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How to write a research plan: Step-by-step guide

Last updated

30 January 2024

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Today’s businesses and institutions rely on data and analytics to inform their product and service decisions. These metrics influence how organizations stay competitive and inspire innovation. However, gathering data and insights requires carefully constructed research, and every research project needs a roadmap. This is where a research plan comes into play.

There’s general research planning; then there’s an official, well-executed research plan. Whatever data-driven research project you’re gearing up for, the research plan will be your framework for execution. The plan should also be detailed and thorough, with a diligent set of criteria to formulate your research efforts. Not including these key elements in your plan can be just as harmful as having no plan at all.

Read this step-by-step guide for writing a detailed research plan that can apply to any project, whether it’s scientific, educational, or business-related.

• What is a research plan?

A research plan is a documented overview of a project in its entirety, from end to end. It details the research efforts, participants, and methods needed, along with any anticipated results. It also outlines the project’s goals and mission, creating layers of steps to achieve those goals within a specified timeline.

Without a research plan, you and your team are flying blind, potentially wasting time and resources to pursue research without structured guidance.

The principal investigator, or PI, is responsible for facilitating the research oversight. They will create the research plan and inform team members and stakeholders of every detail relating to the project. The PI will also use the research plan to inform decision-making throughout the project.

• Why do you need a research plan?

Create a research plan before starting any official research to maximize every effort in pursuing and collecting the research data. Crucially, the plan will model the activities needed at each phase of the research project.

Like any roadmap, a research plan serves as a valuable tool providing direction for those involved in the project—both internally and externally. It will keep you and your immediate team organized and task-focused while also providing necessary definitions and timelines so you can execute your project initiatives with full understanding and transparency.

External stakeholders appreciate a working research plan because it’s a great communication tool, documenting progress and changing dynamics as they arise. Any participants of your planned research sessions will be informed about the purpose of your study, while the exercises will be based on the key messaging outlined in the official plan.

Here are some of the benefits of creating a research plan document for every project:

Project organization and structure

Well-informed participants

All stakeholders and teams align in support of the project

Clearly defined project definitions and purposes

Distractions are eliminated, prioritizing task focus

Timely management of individual task schedules and roles

Costly reworks are avoided

• What should a research plan include?

The different aspects of your research plan will depend on the nature of the project. However, most official research plan documents will include the core elements below. Each aims to define the problem statement, devising an official plan for seeking a solution.

Specific project goals and individual objectives

Ideal strategies or methods for reaching those goals

Required resources

Descriptions of the target audience, sample sizes, demographics, and scopes

Key performance indicators (KPIs)

Project background

Research and testing support

Preliminary studies and progress reporting mechanisms

Cost estimates and change order processes

Depending on the research project’s size and scope, your research plan could be brief—perhaps only a few pages of documented plans. Alternatively, it could be a fully comprehensive report. Either way, it’s an essential first step in dictating your project’s facilitation in the most efficient and effective way.

• How to write a research plan for your project

When you start writing your research plan, aim to be detailed about each step, requirement, and idea. The more time you spend curating your research plan, the more precise your research execution efforts will be.

Account for every potential scenario, and be sure to address each and every aspect of the research.

Consider following this flow to develop a great research plan for your project:

Define your project’s purpose

Start by defining your project’s purpose. Identify what your project aims to accomplish and what you are researching. Remember to use clear language.

Thinking about the project’s purpose will help you set realistic goals and inform how you divide tasks and assign responsibilities. These individual tasks will be your stepping stones to reach your overarching goal.

Additionally, you’ll want to identify the specific problem, the usability metrics needed, and the intended solutions.

Know the following three things about your project’s purpose before you outline anything else:

What you’re doing

Why you’re doing it

What you expect from it

Identify individual objectives

With your overarching project objectives in place, you can identify any individual goals or steps needed to reach those objectives. Break them down into phases or steps. You can work backward from the project goal and identify every process required to facilitate it.

Be mindful to identify each unique task so that you can assign responsibilities to various team members. At this point in your research plan development, you’ll also want to assign priority to those smaller, more manageable steps and phases that require more immediate or dedicated attention.

Select research methods

Research methods might include any of the following:

User interviews: this is a qualitative research method where researchers engage with participants in one-on-one or group conversations. The aim is to gather insights into their experiences, preferences, and opinions to uncover patterns, trends, and data.

Field studies: this approach allows for a contextual understanding of behaviors, interactions, and processes in real-world settings. It involves the researcher immersing themselves in the field, conducting observations, interviews, or experiments to gather in-depth insights.

Card sorting: participants categorize information by sorting content cards into groups based on their perceived similarities. You might use this process to gain insights into participants’ mental models and preferences when navigating or organizing information on websites, apps, or other systems.

Focus groups: use organized discussions among select groups of participants to provide relevant views and experiences about a particular topic.

Diary studies: ask participants to record their experiences, thoughts, and activities in a diary over a specified period. This method provides a deeper understanding of user experiences, uncovers patterns, and identifies areas for improvement.

Five-second testing: participants are shown a design, such as a web page or interface, for just five seconds. They then answer questions about their initial impressions and recall, allowing you to evaluate the design’s effectiveness.

Surveys: get feedback from participant groups with structured surveys. You can use online forms, telephone interviews, or paper questionnaires to reveal trends, patterns, and correlations.

Tree testing: tree testing involves researching web assets through the lens of findability and navigability. Participants are given a textual representation of the site’s hierarchy (the “tree”) and asked to locate specific information or complete tasks by selecting paths.

Usability testing: ask participants to interact with a product, website, or application to evaluate its ease of use. This method enables you to uncover areas for improvement in digital key feature functionality by observing participants using the product.

Live website testing: research and collect analytics that outlines the design, usability, and performance efficiencies of a website in real time.

There are no limits to the number of research methods you could use within your project. Just make sure your research methods help you determine the following:

What do you plan to do with the research findings?

What decisions will this research inform? How can your stakeholders leverage the research data and results?

Recruit participants and allocate tasks

Next, identify the participants needed to complete the research and the resources required to complete the tasks. Different people will be proficient at different tasks, and having a task allocation plan will allow everything to run smoothly.

Prepare a thorough project summary

Every well-designed research plan will feature a project summary. This official summary will guide your research alongside its communications or messaging. You’ll use the summary while recruiting participants and during stakeholder meetings. It can also be useful when conducting field studies.

Ensure this summary includes all the elements of your research project. Separate the steps into an easily explainable piece of text that includes the following:

An introduction: the message you’ll deliver to participants about the interview, pre-planned questioning, and testing tasks.

Interview questions: prepare questions you intend to ask participants as part of your research study, guiding the sessions from start to finish.

An exit message: draft messaging your teams will use to conclude testing or survey sessions. These should include the next steps and express gratitude for the participant’s time.

Create a realistic timeline

While your project might already have a deadline or a results timeline in place, you’ll need to consider the time needed to execute it effectively.

Realistically outline the time needed to properly execute each supporting phase of research and implementation. And, as you evaluate the necessary schedules, be sure to include additional time for achieving each milestone in case any changes or unexpected delays arise.

For this part of your research plan, you might find it helpful to create visuals to ensure your research team and stakeholders fully understand the information.

Determine how to present your results

A research plan must also describe how you intend to present your results. Depending on the nature of your project and its goals, you might dedicate one team member (the PI) or assume responsibility for communicating the findings yourself.

In this part of the research plan, you’ll articulate how you’ll share the results. Detail any materials you’ll use, such as:

Presentations and slides

A project report booklet

A project findings pamphlet

Documents with key takeaways and statistics

Graphic visuals to support your findings

• Format your research plan

As you create your research plan, you can enjoy a little creative freedom. A plan can assume many forms, so format it how you see fit. Determine the best layout based on your specific project, intended communications, and the preferences of your teams and stakeholders.

Find format inspiration among the following layouts:

Written outlines

Narrative storytelling

Visual mapping

Graphic timelines

Remember, the research plan format you choose will be subject to change and adaptation as your research and findings unfold. However, your final format should ideally outline questions, problems, opportunities, and expectations.

• Research plan example

Imagine you’ve been tasked with finding out how to get more customers to order takeout from an online food delivery platform. The goal is to improve satisfaction and retain existing customers. You set out to discover why more people aren’t ordering and what it is they do want to order or experience. 

You identify the need for a research project that helps you understand what drives customer loyalty. But before you jump in and start calling past customers, you need to develop a research plan—the roadmap that provides focus, clarity, and realistic details to the project.

Here’s an example outline of a research plan you might put together:

Project title

Project members involved in the research plan

Purpose of the project (provide a summary of the research plan’s intent)

Objective 1 (provide a short description for each objective)

Objective 2

Objective 3

Proposed timeline

Audience (detail the group you want to research, such as customers or non-customers)

Budget (how much you think it might cost to do the research)

Risk factors/contingencies (any potential risk factors that may impact the project’s success)

Remember, your research plan doesn’t have to reinvent the wheel—it just needs to fit your project’s unique needs and aims.

Customizing a research plan template

Some companies offer research plan templates to help get you started. However, it may make more sense to develop your own customized plan template. Be sure to include the core elements of a great research plan with your template layout, including the following:

Introductions to participants and stakeholders

Background problems and needs statement

Significance, ethics, and purpose

Research methods, questions, and designs

Preliminary beliefs and expectations

Implications and intended outcomes

Realistic timelines for each phase

Conclusion and presentations

How many pages should a research plan be?

Generally, a research plan can vary in length between 500 to 1,500 words. This is roughly three pages of content. More substantial projects will be 2,000 to 3,500 words, taking up four to seven pages of planning documents.

What is the difference between a research plan and a research proposal?

A research plan is a roadmap to success for research teams. A research proposal, on the other hand, is a dissertation aimed at convincing or earning the support of others. Both are relevant in creating a guide to follow to complete a project goal.

What are the seven steps to developing a research plan?

While each research project is different, it’s best to follow these seven general steps to create your research plan:

Defining the problem

Identifying goals

Choosing research methods

Recruiting participants

Preparing the brief or summary

Establishing task timelines

Defining how you will present the findings

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Plan template bundle, what is a research work plan, what is the difference between a research plan and a research proposal, what is the purpose of a research plan, what are the good research questions, what are the components of a work plan, free 12+ research work plan templates in pdf | ms word, 1. research work plan format template, 2. research project communication plan template, 3. free project research work plan template, 4. free research work plan example, 5. free research work group plan template, 6. half yearly research work plan template, 7. sample research work plan template, 8. free research work plan template in pdf, 9. free business research work plan template, 10. free project research work plan example, 11. monthly project research work plan template, 12. transport research work plan template, 13. free research work plan template in doc, how to develop a work plan, advantages of developing a work plan, plan templates, 12+ research work plan templates in pdf | ms word.

A work plan is an overview of a series of objectives and procedures by which a team and/or entity can achieve those goals and provide the reader with a clearer picture of the project’s context. No matter if it is used in professional or academic life, work plans serve the purpose of helping you stay focused when working on a certain project. You disintegrate a process into tiny, manageable tasks by work schedules , and define the tasks you want to achieve.

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Step 1: Think About the Objectives

Step 2: introduction and background, step 3: list the resources, step 4: anticipate and define limitations, step 5: assign roles, step 6: write the strategy, determine goals and objectives, organize teams and leadership, establish project timelines, set project budget, quality assurance and control, more in plan templates, editable research flowchart template, research poster template for keynote, education research agenda template, research agenda template, legal research methodology ppt template, medical research template, research template, qualitative research template, psychology research template, college research template.

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FLEET LIBRARY | Research Guides

Rhode island school of design, create a research plan: research plan.

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A research plan is a framework that shows how you intend to approach your topic. The plan can take many forms: a written outline, a narrative, a visual/concept map or timeline. It's a document that will change and develop as you conduct your research. Components of a research plan

1. Research conceptualization - introduces your research question

2. Research methodology - describes your approach to the research question

3. Literature review, critical evaluation and synthesis - systematic approach to locating,

    reviewing and evaluating the work (text, exhibitions, critiques, etc) relating to your topic

4. Communication - geared toward an intended audience, shows evidence of your inquiry

Research conceptualization refers to the ability to identify specific research questions, problems or opportunities that are worthy of inquiry. Research conceptualization also includes the skills and discipline that go beyond the initial moment of conception, and which enable the researcher to formulate and develop an idea into something researchable ( Newbury 373).

Research methodology refers to the knowledge and skills required to select and apply appropriate methods to carry through the research project ( Newbury 374) .

Method describes a single mode of proceeding; methodology describes the overall process.

Method - a way of doing anything especially according to a defined and regular plan; a mode of procedure in any activity

Methodology - the study of the direction and implications of empirical research, or the sustainability of techniques employed in it; a method or body of methods used in a particular field of study or activity *Browse a list of research methodology books  or this guide on Art & Design Research

Literature Review, critical evaluation & synthesis

A literature review is a systematic approach to locating, reviewing, and evaluating the published work and work in progress of scholars, researchers, and practitioners on a given topic.

Critical evaluation and synthesis is the ability to handle (or process) existing sources. It includes knowledge of the sources of literature and contextual research field within which the person is working ( Newbury 373).

Literature reviews are done for many reasons and situations. Here's a short list:

Sources to consult while conducting a literature review:

Online catalogs of local, regional, national, and special libraries

meta-catalogs such as worldcat , Art Discovery Group , europeana , world digital library or RIBA

subject-specific online article databases (such as the Avery Index, JSTOR, Project Muse)

digital institutional repositories such as Digital Commons @RISD ; see Registry of Open Access Repositories

Open Access Resources recommended by RISD Research LIbrarians

works cited in scholarly books and articles

print bibliographies

the internet-locate major nonprofit, research institutes, museum, university, and government websites

search google scholar to locate grey literature & referenced citations

trade and scholarly publishers

fellow scholars and peers

Communication                              

Communication refers to the ability to

• structure a coherent line of inquiry
• communicate your findings to your intended audience
• make skilled use of visual material to express ideas for presentations, writing, and the creation of exhibitions ( Newbury 374)

Research plan framework: Newbury, Darren. "Research Training in the Creative Arts and Design." The Routledge Companion to Research in the Arts . Ed. Michael Biggs and Henrik Karlsson. New York: Routledge, 2010. 368-87. Print.

