research proposal definition terms

What (Exactly) Is A Research Proposal?

A simple explainer with examples + free template.

By: Derek Jansen (MBA) | Reviewed By: Dr Eunice Rautenbach | June 2020 (Updated April 2023)

Whether you’re nearing the end of your degree and your dissertation is on the horizon, or you’re planning to apply for a PhD program, chances are you’ll need to craft a convincing research proposal . If you’re on this page, you’re probably unsure exactly what the research proposal is all about. Well, you’ve come to the right place.

Overview: Research Proposal Basics

• What a research proposal is
• What a research proposal needs to cover
• How to structure your research proposal
• Example /sample proposals
• Proposal writing FAQs
• Key takeaways & additional resources

What is a research proposal?

Simply put, a research proposal is a structured, formal document that explains what you plan to research (your research topic), why it’s worth researching (your justification), and how  you plan to investigate it (your methodology). 

The purpose of the research proposal (its job, so to speak) is to convince  your research supervisor, committee or university that your research is  suitable  (for the requirements of the degree program) and  manageable  (given the time and resource constraints you will face). 

The most important word here is “ convince ” – in other words, your research proposal needs to  sell  your research idea (to whoever is going to approve it). If it doesn’t convince them (of its suitability and manageability), you’ll need to revise and resubmit . This will cost you valuable time, which will either delay the start of your research or eat into its time allowance (which is bad news). 

What goes into a research proposal?

A good dissertation or thesis proposal needs to cover the “ what “, “ why ” and” how ” of the proposed study. Let’s look at each of these attributes in a little more detail:

Your proposal needs to clearly articulate your research topic . This needs to be specific and unambiguous . Your research topic should make it clear exactly what you plan to research and in what context. Here’s an example of a well-articulated research topic:

An investigation into the factors which impact female Generation Y consumer’s likelihood to promote a specific makeup brand to their peers: a British context

As you can see, this topic is extremely clear. From this one line we can see exactly:

• What’s being investigated – factors that make people promote or advocate for a brand of a specific makeup brand
• Who it involves – female Gen-Y consumers
• In what context – the United Kingdom

So, make sure that your research proposal provides a detailed explanation of your research topic . If possible, also briefly outline your research aims and objectives , and perhaps even your research questions (although in some cases you’ll only develop these at a later stage). Needless to say, don’t start writing your proposal until you have a clear topic in mind , or you’ll end up waffling and your research proposal will suffer as a result of this.

Need a helping hand?

As we touched on earlier, it’s not good enough to simply propose a research topic – you need to justify why your topic is original . In other words, what makes it  unique ? What gap in the current literature does it fill? If it’s simply a rehash of the existing research, it’s probably not going to get approval – it needs to be fresh.

But,  originality  alone is not enough. Once you’ve ticked that box, you also need to justify why your proposed topic is  important . In other words, what value will it add to the world if you achieve your research aims?

As an example, let’s look at the sample research topic we mentioned earlier (factors impacting brand advocacy). In this case, if the research could uncover relevant factors, these findings would be very useful to marketers in the cosmetics industry, and would, therefore, have commercial value . That is a clear justification for the research.

So, when you’re crafting your research proposal, remember that it’s not enough for a topic to simply be unique. It needs to be useful and value-creating – and you need to convey that value in your proposal. If you’re struggling to find a research topic that makes the cut, watch  our video covering how to find a research topic .

It’s all good and well to have a great topic that’s original and valuable, but you’re not going to convince anyone to approve it without discussing the practicalities – in other words:

• How will you actually undertake your research (i.e., your methodology)?
• Is your research methodology appropriate given your research aims?
• Is your approach manageable given your constraints (time, money, etc.)?

While it’s generally not expected that you’ll have a fully fleshed-out methodology at the proposal stage, you’ll likely still need to provide a high-level overview of your research methodology . Here are some important questions you’ll need to address in your research proposal:

• Will you take a qualitative , quantitative or mixed -method approach?
• What sampling strategy will you adopt?
• How will you collect your data (e.g., interviews , surveys, etc)?
• How will you analyse your data (e.g., descriptive and inferential statistics , content analysis, discourse analysis, etc, .)?
• What potential limitations will your methodology carry?

So, be sure to give some thought to the practicalities of your research and have at least a basic methodological plan before you start writing up your proposal. If this all sounds rather intimidating, the video below provides a good introduction to research methodology and the key choices you’ll need to make.

How To Structure A Research Proposal

Now that we’ve covered the key points that need to be addressed in a proposal, you may be wondering, “ But how is a research proposal structured? “.

While the exact structure and format required for a research proposal differs from university to university, there are four “essential ingredients” that commonly make up the structure of a research proposal:

• A rich introduction and background to the proposed research
• An initial literature review covering the existing research
• An overview of the proposed research methodology
• A discussion regarding the practicalities (project plans, timelines, etc.)

In the video below, we unpack each of these four sections, step by step.

Research Proposal Examples/Samples

In the video below, we provide a detailed walkthrough of two successful research proposals (Master’s and PhD-level), as well as our popular free proposal template.

Proposal Writing FAQs

How long should a research proposal be.

This varies tremendously, depending on the university, the field of study (e.g., social sciences vs natural sciences), and the level of the degree (e.g. undergraduate, Masters or PhD) – so it’s always best to check with your university what their specific requirements are before you start planning your proposal.

As a rough guide, a formal research proposal at Masters-level often ranges between 2000-3000 words, while a PhD-level proposal can be far more detailed, ranging from 5000-8000 words. In some cases, a rough outline of the topic is all that’s needed, while in other cases, universities expect a very detailed proposal that essentially forms the first three chapters of the dissertation or thesis.

The takeaway – be sure to check with your institution before you start writing.

How do I choose a topic for my research proposal?

Finding a good research topic is a process that involves multiple steps. We cover the topic ideation process in this video post.

How do I write a literature review for my proposal?

While you typically won’t need a comprehensive literature review at the proposal stage, you still need to demonstrate that you’re familiar with the key literature and are able to synthesise it. We explain the literature review process here.

How do I create a timeline and budget for my proposal?

We explain how to craft a project plan/timeline and budget in Research Proposal Bootcamp .

Which referencing format should I use in my research proposal?

The expectations and requirements regarding formatting and referencing vary from institution to institution. Therefore, you’ll need to check this information with your university.

What common proposal writing mistakes do I need to look out for?

We’ve create a video post about some of the most common mistakes students make when writing a proposal – you can access that here . If you’re short on time, here’s a quick summary:

• The research topic is too broad (or just poorly articulated).
• The research aims, objectives and questions don’t align.
• The research topic is not well justified.
• The study has a weak theoretical foundation.
• The research design is not well articulated well enough.
• Poor writing and sloppy presentation.
• Poor project planning and risk management.
• Not following the university’s specific criteria.

Key Takeaways & Additional Resources

As you write up your research proposal, remember the all-important core purpose:  to convince . Your research proposal needs to sell your study in terms of suitability and viability. So, focus on crafting a convincing narrative to ensure a strong proposal.

At the same time, pay close attention to your university’s requirements. While we’ve covered the essentials here, every institution has its own set of expectations and it’s essential that you follow these to maximise your chances of approval.

By the way, we’ve got plenty more resources to help you fast-track your research proposal. Here are some of our most popular resources to get you started:

• Proposal Writing 101 : A Introductory Webinar
• Research Proposal Bootcamp : The Ultimate Online Course
• Template : A basic template to help you craft your proposal

If you’re looking for 1-on-1 support with your research proposal, be sure to check out our private coaching service , where we hold your hand through the proposal development process (and the entire research journey), step by step.

Psst… there’s more!

This post is an extract from our bestselling short course, Research Proposal Bootcamp . If you want to work smart, you don't want to miss this .

52 Comments

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You’re most welcome. We don’t have any research proposals that we can share (the students own the intellectual property), but you might find our research proposal template useful: https://gradcoach.com/research-proposal-template/

Cheruiyot Moses Kipyegon

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

Glossary of research terms.

• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

This glossary is intended to assist you in understanding commonly used terms and concepts when reading, interpreting, and evaluating scholarly research. Also included are common words and phrases defined within the context of how they apply to research in the social and behavioral sciences.

