research objectives questions and hypotheses

Research Aims, Objectives & Questions

The “Golden Thread” Explained Simply (+ Examples)

By: David Phair (PhD) and Alexandra Shaeffer (PhD) | June 2022

The research aims , objectives and research questions (collectively called the “golden thread”) are arguably the most important thing you need to get right when you’re crafting a research proposal , dissertation or thesis . We receive questions almost every day about this “holy trinity” of research and there’s certainly a lot of confusion out there, so we’ve crafted this post to help you navigate your way through the fog.

Overview: The Golden Thread

• What is the golden thread
• What are research aims ( examples )
• What are research objectives ( examples )
• What are research questions ( examples )
• The importance of alignment in the golden thread

What is the “golden thread”?  

The golden thread simply refers to the collective research aims , research objectives , and research questions for any given project (i.e., a dissertation, thesis, or research paper ). These three elements are bundled together because it’s extremely important that they align with each other, and that the entire research project aligns with them.

Importantly, the golden thread needs to weave its way through the entirety of any research project , from start to end. In other words, it needs to be very clearly defined right at the beginning of the project (the topic ideation and proposal stage) and it needs to inform almost every decision throughout the rest of the project. For example, your research design and methodology will be heavily influenced by the golden thread (we’ll explain this in more detail later), as well as your literature review.

The research aims, objectives and research questions (the golden thread) define the focus and scope ( the delimitations ) of your research project. In other words, they help ringfence your dissertation or thesis to a relatively narrow domain, so that you can “go deep” and really dig into a specific problem or opportunity. They also help keep you on track , as they act as a litmus test for relevance. In other words, if you’re ever unsure whether to include something in your document, simply ask yourself the question, “does this contribute toward my research aims, objectives or questions?”. If it doesn’t, chances are you can drop it.

Alright, enough of the fluffy, conceptual stuff. Let’s get down to business and look at what exactly the research aims, objectives and questions are and outline a few examples to bring these concepts to life.

Research Aims: What are they?

Simply put, the research aim(s) is a statement that reflects the broad overarching goal (s) of the research project. Research aims are fairly high-level (low resolution) as they outline the general direction of the research and what it’s trying to achieve .

Research Aims: Examples  

True to the name, research aims usually start with the wording “this research aims to…”, “this research seeks to…”, and so on. For example:

“This research aims to explore employee experiences of digital transformation in retail HR.”   “This study sets out to assess the interaction between student support and self-care on well-being in engineering graduate students”  

As you can see, these research aims provide a high-level description of what the study is about and what it seeks to achieve. They’re not hyper-specific or action-oriented, but they’re clear about what the study’s focus is and what is being investigated.

Need a helping hand?

Research Objectives: What are they?

The research objectives take the research aims and make them more practical and actionable . In other words, the research objectives showcase the steps that the researcher will take to achieve the research aims.

The research objectives need to be far more specific (higher resolution) and actionable than the research aims. In fact, it’s always a good idea to craft your research objectives using the “SMART” criteria. In other words, they should be specific, measurable, achievable, relevant and time-bound”.

Research Objectives: Examples  

Let’s look at two examples of research objectives. We’ll stick with the topic and research aims we mentioned previously.  

For the digital transformation topic:

To observe the retail HR employees throughout the digital transformation. To assess employee perceptions of digital transformation in retail HR. To identify the barriers and facilitators of digital transformation in retail HR.

And for the student wellness topic:

To determine whether student self-care predicts the well-being score of engineering graduate students. To determine whether student support predicts the well-being score of engineering students. To assess the interaction between student self-care and student support when predicting well-being in engineering graduate students.

  As you can see, these research objectives clearly align with the previously mentioned research aims and effectively translate the low-resolution aims into (comparatively) higher-resolution objectives and action points . They give the research project a clear focus and present something that resembles a research-based “to-do” list.

Research Questions: What are they?

Finally, we arrive at the all-important research questions. The research questions are, as the name suggests, the key questions that your study will seek to answer . Simply put, they are the core purpose of your dissertation, thesis, or research project. You’ll present them at the beginning of your document (either in the introduction chapter or literature review chapter) and you’ll answer them at the end of your document (typically in the discussion and conclusion chapters).  

The research questions will be the driving force throughout the research process. For example, in the literature review chapter, you’ll assess the relevance of any given resource based on whether it helps you move towards answering your research questions. Similarly, your methodology and research design will be heavily influenced by the nature of your research questions. For instance, research questions that are exploratory in nature will usually make use of a qualitative approach, whereas questions that relate to measurement or relationship testing will make use of a quantitative approach.  

Let’s look at some examples of research questions to make this more tangible.

Research Questions: Examples  

Again, we’ll stick with the research aims and research objectives we mentioned previously.  

For the digital transformation topic (which would be qualitative in nature):

How do employees perceive digital transformation in retail HR? What are the barriers and facilitators of digital transformation in retail HR?  

And for the student wellness topic (which would be quantitative in nature):

Does student self-care predict the well-being scores of engineering graduate students? Does student support predict the well-being scores of engineering students? Do student self-care and student support interact when predicting well-being in engineering graduate students?  

You’ll probably notice that there’s quite a formulaic approach to this. In other words, the research questions are basically the research objectives “converted” into question format. While that is true most of the time, it’s not always the case. For example, the first research objective for the digital transformation topic was more or less a step on the path toward the other objectives, and as such, it didn’t warrant its own research question.  

So, don’t rush your research questions and sloppily reword your objectives as questions. Carefully think about what exactly you’re trying to achieve (i.e. your research aim) and the objectives you’ve set out, then craft a set of well-aligned research questions . Also, keep in mind that this can be a somewhat iterative process , where you go back and tweak research objectives and aims to ensure tight alignment throughout the golden thread.

The importance of strong alignment 

Alignment is the keyword here and we have to stress its importance . Simply put, you need to make sure that there is a very tight alignment between all three pieces of the golden thread. If your research aims and research questions don’t align, for example, your project will be pulling in different directions and will lack focus . This is a common problem students face and can cause many headaches (and tears), so be warned.

Take the time to carefully craft your research aims, objectives and research questions before you run off down the research path. Ideally, get your research supervisor/advisor to review and comment on your golden thread before you invest significant time into your project, and certainly before you start collecting data .  

Recap: The golden thread

In this post, we unpacked the golden thread of research, consisting of the research aims , research objectives and research questions . You can jump back to any section using the links below.

As always, feel free to leave a comment below – we always love to hear from you. Also, if you’re interested in 1-on-1 support, take a look at our private coaching service here.

Psst... there’s more!

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

40 Comments

Thank you very much for your great effort put. As an Undergraduate taking Demographic Research & Methodology, I’ve been trying so hard to understand clearly what is a Research Question, Research Aim and the Objectives in a research and the relationship between them etc. But as for now I’m thankful that you’ve solved my problem.

Well appreciated. This has helped me greatly in doing my dissertation.

An so delighted with this wonderful information thank you a lot.

so impressive i have benefited a lot looking forward to learn more on research.

I am very happy to have carefully gone through this well researched article.

Infact,I used to be phobia about anything research, because of my poor understanding of the concepts.

Now,I get to know that my research question is the same as my research objective(s) rephrased in question format.

I please I would need a follow up on the subject,as I intends to join the team of researchers. Thanks once again.

Thanks so much. This was really helpful.

I know you pepole have tried to break things into more understandable and easy format. And God bless you. Keep it up

i found this document so useful towards my study in research methods. thanks so much.

This is my 2nd read topic in your course and I should commend the simplified explanations of each part. I’m beginning to understand and absorb the use of each part of a dissertation/thesis. I’ll keep on reading your free course and might be able to avail the training course! Kudos!

Thank you! Better put that my lecture and helped to easily understand the basics which I feel often get brushed over when beginning dissertation work.

This is quite helpful. I like how the Golden thread has been explained and the needed alignment.

This is quite helpful. I really appreciate!

The article made it simple for researcher students to differentiate between three concepts.

Very innovative and educational in approach to conducting research.

I am very impressed with all these terminology, as I am a fresh student for post graduate, I am highly guided and I promised to continue making consultation when the need arise. Thanks a lot.

A very helpful piece. thanks, I really appreciate it .

Very well explained, and it might be helpful to many people like me.

Wish i had found this (and other) resource(s) at the beginning of my PhD journey… not in my writing up year… 😩 Anyways… just a quick question as i’m having some issues ordering my “golden thread”…. does it matter in what order you mention them? i.e., is it always first aims, then objectives, and finally the questions? or can you first mention the research questions and then the aims and objectives?

Thank you for a very simple explanation that builds upon the concepts in a very logical manner. Just prior to this, I read the research hypothesis article, which was equally very good. This met my primary objective.

My secondary objective was to understand the difference between research questions and research hypothesis, and in which context to use which one. However, I am still not clear on this. Can you kindly please guide?

In research, a research question is a clear and specific inquiry that the researcher wants to answer, while a research hypothesis is a tentative statement or prediction about the relationship between variables or the expected outcome of the study. Research questions are broader and guide the overall study, while hypotheses are specific and testable statements used in quantitative research. Research questions identify the problem, while hypotheses provide a focus for testing in the study.

Exactly what I need in this research journey, I look forward to more of your coaching videos.

This helped a lot. Thanks so much for the effort put into explaining it.

What data source in writing dissertation/Thesis requires?

What is data source covers when writing dessertation/thesis

This is quite useful thanks

I’m excited and thankful. I got so much value which will help me progress in my thesis.

where are the locations of the reserch statement, research objective and research question in a reserach paper? Can you write an ouline that defines their places in the researh paper?

Very helpful and important tips on Aims, Objectives and Questions.

Thank you so much for making research aim, research objectives and research question so clear. This will be helpful to me as i continue with my thesis.

Thanks much for this content. I learned a lot. And I am inspired to learn more. I am still struggling with my preparation for dissertation outline/proposal. But I consistently follow contents and tutorials and the new FB of GRAD Coach. Hope to really become confident in writing my dissertation and successfully defend it.

As a researcher and lecturer, I find splitting research goals into research aims, objectives, and questions is unnecessarily bureaucratic and confusing for students. For most biomedical research projects, including ‘real research’, 1-3 research questions will suffice (numbers may differ by discipline).

Awesome! Very important resources and presented in an informative way to easily understand the golden thread. Indeed, thank you so much.

Well explained

The blog article on research aims, objectives, and questions by Grad Coach is a clear and insightful guide that aligns with my experiences in academic research. The article effectively breaks down the often complex concepts of research aims and objectives, providing a straightforward and accessible explanation. Drawing from my own research endeavors, I appreciate the practical tips offered, such as the need for specificity and clarity when formulating research questions. The article serves as a valuable resource for students and researchers, offering a concise roadmap for crafting well-defined research goals and objectives. Whether you’re a novice or an experienced researcher, this article provides practical insights that contribute to the foundational aspects of a successful research endeavor.

A great thanks for you. it is really amazing explanation. I grasp a lot and one step up to research knowledge.

I really found these tips helpful. Thank you very much Grad Coach.

I found this article helpful. Thanks for sharing this.

thank you so much, the explanation and examples are really helpful

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Research Questions & Hypotheses

Generally, in quantitative studies, reviewers expect hypotheses rather than research questions. However, both research questions and hypotheses serve different purposes and can be beneficial when used together.

Research Questions

Clarify the research’s aim (farrugia et al., 2010).

• Research often begins with an interest in a topic, but a deep understanding of the subject is crucial to formulate an appropriate research question.
• Descriptive: “What factors most influence the academic achievement of senior high school students?”
• Comparative: “What is the performance difference between teaching methods A and B?”
• Relationship-based: “What is the relationship between self-efficacy and academic achievement?”
• Increasing knowledge about a subject can be achieved through systematic literature reviews, in-depth interviews with patients (and proxies), focus groups, and consultations with field experts.
• Some funding bodies, like the Canadian Institute for Health Research, recommend conducting a systematic review or a pilot study before seeking grants for full trials.
• The presence of multiple research questions in a study can complicate the design, statistical analysis, and feasibility.
• It’s advisable to focus on a single primary research question for the study.
• The primary question, clearly stated at the end of a grant proposal’s introduction, usually specifies the study population, intervention, and other relevant factors.
• The FINER criteria underscore aspects that can enhance the chances of a successful research project, including specifying the population of interest, aligning with scientific and public interest, clinical relevance, and contribution to the field, while complying with ethical and national research standards.

• The P ICOT approach is crucial in developing the study’s framework and protocol, influencing inclusion and exclusion criteria and identifying patient groups for inclusion.

• Defining the specific population, intervention, comparator, and outcome helps in selecting the right outcome measurement tool.
• The more precise the population definition and stricter the inclusion and exclusion criteria, the more significant the impact on the interpretation, applicability, and generalizability of the research findings.
• A restricted study population enhances internal validity but may limit the study’s external validity and generalizability to clinical practice.
• A broadly defined study population may better reflect clinical practice but could increase bias and reduce internal validity.
• An inadequately formulated research question can negatively impact study design, potentially leading to ineffective outcomes and affecting publication prospects.

Checklist: Good research questions for social science projects (Panke, 2018)

Research Hypotheses

Present the researcher’s predictions based on specific statements.

• These statements define the research problem or issue and indicate the direction of the researcher’s predictions.
• Formulating the research question and hypothesis from existing data (e.g., a database) can lead to multiple statistical comparisons and potentially spurious findings due to chance.
• The research or clinical hypothesis, derived from the research question, shapes the study’s key elements: sampling strategy, intervention, comparison, and outcome variables.
• Hypotheses can express a single outcome or multiple outcomes.
• After statistical testing, the null hypothesis is either rejected or not rejected based on whether the study’s findings are statistically significant.
• Hypothesis testing helps determine if observed findings are due to true differences and not chance.
• Hypotheses can be 1-sided (specific direction of difference) or 2-sided (presence of a difference without specifying direction).
• 2-sided hypotheses are generally preferred unless there’s a strong justification for a 1-sided hypothesis.
• A solid research hypothesis, informed by a good research question, influences the research design and paves the way for defining clear research objectives.

Types of Research Hypothesis

• In a Y-centered research design, the focus is on the dependent variable (DV) which is specified in the research question. Theories are then used to identify independent variables (IV) and explain their causal relationship with the DV.
• Example: “An increase in teacher-led instructional time (IV) is likely to improve student reading comprehension scores (DV), because extensive guided practice under expert supervision enhances learning retention and skill mastery.”
• Hypothesis Explanation: The dependent variable (student reading comprehension scores) is the focus, and the hypothesis explores how changes in the independent variable (teacher-led instructional time) affect it.
• In X-centered research designs, the independent variable is specified in the research question. Theories are used to determine potential dependent variables and the causal mechanisms at play.
• Example: “Implementing technology-based learning tools (IV) is likely to enhance student engagement in the classroom (DV), because interactive and multimedia content increases student interest and participation.”
• Hypothesis Explanation: The independent variable (technology-based learning tools) is the focus, with the hypothesis exploring its impact on a potential dependent variable (student engagement).
• Probabilistic hypotheses suggest that changes in the independent variable are likely to lead to changes in the dependent variable in a predictable manner, but not with absolute certainty.
• Example: “The more teachers engage in professional development programs (IV), the more their teaching effectiveness (DV) is likely to improve, because continuous training updates pedagogical skills and knowledge.”
• Hypothesis Explanation: This hypothesis implies a probable relationship between the extent of professional development (IV) and teaching effectiveness (DV).
• Deterministic hypotheses state that a specific change in the independent variable will lead to a specific change in the dependent variable, implying a more direct and certain relationship.
• Example: “If the school curriculum changes from traditional lecture-based methods to project-based learning (IV), then student collaboration skills (DV) are expected to improve because project-based learning inherently requires teamwork and peer interaction.”
• Hypothesis Explanation: This hypothesis presumes a direct and definite outcome (improvement in collaboration skills) resulting from a specific change in the teaching method.
• Example : “Students who identify as visual learners will score higher on tests that are presented in a visually rich format compared to tests presented in a text-only format.”
• Explanation : This hypothesis aims to describe the potential difference in test scores between visual learners taking visually rich tests and text-only tests, without implying a direct cause-and-effect relationship.
• Example : “Teaching method A will improve student performance more than method B.”
• Explanation : This hypothesis compares the effectiveness of two different teaching methods, suggesting that one will lead to better student performance than the other. It implies a direct comparison but does not necessarily establish a causal mechanism.
• Example : “Students with higher self-efficacy will show higher levels of academic achievement.”
• Explanation : This hypothesis predicts a relationship between the variable of self-efficacy and academic achievement. Unlike a causal hypothesis, it does not necessarily suggest that one variable causes changes in the other, but rather that they are related in some way.

Tips for developing research questions and hypotheses for research studies

• Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.
• Learn about current trends and technological advances on the topic.
• Seek careful input from experts, mentors, colleagues, and collaborators to refine your research question as this will aid in developing the research question and guide the research study.
• Use the FINER criteria in the development of the research question.
• Ensure that the research question follows PICOT format.
• Develop a research hypothesis from the research question.
• Ensure that the research question and objectives are answerable, feasible, and clinically relevant.

If your research hypotheses are derived from your research questions, particularly when multiple hypotheses address a single question, it’s recommended to use both research questions and hypotheses. However, if this isn’t the case, using hypotheses over research questions is advised. It’s important to note these are general guidelines, not strict rules. If you opt not to use hypotheses, consult with your supervisor for the best approach.

Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives.  Canadian journal of surgery. Journal canadien de chirurgie ,  53 (4), 278–281.

Hulley, S. B., Cummings, S. R., Browner, W. S., Grady, D., & Newman, T. B. (2007). Designing clinical research. Philadelphia.

Panke, D. (2018). Research design & method selection: Making good choices in the social sciences.  Research Design & Method Selection , 1-368.

Educational resources and simple solutions for your research journey

What Are Research Objectives and How to Write Them (with Examples)

Table of Contents

Introduction

Research is at the center of everything researchers do, and setting clear, well-defined research objectives plays a pivotal role in guiding scholars toward their desired outcomes. Research papers are essential instruments for researchers to effectively communicate their work. Among the many sections that constitute a research paper, the introduction plays a key role in providing a background and setting the context. 1 Research objectives, which define the aims of the study, are usually stated in the introduction. Every study has a research question that the authors are trying to answer, and the objective is an active statement about how the study will answer this research question. These objectives help guide the development and design of the study and steer the research in the appropriate direction; if this is not clearly defined, a project can fail!

Research studies have a research question, research hypothesis, and one or more research objectives. A research question is what a study aims to answer, and a research hypothesis is a predictive statement about the relationship between two or more variables, which the study sets out to prove or disprove. Objectives are specific, measurable goals that the study aims to achieve. The difference between these three is illustrated by the following example:

• Research question : How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?
• Research hypothesis : Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).
• Research objective : To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

This article discusses the importance of clear, well-thought out objectives and suggests methods to write them clearly.

What is the introduction in research papers?

Research objectives are usually included in the introduction section. This section is the first that the readers will read so it is essential that it conveys the subject matter appropriately and is well written to create a good first impression. A good introduction sets the tone of the paper and clearly outlines the contents so that the readers get a quick snapshot of what to expect.

A good introduction should aim to: 2,3

• Indicate the main subject area, its importance, and cite previous literature on the subject
• Define the gap(s) in existing research, ask a research question, and state the objectives
• Announce the present research and outline its novelty and significance
• Avoid repeating the Abstract, providing unnecessary information, and claiming novelty without accurate supporting information.

Why are research objectives important?

Objectives can help you stay focused and steer your research in the required direction. They help define and limit the scope of your research, which is important to efficiently manage your resources and time. The objectives help to create and maintain the overall structure, and specify two main things—the variables and the methods of quantifying the variables.

A good research objective:

• defines the scope of the study
• gives direction to the research
• helps maintain focus and avoid diversions from the topic
• minimizes wastage of resources like time, money, and energy

Types of research objectives

Research objectives can be broadly classified into general and specific objectives . 4 General objectives state what the research expects to achieve overall while specific objectives break this down into smaller, logically connected parts, each of which addresses various parts of the research problem. General objectives are the main goals of the study and are usually fewer in number while specific objectives are more in number because they address several aspects of the research problem.

Example (general objective): To investigate the factors influencing the financial performance of firms listed in the New York Stock Exchange market.

Example (specific objective): To assess the influence of firm size on the financial performance of firms listed in the New York Stock Exchange market.

In addition to this broad classification, research objectives can be grouped into several categories depending on the research problem, as given in Table 1.

Table 1: Types of research objectives

Characteristics of research objectives

Research objectives must start with the word “To” because this helps readers identify the objective in the absence of headings and appropriate sectioning in research papers. 5,6

• A good objective is SMART (mostly applicable to specific objectives):
• Specific—clear about the what, why, when, and how
• Measurable—identifies the main variables of the study and quantifies the targets
• Achievable—attainable using the available time and resources
• Realistic—accurately addresses the scope of the problem
• Time-bound—identifies the time in which each step will be completed
• Research objectives clarify the purpose of research.
• They help understand the relationship and dissimilarities between variables.
• They provide a direction that helps the research to reach a definite conclusion.

How to write research objectives?

Research objectives can be written using the following steps: 7

• State your main research question clearly and concisely.
• Describe the ultimate goal of your study, which is similar to the research question but states the intended outcomes more definitively.
• Divide this main goal into subcategories to develop your objectives.
• Limit the number of objectives (1-2 general; 3-4 specific)
• Assess each objective using the SMART
• Start each objective with an action verb like assess, compare, determine, evaluate, etc., which makes the research appear more actionable.
• Use specific language without making the sentence data heavy.
• The most common section to add the objectives is the introduction and after the problem statement.
• Add the objectives to the abstract (if there is one).
• State the general objective first, followed by the specific objectives.

Formulating research objectives

Formulating research objectives has the following five steps, which could help researchers develop a clear objective: 8

• Identify the research problem.
• Review past studies on subjects similar to your problem statement, that is, studies that use similar methods, variables, etc.
• Identify the research gaps the current study should cover based on your literature review. These gaps could be theoretical, methodological, or conceptual.
• Define the research question(s) based on the gaps identified.
• Revise/relate the research problem based on the defined research question and the gaps identified. This is to confirm that there is an actual need for a study on the subject based on the gaps in literature.
• Identify and write the general and specific objectives.
• Incorporate the objectives into the study.

Advantages of research objectives

Adding clear research objectives has the following advantages: 4,8

• Maintains the focus and direction of the research
• Optimizes allocation of resources with minimal wastage
• Acts as a foundation for defining appropriate research questions and hypotheses
• Provides measurable outcomes that can help evaluate the success of the research
• Determines the feasibility of the research by helping to assess the availability of required resources
• Ensures relevance of the study to the subject and its contribution to existing literature

Disadvantages of research objectives

Research objectives also have few disadvantages, as listed below: 8

• Absence of clearly defined objectives can lead to ambiguity in the research process
• Unintentional bias could affect the validity and accuracy of the research findings

Key takeaways

• Research objectives are concise statements that describe what the research is aiming to achieve.
• They define the scope and direction of the research and maintain focus.
• The objectives should be SMART—specific, measurable, achievable, realistic, and time-bound.
• Clear research objectives help avoid collection of data or resources not required for the study.
• Well-formulated specific objectives help develop the overall research methodology, including data collection, analysis, interpretation, and utilization.
• Research objectives should cover all aspects of the problem statement in a coherent way.
• They should be clearly stated using action verbs.