About the author

Except where otherwise noted, this guide is subject to a Creative Commons Attribution license

source document

  Routledge Companion to Research in the Arts

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Top 10 Research Plan Templates with Samples and Examples

Mohammed Sameer

"Research is creating new knowledge."- Neil Armstrong

When we dwell on what we enjoy most about our work — what excites us, what inspires us, what sparks the next big "a-ha" moment — we seldom consider processes or documentation.

But when we reflect on what frustrates us about our work—"next steps" that get delayed, projects that feel unfocused, minor details that obstruct our plans—we frequently blame processes and documentation.

To figure a way out of the dilemma, consider using a research plan to structure your work. 

Even if you don't use a research plan on a regular basis or are unaware of what it means, it can help your next project run more smoothly.

This blog shows you how to write a research plan in a jiffy (with templates) that can save you hours of work, on balance. 

A research plan template is a concise reference point for your project's timeline, goals, key players, and objectives.

Research plan templates provide an overview of the initiative and serve as a project kick-off document. Its beauty lies in its ability to keep your team on track, to ensure that overarching goals are well-defined and agreed upon, and to ensure that the research meets those goals.

Let's explore our top 10 research plan templates .

Template 1: Business Research Plan PPT Template

Framing a successful business research plan involves dealing with many headaches. A well-structured PPT Template is the answer in all cases. SlideTeam presents a business research plan design that explains project context and objectives, a plan of action, and a timeline to track progress. Download now.

Download this template

Template 2: Business Marketing Research Plan PPT Framework

Here’s a six-step business marketing research plan that every company needs. It helps you:

• Know your Business
• Identify your Target Market
• Analyze Competitors
• Outline Strategy
• Set a Budget

Download it now.

Get this template

Template 3: Timeframe for Business Research Plan

Construct a relevant timeframe for your business research plan using this illustrative template. It has multiple stages, including Industry Profiling, Market Assessment, Customer Persona, and Market Entry Strategies. Each stage is allotted a time frame (in weeks) to ensure your research plan is progressing. Get it now.

Grab this template

Template 4: Marketing Research Plan PPT Design

Using our template, perform an in-depth examination of how your product or service will perform in a specified area. It facilitates an analysis of the market and an assessment of the demand for your product or service. With the help of the template, you can even get to evaluate competitors. Get it now.

Template 5: Customer Research Plan PPT Template

Building a customer research plan from scratch will seem like a mountain to climb. With this PPT Template, you can avoid this struggle. It has five detailed steps that encompass the core of a customer research plan:

• Identify the Problem
• Develop the Research Plan
• Conduct Research
• Analyze and Report Findings
• Take Action in terms of Budget, Personnel, etc.

Grab it now.

Template 6: Developing the Research Plan Timeline PPT Template

Developing the research plan timeline is a methodical process that necessitates keen attention and effort. Our template makes it easy. From developing the research plan, collecting information, presenting findings, and analyzing information, our template covers every conceivable scenario. Get it now.

Template 7: Our Market Research Plan PPT Template

Present your organization’s month-wise activities concerning the market research plan. It is designed to foster simplicity and highlights how your market research activities are performing against set targets. You can assign colors to individual activities, which eliminates the risk of duplicacy. Grab it now.

Template 8: Business Marketing Research Plan PPT Template

Business marketing research plans can be troublesome to prepare. With our predesigned PPT Template, you can breathe easy knowing everything concerning your research plan is in place. It helps you build a robust infrastructure of your research plan and reevaluate every critical piece to ensure ideal results. Get it now.

Template 9: Shopper Journey Need Desire Research Plan

This predesigned PowerPoint Presentation helps you prepare an inclusive flowchart encompassing the shoppers’ journey giving special attention to their needs and desires. It also emphasizes the importance of loyalty and experience throughout the journey of these shoppers. This template offers you insights you need to build your research plan. Download it now.

Template 10: Market Research Business Plan Diagram

This four-phase market research business plan is a must-have for every enterprise. This is a four-stage process: Exploration, Design and Development, Planning, and Implementation. Each phase has a space for writing a brief where you can highlight points for your audience. The goal of this template is to allow you to conduct market research and make it easier to comprehend the viability of your plans. Download now.

Summing It Up

Your user research plan is a blueprint for your research project. It's the most straightforward way to set expectations, solicit feedback, and generate enthusiasm and support for your research.

A solid research plan can go a long way toward ensuring a solid research project, whether it actively guides your interviews or provides an active structure for organizing your thoughts.

FAQs on Research Plan Templates

What is a research plan.

A research plan is a framework that outlines your approach to your topic. A written outline, a narrative, a visual/concept map, or a timeline are all examples of plans. It is a living document that evolves as you conduct your research.

Components of a research plan:

• Research conceptualization - presents your research question.
• Research methodology - describes how you intend to approach the research question.
• Literature review, critical evaluation, and synthesis - a methodical approach to locating, reviewing, and evaluating relevant work (texts, exhibitions, critiques, etc.)
• Communication - directed at a specific audience, demonstrating evidence of your investigation

How do you write a research plan?

Steps to writing a research plan:

• Define the project's goal
• Determine individual objectives
• Choose a research method
• Organize participants and assign tasks
• Create a project synopsis
• Make a realistic timetable
• Determine how you will present your findings

What are the 5 elements of research?

The systematic investigation of discovering new knowledge or contributing to generalized knowledge is known as research. It adheres to a specific structure that is specified in the research design. For research to be successful, elements that aid in problem-solving must be included. Here are some examples of good research design elements that produce excellent results:

• Statement of purpose
• Methods of data collection
• Techniques for analyzing research data
• Methodologies of research
• Research difficulties
• Prerequisites for conducting research
• The appropriate time for the research study
• Analysis measurement

What is the difference between a research proposal and a research report?

A research proposal is a medium by which a researcher introduces the research problem and communicates the need for research. It is imperative in the application process. It provides a snapshot of the questions that the researcher hopes to answer using the research. It specifies the researcher's methodology during the research process.

On the other hand, a research report is the culmination of the research effort. It's an excellent way to explain the research and its findings to a group of people. It is the result of a study conducted during the research process.

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How to Prepare a PhD Research Plan/Schedule?

PhD research plan is a structured schedule for completing different objectives and milestones during a given timeframe. Scholars are usually unaware of it. Let us find out how to prepare it. 

Between March 2021 to 2022, I read almost 15 different research proposals from students (for their projects) and only a single one, I found, with a comprehensive research plan for 3 years. Which is still not, kind of practical, probably copied from other students. 

Such entities are not known to over 90% of students, if some know that because their university asked for but unfortunately, this basic procedure lacks penetration among students. I don’t know the exact reason, but students lack a basic understanding of the research process. 

Meaning, that they don’t know or perhaps don’t complete their course work needly. PhD research requires many documents, SOPs and write-ups, before even starting it. For example, a rough research plan, research proposal, initial interview, competence screening, grant proposal and so on. 

However, the requirement varies among universities and thus knowledge regarding basic procedures often also varies among students. So I’m not blaming students but certainly, it is the fault of the university side, as well.  

When you come up with a research proposal with a research schedule or entire plant, certainly it will create a positive image and good reputation. So it is important. But how to prepare it? 

Hey, there I’m Dr Tushar, a PhD tutor and coach. In this article, we will understand how we can prepare a structured plan for the PhD research and how to execute it. 

So let’s get started.  

How to prepare a PhD research plan/schedule?

A PhD research plan or schedule can be prepared using the GANTT chart which includes a month, semester or year-wise planning of the entire PhD research work. 

First, enlist goals and objectives.

It’s not about your research objective enlisted in your proposal. I’m talking about the objectives of your PhD. Take a look at some of the objectives.

Note that these are all the objectives that should be completed during the PhD, but not limited to a specific subject. Note you have to show how you can complete or achieve each objective during the entire tenure of your work. 

And that is what the plan/schedule is all about. Next, explain the time duration. The time required to complete each goal, roughly. For example, a semester or a year to complete the course work or 4 to 8 months for completion of ethical approval. 

Now two things must be known to you, at this point in time. 

• First, enlist the time required to complete each objective, as aforementioned. 
• Second, what goals would you complete during each semester?

For instance, course work takes a semester to complete, but during the period a scholar can also craft their PhD research title, research proposal, ethical approval and grant proposals. 

Now it is also crucial to know that there is no time bound to complete goals, but it should be completed as you explained. Let’s say you can plant it for 3 years, 4 or even 5 years depending on the weightage of your work. 

In summary, the answer to the question of how to prepare a research plan is, 

• Enlist your goals or objectives. 
• Decide the time required to complete each goal.
• Prepare a GANTT chart.  

Now you have prepared zero-date planning for your research but how to present it? The answer is a GANTT chart.   

GANTT chart for PhD research plan: 

GANTT chart is a task manager and graphical presentation of how and how many tasks are completed or should be completed against a given time duration. Take a look at the image below. 

How can you prepare one?

Open MS Excel (on Windows) or numbers (on Mac).

Enlist goals or objectives in a column. 

Enlist years (duration of PhD) in a row and bifurcate them into individual semesters. You can also prepare a month-wise plan, that’s totally up to you. In my opinion, semester-wise planning is good because research is a lengthy and time-consuming process. So monthly planning would not work. 

To make a chart more attractive and readable use colors, as I used. Now mark a ‘cell’ against a column and row showing the objective which you are going to complete in a semester. Take a look. 

After the end of this, your GANTT chart would look like this. 

You can prepare a month-wise planning, individual semester-wise planning and goal-wise planning etc. I will explain these things in upcoming articles on 5 different types of GANTT charts for PhD.  

Custom writing services: 

If you find difficulties in preparing a research plan, synopsis, proposal or GANTT chart. We can work on behalf of you. Our costume services are, 

• Synopsis writing 
• Project writing 
• Research proposal writing 
• Research planning and GANTT chart preparation. 

You can contact us at [email protected] or [email protected] to get more information. 

Wrapping up: 

Planning and executing a research schedule are two different things. Oftentimes, students just prepare as per the requirements and then do work as per their convenience. Then they are stuck in one place and just work around the time. 

Plan things. Make your own GANTT chart, put it on your work table or stick it on a wall so that you can see it daily. Try to achieve each goal in time. Trust me things will work and you will complete your PhD before anyone else.  

Dr. Tushar Chauhan is a Scientist, Blogger and Scientific-writer. He has completed PhD in Genetics. Dr. Chauhan is a PhD coach and tutor.

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10 Free Research Plan Templates for Teams and Professionals

February 13, 2024

Starting a new research project from scratch can feel overwhelming. Without the right tools and templates, you’re left with a blank page and no direction. With them, starting a new project or organizing an existing one feels like a breeze.

That’s why you need to build a library of the best research plan templates. And we’re here to help you do it.

Stick with us as we run through the benefits of using a research plan template and share some of our favorites—all designed to help make your research projects run like magic.

What is a Research Plan Template?

What makes a good research plan template, 1. clickup user research plan template, 2. clickup market research template, 3. clickup research whiteboard template, 4. clickup equity research report template, 5. clickup seo research & management template, 6. clickup research report template, 7. clickup data analysis findings template, 8. clickup personal swot analysis template, 9. clickup case study template, 10. clickup investigation report template, how to write a research plan.

A research plan template is a document that’s designed to help you build the best research management plan possible. Instead of starting from scratch with a blank screen, a research plan document gives you the building blocks to fill in—so you won’t miss anything important.

There are a lot of solid research plan documents out there—covering everything from UX research (user experience) to case study templates . These templates can be helpful for any team, whether you’re working on product development prototypes or research objectives for a marketing project. They’re especially helpful for product design , UX research, and project management teams.

Some of the most popular research plan templates include:

• UX research plan templates
• Usability testing research templates
• Data analysis findings templates
• Project proposal templates
• Case study templates
• Research process templates
• Market research templates
• Competitive analysis templates
• Request for proposal templates

Each is there to guide you towards collecting, reviewing, and reporting on your research in a more strategic and organized way. Think of the research plan as your helpful research buddy—there to make things easier, provide guidance, and help you ace your project execution .

We’re all looking for something different when it comes to project templates. You might favor simplicity and order, while another team might prefer a more creative approach with lots of color and prompts.

Even though your needs are unique, there are some elements that almost always make a research plan template stand out above all the rest.

The best research plan templates:

• Keep you and your product team organized
• Help you standardize the research process and research method you use 
• Keep you focused on the key project goals and deliverables
• Give you suggestions for metrics to record and analyze
• Help you keep your research questions in one place
• Help you stay on target with your project timeline
• Give you a defined place to store your thoughts and research findings

There’s no one perfect template for any individual or team. Consider what your purpose or goal is, what your project management workstreams look like, and which areas you need the most support or guidance in. This will help you choose which templates to feature and how you can use wiki software to build a collection of your go-to templates.

10 Research Plan Templates to Use in 2024

There are hundreds of research plan templates out there, but they’re not all alike. Some of them bring out the best of your project management skills , while others hinder them.

We’ve brought together the best of the best, to share with you the ultimate list of research plan templates to add to your workflow this year. Want to know what’s even better? You don’t need to get buy-in for an expensive pricing plan—these templates are all free!

One of the first things that comes to mind when you say “research plan template” is user research. For development and project teams, this is one step of the process where strategy and staying organized is essential.

The User Research Plan Template by ClickUp makes it easy for you to achieve that and more. There’s space to share your project overview and research goals, research objectives, hypotheses, and more—plus a bonus Interview Research Debrief doc.

This template acts as a central resource for all the stakeholders. Use it to bring your team together, reaffirm your goals and objectives, and stay on track as you execute your qualitative research project.

Bonus: UX design tools !

Planning your market research is a must-have if you want to get the best possible data. Give your team everything they need in one place and it helps your process run smoothly.

To help keep your team informed and ready to go, we developed the Market Research Template by ClickUp . It’s a Task template that brings you key information, all in one place.

Our Market Research Template features five custom fields—a research presentation link, market research type, report document link, data collection technique, and research stage. Add your clickable links, and use the dropdowns to assign the correct stage or type as you progress.

