized study, intention-to-treat analysis is the gold standard for the primary analysis and the intention-to-treat principle is regarded as the most appropriate criteria for the assessment of a new therapy by the Food and Drug Administration and the National Institute of Health. 12 An intention-to-treat data set includes all randomized subjects, whether or not they were compliant or completed the study. Adhering to the ITT principle mirrors what occurs in clinical practice where a patient may discontinue a medication or miss a clinic appointment. This avoids biases that can result from dropouts and missing data. However, the missing data must not bias the treatment comparisons 13 , otherwise the statistics may not be valid. This type of bias could occur if the dropouts or missed study visits are related to a particular treatment group and are not observed equally across all of the treatments.

— the principle that asserts that the effect of a treatment policy can be best assessed by evaluating on the basis of the intention to treat a subject (i.e., the planned treatment regimen) rather than the actual treatment given. It has the consequence that subjects allocated to a treatment group should be followed up, assessed, and analyzed as members of that group irrespective of their compliance with the planned course of treatment.

— the set of subjects that is as close as possible to the ideal implied by the intention-to-treat principle. It is derived from the set of all randomized subjects by minimal and justified elimination of subjects.

(valid cases, efficacy sample, evaluable subjects sample) — the set of data generated by the subset of subjects who complied with the protocol sufficiently to ensure that these data would be likely to exhibit the effects of treatment according to the underlying scientific model. Compliance covers such considerations as exposure to treatment, availability of measurements, and absence of major protocol violations.

From ICH E9: Guidance for Industry - E9 Statistical Principles for Clinical Trials, U.S. Department of Health and Human Services, Food and Drug Administration, September 1998

A true intention-to-treat data set may not be attainable in all clinical trials. There might be no post-randomization or post-treatment data for a study subject who withdraws from the study at the initial study visit. Then the primary analysis might consist of all subjects who took at least one treatment dose or had at least one follow-up visit. 11 Anticipate these possibilities as the study is designed and specify in the statistical analysis plan which subjects and observations will comprise the “full analysis set”. Pre-specification of these data sets prior to statistical analysis is imperative.

Per-protocol analysis

It may be of clinical interest to plan an analysis set which consists of only ‘completers’ or ‘compliers’. A per-protocol analysis, defined in Table 6 , is more likely to be planned as secondary analyses. If the per-protocol analysis results are not consistent with the intention-to-treat analysis results, then closely examine the reasons behind any discrepancy.

Statistical analysis

The statistical analysis plan is driven by the research questions, the study design, and the type of the outcome measurements. The analysis plan includes a detailed description of statistical testing for each of the variables in the Specific Aim(s). If several Specific Aims are proposed, we write an analysis plan for each Specific Aim. Plan descriptive analyses for each group or planned subgroup. If subjects were randomly assigned to groups, it is expected that there will be a description of subject characteristics that include demographic information as well as baseline measurements or co-morbid conditions. Specify anticipated data transformations that may be needed to meet analysis assumptions and describe derived variables to be created such as area under the curve. Incorporate confidence intervals in the analysis plan for reporting treatment effects. Confidence limits are much more informative to the reader than are p-values alone. 14

Statistical details and terminology are not intended to be an obstacle for a young investigator. Instead this is where the statistical expert can be a valuable resource to help the investigators use the appropriate statistical methods and language that address the research hypotheses. Brief statistical analysis descriptions are shown in Table 7 for a randomized study and a longitudinal cohort study. In addition to the general methodology of Table 7 , we explain in the statistical methods section how statistical assumptions or model diagnostics will be evaluated. Describe the hypotheses to be tested with the corresponding statistical tests for the primary, secondary, and exploratory analyses. In the medical literature, statistical analyses such as chi-square and t-tests, analysis of variance, regression modeling, and various nonparametric tests are common. However, the statistician is happy to advise whether these traditional methods are appropriate for the research question at hand or if other approaches would be more suitable.

Statistical analysis plans

. The full analysis set will include patients who have received at least one dose of medication or had one or more post-randomization, follow-up evaluation. Descriptive statistics will be computed for each treatment group, Medians and percentiles will be reported for skewed continuous variables. For primary and secondary outcomes, descriptive statistics and 95 percent confidence intervals will used to summarize the differences between groups. The primary outcome of systolic blood pressure and other continuous variables will be assessed with a repeated measures analysis using a mixed linear model approach. Since many of the inflammatory markers are positively skewed, IL-6 and CRP will be log transformed prior to analysis. The Wilcoxon Rank Sum test will be used to compare pill counts between groups. Hypothesis tests will be two-sided using the 0.05 significance level. Bonferroni type adjustments for multiple testing will be implemented to control type I errors. Statistical analysis will be performed with SAS software (SAS Institute, Cary, NC, USA).