Frequently asked questions on research objectives

Q: what’s the difference between research objectives and aims 9.

A: Research aims are statements that reflect the broad goal(s) of the study and outline the general direction of the research. They are not specific but clearly define the focus of the study.

Example: This research aims to explore employee experiences of digital transformation in retail HR.

Research objectives focus on the action to be taken to achieve the aims. They make the aims more practical and should be specific and actionable.

Example: To observe the retail HR employees throughout the digital transformation.

Q: What are the examples of research objectives, both general and specific?

A: Here are a few examples of research objectives:

• To identify the antiviral chemical constituents in Mumbukura gitoniensis (general)
• To carry out solvent extraction of dried flowers of Mumbukura gitoniensis and isolate the constituents. (specific)
• To determine the antiviral activity of each of the isolated compounds. (specific)
• To examine the extent, range, and method of coral reef rehabilitation projects in five shallow reef areas adjacent to popular tourist destinations in the Philippines.
• To investigate species richness of mammal communities in five protected areas over the past 20 years.
• To evaluate the potential application of AI techniques for estimating best-corrected visual acuity from fundus photographs with and without ancillary information.
• To investigate whether sport influences psychological parameters in the personality of asthmatic children.

Q: How do I develop research objectives?

A: Developing research objectives begins with defining the problem statement clearly, as illustrated by Figure 1. Objectives specify how the research question will be answered and they determine what is to be measured to test the hypothesis.

Q: Are research objectives measurable?

A: The word “measurable” implies that something is quantifiable. In terms of research objectives, this means that the source and method of collecting data are identified and that all these aspects are feasible for the research. Some metrics can be created to measure your progress toward achieving your objectives.

Q: Can research objectives change during the study?

A: Revising research objectives during the study is acceptable in situations when the selected methodology is not progressing toward achieving the objective, or if there are challenges pertaining to resources, etc. One thing to keep in mind is the time and resources you would have to complete your research after revising the objectives. Thus, as long as your problem statement and hypotheses are unchanged, minor revisions to the research objectives are acceptable.

Q: What is the difference between research questions and research objectives? 10

Q: Are research objectives the same as hypotheses?

A: No, hypotheses are predictive theories that are expressed in general terms. Research objectives, which are more specific, are developed from hypotheses and aim to test them. A hypothesis can be tested using several methods and each method will have different objectives because the methodology to be used could be different. A hypothesis is developed based on observation and reasoning; it is a calculated prediction about why a particular phenomenon is occurring. To test this prediction, different research objectives are formulated. Here’s a simple example of both a research hypothesis and research objective.

Research hypothesis : Employees who arrive at work earlier are more productive.

Research objective : To assess whether employees who arrive at work earlier are more productive.

To summarize, research objectives are an important part of research studies and should be written clearly to effectively communicate your research. We hope this article has given you a brief insight into the importance of using clearly defined research objectives and how to formulate them.

• Farrugia P, Petrisor BA, Farrokhyar F, Bhandari M. Practical tips for surgical research: Research questions, hypotheses and objectives. Can J Surg. 2010 Aug;53(4):278-81.
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• Writing a scientific paper: Introduction. UCI libraries website. Accessed June 15, 2023. https://guides.lib.uci.edu/c.php?g=334338&p=2249903
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• Bartle P. SMART Characteristics of good objectives. Community empowerment collective website. Accessed June 16, 2023. https://cec.vcn.bc.ca/cmp/modules/pd-smar.htm
• Research objectives. Studyprobe website. Accessed June 18, 2023. https://www.studyprobe.in/2022/08/research-objectives.html
• Corredor F. How to write objectives in a research paper. wikiHow website. Accessed June 18, 2023. https://www.wikihow.com/Write-Objectives-in-a-Research-Proposal
• Research objectives: Definition, types, characteristics, advantages. AccountingNest website. Accessed June 15, 2023. https://www.accountingnest.com/articles/research/research-objectives
• Phair D., Shaeffer A. Research aims, objectives & questions. GradCoach website. Accessed June 20, 2023. https://gradcoach.com/research-aims-objectives-questions/
• Understanding the difference between research questions and objectives. Accessed June 21, 2023. https://board.researchersjob.com/blog/research-questions-and-objectives

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Research Objectives – Types, Examples and Writing Guide

Table of Contents

Research Objectives

Research objectives refer to the specific goals or aims of a research study. They provide a clear and concise description of what the researcher hopes to achieve by conducting the research . The objectives are typically based on the research questions and hypotheses formulated at the beginning of the study and are used to guide the research process.

Types of Research Objectives

Here are the different types of research objectives in research:

• Exploratory Objectives: These objectives are used to explore a topic, issue, or phenomenon that has not been studied in-depth before. The aim of exploratory research is to gain a better understanding of the subject matter and generate new ideas and hypotheses .
• Descriptive Objectives: These objectives aim to describe the characteristics, features, or attributes of a particular population, group, or phenomenon. Descriptive research answers the “what” questions and provides a snapshot of the subject matter.
• Explanatory Objectives : These objectives aim to explain the relationships between variables or factors. Explanatory research seeks to identify the cause-and-effect relationships between different phenomena.
• Predictive Objectives: These objectives aim to predict future events or outcomes based on existing data or trends. Predictive research uses statistical models to forecast future trends or outcomes.
• Evaluative Objectives : These objectives aim to evaluate the effectiveness or impact of a program, intervention, or policy. Evaluative research seeks to assess the outcomes or results of a particular intervention or program.
• Prescriptive Objectives: These objectives aim to provide recommendations or solutions to a particular problem or issue. Prescriptive research identifies the best course of action based on the results of the study.
• Diagnostic Objectives : These objectives aim to identify the causes or factors contributing to a particular problem or issue. Diagnostic research seeks to uncover the underlying reasons for a particular phenomenon.
• Comparative Objectives: These objectives aim to compare two or more groups, populations, or phenomena to identify similarities and differences. Comparative research is used to determine which group or approach is more effective or has better outcomes.
• Historical Objectives: These objectives aim to examine past events, trends, or phenomena to gain a better understanding of their significance and impact. Historical research uses archival data, documents, and records to study past events.
• Ethnographic Objectives : These objectives aim to understand the culture, beliefs, and practices of a particular group or community. Ethnographic research involves immersive fieldwork and observation to gain an insider’s perspective of the group being studied.
• Action-oriented Objectives: These objectives aim to bring about social or organizational change. Action-oriented research seeks to identify practical solutions to social problems and to promote positive change in society.
• Conceptual Objectives: These objectives aim to develop new theories, models, or frameworks to explain a particular phenomenon or set of phenomena. Conceptual research seeks to provide a deeper understanding of the subject matter by developing new theoretical perspectives.
• Methodological Objectives: These objectives aim to develop and improve research methods and techniques. Methodological research seeks to advance the field of research by improving the validity, reliability, and accuracy of research methods and tools.
• Theoretical Objectives : These objectives aim to test and refine existing theories or to develop new theoretical perspectives. Theoretical research seeks to advance the field of knowledge by testing and refining existing theories or by developing new theoretical frameworks.
• Measurement Objectives : These objectives aim to develop and validate measurement instruments, such as surveys, questionnaires, and tests. Measurement research seeks to improve the quality and reliability of data collection and analysis by developing and testing new measurement tools.
• Design Objectives : These objectives aim to develop and refine research designs, such as experimental, quasi-experimental, and observational designs. Design research seeks to improve the quality and validity of research by developing and testing new research designs.
• Sampling Objectives: These objectives aim to develop and refine sampling techniques, such as probability and non-probability sampling methods. Sampling research seeks to improve the representativeness and generalizability of research findings by developing and testing new sampling techniques.

How to Write Research Objectives

Writing clear and concise research objectives is an important part of any research project, as it helps to guide the study and ensure that it is focused and relevant. Here are some steps to follow when writing research objectives:

• Identify the research problem : Before you can write research objectives, you need to identify the research problem you are trying to address. This should be a clear and specific problem that can be addressed through research.
• Define the research questions : Based on the research problem, define the research questions you want to answer. These questions should be specific and should guide the research process.
• Identify the variables : Identify the key variables that you will be studying in your research. These are the factors that you will be measuring, manipulating, or analyzing to answer your research questions.
• Write specific objectives: Write specific, measurable objectives that will help you answer your research questions. These objectives should be clear and concise and should indicate what you hope to achieve through your research.
• Use the SMART criteria: To ensure that your research objectives are well-defined and achievable, use the SMART criteria. This means that your objectives should be Specific, Measurable, Achievable, Relevant, and Time-bound.
• Revise and refine: Once you have written your research objectives, revise and refine them to ensure that they are clear, concise, and achievable. Make sure that they align with your research questions and variables, and that they will help you answer your research problem.

Example of Research Objectives

Examples of research objectives Could be:

Research Objectives for the topic of “The Impact of Artificial Intelligence on Employment”:

• To investigate the effects of the adoption of AI on employment trends across various industries and occupations.
• To explore the potential for AI to create new job opportunities and transform existing roles in the workforce.
• To examine the social and economic implications of the widespread use of AI for employment, including issues such as income inequality and access to education and training.
• To identify the skills and competencies that will be required for individuals to thrive in an AI-driven workplace, and to explore the role of education and training in developing these skills.
• To evaluate the ethical and legal considerations surrounding the use of AI for employment, including issues such as bias, privacy, and the responsibility of employers and policymakers to protect workers’ rights.

When to Write Research Objectives

• At the beginning of a research project : Research objectives should be identified and written down before starting a research project. This helps to ensure that the project is focused and that data collection and analysis efforts are aligned with the intended purpose of the research.
• When refining research questions: Writing research objectives can help to clarify and refine research questions. Objectives provide a more concrete and specific framework for addressing research questions, which can improve the overall quality and direction of a research project.
• After conducting a literature review : Conducting a literature review can help to identify gaps in knowledge and areas that require further research. Writing research objectives can help to define and focus the research effort in these areas.
• When developing a research proposal: Research objectives are an important component of a research proposal. They help to articulate the purpose and scope of the research, and provide a clear and concise summary of the expected outcomes and contributions of the research.
• When seeking funding for research: Funding agencies often require a detailed description of research objectives as part of a funding proposal. Writing clear and specific research objectives can help to demonstrate the significance and potential impact of a research project, and increase the chances of securing funding.
• When designing a research study : Research objectives guide the design and implementation of a research study. They help to identify the appropriate research methods, sampling strategies, data collection and analysis techniques, and other relevant aspects of the study design.
• When communicating research findings: Research objectives provide a clear and concise summary of the main research questions and outcomes. They are often included in research reports and publications, and can help to ensure that the research findings are communicated effectively and accurately to a wide range of audiences.
• When evaluating research outcomes : Research objectives provide a basis for evaluating the success of a research project. They help to measure the degree to which research questions have been answered and the extent to which research outcomes have been achieved.
• When conducting research in a team : Writing research objectives can facilitate communication and collaboration within a research team. Objectives provide a shared understanding of the research purpose and goals, and can help to ensure that team members are working towards a common objective.

Purpose of Research Objectives

Some of the main purposes of research objectives include:

• To clarify the research question or problem : Research objectives help to define the specific aspects of the research question or problem that the study aims to address. This makes it easier to design a study that is focused and relevant.
• To guide the research design: Research objectives help to determine the research design, including the research methods, data collection techniques, and sampling strategy. This ensures that the study is structured and efficient.
• To measure progress : Research objectives provide a way to measure progress throughout the research process. They help the researcher to evaluate whether they are on track and meeting their goals.
• To communicate the research goals : Research objectives provide a clear and concise description of the research goals. This helps to communicate the purpose of the study to other researchers, stakeholders, and the general public.

Advantages of Research Objectives

Here are some advantages of having well-defined research objectives:

• Focus : Research objectives help to focus the research effort on specific areas of inquiry. By identifying clear research questions, the researcher can narrow down the scope of the study and avoid getting sidetracked by irrelevant information.
• Clarity : Clearly stated research objectives provide a roadmap for the research study. They provide a clear direction for the research, making it easier for the researcher to stay on track and achieve their goals.
• Measurability : Well-defined research objectives provide measurable outcomes that can be used to evaluate the success of the research project. This helps to ensure that the research is effective and that the research goals are achieved.
• Feasibility : Research objectives help to ensure that the research project is feasible. By clearly defining the research goals, the researcher can identify the resources required to achieve those goals and determine whether those resources are available.
• Relevance : Research objectives help to ensure that the research study is relevant and meaningful. By identifying specific research questions, the researcher can ensure that the study addresses important issues and contributes to the existing body of knowledge.

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• 10 Research Question Examples to Guide Your Research Project

10 Research Question Examples to Guide your Research Project

Published on October 30, 2022 by Shona McCombes . Revised on October 19, 2023.

The research question is one of the most important parts of your research paper , thesis or dissertation . It’s important to spend some time assessing and refining your question before you get started.

The exact form of your question will depend on a few things, such as the length of your project, the type of research you’re conducting, the topic , and the research problem . However, all research questions should be focused, specific, and relevant to a timely social or scholarly issue.

Once you’ve read our guide on how to write a research question , you can use these examples to craft your own.

Note that the design of your research question can depend on what method you are pursuing. Here are a few options for qualitative, quantitative, and statistical research questions.

Other interesting articles

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

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Handy Tips To Write A Clear Research Objectives With Examples

Introduction.

Research objectives play a crucial role in any research study. They provide a clear direction and purpose for the research, guiding the researcher in their investigation. Understanding research objectives is essential for conducting a successful study and achieving meaningful results.

In this comprehensive review, we will delve into the definition of research objectives, exploring their characteristics, types, and examples. We will also discuss the relationship between research objectives and research questions, as well as provide insights into how to write effective research objectives. Additionally, we will examine the role of research objectives in research methodology and highlight the importance of them in a study. By the end of this review, you will have a comprehensive understanding of research objectives and their significance in the research process.

Definition of Research Objectives: What Are They?

A research objective is defined as a clear and concise statement that outlines the specific goals and aims of a research study. These objectives are designed to be specific, measurable, achievable, relevant, and time-bound (SMART), ensuring they provide a structured pathway to accomplishing the intended outcomes of the project. Each objective serves as a foundational element that summarizes the purpose of your study, guiding the research activities and helping to measure progress toward the study’s goals. Additionally, research objectives are integral components of the research framework , establishing a clear direction that aligns with the overall research questions and hypotheses. This alignment helps to ensure that the study remains focused and relevant, facilitating the systematic collection, analysis, and interpretation of data.

Characteristics of Effective Research Objectives

Characteristics of research objectives include:

• Specific: Research objectives should be clear about the what, why, when, and how of the study.
• Measurable: Research objectives should identify the main variables of the study that can be measured or observed.
• Relevant: Research objectives should be relevant to the research topic and contribute to the overall understanding of the subject.
• Feasible: Research objectives should be achievable within the constraints of time, resources, and expertise available.
• Logical: Research objectives should follow a logical sequence and build upon each other to achieve the overall research goal.
• Observable: Research objectives should be observable or measurable in order to assess the progress and success of the research project.
• Unambiguous: Research objectives should be clear and unambiguous, leaving no room for interpretation or confusion.
• Measurable: Research objectives should be measurable, allowing for the collection of data and analysis of results.

By incorporating these characteristics into research objectives, researchers can ensure that their study is focused, achievable, and contributes to the body of knowledge in their field.

Types of Research Objectives

Research objective can be broadly classified into general and specific objectives. General objectives are broad statements that define the overall purpose of the research. They provide a broad direction for the study and help in setting the context. Specific objectives, on the other hand, are detailed objectives that describe what will be researched during the study. They are more focused and provide specific outcomes that the researcher aims to achieve. Specific objectives are derived from the general objectives and help in breaking down the research into smaller, manageable parts. The specific objectives should be clear, measurable, and achievable. They should be designed in a way that allows the researcher to answer the research questions and address the research problem.

In addition to general and specific objectives, research objective can also be categorized as descriptive or analytical objectives. Descriptive objectives focus on describing the characteristics or phenomena of a particular subject or population. They involve surveys, observations, and data collection to provide a detailed understanding of the subject. Analytical objectives, on the other hand, aim to analyze the relationships between variables or factors. They involve data analysis and interpretation to gain insights and draw conclusions.

Both descriptive and analytical objectives are important in research as they serve different purposes and contribute to a comprehensive understanding of the research topic.

Examples of Research Objectives

Here are some examples of research objectives in different fields:

1. Objective: To identify key characteristics and styles of Renaissance art.

This objective focuses on exploring the characteristics and styles of art during the Renaissance period. The research may involve analyzing various artworks, studying historical documents, and interviewing experts in the field.

2. Objective: To analyze modern art trends and their impact on society.

This objective aims to examine the current trends in modern art and understand how they influence society. The research may involve analyzing artworks, conducting surveys or interviews with artists and art enthusiasts, and studying the social and cultural implications of modern art.

3. Objective: To investigate the effects of exercise on mental health.

This objective focuses on studying the relationship between exercise and mental health. The research may involve conducting experiments or surveys to assess the impact of exercise on factors such as stress, anxiety, and depression.

4. Objective: To explore the factors influencing consumer purchasing decisions in the fashion industry.

This objective aims to understand the various factors that influence consumers’ purchasing decisions in the fashion industry. The research may involve conducting surveys, analyzing consumer behavior data, and studying the impact of marketing strategies on consumer choices.

5. Objective: To examine the effectiveness of a new drug in treating a specific medical condition.

This objective focuses on evaluating the effectiveness of a newly developed drug in treating a particular medical condition. The research may involve conducting clinical trials, analyzing patient data, and comparing the outcomes of the new drug with existing treatment options.

These examples demonstrate the diversity of research objectives across different disciplines. Each objective is specific, measurable, and achievable, providing a clear direction for the research study.

Aligning Research Objectives with Research Questions

Research objectives and research questions are essential components of a research project. Research objective describe what you intend your research project to accomplish. They summarize the approach and purpose of the project and provide a clear direction for the research. Research questions, on the other hand, are the starting point of any good research. They guide the overall direction of the research and help identify and focus on the research gaps .

The main difference between research questions and objectives is their form. Research questions are stated in a question form, while objectives are specific, measurable, and achievable goals that you aim to accomplish within a specified timeframe. Research questions are broad statements that provide a roadmap for the research, while objectives break down the research aim into smaller, actionable steps.

Research objectives and research questions work together to form the ‘golden thread’ of a research project. The research aim specifies what the study will answer, while the objectives and questions specify how the study will answer it. They provide a clear focus and scope for the research project, helping researchers stay on track and ensure that their study is meaningful and relevant.

When writing research objectives and questions, it is important to be clear, concise, and specific. Each objective or question should address a specific aspect of the research and contribute to the overall goal of the study. They should also be measurable, meaning that their achievement can be assessed and evaluated. Additionally, research objectives and questions should be achievable within the given timeframe and resources of the research project. By clearly defining the objectives and questions, researchers can effectively plan and execute their research, leading to valuable insights and contributions to the field.

Guidelines for Writing Clear Research Objectives

Writing research objective is a crucial step in any research project. The objectives provide a clear direction and purpose for the study, guiding the researcher in their data collection and analysis. Here are some tips on how to write effective research objective:

1. Be clear and specific

Research objective should be written in a clear and specific manner. Avoid vague or ambiguous language that can lead to confusion. Clearly state what you intend to achieve through your research.

2. Use action verbs

Start your research objective with action verbs that describe the desired outcome. Action verbs such as ‘investigate’, ‘analyze’, ‘compare’, ‘evaluate’, or ‘identify’ help to convey the purpose of the study.

3. Align with research questions or hypotheses

Ensure that your research objectives are aligned with your research questions or hypotheses. The objectives should address the main goals of your study and provide a framework for answering your research questions or testing your hypotheses.

4. Be realistic and achievable

Set research objectives that are realistic and achievable within the scope of your study. Consider the available resources, time constraints, and feasibility of your objectives. Unrealistic objectives can lead to frustration and hinder the progress of your research.

5. Consider the significance and relevance

Reflect on the significance and relevance of your research objectives. How will achieving these objectives contribute to the existing knowledge or address a gap in the literature? Ensure that your objectives have a clear purpose and value.

6. Seek feedback

It is beneficial to seek feedback on your research objectives from colleagues, mentors, or experts in your field. They can provide valuable insights and suggestions for improving the clarity and effectiveness of your objectives.

7. Revise and refine

Research objectives are not set in stone. As you progress in your research, you may need to revise and refine your objectives to align with new findings or changes in the research context. Regularly review and update your objectives to ensure they remain relevant and focused.

By following these tips, you can write research objectives that are clear, focused, and aligned with your research goals. Well-defined objectives will guide your research process and help you achieve meaningful outcomes.

The Role of Research Objectives in Research Methodology

Research objectives play a crucial role in the research methodology . In research methodology, research objectives are formulated based on the research questions or problem statement. These objectives help in defining the scope and focus of the study, ensuring that the research is conducted in a systematic and organized manner.

The research objectives in research methodology act as a roadmap for the research project. They help in identifying the key variables to be studied, determining the research design and methodology, and selecting the appropriate data collection methods .

Furthermore, research objectives in research methodology assist in evaluating the success of the study. By setting clear objectives, researchers can assess whether the desired outcomes have been achieved and determine the effectiveness of the research methods employed. It is important to note that research objectives in research methodology should be aligned with the overall research aim. They should address the specific aspects or components of the research aim and provide a framework for achieving the desired outcomes.

Understanding The Dynamic of Research Objectives in Your Study

The research objectives of a study play a crucial role in guiding the research process, ensuring that the study is focused, purposeful, and contributes to the advancement of knowledge in the field. It is important to note that the research objectives may evolve or change as the study progresses. As new information is gathered and analyzed, the researcher may need to revise the objectives to ensure that they remain relevant and achievable.

In summary, research objectives are essential components in writing an effective research paper . They provide a roadmap for the research process, guiding the researcher in their investigation and helping to ensure that the study is purposeful and meaningful. By understanding and effectively utilizing research objectives, researchers can enhance the quality and impact of their research endeavors.

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Unit 7: Should you believe those…Methods?

13 research questions and hypotheses [you choose to ask].

Or possibly… the question that researcher chose to ask. After processing everything through their ologies, and through their paradigm – both through nature (research is a personal thing – for all of us!) and nurture (ways of doing things handed down through education – remember the p roblems with science discussion?). As you know (if you’ve been reading this text at all, and you’re here now reading, obviously, so I’m guessing that you know) the concepts and terms we’re discussing in the remaining units of this course are multi-pronged and interdependent. We’re going to start light – first, where do we even find the RQs and HYs? Step 1: Locate the central question. Turning it over to your student textbook authors:

Learning Objectives

Why are research questions and hypotheses important? Where are they located?

• Research Questions and Hypotheses [you choose to ask]

Research questions and hypotheses (also known as RQs and HYs) are super important because they tell you what the researcher wants to answer! It could be “Why are the Cambus’s never on time?” or “Why is Catlett’s dining hall the most popular?” or “The presence of Spring Break improves student mood.”

To properly identify a research question and or hypothesis, you first need to know where you can find them.

There are three places:

• The literature review (embedded or end)- Which is where the question is first stated.
• The Results- Sometimes authors will re-state the research question in the results section in order to direct the reader to the answer to their original question.
• Discussion- Where the study is discussed with other primary research. Sometimes authors will re-state or paraphrase their research question(s) and/or hypotheses when they compare what they found to previous research.