You can collect user research in so many ways. Questionnaires, user interviews, focus groups, user research sessions, or social media. Another super engaging way to do this is with a whiteboard.

Collaboration and user research feels interactive and fun with the Research Whiteboard Template by ClickUp . Encourage your team to share the insights they’ve collected in this highly visual template, with digital sticky notes instead of empty white boxes.

Use this ClickUp whiteboard template as a more engaging way to view your user research. You can also use this as a tool for internal research projects—invite your stakeholders by link and ask them to comment directly.

If you’re in the business of advising investors on what to do with their money, an equity report is a must-have. Instead of manually writing a new report every time, a research plan template can help you shortcut the process and get straight to the details.

Enter the Equity Research Report Template by ClickUp . It’s designed to help you share what you know in a more strategic way. Share an insight into the company overview, management team, performance, market valuation, and recommendations.

This research plan template has everything you need to present your findings to investors in an organized and effective way. Look like a pro to your investor clients and partners, and store all your data in a meaningful way to reflect on later.

Staying on top of your company’s SEO performance is no easy task. There are so many moving parts, tools, projects, goals, and team members that you need a way to stay organized and productive.

Luckily for you, the SEO Research & Management Template by ClickUp is here to help simplify the process—and make you look good to your boss. This Folder template gives you a dedicated place to work on your SEO goals, with SEO-related custom fields and plenty of custom task types to help your team communicate progress and see roadblocks in your research plan.

Use this template to see at a glance where your SEO projects are, so you can be more proactive about how your team is working. You can also dive in to details and understand time estimates, publish dates, and where your rankings are at.

Check out these AI SEO Tools !

There’s no need to start from scratch every time you’re asked to put a research report together—instead use a template to make all your research questions and study reports as impressive as the last one.

Shortcut your way to success with the Research Report Template by ClickUp . There are sections for your executive summary, introduction, research method and techniques, results & discussion, references, and appendices. Add a report author and contributors, so you can recognize everyone that contributed to the report.

Share your research methods, approach, and findings with stakeholders and clients with this impressive template. It’s a useful foundation to help your team get organized and find a better way to update stakeholders on progress.

The Data Analysis Findings Template by ClickUp helps you present your data to everyone in a more meaningful way. Instead of presenting numbers and graphs, this template can help you go deeper into the problem statement, scope, analysis and research method, findings, and conclusion.

Use this template to help you organize your thoughts and communicate the results of your study in a transparent and easy-to-read way. Explain the context and background information alongside your approach, so your stakeholders can fully understand what the data shows.

A personal SWOT analysis can help you understand your (or your team’s) strengths, weaknesses, opportunities, and threats. This information can not only help you work better, but it means you can be more intentional about your impact on the wider company.

The Personal SWOT Analysis Template by ClickUp can help you remember to work on your SWOT analysis. Find your strengths, weaknesses or pain points, opportunities, and threats. This Task template features several custom fields designed to help you monitor your progress—including your objective, timeline, and completion rate.

This template can be a helpful reminder to focus on your personal SWOT analysis, so you can be more intentional and aware of how you contribute to your team and company’s goals and objectives. Use your personal SWOT to help you set professional goals for work and make a bigger impact.

Case studies give you a powerful insight into what your brands, clients, or competitors are doing. They’re an in-depth look into a specific area of the business, based on your personal research and findings.

Simplify the process of building your case studies with the Case Study Template by ClickUp . This template gives you a strong foundation for presenting clear, insightful case studies with your team, stakeholders, or clients. Introduce the company, your case study objective, solutions and statistics, and your insights.

Use this template to help you create case studies at scale. Present your data in a clear and concise way, with all the context your team or stakeholders need to extract the most value from the case study as possible.

Often our research helps us understand the market, our competitors, or what our own company is doing. Sometimes, it’s to help us understand incidents and challenges instead.

That’s where the Investigation Report Template by ClickUp comes in. This template is designed to help you report on accidents, complaints, incidents, and violations. Explain the case details including a summary and evidence, then move into cross-examination with space for interview questions and answers, and your conclusion.

This template is a must-have for teams and companies that want to demonstrate how they overcome challenges or handle incidents. It’s great for transparency and trust-building, and serves as a useful way to document a trail of evidence for when you need it.

Now that you have a template for your research plan, let’s dive into the details of how to write one. Follow these steps to create an effective research plan that will guide your research and help you achieve your goals.

Step 1: Identify Your Research Question

The first step in writing a research plan is to clearly define your research question or topic. This will serve as the foundation for all of your research and help guide your methods and analysis. Make sure your question is specific, relevant, and achievable within the scope of your project.

Step 2: Outline Your Objectives

Next, you should outline the specific objectives or goals of your research. These objectives should be aligned with your research question and provide a clear roadmap for your project. Be sure to make them measurable and achievable.

Step 3: Choose Your Research Methods

Based on your research question and objectives, you can now determine the appropriate methods for gathering data and conducting analysis. This may include surveys, experiments, interviews, or literature reviews. It’s important to choose methods that are suitable for your research topic and will provide reliable and accurate results.

Step 4: Create a Timeline

A research plan should include a detailed timeline for each stage of the project. This will help you stay on track and ensure that you have enough time to complete each task. Be realistic with your timeline and build in some buffer time for unexpected delays or challenges.

Step 5: Consider Ethical Implications

When conducting research, it’s important to consider any potential ethical implications. This may include obtaining consent from participants, ensuring privacy and confidentiality, or following ethical guidelines set by your institution or governing body.

Step 6: Anticipate Potential Outcomes

As with any research project, there are always potential outcomes that can arise. These could be both positive and negative, and it’s important to anticipate and plan for them. This will help you be prepared for any potential challenges or changes that may occur during your research.

Step 7: Revise and Refine Your Plan

Once you have completed the previous steps, it’s essential to review and revise your research plan as needed. It’s common for plans to change as the project progresses, so be open to making adjustments and tweaking your methods or timeline as needed.

Stay Organized with the Best Research Plan Templates

Nobody likes a disorganized project—especially a research project. Let your team breathe a sigh of relief and make your stakeholders smile when they realize you’ve got it all under control.

Use these free research plan templates to help you get organized, streamline your workflows, and keep everyone informed. Build a collection of templates that work for your projects, and make them a central part of the way you work as a team. Standardize, simplify, and get productive.

All of these research plan templates are available right now, for free, inside our template library . Get access to these user-friendly templates, 100MB of storage, 1,000+ integrations, and more with ClickUp—free now, and forever!

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The scope of work should include the following sections: (1) introduction, (2) statement of objectives, (3) materials and methods, (4) expected results/format of report, and (5) literature citations. Your submitted scope of work should include the following:

1. The Cover Sheet . The cover sheet must include the tentative title, date, author(s), and MP advisor(s). The cover sheet must also include the author(s) and advisor(s) signature(s) to demonstrate faculty approval (Visit #1 on the Final Report page for more information on Cover Sheets or download the template ). 2. Introduction . Describe the problem you will be working on and why it is important. Include a concise literature review to relate your problem to previous work and set the stage for the approach you will take. If applicable, describe the client involved and their interest in the project. [2-4 pages] 3. Objectives . State the research questions your MP will answer or the hypotheses you will test. Be specific and succinct. You should be able to list your questions or hypotheses as a series of no more than 3 or 4 concrete bullet points. While you may fine-tune these questions after you begin your work, the initial description of your methods and expected results should follow directly from these objectives. [<1 page] 4. Methods and Sources of Support . Describe the methods/approach you plan to use including, as appropriate, your research approach, data or means of data collection, and plans for data analysis. Be specific and identify significant subtasks related to each part of your project. State any research support needed in terms of supplies, space, equipment and money. If needed, identify source(s) of financial support (e.g., case study funds, research project of professor, school support, grant, etc.). Note whether you will require Institutional Review Board (IRB) approval for data collection involving human subjects, or whether you will operate under a nondisclosure agreement . [2-3 pages] 5. Expected Results and Format of Report . Identify the expected results of the project and/or the deliverables to be produced (e.g., management plan, geospatial tool, scientific publication, policy recommendations, computer model, etc.). State the format of the final product and its intended audience. If appropriate, identify likely journals for publication of your research. [1-2 pages] 6. Literary Citations . Include full, standard citations for any references referred to in the text of your proposal. The Nicholas School does not require MPs to adhere to one specific citation style as long as citations are formatted consistently throughout the final document. Choose the appropriate manual of style for your project (for guidance, see Duke Libraries citation resources ). 7. Faculty . List all faculty who have agreed to serve as advisors or cooperators in your project, along with their affiliations (e.g., school or department). Indicate the primary adviser(s) responsible for evaluating the project.

Part II: Project Timeline

Part two should contain the timeline with anticipated deliverables, and may be attached as a separate document, if using Excel or other project management formats. Timeline of Tasks, Deliverables, and Events . Outline the various steps of project completion from start to finish. Include all significant milestones and recurring meetings with your teammates (if applicable) and MP advisor. For example, this could include completion of your literature review and other background research, as well as intermediate steps related to your data collection and planned analysis. It is highly recommended that you use a Gantt chart or an equivalent Excel spreadsheet (e.g. with individual tasks and milestones as rows and dates in columns), with ongoing tasks specified at a biweekly resolution. Your timeline should also include standing client meetings, if relevant.

Part III: Team Charter

The team charter should outline roles and responsibilities of the team and advisor. All students, including those completing an individual MPs, must include a team charter in the final Work Plan. If you are participating in an individual project, you and your MP advisor are considered a “team”.

Your team charter should include the following: 1. Team Roles and Responsibilities . Assign each team member a role and associated responsibilities to be fulfilled during completion of the MP. 2. Regular Meeting Schedule. Outline how often, in what way, and with whom your MP team will meet. This includes regular team meetings, as well as standing meetings with your advisor and, if relevant, client. Frequency and content of the meetings is up to the collective discretion of the team. 3. Team Expectations . Describe any additional agreements your MP team comes up with. (e.g., how to handle potential conflicts, preferred means of communication, data sharing and storage, etc.) 4. Team Purpose and Mission . Describe the top priorities and goals of each individual team member during the course of the project. 5. Team Cohesion and Conflict Resolution. Include a brief description that addresses these questions: How will your team resolve conflict? How can you most effectively handle scenarios in which team members are not pulling their weight or not living up to the expectations outlined in this charter? How will you have difficult conversations? What steps will you take to understand and know each other better? How do you want to promote ongoing integration and camaraderie within the team?

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Integration of Analysis Methods and Development of Analysis Plan (2012)

Chapter: chapter 6 - examples of summary work plans.

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

21 C h a p t e r 6 This chapter provides sample work plans for five global research areas: lane-departure crashes, intersection crashes, driver distraction, driver fatigue, and alcohol-impaired driving. Overview of Work plans Each proposed Project S08 work plan should follow this outline: 1. Overview of research topic. 2. Specific research question(s). 2.1. Crash type(s) addressed. 2.2. Proposed surrogate measures. 2.3. Rationale for research questions and use of natural- istic driving data. 2.4. Hypotheses to be tested. 3. Data analysis plan. 3.1. Data sampling, segmentation, and aggregation. 3.2. Model formulation. 3.3. Analytical approach. 3.4. Model validation. 4. Pitfalls and limitations that may be encountered and how to address them. 5. Documentation of results. 6. Expected impact or outcome of research on counter- measures or policy implications. Each work plan needs to clearly identify the global research area being addressed and how the research will contribute to the knowledge in this area. Typically, the research area will be specified by the RFP. Otherwise, the proposer needs to justify the prioritization of the proposed global research question. Proposers should articulate both the importance of the specific research question to SHRP 2 goals and why NDS data are required for their analysis. The specific research ques- tion should also identify what types of crashes the study will address (rear end, angular, intersection, roadway departures, or some combination of crash types). The intent of SHRP 2 is to improve traffic safety, and all funded projects should contribute to the goal of reducing crashes, particularly fatal or injury crashes. Research questions that are mainly relevant to low-severity or low-incidence crash types (e.g., property only) would be a lower priority for SHRP 2. If crash type is not specified, the conclusions of the research cannot direct interventions to improve safety. Accordingly, the work plans should demonstrate that the specific research question is rel- evant to a specific crash type. The S02 team has developed five work plans to demonstrate issues that may be encountered when preparing a proposal and how a team might address some of the RFP requirements. Each example work plan addresses one of the global research topic areas: 1. Lane-departure crashes; 2. Intersection crashes; 3. Driver distraction; 4. Driver fatigue; and 5. Alcohol-impaired driving. The work plans briefly illustrate what might be included in a proposal. It is important to note that these are examples only. The specific research questions used are not neces- sarily the most important questions regarding the particu- lar global topic area, and the data reduction and analytical methods may not be the best or the only tool to answer the questions posed. Project S08 researchers are not expected to use the same examples or generate similar outcomes. On the contrary, the goal is to provide enough examples to guide researchers in the development of their own innovative ideas. These work plans provide concrete examples of the chal- lenges associated with naturalistic research and how they might be overcome. The work plans vary in the level of detail they provide to most efficiently highlight the range of issues that researchers need to consider. The first research plan (the Examples of Summary Work Plans