. We will compute and compare the mean/median, and inter-quartile range of urine biomarker levels in different disease activity groups, after partitioning patients in various ways: patients who attain any of the primary disease outcomes, i.e., WHO Class IIII-or-IV glomerulonephritis, patients with nephritic or nephrotic flares, or end stage renal disease. Additionally, we will define the biomarker levels in patients with the following disease features: anemia, leucopenia, or thrombocytopenia. For comparing multiple patient groups, analysis of variance (ANOVA) or the Kruskal-Wallis test will be used, depending on whether the biomarker values are normality distributed. Data transformations will be performed if necessary. If the omnibus ANOVA or Kruskal-Wallis test yields p<0.05, we will conduct pairwise group comparisons using either t-tests or Wilcoxon rank sum tests with Bonferroni corrections. The generalized estimating equations (GEE) approach will be used to evaluate if urinary biomarkers vary significantly over time among different disease activity classes.

Statistics, like medicine, is a large and diverse field; hence statisticians have specific areas of expertise. Some proposals may require one statistician for the design and analysis of medical imaging studies and another statistician for design and analysis of a microarray study. Often a proposal specifies one statistician as the study statistician and another statistician to serve on a Data and Safety Monitoring Board.

Interim analysis

Conducting a planned interim analysis in an ongoing clinical trial can be beneficial for scientific, economic, and ethical reasons. 15 Formal interim analyses include stopping rules for terminating the study early if a treatment shows futility or clear benefit or harm. The termination of the estrogen plus progestin treatment arm of the Women’s Health Initiative clinical trial in 2002 16 when the treatment risks exceeded benefits demonstrates the strong clinical impact of interim analyses. However, interim analyses are not to be undertaken lightly. Taking unplanned repeated looks at accumulating data is problematic. First, it raises the multiple testing issue so that adjustments to control the overall Type I error rate are necessary. Second, the results can interfere with the conduct of the remainder of the study, creating bias. Pocock 17 and O’Brien & Fleming 18 authored the classic approaches for defining statistical stopping rules. The alpha spending function described by DeMets and Lan 19 provides some flexibility for the timing of interim analyses as well as controlling the Type I error rate. Clinical investigators must seriously consider what decisions might have to be made based upon interim analysis results and how this will affect an ongoing study.

OVERLOOKED OR INADEQUATELY DESCRIBED AREAS

Matching in case-control studies.

A weakness that often surfaces in sessions reviewing research proposals is an inadequate description of matching. Matching is commonly used in case-control studies by selecting for each case a control with the same value of the confounding variable. However, in our experience, the term “matching” is used too loosely. To a reviewer matching implies the recruitment of matched pairs. This may not be the intention of the investigators or the planned statistical analysis approach. A proposal that states that the participants will be matched according the gender, race/ethnicity, age, and body mass index would raise quite a few questions because ‘matching’ on all these variables would be quite difficult to achieve in practice. Often what the investigator really would like to insure is that the study groups will be balanced with respect to these characteristics. This is described as “frequency matching”. For continuous variables, such as age, the range that is considered a “match” needs to be specified. Indicate the target age range that is clinically comparable for your study, e.g., within 2 years or 5 years. Avoid matching on variables that are not known confounders as this may lead to loss of power. 20

Missing data prevention

It is well known that dropouts and certain missing data patterns can impact a study’s validity. Since statistical analyses cannot cure all problems associated with missing data, prevention is the best policy. To minimize dropouts and missed study visits, verify that the proposal has included a retention plan. Incorporate study procedures that may help to reduce the amount missing data, such as making regular calls to participants to better maintain contact as the study is underway. Every member of the research team must appreciate the need to reschedule or repeat key study visits or labs to the extent possible if the primary outcome measurement was not obtained. In order to obtain an analysis set that is consistent with the intention-to-treat principle, continue to schedule follow-up visits and collect primary outcome measurements for subjects who have discontinued their assigned treatment.