Links to articles reference in the video lecture:

Article 1: Kate Magsamen-Conrad, Jeanette Muhleman Dillon, China Billotte Verhoff & Claire Youngnyo Joa (2020) Toward a Theory of HealthIT Adoption Across the Lifespan: Findings from Five Years in the Community, Health Communication, 35:3, 308-321, DOI: 10.1080/10410236.2018.1563027

https://www.tandfonline.com/doi/abs/10.1080/10410236.2018.1563027

https://www.researchgate.net/publication/330460464_Toward_a_Theory_of_HealthIT_Adoption_Across_the_Lifespan_Findings_from_Five_Years_in_the_Community

Article 2: Kate Magsamen-Conrad, Maria K. Venetis, Maria G. Checton & Kathryn Greene (2019) The Role of Response Perceptions in Couples’ Ongoing Cancer-Related Disclosure, Health Communication, 34:9, 999-1009, DOI: 10.1080/10410236.2018.1452091

https://www.researchgate.net/publication/323953823_The_Role_of_Response_Perceptions_in_Couples’_Ongoing_Cancer-Related_Disclosure

https://www.tandfonline.com/doi/abs/10.1080/10410236.2018.1452091?journalCode=hhth20

https://pubmed.ncbi.nlm.nih.gov/29565693/

Why so many links, Doc? Click here to find out.

The literature review (embedded or end)

It is important to know that research questions and hypotheses, or RQs & HYs, can be embedded within the literature review or directly stated at the end.

DocMC again!: Ok, now. Once upon a time, one of your textbook authors told me that I didn’t need to hammer on some of these concepts (particularly in the experiments unit) because ya’ll learned about independent and dependent variables “in like the 5th grade.” Well, low and behold, 2020 hits and my child was in 100% online school and I have a new, deep, somewhat reluctant understanding of what ya’ll may have learned in 7th grade. And d@mned if she wasn’t right. So, for fun, I’ll sprinkle in questions periodically from one of my son Gabe’s 7th grade science class quizzes. They might look a little something like this:

Got ideas for questions to include on the exam?

Click this link to add them!

… Unit 1 … Unit 2 …. Unit 3 … Unit 4 … Unit 5 … Unit 6 … Unit 7 … Unit 8 … Unit 9 … Unit 10 … Unit 11 … Unit 12 … Unit 13 … Unit 14 … Unit 15 … Unit 16 …

• Qualitative vs. Quantitative Research [brief overview]
• You said it was called the What’s That Now? Article Navigation
• Research Questions and Hypotheses [you choose to ask] – Breakout Section!

What a researcher aims to answer

Predicted outcome; not necessarily true

Introduction to Social Scientific Research Methods in Communication (3rd Edition) Copyright © 2023 by Kate Magsamen-Conrad. All Rights Reserved.

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Research Questions, Hypotheses and Objectives

Research questions.

A research question naturally emerges from a research problem that needs to be resolved. Developing a good research question not only rests on the study of some uncertain phenomenon, but also on the rational need for investigating it. It is therefore essential that a systematic review of extant literature on the research topic be conducted, prior to formulating a research question. Awareness of current trends and latest development in the field of study will certainly assist in formulating a relevant question. There may be several research questions, whether primary or secondary, but they should all be developed during the planning stage of a study. Furthermore, it must be ensured that secondary questions do not compromise the primary research question, which forms the basis of research objectives and hypotheses. Lastly, bear in mind that the number of research questions will inevitably make the research design and data analysis more complex.

Hulley et al . (2001) suggested the use of the FINER criteria in developing a good research question:

• F - Feasible : adequate number of subjects and technical expertise, affordability in terms of time and money, manageability in scope
• I - Interesting : getting an answer that intrigues the researcher, the peers and the community
• N - Novel : confirms, refutes or extends previous findings
• E - Ethical : a study that will be approved by an institutional review board
• R - Relevant : to scientific knowledge and future research

A poorly formulated research question may thus affect the choice of study design and hamper the chance of obtaining any significant finding, besides compromising the quality of the study.

Research Hypotheses

First of all, it is essential to understand that it is a hypothesis, not the data, that drives a primary research question . Otherwise, given any dataset, it would be too simple to perform several tests and apply statistical techniques to establish significant associations and/or relationships among variables and/or constructs. In such cases, it would be working backward by using the data to develop the research question, and that would defeat the entire purpose of conducting the study. To make matters worse, spuriously positive findings may result.

Hypothesis testing aims at making inferences about the targeted population on the basis of a random sample taken from that population. A hypothesis must be formulated as a null hypothesis, generally meaning that a prevailing situation has not changed (in the case of finding differences) or that there are no significant relationships among variables and/or constructs. This is the reason why each null hypoythesis must be paired with an alternative hypothesis, should the outcome be significant. The two hypotheses must be mutually exclusive and comprehensively exhaustive, i.e., the acceptance of one would automatically imply the rejection of the other. For a better understanding of the concept of hypothesis testing, you might need to consult our statistician.

At this stage, all you need to know is that the development of a research hypothesis should be supported by a good research question, as it will influence your research design. Once appropriate hypotheses have been developed, you can safely proceed to the formulation of your research objectives.

Research Objectives

You must first learn to distinguish between a research aim and a research objective . While an aim is written in broad terms and explains what is to be achieved at the end of the study, an objective is an active statement that is defined in measurable outcomes via a strong positive statement. The primary objective of a study is paired with the hypothesis of the study, and should be clearly stated in the introduction of the research protocol. Objectives usually state exactly the outcome measures that are going to be used within their statements. Strong verbs like determine , measure , assess , evaluate , identify , examine , investigate , etc., are used in the formulation of objectives.

The importance of objectives is that they guide the development of the protocol and design of study, and play a determining role in sample size calculations. Objectives should be focused on outcomes that are important and relevant to the study.

Research aim To investigate the issue of student indiscipline and its impact on student attainment in Mauritian Secondary Schools Research question What are the various types of student indiscipline currently experienced in secondary schools of Mauritius? Research (null) hypothesis School management style does not impact on student attainment Research objective To identify the most common forms of indiscipline and their level of seriousness

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Research questions, hypotheses and objectives

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PRACTICAL TIPS FOR SURGICAL RESEARCH

There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidencebased practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed.1

OBJECTIVES OF THIS ARTICLE

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

RESEARCH QUESTION

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study.1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study.2 Indeed, Haynes suggests that it is important to know "where the boundary between current knowledge and ignorance lies."1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one's knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions.2...

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• v.63(8); 2019 Aug

Formulating a good research question: Pearls and pitfalls

Wilson fandino.

Guys' and St Thomas' Hospital National Health Service Foundation Trust, London, United Kingdom

The process of formulating a good research question can be challenging and frustrating. While a comprehensive literature review is compulsory, the researcher usually encounters methodological difficulties in the conduct of the study, particularly if the primary study question has not been adequately selected in accordance with the clinical dilemma that needs to be addressed. Therefore, optimising time and resources before embarking in the design of a clinical protocol can make an impact on the final results of the research project. Researchers have developed effective ways to convey the message of how to build a good research question that can be easily recalled under the acronyms of PICOT (population, intervention, comparator, outcome, and time frame) and FINER (feasible, interesting, novel, ethical, and relevant). In line with these concepts, this article highlights the main issues faced by clinicians, when developing a research question.

INTRODUCTION

What is your research question? This is very often one of the first queries made by statisticians, when researchers come up with an interesting idea. In fact, the findings of a study may only acquire relevance if they provide an accurate and unbiased answer to a specific question,[ 1 , 2 ] and it has been suggested that up to one-third of the time spent in the whole process—from the conception of an idea to the publication of the manuscript—could be invested in finding the right primary study question.[ 3 ] Furthermore, selecting a good research question can be a time-consuming and challenging task: in one retrospective study, Mayo et al . reported that 3 out of 10 articles published would have needed a major rewording of the question.[ 1 ] This paper explores some recommendations to consider before starting any research project, and outlines the main difficulties faced by young and experienced clinicians, when it comes time to turn an exciting idea into a valuable and feasible research question.

OPTIMISATION OF TIME AND RESOURCES

Focusing on the primary research question.

The process of developing a new idea usually stems from a dilemma inherent to the clinical practice.[ 2 , 3 , 4 ] However, once the problem has been identified, it is tempting to formulate multiple research questions. Conducting a clinical trial with more than one primary study question would not be feasible. First, because each question may require a different research design, and second, because the necessary statistical power of the study would demand unaffordable sample sizes. It is the duty of editors and reviewers to make sure that authors clearly identify the primary research question, and as a consequence, studies approaching more than one primary research question may not be suitable for publication.

Working in the right environment

Teamwork is essential to find the appropriate research question. Working in the right environment will enable the investigator to interact with colleagues with different backgrounds, and create opportunities to exchange experiences in a collaborative way between clinicians and researchers. Likewise, it is of paramount importance to get involved colleagues with expertise in the field (lead clinicians, education supervisors, research mentors, department chairs, epidemiologists, biostatisticians, and ethical consultants, among others), and ask for their guidance.[ 5 , 6 , 7 , 8 ]

Evaluating the pertinence of the study

The researcher should wonder if, on the basis of the research question formulated, there is a need for a study to address the problem, as clinical research usually entails a large investment of resources and workforce involvement. Thus, if the answer to the posed clinical question seems to be evident before starting the study, investing in research to address the problem would become superfluous. For example, in a clinical trial, Herzog-Niescery et al . compared laryngeal masks with cuffed and uncuffed tracheal tubes, in the context of surgeons' exposure to sevoflurane, in infants undergoing adenoidectomy. However, it appears obvious that cuffed tracheal tubes are preferred to minimise surgeons' exposure to volatile gases, as authors concluded after recruiting 60 patients.[ 9 ]

Conducting a thorough literature review

Any research project requires the identification of at least one of three problems: the evidence is scarce, the existing literature yields conflicting results, or the results could be improved. Hence, a comprehensive review of the topic is imperative, as it allows the researcher to identify this gap in the literature, formulate a hypothesis and develop a research question.[ 2 ] To this end, it is crucial to be attentive to new ideas, keep the imagination roaming with reflective attitude, and remain sceptical to the new-gained information.[ 4 , 7 ]

Narrowing the research question

A broad research question may encompass an unaffordable extensive topic. For instance, do supraglottic devices provide similar conditions for the visualization of the glottis aperture in a German hospital? Such a general research question usually needs to be narrowed, not only by cutting away unnecessary components (a German hospital is irrelevant in this context), but also by defining a target population, a specific intervention, an alternative treatment or procedure to be compared with the intervention, a measurable primary outcome, and a time frame of the study. In contrast, an example of a good research question would be: among children younger than 1 year of age undergoing elective minor procedures, to what extent the insertion times are different, comparing the Supreme™ laryngeal mask airway (LMA) to Proseal™ LMA, when placed after reaching a BIS index <60?[ 10 ] In this example, the core ingredients of the research question can be easily identified as: children <1 year of age undergoing minor elective procedures, Supreme™ LMA, Proseal™ LMA and insertion times at anaesthetic induction when reaching a BIS index <60. These components are usually gathered in the literature under the acronym of PICOT (population, intervention, comparator, outcome and time frame, respectively).[ 1 , 3 , 5 ]

PICOT FRAMEWORK

Table 1 summarises the foremost questions likely to be addressed when working on PICOT frame.[ 1 , 6 , 8 ] These components are also applicable to observational studies, where the exposure takes place of the intervention.[ 1 , 11 ] Remarkably, if after browsing the title and the abstract of a paper, the reader is not able to clearly identify the PICOT parameters, and elucidate the question posed by the authors, there should be reasonable scepticism regarding the scientific rigor of the work.[ 12 , 13 ] All these elements are crucial in the design and methodology of a clinical trial, as they can affect the feasibility and reliability of results. Having formulated the primary study question in the context of the PICOT framework [ Table 1 ],[ 1 , 6 , 8 ] the researcher should be able to elucidate which design is most suitable for their work, determine what type of data needs to be collected, and write a structured introduction tailored to what they want to know, explicitly mentioning the primary study hypothesis, which should lead to formulate the main research question.[ 1 , 2 , 6 , 8 ]

Key questions to be answered when working with the PICOT framework (population, intervention, comparator, outcome, and time frame) in a clinical research design

Occasionally, the intended population of the study needs to be modified, in order to overcome any potential ethical issues, and/or for the sake of convenience and feasibility of the project. Yet, the researcher must be aware that the external validity of the results may be compromised. As an illustration, in a randomised clinical trial, authors compared the ease of tracheal tube insertion between C-MAC video laryngoscope and direct laryngoscopy, in patients presenting to the emergency department with an indication of rapid sequence intubation. However, owing to the existence of ethical concerns, a substantial amount of patients requiring emergency tracheal intubation, including patients with major maxillofacial trauma and ongoing cardiopulmonary resuscitation, had to be excluded from the trial.[ 14 ] In fact, the design of prospective studies to explore this subset of patients can be challenging, not only because of ethical considerations, but because of the low incidence of these cases. In another study, Metterlein et al . compared the glottis visualisation among five different supraglottic airway devices, using fibreroptic-guided tracheal intubation in an adult population. Despite that the study was aimed to explore the ease of intubation in patients with anticipated difficult airway (thus requiring fibreoptic tracheal intubation), authors decided to enrol patients undergoing elective laser treatment for genital condylomas, as a strategy to hasten the recruitment process and optimise resources.[ 15 ]

Intervention

Anaesthetic interventions can be classified into pharmacological (experimental treatment) and nonpharmacological. Among nonpharmacological interventions, the most common include anaesthetic techniques, monitoring instruments and airway devices. For example, it would be appropriate to examine the ease of insertion of Supreme™ LMA, when compared with ProSeal™ LMA. Notwithstanding, a common mistake is the tendency to be focused on the data aimed to be collected (the “stated” objective), rather than the question that needs to be answered (the “latent” objective).[ 1 , 4 ] In one clinical trial, authors stated: “we compared the Supreme™ and ProSeal™ LMAs in infants by measuring their performance characteristics, including insertion features, ventilation parameters, induced changes in haemodynamics, and rates of postoperative complications”.[ 10 ] Here, the research question has been centered on the measurements (insertion characteristics, haemodynamic variables, LMA insertion characteristics, ventilation parameters) rather than the clinical problem that needs to be addressed (is Supreme™ LMA easier to insert than ProSeal™ LMA?).

Comparators in clinical research can also be pharmacological (e.g., gold standard or placebo) or nonpharmacological. Typically, not more than two comparator groups are included in a clinical trial. Multiple comparisons should be generally avoided, unless there is enough statistical power to address the end points of interest, and statistical analyses have been adjusted for multiple testing. For instance, in the aforementioned study of Metterlein et al .,[ 15 ] authors compared five supraglottic airway devices by recruiting only 10--12 participants per group. In spite of the authors' recommendation of using two supraglottic devices based on the results of the study, there was no mention of statistical adjustments for multiple comparisons, and given the small sample size, larger clinical trials will undoubtedly be needed to confirm or refute these findings.[ 15 ]

A clear formulation of the primary outcome results of vital importance in clinical research, as the primary statistical analyses, including the sample size calculation (and therefore, the estimation of the effect size and statistical power), will be derived from the main outcome of interest. While it is clear that using more than one primary outcome would not be appropriate, it would be equally inadequate to include multiple point measurements of the same variable as the primary outcome (e.g., visual analogue scale for pain at 1, 2, 6, and 12 h postoperatively).

Composite outcomes, in which multiple primary endpoints are combined, may make it difficult to draw any conclusions based on the study findings. For example, in a clinical trial, 200 children undergoing ophthalmic surgery were recruited to explore the incidence of respiratory adverse events, when comparing desflurane with sevoflurane, following the removal of flexible LMA during the emergence of the anaesthesia. The primary outcome was the number of respiratory events, including breath holding, coughing, secretions requiring suction, laryngospasm, bronchospasm, and mild desaturation.[ 16 ] Should authors had claimed a significant difference between these anaesthetic volatiles, it would have been important to elucidate whether those differences were due to serious adverse events, like laryngospasm or bronchospasm, or the results were explained by any of the other events (e.g., secretions requiring suction). While it is true that clinical trials evaluating the occurrence of adverse events like laryngospasm/bronchospasm,[ 16 , 17 ] or life-threating complications following a tracheal intubation (e.g., inadvertent oesophageal placement, dental damage or injury of the larynx/pharynx)[ 14 ] are almost invariably underpowered, because the incidence of such events is expected to be low, subjective outcomes like coughing or secretions requiring suction should be avoided, as they are highly dependent on the examiner's criteria.[ 16 ]

Secondary outcomes are useful to document potential side effects (e.g., gastric insufflation after placing a supraglottic device), and evaluate the adherence (say, airway leak pressure) and safety of the intervention (for instance, occurrence, or laryngospasm/bronchospasm).[ 17 ] Nevertheless, the problem of addressing multiple secondary outcomes without the adequate statistical power is habitual in medical literature. A good illustration of this issue can be found in a study evaluating the performance of two supraglottic devices in 50 anaesthetised infants and neonates, whereby authors could not draw any conclusions in regard to potential differences in the occurrence of complications, because the sample size calculated made the study underpowered to explore those differences.[ 17 ]

Among PICOT components, the time frame is the most likely to be omitted or inappropriate.[ 1 , 12 ] There are two key aspects of the time component that need to be clearly specified in the research question: the time of measuring the outcome variables (e.g. visual analogue scale for pain at 1, 2, 6, and 12 h postoperatively), and the duration of each measurement (when indicated). The omission of these details in the study protocol might lead to substantial differences in the methodology used. For instance, if a study is designed to compare the insertion times of three different supraglottic devices, and researchers do not specify the exact moment of LMA insertion in the clinical trial protocol (i.e., at the anaesthetic induction after reaching a BIS index < 60), placing an LMA with insufficient depth of anaesthesia would have compromised the internal validity of the results, because inserting a supraglottic device in those patients would have resulted in failed attempts and longer insertion times.[ 10 ]

FINER CRITERIA

A well-elaborated research question may not necessarily be a good question. The proposed study also requires being achievable from both ethical and realistic perspectives, interesting and useful to the clinical practice, and capable to formulate new hypotheses, that may contribute to the generation of knowledge. Researchers have developed an effective way to convey the message of how to build a good research question, that is usually recalled under the acronym of FINER (feasible, interesting, novel, ethical and relevant).[ 5 , 6 , 7 ] Table 2 highlights the main characteristics of FINER criteria.[ 7 ]

Main features of FINER criteria (Feasibility, interest, novelty, ethics, and relevance) to formulate a good research question. Adapted from Cummings et al .[ 7 ]

Novelty and relevance

Although it is clear that any research project should commence with an accurate literature interpretation, in many instances it represents the start and the end of the research: the reader will soon realise that the answer to several questions can be easily found in the published literature.[ 5 ] When the question overcomes the test of a thorough literature review, the project may become novel (there is a gap in the knowledge, and therefore, there is a need for new evidence on the topic) and relevant (the paper may contribute to change the clinical practice). In this context, it is important to distinguish the difference between statistical significance and clinical relevance: in the aforementioned study of Oba et al .,[ 10 ] despite the means of insertion times were reported as significant for the Supreme™ LMA, as compared with ProSeal™ LMA, the difference found in the insertion times (528 vs. 486 sec, respectively), although reported as significant, had little or no clinical relevance.[ 10 ] Conversely, a statistically significant difference of 12 sec might be of clinical relevance in neonates weighing <5 kg.[ 17 ] Thus, statistical tests must be interpreted in the context of a clinically meaningful effect size, which should be previously defined by the researcher.

Feasibility and ethical aspects

Among FINER criteria, there are two potential barriers that may prevent the successful conduct of the project and publication of the manuscript: feasibility and ethical aspects. These obstacles are usually related to the target population, as discussed above. Feasibility refers not only to the budget but also to the complexity of the design, recruitment strategy, blinding, adequacy of the sample size, measurement of the outcome, time of follow-up of participants, and commitment of clinicians, among others.[ 3 , 7 ] Funding, as a component of feasibility, may also be implicated in the ethical principles of clinical research, because the choice of the primary study question may be markedly influenced by the specific criteria demanded in the interest of potential funders.

Discussing ethical issues with local committees is compulsory, as rules applied might vary among countries.[ 18 ] Potential risks and benefits need to be carefully weighed, based upon the four principles of respect for autonomy, beneficence, non-maleficence, and justice.[ 19 ] Although many of these issues may be related to the population target (e.g., conducting a clinical trial in patients with ongoing cardiopulmonary resuscitation would be inappropriate, as would be anaesthetising patients undergoing elective LASER treatment for condylomas, to examine the performance of supraglottic airway devices),[ 14 , 15 ] ethical conflicts may also arise from the intervention (particularly those involving the occurrence of side effects or complications, and their potential for reversibility), comparison (e.g., use of placebo or sham procedures),[ 19 ] outcome (surrogate outcomes should be considered in lieu of long term outcomes), or time frame (e.g., unnecessary longer exposition to an intervention). Thus, FINER criteria should not be conceived without a concomitant examination of the PICOT checklist, and consequently, PICOT framework and FINER criteria should not be seen as separated components, but rather complementary ingredients of a good research question.

Undoubtedly, no research project can be conducted if it is deemed unfeasible, and most institutional review boards would not be in a position to approve a work with major ethical problems. Nonetheless, whether or not the findings are interesting, is a subjective matter. Engaging the attention of readers also depends upon a number of factors, including the manner of presenting the problem, the background of the topic, the intended audience, and the reader's expectations. Furthermore, the interest is usually linked to the novelty and relevance of the topic, and it is worth nothing that editors and peer reviewers of high-impact medical journals are usually reluctant to accept any publication, if there is no novelty inherent to the research hypothesis, or there is a lack of relevance in the results.[ 11 ] Nevertheless, a considerable number of papers have been published without any novelty or relevance in the topic addressed. This is probably reflected in a recent survey, according to which only a third of respondents declared to have read thoroughly the most recent papers downloaded, and at least half of those manuscripts remained unread.[ 20 ] The same study reported that up to one-third of papers examined remained uncited after 5 years of publication, and only 20% of papers accounted for 80% of the citations.[ 20 ]

Formulating a good research question can be fascinating, albeit challenging, even for experienced investigators. While it is clear that clinical experience in combination with the accurate interpretation of literature and teamwork are essential to develop new ideas, the formulation of a clinical problem usually requires the compliance with PICOT framework in conjunction with FINER criteria, in order to translate a clinical dilemma into a researchable question. Working in the right environment with the adequate support of experienced researchers, will certainly make a difference in the generation of knowledge. By doing this, a lot of time will be saved in the search of the primary study question, and undoubtedly, there will be more chances to become a successful researcher.