22 Proposed Surrogate Measures Because crashes are relatively rare events, other factors, such as the amount of lane deviation, will be used as a crash sur- rogate. While it is assumed that lane deviation is correlated to crash occurrence, the team is not aware of any studies that have proven this relationship. If a sufficient number of relevant crashes are available in the final data set, the team will devote some resources toward developing a model that relates lane deviation to lane-departure crash occurrence. Related measures, including time to lane crossing and yaw rate, have also been shown to be effective surrogates for lane departures. Several studies have used lane position as a surrogate mea- sure to assess the effectiveness of lane-departure counter- measures. Stimpson et al. (1977) identified lateral placement and speed as the best indicators for assessing driver behavior on horizontal curves. Donnell et al. (2006) used mean lat- eral vehicle position within the travel lane to assess the effect of wider-edge lines on horizontal curves on two-lane rural highways. Zador et al. (1987) used lane placement to assess the effect of post-mounted delineators and raised pavement markers on driver behavior at curves. Charlton (2007) used speed and lane position to compare the effectiveness of advance warning, delineation, and road marking treatments on horizontal curves. Finally, Porter et al. (2004), Taylor et al. (2005), and Hallmark et al. (2009) also used lane position to assess the effectiveness of lane-departure countermeasures. Rationale for Research Question and Use of Naturalistic Driving Data Importance of answerIng research QuestIon FHWA (2009) estimates that 58% of roadway fatalities are lane departures and 40% of fatalities are single-vehicle ROR crashes. Horizontal curves have been correlated with crash occurrence in a number of studies. Curves have approximately three times the crash rate of tangent sections. Seventy-six per- cent of curve-related fatal crashes are single vehicles leaving the roadway and either striking a fixed object or overturning; another 11% of curve-related crashes are head-on collisions (AASHTO 2008). Studies on roadway factors such as degree of curve, pres- ence of spirals, or shoulder width and type suggest that curve characteristics are the most relevant factors to crash occur- rence, but information is still lacking. In addition, little infor- mation is available that identifies which driver behaviors contribute to curve crashes. As a result, a better understand- ing of how drivers interact with various roadway features, such as curve radius or countermeasures like advance sig- nage, will provide valuable information to highway agen- cies in determining how resources can best be allocated to lane departure work plan) provides the most detail, and the subsequent plans highlight issue-specific considerations, such as alcohol-impaired driving. The research question related to crash surrogates is not a separate work plan but is incorporated into each example work plan. The crash surrogate question can of course be a research question in itself, but for the purpose of these examples, the team elected to focus on the various ways crash surrogates can be used for driver behavior and roadway improvements. With minor variations, each sample work plan follows the suggested six-part outline shown above. Explanatory com- ments from the S02 team are included in italics. These com- ments concern the main issues associated with each section of the proposal and the information that should be provided in each part of the work plan. example Work plan 1: Lane-Departure Crashes Overview of Research Topic This work plan focuses on the global topic area of “the influence of driver interactions with roadway features on lane-departure crashes.” This topic area was assigned a pri- ority one level on the Project S02 decision tree described in the Phase I report because research on driver interactions with roadway features leading to lane-departure crashes is relevant to safety, has the potential to reduce fatalities, would benefit from additional data sources, and is focused on driver behavior. Moreover, the behaviors are available in the natu- ralistic driving data set, and research outcomes may propose straightforward interventions, as well as provide broader insights into driving safety. Comment: This example summarizes one research approach to addressing a question within this topic area. It does not represent a complete or ideal work plan, nor does it advocate a preferred research methodology. Specific Research Question What roadway and driver factors influence the frequency and outcome of lane departures on horizontal curves? Crash Type(s) Addressed The ROR crash is the most likely crash type to result from right-side lane departures; however, head-on crashes and opposite-direction sideswipes can occur when a vehicle runs off to the right and then overcorrects in returning to the roadway. Head-on crashes and opposite-direction side- swipes are the typical crash types associated with left-side lane departures.

23 question. Specific tasks are not broken out, but the components could easily be divided into tasks. Data Sampling, Segmentation, and Aggregation Data reQuest Since the SHRP 2 NDS will generate a significant amount of data and because a disproportionate number of lane-departure crashes occur on two-lane roadways, the focus of the research will be curves on rural, dry, paved, two-lane roadways. Gårder (2006) indicated that two-thirds of all fatal crashes in Maine take place on rural, two-lane collector or arterial roads. Studies from other states have also indicated that a large number of lane-departure crashes occur on rural two-lane roads (ETSC 1995; Fitzpatrick et al. 2002; KTC 2006). FHWA (2009) also found that most lane-departure crashes occur on two-lane rural roadways. It is also relevant that a review of the most recently available information on the lane-positioning system that will be included with the SHRP 2 data acquisition sys- tem (DAS) suggests that the system does not perform well on unpaved roadways (Dingus et al. 2008a; Dingus et al. 2008b). As a result, the research team does not expect to be able to include unpaved roadways in the analysis. As to the focus on dry roadways, although adverse weather conditions increase the likelihood of lane-departure crashes, inclusion of varying weather and road conditions increases the complexity of the model and would require much larger sample sizes than can be included in the scope of this research. A review of data that will be collected at the NDS sites sug- gests that several of the sites are likely to include a large amount of rural road driving. (This is based on information available as of December 2009.) The team will therefore request data from the following sites: • Central Indiana will have 150 DAS units. The study area has 192 miles of rural principal arterial and 202 miles of rural minor arterial roadways. • Durham, North Carolina, will have 300 units. Although they do not distinguish between rural and urban in their study site description, they list 605 miles of primary road without limited access that are expected to include a large number of rural two-lane roadways. • Erie County, New York, will have 450 units. Although they do not list by rural and urban, the scheduled study site has 185 miles of primary road without limited access and 1,117 miles of secondary state and county highways that are expected to include a significant number of rural two- lane roadways. • Central Pennsylvania will have 150 units and has 568 miles of principal arterial and 734 miles of minor arterial road- ways, the majority of which are expected to be rural road- ways given the location of the study site. maximize driver performance and reduce the incidence and severity of crashes. The study results related to this research question should provide agencies with additional information to implement curve countermeasures or policies that will allow them to make better decisions to target resources in order to improve safety. Information that leads to a reduction in crash severity is a high priority for state highway agencies. ratIonale for use of nDs Data Crash data can be used to evaluate some factors related to curve crashes, but such analysis is limited by the amount and type of data provided in crash reports. Usually only aggregate information about roadway features is requested on crash forms. Even when more specific information might be avail- able, police officers may not choose to spend time collecting details on all roadway variables. Thus such data are incon- sistently reported. For example, an officer may code a crash as occurring on a curve, but will most likely not include any information about curve geometry or signage. In addition, minimal information on driver distraction is requested on police crash reports, and what is reported can be highly sub- jective. For example, an officer may or may not report that the driver was distracted by cell phone use, depending on whether that officer attempted to ascertain whether this was the case and whether it was a contributing factor. Other driver factors, such as driver forward attention, are not included in crash report data. A driving simulator could be used to assess how drivers interact with roadway features, since simulator data can pro- vide information on a specific driver’s performance. However, simulator studies do not represent normal driver behavior (e.g., natural engagement in driver distraction). In addition, addressing the wide variety of curve radii and varying road- way features that would be necessary to have representative samples would result in a very large and costly simulation study requiring a large number of drivers. Moreover, simula- tor studies, like crash data, do not yield exposure data. Thus, the use of NDS data was determined to be the best method to address this research question. Hypothesis to Be Tested Relationships exist between driver and roadway factors and the frequency and outcome of lane departures. To assess these relationships, the study will use lane deviation as a crash sur- rogate as described in the crash surrogate measures section. Data Analysis Plan Comment: The following sections outline the major components that would need to be included in a work plan for this research

24 possible to collect eye-tracking information. Since eye-tracking data are not available in naturalistic driving studies, forward scan position will be used as a proxy. DAS is expected to have some eyeglance positioning capabilities. Information on driver face position will be used to infer driver scan location. Bao and Boyle (2009) used driver scan behavior as a metric to assess age-related differences in how drivers perform various turning maneuvers at rural expressway intersections. They divided the forward view into seven scan locations (see Figure 6.1). A pro- tocol to measure location of driver forward scan position for each sampling period will be developed based on the method used by Bao and Boyle (2009). Comment: At this point the final accuracy and resolution of data from the various data sources have not been finalized. As a result, the accuracies desired for this sample work plan may not be available in the final data sets. If accuracies for certain elements are lower than desired, a determination will have to be made as to whether the accuracy is sufficient to answer the research question. The expected accuracy should be available for the S08 studies, and researchers should demonstrate that they understand what will be available and whether it is adequate to answer the specific research question they have posed. Database structure The database will be set up so that it can be shared with other researchers. Shared information will include a description of the data extraction, reduction, and processing methods used, as well as a data dictionary with an operational definition for each term or variable used. Irb reQuIrements Although the final requirements are not available, it is antici- pated that the team can meet the IRB requirements in order to obtain and use forward video, driver face video, and GPS data for the sections sampled. segmentatIon approach The sequential block data segmentation approach was selected as the most appropriate method for sampling data for clas- sification and regression tree (CART) analysis. Data will be sampled at 30-second intervals on tangent sections and at four points for each curve. Campbell et al. (2008) indicated that the driving task through a curve can be divided into four areas (approach, curve discovery, entry and negotiation, and exit). Each area requires different levels of attention and involves different driving tasks, so every curve will be sampled at each of the four points. Driver (e.g., distraction type, head posi- tion), vehicle (e.g., speed, acceleration, lane position), road- way (e.g., lane width, shoulder type and width, curve radius), and environmental (e.g., day, night) factors will be reduced from the corresponding data for 1 second for each sampling point. A 30-second sampling period for the tangent sections • Seattle, Washington, will have 450 units with a total of 174 miles of rural principal and 444 miles of minor rural arterial roadways. The Tampa, Florida, site has 450 units scheduled, but the site description specifies only 23 miles of rural principal arte- rial and 37 miles of rural minor arterial roadway. As a result, data will not be requested from the Tampa site. Comment: More detailed information about the amount and location of data to be collected and the schedule for data col­ lection will be available once NDS data collection begins and researchers from Project S04A prioritize collection of roadway data elements. S08 researchers should indicate that they under­ stand where roadway and NDS data will occur and what data collection schedule constraints may exist that will affect their ability to obtain data in a timely manner. extractIon of Data elements The data elements necessary to answer the research ques- tion will include roadway, vehicle, driver, and environmental characteristics. Table 6.1 indicates the necessary data elements and their expected source. The accuracy necessary for each data element is also provided. The majority of roadway data elements will be collected by using mobile mapping or will be gleaned from existing state databases. Depending on where mobile mapping data are col- lected and what other data sets are available, some data will need to be reduced from sources such as forward images or aerial images. Vehicle factors (e.g., speed, acceleration, spatial position, and lane position) will be provided by DAS. Driver face video, passive alcohol sensor data, and potentially some face tracking will also be available from DAS. All other driver factors will have to be reduced from the video. Because the analysis will include only dry road conditions, it will be necessary to determine a method for selecting only the desired environmental conditions, since roadway surface or ambient environmental conditions will not be provided by any of the data sources that are expected to be available. If possible, archived roadway weather information system (RWIS) data or other meteorological records may be used. Number and type of driver distractions will be extracted for each sampling interval. To ensure consistency among research team members, a protocol for extracting and cod- ing driver distractions will be developed based on the driver distraction coding system developed by VTTI for the 100-car NDS (Dingus et al. 2008a; Dingus et al. 2008b). Driver forward attention will be measured by the location of driver focus for each sampling interval. Scan position or eye movement has been used by several researchers to gather and process information about how drivers negotiate curves (Shinar et al. 1977; Suh et al. 2006). The majority of these researchers conducted simulator studies in which it was

25 Table 6.1. Necessary Data Elements for Lane-Departure Work Plan Data Element Data Stream Minimum Vehicle Factors Latitude, longitude In-vehicle DAS ±6.6 ft Distance between vehicle and nearest strikeable object In-vehicle DAS ±6.6 ft Vehicle position from lane center DAS lane position tracking system ±0.1 ft Forward and lateral acceleration and speed In-vehicle DAS ±0.1 ft/s2 and 0.1 ft/s Pitch, roll, yaw In-vehicle DAS NA Roadway Factors Lane and shoulder widths Mobile mapping ±0.25 ft Roadway and shoulder surface types, number of lanes, presence and type of edge and centerline rumble strips Mobile mapping NA Horizontal and vertical curve lengths and radii, distance between successive curves, type and characteristics of curve spirals, curve start and end points Mobile mapping ±25 ft Superelevation, grade Mobile mapping ±0.5% Lane cross slope Mobile mapping ±0.1% Curve direction Will be extracted using DAS forward imagery NA Type and location of signage (e.g., chevrons), type and location of roadside objects Mobile mapping ±6.6 ft Pavement marking type and condition Extracted from DAS forward imagery NA Location and type of roadside objects Mobile mapping ±6.6 ft Speed limit and curve advisory speed Mobile mapping NA Exposure Factors Traffic volume (annual average daily traffic) State databases NA Time into trip Extracted from DAS NA Traffic density Extracted from DAS forward imagery NA Lane-departure crash data State databases NA Percentage of time driving on various roadway types under different conditions Extracted from DAS NA Driver Factors Age and gender Driver questionnaire NA Driver distraction Extracted from DAS driver videos NA Alcohol use Inferred from DAS NA Driver fatigue Extracted from driver video NA Driver forward attention Inferred from driver face tracking NA Note: DAS = data acquisition system; NA = not applicable.