• Acculturation -- refers to the process of adapting to another culture, particularly in reference to blending in with the majority population [e.g., an immigrant adopting American customs]. However, acculturation also implies that both cultures add something to one another, but still remain distinct groups unto themselves.
• Accuracy -- a term used in survey research to refer to the match between the target population and the sample.
• Affective Measures -- procedures or devices used to obtain quantified descriptions of an individual's feelings, emotional states, or dispositions.
• Aggregate -- a total created from smaller units. For instance, the population of a county is an aggregate of the populations of the cities, rural areas, etc. that comprise the county. As a verb, it refers to total data from smaller units into a large unit.
• Anonymity -- a research condition in which no one, including the researcher, knows the identities of research participants.
• Baseline -- a control measurement carried out before an experimental treatment.
• Behaviorism -- school of psychological thought concerned with the observable, tangible, objective facts of behavior, rather than with subjective phenomena such as thoughts, emotions, or impulses. Contemporary behaviorism also emphasizes the study of mental states such as feelings and fantasies to the extent that they can be directly observed and measured.
• Beliefs -- ideas, doctrines, tenets, etc. that are accepted as true on grounds which are not immediately susceptible to rigorous proof.
• Benchmarking -- systematically measuring and comparing the operations and outcomes of organizations, systems, processes, etc., against agreed upon "best-in-class" frames of reference.
• Bias -- a loss of balance and accuracy in the use of research methods. It can appear in research via the sampling frame, random sampling, or non-response. It can also occur at other stages in research, such as while interviewing, in the design of questions, or in the way data are analyzed and presented. Bias means that the research findings will not be representative of, or generalizable to, a wider population.
• Case Study -- the collection and presentation of detailed information about a particular participant or small group, frequently including data derived from the subjects themselves.
• Causal Hypothesis -- a statement hypothesizing that the independent variable affects the dependent variable in some way.
• Causal Relationship -- the relationship established that shows that an independent variable, and nothing else, causes a change in a dependent variable. It also establishes how much of a change is shown in the dependent variable.
• Causality -- the relation between cause and effect.
• Central Tendency -- any way of describing or characterizing typical, average, or common values in some distribution.
• Chi-square Analysis -- a common non-parametric statistical test which compares an expected proportion or ratio to an actual proportion or ratio.
• Claim -- a statement, similar to a hypothesis, which is made in response to the research question and that is affirmed with evidence based on research.
• Classification -- ordering of related phenomena into categories, groups, or systems according to characteristics or attributes.
• Cluster Analysis -- a method of statistical analysis where data that share a common trait are grouped together. The data is collected in a way that allows the data collector to group data according to certain characteristics.
• Cohort Analysis -- group by group analytic treatment of individuals having a statistical factor in common to each group. Group members share a particular characteristic [e.g., born in a given year] or a common experience [e.g., entering a college at a given time].
• Confidentiality -- a research condition in which no one except the researcher(s) knows the identities of the participants in a study. It refers to the treatment of information that a participant has disclosed to the researcher in a relationship of trust and with the expectation that it will not be revealed to others in ways that violate the original consent agreement, unless permission is granted by the participant.
• Confirmability Objectivity -- the findings of the study could be confirmed by another person conducting the same study.
• Construct -- refers to any of the following: something that exists theoretically but is not directly observable; a concept developed [constructed] for describing relations among phenomena or for other research purposes; or, a theoretical definition in which concepts are defined in terms of other concepts. For example, intelligence cannot be directly observed or measured; it is a construct.
• Construct Validity -- seeks an agreement between a theoretical concept and a specific measuring device, such as observation.
• Constructivism -- the idea that reality is socially constructed. It is the view that reality cannot be understood outside of the way humans interact and that the idea that knowledge is constructed, not discovered. Constructivists believe that learning is more active and self-directed than either behaviorism or cognitive theory would postulate.
• Content Analysis -- the systematic, objective, and quantitative description of the manifest or latent content of print or nonprint communications.
• Context Sensitivity -- awareness by a qualitative researcher of factors such as values and beliefs that influence cultural behaviors.
• Control Group -- the group in an experimental design that receives either no treatment or a different treatment from the experimental group. This group can thus be compared to the experimental group.
• Controlled Experiment -- an experimental design with two or more randomly selected groups [an experimental group and control group] in which the researcher controls or introduces the independent variable and measures the dependent variable at least two times [pre- and post-test measurements].
• Correlation -- a common statistical analysis, usually abbreviated as r, that measures the degree of relationship between pairs of interval variables in a sample. The range of correlation is from -1.00 to zero to +1.00. Also, a non-cause and effect relationship between two variables.
• Covariate -- a product of the correlation of two related variables times their standard deviations. Used in true experiments to measure the difference of treatment between them.
• Credibility -- a researcher's ability to demonstrate that the object of a study is accurately identified and described based on the way in which the study was conducted.
• Critical Theory -- an evaluative approach to social science research, associated with Germany's neo-Marxist “Frankfurt School,” that aims to criticize as well as analyze society, opposing the political orthodoxy of modern communism. Its goal is to promote human emancipatory forces and to expose ideas and systems that impede them.
• Data -- factual information [as measurements or statistics] used as a basis for reasoning, discussion, or calculation.
• Data Mining -- the process of analyzing data from different perspectives and summarizing it into useful information, often to discover patterns and/or systematic relationships among variables.
• Data Quality -- this is the degree to which the collected data [results of measurement or observation] meet the standards of quality to be considered valid [trustworthy] and  reliable [dependable].
• Deductive -- a form of reasoning in which conclusions are formulated about particulars from general or universal premises.
• Dependability -- being able to account for changes in the design of the study and the changing conditions surrounding what was studied.
• Dependent Variable -- a variable that varies due, at least in part, to the impact of the independent variable. In other words, its value “depends” on the value of the independent variable. For example, in the variables “gender” and “academic major,” academic major is the dependent variable, meaning that your major cannot determine whether you are male or female, but your gender might indirectly lead you to favor one major over another.
• Deviation -- the distance between the mean and a particular data point in a given distribution.
• Discourse Community -- a community of scholars and researchers in a given field who respond to and communicate to each other through published articles in the community's journals and presentations at conventions. All members of the discourse community adhere to certain conventions for the presentation of their theories and research.
• Discrete Variable -- a variable that is measured solely in whole units, such as, gender and number of siblings.
• Distribution -- the range of values of a particular variable.
• Effect Size -- the amount of change in a dependent variable that can be attributed to manipulations of the independent variable. A large effect size exists when the value of the dependent variable is strongly influenced by the independent variable. It is the mean difference on a variable between experimental and control groups divided by the standard deviation on that variable of the pooled groups or of the control group alone.
• Emancipatory Research -- research is conducted on and with people from marginalized groups or communities. It is led by a researcher or research team who is either an indigenous or external insider; is interpreted within intellectual frameworks of that group; and, is conducted largely for the purpose of empowering members of that community and improving services for them. It also engages members of the community as co-constructors or validators of knowledge.
• Empirical Research -- the process of developing systematized knowledge gained from observations that are formulated to support insights and generalizations about the phenomena being researched.
• Epistemology -- concerns knowledge construction; asks what constitutes knowledge and how knowledge is validated.
• Ethnography -- method to study groups and/or cultures over a period of time. The goal of this type of research is to comprehend the particular group/culture through immersion into the culture or group. Research is completed through various methods but, since the researcher is immersed within the group for an extended period of time, more detailed information is usually collected during the research.
• Expectancy Effect -- any unconscious or conscious cues that convey to the participant in a study how the researcher wants them to respond. Expecting someone to behave in a particular way has been shown to promote the expected behavior. Expectancy effects can be minimized by using standardized interactions with subjects, automated data-gathering methods, and double blind protocols.
• External Validity -- the extent to which the results of a study are generalizable or transferable.
• Factor Analysis -- a statistical test that explores relationships among data. The test explores which variables in a data set are most related to each other. In a carefully constructed survey, for example, factor analysis can yield information on patterns of responses, not simply data on a single response. Larger tendencies may then be interpreted, indicating behavior trends rather than simply responses to specific questions.
• Field Studies -- academic or other investigative studies undertaken in a natural setting, rather than in laboratories, classrooms, or other structured environments.
• Focus Groups -- small, roundtable discussion groups charged with examining specific topics or problems, including possible options or solutions. Focus groups usually consist of 4-12 participants, guided by moderators to keep the discussion flowing and to collect and report the results.
• Framework -- the structure and support that may be used as both the launching point and the on-going guidelines for investigating a research problem.
• Generalizability -- the extent to which research findings and conclusions conducted on a specific study to groups or situations can be applied to the population at large.
• Grey Literature -- research produced by organizations outside of commercial and academic publishing that publish materials, such as, working papers, research reports, and briefing papers.
• Grounded Theory -- practice of developing other theories that emerge from observing a group. Theories are grounded in the group's observable experiences, but researchers add their own insight into why those experiences exist.
• Group Behavior -- behaviors of a group as a whole, as well as the behavior of an individual as influenced by his or her membership in a group.
• Hypothesis -- a tentative explanation based on theory to predict a causal relationship between variables.
• Independent Variable -- the conditions of an experiment that are systematically manipulated by the researcher. A variable that is not impacted by the dependent variable, and that itself impacts the dependent variable. In the earlier example of "gender" and "academic major," (see Dependent Variable) gender is the independent variable.
• Individualism -- a theory or policy having primary regard for the liberty, rights, or independent actions of individuals.
• Inductive -- a form of reasoning in which a generalized conclusion is formulated from particular instances.
• Inductive Analysis -- a form of analysis based on inductive reasoning; a researcher using inductive analysis starts with answers, but formulates questions throughout the research process.
• Insiderness -- a concept in qualitative research that refers to the degree to which a researcher has access to and an understanding of persons, places, or things within a group or community based on being a member of that group or community.
• Internal Consistency -- the extent to which all questions or items assess the same characteristic, skill, or quality.
• Internal Validity -- the rigor with which the study was conducted [e.g., the study's design, the care taken to conduct measurements, and decisions concerning what was and was not measured]. It is also the extent to which the designers of a study have taken into account alternative explanations for any causal relationships they explore. In studies that do not explore causal relationships, only the first of these definitions should be considered when assessing internal validity.
• Life History -- a record of an event/events in a respondent's life told [written down, but increasingly audio or video recorded] by the respondent from his/her own perspective in his/her own words. A life history is different from a "research story" in that it covers a longer time span, perhaps a complete life, or a significant period in a life.
• Margin of Error -- the permittable or acceptable deviation from the target or a specific value. The allowance for slight error or miscalculation or changing circumstances in a study.
• Measurement -- process of obtaining a numerical description of the extent to which persons, organizations, or things possess specified characteristics.
• Meta-Analysis -- an analysis combining the results of several studies that address a set of related hypotheses.
• Methodology -- a theory or analysis of how research does and should proceed.
• Methods -- systematic approaches to the conduct of an operation or process. It includes steps of procedure, application of techniques, systems of reasoning or analysis, and the modes of inquiry employed by a discipline.
• Mixed-Methods -- a research approach that uses two or more methods from both the quantitative and qualitative research categories. It is also referred to as blended methods, combined methods, or methodological triangulation.
• Modeling -- the creation of a physical or computer analogy to understand a particular phenomenon. Modeling helps in estimating the relative magnitude of various factors involved in a phenomenon. A successful model can be shown to account for unexpected behavior that has been observed, to predict certain behaviors, which can then be tested experimentally, and to demonstrate that a given theory cannot account for certain phenomenon.
• Models -- representations of objects, principles, processes, or ideas often used for imitation or emulation.
• Naturalistic Observation -- observation of behaviors and events in natural settings without experimental manipulation or other forms of interference.
• Norm -- the norm in statistics is the average or usual performance. For example, students usually complete their high school graduation requirements when they are 18 years old. Even though some students graduate when they are younger or older, the norm is that any given student will graduate when he or she is 18 years old.
• Null Hypothesis -- the proposition, to be tested statistically, that the experimental intervention has "no effect," meaning that the treatment and control groups will not differ as a result of the intervention. Investigators usually hope that the data will demonstrate some effect from the intervention, thus allowing the investigator to reject the null hypothesis.
• Ontology -- a discipline of philosophy that explores the science of what is, the kinds and structures of objects, properties, events, processes, and relations in every area of reality.
• Panel Study -- a longitudinal study in which a group of individuals is interviewed at intervals over a period of time.
• Participant -- individuals whose physiological and/or behavioral characteristics and responses are the object of study in a research project.
• Peer-Review -- the process in which the author of a book, article, or other type of publication submits his or her work to experts in the field for critical evaluation, usually prior to publication. This is standard procedure in publishing scholarly research.
• Phenomenology -- a qualitative research approach concerned with understanding certain group behaviors from that group's point of view.
• Philosophy -- critical examination of the grounds for fundamental beliefs and analysis of the basic concepts, doctrines, or practices that express such beliefs.
• Phonology -- the study of the ways in which speech sounds form systems and patterns in language.
• Policy -- governing principles that serve as guidelines or rules for decision making and action in a given area.
• Policy Analysis -- systematic study of the nature, rationale, cost, impact, effectiveness, implications, etc., of existing or alternative policies, using the theories and methodologies of relevant social science disciplines.
• Population -- the target group under investigation. The population is the entire set under consideration. Samples are drawn from populations.
• Position Papers -- statements of official or organizational viewpoints, often recommending a particular course of action or response to a situation.
• Positivism -- a doctrine in the philosophy of science, positivism argues that science can only deal with observable entities known directly to experience. The positivist aims to construct general laws, or theories, which express relationships between phenomena. Observation and experiment is used to show whether the phenomena fit the theory.
• Predictive Measurement -- use of tests, inventories, or other measures to determine or estimate future events, conditions, outcomes, or trends.
• Principal Investigator -- the scientist or scholar with primary responsibility for the design and conduct of a research project.
• Probability -- the chance that a phenomenon will occur randomly. As a statistical measure, it is shown as p [the "p" factor].
• Questionnaire -- structured sets of questions on specified subjects that are used to gather information, attitudes, or opinions.
• Random Sampling -- a process used in research to draw a sample of a population strictly by chance, yielding no discernible pattern beyond chance. Random sampling can be accomplished by first numbering the population, then selecting the sample according to a table of random numbers or using a random-number computer generator. The sample is said to be random because there is no regular or discernible pattern or order. Random sample selection is used under the assumption that sufficiently large samples assigned randomly will exhibit a distribution comparable to that of the population from which the sample is drawn. The random assignment of participants increases the probability that differences observed between participant groups are the result of the experimental intervention.
• Reliability -- the degree to which a measure yields consistent results. If the measuring instrument [e.g., survey] is reliable, then administering it to similar groups would yield similar results. Reliability is a prerequisite for validity. An unreliable indicator cannot produce trustworthy results.
• Representative Sample -- sample in which the participants closely match the characteristics of the population, and thus, all segments of the population are represented in the sample. A representative sample allows results to be generalized from the sample to the population.
• Rigor -- degree to which research methods are scrupulously and meticulously carried out in order to recognize important influences occurring in an experimental study.
• Sample -- the population researched in a particular study. Usually, attempts are made to select a "sample population" that is considered representative of groups of people to whom results will be generalized or transferred. In studies that use inferential statistics to analyze results or which are designed to be generalizable, sample size is critical, generally the larger the number in the sample, the higher the likelihood of a representative distribution of the population.
• Sampling Error -- the degree to which the results from the sample deviate from those that would be obtained from the entire population, because of random error in the selection of respondent and the corresponding reduction in reliability.
• Saturation -- a situation in which data analysis begins to reveal repetition and redundancy and when new data tend to confirm existing findings rather than expand upon them.
• Semantics -- the relationship between symbols and meaning in a linguistic system. Also, the cuing system that connects what is written in the text to what is stored in the reader's prior knowledge.
• Social Theories -- theories about the structure, organization, and functioning of human societies.
• Sociolinguistics -- the study of language in society and, more specifically, the study of language varieties, their functions, and their speakers.
• Standard Deviation -- a measure of variation that indicates the typical distance between the scores of a distribution and the mean; it is determined by taking the square root of the average of the squared deviations in a given distribution. It can be used to indicate the proportion of data within certain ranges of scale values when the distribution conforms closely to the normal curve.
• Statistical Analysis -- application of statistical processes and theory to the compilation, presentation, discussion, and interpretation of numerical data.
• Statistical Bias -- characteristics of an experimental or sampling design, or the mathematical treatment of data, that systematically affects the results of a study so as to produce incorrect, unjustified, or inappropriate inferences or conclusions.
• Statistical Significance -- the probability that the difference between the outcomes of the control and experimental group are great enough that it is unlikely due solely to chance. The probability that the null hypothesis can be rejected at a predetermined significance level [0.05 or 0.01].
• Statistical Tests -- researchers use statistical tests to make quantitative decisions about whether a study's data indicate a significant effect from the intervention and allow the researcher to reject the null hypothesis. That is, statistical tests show whether the differences between the outcomes of the control and experimental groups are great enough to be statistically significant. If differences are found to be statistically significant, it means that the probability [likelihood] that these differences occurred solely due to chance is relatively low. Most researchers agree that a significance value of .05 or less [i.e., there is a 95% probability that the differences are real] sufficiently determines significance.
• Subcultures -- ethnic, regional, economic, or social groups exhibiting characteristic patterns of behavior sufficient to distinguish them from the larger society to which they belong.
• Testing -- the act of gathering and processing information about individuals' ability, skill, understanding, or knowledge under controlled conditions.
• Theory -- a general explanation about a specific behavior or set of events that is based on known principles and serves to organize related events in a meaningful way. A theory is not as specific as a hypothesis.
• Treatment -- the stimulus given to a dependent variable.
• Trend Samples -- method of sampling different groups of people at different points in time from the same population.
• Triangulation -- a multi-method or pluralistic approach, using different methods in order to focus on the research topic from different viewpoints and to produce a multi-faceted set of data. Also used to check the validity of findings from any one method.
• Unit of Analysis -- the basic observable entity or phenomenon being analyzed by a study and for which data are collected in the form of variables.
• Validity -- the degree to which a study accurately reflects or assesses the specific concept that the researcher is attempting to measure. A method can be reliable, consistently measuring the same thing, but not valid.
• Variable -- any characteristic or trait that can vary from one person to another [race, gender, academic major] or for one person over time [age, political beliefs].
• Weighted Scores -- scores in which the components are modified by different multipliers to reflect their relative importance.
• White Paper -- an authoritative report that often states the position or philosophy about a social, political, or other subject, or a general explanation of an architecture, framework, or product technology written by a group of researchers. A white paper seeks to contain unbiased information and analysis regarding a business or policy problem that the researchers may be facing.