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There is an increasing familiarity with the principles of evidence-based medicine in the surgical community. As surgeons become more aware of the hierarchy of evidence, grades of recommendations and the principles of critical appraisal, they develop an increasing familiarity with research design. Surgeons and clinicians are looking more and more to the literature and clinical trials to guide their practice; as such, it is becoming a responsibility of the clinical research community to attempt to answer questions that are not only well thought out but also clinically relevant. The development of the research question, including a supportive hypothesis and objectives, is a necessary key step in producing clinically relevant results to be used in evidence-based practice. A well-defined and specific research question is more likely to help guide us in making decisions about study design and population and subsequently what data will be collected and analyzed. 1

Objectives of this article

In this article, we discuss important considerations in the development of a research question and hypothesis and in defining objectives for research. By the end of this article, the reader will be able to appreciate the significance of constructing a good research question and developing hypotheses and research objectives for the successful design of a research study. The following article is divided into 3 sections: research question, research hypothesis and research objectives.

Research question

Interest in a particular topic usually begins the research process, but it is the familiarity with the subject that helps define an appropriate research question for a study. 1 Questions then arise out of a perceived knowledge deficit within a subject area or field of study. 2 Indeed, Haynes suggests that it is important to know “where the boundary between current knowledge and ignorance lies.” 1 The challenge in developing an appropriate research question is in determining which clinical uncertainties could or should be studied and also rationalizing the need for their investigation.

Increasing one’s knowledge about the subject of interest can be accomplished in many ways. Appropriate methods include systematically searching the literature, in-depth interviews and focus groups with patients (and proxies) and interviews with experts in the field. In addition, awareness of current trends and technological advances can assist with the development of research questions. 2 It is imperative to understand what has been studied about a topic to date in order to further the knowledge that has been previously gathered on a topic. Indeed, some granting institutions (e.g., Canadian Institute for Health Research) encourage applicants to conduct a systematic review of the available evidence if a recent review does not already exist and preferably a pilot or feasibility study before applying for a grant for a full trial.

In-depth knowledge about a subject may generate a number of questions. It then becomes necessary to ask whether these questions can be answered through one study or if more than one study needed. 1 Additional research questions can be developed, but several basic principles should be taken into consideration. 1 All questions, primary and secondary, should be developed at the beginning and planning stages of a study. Any additional questions should never compromise the primary question because it is the primary research question that forms the basis of the hypothesis and study objectives. It must be kept in mind that within the scope of one study, the presence of a number of research questions will affect and potentially increase the complexity of both the study design and subsequent statistical analyses, not to mention the actual feasibility of answering every question. 1 A sensible strategy is to establish a single primary research question around which to focus the study plan. 3 In a study, the primary research question should be clearly stated at the end of the introduction of the grant proposal, and it usually specifies the population to be studied, the intervention to be implemented and other circumstantial factors. 4

Hulley and colleagues 2 have suggested the use of the FINER criteria in the development of a good research question ( Box 1 ). The FINER criteria highlight useful points that may increase the chances of developing a successful research project. A good research question should specify the population of interest, be of interest to the scientific community and potentially to the public, have clinical relevance and further current knowledge in the field (and of course be compliant with the standards of ethical boards and national research standards).

FINER criteria for a good research question

Adapted with permission from Wolters Kluwer Health. 2

Whereas the FINER criteria outline the important aspects of the question in general, a useful format to use in the development of a specific research question is the PICO format — consider the population (P) of interest, the intervention (I) being studied, the comparison (C) group (or to what is the intervention being compared) and the outcome of interest (O). 3 , 5 , 6 Often timing (T) is added to PICO ( Box 2 ) — that is, “Over what time frame will the study take place?” 1 The PICOT approach helps generate a question that aids in constructing the framework of the study and subsequently in protocol development by alluding to the inclusion and exclusion criteria and identifying the groups of patients to be included. Knowing the specific population of interest, intervention (and comparator) and outcome of interest may also help the researcher identify an appropriate outcome measurement tool. 7 The more defined the population of interest, and thus the more stringent the inclusion and exclusion criteria, the greater the effect on the interpretation and subsequent applicability and generalizability of the research findings. 1 , 2 A restricted study population (and exclusion criteria) may limit bias and increase the internal validity of the study; however, this approach will limit external validity of the study and, thus, the generalizability of the findings to the practical clinical setting. Conversely, a broadly defined study population and inclusion criteria may be representative of practical clinical practice but may increase bias and reduce the internal validity of the study.

PICOT criteria 1

A poorly devised research question may affect the choice of study design, potentially lead to futile situations and, thus, hamper the chance of determining anything of clinical significance, which will then affect the potential for publication. Without devoting appropriate resources to developing the research question, the quality of the study and subsequent results may be compromised. During the initial stages of any research study, it is therefore imperative to formulate a research question that is both clinically relevant and answerable.

Research hypothesis

The primary research question should be driven by the hypothesis rather than the data. 1 , 2 That is, the research question and hypothesis should be developed before the start of the study. This sounds intuitive; however, if we take, for example, a database of information, it is potentially possible to perform multiple statistical comparisons of groups within the database to find a statistically significant association. This could then lead one to work backward from the data and develop the “question.” This is counterintuitive to the process because the question is asked specifically to then find the answer, thus collecting data along the way (i.e., in a prospective manner). Multiple statistical testing of associations from data previously collected could potentially lead to spuriously positive findings of association through chance alone. 2 Therefore, a good hypothesis must be based on a good research question at the start of a trial and, indeed, drive data collection for the study.

The research or clinical hypothesis is developed from the research question and then the main elements of the study — sampling strategy, intervention (if applicable), comparison and outcome variables — are summarized in a form that establishes the basis for testing, statistical and ultimately clinical significance. 3 For example, in a research study comparing computer-assisted acetabular component insertion versus freehand acetabular component placement in patients in need of total hip arthroplasty, the experimental group would be computer-assisted insertion and the control/conventional group would be free-hand placement. The investigative team would first state a research hypothesis. This could be expressed as a single outcome (e.g., computer-assisted acetabular component placement leads to improved functional outcome) or potentially as a complex/composite outcome; that is, more than one outcome (e.g., computer-assisted acetabular component placement leads to both improved radiographic cup placement and improved functional outcome).

However, when formally testing statistical significance, the hypothesis should be stated as a “null” hypothesis. 2 The purpose of hypothesis testing is to make an inference about the population of interest on the basis of a random sample taken from that population. The null hypothesis for the preceding research hypothesis then would be that there is no difference in mean functional outcome between the computer-assisted insertion and free-hand placement techniques. After forming the null hypothesis, the researchers would form an alternate hypothesis stating the nature of the difference, if it should appear. The alternate hypothesis would be that there is a difference in mean functional outcome between these techniques. At the end of the study, the null hypothesis is then tested statistically. If the findings of the study are not statistically significant (i.e., there is no difference in functional outcome between the groups in a statistical sense), we cannot reject the null hypothesis, whereas if the findings were significant, we can reject the null hypothesis and accept the alternate hypothesis (i.e., there is a difference in mean functional outcome between the study groups), errors in testing notwithstanding. In other words, hypothesis testing confirms or refutes the statement that the observed findings did not occur by chance alone but rather occurred because there was a true difference in outcomes between these surgical procedures. The concept of statistical hypothesis testing is complex, and the details are beyond the scope of this article.

Another important concept inherent in hypothesis testing is whether the hypotheses will be 1-sided or 2-sided. A 2-sided hypothesis states that there is a difference between the experimental group and the control group, but it does not specify in advance the expected direction of the difference. For example, we asked whether there is there an improvement in outcomes with computer-assisted surgery or whether the outcomes worse with computer-assisted surgery. We presented a 2-sided test in the above example because we did not specify the direction of the difference. A 1-sided hypothesis states a specific direction (e.g., there is an improvement in outcomes with computer-assisted surgery). A 2-sided hypothesis should be used unless there is a good justification for using a 1-sided hypothesis. As Bland and Atlman 8 stated, “One-sided hypothesis testing should never be used as a device to make a conventionally nonsignificant difference significant.”

The research hypothesis should be stated at the beginning of the study to guide the objectives for research. Whereas the investigators may state the hypothesis as being 1-sided (there is an improvement with treatment), the study and investigators must adhere to the concept of clinical equipoise. According to this principle, a clinical (or surgical) trial is ethical only if the expert community is uncertain about the relative therapeutic merits of the experimental and control groups being evaluated. 9 It means there must exist an honest and professional disagreement among expert clinicians about the preferred treatment. 9

Designing a research hypothesis is supported by a good research question and will influence the type of research design for the study. Acting on the principles of appropriate hypothesis development, the study can then confidently proceed to the development of the research objective.

Research objective

The primary objective should be coupled with the hypothesis of the study. Study objectives define the specific aims of the study and should be clearly stated in the introduction of the research protocol. 7 From our previous example and using the investigative hypothesis that there is a difference in functional outcomes between computer-assisted acetabular component placement and free-hand placement, the primary objective can be stated as follows: this study will compare the functional outcomes of computer-assisted acetabular component insertion versus free-hand placement in patients undergoing total hip arthroplasty. Note that the study objective is an active statement about how the study is going to answer the specific research question. Objectives can (and often do) state exactly which outcome measures are going to be used within their statements. They are important because they not only help guide the development of the protocol and design of study but also play a role in sample size calculations and determining the power of the study. 7 These concepts will be discussed in other articles in this series.

From the surgeon’s point of view, it is important for the study objectives to be focused on outcomes that are important to patients and clinically relevant. For example, the most methodologically sound randomized controlled trial comparing 2 techniques of distal radial fixation would have little or no clinical impact if the primary objective was to determine the effect of treatment A as compared to treatment B on intraoperative fluoroscopy time. However, if the objective was to determine the effect of treatment A as compared to treatment B on patient functional outcome at 1 year, this would have a much more significant impact on clinical decision-making. Second, more meaningful surgeon–patient discussions could ensue, incorporating patient values and preferences with the results from this study. 6 , 7 It is the precise objective and what the investigator is trying to measure that is of clinical relevance in the practical setting.

The following is an example from the literature about the relation between the research question, hypothesis and study objectives:

Study: Warden SJ, Metcalf BR, Kiss ZS, et al. Low-intensity pulsed ultrasound for chronic patellar tendinopathy: a randomized, double-blind, placebo-controlled trial. Rheumatology 2008;47:467–71.

Research question: How does low-intensity pulsed ultrasound (LIPUS) compare with a placebo device in managing the symptoms of skeletally mature patients with patellar tendinopathy?

Research hypothesis: Pain levels are reduced in patients who receive daily active-LIPUS (treatment) for 12 weeks compared with individuals who receive inactive-LIPUS (placebo).

Objective: To investigate the clinical efficacy of LIPUS in the management of patellar tendinopathy symptoms.

The development of the research question is the most important aspect of a research project. A research project can fail if the objectives and hypothesis are poorly focused and underdeveloped. Useful tips for surgical researchers are provided in Box 3 . Designing and developing an appropriate and relevant research question, hypothesis and objectives can be a difficult task. The critical appraisal of the research question used in a study is vital to the application of the findings to clinical practice. Focusing resources, time and dedication to these 3 very important tasks will help to guide a successful research project, influence interpretation of the results and affect future publication efforts.

Tips for developing research questions, hypotheses and objectives for research studies

Perform a systematic literature review (if one has not been done) to increase knowledge and familiarity with the topic and to assist with research development.

Learn about current trends and technological advances on the topic.

Seek careful input from experts, mentors, colleagues and collaborators to refine your research question as this will aid in developing the research question and guide the research study.

Use the FINER criteria in the development of the research question.

Ensure that the research question follows PICOT format.

Develop a research hypothesis from the research question.

Develop clear and well-defined primary and secondary (if needed) objectives.

Ensure that the research question and objectives are answerable, feasible and clinically relevant.

FINER = feasible, interesting, novel, ethical, relevant; PICOT = population (patients), intervention (for intervention studies only), comparison group, outcome of interest, time.

Competing interests: No funding was received in preparation of this paper. Dr. Bhandari was funded, in part, by a Canada Research Chair, McMaster University.

• Accepted January 27, 2009.
• Brian Haynes R
• Cummings S ,
• Browner W ,
• Sackett D ,
• Strauss S ,
• Richardson W ,
• Fisher CG ,
• Haynes RB ,
• Sackett DL ,
• Guyatt GH ,

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Part 1. Overview Information

National Institutes of Health ( NIH )

National Institute of Allergy and Infectious Diseases ( NIAID )

National Institute of Dental and Craniofacial Research ( NIDCR )

National Institute on Drug Abuse ( NIDA )

National Institute of Mental Health ( NIMH )

U19 Research Program – Cooperative Agreements

• April 4, 2024  - Overview of Grant Application and Review Changes for Due Dates on or after January 25, 2025. See Notice NOT-OD-24-084 .
• August 31, 2022 - Implementation Changes for Genomic Data Sharing Plans Included with Applications Due on or after January 25, 2023. See Notice  NOT-OD-22-198 .
• August 5, 2022 - Implementation Details for the NIH Data Management and Sharing Policy. See Notice  NOT-OD-22-189 .

See Section III. 3. Additional Information on Eligibility .

The purpose of this NOFO is to support the Pediatric HIV/AIDS Cohort Study (PHACS) as a transformative and agile program addressing the developmental and clinical course of persons living with HIV, and perinatally  acquired HIV, with an emphasis on youth through reproductive age in the United States.     

This Notice of Fuding Opportunity (NOFO) requires a Plan for Enhancing Diverse Perspectives (PEDP).

November 5, 2024

All applications are due by 5:00 PM local time of applicant organization. 

Applicants are encouraged to apply early to allow adequate time to make any corrections to errors found in the application during the submission process by the due date.

Not Applicable

It is critical that applicants follow the Multi-Project (M) Instructions in the How to Apply - Application Guide , except where instructed to do otherwise (in this NOFO or in a Notice from the NIH Guide for Grants and Contracts ). Conformance to all requirements (both in the How to Apply - Application Guide and the NOFO) is required and strictly enforced. Applicants must read and follow all application instructions in the How to Apply - Application Guide as well as any program-specific instructions noted in Section IV. When the program-specific instructions deviate from those in the How to Apply - Application Guide , follow the program-specific instructions. Applications that do not comply with these instructions may be delayed or not accepted for review.

There are several options available to submit your application through Grants.gov to NIH and Department of Health and Human Services partners. You must use one of these submission options to access the application forms for this opportunity.

• Use the NIH ASSIST system to prepare, submit and track your application online.
• Use an institutional system-to-system (S2S) solution to prepare and submit your application to Grants.gov and eRA Commons to track your application. Check with your institutional officials regarding availability.

Part 2. Full Text of Announcement

Section i. notice of funding opportunity description.

The purpose of this NOFO is to support the Pediatric HIV/AIDS Cohort Study (PHACS) cohorts as a transformative, streamlined, and agile program addressing the developmental and clinical course of persons living with HIV and perinatally  acquired HIV in the United States. The integration of investigators with experience using streamlined scientific and administrative methods and approaches to enhance the function and scientific vision of the cohorts is encouraged.

The goals of this initiative are to support research on the developmental and clinical course of persons living with HIV and perinatally acquired HIV, including the effects of HIV and HIV treatment on fertility, pregnancy and post-partum outcomes, complications, co-morbidities, and co-infections including gynecologic conditions and sexually transmitted infections (STI's) for example syphilis, chlamydia, gonorrhea, HPV, trichomoniasis and CMV. The transition to adulthood of youth living with vertically acquired HIV who have been on ART for an extended period provides an important opportunity to understand many early developing health issues, including cardiovascular, metabolic, and immune. Early changes in oral health, alcohol, and substance use, behavioral, mental, social, health outcomes may also be evaluated.

The WHO has reported that in 2022 there were approximately 1.2 M pregnant women and 1.5 M children living with HIV. At the end of 2022 there were 9.9K women and 8.8K children accessing ART globally. Individuals with perinatally acquired HIV live with a chronic illness and face the developmental consequences of prolonged HIV, associated co-morbidities, and long-term ART that can affect health, starting with the development of the immune system and over the life course into young adulthood. Of the >1 M people in the US diagnosed with HIV at the end of 2019, 12,355 were among people diagnosed with vertically acquired HIV. The total number of people diagnosed with vertically acquired HIV in the US is disproportionate and occurs among certain racial and ethnic groups with the largest number among Black and Hispanic populations. People with vertically acquired HIV face the developmental consequences of exposure to HIV in utero, long-term antiretroviral therapy (ART) use and associated co-morbidities and co-infections, that affect health throughout life.

There were 2.5 million new cases of syphilis, chlamydia, and gonorrhea reported by the CDC in 2022, with a 555% increase in syphilis reported. These STIs are syndemic with HIV and affect similar populations. Included in the groups most affected by STI's and HIV are pregnant people, those who misuse substances and those aged 13-24.

The findings from United States (US) based initiatives have great relevance internationally since millions of children living with HIV in resource-constrained settings receive treatment and survive into adolescence and adulthood, and many pregnant people with HIV have access to and use combination antiretroviral therapy to prevent transmission of HIV to their infants and preserve their own health. The increased availability of antiretrovirals for HIV treatment and prevention has allowed for an increased number of children with perinatally acquired HIV to age into adulthood globally. There is limited clinical data on the long-term impact of HIV and its treatments on this population as they enter reproductive age and have children of their own.

Building on the infrastructure, community connections, and data obtained in the PHACS and similar US cohorts for perinatally acquired HIV individuals, opportunities to study the generational consequences of lifelong ART therapy is critical. For example, the PHACS Adolescent Master Protocol Cohort (AMP Series) includes youth who received very early treatment and may have had nearly lifelong HIV suppression. These data may also inform HIV cure research. Collaborations will continue to be encouraged with other similar cohorts in both resource-rich and resource-constrained settings for data harmonization and sharing.

Cohorts of Interest:

Cohorts of 500 to 1,000 individuals at risk for or living with perinatally or behaviorally acquired HIV, including youth and women of reproductive age are of interest. Recruitment of pregnant and non-pregnant individuals at high risk for or living with HIV (including perinatally acquired) and their children will continue to be encouraged. New enrollments will continue to capture the evolving type and timing of antiretrovirals used as youth transition to adulthood and during pregnancy. The impact of new HIV regimens in these populations will inform the future direction of long-acting antiretrovirals, multipurpose prevention technologies, and vaccines. Activities to support the maintenance and enrichment of the foundational cohorts proposed for study will continue to follow the needed numbers of participants in proposed protocols, but at least 200 new individuals, including children, will be recruited as an addition to the active cohorts each year.

It is expected appropriate control groups, pertinent to the cohorts being studied are included.

Cohorts will also be used in focused Research Pilots (sub-studies) to answer new questions as the research landscape evolves. This will enable the study of priority scientific investigations more rapidly than could be accomplished by individual projects alone.

The collection of basic information in areas of interest is expected to continue through base protocols and in other supported studies and should include but not be limited to:

• The outcomes and the generational consequences of lifelong ART therapy on:
• Reproductive system pathology and other gynecologic conditions, fertility, sexual maturation, nutrition, growth, endocrine, and bone development
• Cardiovascular, pulmonary, and renal disease risk and complications
• Genomic and metabolomic outcomes of exposure to ART and HIV in reproductive age people
• Correlates of immune system development, breath and HIV control.
• Studies on epigenetic aging on the early development of co-morbidities, and complications.
• Studies on neurodevelopmental, cognitive, and behavioral, outcomes including central nervous system imaging, and peripheral nervous system complications.
• Studies evaluating biomarker correlates of cognitive, cerebrovascular, neuro-inflammatory, neurodegenerative, behavioral and substance exposures use.
• Research on the effects of ART treatment on oral cells/tissues, bone mineral density, and tooth development; mucosal immunity; disease biomarkers; persistence, latency and reservoirs for HIV and pathogens causing oral diseases.
• Studies on the effect of Human Papillomavirus (HPV) vaccine on mucosal immunity and HPV acquisition, clearance, and persistence among those living with HIV.
• Studies on alcohol and drug exposure outcomes in individuals living with perinatally acquired HIV.
• Studies of HIV persistence, latency, reservoirs, and vaccines in reproductive age people
• Research on the effects of HIV and HIV treatment on fertility, pregnancy and post-partum outcomes, complications, co-morbidities, and co-infections including sexually transmitted infections (STI's) (for example syphilis, chlamydia, gonorrhea, HPV, trichomoniasis and CMV).

Examples of activities supported and encouraged under this NOFO include, but are not limited to:

• Development and implementation of a quality assurance and quality control plan spanning the full data life cycle
• Development of a centralized data storage system for site data.
• Creation and roll-out of a searchable web-based platform to enrich data sharing and broader integration
• Development and utilization of standard, unambiguous terminology for data linkage and integration with multiple data sets, including data dictionaries
• Development of methodologies to support the interoperability of PHACS data with other data sets such as common and metadata standards.
• Establishment of scientific collaborations with investigators.  

Essential Features of the U19 Structure

Scientific Administrative Core (SAC) (required)

The Scientific Administrative Core (SAC) provides overall management, communication, coordination, and supervision of the Program. The SAC administers the plan provided in the application to address the short- and long-term management of the Program. The SAC will monitor progress, develop, and implement a project management plan, and define timelines. Additionally, the Scientific Administrative Core will coordinate detailed communication of efforts and progress with NICHD and participating NIH program staff.

The SAC will provide outreach and establish collaborations with other networks and studies, develop and maintain bylaws and policies and mitigate conflicts of interest. In addition, the SAC will convene a Scientific Leadership Group and an Executive Committee, recruit and support the activities of an External Advisory Group (EAG).

The SAC will bring necessary expertise and resources for collaborative protocol development that will ensure feasible and acceptable study design(s), with proven ability to recruit and retain these unique populations through 5-15 competitive subcontracts to clinical sites with demonstrated high level prior performance .

The SAC will maintain discretionary funds to support the Emerging Research Pilots (ERPs) and may conduct an annual competition for an Early Career Investigator Award. The SAC will also be responsible for developing plans to mentor new and early-stage investigators to develop independent research careers.

The SAC will also be responsible for holding an annual group meeting to review accomplishments and plan the project agenda.

Data Management and Analysis Core (DMAC) (required)

The Data Management and Analysis Core (DMAC) will be responsible for providing central data storage, data management with safeguards to protect the integrity of the data to all projects within a U19 application and will be responsible for ensuring the submission of data, meta-data and related data analyses to DASH, or other appropriate public databases approved by NICHD. The core will also provide analytic support and development of methods, as needed, to integrate and/or harmonize data and methods for activities across research projects.The DMAC must demonstrate that existing datasets pertinent to the research proposed are usable and accessible through DASH or other publicly accessible data systems.

The DMAC will develop and direct the overarching Project Management Plan for the Cores and Research Projects.  The project management plan must include a transition plan to another responsible steward and long-term archival of the data. This is required if the current team no longer manages the data resource, or the entire resource is sunset.

The DMAC will:

• Execute and manage subcontracts for collaborating partners and clinical sites 
• Provide support to the Single IRB (sIRB) process and regulatory and compliance management for clinical sites.
• Utilize its relationship with and direct access to clinical sites, personnel, including Clinical Investigators, Study Staff and Community Board (CB) members, to implement all Research Projects and manage and monitor performance efficiently and effectively.
• Serve as the centralized data and resource management entity.
• Provide sample tracking, manage sample storage, oversee laboratory data management and data storage and access.
• Oversee and execute the sharing and transfer of samples and data and the receipt of data for collaborations.
• Provide programming and analytical core services for integrated data analysis. 
• Serve as a shared resource to all Cores and Research Projects.