26 The sequential block method was selected for several reasons. Continuous data segmentation would represent all driving situations and would provide a high level of confidence that meaningful patterns in the data could be detected. However, reduction of driver data at the continuous level is not prac- tical given the amount of data that is expected to be avail- able for rural two-lane paved roadways. A sample-based segmentation approach would result in data that were overly aggregated. An event-based approach could also be used. However, the purpose of data mining is to uncover patterns in the data, and an event-based approach focuses on pre- determined events that may prejudice the results. In addition, unless combined with another approach, an event-based approach may exclude drivers who did not engage in a pre- defined event. As a result, it was decided that the sequential block data segmentation approach provided sufficient ran- domness to uncover data patterns and was achievable given the time and resource constraints. Development of analytIcal tools The team will develop a processing tool since none is expected to be available. Model Formulation CART analysis, a data mining approach, will be used to address this research question and evaluate the data. This approach iteratively generates a tree structure by splitting the sample data set into two subsets based on a predictor variable and the value of that variable that produces the maximum reduction in variability. The algorithm will continue creating splits until some minimum criteria are met. Tree-based models are used for both classification and regression. A tree-based analysis uses a response variable (Y) that can be either quantitative or qualitative and a set of clas- sification or predictor variables (Xi) that may be a mixture of ordinal or nominal variables. For classification trees the response is categorical, and for regression trees the depen- dent variable is quantitative (Nagpual 2009). Classification and regression trees use algorithms to determine a set of if– then logical split conditions that divide the data into subsets. One of the advantages of regression tree analysis over tradi- tional regression analysis is that because it is a nonparamet- ric method and does not require assumptions of a particular distribution, it is more resistant to the effects of outliers, since splits usually occur at nonoutlier values. Tree models are nonlinear; that is, there are no assumptions about the underlying relationships between the response and explana- tory variables. In addition, independent variables do not have to be specified in advance. A regression tree selects only the most important indepen- dent variables—and values of those variables that result in was selected because reduction of driver video is expected to be time consuming and resource intensive. Sampling on tangent sections will be used to account for exposure. The oversampling of curves compared with tangent sections will be accounted for in the analysis. Vehicle variables can be extracted automatically by using an extraction tool that will be developed. Most roadway vari- ables will also be extracted from existing data (i.e., roadway data sets and mobile mapping data). Some roadway data may need to be extracted from the forward video or other sources such as aerial photos. However, at this point the amount of data reduction is unknown. Data reduction can be time consuming and depends on the time frame, the number of variables, and the amount of data filtering needed. For data sampled at 30-second intervals, data reduction time can be anywhere from 2 weeks to 2 months depending on the amount of data. The amount of data available on rural, dry, two-lane roadways in the NDS is unknown at this point, so an estimate cannot be made of the total resources that would be required to reduce the data. Depending on the amount of data available and project resources requested, a subset of the data meeting study conditions can be reduced. Comment: Although information on possible sample size and resource needs cannot be estimated at this point given the avail­ able information, S08 proposers should demonstrate that they understand what data are likely to be available and the resources that will be required to reduce sufficient data to ensure a statisti­ cally representative sample. JustIfIcatIon for samplIng approach Several different sampling approaches (e.g., continuous, sample based, or event based) could be used to extract data. 3 FAR RIGHT 1 FAR LEFT 2 CLOSE LEFT 4 CLOSE RIGHT 6 REARVIEW MIRROR 5 STRAIGHT AHEAD 7 OTHER Driver Figure 6.1. Seven visual scanning areas as defined by Bao and Boyle (2009).

27 were reduced from UMTRI’s lane-departure and collision- warning system FOT data. Odds ratios were calculated using logistic regression. A time–series analysis that used continu- ous data from UMTRI’s FOT data was also used to predict vehicle position according to a vehicle’s previous position and some roadway characteristics. Comment: CART analysis was selected for this sample work plan because the S02 team wanted to showcase a different analy­ sis method for each work plan presented in Chapter 6. The goal was to allow potential task force members and S08 proposers to see a variety of segmentation and analytical approaches that could be used to answer research questions. Other work plans presented in this report showcase time–series analysis, risk ratio, odds ratio, and crossover design. Data mining using CART analysis was selected for this first work plan because it was thought that data mining might not be as familiar to task force members as other methods. The authors acknowledge that this is certainly not the only valid approach for analysis of lane departures, and might not even be the best approach to answer the research question posed. Model Validation To assess how well the data mining models perform against real data, the data will be randomly partitioned into training and testing sets. The training set will be used to develop the model, and the testing set will be used to validate the model’s accuracy (how well the model correlates with the attributes in the data provided), reliability (how well the model works for multiple situations), and usefulness (whether the model provides useful information for the stakeholders). Pitfalls or Limitations That May Be Encountered and How to Address Them In order to represent a large number of lane-departure crashes and surrogates so that patterns in the data can emerge, a large amount of data is required, and reducing the data required at the level of 30-second intervals will be quite time consuming. When possible, the team will automate the process. Another limitation lies in whether the research team can demonstrate that lane deviation is a reliable surrogate for lane-departure crashes. In addition, data mining using CART analysis is not as commonly used as methods such as calculation of odds ratios or generalized linear models, and as a result it may not be well understood by the highway agencies that will need to use the information. Care will be taken in the dissemination of the project results so that stakeholders will have a general idea of how data are derived from data mining using CART analysis and specific knowledge of how such data can most reasonably be used. the maximum reduction in deviance—and does not require an assumption of best fit (Roberts et al. 1999). Regression tree analysis allows patterns in data to emerge that may not be uncovered using other approaches. Regres- sion tree analysis also reveals relationships between variables that may not be determined using other methods (StatSoft 2011). This method only allows variables to split at the value at which a correlation exists. For instance, shoulder width may only be relevant in determining whether a right-side lane departure results in a lane-departure crash on curves of a certain radius, and may be completely irrelevant for tangent sections or curves with larger radii. The response variable will be the amount of right- or left- side deviation from the lane center. Explanatory variables will include driver, roadway, vehicle, and environmental factors as discussed in the sections above on data extraction and segmentation approach. A sample analysis is presented in Figure 6.2; the data are plausible but hypothetical and are provided to illustrate how the method works. As shown, the hypothetical probability of having a right-side lane-departure conflict is related to curve radius, driver distraction level, and presence of edge-line rumble strips. Analytical Approach Various methodologies could be used to answer the research question and identify relationships between lane depar- tures on curves and driver and roadway factors. Jovanis et al. (2009) evaluated data from the VTTI 100-car study by using both event-based and driver-based approaches and generalized linear and Bayesian models. Gordon et al. (2009) used NDS data from existing FOTs to capture the associa- tion between highway factors, crashes, and driving behavior. They used Bayesian multivariate generalized models, SURs, and extreme value theory to test this association. Hallmark et al. (2009) used an event-based approach to model the rela- tionship between lane departures and roadway factors. Data Figure 6.2. Example of regression tree analysis.

28 of left- and right-turn lanes, signal phasing, roundabouts, pedestrian crossings, and signage. Specific Research Questions The research outlined in this proposal will answer the follow- ing specific research questions: How do intersection geomet- ric and operational features influence driver scan behavior and response? What is the effect of those influences on RLR violation and crash risk? Comment: This sample work plan is based on the assump­ tions that either the forward video includes the signal head suf­ ficiently far in advance so that the researcher can see if a driver ran a red light or that some other method will be available to identify instances of RLR. A review of the latest version of the forward video as shown in Figure 5.8 suggests that data reduc­ tionists will be able to determine signal head state, but at present the S02 team does not have sufficient information to determine whether a data reductionist would be able to identify RLR using only the forward video. A proposer who addressed the research question in this sample work plan would need to determine how RLR would be identified in the database, estimate the amount of resources to identify and reduce RLR events, and determine whether that method would be practical. Crash Type(s) Addressed Right-angle crashes are the most common crash type in RLR. Left-turn oncoming crash type, and opposite-direction side- swipe can also result from RLR. Some countermeasures, such as RLR camera enforcement, are believed to contribute to an increase in rear-end crashes. Proposed Surrogate Measures Even with the amount of data that will be provided by the full-scale NDS, it is expected that crashes will be rare events. RLR violation has been used as a safety surrogate for RLR crashes in a number of studies evaluating the safety impact of camera enforcement. Although a robust crash study requires several years of data after installation of the cameras, agencies often wish to evaluate the immediate impact of the cameras in order to justify their investment. As a result, a reduction in the number of RLR violations has been used by agencies as a safety performance measure (Bonneson et al. 2002; Fitzsim- mons et al. 2007; Retting et al. 2007). While it logically fol- lows that a strong correlation exists between RLR violations and RLR-related crashes, no studies were found that estab- lished a direct relationship between the two. RLR violations will be the basic crash surrogate for RLR crashes. An RLR violation is defined as a vehicle crossing the stop line after onset of the red phase. Time to conflict between Documentation of Results Project outcomes will be presented in a final report that will include a description of the data, data reduction, model for- mulation, analysis, results, and conclusions. The team will also prepare several two-page technical briefs geared toward nontechnical persons who may benefit from project results. Expected Impact or Outcome of Research on Countermeasures or Policy Implications This research is important because a large number of fatal crashes occur on curves. The results will aid agencies in understanding the relationship between driver behavior, including distractions, and curve negotiation. The results will also allow agencies to better understand which curve treatments result in fewer and less severe lane departures and will provide insight into which distractions have the most significant impact on the likelihood of a lane departure on a curve. Most highway agencies are proactive in implementing a range of countermeasures to reduce lane departures on curves, but they are hampered by having only limited infor- mation about the effectiveness of different countermeasures. The results of this research will provide more information about which specific roadway features are correlated to increased risk of lane departure. Study outcomes will also provide valuable information about how drivers interact with roadway features and how those interactions demon- strate the effectiveness of countermeasures. Understanding how drivers approach the task of negotiating curves, for example, will help to explain why certain countermeasures work. Increased understanding of the interactions of drivers with roadway features will allow agencies to make better deci- sion about selection of countermeasures. The research has implications for roadway design, selec- tion of sign type and placement, sight distance, and selection and application of countermeasures. It is expected that more appropriate application of countermeasures to mitigate ROR or head-on crashes on curves will result in fewer fatal crashes. example Work plan 2: Intersections and Crash Likelihood Overview of Research Topic Several issues associated with the interaction between driv- ers and the configuration and operation of intersections were observed in the S01 reports and the report for the S05 proj- ect, Design of the In­Vehicle Driving Behavior and Crash Risk Study (Antin et al. 2011). The questions related to the role of intersection characteristics include the effects on crash risk

29 information necessary to make the correct decision about whether to slow down or continue through the intersection during the yellow interval. The study hypothesis is that visual scanning behavior is a function of intersection geometry, operational factors, and driver factors and can be correlated to RLR crash risk. Data Analysis Plan Comment: The following sections provide a brief example of how a researcher might approach populating the work plan for this research question. A time–series analysis will be used to evaluate the data. Data will be sampled using a continuous segmentation approach. The data needed to answer the research question are listed, and how the data will be sampled, extracted, and aggregated for the time–series analysis is discussed. Data Sampling, Segmentation, and Aggregation Data neeDs The data variables necessary to answer the research question include intersection geometry, intersection operation, vehicle, and driver factors. Intersection factors include lane width and approach grade. Most roadway factors are expected to be avail- able from either existing databases or from mobile mapping data. Some data elements, such as signal head configuration and sight distance, may need to be reduced from the forward video. It will also be necessary to develop a metric that subjec- tively measures intersection level of service from the forward or other outward-facing videos. It is assumed that a database that indicates location of signalized intersections will be available from either existing data sets or from mobile mapping data sets. Signal timing, particularly clearance interval length, is an important factor in RLR. However, most agencies do not maintain a database of intersection timing, and even when this information is available, it is often out of date. Signal timing information will not be available from the mobile mapping data. As a result, there will be no way to obtain green or red phase lengths. The only option for obtaining clearance intervals will be from the forward video if the signal head is visible for the entire interval. The team will evaluate 100 RLR incidents when the data are first received to determine whether it is feasible to include clearance interval length. If a success rate of 80% is not achieved, clearance interval will not be included as a covariate. Seconds into the red interval (Rtime) can be determined from the forward video. Rtime will be calculated by measuring the amount of time that elapses from the time the signal head turns red until the vehicle crosses the intersection stop bar. The stop bar can be identified by locating a regular stop bar or pedestrian crosswalk or by observing the edge of the curb loca- tion in the forward video. A protocol will be established to mea- sure Rtime. Seconds into the red interval requires an accuracy of the study vehicle and an opposing vehicle or pedestrian once an RLR violation has occurred is the metric that will be used to evaluate risk. The model will attempt to derive a relation- ship between RLR crashes and time to conflict, if feasible. Rationale for Research Questions and Use of Naturalistic Driving Data Importance of answerIng research QuestIons FHWA (2006) estimates that RLR contributes to more than 100,000 crashes and 1,000 fatalities annually and results in an estimated economic loss of over $14 billion per year in the United States. Retting et al. (1995) found that occupant injuries occurred in 45% of RLR crashes as compared with other urban crashes and that such crashes accounted for 16% to 20% of total crashes at urban signalized intersections. In Iowa, RLR crashes are estimated to account for 35% of fatal and major injury crashes at signalized intersections (Hall- mark and McDonald 2007). ratIonale for use of nDs Data The goal of this project is to evaluate how drivers visually scan signalized intersections and what geometric, opera- tional, and driver factors result in diminished scanning that can potentially lead to RLR. Visual scanning is very impor- tant in understanding how drivers perform under certain situations. Drivers must process information from a number of sources, and most studies agree that visual information plays a significant role in how they perceive and respond to driving situations (Bao and Boyle 2009). Scanning behavior data cannot be obtained from crash data, nor can such data be easily captured in driver-in-the-loop sim- ulation because of the wide variety of factors (e.g., inattention, aggressive driving, insufficient sight distance) that contribute to RLR. Since a large number of factors may contribute to RLR, use of a simulator would require a large number of scenarios to isolate factors such as clearance interval length. In addition, some studies have shown that RLR is related to driver factors that may be hard to replicate in a simulator. For example, being in a hurry (particularly if a driver is late for work) was identi- fied as a factor in why drivers run red lights. However, it would be difficult to replicate being late in a simulator. Moreover, setting up a simulation to test a wide variety of intersection and signal timing configurations would be costly. Scanning behavior must be collected from within the vehi- cle, and only NDS data will provide the amount and types of data necessary for a careful examination of drivers’ scanning behavior relative to RLR. Hypothesis to Be Tested Certain intersection features may contribute to driver over- load that reduces a driver’s ability to perceive and process the