Elliot, Mark, Fairweather, Ian, Olsen, Wendy Kay, and Pampaka, Maria. A Dictionary of Social Research Methods. Oxford, UK: Oxford University Press, 2016; Free Social Science Dictionary. Socialsciencedictionary.com [2008]. Glossary. Institutional Review Board. Colorado College; Glossary of Key Terms. Writing@CSU. Colorado State University; Glossary A-Z. Education.com; Glossary of Research Terms. Research Mindedness Virtual Learning Resource. Centre for Human Servive Technology. University of Southampton; Miller, Robert L. and Brewer, John D. The A-Z of Social Research: A Dictionary of Key Social Science Research Concepts London: SAGE, 2003; Jupp, Victor. The SAGE Dictionary of Social and Cultural Research Methods . London: Sage, 2006.

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What is a research proposal?

A research proposal is a type of text which maps out a proposed central research problem or question and a suggested approach to its investigation.

In many universities, including RMIT, the research proposal is a formal requirement. It is central to achieving your first milestone: your Confirmation of Candidature. The research proposal is useful for both you and the University: it gives you the opportunity to get valuable feedback about your intended research aims, objectives and design. It also confirms that your proposed research is worth doing, which puts you on track for a successful candidature supported by your School and the University. 

Although there may be specific School or disciplinary requirements that you need to be aware of, all research proposals address the following central themes:

• what   you propose to research
• why   the topic needs to be researched
• how  you plan to research it.

Purpose and audience

Before venturing into writing a research purposal, it is important to think about the  purpose  and  audience of this type of text.  Spend a moment or two to reflect on what these might be.

What do you think is the purpose of your research proposal and who is your audience?

The purpose of your research proposal is:

1. To allow experienced researchers (your supervisors and their peers) to assess whether

• the research question or problem is viable (that is, answers or solutions are possible)
• the research is worth doing in terms of its contribution to the field of study and benefits to stakeholders
• the scope is appropriate to the degree (Masters or PhD)
• you’ve understood the relevant key literature and identified the gap for your research
• you’ve chosen an appropriate methodological approach.

2. To help you clarify and focus on what you want to do, why you want to do it, and how you’ll do it. The research proposal helps you position yourself as a researcher in your field. It will also allow you to:

• systematically think through your proposed research, argue for its significance and identify the scope
• show a critical understanding of the scholarly field around your proposed research
• show the gap in the literature that your research will address
• justify your proposed research design
• identify all tasks that need to be done through a realistic timetable
• anticipate potential problems
• hone organisational skills that you will need for your research
• become familiar with relevant search engines and databases
• develop skills in research writing.

The main audience for your research proposal is your reviewers. Universities usually assign a panel of reviewers to which you need to submit your research proposal. Often this is within the first year of study for PhD candidates, and within the first six months for Masters by Research candidates.

Your reviewers may have a strong disciplinary understanding of the area of your proposed research, but depending on your specialisation, they may not. It is therefore important to create a clear context, rationale and framework for your proposed research. Limit jargon and specialist terminology so that non-specialists can comprehend it. You need to convince the reviewers that your proposed research is worth doing and that you will be able to effectively ‘interrogate’ your research questions or address the research problems through your chosen research design.

Your review panel will expect you to demonstrate:

• a clearly defined and feasible research project
• a clearly explained rationale for your research
• evidence that your research will make an original contribution through a critical review of the literature
• written skills appropriate to graduate research study.

Research and Writing Skills for Academic and Graduate Researchers Copyright © 2022 by RMIT University is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

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

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

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

Introduction

Literature review.

• Research design

Reference list

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

Table of contents

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

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

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

Research proposal length

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

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

Download our research proposal template

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

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

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

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

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

Your introduction should:

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

To guide your introduction , include information about:

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

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

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

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

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

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

For example, your results might have implications for:

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

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

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

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

Download our research schedule template

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

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

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

To determine your budget, think about:

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

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

Methodology

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

 Statistics

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

Research bias

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

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

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

I will compare …

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

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

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

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

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

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

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

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

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

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How to write a research proposal?

Department of Anaesthesiology, Bangalore Medical College and Research Institute, Bengaluru, Karnataka, India

Devika Rani Duggappa

Writing the proposal of a research work in the present era is a challenging task due to the constantly evolving trends in the qualitative research design and the need to incorporate medical advances into the methodology. The proposal is a detailed plan or ‘blueprint’ for the intended study, and once it is completed, the research project should flow smoothly. Even today, many of the proposals at post-graduate evaluation committees and application proposals for funding are substandard. A search was conducted with keywords such as research proposal, writing proposal and qualitative using search engines, namely, PubMed and Google Scholar, and an attempt has been made to provide broad guidelines for writing a scientifically appropriate research proposal.

INTRODUCTION

A clean, well-thought-out proposal forms the backbone for the research itself and hence becomes the most important step in the process of conduct of research.[ 1 ] The objective of preparing a research proposal would be to obtain approvals from various committees including ethics committee [details under ‘Research methodology II’ section [ Table 1 ] in this issue of IJA) and to request for grants. However, there are very few universally accepted guidelines for preparation of a good quality research proposal. A search was performed with keywords such as research proposal, funding, qualitative and writing proposals using search engines, namely, PubMed, Google Scholar and Scopus.

Five ‘C’s while writing a literature review

BASIC REQUIREMENTS OF A RESEARCH PROPOSAL

A proposal needs to show how your work fits into what is already known about the topic and what new paradigm will it add to the literature, while specifying the question that the research will answer, establishing its significance, and the implications of the answer.[ 2 ] The proposal must be capable of convincing the evaluation committee about the credibility, achievability, practicality and reproducibility (repeatability) of the research design.[ 3 ] Four categories of audience with different expectations may be present in the evaluation committees, namely academic colleagues, policy-makers, practitioners and lay audiences who evaluate the research proposal. Tips for preparation of a good research proposal include; ‘be practical, be persuasive, make broader links, aim for crystal clarity and plan before you write’. A researcher must be balanced, with a realistic understanding of what can be achieved. Being persuasive implies that researcher must be able to convince other researchers, research funding agencies, educational institutions and supervisors that the research is worth getting approval. The aim of the researcher should be clearly stated in simple language that describes the research in a way that non-specialists can comprehend, without use of jargons. The proposal must not only demonstrate that it is based on an intelligent understanding of the existing literature but also show that the writer has thought about the time needed to conduct each stage of the research.[ 4 , 5 ]

CONTENTS OF A RESEARCH PROPOSAL

The contents or formats of a research proposal vary depending on the requirements of evaluation committee and are generally provided by the evaluation committee or the institution.

In general, a cover page should contain the (i) title of the proposal, (ii) name and affiliation of the researcher (principal investigator) and co-investigators, (iii) institutional affiliation (degree of the investigator and the name of institution where the study will be performed), details of contact such as phone numbers, E-mail id's and lines for signatures of investigators.

The main contents of the proposal may be presented under the following headings: (i) introduction, (ii) review of literature, (iii) aims and objectives, (iv) research design and methods, (v) ethical considerations, (vi) budget, (vii) appendices and (viii) citations.[ 4 ]

Introduction

It is also sometimes termed as ‘need for study’ or ‘abstract’. Introduction is an initial pitch of an idea; it sets the scene and puts the research in context.[ 6 ] The introduction should be designed to create interest in the reader about the topic and proposal. It should convey to the reader, what you want to do, what necessitates the study and your passion for the topic.[ 7 ] Some questions that can be used to assess the significance of the study are: (i) Who has an interest in the domain of inquiry? (ii) What do we already know about the topic? (iii) What has not been answered adequately in previous research and practice? (iv) How will this research add to knowledge, practice and policy in this area? Some of the evaluation committees, expect the last two questions, elaborated under a separate heading of ‘background and significance’.[ 8 ] Introduction should also contain the hypothesis behind the research design. If hypothesis cannot be constructed, the line of inquiry to be used in the research must be indicated.

Review of literature

It refers to all sources of scientific evidence pertaining to the topic in interest. In the present era of digitalisation and easy accessibility, there is an enormous amount of relevant data available, making it a challenge for the researcher to include all of it in his/her review.[ 9 ] It is crucial to structure this section intelligently so that the reader can grasp the argument related to your study in relation to that of other researchers, while still demonstrating to your readers that your work is original and innovative. It is preferable to summarise each article in a paragraph, highlighting the details pertinent to the topic of interest. The progression of review can move from the more general to the more focused studies, or a historical progression can be used to develop the story, without making it exhaustive.[ 1 ] Literature should include supporting data, disagreements and controversies. Five ‘C's may be kept in mind while writing a literature review[ 10 ] [ Table 1 ].

Aims and objectives

The research purpose (or goal or aim) gives a broad indication of what the researcher wishes to achieve in the research. The hypothesis to be tested can be the aim of the study. The objectives related to parameters or tools used to achieve the aim are generally categorised as primary and secondary objectives.

Research design and method

The objective here is to convince the reader that the overall research design and methods of analysis will correctly address the research problem and to impress upon the reader that the methodology/sources chosen are appropriate for the specific topic. It should be unmistakably tied to the specific aims of your study.

In this section, the methods and sources used to conduct the research must be discussed, including specific references to sites, databases, key texts or authors that will be indispensable to the project. There should be specific mention about the methodological approaches to be undertaken to gather information, about the techniques to be used to analyse it and about the tests of external validity to which researcher is committed.[ 10 , 11 ]

The components of this section include the following:[ 4 ]

Population and sample

Population refers to all the elements (individuals, objects or substances) that meet certain criteria for inclusion in a given universe,[ 12 ] and sample refers to subset of population which meets the inclusion criteria for enrolment into the study. The inclusion and exclusion criteria should be clearly defined. The details pertaining to sample size are discussed in the article “Sample size calculation: Basic priniciples” published in this issue of IJA.

Data collection

The researcher is expected to give a detailed account of the methodology adopted for collection of data, which include the time frame required for the research. The methodology should be tested for its validity and ensure that, in pursuit of achieving the results, the participant's life is not jeopardised. The author should anticipate and acknowledge any potential barrier and pitfall in carrying out the research design and explain plans to address them, thereby avoiding lacunae due to incomplete data collection. If the researcher is planning to acquire data through interviews or questionnaires, copy of the questions used for the same should be attached as an annexure with the proposal.

Rigor (soundness of the research)

This addresses the strength of the research with respect to its neutrality, consistency and applicability. Rigor must be reflected throughout the proposal.

It refers to the robustness of a research method against bias. The author should convey the measures taken to avoid bias, viz. blinding and randomisation, in an elaborate way, thus ensuring that the result obtained from the adopted method is purely as chance and not influenced by other confounding variables.

Consistency

Consistency considers whether the findings will be consistent if the inquiry was replicated with the same participants and in a similar context. This can be achieved by adopting standard and universally accepted methods and scales.

Applicability

Applicability refers to the degree to which the findings can be applied to different contexts and groups.[ 13 ]

Data analysis

This section deals with the reduction and reconstruction of data and its analysis including sample size calculation. The researcher is expected to explain the steps adopted for coding and sorting the data obtained. Various tests to be used to analyse the data for its robustness, significance should be clearly stated. Author should also mention the names of statistician and suitable software which will be used in due course of data analysis and their contribution to data analysis and sample calculation.[ 9 ]

Ethical considerations

Medical research introduces special moral and ethical problems that are not usually encountered by other researchers during data collection, and hence, the researcher should take special care in ensuring that ethical standards are met. Ethical considerations refer to the protection of the participants' rights (right to self-determination, right to privacy, right to autonomy and confidentiality, right to fair treatment and right to protection from discomfort and harm), obtaining informed consent and the institutional review process (ethical approval). The researcher needs to provide adequate information on each of these aspects.

Informed consent needs to be obtained from the participants (details discussed in further chapters), as well as the research site and the relevant authorities.

When the researcher prepares a research budget, he/she should predict and cost all aspects of the research and then add an additional allowance for unpredictable disasters, delays and rising costs. All items in the budget should be justified.

Appendices are documents that support the proposal and application. The appendices will be specific for each proposal but documents that are usually required include informed consent form, supporting documents, questionnaires, measurement tools and patient information of the study in layman's language.

As with any scholarly research paper, you must cite the sources you used in composing your proposal. Although the words ‘references and bibliography’ are different, they are used interchangeably. It refers to all references cited in the research proposal.

Successful, qualitative research proposals should communicate the researcher's knowledge of the field and method and convey the emergent nature of the qualitative design. The proposal should follow a discernible logic from the introduction to presentation of the appendices.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

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What is a research proposal?

A research proposal is a concise and coherent summary of your proposed research.

Your research proposal should set out the central issues or questions that you intend to address. It should outline the general area of study within which your research falls, referring to the current state of knowledge and any recent debates on the topic, as well as demonstrate the originality of your proposed research.

The proposal also gives you an opportunity to show that you have the aptitude for postgraduate level research by demonstrating that you have the ability to communicate complex ideas clearly, concisely and critically.

In addition, the proposal also helps us to match your research interest with an appropriate supervisor. The proposal is a key part of your application, on which potential supervisors will decide if your research is something they can support. 

Read our advice on research proposal preparation

Additional guidance for applicants from the USA

Writing a Research Proposal

Parts of a research proposal, prosana model, introduction, research question, methodology.

• Structure of a Research Proposal
• Common Proposal Writing Mistakes
• Proposal Writing Resources

A research proposal's purpose is to capture the evaluator's attention, demonstrate the study's potential benefits, and prove that it is a logical and consistent approach (Van Ekelenburg, 2010).  To ensure that your research proposal contains these elements, there are several aspects to include in your proposal (Al-Riyami, 2008):

• Objective(s)
• Variables (independent and dependent)
• Research Question and/or hypothesis

Details about what to include in each element are included in the boxes below.  Depending on the topic of your study, some parts may not apply to your proposal.  You can also watch the video below for a brief overview about writing a successful research proposal.