The integrity of the statistical analysis depends on the quality of the data. Obviously a study must contain high quality data (garbage in, garbage out), but steps to ensure this are frequently overlooked. Describe in the research proposal how data will be collected, de-identified, stored, and protected. It is vital that the clinical research team becomes skilled at data management. Meet with a database expert early to discuss the design of a database and related forms and involve the statistician in the review of the forms. Development of the proper data forms and database prior to study activation is essential.

We have presented guidance to be considered when developing the statistical plan in proposals for clinical and translational research. All these approaches have the common theme of eliminating or reducing bias and improving study quality. Planning the statistical methodology IN ADVANCE is crucial for maintaining the integrity of clinical research. We hope we have conveyed that developing the statistical methods for a research proposal is a collaborative effort between statistical and clinical research professionals.

Writing the statistical plan is a multidisciplinary effort. Both the clinical investigator and statistician on the research team need to carefully review the final product and ensure that the science and statistics correspond correctly. Just as a statistician who can understand the clinical aspect of the research is particularly advantageous, endeavor to learn all you can from the statistical expert. Ask the statistician to explain the rationale of the statistical methodology so you can defend the statistical plans without the statistician at your side. The clinical investigator may not have to know how to perform complex analyses but does need to understand the general statistical reasoning behind the proposed statistical design and analysis. When clinical investigators have a basic proficiency in statistical methodology, not only are collaborations with statisticians more dynamic and fruitful, but the potential to develop into a strong, independent clinical investigator and mentor increases. This leads to the design and execution of more efficient and advanced research, increasing the productivity of the entire research team.

Statistical Resources and Education

What if the researcher does not have funding to support a biostatistician? One option is to include a biostatistician as a co-investigator in your grant proposal to cover salary and supplies needed to implement the statistical methods described in the grant. Hopefully there is a department or division of Biostatistics or related field at your or a nearby institution. If not, long distance collaborations can succeed via conference calls and email. The American Statistical Association (ASA) has an ASA consulting section < http://www.amstat.org/sections/cnsl/ > where a clinical investigator can get assistance in finding a statistical consultant.

Some useful statistical websites for general statistical information and definitions include “The Little Handbook of Statistical Practice” < http://www.tufts.edu/~gdallal/LHSP.HTM >; “HyperStat Online Statistics Textbook” < http://davidmlane.com/hyperstat/index.html >; WISE Web Interface for Statistical Education < http://wise.cgu.edu/index.html >. Clinical trial statistical guidelines are documented in the International Conference on Harmonisation (ICH) Guidance for industry: E9 Statistical principles for clinical trials < http://www.fda.gov/ >. 11

As of September 2009, 46 medical research institutions in the United States have been granted a Clinical and Translational Science Award (CTSA, < www.ncrr.nih.gov/crctsa >). When the CTSA program is fully implemented, it will support approximately 60 centers across the nation. Some CTSA awardees offer biostatistical collaboration or institutional pilot grants for early career clinical investigators in need of statistical expertise. Many of these research centers offer Biostatistics courses or seminar series that are specifically designed for clinical researchers. This paper evolved from a CTSA course, “Clinical Research from Proposal to Implementation”, taught at the University of Texas Southwestern at Dallas. Take advantage of any such course offerings and resources.

A successful research proposal requires solid statistical methodology. The written statistical methods section is the result of teamwork between the clinical investigators and statisticians. Collaborating with a statistician early and often will help the study proposal evolve into a strong application that increases opportunities for scientific acceptance and funding for conducting important clinical research studies.

Acknowledgments

Grant support: NIH CTSA grant UL1 RR024982

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Measuring variables

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Sampling for statistical analysis

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Calculate measures of central tendency

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Hypothesis testing

Parametric tests

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Statistical significance

Effect size

Decision errors

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Top 10 Statistical Analysis Research Proposal Templates with Samples and Examples

Densil Nazimudeen

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

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

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

Template 4: Timeline for Statistical Analysis of Research Findings Template

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

Template 6: Statistical Analysis Cost Structure 1/2 Template

Template 7: Statistical Analysis Cost Structure 2/2 Template

Template 8: Statistical Analysis Team Cost Structure Template

Template 9: Why Our Statistical Analysis Company Template

Template 10: About Our Statistical Analysis of Research Findings Template

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

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17 Research Proposal Examples

Research Proposal Sample Structure

Research Proposal Examples

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