30 night lighted). Sight distance and field of view are decreased for nighttime conditions even when street lighting is pres- ent. Glare from oncoming vehicles, overhead street lighting, traffic signals, and other sources of light pollution may also significantly affect drivers. However, inclusion of light con- ditions would also significantly increase the complexity of the model and data needs; consequently the study will be limited to daytime hours. It should be noted that it is also difficult to account for the effect of glare on driver response and perception–reaction time. The research team assumes that a database that spatially locates all signalized intersections will be available for each study area. Vehicle traces will be requested for all signalized intersections within a study area that meet the geometry requirements and occur during daytime hours. The team expects that environmental conditions will not be reduced, so vehicle traces in which rain, snow, fog, ice, or other low- visibility conditions exist will need to be identified and dis- carded. Data will be requested for 500 feet upstream and 20 feet downstream for each 40+ mph intersection approach; this area will define the vehicle trace for this RLR study. Sev- eral researchers have identified an intersection area of influ- ence that extends beyond the boundary of the intersection itself. Upstream of an intersection, drivers must perceive and react to downstream conditions such as turning traffic, traf- fic signal changes, and queues. Although it is not simple to define the exact extent of the area of influence, studies suggest that an influence area of between 500 and 600 feet exists for a 40-mph approach (Stollof 2008; Stover and Koepke 2002). Certain age groups may be more or less likely to use four- lane arterials than others and will therefore be over- or under- represented. Older drivers, for instance, may be more likely to use lower-speed alternate roadways. Since not all instances of RLR will be included, the team will attempt to sample each age cohort as it appears in the population of drivers selected for the NDS. For example, if 5% of drivers are aged 18 to 25 years, the team will attempt to sample 18- to 25-year-old drivers at the same rate. Even with the constraints on geometry, time of day, weather conditions, and driver age cohorts, it is expected that a large number of vehicle traces will occur in the full NDS data set that meet the above criteria. A review of data that will be collected at the SHRP 2 NDS sites suggests that all but one of the sites is likely to include many signalized intersections. Based on this information, which was available as of January 2010, the team will request data from the following sites: • Tampa, Florida, has 450 units scheduled. The study area will include mostly urban roadways. • Central Indiana will have 150 DAS units. The study area has 380 miles of minor and other principal arterial roadways. at least 0.1 second. The GPS trace will not be sufficiently pre- cise to determine a vehicle’s position to that level of accuracy. Figure 5.8 shows the forward view from an interim version of the DAS. Although the final DAS will have some differences, it is assumed that details related to the signal head and phasing can be determined from the camera views. Driver distraction variables and driver scan behavior will be reduced from driver face video. Driver distraction will be coded using the methodology used by VTTI in the 100-car study (Dingus et al. 2006). Comment: As of January 2010, information was not avail­ able on the quality of driver face video, camera angle in relation­ ship to the driver’s face, field of view, and other parameters that would be necessary to determine how driver scan behavior can be extracted. Face tracking may also be available in the DAS. It will be important for S08 researchers to be familiar with the types of data that will be available to assess driver scan behavior and driver distraction and to provide a methodology for how they will define and extract such behaviors. Static vehicle characteristics (e.g., vehicle length and engine size), as well as dynamic vehicle characteristics (e.g., speed, acceleration, spatial position, and lane position), will be available from the DAS units in the SHRP 2 NDS. Table 6.2 indicates which data elements are necessary and their expected sources. The accuracy necessary for each data element is also provided. At the time this proposal was submitted the final accuracy and resolution of data from the various sources (e.g., Proj- ect S07 NDS and Project S04B) had not been finalized. As a result, the desired accuracies may not be available in the final data sets. If accuracies for certain elements are lower than desired, a determination will have to be made as to whether the accuracy is sufficient to answer the research question. Comment: The expected accuracy should be available for S08 researchers, and they should demonstrate that they understand what will be available and whether it is adequate to answer the specific research question they have posed. Data reQuest The study will focus on intersections on four-lane arterials with posted approach speeds of 40 mph or greater on dry roads during daylight conditions and with approach grades of less than ±4%. Although RLR can be dangerous in any situ- ation, the consequences are greater at higher speeds. Weather conditions can affect intersection crash risk and may contrib- ute to increased likelihood of RLR. Weather will also affect traffic operations. However, although weather may be an important factor, inclusion of environmental factors would increase the complexity of the model and the amount of data required. As a result, the model will be limited to dry roads. Likewise, it is assumed that driver behavior is affected by light conditions (e.g., daytime, dawn, dusk, night unlighted, or

31 Table 6.2. Necessary Data Elements for Intersection Work Plan Data Element Data Stream Minimum Vehicle Factors Vehicle length, center of gravity, acceleration capability, engine size Driver questionnaire NA Latitude, longitude In-vehicle DAS ±6.6 ft Distance to nearest vehicle or pedestrian crossing path In-vehicle DAS, forward video, forward or side radar ±6.6 ft Forward and lateral acceleration and speed In-vehicle DAS ±0.1 ft/s2 and 0.1 ft/s Pitch, roll, yaw In-vehicle DAS NA Distance from intersection stop line DAS, forward video ±1.0 ft Clearance interval time Forward video 0.1 s Rtime DAS and forward video 0.1 s Intersection Factors Lane width Mobile mapping ±0.25 ft Roadway and shoulder surface type, number of lanes, presence of bike lane, turn lane configuration Mobile mapping NA Approach grade Mobile mapping ±0.5% Approach speed limit Mobile mapping NA Presence, type, and condition of crosswalk Mobile mapping or forward video NA Sight distance to signal head Measured from forward video ±6.6 ft Signal head type and configuration Mobile mapping or forward video NA Clearance interval Forward video 0.1 s Exposure Factors Daily entering vehicles State databases NA Time into trip Extracted from DAS NA Traffic density Extracted from DAS forward imagery NA Intersection crash data State databases NA Percentage of time driving on four-lane arterials Extracted from DAS NA Driver Factors Age and gender Driver questionnaire NA Driver distraction Extracted from DAS driver videos NA Alcohol use Inferred from DAS NA Driver fatigue Extracted from driver video NA Driver scan behavior Inferred from driver face tracking NA

32 approximately 15 seconds, and calculation of time into red requires 30 seconds, for a total of about 45 seconds. The above estimates result in 312 + 15 + 30 = 357 seconds (5.95 minutes) to find and reduce intersection variables for a single RLR. Vehicle and other variables available in raw format from DAS (e.g., speed and lateral acceleration) will be retained at the 0.1-second resolution. Driver scan position will be reduced at an interval of 0.5 seconds; it will be assumed to be constant over the 0.5-second interval and can be mapped to 0.1-second intervals. The amount of eyeglance data to be coded depends on the first defining the relevant surrounding of the intersection. Coding data would be extracted approximately 500 feet (inter- section influence area) before the intersection, 48 feet while in the intersection, and an additional 20 feet after the intersection for a total of 568 feet. If the minimum speed is around 40 mph (approximately 59 ft/s), then it would take 9.6 seconds to travel through the designated area: 568 ft ÷ 59 ft/s = 9.6 s. From previous experience, it estimated that manual data reduction could take between five and six times the length of the actual video clip. As a result, driver data reduction will require approximately 9.6 s × 5 = 48 s per clip to code. Summarizing all the above, the total time to identify, char- acterize, and code is 312 s + 45 s + 48 s = 415 s. This includes time to scan traces in which an RLR event did not occur. In addition to this estimate, locating files within the databases and opening files will take more time. Estimates of coding should consider such peripheral logistics that also add time to each clip that is analyzed. Comment: The example is provided as an illustration that could be used to demonstrate that the proposer understands the amount of time and resources necessary for data reduction. Although it may not be necessary to provide this much detail, S08 researchers should provide some basis for why a data reduc­ tion interval was selected and should also make some estimate of how much time will be required to reduce data at the indicated level of segmentation. JustIfIcatIon for segmentatIon approach As time–series analysis was selected, a continuous approach was the only logical choice for data segmentation. It is acknowledged that this method will require a large amount of time for data reduction and will limit the number of samples that can be included in the model. Model Formulation A time–series analysis will be used to examine the propensity for RLR based on driver’s visual scan patterns. The main advan- tage of using time–series analysis for NDS data is that it allows one to model the driver’s scan behavior as the driver progress • Durham, North Carolina, will have 300 units, and the major- ity of the study area appears to be in an urban area. Although they do not describe their study site as either rural or urban, they list 605 miles of primary road without limited access. • Erie County, New York, will have 450 units and is located in a major urban area. Although they do not list by rural or urban, the scheduled study site has 185 miles of primary road without limited access. • Seattle, Washington, will have 450 units with a total of 1,958 miles of urban principal arterial roadways. The central Pennsylvania area is expected to have about 150 units and appears to be predominately rural. As a result, data will not be requested from the Pennsylvania site. Comment: More detailed information about the amount and location of data to be collected and the schedule for data collection will be available once the SHRP 2 NDS data collec­ tion begins and researchers from S04A prioritize collection of roadway data elements. S08 proposers should indicate that they understand where roadway and NDS data will be obtained and what potential data collection schedule constraints may affect their ability to obtain data in a timely manner. Database structure The database will be set up so that it can be shared with other researchers. Shared information will include a description of the data extraction, reduction, and processing methods used, as well as a data dictionary with an operational definition for each term or variable used. Irb reQuIrements Although the final requirements are not available, it is antici- pated that the team can meet the IRB requirements to obtain forward video, driver face video, and GPS data for the sec- tions sampled. Data segmentatIon anD reDuctIon approach Data will be extracted and used at the continuous level (col- lected at the frame rate). This is expected be 10 Hz or 0.1-second intervals. It is acknowledged that reducing data at this level of segmentation will require significant resources. The fol- lowing description provides a rough estimate of the time resources required to reduce a single vehicle trace. A review of a short video clip indicated that an experienced data reductionist would require approximately 10 seconds for each vehicle trace to determine whether a driver ran the red light. Retting et al. (2007) found that approximately 3.2% of drivers run red lights. Using the Retting data as a refer- ence, it will require approximately 1,000 seconds to find three red light running (RLR) vehicle traces, or 312 seconds per single RLR trace. Once an RLR event is identified, reduction of signal head configuration and sight distance requires

33 Documentation of Results Project outcomes will be presented in a report that will include a description of the data, data reduction, model formulation, analysis, results, and conclusions. The team will generate a white paper on the algorithm developed to flag RLR. The team will also make several presentations at national conferences. Expected Impact or Outcome of Research on Countermeasures or Policy Implications The main outcome will be information about which inter- section geometric, operational, and driver characteristics result in increased RLR and crash risk. This information will be useful for cities and other transportation agencies to make intersection improvements. For example, if the study demonstrated that yellow-phase length is correlated to RLR, traffic engineers could make recommendations for better signal-timing practices. This research has implications for intersection design, intersection signal timing and coordination, and for appli- cation of countermeasures such as all-red phasing or use of RLR camera enforcement. example Work plan 3: Driver Distraction and Crash Likelihood Overview of Research Topic Driver distraction has recently emerged as a high-profile driv- ing safety concern. The increasing popularity and complexity of electronic devices that are either built-in or carried into cars makes distraction an increasing threat to driving safety. Design- ers and legislatures work to balance convenience and access to information with driving safety, but much critical research regarding the risks posed by various types of distractions is still missing. In addition, sophisticated sensor systems may enable future vehicles to track drivers’ eye movements, identify dis- tracted drivers, and potentially warn drivers before mishaps occur. Naturalistic driving data can help identify the distrac- tions associated with different activities and provide the data necessary for the development of eye-tracking sensor systems. Specific Research Question What pattern of glances away from the road and steering wheel movements predicts breakdowns in lateral and longi- tudinal control? Crash Type(s) Addressed Rear-end crashes are most commonly associated with dis- traction, which was found to have contributed to 93% of through the intersection and to examine how changes in one time period affect the scan patterns in the next time period. Drivers’ scan patterns can be examined in two time peri- ods: before entering the intersection and while maneuvering through the intersection. The expectation is that the sequence of events in the first time period will provide insights into the likelihood of running a red light in the next time period. An autoregressive moving average approach may be appropriate to model and predict various types of driver behavior based on various scenarios. Analytical Approach A time–series analysis will be used to examine drivers as they encounter and go through a yellow phase as exhibited by speed and acceleration patterns. Speed and acceleration patterns in the vicinity of a signalized intersection that dif- fer significantly, indicating RLR, can then be related to inter- section geometry and operational factors. The model will be developed for dry roads during daylight hours to control for environmental factors. Data will be modeled for vehicles traveling through the intersection (not turning left or right). Crash risk will be modeled by time to conflict. Comment: The section above on issues related to time­dependent variables discusses issues related to time­dependent methods. As a result, model formulation is not further expanded on here. A time–series approach was selected because it was deter- mined to be the best method to account for dependencies in driver behaviors from previous time intervals. The main advantage of time–series analysis for naturalistic driving data is that it allows relationships between variables across time to be incorporated into the model. As a result, relationships such as driver distraction in previous time periods and probability of an RLR crash in a subsequent time period can be established. Comment: The authors acknowledge that this is certainly not the only valid approach for the research question and may not even be the best approach. Model Validation Approximately three-fourths of the data will be used to develop a time–series model. The remaining one-fourth of the data will be input to the model to determine how well it performs. Pitfalls or Limitations That May Be Encountered and How to Address Them It is expected that the request for vehicle activity in the vicin- ity of signalized intersections along major arterials will result in a significant amount of data to process. The time–series method may also be more difficult to present to lay persons at transportation agencies who are the most likely stakeholders to benefit from the results.