Van Ekelenburg (2010) uses the PROSANA Model to guide researchers in developing rationale and justification for their research projects.  It is an acronym that connects the problem, solution, and benefits of a particular research project.  It is an easy way to remember the critical parts of a research proposal and how they relate to one another.  It includes the following letters (Van Ekelenburg, 2010):

• Problem: Describing the main problem that the researcher is trying to solve.
• Root causes: Describing what is causing the problem.  Why is the topic an issue?
• fOcus: Narrowing down one of the underlying causes on which the researcher will focus for their research project.
• Solutions: Listing potential solutions or approaches to fix to the problem.  There could be more than one.
• Approach: Selecting the solution that the researcher will want to focus on.
• Novelty: Describing how the solution will address or solve the problem.
• Arguments: Explaining how the proposed solution will benefit the problem.

Research proposal titles should be concise and to the point, but informative.  The title of your proposal may be different from the title of your final research project, but that is completely normal!  Your findings may help you come up with a title that is more fitting for the final project.  Characteristics of good proposal titles are (Al-Riyami, 2008):

• Catchy: It catches the reader's attention by peaking their interest.
• Positive: It spins your project in a positive way towards the reader.
• Transparent: It identifies the independent and dependent variables.

It is also common for proposal titles to be very similar to your research question, hypothesis, or thesis statement (Locke et al., 2007).

An abstract is a brief summary (about 300 words) of the study you are proposing.  It includes the following elements (Al-Riyami, 2008):

• Your primary research question(s).
• Hypothesis or main argument.
• Method you will use to complete the study.  This may include the design, sample population, or measuring instruments that you plan to use.

Our guide on writing summaries may help you with this step.

The purpose of the introduction is to give readers background information about your topic.  it gives the readers a basic understanding of your topic so that they can further understand the significance of your proposal.  A good introduction will explain (Al-Riyami, 2008):

• How it relates to other research done on the topic
• Why your research is significant to the field
• The relevance of your study

Your research objectives are the desired outcomes that you will achieve from the research project.  Depending on your research design, these may be generic or very specific.  You may also have more than one objective (Al-Riyami, 2008).

• General objectives are what the research project will accomplish
• Specific objectives relate to the research questions that the researcher aims to answer through the study.

Be careful not to have too many objectives in your proposal, as having too many can make your project lose focus.  Plus, it may not be possible to achieve several objectives in one study.

This section describes the different types of variables that you plan to have in your study and how you will measure them.  According to Al-Riyami (2008), there are four types of research variables:

• Independent:  The person, object, or idea that is manipulated by the researcher.
• Dependent:  The person, object, or idea whose changes are dependent upon the independent variable.  Typically, it is the item that the researcher is measuring for the study.
• Confounding/Intervening:  Factors that may influence the effect of the independent variable on the dependent variable.  These include physical and mental barriers.  Not every study will have intervening variables, but they should be studied if applicable.
• Background:   Factors that are relevant to the study's data and how it can be generalized.  Examples include demographic information such as age, sex, and ethnicity.

Your research proposal should describe each of your variables and how they relate to one another.  Depending on your study, you may not have all four types of variables present.  However, there will always be an independent and dependent variable.

A research question is the main piece of your research project because it explains what your study will discover to the reader.  It is the question that fuels the study, so it is important for it to be precise and unique.  You do not want it to be too broad, and it should identify a relationship between two variables (an independent and a dependent) (Al-Riyami, 2008).  There are six types of research questions (Academic Writer, n.d.):

• Example: "Do people get nervous before speaking in front of an audience?"
• Example: "What are the study habits of college freshmen at Tiffin University?"
• Example: "What primary traits create a successful romantic relationship?"
• Example: "Is there a relationship between a child's performance in school and their parents' socioeconomic status?"
• Example: "Are high school seniors more motivated than high school freshmen?"
• Example: "Do news media outlets impact a person's political opinions?"

For more information on the different types of research questions, you can view the "Research Questions and Hypotheses" tutorial on Academic Writer, located below.  If you are unfamiliar with Academic Writer, we also have a tutorial on using the database located below.

Compose papers in pre-formatted APA templates. Manage references in forms that help craft APA citations. Learn the rules of APA style through tutorials and practice quizzes.

Academic Writer will continue to use the 6th edition guidelines until August 2020. A preview of the 7th edition is available in the footer of the resource's site. Previously known as APA Style Central.

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If you know enough about your research topic that you believe a particular outcome may occur as a result of the study, you can include a hypothesis (thesis statement) in your proposal.  A hypothesis is a prediction that you believe will be the outcome of your study.  It explains what you think the relationship will be between the independent and dependent variable (Al-Riyami, 2008).  It is ok if the hypothesis in your proposal turns out to be incorrect, because it is only a prediction!  If you are writing a proposal in the humanities, you may be writing a thesis statement instead of a hypothesis.  A thesis presents the main argument of your research project and leads to corresponding evidence to support your argument.

Hypotheses vs. Theories

Hypotheses are different from theories in that theories represent general principles and sets of rules that explain different phenomena.  They typically represent large areas of study because they are applicable to anything in a particular field.  Hypotheses focus on specific areas within a field and are educated guesses, meaning that they have the potential to be proven wrong (Academic Writer, n.d.).  Because of this, hypotheses can also be formed from theories.

For more information on writing effective thesis statements, you can view our guide on writing thesis statements below.

In a research proposal, you must thoroughly explain how you will conduct your study.  This includes things such as (Al-Riyami, 2008):

• Research design:  What research approach will your study take?  Will it be quantitative or qualitative?
• Research subjects/participants:  Who will be participating in your study?  Does your study require human participants?  How will you determine who to study?
• Sample size:  How many participants will your study require?  If you are not using human participants, how much of the sample will you be studying?
• Timeline:  A proposed list of the general tasks and events that you plan to complete the study.  This will include a time frame for each task/event and the order in which they will be completed.
• Interventions:  If you plan on using anything on human participants for the study, you must include information it here.  This is especially important if you plan on using any substances on human subjects.
• Ethical issues:  Are there any potential ethical issues surrounding this study?
• Potential limitations:  Are there any limitations that could skew the data and findings from your study?
• Appendixes:  If you need to present any consent forms, interview questions, surveys, questionnaires, or other items that will be used in your study, you should include samples of each item with an appendix to reference them.  If you are using a copyrighted document, you may need written permission from the original creator to use it in your study.  A copy of the written permission should be included in your proposal.
• Setting:  Where will you be conducting the study?
• Study instruments:  What measuring tools or computer software will you be using to collect data?  How will you collect the data?
• How you will analyze the data:  What strategies or tools will you use to analyze the data you collect?
• Quality control:  Will you have precautions in place to ensure that the study is conducted consistently and that outside factors will not skew the data?
• Budget:  What type of funding will you need for your study?  This will include the funds needed to afford measuring tools, software, etc.
• How you will share the study's findings:  What will you plan to do with the findings?
• Significance of the study: How will your study expand on existing knowledge of the subject area?

For more information on research methodologies, you can view our guide on research methods and methodologies below.

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11.2 Steps in Developing a Research Proposal

Learning objectives.

• Identify the steps in developing a research proposal.
• Choose a topic and formulate a research question and working thesis.
• Develop a research proposal.

Writing a good research paper takes time, thought, and effort. Although this assignment is challenging, it is manageable. Focusing on one step at a time will help you develop a thoughtful, informative, well-supported research paper.

Your first step is to choose a topic and then to develop research questions, a working thesis, and a written research proposal. Set aside adequate time for this part of the process. Fully exploring ideas will help you build a solid foundation for your paper.

Choosing a Topic

When you choose a topic for a research paper, you are making a major commitment. Your choice will help determine whether you enjoy the lengthy process of research and writing—and whether your final paper fulfills the assignment requirements. If you choose your topic hastily, you may later find it difficult to work with your topic. By taking your time and choosing carefully, you can ensure that this assignment is not only challenging but also rewarding.

Writers understand the importance of choosing a topic that fulfills the assignment requirements and fits the assignment’s purpose and audience. (For more information about purpose and audience, see Chapter 6 “Writing Paragraphs: Separating Ideas and Shaping Content” .) Choosing a topic that interests you is also crucial. You instructor may provide a list of suggested topics or ask that you develop a topic on your own. In either case, try to identify topics that genuinely interest you.

After identifying potential topic ideas, you will need to evaluate your ideas and choose one topic to pursue. Will you be able to find enough information about the topic? Can you develop a paper about this topic that presents and supports your original ideas? Is the topic too broad or too narrow for the scope of the assignment? If so, can you modify it so it is more manageable? You will ask these questions during this preliminary phase of the research process.

Identifying Potential Topics

Sometimes, your instructor may provide a list of suggested topics. If so, you may benefit from identifying several possibilities before committing to one idea. It is important to know how to narrow down your ideas into a concise, manageable thesis. You may also use the list as a starting point to help you identify additional, related topics. Discussing your ideas with your instructor will help ensure that you choose a manageable topic that fits the requirements of the assignment.

In this chapter, you will follow a writer named Jorge, who is studying health care administration, as he prepares a research paper. You will also plan, research, and draft your own research paper.

Jorge was assigned to write a research paper on health and the media for an introductory course in health care. Although a general topic was selected for the students, Jorge had to decide which specific issues interested him. He brainstormed a list of possibilities.

If you are writing a research paper for a specialized course, look back through your notes and course activities. Identify reading assignments and class discussions that especially engaged you. Doing so can help you identify topics to pursue.

• Health Maintenance Organizations (HMOs) in the news
• Sexual education programs
• Hollywood and eating disorders
• Americans’ access to public health information
• Media portrayal of health care reform bill
• Depictions of drugs on television
• The effect of the Internet on mental health
• Popularized diets (such as low-carbohydrate diets)
• Fear of pandemics (bird flu, HINI, SARS)
• Electronic entertainment and obesity
• Advertisements for prescription drugs
• Public education and disease prevention

Set a timer for five minutes. Use brainstorming or idea mapping to create a list of topics you would be interested in researching for a paper about the influence of the Internet on social networking. Do you closely follow the media coverage of a particular website, such as Twitter? Would you like to learn more about a certain industry, such as online dating? Which social networking sites do you and your friends use? List as many ideas related to this topic as you can.

Narrowing Your Topic

Once you have a list of potential topics, you will need to choose one as the focus of your essay. You will also need to narrow your topic. Most writers find that the topics they listed during brainstorming or idea mapping are broad—too broad for the scope of the assignment. Working with an overly broad topic, such as sexual education programs or popularized diets, can be frustrating and overwhelming. Each topic has so many facets that it would be impossible to cover them all in a college research paper. However, more specific choices, such as the pros and cons of sexual education in kids’ television programs or the physical effects of the South Beach diet, are specific enough to write about without being too narrow to sustain an entire research paper.

A good research paper provides focused, in-depth information and analysis. If your topic is too broad, you will find it difficult to do more than skim the surface when you research it and write about it. Narrowing your focus is essential to making your topic manageable. To narrow your focus, explore your topic in writing, conduct preliminary research, and discuss both the topic and the research with others.

Exploring Your Topic in Writing

“How am I supposed to narrow my topic when I haven’t even begun researching yet?” In fact, you may already know more than you realize. Review your list and identify your top two or three topics. Set aside some time to explore each one through freewriting. (For more information about freewriting, see Chapter 8 “The Writing Process: How Do I Begin?” .) Simply taking the time to focus on your topic may yield fresh angles.

Jorge knew that he was especially interested in the topic of diet fads, but he also knew that it was much too broad for his assignment. He used freewriting to explore his thoughts so he could narrow his topic. Read Jorge’s ideas.

Conducting Preliminary Research

Another way writers may focus a topic is to conduct preliminary research . Like freewriting, exploratory reading can help you identify interesting angles. Surfing the web and browsing through newspaper and magazine articles are good ways to start. Find out what people are saying about your topic on blogs and online discussion groups. Discussing your topic with others can also inspire you. Talk about your ideas with your classmates, your friends, or your instructor.

Jorge’s freewriting exercise helped him realize that the assigned topic of health and the media intersected with a few of his interests—diet, nutrition, and obesity. Preliminary online research and discussions with his classmates strengthened his impression that many people are confused or misled by media coverage of these subjects.

Jorge decided to focus his paper on a topic that had garnered a great deal of media attention—low-carbohydrate diets. He wanted to find out whether low-carbohydrate diets were as effective as their proponents claimed.

Writing at Work

At work, you may need to research a topic quickly to find general information. This information can be useful in understanding trends in a given industry or generating competition. For example, a company may research a competitor’s prices and use the information when pricing their own product. You may find it useful to skim a variety of reliable sources and take notes on your findings.

The reliability of online sources varies greatly. In this exploratory phase of your research, you do not need to evaluate sources as closely as you will later. However, use common sense as you refine your paper topic. If you read a fascinating blog comment that gives you a new idea for your paper, be sure to check out other, more reliable sources as well to make sure the idea is worth pursuing.

Review the list of topics you created in Note 11.18 “Exercise 1” and identify two or three topics you would like to explore further. For each of these topics, spend five to ten minutes writing about the topic without stopping. Then review your writing to identify possible areas of focus.

Set aside time to conduct preliminary research about your potential topics. Then choose a topic to pursue for your research paper.

Collaboration

Please share your topic list with a classmate. Select one or two topics on his or her list that you would like to learn more about and return it to him or her. Discuss why you found the topics interesting, and learn which of your topics your classmate selected and why.

A Plan for Research

Your freewriting and preliminary research have helped you choose a focused, manageable topic for your research paper. To work with your topic successfully, you will need to determine what exactly you want to learn about it—and later, what you want to say about it. Before you begin conducting in-depth research, you will further define your focus by developing a research question , a working thesis, and a research proposal.