The Core Lead is responsible for ensuring that shared scientific and analytic resources/facilities are available and utilized to the maximum extent possible and that procedures are developed to ensure that such resources are available to members of the research team in a timely manner. The data management and analysis core will also be responsible for ensuring compliance with data sharing policies.  The DMAC is encouraged to provide data as it becomes available.  To achieve the goal of data sharing from large epidemiologic studies in which data are collected over several discrete time periods or waves, it is reasonable to expect that these data would be released in waves as data become available or main findings from waves of the data are published.

It is expected and encouraged that the DMAC will lead an effort to engage the community to inform the research project, implementation, and dissemination of research findings. This may include translation of findings into resources of interest, coordination of dissemination activities with community members, partner organizations, and relevant service organizations or policymakers.

The effort may also include the support of a community advisory board and/or utilization of a community-based participatory research approach as applicable. Using plain language strategies, dissemination activities should include an effort to translate findings from projects and strategic planning into sustainable community and system-level changes.

The PHACS U19 Research Projects

Each U19 will include a maximum of 3 Research Projects along with Core(s) necessary to support the projects. Research projects should focus on the effects of antiretroviral treatment (ART) treatment on HIV during reproductive years and/or the developmental and clinical course of persons living with perinatally transmitted HIV. The U19 research program will be facilitated by the sharing of ideas, data, and specialized resources, such as equipment, services, and clinical facilities. The Research Projects proposed must be scientifically meritorious, and complement one another, be synergistic, and support the program's overall theme. Thus, the program's overall scientific merit should be greater than the sum of its parts.

Research Projects require the participation of established investigators in several disciplines or investigators with special expertise in several areas of one discipline. All Senior/Key Personnel (PDs/PIs, Project Leads, Core Leads) must contribute to, and share in, the responsibilities of fulfilling the program objectives.

Each Research Project should contribute materially and intellectually to the specific goals and objectives of the Program Project, contribute expertise and/or resources toward the aims of the Program Project and emphasize collaboration across all components of the U19. Each Research Project should contain the scientific vision which anticipates the ongoing evolution of the field and an emerging scientific agenda by briefly addressing the current state of knowledge on the clinical course of vertically transmitted HIV in children and adolescents, and the critical scientific questions in the clinical course of HIV from preconception to post-partum including the significant scientific gaps and opportunities, and the research, tools, resources and collaborations needed to progress toward filling those gaps to improve health outcomes in these populations.

Research Projects should be supported by the Scientific Administrative Core (SAC), Data Management and Analysis Core (DMAC) and any other optional appropriate Cores to enhance the research objectives.

The PD/PI must possess recognized scientific and administrative competence, devote a substantial commitment of effort to the program, and exercise leadership in maintaining program quality.

Optional Core (Optional) 

Up to 2 optional cores may be proposed to support the research projects proposed for the U19.

Cores are optional and may be included to provide investigators with core resources and/or facilities that are essential for the activities of two or more Research Projects. Core activities must not overlap with each other or with the activities of a Research Project.  The Core (optional) will be evaluated as Acceptable or Not Acceptable based on whether it is essential for the proposed research and has the capability to fulfill the proposed function. 

Annual Programmatic Meetings

A one- or two-day annual meeting will be held at a location at or near Bethesda, MD or at another NICHD-approved site or may be held virtually as needed. Costs associated with this meeting(s) should be included in the budget.

External Advisory Group (EAG)

An independent external advisory Group (EAG) of investigators who are not current collaborators of the funded programs is expected to be constituted by the PD/PI(s) of the U19 program project and the NIH. The advisory board will meet at least biannually to review the progress in achieving the goals of all research projects participating in the program. The EAG will make recommendations in writing for the continuation or re-direction of any or all projects and activities. Costs associated with the EAG should be included in the budget.

 NICHD Data Sharing Expectations and Requirements

The NIH Policy for Data Management and Sharing (Policy) expects researchers maximize the sharing of scientific data and data be accessible as soon as possible and no later than the time of an associated publication or the end of the award period, whichever comes first. NIH requires all applications submitted in response to this NOFO to include a Data Management and Sharing Plan (DMS Plan). The DMS Plan is expected to address the Elements as described in Supplemental Information to the NIH Policy for Data Management and Sharing: Elements of an NIH Data Management and Sharing Plan (NOT-OD-21-014) The DMS Plan will be reviewed and approved by NIH Program Staff prior to award. Awardees will be required to comply with their approved DMS Plan and any approved updates.

For human data, NICHD encourages the use of the Data and Specimen Hub (DASH), a centralized resource for researchers to store and access de-identified data from studies funded by NICHD. Information about DASH may be obtained at https://dash.nichd.nih.gov/. For projects generating large-scale human genetic data, applicants should provide a Provisional or Institutional Certification specifying whether the individual-level data can be shared through an NIH approved repository, such as dbGaP and the Sequence Read Archive, in line with the NIH Genomic Data Sharing Policy.

If use of DASH is not feasible, NICHD expects awardees to share data through other equivalent broad-sharing data repositories. For applications that aim to analyze existing data, DMS Plans should describe where and how other researchers can access that data to enable reproducibility and reuse. Additional information on the Data Management and Sharing Policy is available on the NICHD Office of Data Science and Sharing website.

See Section VIII. Other Information for award authorities and regulations.

Plan for Enhancing Diverse Perspectives (PEDP) The NIH recognizes that teams comprised of investigators with diverse perspectives working together and capitalizing on innovative ideas and distinct viewpoints outperform homogeneous teams. There are many benefits that flow from a scientific workforce rich with diverse perspectives, including: fostering scientific innovation, enhancing global competitiveness, contributing to robust learning environments, improving the quality of the research, advancing the likelihood that underserved populations participate in, and benefit from research, and enhancing public trust. To support the best science, the NIH encourages inclusivity in research guided by the consideration of diverse perspectives. Broadly, diverse perspectives can include but are not limited to the educational background and scientific expertise of the people who perform the research; the populations who participate as human subjects in research studies; and the places where research is done. This NOFO requires a Plan for Enhancing Diverse Perspectives (PEDP), which will be assessed as part of the scientific and technical peer review evaluation.  Assessment of applications containing a PEDP are based on the scientific and technical merit of the proposed project. Consistent with federal law, the race, ethnicity, or sex of a researcher, award participant, or trainee will not be considered during the application review process or when making funding decisions.  Applications that fail to include a PEDP will be considered incomplete and will be administratively withdrawn before review. The PEDP will be submitted as Other Project Information as an attachment (see Section IV).  Applicants are strongly encouraged to read the NOFO instructions carefully and view the available PEDP guidance materials .

Section II. Award Information

Cooperative Agreement: A financial assistance mechanism used when there will be substantial Federal scientific or programmatic involvement. Substantial involvement means that, after award, NIH scientific or program staff will assist, guide, coordinate, or participate in project activities. See Section VI.2 for additional information about the substantial involvement for this NOFO.

The  OER Glossary  and the How to Apply - Application Guide provides details on these application types. Only those application types listed here are allowed for this NOFO.

Not Allowed: Only accepting applications that do not propose clinical trials.

Issuing IC, NICHD, and partner  components intend to commit an estimated total of $11M to fund 1-2 awards.

Application budgets may not exceed $5.5 M direct costs per year but need to reflect the actual needs of the proposed project.

NIH grants policies as described in the NIH Grants Policy Statement will apply to the applications submitted and awards made from this NOFO.

Section III. Eligibility Information

1. eligible applicants eligible organizations higher education institutions public/state controlled institutions of higher education private institutions of higher education the following types of higher education institutions are always encouraged to apply for nih support as public or private institutions of higher education: hispanic-serving institutions historically black colleges and universities (hbcus) tribally controlled colleges and universities (tccus) alaska native and native hawaiian serving institutions asian american native american pacific islander serving institutions (aanapisis) nonprofits other than institutions of higher education nonprofits with 501(c)(3) irs status (other than institutions of higher education) nonprofits without 501(c)(3) irs status (other than institutions of higher education) for-profit organizations small businesses for-profit organizations (other than small businesses) local governments state governments county governments city or township governments special district governments indian/native american tribal governments (federally recognized) indian/native american tribal governments (other than federally recognized) federal governments eligible agencies of the federal government u.s. territory or possession other independent school districts public housing authorities/indian housing authorities native american tribal organizations (other than federally recognized tribal governments) faith-based or community-based organizations regional organizations foreign organizations non-domestic (non-u.s.) entities (foreign organization) are not eligible to apply. non-domestic (non-u.s.) components of u.s. organizations are not eligible to apply. foreign components, as defined in the nih grants policy statement , are not allowed.  required registrations applicant organizations applicant organizations must complete and maintain the following registrations as described in the how to apply- application guide to be eligible to apply for or receive an award. all registrations must be completed prior to the application being submitted. registration can take 6 weeks or more, so applicants should begin the registration process as soon as possible. failure to complete registrations in advance of a due date is not a valid reason for a late submission, please reference  nih grants policy statement section 2.3.9.2 electronically submitted applications  for additional information. system for award management (sam) – applicants must complete and maintain an active registration, which requires renewal at least annually . the renewal process may require as much time as the initial registration. sam registration includes the assignment of a commercial and government entity (cage) code for domestic organizations which have not already been assigned a cage code. nato commercial and government entity (ncage) code – foreign organizations must obtain an ncage code (in lieu of a cage code) in order to register in sam. unique entity identifier (uei) - a uei is issued as part of the sam.gov registration process. the same uei must be used for all registrations, as well as on the grant application. era commons - once the unique organization identifier is established, organizations can register with era commons in tandem with completing their grants.gov registration; all registrations must be in place by time of submission. era commons requires organizations to identify at least one signing official (so) and at least one program director/principal investigator (pd/pi) account in order to submit an application. grants.gov – applicants must have an active sam registration in order to complete the grants.gov registration. program directors/principal investigators (pd(s)/pi(s)) all pd(s)/pi(s) must have an era commons account.  pd(s)/pi(s) should work with their organizational officials to either create a new account or to affiliate their existing account with the applicant organization in era commons. if the pd/pi is also the organizational signing official, they must have two distinct era commons accounts, one for each role. obtaining an era commons account can take up to 2 weeks. eligible individuals (program director/principal investigator) any individual(s) with the skills, knowledge, and resources necessary to carry out the proposed research as the program director(s)/principal investigator(s) (pd(s)/pi(s)) is invited to work with his/her organization to develop an application for support. individuals from diverse backgrounds, including underrepresented racial and ethnic groups, individuals with disabilities, and women are always encouraged to apply for nih support. see, reminder: notice of nih's encouragement of applications supporting individuals from underrepresented ethnic and racial groups as well as individuals with disabilities, not-od-22-019 .  for institutions/organizations proposing multiple pds/pis, visit the multiple program director/principal investigator policy and submission details in the senior/key person profile (expanded) component of the how to apply - application guide . 2. cost sharing.

This NOFO does not require cost sharing as defined in the NIH Grants Policy Statement  Section 1.2- Definitions of Terms.

3. Additional Information on Eligibility

Number of applications.

Applicant organizations may submit more than one application, provided that each application is scientifically distinct.

The NIH will not accept duplicate or highly overlapping applications under review at the same time per NIH Grants Policy Statement Section 2.3.7.4 Submission of Resubmission Application . This means that the NIH will not accept:

• A new (A0) application that is submitted before issuance of the summary statement from the review of an overlapping new (A0) or resubmission (A1) application.
• A resubmission (A1) application that is submitted before issuance of the summary statement from the review of the previous new (A0) application.
• An application that has substantial overlap with another application pending appeal of initial peer review (see NIH Grants Policy Statement 2.3.9.4 Similar, Essentially Identical, or Identical Applications ).

Section IV. Application and Submission Information

1. requesting an application package.

The application forms package specific to this opportunity must be accessed through ASSIST or an institutional system-to-system solution. A button to apply using ASSIST is available in Part 1 of this NOFO. See the administrative office for instructions if planning to use an institutional system-to-system solution.

2. Content and Form of Application Submission

It is critical that applicants follow the Multi-Project (M) Instructions in the How to Apply - Application Guide , except where instructed in this notice of funding opportunity to do otherwise and where instructions in the How to Apply - Application Guide are directly related to the Grants.gov downloadable forms currently used with most NIH opportunities. Conformance to the requirements in the How to Apply - Application Guide is required and strictly enforced. Applications that are out of compliance with these instructions may be delayed or not accepted for review.

Letter of Intent

Although a letter of intent is not required, is not binding, and does not enter into the review of a subsequent application, the information that it contains allows IC staff to estimate the potential review workload and plan the review.

By the date listed in Part 1. Overview Information , prospective applicants are asked to submit a letter of intent that includes the following information:

• Descriptive title of proposed activity
• Name(s), address(es), and telephone number(s) of the PD(s)/PI(s)
• Names of other key personnel
• Participating institution(s)
• Number and title of this funding opportunity

The letter of intent should be sent to:

Denise Russo, Ph.D. Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Telephone: 301-435-6871  Email:   [email protected]  

Page Limitations

All page limitations described in the How to Apply- Application Guide and the Table of Page Limits must be followed.

Instructions for the Submission of Multi-Component Applications The following section supplements the instructions found in How to Apply- Application Guide and should be used for preparing a multi-component application. The application should consist of the following components: Overall: required, page limit 12 Scientific Administrative Core (SAC): required, page limit 12 Cores: optional, page limit 6, maximum 2 Data Management and Analysis Core (DMAC): required, page limit 12 Projects: required, page limit 12, minimum 2, maximum 3 Overall Component

When preparing the application, use Component Type ‘Overall’.

All instructions in the How to Apply - Application Guide must be followed, with the following additional instructions, as noted.

SF424(R&R) Cover (Overall)

Complete entire form.

PHS 398 Cover Page Supplement (Overall)

Note: Human Embryonic Stem Cell lines from other components should be repeated in cell line table in Overall component.

Research & Related Other Project Information (Overall)

Follow standard instructions.

Plan for Enhancing Diverse Perspectives (PEDP)

• In an "Other Attachment" entitled "Plan for Enhancing Diverse Perspectives," all applicants must include a summary of actionable strategies to advance the scientific and technical merit of the proposed project through expanded inclusivity.
• Applicants should align their proposed strategies for PEDP with the research strategy section, providing a holistic and integrated view of how enhancing diverse perspectives and inclusivity are buoyed throughout the application.
• The PEDP will vary depending on the scientific aims, expertise required, the environment and performance site(s), as well as how the project aims are structured.
• Actionable strategies using defined approaches for the inclusion of diverse perspectives in the project;
• Description of how the PEDP will advance the scientific and technical merit of the proposed project;
• Anticipated timeline of proposed PEDP activities;
• Evaluation methods for assessing the progress and success of PEDP activities.

Examples of items that advance inclusivity in research and may be appropriate for a PEDP can include, but are not limited to:

• Partnerships with different types of institutions and organizations (e.g., research-intensive; undergraduate-focused; HBCUs; emerging research institutions; community-based organizations).
• Project frameworks that enable communities and researchers to work collaboratively as equal partners in all phases of the research process.
• Outreach and planned engagement activities to enhance recruitment of individuals from diverse groups as human subjects in clinical trials, including those from underrepresented backgrounds.
• Description of planned partnerships that may enhance geographic and regional diversity.
• Outreach and recruiting activities intended to diversify the pool of applicants for research training programs, such as outreach to prospective applicants from groups underrepresented in the biomedical sciences, for example, individuals from underrepresented racial and ethnic groups, those with disabilities, those from disadvantaged backgrounds, and women.
• Plans to utilize the project infrastructure (i.e., research and structure) to enhance the research environment and support career-advancing opportunities for junior, early- and mid-career researchers.
• Transdisciplinary research projects and collaborations among researchers from fields beyond the biological sciences, such as physics, engineering, mathematics, computational biology, computer and data sciences, as well as bioethics.

Examples of items that are not appropriate in a PEDP include, but are not limited to:

• Selection or hiring of personnel for a research team based on their race, ethnicity, or sex.
• A training or mentorship program limited to certain researchers based on their race, ethnicity, or sex.

For further information on the Plan for Enhancing Diverse Perspectives (PEDP), please see PEDP guidance materials .

Project/Performance Site Locations (Overall)

Enter primary site only.

A summary of Project/Performance Sites in the Overall section of the assembled application image in eRA Commons compiled from data collected in the other components will be generated upon submission.

Research and Related Senior/Key Person Profile (Overall)

Include only the Project Director/Principal Investigator (PD/PI) and any multi-PDs/PIs (if applicable to this NOFO) for the entire application.

The U19 Program Project PD/PI (s)

• Should be established investigator(s) with demonstrated leadership and administrative capabilities in multidisciplinary research.
• Will be responsible for the projects and cores within the U19 and for communication, collaboration and coordination with other research networks and investigators.
• Will participate and collaborate with the Research Project leads, Core leads and others within the U19 to guide the Program in implementing scientific and administrative decisions.
• Should demonstrate a capacity to ensure participant recruitment based on demonstrated prior site experience and performance.
• Should demonstrate a track record of proactive community engagement in development of research activities.

A summary of Senior/Key Persons followed by their Biographical Sketches in the Overall section of the assembled application image in eRA Commons will be generated upon submission.

Budget (Overall)

The only budget information included in the Overall component is the Estimated Project Funding section of the SF424 (R&R) Cover.

PEDP implementation costs: Applicants may include allowable costs associated with PEDP implementation (as outlined in the Grants Policy Statement section 7): https://grants.nih.gov/grants/policy/nihgps/html5/section_7/7.1_general.htm.

A budget summary in the Overall section of the assembled application image in eRA Commons compiled from detailed budget data collected in the other components will be generated upon submission.

PHS 398 Research Plan (Overall)

Introduction to Application: For Resubmission and Revision applications, an Introduction to Application is required in the Overall component.

Specific Aims should comprehensively address the overall goals of the U19  

Research Strategy: Summarize the overall research objectives and strategic plan for the multi-project application. Applications responding to this FOA should describe the central theme of the proposed Program and explain how the proposed Research Projects are synergistic and fit under the overarching Program theme.

• Describe the conceptual wholeness to the overall program project by giving a statement of the general problem area and by laying out a broad strategy for addressing the problems.
• As the strategy develops, cite each research project and core to describe its place in the overall scheme.
• Concisely describe the hypothesis or hypotheses to be tested.
• Highlight the innovation, approach, and significance across the Program Project, including in the research projects.
• Explain ongoing, planned, and potential collaborations nationally to conduct epidemiologic and clinical research.
• Describe the program project's plan and timeline for  dissemination of  the research data and resources generated. t
• Include an overview of the program project's outreach activities.
• Describe the program project's commitment and plan for developing and mentoring new talent, including new PIs and early stage investigators, toward leadership roles in the cohort and as PIs of R01's.
• Describe prior collaborative arrangements between investigators in the group to explain the development of the current application.
• Describe a Project Management Plan that articulates the strategies and processes that will be used to manage the U19 and achieve the overall goals, including monitoring progress on achievement of Milestones, implementation of the Plan, and proposed Timelines.
• Explain how the proposed program project would enable the stated objectives of the proposed research to be addressed more efficiently and effectively than by a group of individual research project grants.
• Briefly describe the components of the Program Project and how they will interact and synergize to provide a program that is greater than the sum of its parts.
• Explain the strategy for achieving the goals defined for the overall program and how each Core and Research Project relate to that strategy.
• Demonstrate how partnerships between academia and the community have influenced and will continue to facilitate the design and implementation of interventions that leverage new and existing relationships (e.g., academic-based clinical and research sites, and community-based organizations, public health authorities and other private organizations) to optimize the research objectives. 

Letters of Support: Include letters of support/agreement for any collaborative/cooperative arrangements, subcontracts, or consultants. Letter of support for the U19 Cooperative Multi-Program Projects overall should be included with the Overall Component. Letter of support for individual Research Projects or Cores should be included with those components of the applications. For program activities to be conducted off site, i.e., at an institution other than the application institution, a letter of assurance or comparable documentation, signed by the collaborator as well as the off-site institutional officials, must be submitted with the application. 

Resource Sharing Plan : Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the How to Apply - Application Guide .

Other Plan(s):  

All instructions in the How to Apply- Application Guide must be followed, with the following additional instructions:

• All applicants planning research (funded or conducted in whole or in part by NIH) that results in the generation of scientific data are required to comply with the instructions for the Data Management and Sharing Plan. All applications, regardless of the amount of direct costs requested for any one year, must address a Data Management and Sharing Plan.

Only limited items are allowed in the Appendix. Follow all instructions for the Appendix as described in How to Apply- Application Guide ; any instructions provided here are in addition to the How to Apply - Application Guide instructions.

PHS Human Subjects and Clinical Trials Information (Overall)

When involving human subjects research, clinical research, and/or NIH-defined clinical trials follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply - Application Guide , with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, there must be at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or a Delayed Onset Study record within the application. The study record(s) must be included in the component(s) where the work is being done, unless the same study spans multiple components. To avoid the creation of duplicate study records, a single study record with sufficient information for all involved components must be included in the Overall component when the same study spans multiple components.

Study Record: PHS Human Subjects and Clinical Trials Information

All instructions in the How to Apply - Application Guide must be followed.

Delayed Onset Study

Note:  Delayed onset  does NOT apply to a study that can be described but will not start immediately (i.e., delayed start). All instructions in the How to Apply- Application Guide must be followed.

PHS Assignment Request Form (Overall)

All instructions in the How to Apply- Application Guide must be followed.

Scientific Administrative Core 

When preparing your application, use Component Type ‘[ Administrative Core] ’

All instructions in the How to Apply- Application Guide must be followed, with the following additional instructions, as noted. 

Note: Effective for due dates on or after January 25, 2023, the Data Management and Sharing Plan will be attached in the Other Plan(s) attachment in FORMS-H application forms packages. If required, the Data Management and Sharing (DMS) Plan must be provided in the Overall component.

SF424 (R&R) Cover (Scientific Administrative Core)

Complete only the following fields:

• Applicant Information
• Type of Applicant (optional)
• Descriptive Title of Applicant’s Project
• Proposed Project Start/Ending Dates

PHS 398 Cover Page Supplement ( Scientific Administrative Core )

Enter Human Embryonic Stem Cells in each relevant component.

Research & Related Other Project Information ( Scientific Administrative Core )

Human Subjects: Answer only the ‘Are Human Subjects Involved?’ and 'Is the Project Exempt from Federal regulations?’ questions.

Vertebrate Animals: Answer only the ‘Are Vertebrate Animals Used?’ question.

Project Narrative: Do not complete. Note: ASSIST screens will show an asterisk for this attachment indicating it is required. However, eRA systems only enforce this requirement in the Overall component and applications will not receive an error if omitted in other components.

Project /Performance Site Location(s) ( Scientific Administrative Core )

List all performance sites that apply to the specific component.

Note: The Project Performance Site form allows up to 300 sites, prior to using additional attachment for additional entries.

Research & Related Senior/Key Person Profile ( Scientific Administrative Core)

• In the Project Director/Principal Investigator section of the form, use Project Role of ‘Other’ with Category of ‘Project Lead’ and provide a valid eRA Commons ID in the Credential field.
• In the additional Senior/Key Profiles section, list Senior/Key persons that are working in the component.
• Include a single Biographical Sketch for each Senior/Key person listed in the application regardless of the number of components in which they participate. When a Senior/Key person is listed in multiple components, the Biographical Sketch can be included in any one component.
• If more than 100 Senior/Key persons are included in a component, the Additional Senior Key Person attachments should be used. 
• The applicant should assemble the necessary multidisciplinary team of established investigators to establish the Scientific Leadership Committee (SLC). Disciplines should be included as required to support the purposes of this initiative .   