34 2009). While a great diversity of sources both within and out- side the vehicle account for these deaths (e.g., eating, groom- ing, other motorists, and billboards), the soaring popularity of in-vehicle information and entertainment systems (e.g., navigation systems, MP3 players, and cell phones) and the potentially distracting tasks introduced by these systems could increase the influence of distraction on driving safety. Although some tasks associated with these systems are driv- ing related, such as in the case of navigation systems, these tasks may interfere with safe driving and thus are still con- sidered distracting (Lee et al. 2008). Developing distraction countermeasures and reducing distraction-related crashes require an understanding of how the broad variety of second- ary tasks associated with new information technology affects driver behavior. ratIonale for use of nDs Data Over the last decade, hundreds of studies have investigated how distraction can undermine driver performance and safety. The vast majority of these studies involve experiments conducted in driving simulators. These studies carefully con- trol for confounding variables and provide a precise indicator of how distraction affects drivers’ performance in control- ling the vehicle. Generalizing the effect of distractions on performance in the simulator to driving safety on the road represents an important challenge. Drivers may adapt their driving and engagement in distracting activities on the road in a way that they do not in the simulator. Other studies have used epidemiological analysis of crash data to estimate the contribution of distraction to crashes. Understanding the effect of distraction on driving safety from such analysis is problematic because crash records may fail to identify the presence of a distraction. Moreover, crash data do not pro- vide a detailed description of the role of the distraction as a contributor to the crash. Naturalistic driving data help to fill the gap between simulator and crash data by providing a detailed account of the driver’s engagement in the distracting activity in the driver’s natural environment. Hypothesis to Be Tested Drivers’ glance patterns and steering behavior can indicate increased crash risk associated with breakdowns of both lat- eral control (e.g., lane-departure crashes) and longitudinal control (e.g., rear-end collisions). Both lateral and longitu- dinal events will be examined to assess whether distraction indicators from steering control can predict breakdown in longitudinal control. This relationship will be robust to dif- ferences in road type, distraction type, and driver age and gender. An extended interaction with a distraction will magnify crash risk defined by glance patterns and steering behavior. rear-end crashes in a recent NDS (Klauer et al. 2006). Less commonly, distraction also contributes to ROR crashes and head-on crashes. Although less frequent, ROR and head-on crashes are disproportionally responsible for fatalities and serious injuries. Recent crash data show that distraction con- tributed to 16% of fatal crashes and 21% of injury crashes (NHTSA 2009). Similarly, in a study of NDS data, Klauer et al. (2006) report that distraction contributed to 25% of all crash and near-crash events and approximately 65% of rear- end crashes and near crashes. Proposed Surrogate Measures Two general approaches to selecting crash surrogates will be employed to assess the sensitivity of the results to the choice of surrogate. The first approach considers driver response. In the case of rear-end crashes, a crash surrogate based on driver response would be a severe braking event. Severe could be operationalized as an absolute value, such as 0.5 g. Because maximum deceleration value is a function of initial speed, vehicle braking system characteristics, and driver response, a normalized value such as 99% for a given speed range would likely provide a more precise indicator of severe braking events. Dividing the speeds into ranges based on a maximum entropy function would guarantee a well-distributed set of ranges (Tan n.d.; Tan and Taniar 2007), but this choice would be vulnerable to any unequal distribution of rear-end crashes across speeds. To address this problem, a second approach uses information regarding close proximity to the vehicle ahead as a crash surrogate. Close could be operationalized using algorithms developed to trigger forward-collision warn- ing alerts. A simple forward-collision alert algorithm could be triggered by situations that cross a time-to-collision threshold of 2 seconds. A more complex algorithm might include the distance, speed, and acceleration of the two vehicles. Most likely these crash surrogates will co-occur—if a driver gets very close to the vehicle ahead, the driver is more likely to need to brake severely. Therefore, three analyses will be performed: one using the instances in which both surro- gates agree, one in which only severe braking occurs, and one in which the driver only gets dangerously close to the vehicle ahead. Assessing the type and degree of distraction associated with these three crash surrogate combinations might suggest different crash types and crash severity associated with differ- ent types of distraction. Rationale for Research Question and Use of Naturalistic Driving Data Importance of answerIng research QuestIon Distraction represents a clear threat to driving safety, account- ing for 5,870 deaths and 515,000 injuries in 2008 (NHTSA

35 of the driver and vehicle sampled for each 0.1 second of the epoch. Driver and epoch number will key a second database to this database. The second database will summarize the first by aggregating the data to the level of the epoch. Each row will represent a single epoch, and the eye gaze and steering data will be combined in several possible algorithms that can represent crash risk associated with different patterns of eye gaze and steering movements. Analytical Approach The statistical modeling involves two phases: the first com- bines gaze and steering data over the 180-second epoch pre- ceding the event to arrive at an index of expected risk. Long glances away from the road, short glances back to the road, a long interaction with a distraction, and lapses in steering followed by abrupt corrections might contribute to a higher degree of expected risk of a mishap. This index of expected risk can be derived from previous research or through data mining methods that identify the combination of variables and their weighting that best reflect the likelihood of a crash or near-crash event. The second phase evaluates the ability of this index to differentiate between crash and near-crash events. A conditional logistic regression model will calculate the odds ratios associated with the various risk indices. Risk indices associated with high odds ratios are those that accu- rately integrate gaze and steering data to predict distraction- related crash and near-crash events. Model Validation The statistical model will be validated with a sensitivity analysis that will examine the extent to which model predic- tions depend on the parameter values. The model will also be validated by assessing its performance by using a subset of the data that is withheld from the data used for model estimation. Pitfalls or Limitations That May Be Encountered and How to Address Them Video review and coding could be prohibitively expensive. Machine vision approaches to automatic gaze tracking are at the research prototype stage, and their output would require validation. Coding of distraction types would require man- ual coding. Currently available surrogate measures reflect abrupt responses of the driver in the form of braking and steering wheel reversals and do not capture lapses related to near-crash events such as failing to stop for a red light. The surrogate defined by driver response and by the lack of driver response outlined in this proposal begins to address this problem, but only for rear-end crashes. Data Analysis Plan Comment: The following sections provide a brief example of how a researcher might populate the work plan to address this research question. Data Sampling, Segmentation, and Aggregation An event-based sampling approach will be used to describe the 180-second period preceding the event and the 5 sec- onds following the event. The relatively long period pre- ceding the event will be used to assess the broad driving context leading up to the event and the contribution of the duration of the distraction to the likelihood of a crash or near-crash event. A case–control method will match the event-triggered sample as closely as possible with another sample selected at the same time of day on the same day of the week for the same driver in the week preceding that in which the event occurred. The continuous data will be aggregated using algorithms that maximize the ability of a sequence of eye movements to differentiate between dis- tracted and attentive drivers. The data variables necessary to answer the research question include road type (e.g., residential urban arterial, freeway), driver factors (e.g., age, gender), and driver behavior (e.g., steering wheel move- ment, speed, frequency and duration of off-road glances, distraction type). Driver distraction variables will be reduced from the driver’s video data. Ideally, on-road and in-vehicle gaze location information will be extracted with a machine vision algorithm. It is assumed that a database that links GPS location to road will be available either from existing data sets or from mobile mapping data sets. Data reQuest Epochs consisting of the 180 seconds preceding and the 5 seconds following each crash and near-crash event will be requested, along with three matching epochs. The epochs will be matched by driver, road type, time of day, and type of day (weekday versus weekend) rather than on a random selection to minimize extraneous variation and to identify the increased risk of distractions associated with the behavior of an individual rather than the overall safety profile of that individual. Database structure The database will be set up so that it can be shared with other researchers. Descriptions of data extraction, reduction, and processing methods, as well as a data dictionary with an operational definition for each term or variable used, will be provided. One database will include the continuous data for each epoch, including steering behavior, speed, and eye gaze location. Each row of this database will represent the state

36 to relate to crash likelihood in drowsy drivers. It is likely that crashes will be rare events in the naturalistic driving data, and ROR crashes may be even rarer. Hence, lane-departure and lateral drift events and standard deviation of lane posi- tion are considered as ROR crash surrogates for this specific research question. Other possible crash surrogates include lateral acceleration and speeding, which have been shown to relate to the likelihood of ROR events and safe negotiation on curves and through intersections (Reymond et al. 2001; Classen et al. 2007; Fildes et al. 2005). Each crash surrogate will be evaluated, and a determination of the best crash sur- rogate will be made after examining the available naturalistic driving data. Rationale for Research Question and Use of Naturalistic Driving Data Importance of answerIng research QuestIon Driver fatigue is a major contributor to motor vehicle crashes and is responsible for approximately 40,000 injuries and 1,500 deaths each year in the United States alone (Knipling and Wang 1995; Laube et al. 1998; Lyznicki et al. 1998). Royal (2003) estimated that 1.35 million drivers were involved in a fatigued driving–related crash over a 5-year period. A NHTSA study revealed that there are six million crashes annually resulting in an economic impact of over $230 bil- lion (Blincoe et al. 2002). Thus, over 4% of these costs are probably attributed to fatigue, and even this estimate may be low. In a separate study conducted by McCartt et al. (1996) approximately 55% of 1,000 drivers surveyed indicated they had driven while drowsy, and 23% had fallen asleep at the wheel. This confirms other findings that fatigue may play a role in crashes that are erroneously attributed to other causes (Connor et al. 2001). ratIonale for use of nDs Data Naturalistic driving data can provide insights into how fre- quently drivers exhibit safety-relevant errors while fatigued. Crash data are generally poor at identifying behavioral causes, and driving simulators cannot tap into how frequently or at what time of day or night such events occur. Crash data do not provide enough details to answer this specific research question since there is no preimpact information. Obviously crash data do not describe how frequently a fatigue event has occurred without a negative outcome (e.g., crash). There is also no way to observe speed, acceleration, and lane offset as the driver progresses on his or her trip or gets sleepier. Driving simulator studies can control for environmen- tal situations and can capture the performance of fatigued drivers by having them traverse over monotonous drives (Boyle et al. 2008; Reyner and Horne 1998). However, sleepi- ness can occur even during complex driving situations that Documentation of Results Project outcomes will be presented in a report that will include a description of the data, data reduction, model formulation, analysis, results, and conclusions. Beyond the standard report- ing of the overall results, a detailed description of the algorithms used for data segmentation and aggregation will be produced so that the process can be exactly duplicated. This description of the algorithms will be accompanied by intermediate data sets. Expected Impact or Outcome of Research on Countermeasures or Policy Implications The main outcome will be an algorithm that relates patterns of eyeglances and steering wheel reversals to crash risk. If such an algorithm predicts rather than coincides with crash risk, then it might be used as the basis for an in-vehicle counter- measure to mitigate driver distraction. To be useful, the algo- rithm must also be robust across different types of drivers and roadway situations. This research could identify effective algorithms for detect- ing distraction and thereby support interventions to prevent or mitigate distraction. The role of extended interactions with distractions could provide justification for greater legal sanctions associated with such behavior, such as those being adopted in Great Britain. example Work plan 4: Driver Fatigue and Crash Likelihood Overview of Research Topic Several issues associated with driver fatigue were observed in the S01 and S05 reports. The original questions related to the role of driver fatigue in various crash types (i.e., rear-end, head-on, backing, and lane change) and in crashes involving other vehicles, pedestrians, and other objects. Issues related to the influence of fatigue or drowsiness on driver behavior were also of concern. Specific Research Question How do episodes of fatigue affect drivers’ lane-keeping ability? Crash Type Addressed This study focuses on ROR crashes. Drivers’ lane-keeping ability is influenced by driver fatigue and sleepiness and hence may affect ROR crashes. Proposed Surrogate Measures This specific research question is based on examining crash surrogate measures (i.e., lane keeping) that have been shown

37 assumption of data independence would not be appropri- ate. Thus, a repeated measures analysis of variation and con- ditional logistic regression will be used depending on the nature of the dependent variables. Fatigued episodes will be identified from driver face video. Screening criteria for fatigue can include driver’s eye move- ments (e.g., eyelid closures for more than 2 seconds, multiple blinks), number of head-nodding events (Heitmann et al. 2001), and yawning. However, the research team notes that an examination of eye movements is highly dependent on the driver’s eye and eyelid geometries and whether sunglasses conceal the eyes. samplIng approaches The continuous data will be examined during the events when drowsy episodes are observed. Continuous data are necessary because they provide the only means for observing fine eye movements. A comparative sampling set will be needed for two nondrowsy episodes (or epochs) for the same driver. A 5-second sample of the vehicle kinematics will be used for the case and crossover events. This sample-based approach will help reduce the fluctuation and noise that are typically observed when examining raw data. Hence, calcula- tions of mean and standard deviation for vehicle kinematic information (e.g., speed, acceleration, lane position) will be potentially smoothed at this level. Data for all roadway types will be requested from the NDS. Video and driver (e.g., driver eye position, distraction type), vehicle (e.g., speed, acceleration, lane position), roadway (e.g., lane width, curve radius), and environmental (e.g., time, weather, lighting) factors will be used for the baseline and case episodes. The influence of sleepiness will be time dependent and will most likely degrade driver performance over the length of a trip. Since the effect of fatigue is continuous and may have an extended duration within a trip, the baseline episodes will come from separate trips from the same driver. Sampling both urban and rural roadways will allow the research team to compare the effect of fatigue under differ- ent road type and traffic conditions; it is expected that the driving environment will be more critical (i.e., there will be more traffic and the distance between vehicles will be closer) in urban areas. Database structure The database will be a flat file indexed by driver number, event index, and state of the driver. Each line will represent a summary of 5-second interval data, including numerical vehicle kinematic data (e.g., speed, acceleration, lane posi- tion), environment information (e.g., weather, roadway con- ditions, time of day), and reduced driver behavior data for that time interval (e.g., normal driving, fatigue). can increase the already high workload encountered by the sleepy driver. These complex and varied situations can only be observed in a naturalistic environment. Hypothesis to Be Tested It is hypothesized that drivers’ lateral control ability is affected by fatigue. By capturing the influence of fatigue on lateral control, insight can be gained on ROR crashes. Data Analysis Plan Comment: The following sections provide a brief example of how a researcher might approach populating the work plan for this research question. Data Sampling, Segmentation, and Aggregation The specific research question will be answered with data gathered from random and event epochs. The specific data will include roadway features such as road type, curve radius, and lane width; time of day; and weather and lighting. The majority of these elements would need to be provided from mobile mapping or state databases, as well as in-vehicle data collection. Weather and lighting information may need to be reduced from forward images. Vehicle kinematic data such as speed, acceleration, curve speed information, and lane position will be obtained from the in-vehicle DAS. Lane and roadway departures will be detected by automated lane position data from DAS. Driver face video and face and eye tracking would also be used and captured from DAS. If possible, evidence of eyelid closure (e.g., PERCLOS) would be determined. Model Formulation A case-crossover design will be used to compare cases (drivers during fatigued episodes) with control or baseline situations (drivers during nonfatigued episodes). In this analysis, each participant will serve as his or her own control, thus minimiz- ing confounding effects of age, gender, driving records, and other fixed characteristics (Maclure 1991). Thus, data from multiple baseline drives and events will be needed for each driver. If there are not enough fatigued episodes, the study can be set up to oversample the control condition such that a 2:1 matched approach can be used with one case (fatigue) episode matched to two control (nonfatigue) episodes, with all episodes based on the same roadway condition (urban or rural) for the same driver. It is important to note that because driving performance tends to be similar for each driver (i.e., within-driver data are highly correlated), using analysis measures based on an