Formulating a Research Question

In forming a research question, you are setting a goal for your research. Your main research question should be substantial enough to form the guiding principle of your paper—but focused enough to guide your research. A strong research question requires you not only to find information but also to put together different pieces of information, interpret and analyze them, and figure out what you think. As you consider potential research questions, ask yourself whether they would be too hard or too easy to answer.

To determine your research question, review the freewriting you completed earlier. Skim through books, articles, and websites and list the questions you have. (You may wish to use the 5WH strategy to help you formulate questions. See Chapter 8 “The Writing Process: How Do I Begin?” for more information about 5WH questions.) Include simple, factual questions and more complex questions that would require analysis and interpretation. Determine your main question—the primary focus of your paper—and several subquestions that you will need to research to answer your main question.

Here are the research questions Jorge will use to focus his research. Notice that his main research question has no obvious, straightforward answer. Jorge will need to research his subquestions, which address narrower topics, to answer his main question.

Using the topic you selected in Note 11.24 “Exercise 2” , write your main research question and at least four to five subquestions. Check that your main research question is appropriately complex for your assignment.

Constructing a Working ThesIs

A working thesis concisely states a writer’s initial answer to the main research question. It does not merely state a fact or present a subjective opinion. Instead, it expresses a debatable idea or claim that you hope to prove through additional research. Your working thesis is called a working thesis for a reason—it is subject to change. As you learn more about your topic, you may change your thinking in light of your research findings. Let your working thesis serve as a guide to your research, but do not be afraid to modify it based on what you learn.

Jorge began his research with a strong point of view based on his preliminary writing and research. Read his working thesis statement, which presents the point he will argue. Notice how it states Jorge’s tentative answer to his research question.

One way to determine your working thesis is to consider how you would complete sentences such as I believe or My opinion is . However, keep in mind that academic writing generally does not use first-person pronouns. These statements are useful starting points, but formal research papers use an objective voice.

Write a working thesis statement that presents your preliminary answer to the research question you wrote in Note 11.27 “Exercise 3” . Check that your working thesis statement presents an idea or claim that could be supported or refuted by evidence from research.

Creating a Research Proposal

A research proposal is a brief document—no more than one typed page—that summarizes the preliminary work you have completed. Your purpose in writing it is to formalize your plan for research and present it to your instructor for feedback. In your research proposal, you will present your main research question, related subquestions, and working thesis. You will also briefly discuss the value of researching this topic and indicate how you plan to gather information.

When Jorge began drafting his research proposal, he realized that he had already created most of the pieces he needed. However, he knew he also had to explain how his research would be relevant to other future health care professionals. In addition, he wanted to form a general plan for doing the research and identifying potentially useful sources. Read Jorge’s research proposal.

Before you begin a new project at work, you may have to develop a project summary document that states the purpose of the project, explains why it would be a wise use of company resources, and briefly outlines the steps involved in completing the project. This type of document is similar to a research proposal. Both documents define and limit a project, explain its value, discuss how to proceed, and identify what resources you will use.

Writing Your Own Research Proposal

Now you may write your own research proposal, if you have not done so already. Follow the guidelines provided in this lesson.

Key Takeaways

• Developing a research proposal involves the following preliminary steps: identifying potential ideas, choosing ideas to explore further, choosing and narrowing a topic, formulating a research question, and developing a working thesis.
• A good topic for a research paper interests the writer and fulfills the requirements of the assignment.
• Defining and narrowing a topic helps writers conduct focused, in-depth research.
• Writers conduct preliminary research to identify possible topics and research questions and to develop a working thesis.
• A good research question interests readers, is neither too broad nor too narrow, and has no obvious answer.
• A good working thesis expresses a debatable idea or claim that can be supported with evidence from research.
• Writers create a research proposal to present their topic, main research question, subquestions, and working thesis to an instructor for approval or feedback.

Writing for Success Copyright © 2015 by University of Minnesota is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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]

Learn about our Editorial Process

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.

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
• Chris Drew (PhD) https://helpfulprofessor.com/author/chris-drew-phd-2/ 101 Hidden Talents Examples

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 👌

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Analysis of Harris’s Billionaire Minimum Tax on Unrealized Capital Gains

In her campaign for president, Vice President Kamala Harris has embraced all the tax A tax is a mandatory payment or charge collected by local, state, and national governments from individuals or businesses to cover the costs of general government services, goods, and activities. increases President Biden proposed in the White House fiscal year 2025 budget —including a new idea that would require taxpayers with net wealth above $100 million to pay a minimum tax on their unrealized capital gains from assets such as stocks, bonds, or privately held companies.

The so-called billionaire minimum tax would take the tax code in the wrong direction by imposing a complicated tax on a narrow segment of high-earning taxpayers in a way that’s never been tried. This proposal would add new compliance burdens for taxpayers and administrative challenges for the IRS while weakening the US economy by raising the tax burden on saving and entrepreneurship. It would also require a new wealth reporting system, allowing the IRS to track the wealth of an unspecified number of Americans every year (some people with less than $100 million of wealth would be required to report it to the IRS).

Under current law, taxpayers pay taxes on the growth in the value of their assets when they are sold (or realized). Gains on assets held for less than one year are subject to ordinary income tax rates, while gains on assets held for longer than one year are taxed at a top rate of 23.8 percent. Additionally, inherited assets receive a “step-up” in tax basis, eliminating tax owed on capital gains.

Under the new proposal, taxpayers with net wealth above $100 million would be required to pay a minimum effective tax rate of 25 percent on an expanded measure of income that includes their unrealized capital gains. Taxpayers would calculate their effective tax rate for the minimum tax and, if it fell below 25 percent, would owe additional taxes to bring their effective rate to 25 percent. Any additional taxes owed because of the minimum tax would be payable over nine years initially, and over five years going forward.

The change means wealthy taxpayers would owe taxes on capital gains each year, even if the underlying asset had not been sold. Any amounts paid would be treated as prepayments of future capital gains tax liability. For example, consider a taxpayer with net wealth of $200 million, $5 million in ordinary income, $10 million in accumulated unrealized capital gains from a privately held company, and an ordinary tax liability of $1.8 million (see accompanying table). When including unrealized capital gains as income, the household’s effective tax rate is 12 percent, below the proposed 25 percent minimum.

To increase their effective tax rate to 25 percent, the household would owe an additional $1.95 million in tax (resulting in a combined $3.75 million in taxes owed on $15 million of income when including unrealized gains). The $1.95 million could be paid in equal installments over nine years (for capital gains moving forward, minimum tax liability can be split into five annual installments) and would be credited against future capital gains tax A capital gains tax is levied on the profit made from selling an asset and is often in addition to corporate income taxes, frequently resulting in double taxation. These taxes create a bias against saving, leading to a lower level of national income by encouraging present consumption over investment. liability on the asset when sold.

When the taxpayer eventually sells the company, the taxpayer would square up their taxes by using the prepaid taxes to offset capital gains tax liability. For example, if the household sold the private company in the future and owed $2 million in capital gains taxes, the taxpayer would reduce their liability by the $1.95 million prepayment and only owe an additional $50,000.

If the asset declined in value in a future year before being sold, it would produce an unrealized capital loss, reducing the taxpayer’s tax liability. An unrealized loss would first reduce remaining installments of tax owed on previous unrealized gains before being refunded in cash.

Overall, the proposal moves in the opposite direction of sound tax policy.

Changing the definition of taxable income to include unrealized capital gains presents significant administrative challenges, including how to value non-tradable assets and how to treat illiquid taxpayers who may have paper gains but lack cash on hand to pay their minimum tax bill.

The proposal attempts to meet such challenges with an entirely separate tax regime involving a deferral charge instead of prepayments for non-tradable assets and by allowing payment periods of nine years and five years. All options, however, introduce new complexities, opportunities for tax planning, and the potential of disputes with the IRS—in other words, economically wasteful activities. Additionally, the tax is levied on assets net of debts, meaning it could encourage additional borrowing to avoid the tax , unless mitigating rules are added.

The proposal would increase the tax burden on US savers, placing foreign savers at a relative advantage as they would not face the minimum tax. Raising taxes on domestic savers reduces the amount of domestic saving in the economy. In turn, foreign savers would finance a greater share of investment opportunities in the US. Over the long run, American incomes would fall as investment returns flowed to foreign savers instead of American savers. It would also manifest in a shifted balance of trade, increasing the trade deficit, all else held equal.

A higher effective tax rate on capital gains could also discourage angel investing , entrepreneurship, and risk-taking , reducing financing options for start-ups and leading to less economic dynamism.

The proposal runs contrary to international norms, as most countries in the Organisation for Economic Co-operation and Development tax capital gains when they are realized, and at lower rates than the US, and tax capital income overall at lower average tax rates .

Another challenge is the minimum tax would likely be an unstable source of revenue. Much of the estimated revenue it would raise in the first decade is from taxing previously accumulated capital gains. Once taxes on the past accumulation of capital gains are paid, the permanent increase in revenue going forward would be smaller. Furthermore, because the revenue would depend on how taxpayers’ assets perform on paper , volatility in the stock market and the broader economy would translate into volatile revenue collections.

Harris’s billionaire minimum tax proposal would be a highly complicated new tax regime, creating new compliance costs for taxpayers and difficult administrative challenges for an already overwhelmed IRS without serving as a stable source of permanent funding. In addition, the economic harm could be substantial, as it would reduce saving, entrepreneurship, and economic dynamism. If lawmakers are seeking to raise revenue from top earners, they have much better options , such as progressive consumption taxes.

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• Volume 83, Issue 9
• ASAS consensus definition of early axial spondyloarthritis
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• http://orcid.org/0000-0002-4527-852X Victoria Navarro-Compán 1 , 2 ,
• http://orcid.org/0000-0001-9119-5330 Diego Benavent 1 , 2 ,
• http://orcid.org/0000-0001-9336-0416 Dafne Capelusnik 3 , 4 ,
• http://orcid.org/0000-0002-5781-158X Désirée van der Heijde 5 ,
• http://orcid.org/0000-0002-0577-6620 Robert BM Landewé 6 , 7 ,
• http://orcid.org/0000-0002-4537-6015 Denis Poddubnyy 8 , 9 ,
• http://orcid.org/0000-0001-8477-0683 Astrid van Tubergen 10 , 11 ,
• http://orcid.org/0000-0002-9475-9362 Xenofon Baraliakos 12 , 13 ,
• http://orcid.org/0000-0002-3561-5932 Filip E Van den Bosch 14 , 15 ,
• http://orcid.org/0000-0001-8448-7407 Floris A van Gaalen 5 ,
• http://orcid.org/0000-0001-6314-5336 Lianne Gensler 16 ,
• http://orcid.org/0000-0002-2309-5837 Clementina López-Medina 17 , 18 ,
• http://orcid.org/0000-0002-9683-3407 Helena Marzo-Ortega 19 , 20 ,
• http://orcid.org/0000-0003-2246-1986 Anna Molto 21 , 22 ,
• Rodolfo Pérez-Alamino 23 ,
• http://orcid.org/0000-0001-5445-548X Martin Rudwaleit 24 ,
• Marleen van de Sande 25 ,
• http://orcid.org/0000-0002-9720-0396 Raj Sengupta 26 ,
• http://orcid.org/0000-0001-6701-670X Ulrich Weber 27 ,
• http://orcid.org/0000-0002-8899-9087 Sofia Ramiro 5 , 7
• 1 Rheumatology , La Paz University Hospital , Madrid , Spain
• 2 IdiPAZ , Madrid , Spain
• 3 Universiteit Maastricht Care and Public Health Research Institute , Maastricht , The Netherlands
• 4 Rheumatology , Tel Aviv Sourasky Medical Center , Tel Aviv , Israel
• 5 Rheumatology , Leiden University Medical Center , Leiden , The Netherlands
• 6 Department of Rheumatology & Clinical Immunology , Amsterdam University Medical Centres , Duivendrecht , The Netherlands
• 7 Rheumatology , Zuyderland Medical Centre Heerlen , Heerlen , The Netherlands
• 8 Department of Gastroenterology, Infectious Diseases and Rheumatology , Charite Universitatsmedizin Berlin , Berlin , Germany
• 9 German Rheumatism Research Center , Berlin , Germany
• 10 Maastricht University Care and Public Health Research Institute , Maastricht , The Netherlands
• 11 Rheumatology , Maastricht University Medical Centre+ , Maastricht , The Netherlands
• 12 Rheumatology , Rheumazentrum Ruhrgebiet , Herne , Germany
• 13 Ruhr-Universitat Bochum , Bochum , Germany
• 14 Internal Medicine and Pediatrics , VIB-UGent Center for Inflammation Research , Zwijnaarde , Belgium
• 15 Ghent University , Gent , Belgium
• 16 Medicine, Division of Rheumatology , University of California , San Francisco , California , USA
• 17 Rheumatology , Reina Sofia University Hospital , Cordoba , Spain
• 18 Maimonides Biomedical Research Institute of Cordoba , Cordoba , Spain
• 19 Rheumatology , Leeds Biomedical Research Centre , Leeds , UK
• 20 University of Leeds Leeds Institute of Rheumatic and Musculoskeletal Medicine , Leeds , UK
• 21 APHP, INSERM U-1158, Rheumatology , Hospital Cochin , Paris , France
• 22 Center of Research in Epidemiology and Statistics Sorbonne Paris Cité , Paris , France
• 23 Rheumatology , Avellaneda Hospital , Tucuman , Argentina
• 24 Internal Medicine and Rheumatology , Klinikum Bielefeld Rosenhohe , Bielefeld , Germany
• 25 Department of Rheumatology and Clinical Immunology , University of Amsterdam , Amsterdam , The Netherlands
• 26 Rheumatology , Royal National Hospital for Rheumatic Diseases , Bath , UK
• 27 Practice Buchsbaum, Rheumatology , Schaffhausen Hospitals , Schaffhausen , Switzerland
• Correspondence to Dr Victoria Navarro-Compán; mvictoria.navarroc{at}gmail.com

Objectives To develop a consensual definition for the term ‘early axial spondyloarthritis—axSpA’—and ‘early peripheral spondyloarthritis—pSpA’.