Budget (Scientific Administrative Core)

Funding for the overall administrative efforts, including secretarial, and/or other administrative services, expenses for publications demonstrating collaborative efforts, and communication expenses should be requested in the budget for this core. Additionally,

• The U19 PD(s)/PI(s) will be expected to provide at least 25% FTE (3 person-months) to the Program and will lead this Core; increased effort is expected if the U19 PD(s)/PI(s) plan to also lead a research project within the U19.
• The U19 PD(s)/PI(s) should provide a discretionary budget to be used for funding of the focused Emerging Research Pilots (sub-studies), for supporting collaboration or co-endorsement agreements with other research networks as indicated, and for accommodating central sub-study-mandated requirements (e.g., specimen shipping costs) on an as-needed basis.
• Funds for one yearly meeting should be included in the budget

Budget forms appropriate for the specific component will be included in the application package.

Note: The R&R Budget form included in many of the component types allows for up to 100 Senior/Key Persons in section A and 100 Equipment Items in section C prior to using attachments for additional entries. All other SF424 (R&R) instructions apply.

PHS 398 Research Plan ( Scientific Administrative Core)

Specific Aims: List in priority order, the broad, long-range objectives, and goals of the Scientific Administrative Core. State the Core’s relationship to the multi-project program goals and how it relates to the Research Projects and any other Cores in the application. Include a brief list of Specific Aims outlining the objectives and functions of the Scientific Administrative Core.

Research Strategy: The overview of the Scientific Administrative Core should articulate the strategy that the Program Project will adopt to achieve the scientific goals and describe the processes/approaches that will be used in decision-making and implementation of activities, including the establishment of scientific priorities, strategies used to manage the Program Project.

• As part of the Core, describe the structure and plans for coordination, administration, fiscal accountability, allocation of funds and other resources, problem identification and resolution, and training.
• Describe the services provided and how the SAC resources will contribute to the objectives of the Research Projects.
• Provide information on how the SAC will provide oversight of the Cores and Research Projects and will promote coordination and collaboration within the program and with investigators and organizations outside the program. 
• A plan for determining how the research pilots will be selected should be included.

Letters of Support: Provide letters of support specific to this component.

Resource Sharing Plan: Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the How to Apply- Application Guide , The Data Management and Sharing (DMS) Plan must be provided in the Overall component.

Only limited items are allowed in the Appendix. Follow all instructions for the How to Apply- Application Guide ; any instructions provided here are in addition to those in the Application Guide instructions.

PHS Human Subjects and Clinical Trials Information ( Scientific Administrative Core)

When involving human subjects research, clinical research, and/or NIH-defined clinical trials follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the How to Apply- Application Guide, with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information  form or a Delayed Onset Study record.

Delayed Onset Study:

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start).All instructions in the How to Apply- Application Guide must be followed.

Data Management and Analysis Core (DMAC)

When preparing your application, use Component Type ‘ Data Core .’ 

All instructions in the SF424 (R&R) Application Guide must be followed, with the following additional instructions, as noted.

SF424 (R&R) Cover (Data Management and Analysis Core)

Phs 398 cover page supplement (data management and analysis core), research & related other project information (data management and analysis core).

Project Narrative:  Do not complete. Note: ASSIST screens will show an asterisk for this attachment indicating it is required. However, eRA systems only enforce this requirement in the Overall component and applications will not receive an error if omitted in other components.

Project /Performance Site Location(s) Data Management and Analysis Core

Research & related senior/key person profile (data management and analysis core).

• In the Project Director/Principal Investigator section of the form, use Project Role of ‘Other’ with Category of ‘Core Lead’ and provide a valid eRA Commons ID in the Credential field.

Budget (Data Management and Analysis Core)

Phs 398 research plan (data management and analysis core).

Specific Aims : List in priority order, the broad, long-range objectives, and goals of the proposed Core. In addition, state the Core's relationship to the Program Project and how it relates to the individual Research Projects or other Cores in the application.  Include a brief list of Specific Aims outlining the objectives and functions of the Scientific Administrative Core.    

Research Strategy: Describe the organizational structure and role of the Data Management and Analysis Core in the overall Program Project research activities and include a strategy for management of data activities that describes internal and external data acquisition strategies to achieve harmonization of systems and procedures for data management, data quality, data analyses, and dissemination for all data and data-related materials. Provide information on innovative capabilities in data analysis and visualization and how these will be developed. Describe the strategies and processes that will be used to manage the DMAC and achieve the overall goals, including monitoring progress on milestones, implementation of the Project Management Plan and proposed Timelines. The DMAC must demonstrate that existing datasets pertinent to the research proposed are usable and accessible through DASH or other publicly accessible data systems.

Describe the utilization of the Core and include the following :

• The Project Management Plan for the Cores and Research Projects.
• A plan for executing and managing subcontracts for collaborating partners. 
• Clinical site Single IRB (sIRB) process, regulatory and compliance management.
• DMAC relationship with and direct access to clinical sites, personnel, including Clinical Investigators, Study Staff and Community Board (CB) members, to implement all Research Projects and manage and monitor performance efficiently and effectively.
• The plan for serving as the centralized data and resource management entity.
• How sample tracking, management of sample storage, oversight of laboratory data management and data storage and access will be provided.
• How oversight and execution of the sharing and transfer of samples/data and the receipt of data for collaborations will be managed.
• How programming and analytical core services for integrated data analysis will be provided and how DMAC will serve as a shared resource to all Cores and Research Projects. 

Describe how DMAC will demonstrate that existing datasets pertinent to the research proposed are usable and accessible through DASH or other publicly accessible data systems.

Resource Sharing Plan:

Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the SF424 (R&R) Application Guide. The Data Management and Sharing (DMS) Plan must be provided in the Overall component.

Only limited items are allowed in the Appendix. Follow all instructions for the Appendix as described in the SF424 (R&R) Application Guide; any instructions provided here are in addition to the SF424 (R&R) Application Guide instructions.   

PHS Human Subjects and Clinical Trials Information (Data management and Analysis Core )

When involving human subjects research, clinical research, and/or NIH-defined clinical trials follow all instructions for the PHS Human Subjects and Clinical Trials Information form in the SF424 (R&R) Application Guide, with the following additional instructions:

If you answered “Yes” to the question “Are Human Subjects Involved?” on the R&R Other Project Information form, you must include at least one human subjects study record using the Study Record: PHS Human Subjects and Clinical Trials Information form or a Delayed Onset Study record.

All instructions in the SF424 (R&R) Application Guide must be followed.

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start). All instructions in the SF424 (R&R) Application Guide must be followed

Research Project

When preparing your application, use Component Type ‘ Project .’

SF424 (R&R) Cover Research project)

Phs 398 cover page supplement (research project), research & related other project information (research project), project /performance site location(s) (research project), research & related senior/key person profile (research project).

• In the Project Director/Principal Investigator section of the form, use Project Role of ‘Other’ with Category of ‘ Core Lead’ and provide a valid eRA Commons ID in the Credential field.

Budget (Research Project)

Phs 398 research plan (research project).

Specific Aims:  Provide Specific Aims for the Research Project

Research Strategy: Following the instructions in the SF424 (R&R) Application Guide, start each section with the appropriate section heading—Significance, Innovation, Approach.

• Clearly describe the project's objectives and explain its relevance to the overall program's theme.
• As part of the Research Strategy, include information on preliminary studies, data, and/or prior experience pertinent to this application.
• Specify the scientific hypotheses and biomedical significance of the work proposed.
• Describe the Research Project's use of Core services, including why the services are needed and the advantages and cost-effectiveness of Core usage for the Project.
• Provide a timeline and recruitment objectives if pertinent.
• Describe the strategies, techniques and processes that will be used to manage the research project and achieve the overall goals, including monitoring progress with respect to Milestones, and proposed Timelines.

Letters of Support: Provide letters of support specific to the Research Projects.

Individuals are required to comply with the instructions for the Resource Sharing Plans as provided in the SF424 (R&R) Application Guide.  The Data Management and Sharing (DMS) Plan must be provided in the Overall component.

PHS Human Subjects and Clinical Trials Information (Research Project)

Note: Delayed onset does NOT apply to a study that can be described but will not start immediately (i.e., delayed start).All instructions in the SF424 (R&R) Application Guide must be followed

Optional Core

When preparing your application, use Component Type ‘CORE.’

Cores are optional and may be included to provide investigators with core resources and/or facilities that are essential for the activities of two or more Research Projects. Core activities must not overlap with each other or with the activities of a Research Project. 

SF424 (R&R) Cover Optional Core )

Phs 398 cover page supplement (optional core ), research & related other project information (optional core).

The Core (optional) will be evaluated as Acceptable or "Not Acceptable based on whether it is essential for the proposed research and has the capability to fulfill the proposed function.

Project /Performance Site Location(s) (Optional Core)

Research & related senior/key person profile (optional core), budget (optional core ), phs 398 research plan (optional core).

Specific Aims: 

Include a brief list of Specific Aims outlining the objectives and functions of the Core.

Research Strategy:

Provide the following information:

• Objectives: Description of the objectives of the Core.
• Staffing: Brief description of scientific, technical, and support staff.
• Resources: Description of how Core resources will contribute to the objectives of the Research Projects, SAC, DMAC.    
• Services provided: Description of current and projected services to other Core and Research Components, as well as the process for prioritizing requests for use of Core facilities by the various Research Projects.
• Management: Description of overall management of the Core, decision-making process for use of Core services, and plans for cost-effectiveness and quality control.
• Utilization of Core: Provide a summary of past and/or projected usage of Core services (e.g., assays performed, etc.).  Include estimates of the percentage use of Core unit by the affiliated Research Project components.       

Letters of Support: Include letters of support specific to this component.

Only limited items are allowed in the Appendix. Follow all instructions for the Appendix as described in the SF424 (R&R) Application Guide; any instructions provided here are in addition to the SF424 (R&R) Application Guide instructions.    

PHS Human Subjects and Clinical Trials Information (Optional Core)

3. unique entity identifier and system for award management (sam).

See Part 2. Section III.1 for information regarding the requirement for obtaining a unique entity identifier and for completing and maintaining active registrations in System for Award Management (SAM), NATO Commercial and Government Entity (NCAGE) Code (if applicable), eRA Commons, and Grants.gov

4. Submission Dates and Times

Part I. contains information about Key Dates and times. Applicants are encouraged to submit applications before the due date to ensure they have time to make any application corrections that might be necessary for successful submission. When a submission date falls on a weekend or Federal holiday , the application deadline is automatically extended to the next business day.

Organizations must submit applications to Grants.gov (the online portal to find and apply for grants across all Federal agencies) using ASSIST or other electronic submission systems. Applicants must then complete the submission process by tracking the status of the application in the eRA Commons , NIH’s electronic system for grants administration. NIH and Grants.gov systems check the application against many of the application instructions upon submission. Errors must be corrected and a changed/corrected application must be submitted to Grants.gov on or before the application due date and time. If a Changed/Corrected application is submitted after the deadline, the application will be considered late. Applications that miss the due date and time are subjected to the NIH Grants Policy Statement Section 2.3.9.2 Electronically Submitted Applications .

Applicants are responsible for viewing their application before the due date in the eRA Commons to ensure accurate and successful submission.

Information on the submission process and a definition of on-time submission are provided in How to Apply- Application Guide.

5. Intergovernmental Review (E.O. 12372)

This initiative is not subject to intergovernmental review .

6. Funding Restrictions

All NIH awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Pre-award costs are allowable only as described in the  NIH Grants Policy Statement  Section 7.9.1 Selected Items of Cost.

Applications must be submitted electronically following the instructions described in the How to Apply - Application Guide . Paper applications will not be accepted.

Applicants must complete all required registrations before the application due date. Section III. Eligibility Information contains information about registration.

For assistance with your electronic application or for more information on the electronic submission process, visit How to Apply – Application Guide . If you encounter a system issue beyond your control that threatens your ability to complete the submission process on-time, you must follow the Dealing with System Issues guidance. For assistance with application submission, contact the Application Submission Contacts in Section VII .

Important reminders:

All PD(s)/PI(s) must include their eRA Commons ID in the Credential field of the Senior/Key Person Profile form . Failure to register in the Commons and to include a valid PD/PI Commons ID in the credential field will prevent the successful submission of an electronic application to NIH. See Section III of this NOFO for information on registration requirements.

The applicant organization must ensure that the unique entity identifier provided on the application is the same identifier used in the organization’s profile in the eRA Commons and for the System for Award Management. Additional information may be found in the How to Apply - Application Guide .

See more tips for avoiding common errors.

Applications must include a PEDP submitted as Other Project Information as an attachment. Applications that fail to include a PEDP will be considered incomplete and will be administratively withdrawn before review.

Upon receipt, applications will be evaluated for completeness and compliance with application instructions by the Center for Scientific Review and responsiveness by components of participating organizations, NIH. Applications that are incomplete, non-compliant and/or nonresponsive will not be reviewed.

Use of Common Data Elements in NIH-funded Research

Many NIH ICs encourage the use of common data elements (CDEs) in basic, clinical, and applied research, patient registries, and other human subject research to facilitate broader and more effective use of data and advance research across studies. CDEs are data elements that have been identified and defined for use in multiple data sets across different studies. Use of CDEs can facilitate data sharing and standardization to improve data quality and enable data integration from multiple studies and sources, including electronic health records. NIH ICs have identified CDEs for many clinical domains (e.g., neurological disease), types of studies (e.g. genome-wide association studies (GWAS)), types of outcomes (e.g., patient-reported outcomes), and patient registries (e.g., the Global Rare Diseases Patient Registry and Data Repository). NIH has established a Common Data Element (CDE) Resource Portal" ( http://cde.nih.gov/ ) to assist investigators in identifying NIH-supported CDEs when developing protocols, case report forms, and other instruments for data collection. The Portal provides guidance about and access to NIH-supported CDE initiatives and other tools and resources for the appropriate use of CDEs and data standards in NIH-funded research. Investigators are encouraged to consult the Portal and describe in their applications any use they will make of NIH-supported CDEs in their projects.

Recipients or subrecipients must submit any information related to violations of federal criminal law involving fraud, bribery, or gratuity violations potentially affecting the federal award. See Mandatory Disclosures,  2 CFR 200.113 and  NIH Grants Policy Statement Section 4.1.35 .

Send written disclosures to the NIH Chief Grants Management Officer listed on the Notice of Award for the IC that funded the award and to the  HHS Office of Inspector Grant Self Disclosure Program at  [email protected] .

Post Submission Materials

Applicants are required to follow the instructions for post-submission materials, as described in the policy .

Section V. Application Review Information

1. criteria.

Only the review criteria described below will be considered in the review process. Applications submitted to NIH in support of the NIH mission are evaluated for scientific and technical merit through the NIH peer review system.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood for the program to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following review criteria and additional review criteria (as applicable for the program proposed).

As part of the overall impact score, reviewers should consider and indicate how the Plan to Enhance Diverse Perspectives affects the scientific merit of the program.

Reviewers will consider each of the review criteria below in the determination of scientific merit and give a separate score for each. An application does not need to be strong in all categories to be judged likely to have major scientific impact. For example, a program that by its nature is not innovative may be essential to advance a field.

Significance

Does the program address an important problem or a critical barrier to progress in the field? Is the prior research that serves as the key support for the proposed program rigorous? If the aims of the program are achieved, how will scientific knowledge, technical capability, and/or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?

Investigator(s)

Are the PD(s)/PI(s), collaborators, and other researchers well suited to the program? If Early Stage Investigators or those in the early stages of independent careers, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the program is collaborative or multi-PD/PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance and organizational structure appropriate for the program?

Specific to this NOFO :  Are Early stage investigators (ESI) involved in different components of the program application? If a multi PD/PI application, are they part of the leadership plan?

Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?

Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the program? Have the investigators included plans to address weaknesses in the rigor of prior research that serves as the key support for the proposed program? Have the investigators presented strategies to ensure a robust and unbiased approach, as appropriate for the work proposed? Are potential problems, alternative strategies, and benchmarks for success presented? If the program is in the early stages of development, will the strategy establish feasibility and will particularly risky aspects be managed? Have the investigators presented adequate plans to address relevant biological variables, such as sex, for studies in vertebrate animals or human subjects?

If the program involves human subjects and/or NIH-defined clinical research, are the plans to address:

1) the protection of human subjects from research risks, and 2) inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion or exclusion of individuals of all ages (including children and older adults), justified in terms of the scientific goals and research strategy proposed?

Specific to this NOFO :

Is there robust synergy/integration across the programs proposed including the projects and cores? 

Environment

Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment and other physical resources available to the investigators adequate for the program proposed? Will the program benefit from unique features of the scientific environment, subject populations, or collaborative arrangements?

Additional Review Criteria - Overall

As applicable for the program proposed, reviewers will evaluate the following additional items while determining scientific and technical merit, and in providing an overall impact score, but will not give separate scores for these items.

Protections for Human Subjects

For research that involves human subjects but does not involve one of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate the justification for involvement of human subjects and the proposed protections from research risk relating to their participation according to the following five review criteria: 1) risk to subjects, 2) adequacy of protection against risks, 3) potential benefits to the subjects and others, 4) importance of the knowledge to be gained, and 5) data and safety monitoring for clinical trials.

For research that involves human subjects and meets the criteria for one or more of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate: 1) the justification for the exemption, 2) human subjects involvement and characteristics, and 3) sources of materials. For additional information on review of the Human Subjects section, please refer to the Guidelines for the Review of Human Subjects .

Inclusion of Women, Minorities, and Individuals Across the Lifespan

When the proposed program involves human subjects and/or NIH-defined clinical research, the committee will evaluate the proposed plans for the inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion (or exclusion) of individuals of all ages (including children and older adults) to determine if it is justified in terms of the scientific goals and research strategy proposed. For additional information on review of the Inclusion section, please refer to the Guidelines for the Review of Inclusion in Clinical Research .

Vertebrate Animals

The committee will evaluate the involvement of live vertebrate animals as part of the scientific assessment according to the following three points: (1) a complete description of all proposed procedures including the species, strains, ages, sex, and total numbers of animals to be used; (2) justifications that the species is appropriate for the proposed research and why the research goals cannot be accomplished using an alternative non-animal model; and (3) interventions including analgesia, anesthesia, sedation, palliative care, and humane endpoints that will be used to limit any unavoidable discomfort, distress, pain and injury in the conduct of scientifically valuable research. Methods of euthanasia and justification for selected methods, if NOT consistent with the AVMA Guidelines for the Euthanasia of Animals, is also required but is found in a separate section of the application. For additional information on review of the Vertebrate Animals Section, please refer to the Worksheet for Review of the Vertebrate Animals Section.

Reviewers will assess whether materials or procedures proposed are potentially hazardous to research personnel and/or the environment, and if needed, determine whether adequate protection is proposed.

Resubmissions

Additional review considerations - overall.

As applicable for the program proposed, reviewers will consider each of the following items, but will not give scores for these items, and should not consider them in providing an overall impact score.

Applications from Foreign Organizations

Select agent research.

Reviewers will assess the information provided in this section of the application, including 1) the Select Agent(s) to be used in the proposed research, 2) the registration status of all entities where Select Agent(s) will be used, 3) the procedures that will be used to monitor possession use and transfer of Select Agent(s), and 4) plans for appropriate biosafety, biocontainment, and security of the Select Agent(s).

Resource Sharing Plans

Reviewers will comment on whether the Resource Sharing Plan(s) (e.g., Sharing Model Organisms ) or the rationale for not sharing the resources, is reasonable.

Authentication of Key Biological and/or Chemical Resources:

For programs involving key biological and/or chemical resources, reviewers will comment on the brief plans proposed for identifying and ensuring the validity of those resources.

Budget and Period of Support

Reviewers will consider whether the budget and the requested period of support are fully justified and reasonable in relation to the proposed research.

Review Criteria - Scientific Administrative Core 

Reviewers will evaluate the following items in determining scientific and technical merit. Reviewers will provide a single impact score for the Science Administrative Core. Reviewers will not give separate scores for the individual items. Reviewers will not provide criteria scores.

• Have the planning and coordination of research activities been adequately described?
• Have the strategies and processes to be used for U19 management to achieve the overall goals, including monitoring progress with respect to Milestones, implementation of the overall Project Management Plan, and proposed Timelines been discussed?
• Are the plans for ongoing communication, and plans for evaluation of the Program Project by internal or external advisory board clearly delineated?
• Are the qualifications, experience, and commitment of the Core director and other Core personnel appropriate? 

Review Criteria - Data Management and Analysis Core

Reviewers will evaluate the following items in determining scientific and technical merit. Reviewers will provide a single impact score for the Data Management and Analysis Core. Reviewers will not give separate scores for the individual items. Reviewers will not provide criteria scores.

• Is the plan for services and the process for prioritizing requests for use of Core facilities by the various Research Projects adequately formulated?
• Has information been provided on innovative capabilities in data analysis and visualization, including, GPS and artificial intelligence (AI) strategies to enhance data integration workflows and pipelines, and how these will be developed to serve as community accepted standards?
• Has a description of plans for data harmonization between research projects and cores been provided?
• Has a description of plans for statistical and data management support and for creating and maintaining the necessary clinical infrastructure been provided?
• Is there a description of how DMAC has provided to the public, existing datasets pertinent to the research proposed, that are usable and accessible through DASH or other publicly accessible data systems?
• Are the qualifications, experience, and commitment of the Core director and other Core personnel appropriate?
• Are Core's governance and organizational structure appropriate?
• Has a plan for the development of a centralized information center been described?

Review Criteria - Optional Core

Reviewers will rate the Optional Core as Acceptable or Not Acceptable based on whether it is essential and justified for the proposed research and has the capability to fulfill the proposed function (reviewers will evaluate the number of Projects serviced by the Core; the Core must service two or more Projects).

Reviewers will evaluate the following items in determining scientific and technical merit. 

The following items should be considered in providing an overall evaluation of the optional Core(s) as Acceptable or Unacceptable

• Are the cores sufficiently justified?
• Do they support at least two research projects?
• Are the cores adequately connected to the focus of the overall program?
• Are the facilities or services provided by the cores (including procedures, techniques, and quality control) high quality and appropriate?
• Will the services be used effectively?
• Are the core leader(s) and key personnel well qualified and is there an adequate commitment of time?

Review Criteria - Research Projects

Overall impact - research projects.

Reviewers will provide an overall impact score to reflect their assessment of the likelihood for the project to exert a sustained, powerful influence on the research field(s) involved, in consideration of the following review criteria and additional review criteria (as applicable for the project proposed).

Scored Review Criteria - Research Projects

Reviewers will consider each of the review criteria below in the determination of scientific merit and give a separate score for each. An application does not need to be strong in all categories to be judged likely to have major scientific impact. For example, a project that by its nature is not innovative may be essential to advance a field.

Does the project address an important problem or a critical barrier to progress in the field? Is the prior research that serves as the key support for the proposed project rigorous?  If the aims of the project are achieved, how will scientific knowledge, technical capability, and/or clinical practice be improved? How will successful completion of the aims change the concepts, methods, technologies, treatments, services, or preventative interventions that drive this field?   

Are the Project Leads, collaborators, and other researchers well suited to the project? If Early Stage Investigators or those in the early stages of independent careers, do they have appropriate experience and training? If established, have they demonstrated an ongoing record of accomplishments that have advanced their field(s)? If the project is collaborative or multi-PD/PI, do the investigators have complementary and integrated expertise; are their leadership approach, governance, and organizational structure appropriate for the project?

Does the application challenge and seek to shift current research or clinical practice paradigms by utilizing novel theoretical concepts, approaches or methodologies, instrumentation, or interventions? Are the concepts, approaches or methodologies, instrumentation, or interventions novel to one field of research or novel in a broad sense? Is a refinement, improvement, or new application of theoretical concepts, approaches or methodologies, instrumentation, or interventions proposed?  

Are the overall strategy, methodology, and analyses well-reasoned and appropriate to accomplish the specific aims of the project? Have investigators included plans to address weaknesses in the rigor of prior research that serves as the key support for the proposed project? Have the investigators presented strategies to ensure a robust and unbiased approach, as appropriate for the work proposed?  Are potential problems, alternative strategies, and benchmarks for success presented? If the project is in the early stages of development, will the strategy establish feasibility, and will particularly risky aspects be managed? Have the investigators presented adequate plans to address relevant biological variables, such as sex, for studies in vertebrate animals or human subjects?

If the project involves human subjects and/or NIH-defined clinical research, are the plans to address:

1) the protection of human subjects from research risks, and

2) inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion or exclusion of individuals of all ages (including children and older adults), justified in terms of the scientific goals and research strategy proposed? 

Specific to this NOFO : Has the research project's use of the Core services, including why they are needed, been adequately explained?

Will the scientific environment in which the work will be done contribute to the probability of success? Are the institutional support, equipment, and other physical resources available to the investigators adequate for the project proposed? Will the project benefit from unique features of the scientific environment, subject populations, or collaborative arrangements?      

Additional Review Criteria - Cores and Research Projects

For research that involves human subjects but does not involve one of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate the justification for involvement of human subjects and the proposed protections from research risk relating to their participation according to the following five review criteria: 1) risk to subjects, 2) adequacy of protection against risks, 3) potential benefits to the subjects and others, 4) importance of the knowledge to be gained, and 5) data and safety monitoring for clinical trials.

For research that involves human subjects and meets the criteria for one or more of the categories of research that are exempt under 45 CFR Part 46, the committee will evaluate: 1) the justification for the exemption, 2) human subjects' involvement and characteristics, and 3) sources of materials. For additional information on review of the Human Subjects section, please refer to the  Guidelines for the Review of Human Subjects .

Inclusion of Women, Minorities, and Individuals Across the Lifespan   

When the proposed project involves human subjects and/or NIH-defined clinical research, the committee will evaluate the proposed plans for the inclusion (or exclusion) of individuals on the basis of sex/gender, race, and ethnicity, as well as the inclusion (or exclusion) of individuals of all ages (including children and older adults) to determine if it is justified in terms of the scientific goals and research strategy proposed.  For additional information on review of the Inclusion section, please refer to the  Guidelines for the Review of Inclusion in Clinical Research .

The committee will evaluate the involvement of live vertebrate animals as part of the scientific assessment according to the following criteria: (1) description of proposed procedures involving animals, including species, strains, ages, sex, and total number to be used; (2) justifications for the use of animals versus alternative models and for the appropriateness of the species proposed; (3) interventions to minimize discomfort, distress, pain and injury; and (4) justification for euthanasia method if NOT consistent with the AVMA Guidelines for the Euthanasia of Animals. Reviewers will assess the use of chimpanzees as they would any other application proposing the use of vertebrate animals. For additional information on review of the Vertebrate Animals section, please refer to the Worksheet for Review of the Vertebrate Animals Section .

 Not Applicable

Additional Review Considerations - Cores and Research Projects 

Reviewers will comment on whether the Resource Sharing Plan(s) (e.g.,  Sharing Model Organisms ) or the rationale for not sharing the resources, is reasonable.

Authentication of Key Biological and/or Chemical Resources

For projects involving key biological and/or chemical resources, reviewers will comment on the brief plans proposed for identifying and ensuring the validity of those resources.

2. Review and Selection Process Applications will be evaluated for scientific and technical merit by (an) appropriate Scientific Review Group(s) convened by NICHD, in accordance with NIH peer review policies and practices , using the stated review criteria. Assignment to a Scientific Review Group will be shown in the eRA Commons. As part of the scientific peer review, all applications will receive a written critique. Applications may undergo a selection process in which only those applications deemed to have the highest scientific and technical merit (generally the top half of applications under review) will be discussed and assigned an overall impact score. Appeals  of initial peer review will not be accepted for applications submitted in response to this NOFO. Applications will be assigned on the basis of established PHS referral guidelines to the appropriate NIH Institute or Center. Applications will compete for available funds with all other recommended applications submitted in response to this NOFO. Following initial peer review, recommended applications will receive a second level of review by the appropriate national Advisory Council or Board. The following will be considered in making funding decisions: Scientific and technical merit of the proposed project, including the PEDP, as determined by scientific peer review Availability of funds. Relevance of the proposed project to program priorities. If the application is under consideration for funding, NIH will request "just-in-time" information from the applicant as described in the  NIH Grants Policy Statement Section 2.5.1. Just-in-Time Procedures . This request is not a Notice of Award nor should it be construed to be an indicator of possible funding. Prior to making an award, NIH reviews an applicant’s federal award history in SAM.gov to ensure sound business practices. An applicant can review and comment on any information in the Responsibility/Qualification records available in SAM.gov.  NIH will consider any comments by the applicant in the Responsibility/Qualification records in SAM.gov to ascertain the applicant’s integrity, business ethics, and performance record of managing Federal awards per 2 CFR Part 200.206 “Federal awarding agency review of risk posed by applicants.”  This provision will apply to all NIH grants and cooperative agreements except fellowships. 3. Anticipated Announcement and Award Dates

After the peer review of the application is completed, the PD/PI will be able to access their Summary Statement (written critique) via the  eRA Commons . Refer to Part 1 for dates for peer review, advisory council review, and earliest start date.

Information regarding the disposition of applications is available in the NIH Grants Policy Statement Section 2.4.4 Disposition of Applications .

Section VI. Award Administration Information

1. award notices.

A Notice of Award (NoA) is the official authorizing document notifying the applicant that an award has been made and that funds may be requested from the designated HHS payment system or office. The NoA is signed by the Grants Management Officer and emailed to the recipient’s business official.

In accepting the award, the recipient agrees that any activities under the award are subject to all provisions currently in effect or implemented during the period of the award, other Department regulations and policies in effect at the time of the award, and applicable statutory provisions.

Recipients must comply with any funding restrictions described in  Section IV.6. Funding Restrictions . Any pre-award costs incurred before receipt of the NoA are at the applicant's own risk.  For more information on the Notice of Award, please refer to the  NIH Grants Policy Statement Section 5. The Notice of Award and NIH Grants & Funding website, see  Award Process.

Institutional Review Board or Independent Ethics Committee Approval: Grantee institutions must ensure that protocols are reviewed by their IRB or IEC. To help ensure the safety of participants enrolled in NIH-funded studies, the recipient must provide NIH copies of documents related to all major changes in the status of ongoing protocols.

Prior Approval of Pilot Projects

Recipient-selected projects that involve {clinical trials or studies involving greater than minimal risk to human subjects} require prior approval by NIH prior to initiation.

• The recipient institution will comply with the NIH Guidance on Changes That Involve Human Subjects in Active Awards and That Will Require Prior NIH Approval .
• The recipient institution will provide NIH with specific plans for data and safety monitoring, and will notify the IRB and NIH of serious adverse events and unanticipated problems, consistent with NIH DSMP policies .

2. Administrative and National Policy Requirements

The following Federal wide and HHS-specific policy requirements apply to awards funded through NIH:

• The rules listed at 2 CFR Part 200 , Uniform Administrative Requirements, Cost Principles, and Audit Requirements for Federal Awards.
• All NIH grant and cooperative agreement awards include the NIH Grants Policy Statement as part of the terms and conditions in the Notice of Award (NoA). The NoA includes the requirements of this NOFO. For these terms of award, see the NIH Grants Policy Statement Part II: Terms and Conditions of NIH Grant Awards, Subpart A: General and Part II: Terms and Conditions of NIH Grant Awards, Subpart B: Terms and Conditions for Specific Types of Grants, Recipients, and Activities .
• HHS recognizes that NIH research projects are often limited in scope for many reasons that are nondiscriminatory, such as the principal investigator’s scientific interest, funding limitations, recruitment requirements, and other considerations. Thus, criteria in research protocols that target or exclude certain populations are warranted where nondiscriminatory justifications establish that such criteria are appropriate with respect to the health or safety of the subjects, the scientific study design, or the purpose of the research. For additional guidance regarding how the provisions apply to NIH grant programs, please contact the Scientific/Research Contact that is identified in Section VII under Agency Contacts of this NOFO.

All federal statutes and regulations relevant to federal financial assistance, including those highlighted in  NIH Grants Policy Statement Section 4 Public Policy Requirements, Objectives and Other Appropriation Mandates.

Recipients are responsible for ensuring that their activities comply with all applicable federal regulations.  NIH may terminate awards under certain circumstances.  See  2 CFR Part 200.340 Termination and  NIH Grants Policy Statement Section 8.5.2 Remedies for Noncompliance or Enforcement Actions: Suspension, Termination, and Withholding of Support .

The following special terms of award are in addition to, and not in lieu of, otherwise applicable U.S. Office of Management and Budget (OMB) administrative guidelines, U.S. Department of Health and Human Services (HHS) grant administration regulations at 2 CFR Part 200, and other HHS, PHS, and NIH grant administration policies.

The administrative and funding instrument used for this program will be the cooperative agreement, an "assistance" mechanism (rather than an "acquisition" mechanism), in which substantial NIH programmatic involvement with the recipients is anticipated during the performance of the activities. Under the cooperative agreement, the NIH purpose is to support and stimulate the recipients' activities by involvement in and otherwise working jointly with the recipients in a partnership role; it is not to assume direction, prime responsibility, or a dominant role in the activities. Consistent with this concept, the dominant role and prime responsibility resides with the recipients for the project as a whole, although specific tasks and activities may be shared among the recipients and NIH as defined below.

The structure of this cooperative agreement encourages interaction and discussion among NIH staff and all involved investigators leading to more robust and innovative research strategies and methods for clinical research to enroll and retain vulnerable reproductive age young adult populations at risk for and living with HIV or at high risk for HIV. Substantive and frequent scientific and administrative involvement of the NICHD and the co-funding ICs (Institutes) Project Scientists will assist the investigators in developing the scientific agenda, refining study protocols, monitoring the progress of the clinical research and participant safety, and coordinating the activities of the Cohorts, including plans for data harmonization, curating, archiving and utilization. The cooperative agreement mechanism will also serve to facilitate cross-Cohort and multi-agency Collaborations, including efforts to ensure participants are prioritized in behavioral and biomedical clinical research.

PD(s)/PI(s) Responsibilities

PD(s)/PI(s) will have the primary responsibility for coordinating the Projects and Cores within the overall Program. Specifically, the PD(s)/PI(s) have primary responsibility as described below.

• The PD(s)/PI(s) will be responsible for defining the research objectives, approaches, and details of the projects within the guidelines of the NOFO and retains primary responsibility for the planning, directing, and executing the proposed scientific activities.
• The PD(s)/PI(s) will monitor all Research Projects and actively promote efforts that foster integration, collaboration, and synergy across the projects.
• The PD(s)/PI(s) will be responsible for ensuring timely compliance with data sharing requirements.
• Recipients will retain custody of and have primary rights to the data and software developed under these awards, subject to Government rights of access consistent with current HHS, PHS, and NIH policies.  The PD(s)/PI(s) will be responsible for ensuring optimal dissemination of research results by expedient data analysis and key publications within 1 year of study completion and implement explicit data sharing policies for public use thereafter.
• The PD(s)/PI(s) is responsible for the timely presentation/publication of work supported in part or in whole by this Cooperative Agreement. Prior notification to the NICHD regarding any presentations or publications and appropriate acknowledgement of NICHD support are required.

NIH staff have substantial programmatic involvement that is above and beyond the normal stewardship role in awards, as described below:

The NIH Project Scientists, representing each of the Institutes co-sponsoring the NOFO, will:

• Provide technical assistance, advice, and coordination, and interact with the PD(s)/PI(s) on a regular basis to monitor study progress, regulatory compliance, and quality assurance to ensure the production of high-quality, unbiased results. Monitoring may include: (1) regular communication with the PD(s)/PI(s) and staff, (2) periodic site visits for discussions with recipients' research teams, and (3) observation of activities, quality control, and other relevant matters, as well as (4) attendance at and participation in annual meetings.
• The Project Scientist will work closely with the PD(s)/PI(s) and other Program member scientists to facilitate collaborations and to leverage the resources available to the Program Project.
• Assist PD/PI and SAC in monitoring the progress of ongoing studies, including field data collection, standardization of methods across study sites, and adherence to protocol and quality control measures.
• Assist in data analyses, interpretation, and publication of study results.
• Assist in identifying the need to terminate or curtail the study (or an individual award) in the event of non-participation in the committee/group activities, substantial shortfall in participant recruitment, follow-up, data reporting, quality control, or other major breach of protocol or substantive protocol changes without prior approval from NIH.
• Collaborate with PD(s)/PI(s) and SAC in overseeing the establishment, maintenance and collaborative scientific efforts of the Program Project and its progress in achieving program goals.

Additionally, an agency program official or IC program director will be responsible for the normal scientific and programmatic stewardship of the award and will be named in the award notice.

The duties of the agency Program Official include:

• Carrying out continuous review of all activities to ensure that the objectives are being met and that all regulatory, fiscal, and administrative matters are handled according to NIH guidelines.
• Having the option to withhold support to a participating institution if technical performance requirements are not met.
• Performing other duties required for normal program stewardship of grants.

Areas of Joint Responsibility include:

The Project Scientist and the PD(s)/PI(s) will hold regular program-wide discussions to facilitate the achievement of program goals.

The Project Scientist and the PD(s)/PI(s) will collaborate during the course of the award to revise and/or update project milestones as appropriate.

Dispute Resolution:

Any disagreements that may arise in scientific or programmatic matters (within the scope of the award) between recipients and NIH may be brought to Dispute Resolution. A Dispute Resolution Panel composed of three members will be convened: a designee of the Steering Committee chosen without NIH staff voting, one NIH designee, and a third designee with expertise in the relevant area who is chosen by the other two; in the case of individual disagreement, the first member may be chosen by the individual recipient. This special dispute resolution procedure does not alter the recipient's right to appeal an adverse action that is otherwise appealable in accordance with PHS regulation 42 CFR Part 50, Subpart D and HHS regulation 45 CFR Part 16.

We encourage inquiries concerning this funding opportunity and welcome the opportunity to answer questions from potential applicants.

eRA Service Desk (Questions regarding ASSIST, eRA Commons, application errors and warnings, documenting system problems that threaten submission by the due date, and post-submission issues)

Finding Help Online:  https://www.era.nih.gov/need-help  (preferred method of contact) Telephone: 301-402-7469 or 866-504-9552 (Toll Free)

General Grants Information (Questions regarding application instructions, application processes, and NIH grant resources) Email:  [email protected]  (preferred method of contact) Telephone: 301-480-7075

Grants.gov Customer Support (Questions regarding Grants.gov registration and Workspace) Contact Center Telephone: 800-518-4726 Email:  [email protected]

Denise Russo, Ph.D. Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Telephone: 301-435-6871  Email: [email protected]    

Kathleen Ruth Borgmann NIDA - NATIONAL INSTITUTE ON DRUG ABUSE Phone: (301) 594-6561 E-mail: [email protected]

Anais Stenson, PhD National Institute of Mental Health (NIMH) Telephone: 240-926-7572 Email: [email protected]

Hiroko Iida, DDS, MPH NIDCR - NATIONAL INSTITUTE OF DENTAL & CRANIOFACIAL RESEARCH Phone: 301-594-7404 E-mail: [email protected]

Tia Morton, RN, MS   National Institute of Allergy and Infectious Diseases (NIAID) Telephone: 240-627-3073 Email: [email protected]

Joanna Kubler-Kielb, PhD Eunice Kennedy Shriver  National Institute of Child Health and Human Development (NICHD) Telephone: 301-435-6916 Email:  [email protected]

Rehana A. Chowdhury Eunice Kennedy Shriver  National Institute of Child Health and Human Development (NICHD) Telephone: 301-979-0259 Email:  [email protected]

Pamela G Fleming NIDA - NATIONAL INSTITUTE ON DRUG ABUSE Phone: 301-480-1159 E-mail: [email protected]

Rita Sisco National Institute of Mental Health (NIMH) Telephone: 301-443-2805 Email: [email protected]

Gabriel Hidalgo, MBA NIDCR - NATIONAL INSTITUTE OF DENTAL & CRANIOFACIAL RESEARCH Phone: 301-827-4630 E-mail: [email protected]

Ann Devine National Institute of Allergy and Infectious Diseases (NIAID) Telephone: 240-669-2988 Email:  [email protected]  

Section VIII. Other Information

Recently issued trans-NIH policy notices may affect your application submission. A full list of policy notices published by NIH is provided in the NIH Guide for Grants and Contracts . All awards are subject to the terms and conditions, cost principles, and other considerations described in the NIH Grants Policy Statement .

Awards are made under the authorization of Sections 301 and 405 of the Public Health Service Act as amended (42 USC 241 and 284) and under Federal Regulations 42 CFR Part 52 and 2 CFR Part 200.

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Why hypothesis testing is important in research ?

Hypothesis Testing allows researchers to evaluate the validity of their assumptions and draw conclusions based on evidence. It provides a framework for making predictions and determining whether observed results are statistically significant or just occurred by chance. By applying various statistical tests, researchers can measure the strength of the evidence and quantify the uncertainty associated with their findings.

Table of Content

Importance of Hypothesis Testing in Research

Types of hypothesis tests, common errors in hypothesis testing, interpreting the results of hypothesis tests, examples of hypothesis testing in different fields, tools and software for conducting hypothesis tests.

Understanding the importance of hypothesis testing is essential for conducting rigorous and reliable research. It enables researchers to make well-informed decisions, support or challenge existing theories, and contribute to the advancement of knowledge in their respective fields. So, whether you are a scientist, a market analyst, or a student working on a research project, grasp the power of hypothesis testing and elevate the impact of your data analysis.

Hypothesis testing is the cornerstone of the scientific method and plays a vital role in the research process. It allows researchers to make informed decisions and draw reliable conclusions from their data. By formulating a hypothesis and then testing it against the observed data, researchers can determine whether their initial assumptions are supported or refuted. This systematic approach is crucial for advancing knowledge and understanding in various fields, from medicine and psychology to economics and engineering. Hypothesis testing enables researchers to move beyond mere observations and anecdotal evidence, and instead rely on statistical analysis to quantify the strength of their findings. It helps them differentiate between genuine effects and random fluctuations, ensuring that the conclusions drawn are based on rigorous and objective analysis.

Moreover, hypothesis testing is not limited to academic research; it is equally important in the business world, where data-driven decision-making is essential for success. Marketers, for instance, can use hypothesis testing to evaluate the effectiveness of their advertising campaigns, while financial analysts can use it to assess the performance of investment strategies. By incorporating hypothesis testing into their decision-making processes, organizations can make more informed choices and optimize their operations.

Understanding the Null and Alternative Hypotheses

At the heart of hypothesis testing lies the distinction between the null hypothesis (H0) and the alternative hypothesis (H1). The null hypothesis represents the status quo or the assumption that there is no significant difference or relationship between the variables being studied. Conversely, the alternative hypothesis suggests that there is a meaningful difference or relationship that is worth investigating.

Researchers begin by formulating their null and alternative hypotheses based on their research question and existing knowledge. For example, in a study examining the effect of a new drug on blood pressure, the null hypothesis might be that the drug has no effect on blood pressure, while the alternative hypothesis would be that the drug does have an effect on blood pressure.

Steps Involved in Hypothesis Testing

Hypothesis testing is a structured process that involves several key steps:

• Clearly define the research question and formulate the null and alternative hypotheses.
• Select an appropriate statistical test based on the nature of the data and research question.
• Collect and organize data, ensuring it meets the assumptions required for the chosen test.
• Calculate the test statistic and compare it to the critical value or p-value to determine significance.
• Interpret the results and draw conclusions about the research question.
• One-Sample Tests : These tests compare the mean or proportion of a single sample to a known or hypothesized value. Examples include the one-sample t-test and the one-sample z-test.
• Two-Sample Tests: These tests compare the means or proportions of two independent samples. Examples include the two-sample t-test, the Mann-Whitney U test, and the chi-square test of independence.
• Paired Tests: These tests compare the means or proportions of two related or paired samples, such as before-and-after measurements or matched pairs. Examples include the paired t-test and the Wilcoxon signed-rank test.
• ANOVA Tests : These tests compare the means of three or more independent samples. Examples include one-way ANOVA, two-way ANOVA, and repeated-measures ANOVA.
• Correlation and Regression Tests: These tests examine the relationship between two or more variables. Examples include Pearson’s correlation, Spearman’s rank correlation, and linear regression analysis.

While hypothesis testing is a powerful tool for data analysis, it is not immune to errors. Two common types of errors in hypothesis testing are Type I errors and Type II errors.

Type I Error

A Type I error occurs when the null hypothesis is true, but it is incorrectly rejected. The probability of making a Type I error is typically denoted by the significance level (α), which is the threshold used to determine statistical significance.

Type II Error

Conversely, a Type II error occurs when the null hypothesis is false, but it is not rejected. In this case, the researcher fails to detect a significant effect that is actually present. The probability of making a Type II error is denoted by β.

When a hypothesis test is conducted, the researcher is provided with a p-value, which represents the probability of obtaining the observed results if the null hypothesis is true. If the p-value is less than the chosen significance level (typically 0.05), the null hypothesis is rejected, and the alternative hypothesis is supported.

• Medicine and Pharmacology: Researchers use hypothesis testing to evaluate the effectiveness of new drugs, treatments, or interventions. For example, a clinical trial might test the null hypothesis that a new drug has no effect on a health outcome.
• Psychology and Behavioral Sciences: Psychologists use hypothesis testing to investigate human behavior, cognition, and social phenomena. For instance, a researcher might hypothesize that a new therapy has no effect on depression symptoms.
• Economics and Finance: Economists use hypothesis testing to evaluate market performance, economic policies, and investment strategies. For example, testing the hypothesis that interest rates have no effect on the stock market.
• Engineering and Technology: Engineers use hypothesis testing to optimize product designs, test system reliability, and evaluate new technologies. For example, testing a new manufacturing process to reduce defects.
• Environmental Science: Environmental scientists use hypothesis testing to evaluate the impact of human activities, conservation efforts, and climate change effects.

Various tools are available for conducting hypothesis tests:

• IBM SPSS Statistics : A user-friendly statistical software widely used for hypothesis testing.
• R : An open-source programming language for statistical computing, offering packages like stats , ggplot2 , and dplyr for hypothesis testing.
• Online Tools : Websites such as StatisticsHowTo.com offer hypothesis test calculators for quick analyses.