Methods The ASAS (Assessment of SpondyloArthritis international Society-Spondyloarthritis EARly definition) steering committee convened an international working group (WG). Five consecutive steps were followed: (1) systematic literature review (SLR); (2) discussion of SLR results within the WG and ASAS community; (3) a three-round Delphi survey inviting all ASAS members to select the items that should be considered for the definition; (4) presentation of Delphi results to the WG and ASAS community and (5) ASAS voting and endorsement (2023 annual meeting).

Results Following the SLR, consensus was to proceed with an expert-based definition for early axSpA (81% in favour) but not for pSpA (54% against). Importantly, early axSpA should be based on symptom duration taking solely axial symptoms into account. 151–164 ASAS members participated in the Delphi surveys. Consensus was achieved for considering the following items within early axSpA definition: duration of symptoms ≤2 years; axial symptoms defined as cervical/thoracic/back/buttock pain or morning stiffness; regardless of the presence/absence of radiographic damage. The WG agreed that in patients with a diagnosis of axSpA ‘early axSpA’ should be defined as a duration of ≤2 years of axial symptoms. Axial symptoms should include spinal/buttock pain or morning stiffness and should be considered by a rheumatologist as related to axSpA. The ASAS community endorsed this proposal (88% in favour).

Conclusions Early axSpA has newly been defined, based on expert consensus. This ASAS definition should be adopted in research studies addressing early axSpA.

• Spondylitis, Ankylosing
• Outcome Assessment, Health Care

https://doi.org/10.1136/ard-2023-224232

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Handling editor Josef S Smolen

X @annamolto, @sofiaramiro82

Contributors VN-C and SR designed the study and developed the study protocol. DB and DC performed the survey and summarised the data. All authors participated actively in the project. VN-C and SR wrote the first draft of the manuscript. All authors critically reviewed the manuscript for important intellectual contribution and approved the final version.

Funding The Assessment of SpondyloArthritis international Society (ASAS) funded Diego Benavent to work on this project.

Competing interests VN-C: Speaker fees—AbbVie, Eli Lilly, Janssen, MSD, Novartis, Pfizer, UCB Pharma; Consultancy fees- AbbVie, Eli Lilly, Galapagos, MoonLake, MSD, Novartis, Pfizer, UCB Pharma; Grants: AbbVie, Novartis. DB: Grant/research support from Novartis, and speaker fees from Janssen, Abbvie, and Galapagos. DvdH: Consulting AbbVie, Bayer, BMS, Cyxone, Eisai, Galapagos, Gilead, Glaxo-Smith-Kline, Janssen, Lilly, Novartis, Pfizer, UCB Pharma. Director of Imaging Rheumatology bv. RBML: Consulting AbbVie, Eli-Lilly, Janssen, Galapagos, Gilead, Novartis, Pfizer, UCB. Director of Rheumatology Consultancy BVD. DP: Research grant from AbbVie, Eli Lilly, MSD, Novartis, Pfizer, Consultation AbbVie, Biocad, BMS, Eli Lilly, Gilead, MSD, Novartis, Pfizer, Samsung Bioepis, UCB, Speaker AbbVie, BMS, Lilly, MSD, Novartis, Pfizer, UCB. AvT: Speaker fees: Pfizer; Consulting fees: Novartis, Galapagos, UCB; Grants: Pfizer, UCB, Novartis XB: Abbvie, Amgen, Chugai, Eli Lilly, Galapagos, Janssen, MSD, Novartis, Pfizer, Roche, Sandoz, UCB. FEVdB: received speaker and/or consultancy fees from AbbVie, Amgen, Eli Lilly, Galapagos, Janssen, Moonlake, Novartis, Pfizer and UCB FAvG: Research Grants—Novartis; consultancy -MSD, AbbVie, Novartis and BMS LG: Research grants UCB, Novartis, Consulting fees AbbVie, Acelyrin, Eli Lilly, Fresenius Kabi, Janssen, Novartis, Pfizer, UCB Pharma CL-M: Speaker fees AbbVie, Eli Lilly, Novartis, Janssen, UCB Pharma. Consulting fees Eli Lilly, Novartis, UCB Pharma. HM-O: Speaker fees/consultancy: ABvie, Eli-Lilly, Janssen, Moonlake, Novartis, Pfizer and UCB. Research grants from Janssen, Novartis and UCB. AM: Consulting fees AbbVie, Biogen, BMS, Cyxone, Eisai, Galapagos, Gilead, Janssen, Lilly, Novartis, Pfizer, UCB Pharma. Grants: UCB RP-A: Speaker fees Abbvie, Eli Lilly, Novartis, Janssen, Pfizer. Consulting fees Abbvie, Eli Lilly, Janssen, Novartis. MR: Speaker- AbbVie, Boehringer Ingelheim, Eli Lilly, Janssen, Novartis, UCB Pharma; Consultancy AbbVie, Eli Lilly, Novartis, Pfizer, UCB Pharma MvdS: Speaker -Janssen, Novartis, UCB; Consultancy Abbvie, Eli Lilly, Novartis, UCB; Research Grants: Eli Lily, Novartis, UCB RS: Speaker - AbbVie, Biogen, Eli Lilly, MSD, Novartis, UCB; Consultancy—AbbVie, Eli Lilly, Novartis, Pfizer, UCB. Grants: AbbVie, Novartis, UCB UW: Speaker fees NovartisS. SR: Research Grants—AbbVie, Galapagos, MSD, Novartis, Pfizer, UCB; consultancy—AbbVie, Eli Lilly, MSD, Novartis, Pfizer, Sanofi, UCB.

Provenance and peer review Not commissioned; externally peer reviewed.

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Distributions for sex (A), decedents (B), race and ethnicity (C), and dual-eligibility (D). Beneficiary race and ethnicity was determined using the Research Triangle Institute race code; Other and unknown race and ethnicity category includes Asian and Pacific Islander, American Indian or Alaska Native, and any race or ethnicity not otherwise specified. ASR indicates age-standardized rate.

eAppendix. Literature Review Protocol

eTable 1.  ICD-10-CM Codes and Prescription Drugs Used in the CCW and 21 Unique Researcher-Developed Claims-Based Dementia Identification Algorithms

eTable 2. Characteristics of Beneficiaries Categorized Into Each Tier of ICD-10-CM Codes (as Defined by Frequency of Use Across the CCW and Researcher-Developed Algorithms) and NDCs

eTable 3 . Raw and Age-Adjusted Characteristics of Beneficiaries Identified as Having Highly Likely ADRD, Likely ADRD, Possible ADRD, and No Evidence of ADRD

eTable 4. Beneficiary Age Distribution in the Full Sample, Within LTC Users and Non-Users, and Within Decedents and Non-Decedents

eTable 5. New Subcodes Associated With F01, F02, and F03 That Went Into Effect in October 2022

eReferences

Data Sharing Statement

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Gianattasio KZ , Wachsmuth J , Murphy R, et al. Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare. JAMA Netw Open. 2024;7(9):e2427610. doi:10.1001/jamanetworkopen.2024.27610

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Case Definition for Diagnosed Alzheimer Disease and Related Dementias in Medicare

• 1 NORC at the University of Chicago, Bethesda, Maryland
• 2 Department of Epidemiology, George Washington University School of Public Health, Washington, DC

Question   How many Medicare beneficiaries have diagnostic codes or drug prescriptions indicating Alzheimer disease and related dementias (ADRD) using a refined case definition, and what are the characteristics of these beneficiaries?

Findings   This cross-sectional study of more than 60 million Medicare beneficiaries identified 7.2% with evidence of highly likely ADRD, 1.9% with likely ADRD, and 4.3% with possible ADRD. Beneficiaries with evidence of ADRD were older, more frail, more likely to use long-term care, and more likely to die than those without evidence of ADRD; these differences persisted after age-standardization.

Meaning   In this cross-sectional study, more than 5.4 million Medicare beneficiaries (9.1%) had evidence of likely or highly likely ADRD in 2019; pending validation, this case definition can be adopted provisionally for national surveillance of persons with diagnosed dementia in the Medicare system.

Importance   Lack of a US dementia surveillance system hinders efforts to support and address disparities among persons living with Alzheimer disease and related dementias (ADRD).

Objective   To review diagnosis and prescription drug code ADRD identification algorithms to develop and implement case definitions for national surveillance.

Design, Setting, and Participants   In this cross-sectional study, a systematic literature review was conducted to identify unique International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) and prescription drug codes used by researchers to identify ADRD in administrative records. Code frequency of use, characteristics of beneficiaries identified by codes, and expert and author consensus around code definitions informed code placement into categories indicating highly likely, likely, and possible ADRD. These definitions were applied cross-sectionally to 2017 to 2019 Medicare fee-for-service (FFS) claims and Medicare Advantage (MA) encounter data to classify January 2019 Medicare enrollees. Data analysis was conducted from September 2022 to March 2024.

Exposures   ICD-10-CM and national drug codes in FFS claims or MA encounters.

Main Outcomes and Measures   The primary outcome was counts and rates of beneficiaries meeting each case definition. Category-specific age, sex, race and ethnicity, MA enrollment, dual-eligibility, long-term care utilization, mortality, and rural residence distributions, as well as frailty scores and FFS monthly expenditures were also analyzed. Beneficiary characteristics were compared across categories, and age-standardized to minimize confounding by age.

Results   Of the 60 000 869 beneficiaries included (50 853 806 aged 65 years or older [84.8%]; 32 567 891 female [54.3%]; 5 555 571 Hispanic [9.3%]; 6 318 194 non-Hispanic Black [10.5%]; 44 384 980 non-Hispanic White [74.0%]), there were 4 312 496 (7.2%) with highly likely ADRD, 1 124 080 (1.9%) with likely ADRD, and 2 572 176 (4.3%) with possible ADRD, totaling more than 8.0 million with diagnostic evidence of at least possible ADRD. These beneficiaries were older, more frail, more likely to be female, more likely to be dual-eligible, more likely to use long-term care, and more likely to die in 2019 compared with beneficiaries with no evidence of ADRD. These differences became larger when moving from the possible ADRD group to the highly likely ADRD group. Mean (SD) FFS monthly spending was $2966 ($4921) among beneficiaries with highly likely ADRD compared with $936 ($2952) for beneficiaries with no evidence of ADRD. Differences persisted after age standardization.

Conclusions and Relevance   This cross-sectional study of 2019 Medicare beneficiaries identified more than 5.4 million Medicare beneficiaries with evidence of at least likely ADRD in 2019 using the diagnostic case definition. Pending validation against clinical and other methods of ascertainment, this approach can be adopted provisionally for national surveillance.

Surveillance is a fundamental public health activity. Lack of a US dementia surveillance system hinders public health efforts to support persons living with Alzheimer disease and related dementias (ADRD), address health disparities, and plan ADRD care resources.

Medicare administrative data are an attractive source upon which to build a dementia surveillance system and are commonly used to identify persons living with ADRD, but a consensus diagnostic code case definition does not exist. Perhaps the most widely used definition (the Centers for Medicare and Medicaid Services [CMS] Chronic Conditions Warehouse [CCW] algorithm) uses 22 International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes, from the commonly accepted G30.X (Alzheimer disease) and F01.XX (vascular dementia), to less specific codes such as R54 (age-related physical debility). 1 In contrast, most researcher-developed ICD-10 - CM –based algorithms exclude R54, but may include codes such as G31.0 (frontotemporal dementia) that are not in the CCW algorithm. 2 - 4 Moreover, while some algorithms use Medicare Part D data to identify prescriptions for Alzheimer disease–related drugs, 5 - 8 most do not.

The impact of using different ICD-10-CM or prescription codes on the number of people identified or their characteristics is unknown. Because ICD-10-CM codes are used for billing (rather than diagnostic) purposes, specific codes may not be sensitive nor specific to dementia, and coding practices may differ systematically by health care practice, patient characteristics, and geography.

We examined how choices of ICD-10-CM and prescription drug codes used to identify persons with clinically recognized ADRD in Medicare fee-for-service (FFS) claims and Medicare Advantage (MA) encounter data affect dementia prevalence estimates and characteristics of the people identified. We synthesized this information to develop a new case definition using diagnostic and prescription drug codes that can be applied to administrative data to support surveillance of persons with diagnosed dementia in the Medicare system.

This cross-sectional study was deemed exempt from review and the requirement of informed consent by the NORC Institutional Review Board. The reporting of this research follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline.

We searched PubMed for articles published from 2012 to 2022, with all-cause dementia or ADRD as a primary exposure or primary outcome, or where the research population of interest was persons living with all-cause dementia or ADRD (eAppendix in Supplement 1 ). We found 28 studies utilizing 20 distinct researcher-developed ICD-10-CM or prescription drug code algorithms in addition to the CCW algorithm (eTable 1 in Supplement 1 ). 2 - 21

We extracted 43 ICD-10-CM codes and 5 prescription drugs across algorithms ( Table 1 and eTable 1 in Supplement 1 ). We shared the codes with 3 clinicians (2 neurology clinicians and 1 geriatrics clinician) who provide care to persons living with dementia, who recommended excluding 8 codes deemed to not indicate dementia ( Table 1 ). We grouped the remaining codes into tiers by use frequency (tier 1, ≥15 algorithms; tier 2, 10-14 algorithms; tier 3, 5-9 algorithms; and tier 4, 1-4 algorithms). We designated prescriptions for ADRD-targeting drugs as indicated by National Drug Codes (NDC) without presence of an ADRD ICD-10-CM code as tier 5.