Hypothesis testing is a crucial tool for researchers across many disciplines. It allows them to make informed decisions, support or challenge theories, and contribute to knowledge advancement. By understanding and mastering hypothesis testing techniques, researchers can significantly enhance their data analysis impact.

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Reconciling Methodological Paradigms: Employing Large Language Models as Novice Qualitative Research Assistants in Talent Management Research

Qualitative data collection and analysis approaches, such as those employing interviews and focus groups, provide rich insights into customer attitudes, sentiment, and behavior. However, manually analyzing qualitative data requires extensive time and effort to identify relevant topics and thematic insights. This study proposes a novel approach to address this challenge by leveraging Retrieval Augmented Generation (RAG) based Large Language Models (LLMs) for analyzing interview transcripts. The novelty of this work lies in strategizing the research inquiry as one that is augmented by an LLM that serves as a novice research assistant. This research explores the mental model of LLMs to serve as novice qualitative research assistants for researchers in the talent management space. A RAG-based LLM approach is extended to enable topic modeling of semi-structured interview data, showcasing the versatility of these models beyond their traditional use in information retrieval and search. Our findings demonstrate that the LLM-augmented RAG approach can successfully extract topics of interest, with significant coverage compared to manually generated topics from the same dataset. This establishes the viability of employing LLMs as novice qualitative research assistants. Additionally, the study recommends that researchers leveraging such models lean heavily on quality criteria used in traditional qualitative research to ensure rigor and trustworthiness of their approach. Finally, the paper presents key recommendations for industry practitioners seeking to reconcile the use of LLMs with established qualitative research paradigms, providing a roadmap for the effective integration of these powerful, albeit novice, AI tools in the analysis of qualitative datasets within talent management research.

1. Introduction

Talent management researchers frequently work backwards from their customers, the employees at the organization. Understanding employee sentiment and behavior often involves conducting deep-dive interviews, explanatory in nature – e.g., demystifying the why behind customer choices, attitudes or behaviors (e.g., (Leino and Räihä, 2007 ) ). Talent management research, at its core, seeks to use science to equip every employee with resources to help them best navigate their careers (Zhao, 2023 ) .

Consequently, qualitative research methodology plays a critical role in talent management. Many of the key considerations around employee engagement, motivation, and workforce culture involve subjective, context-dependent factors that are best explored through in-depth interviews, focus groups, and other qualitative data collection approaches. Talent management professionals often rely on rich qualitative datasets to gain deep insights into employee experiences, organizational dynamics, and the nuances of human capital. However, these qualitative paradigms can clash with the more positivist, quantitative worldview that underlies many of the analytic tools used to evaluate talent management data. Talent management researchers may find that standard statistical techniques and data visualization approaches struggle to fully capture the complexities inherent in qualitative datasets, leading to potential misinterpretations or oversimplifications of the human elements involved in managing an organization’s workforce. Navigating this tension between qualitative and quantitative approaches is an ongoing challenge for talent management professionals.

Large language models (LLMs) like BERT, GPT-3 and PaLM have demonstrated strong aptitude for summarization (e.g., (Yang et al . , 2023 ) ), classification (e.g., (Pelaez et al . , 2024 ) ), and information extraction (e.g., (Dunn et al . , 2022 ) ) for text-based data. Consequently, LLMs are also increasingly being leveraged within talent management contexts for tasks such as interview analysis. However, language models are themselves designed primarily from a quantitative, data-driven paradigm. These models are trained on vast troves of text data using statistical machine learning techniques optimized for numerical patterns and correlations. While powerful at extracting insights from large-scale datasets, LLMs can often struggle to fully capture the nuanced, contextual nature of language (Bender et al . , 2021 ) , (Dwivedi et al . , 2023 ) that is critical for qualitative information sourced from interviews, focus groups, and other qualitative research methods common in talent management.

Talent management professionals must therefore continuously navigate a tension between the quantitative orientation of their analytical tools and the qualitative richness of the human dynamics they seek to understand. Bridging this gap requires innovative approaches that combine the opportunity for scale and speed offered by LLM-powered analysis augmented by borrowing evaluative nuances of traditional qualitative techniques. Talent leaders, thus, must carefully select and configure their AI-powered tools to ensure the voices and experiences of employees are authentically represented, rather than reduced to oversimplified metrics. Mastering this balance is an ongoing challenge, but one that is critical for talent management to yield truly holistic and impactful insights.

This paper presents results from leveraging LLMs as a novice qualitative researcher to augment qualitative research workstreams, specifically for data generated through semi-structured interviews.

The purpose of this paper is two-fold – 1) provide an overview of a successful implementation of a Retrieval Augmented Generation-based model for analyzing semi-structured interviews, and more importantly, 2) enumerate pragmatic take-aways and learnings drawing from traditional qualitative research to help fellow industry practitioners in reconciling the methodological paradigms. We posit the second purpose to be valuable to the larger discussion within talent management research communities on how and where to integrate AI capabilities across different talent management workstreams.

2. Quantitative and Qualitative Paradigms

Quantitative and qualitative research represent two fundamental paradigms or philosophical frameworks that guide research strategies, methods, analysis, and use of results (Yilmaz, 2013 ) . While both methodological approaches seek to rigorously study research problems, they are based on distinct assumptions and procedures adapted to investigating particular types of questions and drawing different conclusions. Quantitative research is based on the assumptions of positivism, the philosophical tradition premised on the application of natural science methods to the study of social reality and beyond (Bryman, 2016 ) . Quantitative researchers believe that objective facts and truths about human behavior and society can be measured and quantified numerically. Quantitative methods such as surveys, structured observations, and experiments aim to test hypotheses derived from theories by examining relationships between precisely measured variables statistically analyzed using large sample sizes (Creswell and Creswell, 2017 ) . These methods seek to minimize subjectivity and generalize findings to a population. In contrast, qualitative research aligns with interpretivist and constructivist philosophical traditions by embracing subjectivity and focused meaning-making by and with research participants (Denzin et al . , 2023 ) .

Qualitative researchers often use an inductive approach aimed at discovering and understanding processes, experiences, and worldviews by collecting non-numerical data through methods like in-depth interviews, ethnographic fieldwork, and document analysis. Findings derive from themes that emerge openly from the data rather than testing predetermined hypotheses. Samples tend to be small and purposely selected to illuminate a phenomenon in depth and detail. The aim is particularization rather than generalization, with a priority on ecological validity and multiple realities situated in time, place, culture, and context.

While debates once positioned these paradigms in opposition, contemporary mixed methods research leverages the complementary strengths of quantitative and qualitative approaches (Halcomb and Hickman, 2015 ) . Mixed methods investigations integrate quantitative and qualitative data collection and analysis within a single program of inquiry by combining these approaches in creative ways to deepen understanding (Creamer, 2017 ) (Creamer, 2018 ) (Greene, 2008 ) . This reconciliation of methodological perspectives offers opportunities to generate more robust, contextualized insights to address complex research problems. The use of large language models (LLMs) as novice qualitative research assistants, as explored in this paper, can be considered an exercise in mixed methods research design.

Prior to LLMs, in previous work, Natural Language Processing based modeling of qualitative data from social science contexts, have also been used as "novice insight" augmented by the more expert contextualization provided by human researchers (e.g., (Bhaduri, 2018 ) , (Bhaduri et al . , 2021 ) ). Popular traditional topic modeling techniques (e.g. Latent Dirichlet Allocation), however, suffer from several limitations (e.g. specifying number of clusters) when compared to existing deep learning-based methods. They also often fail to capture the contextual nuances and ambiguities inherent in natural language, as they rely heavily on predefined rules and patterns (Devlin, 2018 ) (Radford et al . , 2019 ) . This can make it challenging to handle the complexities and variations present in real-world text data, and may require domain-specific knowledge or fine-tuning to achieve acceptable performance (Lee and Hsiang, 2019 ) . Recent advancements in LLMs, such as BERT and GPT, have largely overcome these limitations by leveraging deep neural networks to learn rich, contextual representations from large amounts of text data (Vaswani et al . , 2017 ) (Devlin, 2018 ) . These powerful models can capture subtle semantic and pragmatic features of language, and demonstrate strong generalization capabilities through transfer learning (Brown, 2020 ) (Radford et al . , 2019 ) .

Further, in traditional qualitative research, thematic analysis is the process of gathering themes across topics from qualitative data, such as interview data, through iteratively analyzing the dataset for topics of interest (Creamer, 2017 ) . Inductive coding and deductive coding are two approaches to analyzing data from semi-structured interviews. Inductive coding involves starting with raw data and gradually developing codes and categories based on patterns and topics that emerge from the data as the researcher manually interacts with it (Patton, 2014 ) (Strauss and Corbin, 1998 ) . This approach is bottom-up, where the data drives the development of codes and theories (Glaser, 1965 ) . Deductive coding, on the other hand, involves starting with preconceived codes or theories and applying them to the data (Pearse, 2019 ) . This approach is top-down, where existing theories or frameworks guide the coding process (Maxwell, 2018 ) . Researchers in industry typically work backwards from research question of interest. Most of the research questions in industry driving qualitative data collection are also explanatory (i.e., tend to explain the quantitative findings such as low customer satisfaction, low product adoption numbers), rather than exploratory (i.e., ethnography of a community of interest or a phenomenon) and as a result deductive approaches are often more popular than inductive coding.

Ultimately, by augmenting traditional deep-dive qualitative analysis with the time and resource efficient pattern recognition and text processing capabilities of LLMs, researchers can integrate quantitative and qualitative techniques to enhance the speed, depth, and rigor of their investigations. This mental model of a novice-LLM approach holds promise for bridging the divide between positivist and interpretive paradigms, ultimately working towards a more comprehensive understanding of the phenomenon under study.

We used an open-source dataset (Paskevicius, 2018 ) to demonstrate how an LLM prompted as a novice researcher can enhance traditional qualitative deductive thematic coding. This dataset was originally collected to explore educators’ experiences implementing open educational practices (Paskevicius, 2018 ) . The dataset contains eight transcripts each from hour-long interviews conducted with educators to understand how they are using openly accessible sources of knowledge and open-source tools. The original research involved a deep-dive qualitative analysis through using a phenomenological approach to extract topics manually from the dataset. We chose this open-source dataset for two reasons – 1) structural match to proprietary dataset, and 2) rich description and manually identified topics by an expert to serve as a gold standard to measure the efficacy of our LLM based approach. Semi-structured interviews provide critical insights through participant perspectives, making them foundational in various industry settings.

The semi-structured approach used to create this dataset is a close match to proprietary talent management data from our organization, where employees are interviewed on a particular phenomenon to get deeper understanding of their related sentiment, attitudes, and behaviors. Manually extracted topics serve as gold standard for benchmarking findings from our LLM-based approach. The paper (Paskevicius, 2018 ) describing the dataset explains the manual process establishing how each transcript was read twice: first, for a comprehensive analysis, and subsequently, to initiate a thematic exploration. Additional reviewing continued as codes and topics emerged and intersected among the interviews. A manual qualitative coding approach was applied at each iteration to reveal themes, following constant comparison methodology (Glaser, 1965 ) .

We posit that our approach, as demonstrated on this sample semi-structured interview dataset, can easily extend to multiple industry settings in talent management research where researchers conduct interviews and focus groups.

4. Thematic Analysis Using LLMs

In traditional, manual qualitative research, deductive thematic analysis process begins with the researcher first formulating the research questions. Then, upon collection of the data, such as interview transcripts, the researcher iterates manually through the transcripts to identify and extract themes or topics of interest. This labor-intensive process involves carefully reading through the data, taking notes, and organizing the topics iteratively into broader coherent themes that address the research questions. The researcher may go through multiple rounds of coding and analysis to refine the themes and ensure they comprehensively capture the key insights from the data. Our approach finds that LLMs can quickly uncover topics of interest from the dataset which can then be iterated upon to garner broader themes of interest across topics. Thus, for our novice-LLM led approach, we leveraged the power of Large Language Models (LLMs) as a novice research assistant in the thematic analysis process. Specifically, we used the open-source framework called Langchain to create dynamic prompt templates, such as few-shot prompts and chain of thoughts, that guided the LLM in performing topic modeling and generating insights from the interview transcripts. We then opted to use Anthropic’s Claude2 model to execute these prompts and extract the relevant themes.

To initiate the analysis, we first selected a main research question and corresponding sub-questions from our dataset (Paskevicius, 2018 ) . We then fed these research questions, along with the interview transcripts, into the LLM-powered Langchain framework. The model was able to quickly identify and summarize the key topics, and iteratively, themes emerging from the data. This approach provided a quick yet relatively comprehensive analysis that would have taken a human researcher significant time and effort to reproduce manually.

4.1. Thematic analysis enhanced through Retrieval Augmented Generation (RAG)

In our LLM based approaches, we experiment with four methods - zero-shot prompting, few-shot prompting, chain-of-thought reasoning, and Retrieval Augmented Generation based Question Answering. In zero-shot prompting we provide a single prompt to the model. In few-shot prompting, we provide a set of topics and anecdotes to the model as examples. In the chain of thought (COT) approach, we provide a set of instructions for the model to follow. Finally, for Retrieval Augmented Generation (RAG) we provide context and questions to the model, from which it extracts information.

Zero-shot prompts are simple instructions or tasks given to an LLM that have not been specifically trained on that task. It serves as a baseline because it demonstrates the model’s fundamental ability to understand and respond to prompts based solely on its pre-training (Kong et al . , 2023 ) . In few-shot prompting, a small set of examples illustrating the desired outcome are manually selected and provided to the LLM. These examples allow the model to understand the tasks at hand and generate similar results (Brown, 2020 ) . Chain-of-thought prompting provides a set of intermediate steps to guide the LLM to mimic human-like reasoning. This significantly improves the capability of the LLM to understand complex reasoning and generate better topics (Wei et al . , [n. d.] ) . Retrieval-augmented generation (RAG) combines the capabilities of an LLM with a retrieval system to source and integrate additional information into its responses (Lewis et al . , 2020 ) . This effort provides contextually richer and ultimately more accurate outputs. We do this by providing all the interview transcripts to the LLM as a custom knowledge base. Two considerations helped the RAG approach outperform the other approaches:

4.1.1. Focused Analysis:

In our approach, LLM searches the knowledge base to find and retrieve parts of documents that are most relevant to the question in the query. This narrows the focus to the most relevant information and ensures attention to critical topics and nuances.

4.1.2. Context Dilution/Managing Information Overload:

Using all transcripts as input in a single instance creates information overload scenarios, ultimately leading to dilution of important topics or nuances. If the dataset is too large or complex, LLM might lose track of what’s most relevant to specific query, leading hallucinations. Hallucinations or inaccuracies within this context refers to instances where the model generates information which is not grounded in input data. In our approach, the use of RAG mitigates some of the hallucination by anchoring LLM responses relevant information, and providing a form of contextual validation for the output.

5. Findings

In the paper describing the dataset leveraged for this work, the authors collected and conducted a manual analysis (Paskevicius, 2018 ) . Their research led to identification of significant, recurring topics within the interviews. Our evaluation strategy uses these manually generated topics from the paper’s work as gold standard to compare against topics generated by the LLMs-based approach. We use Precision (Equation 1), Recall (Equation 2), and F1-score (Equation 3) to benchmark topics generated by our LLM-augmented qualitative research approach against the topics generated by the human researcher.

These metrics are the current evaluation standard for classification models, but they can be adapted for text generation tasks (Zhang et al . , 2019 ) . Precision and Recall measure the proportion of correctly identified positive cases. In the context of our experiment, every word from predicted text gets matched to a word in the referenced text to compute recall. This process is inverted to then compute precision. The precision and recall values are then combined to compute an F1 score. These metrics use cosine similarity (Equation 4) in which each predicted word is paired with its closest corresponding word from the reference text with the aim of maximizing the similarity score.

In Table 1, the performance of various LLM prompting techniques including Chain of Thought, Few Shot, Zero Shot and RAG, are compared across different embedding models (Distillbert-base-uncased, Bert-base-uncased, and Roberta-large). This comparison aims to evaluate the robustness and effectiveness of these prompting techniques. Our results indicate that while each prompting technique shows varying level of precision, recall and F1-score, RAG consistently outperform the others on all three metrics, achieving highest performance across all models.

6. Learnings

Treating large language models (LLMs) as novice research assistants during thematic analysis offered valuable insights for our research. By framing the LLM as a novice collaborator with little knowledge or insight of the context, prompts can be crafted to better guide the model and leverage its capabilities. Used prudently, similar novice LLM-augmented approaches can significantly increase time and resource efficiency compared to traditional qualitative coding methods in talent management research. The following sections explore some of our key learnings that may benefit other researchers considering designing LLMs as novice researchers to optimize thematic analysis.

6.1. Approaching LLMs as Novice Research Assistants can help prepare better prompts

A novice is a person who, “has no experience with the situations in which they are expected to perform tasks” (Benner, 1982 ) . The novice is thus at a basic proficiency level for skill acquisition, with limited information and prior experience related to a task at hand (Montfort et al . , 2013 ) . For large qualitative datasets analyzed using LLMs we propose that a novice-led approach to analysis is a good fit. In our approach the human behaves as an expert prompting the novice LLM to provide insights related to topics of interest. We found this framework as a helpful mental model to ground the primary researcher prompting the LLM as they iteratively uncover insights from the dataset.

6.2. Used prudently, LLMs can help increase time effectiveness and resource efficiency

LLMs have advanced the field of natural language processing with their ability to understand and generate responses that closely mimic human language (Shanahan, 2024 ) . The strengths of LLMs extend beyond metrics, these models are adept at processing vast amounts of text rapidly, demonstrating a level of topic modeling that can mimic human analysis. Manual topic modeling is human labor intensive and time inefficient (Clarke and Braun, 2017 ) . LLMs also enhance efficiency by streamlining the processing of large datasets, allowing for the extraction of topics from qualitative data more quickly. Improvisations of these model using techniques like few-shot and zero-shot learning capabilities further reduce the need for expensive data labeling and annotations. In a nutshell, LLMs boost speed, reduce human effort, scale to massive datasets, and lower labeling costs. However, human expertise is still essential for judgment, validation and end-to-end framework design.

6.3. LLM augmented approaches offer significant increase in ease and enhanced context compared to traditional NLP approaches.

Using a RAG approach towards an LLM-augmented qualitative research analyzing semi-structure interviews shows great promise compared to natural language processing methods like Latent Dirichlet allocation (LDA). Currently, there are no widely accepted methods for comparing the two approaches as there is no bridge to compare keywords to themes, except from a human-evaluator ease of interpretability standpoint. We performed topic modeling analysis on the same dataset with the broader aim of finding themes. Manually comparing both approaches, each researcher of this workstream independently found that any of the approaches using an LLM yielded much greater context and consequently, better interpretability than the traditional LDA approach. This is likely because, with LDA, the model outputs a list of words and probability for each topic. With these words, the researcher would then have to manually define the topic. While this approach increases researcher flexibility, it remains time and resource consuming. In contrast, with the LLM approach, the output is richer in context of what particular topics mean. For example, our LDA model yielded 5 topics (see: Appendix A Figure 3). The first 10 words for topic 1 can also be seen in Table 2. Putting these words together into a comprehensive theme can be challenging without more context. However, an LLM is able to generate context grounded in the participant’s voice for researchers to work with. An example of an extracted theme and its corresponding anecdote using an LLM can also be seen in Table 2, above.

7. Recommendations

Traditional qualitative research is evaluated based on several criteria that ensure quality and rigor of the research, both in terms of methods as well as findings. Prior research has established four criteria for increased rigor and trustworthiness of qualitative research studies around credibility, dependability, confirmability, and transferability (Lincoln and Guba, 1988 ) . We recommend three ways in which quality criteria from traditional qualitative research can be used by practitioners employing LLM augmented analysis of qualitative data.

7.1. Establishing credibility of findings by incorporating mechanism for member checks.

Member checks, i.e., the strategy of soliciting insights from research participants on research findings, are often relied on as the gold standard for increasing trustworthiness of qualitative research approaches (e.g., (Patton, 2014 ) (Kornbluh, 2015 ) ). Qualitative researchers employing LLMs can work on deepening their understanding of the research context using appropriate data-collection methods and tools that work best for particular contexts, as well as conduct adequate member checking to ensure the accuracy of findings.

7.2. Practicing increased researcher reflexivity.

Qualitative researchers are recommended that they acknowledge and address their own biases, thus recognizing the influence of their own experiences and opinions on the research process (Finlay, 2002 ) . Similar exercises on reflectivity can also be helpful for researchers augmenting qualitative data analysis through employing LLMs. Researcher reflexivity in such instances can extend to querying the LLM to ask for rationale on why certain topics were extracted, grounding topics in anecdotes from the transcripts, and recognizing the influence the human researcher’s prior knowledge and biases will have on the prompts used. Future work in extending LLMs for qualitative research should continue to draw on evaluation criteria grounded in traditional qualitative research paradigm.

7.3. Increasing transparency of decisions made throughout the research study.

Qualitative researchers are recommended to thoroughly document all decisions that guide their analysis process by providing thick descriptions, allowing for increased transparency. This practice enhances reliability and reproducibility of the research (Lincoln and Guba, 1988 ) . Qualitative researchers employing LLMs should also similarly strategize maximizing transparency through mechanisms such as documenting changes in workflow, sharing prompts, and detailing model preferences.

8. Closing Thoughts

The approach outlined in this paper offers a promising avenue for industry-based talent management practitioners seeking to increase the time and resource efficiency of qualitative interview data analysis. By leveraging large language models (LLMs) as novice qualitative research assistants, organizations can potentially accelerate the coding, categorization, and thematic synthesis of rich interview data - a critical bottleneck in many talent management research initiatives.

However, as the field of LLM-assisted qualitative research matures, it will be essential to not only benchmark model performance against traditional quantitative evaluation metrics, but also consider quality criteria more prominent within the qualitative research paradigm. Factors such as credibility, transferability, dependability, and confirmability will need to be carefully evaluated as LLMs are integrated into qualitative workflows. Furthermore, the ethical use of AI assistants in sensitive domains like talent management will require close, multi-disciplinary attention to issues at the intersection of data privacy, algorithmic bias, and model transparency, for which researchers will have to be trained (Mackenzie et al . , 2024 ) .

Future research should seek to establish guidelines and best practices for LLM-augmented qualitative analysis that uphold the rigor and trustworthiness expected within the qualitative research community. Only by doing so can talent management scholars and practitioners unlock the full potential of these powerful language models, while respecting the epistemological foundations of qualitative inquiry. As the field evolves, we believe that a judicious, ethically-grounded approach to LLM integration can yield substantial gains in research efficiency and organizational impact.

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Appendix A Results from analyzing the same dataset using an LDA Approach.

Traditional topic modeling using approaches such as Latent Dirichlet Allocation (LDA) often present the most representative words for each generated topic. For instance, for Topic 1 words such as "students", "develop", "institution", "science", etc. were found important. Attempting to interpret the underlying thematic meaning of these word lists can be challenging without additional contextual information about how those words were used within the original corpus. In contrast, large language models (LLMs) have demonstrated the capability to synthesize the semantically related words and phrases into more coherent topical representations. This ability of LLMs to generate primitive yet formative contextual information threading together words and phrases of interest and thereby provide researchers with a more insightful starting point for further analysis and interpretation of the latent topics uncovered through the LDA process.