We used 100% of the 2017 to 2019 Medicare FFS inpatient, outpatient, carrier, skilled nursing facility, home health agency, and hospice claims; MA inpatient, outpatient, carrier, skilled nursing facility, and home health agency encounter data; and Medicare Part D prescription drug event (PDE) data. We used the minimum dataset (MDS 3.0) to identify long-term care (LTC) utilization. We limited analysis to Medicare beneficiaries with at least Part A (the premium-free Medicare benefit) enrollment in January 2019, nonmissing sex, and a valid US state or territory code based on the Medicare beneficiary summary files. We did not exclude beneficiaries based on age or lack of Part B enrollment because our aim was to identify all people in the Medicare system with evidence of ADRD.

To categorize beneficiaries with or without evidence of dementia as of 2019, we conducted a cross-sectional analysis of January 2017 to December 2019 FFS and MA data to identify all claims and encounters with a relevant ICD-10-CM code listed in any position, and all PDE claims for a relevant NDC. We classified beneficiaries hierarchically, first with a tier 1 ICD-10-CM code, then with a tier 2 code among remaining beneficiaries, and so on, identifying only the incremental beneficiaries in each tier if they had not been classified earlier. We compared distributions of age, sex, race and ethnicity (as indicated by the Research Triangle Institute race code 22 ), MA enrollment, LTC use, and 2019 mortality across tiers. Race and ethnicity categories included American Indian or Alaska Native, Asian or Pacific Islander, Hispanic, non-Hispanic Black, non-Hispanic White, unknown, and other (defined as any race or ethnicity not otherwise specified); race and ethnicity were included because existing evidence shows that there are disparities in dementia prevalence across race and ethnicity groups. We compared cross-tier beneficiary frailty using a claims-based frailty index (CFI), 23 an adapted CFI that excludes ADRD codes in tiers 1 to 4, and per-member-per-month (PMPM) spending, averaged across all months of 2019 FFS coverage.

Using these data (eTable 2 in Supplement 1 ), we found that beneficiaries in tiers 1 and 2 were older, more frail, more likely to be female, in LTC, and die than those in tiers 3 to 5. There were minimal differences in race and ethnicity across tiers, with exception of a higher-than-expected representation of Hispanic and Asian and Pacific Islander beneficiaries in tier 5; however, the overall size of the sample categorized as tier 5 was very small, at just 0.1% (52 338 of 60 000 869 beneficiaries). Based on the findings from the cross-tier comparison and author consensus, we further aggregated codes into 3 categories with decreasing confidence of having a true ADRD diagnosis: a highly likely ADRD category requiring at least 2 claims or encounters on different dates with ICD-10-CM codes from tiers 1 or 2; a likely ADRD category requiring 1 claim or encounter with an ICD-10-10-CM code from tiers 1 or 2; and a possible ADRD category requiring at least 1 claim or encounter with an ICD-10-CM or NDC code from tiers 3, 4, or 5 over a 3-year lookback period. We categorized beneficiaries and reevaluated group demographics, health insurance type, frailty and mortality, and rural residency. We then computed prevalence of highly likely, likely, and possible ADRD within population subgroups defined by these characteristics. We age-standardized to the full analytical population to evaluate differences unconfounded by age.

All analyses were conducted in SAS Enterprise Guide 7.1 and SAS Studio version 3.81 (SAS Institute). Data analysis was conducted from September 2022 to March 2024.

Of 64 430 729 2019 Medicare beneficiaries, we excluded 3 940 831 due to lack of Part A enrollment in January, 8 due to missing sex, and 489 021 due to a nonvalid US state or territory code, resulting in a total of 60 000 869 beneficiaries (50 853 806 aged 65 years or older [84.8%]; 32 567 891 female [54.3%]; 5 555 571 Hispanic [9.3%]; 6 318 194 non-Hispanic Black [10.5%]; 44 384 980 non-Hispanic White [74.0%]) included in the study sample. Of all beneficiaries, 11 502 479 (19.2%) had Medicaid dual-eligibility, while 23 607 426 (39.3%) had MA. Mean (SD) FFS PMPM spending in 2019 was $1220 ($3426) ( Table 2 ).

We identified 4 312 496 beneficiaries (7.2%) as having highly likely ADRD, and 1 124 080 (1.9%) as having likely ADRD ( Table 3 ). The proportion of beneficiaries with highly likely ADRD increased to 8.1% (4 125 639 beneficiaries) after limiting age to 65 years or older, and to 8.8% (4 093 008 beneficiaries) when further limiting to those with both Parts A and B enrollment. The proportion of beneficiaries with likely ADRD increased to 2.1% (996 379 beneficiaries) after these restrictions. Compared with those with likely ADRD, those with highly likely ADRD were older and more frail, more likely to be female and dual-eligible, had over 3 times the rate of LTC utilization (681 923 of 4 312 496 beneficiaries [15.8%] vs 51 332 of 1 124 080 beneficiaries [4.6%]), and almost double the rate of death (828 366 of 4 312 496 beneficiaries [19.2%] vs 129 705 of 1 124 080 beneficiaries [11.5%]). We identified an additional 2 572 176 beneficiaries (4.3%) as having possible ADRD; this percentage increased to 4.8% (2 231 673 beneficiaries) after restricting to beneficiaries aged 65 years or older with Parts A and B enrollment. The possible ADRD group was younger and healthier (lower CFI, mortality, and LTC utilization) than those with highly likely or likely ADRD but was older and less healthy than those with no evidence of ADRD (51 992 117 beneficiaries). Mean (SD) PMPM spending was approximately 3 times as high in the ADRD groups (ranging from $2559 [$2952] among those with possible ADRD to $2966 [$4921] among those with highly likely ADRD) as that of the no ADRD group ($936 [$2952]). Age standardization narrowed differences in sex distribution and death rates, widened differences in race and ethnicity distribution and dual-eligible rates, and had minimal impact on differences in MA enrollment, LTC utilization, and frailty ( Figure and eTable 3 in Supplement 1 ). FFS spending increased slightly for all categories after age standardization.

The proportion of beneficiaries with any evidence of ADRD increased with age, from 6.5% (1 931 517 of 29 878 739 beneficiaries) among beneficiaries aged 65 to 74 years to 42.5% (2 544 205 of 5 983 967) among those aged 85 years or older, with the largest increase seen in the percentage of those with highly likely ADRD (2.6% [770 296 of 29 878 739 beneficiaries] to 29.1% [1 739 705 of 5 983 967 beneficiaries]) ( Table 4 ). Prevalence of any ADRD was higher in females than in males but was similar between non-Hispanic White (5 950 598 beneficiaries [13.4%]), non-Hispanic Black (892 541 beneficiaries [14.1%]), and Hispanic (792 948 beneficiaries [14.3%]) beneficiaries. Those with LTC use were substantially more likely to have ADRD than those with no LTC (681 923 of 937 248 beneficiaries [72.8%] vs 3 630 573 of 59 063 621 beneficiaries [6.1%] categorized as highly likely). Similarly, prevalence of highly likely or likely ADRD was much higher in decedents (958 071 of 2 285 257 beneficiaries [41.9%]) than nondecedents (4 478 505 of 57 715 612 beneficiaries [7.7%]) and in those who were dual-eligible (1 917 434 of 11 502 479 beneficiaries [16.7%]) than among those who were not (3 519 142 of 48 498 390 beneficiaries [7.2%]). MA beneficiaries had a higher prevalence of highly likely or likely ADRD (2 296 154 of 23 607 426 beneficiaries [9.7%]) than FFS beneficiaries (3 140 422 of 36 393 443 beneficiaries [8.6%]), and any evidence of ADRD (4 595 211 of 23 607 426 beneficiaries [14.5%] for MA vs 4 593 211 of 36 393 443 beneficiaries [12.6%] for FFS).

Age-standardizing subgroups to the age distribution of the Medicare population resulted in changes in ADRD prevalence estimates in some groups ( Table 4 ). Relative differences in ADRD prevalence narrowed across sex but widened across race and ethnicity groups. Most notably, non-Hispanic White beneficiaries became less likely to have any evidence of ADRD (12.9% across categories), while racial and ethnic minority groups became more likely to have evidence of ADRD (non-Hispanic Black beneficiaries, 16.5%; Hispanic beneficiaries, 15.3%). Among non-Hispanic Black beneficiaries, age standardization resulted in a substantial increase in the proportion of those with highly likely or likely ADRD (9.9% to 12.0%). Age standardization also reduced ADRD prevalence among LTC users (from 72.8% to 62.8% with highly likely ADRD) and decedents (from 36.2% to 23.8% with highly likely ADRD) but had minimal impact in ADRD prevalence among non–LTC users and nondecedents; this is because LTC-users and decedent groups were heavily skewed toward older ages, while the age distribution of the non–LTC users and nondecedent groups mimicked that of the general Medicare population (eTable 4 in Supplement 1 ).

Among 2019 Medicare beneficiaries in this cross-sectional study, we identified approximately 4.3 million (7.2%) with highly likely ADRD, 1.1 million (1.9%) with likely ADRD, and 2.6 million (4.3%) with possible ADRD, for a total of more than 8.0 million (13.4%) in any category. Specifically, we developed new diagnosis and NDC code ADRD case definitions informed by a systematic review of previous algorithms, author and expert input, and analyses of Medicare data. The review identified 43 ICD-10-CM codes and 5 prescription drugs used by the CCW and 20 researcher-developed algorithms to identify ADRD in Medicare data. We divided codes into categories that were likely to indicate ADRD vs those that were possibly ADRD based on past frequency of use by other researchers, characteristics of beneficiaries identified by codes, and author and expert consensus around code definitions. We then added a highly likely category to describe beneficiaries who received 2 or more likely codes on different dates of service. We posit that these categories are superior to previous definitions for provisional use in surveillance systems, but caution that validation is necessary. To our knowledge, this is the first application of claims identification algorithms to all-age FFS and MA beneficiaries. We have used this case definition to compute provisional national-, state-, and county-level estimates of ADRD prevalence and incidence in 2020 Medicare and published them on our dementia surveillance website. 24 Estimates will be refined pending validation and updated with additional years of data as they become available.

Our 3-level case definition is novel in that it was driven by researcher-consensus as well as data analysis and identifies dementia with varying degrees of certainty. Of note, ICD-10-CM codes used to identify possible ADRD have lower researcher consensus and less specific code descriptions (ie, do not contain dementia or Alzheimer ). Use of the possible ADRD codes may reflect physician uncertainty about a dementia diagnosis or medical events involving ADRD-like symptoms in patients without underlying dementia. 25 - 27 Our definition also excludes several previously used codes that were determined to not indicate ADRD by expert clinicians. Compared with the commonly used CCW algorithm, which similarly uses a 3-year look-back period, our case definition is more specific when limited to the highly likely and likely categories, but broader when also including the possible ADRD category. The CCW algorithm estimated prevalence of 10.7% in 2019 Medicare FFS beneficiaries 28 falls between our estimates for FFS beneficiaries of 8.6% for highly likely or likely ADRD and 12.6% for all 3 categories.

Importantly, we saw expected and meaningful differences between beneficiaries identified in each ADRD category. Moving from the no evidence of ADRD to the highly likely ADRD groups, beneficiaries became progressively older and more frail and had greater rates of dual-eligibility, LTC use, and death, which is consistent with prior research. 29 - 35 Notably, prevalence of highly likely ADRD was 29.1% in beneficiaries aged 85 years or older, 72.8% in LTC users, and 36.2% in decedents, compared with 7.2% in the general Medicare population. Higher rates of dual-eligibility in ADRD groups may be driven by ADRD beneficiaries spending down assets to qualify for Medicaid and obtain LTC coverage. These differences persisted after age standardization and lend confidence to our case definitions.

Application of our case definitions also showed disparities in diagnosis rates by race in the expected direction—higher dementia risk among non-Hispanic Black beneficiaries relative to non-Hispanic White beneficiaries 36 , 37 —after age standardization to account for lower life expectancy among non-Hispanic Black individuals. 38 However, because non-Hispanic Black individuals also have a greater risk of under-diagnosis of ADRD than non-Hispanic White individuals, 39 disparities in true underlying rates may be higher than observed. Additionally, we found higher-than-expected representation of Hispanic and Asian and Pacific Islander beneficiaries among those that had an ADRD-targeting drug without diagnostic ( ICD-10-CM ) evidence. We hypothesize that differences in cultural perceptions around dementia and cognitive decline (eg, memory loss as a normal aging process) 40 , 41 may result in lower utilization of diagnosis codes when providers suspect dementia. Using PDE claims may result in higher and more accurate rates of ADRD among Hispanic and Asian and Pacific Islander individuals despite the overall small number of beneficiaries identified by PDE claims alone.

Finally, also consistent with past research, 29 , 35 , 42 PMPM FFS spending was substantially higher for beneficiaries with evidence of ADRD compared with those with no evidence of ADRD. Medicare FFS PMPM spending was relatively similar across the highly likely, likely, and possible ADRD groups despite differences in frailty and mortality. Medicare FFS spending may not be generalizable to those with MA (for whom costs cannot be computed) and is only part of the economic story. Medicaid is the primary US payer of LTC; higher rates of dual-eligibility and LTC use among the highly likely ADRD group indicate that differences in total federal and state spending between the highly likely ADRD and other groups are likely larger. We also did not capture patient and family health–related out-of-pocket expenses and informal care costs ($203 117 in families caring for a patient living with dementia vs $102 955 in families caring for a patient without dementia over the last 7 years of the patient’s life 42 ), forgone wages, or other impacts on informal caregivers, and payments made by other assistance programs. Finally, we caution that our spending measure represents total Medicare FFS spending, rather than the incremental ADRD costs.

This study is limited by at least the following. First, our ADRD case definition was driven by researcher-consensus, and validation against other dementia ascertainment methods (including ascertainment based on in-person clinical and neuropsychological assessments) is necessary. Both over- and under-diagnosis of ADRD have been documented in Medicare claims, 35 , 39 and the 8.0 million beneficiaries identified as having some evidence of ADRD by our case definition will include some without ADRD, especially those in the possible category. Similarly, this method only captures documented cases of dementia in Medicare administrative records and cannot capture beneficiaries with unrecognized and/or undocumented ADRD. If we assume a 60% rate of undetected dementia in the US 43 our estimates would suggest an additional 12 million beneficiaries may be living with ADRD. Additionally, our data show a marginally higher rate of ADRD in MA than in FFS enrollees (14.5% vs 12.6% across the 3 categories), which may reflect beneficiary selection in MA plans, MA vs FFS differences in clinical ADRD assessment and diagnosis rates, differences in claims or encounter documentation, or a combination thereof. Given the rapid rise in MA participation (from 33% in 2017 to 51% in 2023) and variation in MA penetration across counties, 44 , 45 it is also important to understand potential differences in performance of this case definition between MA and FFS beneficiaries. As such, validation of this case definition against in-person clinical and other ascertainment methods to assess performance (including sensitivity, specificity, positive predictive value, and negative predictive value), separately for Medicare FFS and MA, is critical for refining and calibrating estimates to accurately capture the diagnosed prevalence and incidence of dementia. Pending validation, our case definitions should be considered provisional. Notably, we expect the possible ADRD category to identify a higher proportion of individuals who do not have ADRD. Thus, it is important to report the possible ADRD category separately from the likely and highly likely ADRD categories in research and surveillance efforts using these case definitions.

Second, evidence for ADRD documented in electronic health or insurance records outside the Medicare system is not captured by our method; this is particularly problematic for beneficiaries without Medicare Parts B or D (7.5% and 25.6% of Medicare enrollees, respectively 43 , 46 ). Third, we deliberately used data from 2017 to 2019 to avoid the COVID-19 pandemic years, which resulted in secular shocks, including excess senior deaths, forgone or deferred care, and increased telehealth, which may have impacted dementia diagnosis. Research is necessary to understand these effects but will necessarily be delayed pending new data. Fourth, Namzaric, a memantine and donepezil combination drug approved in 2014, was not included by any prescription-drug based identification strategy; while the impact of including this drug necessitates further investigation, we anticipate a negligible effect given that just 0.1% of the sample had an ADRD-targeting prescription drug without ICD-10-CM evidence. Similarly, ICD-10 -CM code updates from October 2022 added 29 highly specific codes each under code roots F01 (vascular dementia) F02 (dementia in other diseases classified elsewhere), and F03 (unspecified dementia) (eTable 5 in Supplement 1 ). 47 We recommend that applications of our approach to Medicare records beginning in October 2022 include these for identifying highly likely and likely ADRD. Fifth, in developing our case definitions, we only considered use of ICD-10-CM codes and prescription drugs but did not consider other criteria of existing ADRD-identification algorithms, including look-back period, types of claims or encounter data considered, number of claims or encounters with relevant ICD-10-CM codes required, and time elapsed between claims and encounters; sensitivity analyses around these different criteria are beyond the scope of this paper.

In this cross-sectional study, our novel case definition for ADRD identified approximately 5.4 million Medicare beneficiaries with evidence of at least likely ADRD in 2019. Pending validation against in-person clinical and other ascertainment methods, this definition can be adopted for provisional use in national surveillance efforts.

Accepted for Publication: June 17, 2024.

Published: September 3, 2024. doi:10.1001/jamanetworkopen.2024.27610

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Gianattasio KZ et al. JAMA Network Open .

Corresponding Author: Kan Z. Gianattasio, PhD, NORC at the University of Chicago, 4350 East-West Hwy 8th Floor, Bethesda, MD 20814 ( [email protected] ).

Author Contributions: Mr Wachsmuth and Mr Murphy had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Gianattasio, Hartzman, Wittenborn, Power, Rein.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Gianattasio, Wachsmuth, Hartzman, Cutroneo, Wittenborn, Rein.

Critical review of the manuscript for important intellectual content: Gianattasio, Murphy, Hartzman, Montazer, Power, Rein.

Statistical analysis: Gianattasio, Wachsmuth, Murphy.

Obtained funding: Wittenborn, Rein.

Administrative, technical, or material support: Hartzman, Montazer, Cutroneo.

Supervision: Hartzman, Rein.

Conflict of Interest Disclosures: None reported.

Funding/Support: Research reported in this publication was supported by the National Institute on Aging of the National Institutes of Health (Award No. R01AG075730).

Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: We would like to thank Christina Prather, MD (George Washington University); Tania Alchalabi, MD (George Washington University); and Raymond Scott Turner, MD, PhD (Georgetown University), for providing critical review of the International Statistical Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes from a clinical perspective. Drs Prather, Alchalabi, and Turner did not receive compensation for their contributions to this work. We also wish to acknowledge the critical input into data processing and analysis decisions made by Qian Gu, PhD (KPMG); Carrie Bao, BS (KPMG); and Samuel Knisely, BA, (KPMG). Dr Gu, Ms Bao, and Mr Knisely received funding from the same National Institute on Aging grant (R01AG075730) that funded this study for their input.

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What Is Unrealized Capital Gains Tax? Unpacking Kamala Harris-Backed Proposal On Ultra Wealthy

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Vice President Kamala Harris’ economic policy plans include a backing of President Joe Biden’s proposal to tax unrealized capital gains on the U.S.’ richest people, a plan unsurprisingly denounced by former President Donald Trump and his associates, though some criticism of the plan is rooted in misinformation.

Kamala Harris speaks at the National Forum on Wages and Working People: Creating an Economy That ... [+] Works for All in Las Vegas in 2019.

The Democratic presidential nominee supports a plan first introduced in 2022 by Biden to impose a 25% minimum tax on unrealized capital gains accrued by Americans with net worths of $100 million or more, according to the Biden administration’s latest 2025 tax proposal released in March—which would alter a fundamental principle of how capital gains are taxed in the U.S., but only affect a small fraction of America’s wealthiest population.

As a refresher, capital gains taxes currently only apply to physical and financial assets, such as real estate properties, shares in publicly traded companies and jewelry, which were sold at a profit and thus are taxed as income.

That means the affected individuals would pay taxes on assets that appreciated in value through a year, even if they did not sell, bucking the current standard that only charges capital gains taxes on profits tallied from those assets.

For example, if the world’s richest man Elon Musk saw his $102 billion stake in Tesla at the end of 2023 rise to $122 billion by year’s end due to an increase in Tesla’s share price, he would pay taxes on the $20 billion increase in the value of his Tesla stock; current tax code requires he would have only had to pay capital gains tax if he actually offloaded his shares in the electric vehicle company.

Far less than .01% of taxpayers cleared the $100 million net worth threshold to pay unrealized capital gains taxes as just 9,850 Americans were worth that amount or more at the end of 2023, according to estimates from Henley & Partners and New World Wealth.

Chief Critics

Still, recent viral social media posts falsely claim the proposal will cause everyday Americans worth far below the threshold to feel impacts—the median American family has a net worth of $192,900, according to the Federal Reserve’s most recent data. Trump claimed at an Aug. 23 rally the Harris proposal on unrealized capital gains will impact “small business owners” who will be forced to sell their “restaurant immediately,” a seemingly misguided statement considering the small group the plan would impact. Trump allies have panned the proposal: Musk said it’s part of a policy plan that will lead to “bread lines” and billionaire venture capitalist Marc Andreessen said founding startups in the U.S. would become “completely implausible” due to the perceived disincentivization the proposed taxes would have for founders looking to build massive fortunes. The unrealized capital gains tax is “one of the worst tax ideas floating around,” Jay Clayton, commissioner of the Securities and Exchange Commission under Trump, told CNBC on Tuesday.

Key Background

The Biden administration estimates the unrealized capital gains tax would generate about $503 billion in tax revenue from the government’s 2025 to 2034 fiscal years. Experts suggest it’s unlikely the proposal makes much headway in Washington, considering Biden failed to pass the proposal when Democrats controlled both chambers of Congress from 2021 to 2023 and the headaches policymakers would have in enforcement. Several experts identified the issue that determining what actually qualifies as unrealized capital gains is a challenging exercise considering it’s not based on actual transactions, creating a “very onerous process,” Raymond James’ chief investment officer Larry Adam recently told Yahoo Finance.

The idea of taxing the U.S.’ richest residents is generally popular, and a 2022 YouGov survey found 57% of Americans believe billionaires don’t pay enough in taxes. A 2021 White House study found billionaires on Forbes’ annual list of the 400 richest Americans paid an effective average federal tax rate of 8.2% from 2010 to 2018, using a methodology including unrealized capital gains, far below the average federal income tax of more than 18% during the period, according to Tax Policy Center.

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Where the Trump and Harris campaigns agree on care proposals

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When you cut your way through all of the highly divisive political rhetoric surrounding this year’s election , you find there is actually common ground in some policy areas, especially when it comes to economic policy.

And there is some — emphasis on some — limited bipartisan focus on policies that aim to support families raising children. One factor behind this interest in so-called “care proposals” may be a shift in economic research interested in measuring not just what happens to caregivers and parents when they get support for their families, but the long-run effects on how these children fare as they grow up.

For more on this, “Marketplace Morning Report” host Sabri Ben-Achour spoke with Marketplace’s senior economics contributor Chris Farrell. Below is an edited transcript of their conversation.

Sabri Ben-Achour: So just how much agreement do you see across party lines when it comes to economic support for families with kids?

Chris Farrell: There are deep divisions in the best ways to help struggling families and their children. But there does seem to be more child-centric initiatives in-play on both the federal and state level. Republican Vice Presidential candidate JD Vance says he’d like to more than double the current federal child tax credit to $5,000. Democratic Presidential candidate Kamala Harris wants families with a newborn to get a $6,000 child tax credit and to boost the credit for other kids in the family . Now, the details differ in important ways, of course, but the shared emphasis, it’s intriguing.

Ben-Achour: Sometimes when there is a consensus on something in research, it does not necessarily translate to a consensus in politics on that issue. So I’m wondering, why do you think there is this slight alignment on the issue of children and families?

Farrell: Economist and blogger Noah Smith , he notes that economic paradigms are more important as ways of thinking about policy, rather than a list of actual policies. And I thought about that framing while reviewing a National Bureau of Economic Research program report on a conference on children and families. And the report notes that historically, research largely focused on the risk that unconditional support for low-income families it might sustain rather than alleviate poverty.

Ben-Achour: That idea is something I remember hearing in decades past. You know, the idea that that helping people disincentivizes them from working. Does that actually hold any water when you look at it?

Farrell: You know many researchers these days, they find little effect on labor supply or marriage rates from safety net programs. Instead, academic research now puts a greater emphasis on investigating the long-term impact of safety net programs on children and their human capital development. And the research agenda in recent years has shown, and I quote from the summary, significant positive effects of safety net programs on short-run children outcomes , as well as longer term measures.

Ben-Achour: So if there has been a shift in the focus of economic research focusing on the cost of caregiving, the potential gains for children from public policy, do you think that politicians are registering that and trying to fix it?

Farrell: My sense is that everyone acknowledges that parents and families are really stretched. The kind of research we’re talking about, it’s an inspiration for ideas and policies with the best chance of healthy human capital development for children from families that live on low and unstable incomes.

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What We Know About Kamala Harris’s $5 Trillion Tax Plan So Far

The vice president supports the tax increases proposed by the Biden White House, according to her campaign.

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By Andrew Duehren

Reporting from Washington

In a campaign otherwise light on policy specifics, Vice President Kamala Harris this week quietly rolled out her most detailed, far-ranging proposal yet: nearly $5 trillion in tax increases over a decade.

That’s how much more revenue the federal government would raise if it adopted a number of tax increases that President Biden proposed in the spring . Ms. Harris’s campaign said this week that she supported those tax hikes, which were thoroughly laid out in the most recent federal budget plan prepared by the Biden administration.

No one making less than $400,000 a year would see their taxes go up under the plan. Instead, Ms. Harris is seeking to significantly raise taxes on the wealthiest Americans and large corporations. Congress has previously rejected many of these tax ideas, even when Democrats controlled both chambers.

While tax policy is right now a subplot in a turbulent presidential campaign, it will be a primary policy issue in Washington next year. The next president will have to work with Congress to address the tax cuts Donald J. Trump signed into law in 2017. Many of those tax cuts expire after 2025, meaning millions of Americans will see their taxes go up if lawmakers don’t reach a deal next year.

Here’s an overview of what we now know — and still don’t know — about the Democratic nominee’s views on taxes.

Higher taxes on corporations

The most recent White House budget includes several proposals that would raise taxes on large corporations . Chief among them is raising the corporate tax rate to 28 percent from 21 percent, a step that the Treasury Department estimated could bring in $1.3 trillion in revenue over the next 10 years.

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