38 research team will gain a better understanding of the role that fatigue plays in safety incidents and ROR crashes. Such data will also help determine what types of countermeasure techno logies are most effective. This research will have implications for the development of driving assistance systems, such as drowsy driver detection and alerting systems, as well as for driver education policies. example Work plan 5: Influence of Driver Impairment Caused by alcohol on Crash Likelihood Comment: This work plan demonstrates the issues and implica­ tions related to the use of the NDS alcohol sensor data. The alco­ hol sensor provides continuous sampling of cabin air and may have some utility in identifying driving segments where alcohol may be present. Several confounding factors include whether a window is open or the HVAC system of the vehicle is on refresh (i.e., if the cabin air is recirculated) and the possibility that the alcohol being detected stems from mouthwash or even perfume. Hence, the following example is focused on the challenges that can be encountered if a proposal includes alcohol sensor data. Data from other sensors can also produce ambiguous and noisy estimates (e.g., precision of lane position depends on the clarity of lane markings and other road textures), and each S08 pro­ poser needs to account for and manage these limitations. Specific Research Questions Do speed and variation in steering and speed differ when alcohol is detected in a vehicle compared with situations in which alcohol is not detected? If differences exist, how do they affect the likelihood of ROR crashes? Crash Type(s) Addressed All crash types related to speeding are relevant, most par- ticularly ROR crashes on both straight and curved roadway segments. Most ROR crashes occur on curves and are more typical in rural roadway environments (Liu and Subrama- nian 2009). Hence, this specific research question has high relevance to SHRP 2 safety improvement objectives. Proposed Surrogate Measures A key finding of the UMTRI S01 study was that yaw rate error could be a good surrogate for roadway departures on curves. As described by UMTRI, the yaw rate error generates a smooth, continuous, and unique data series, even when a lane boundary crossing occurs and appears to be a better pre- dictor than TTEC. Both these measures will be used as initial surrogates for ROR probability. However, their performance The database will be set up so that it can be shared with other researchers. Shared information will include a descrip- tion of data extraction, reduction, and processing methods, as well as a data dictionary with an operational definition for each term or variable that will be used. statIstIcal analysIs Repeated measures analysis of variance (ANOVA) will be used to analyze the speed and standard deviation of lane position. Conditional logistic regression will be used to analyze the like- lihood of lane departure and lateral drift incidents. Depend- ing on data availability, time of day, weather, lighting, and roadway features will be controlled to investigate the influ- ence of fatigue under different environmental conditions. Model Validation The statistical models will be validated with a sensitivity analy- sis that will examine the extent to which model predictions depend on the parameter values. The model will also be vali- dated by assessing its performance by using a subset of the data that will be withheld from the data used for model estimation. Pitfalls or Limitations That May Be Encountered and How to Address Them The raw video data will require extensive data reduction to capture driver fatigue. This process can be very time consum- ing and will add to the cost of this study. It is also important to note that distinctions between driver sleepiness, fatigue, and drowsiness will not be made because video data can only provide the analyst information on whether the driver appears to be tired. In addition, distinguishing between a driver’s fatigued and normal appearance using naturalistic driving data might be difficult and depends on individual eye and eyelid geometries (e.g., a driver with droopy eyelids may always look tired). However, the kinematic variables in combination with the video data will provide insights on the propensity of drivers to drive in an unsafe manner given the indicators of sleepiness. Documentation of Results Project outcomes will be presented in a report that will include a description of the data, data reduction, model for- mulation, analysis, results, and conclusions. Expected Impact or Outcome of Research on Countermeasures or Policy Implications The results of this research will provide insights in how fatigue could affect driving safety. By analyzing lateral control, the

39 Data Analysis Plan The data used for this analysis will be restricted to single- occupant vehicles (vehicles without passengers) to avoid any potential confounders from alcohol use by other occupants of the vehicle. Data Sampling and Aggregation Included in the data collected in the NDS are GPS location, the outcome of the alcohol trigger (alcohol presence), yaw rate, and steering and speed variability. Drivers tend to stay on familiar routes when traveling between home, work, and other routine destinations. Cap- turing driving during segments when no alcohol is detected on routine roadway links could be considered baseline (i.e., nonimpaired) driving. Aggregated baseline profiles for a par- ticular roadway link would be compared with instances of positive alcohol sensing on the same link. When a driver is impaired, the profile would change based on differences in speed, steering and/or yaw rate. Positive alcohol triggers on curves and straight road segments will be matched within each route to segments with no alcohol detection. Roadway, regional, and environmental characteristics (e.g., weather, day or night) will also be examined for each road segment and included in the model as covariates as appro- priate. Information on roadway characteristics (e.g., road type, lane width, cross slope, and type and width of shoul- der) will be provided by mobile mapping or will be available from existing state databases. These databases should be cross referenced with the vehicle GPS coordinates to identify fea- ture-based epochs of data upstream of the point of curvature (Campbell et al. 2008). Regions can be classified as rural and urban according to the vehicle GPS coordinates and state or national standards. Weather and other environmental condi- tions will most likely be extracted from the forward video. Comment: Other data sets may also be needed depending on available information (e.g., aerial images), and the proposer will need to identify where such information will be obtained. Model Formulation samplIng approach Epochs for each event will be collected for straight and curved segments with and without the detection of alcohol. Data for this specific research question will be reduced to 30-second blocks and aggregated across a road segment, depending on the length of the segment. Database structure The database will be set up so that it can be shared with other researchers. Shared information will include a description of data extraction, reduction, and processing methods, as well is based on a field operational test that considered only one vehicle type and whose primary focus was on evaluating a roadway departure system. Therefore, other variables will also be considered for crash surrogates in SHRP 2, and the outcomes will be compared to yaw rate error. Rationale for Research Questions and Use of Naturalistic Driving Data Importance of answerIng research QuestIons In 2006, there were 35,588 fatal crashes in the United States (NHTSA 2007). Twenty-eight percent of drivers (and motor- cycle operators) involved in fatal crashes failed to keep proper lane position or ran off the road (NHTSA 2007). ROR crashes are related to both alcohol and speeding (Liu and Subrama- nian 2009). In addition, alcohol and speeding are significant behavioral risk factors for other crash types (ETSC 1995; NHTSA 2001). Therefore, this research is relevant to the SHRP 2 traffic safety goals. The influence of alcohol is a behavioral crash factor of great concern given its impact on crash fatalities (NHTSA 2001). The research questions also identify the targeted form of driver error in terms of lane-keeping and selection of appropriate speeds (ETSC 1995). ratIonale for use of nDs Data Current research on the crash risk associated with alcohol use is based on epidemiological studies or derived from driving simu- lator or test track experiments. However, the data and conclu- sions from these studies are limited. Simulator and test track studies can identify the intervening behaviors that result in increased risk, but such studies involve artificial environments that lack the natural motivation factors inherent in the real world that might affect behavior such as speed choice. Driv- ers are less likely to misbehave in a driving simulator given the demand characteristics of a controlled experiment in which the driver knows he or she is being observed. Further, it is not ethi- cally possible to test alcohol involvement at the high blood alco- hol concentration (BAC) levels encountered in fatal crashes. Hypothesis to Be Tested Crash and near-crash events that occur when alcohol is detected are preceded by systematically different patterns of steering behavior and speed adjustment compared with those events when alcohol is not detected. Several shifts in driv- ing behavior can occur as a result of alcohol-related driver impairment: (1) increased, but less effective, steering behav- ior; (2) diminished steering input punctuated by lapses; or (3) increased speed combined with less effective steering. The analysis will assess the prominence of these potential alcohol- induced shifts in steering behavior.

40 Pitfalls or Limitations That May Be Encountered and How to Address Them Perfumes and other substances could falsely be identified as alcohol by the current NDS alcohol-detection system. Additional data coding resources may be needed to sepa- rate single- and multiple-occupant situations. Even though the proposed scenario is constrained to one occupant (i.e., the driver), it would still be difficult to determine whether the driver is under the influence of alcohol. Thus, video data will also be examined to assess whether the driver appears to have been drinking in the moments leading up to the episode being examined or whether he or she was using some other alcohol- based substance that was not ingested. Eliminating situations that might cause inaccurate indica- tions of alcohol presence might bias the data (few drivers will have their windows open in northern states in the winter), consume substantial effort, and diminish the sample size. However, according to current reports in the literature, there will be many instances of drivers drinking alcohol (NHTSA 2001); thus, a large-enough sample size to achieve adequate statistical power may be possible to mitigate these biases. Documentation of Results Project outcomes will be presented in a final report that will include a description of the data, data reduction, model for- mulation, analysis, results, and conclusions. Expected Impact or Outcome of Research on Countermeasures or Policy Implications The research is important because a large number of fatal alcohol-related crashes occur, with the highest rate of alcohol use and fatal crashes occurring in rural areas. This research will provide results that could further aid agencies in under- standing the relationship between alcohol consumption and driving safety. However, without BAC data, providing addi- tional support for alcohol policies that relate to the BAC level considered impaired and the potential methods for detecting impaired drivers from their driving performance measures is not possible. The profiles of behavior that significantly correlate with driver impairment can provide valuable information (1) to support methods for officers to detect impaired drivers from observed actions of vehicles and (2) to develop vehicle-based systems to monitor vehicle control and diagnose inferred impairment states. For example, real-time measures of vehicle speed in relation to posted speed limits can be used with intel- ligent speed adaptation systems that can automatically warn drivers and control speed for alcohol-impaired drivers. This study could, therefore, demonstrate the value of controlling the speed of cars that are being driven by alcohol-impaired drivers. as a data dictionary with an operational definition for each term or variable used. Based on the sampling approach and specified data, a processed database will be derived from the integration of the vehicle, roadway, and environmental data elements (see above). This processed database will have col- umns to represent the data shown below each epoch: • Trip information: date, trip number, and segment number; • Driver information: driver ID, driver face video, and eye point of gaze; • Vehicle information: 44 Alcohol presence or absence, 44 Speed variation on road link when alcohol is and is not detected, 44 Speed and steering variation on road link when alcohol is and is not detected, 44 Yaw rate when alcohol is and is not detected, 44 TTEC when alcohol is and is not detected, 44 Vehicle speed at curve averaged between 5 meters before and 5 meters after the point of curvature, and 44 Vehicle speed on roadway averaged between a distance 60 and 50 meters before the curve entrance; • Roadway information: 44 Road type, 44 Lanes (number, marking type, width), 44 Speed limit (posted and roadway), 44 Road curvature (horizontal, vertical), 44 Indicator for roadway speed >9 mph above the posted speed limit, 44 Indicator for curve entrance speed >9 mph above the posted speed limit; and • Regional information: urban or rural based on GPS. statIstIcal analysIs The central focus of this analysis is on the quantification of steering behavior and speed associated with situations in which alcohol is detected. These steering patterns can be defined in various ways. One approach is to describe the steering behavior in the time domain with traditional measures of steering perfor- mance, such as standard deviation or the frequency of steering reversals greater than some threshold. Another approach is to use a frequency domain description that describes the steering behavior in terms of a power spectrum using Fourier analysis or techniques such as wavelet analysis that are more robust to the characteristics of naturalistic driving data. Parameters extracted from the time and frequency domain analysis could then be evaluated with a cluster analysis to identify distinct types of steering behavior for alcohol and no-alcohol cases. Risk ratios applied to event counts (e.g., alcohol detected) and other forms of event-based analysis such as logistic regression are an appro- priate statistical method to test the association of alcohol with cluster membership (types of steering behavior) and cluster membership with crash and near-crash ROR events.

TRB’s second Strategic Highway Research Program (SHRP 2). Report S2-S02-RW-1:Integration of Analysis Methods and Development of Analysis Plan provides an analysis plan for the SHRP 2 Naturalistic Driving Study (NDS) to help guide the development of Project S08, Analysis of In-Vehicle Field Study Data and Countermeasure Implications, and to help assist researchers planning to use the SHRP 2 NDS data.

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• Knowledge Base

Methodology

• Sampling Methods | Types, Techniques & Examples

Sampling Methods | Types, Techniques & Examples

Published on September 19, 2019 by Shona McCombes . Revised on June 22, 2023.

When you conduct research about a group of people, it’s rarely possible to collect data from every person in that group. Instead, you select a sample . The sample is the group of individuals who will actually participate in the research.

To draw valid conclusions from your results, you have to carefully decide how you will select a sample that is representative of the group as a whole. This is called a sampling method . There are two primary types of sampling methods that you can use in your research:

• Probability sampling involves random selection, allowing you to make strong statistical inferences about the whole group.
• Non-probability sampling involves non-random selection based on convenience or other criteria, allowing you to easily collect data.

You should clearly explain how you selected your sample in the methodology section of your paper or thesis, as well as how you approached minimizing research bias in your work.

Table of contents

Population vs. sample, probability sampling methods, non-probability sampling methods, other interesting articles, frequently asked questions about sampling.

First, you need to understand the difference between a population and a sample , and identify the target population of your research.

• The population is the entire group that you want to draw conclusions about.
• The sample is the specific group of individuals that you will collect data from.

The population can be defined in terms of geographical location, age, income, or many other characteristics.

It is important to carefully define your target population according to the purpose and practicalities of your project.

If the population is very large, demographically mixed, and geographically dispersed, it might be difficult to gain access to a representative sample. A lack of a representative sample affects the validity of your results, and can lead to several research biases , particularly sampling bias .

Sampling frame

The sampling frame is the actual list of individuals that the sample will be drawn from. Ideally, it should include the entire target population (and nobody who is not part of that population).

Sample size

The number of individuals you should include in your sample depends on various factors, including the size and variability of the population and your research design. There are different sample size calculators and formulas depending on what you want to achieve with statistical analysis .

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Probability sampling means that every member of the population has a chance of being selected. It is mainly used in quantitative research . If you want to produce results that are representative of the whole population, probability sampling techniques are the most valid choice.

There are four main types of probability sample.

1. Simple random sampling

In a simple random sample, every member of the population has an equal chance of being selected. Your sampling frame should include the whole population.

To conduct this type of sampling, you can use tools like random number generators or other techniques that are based entirely on chance.

2. Systematic sampling

Systematic sampling is similar to simple random sampling, but it is usually slightly easier to conduct. Every member of the population is listed with a number, but instead of randomly generating numbers, individuals are chosen at regular intervals.

If you use this technique, it is important to make sure that there is no hidden pattern in the list that might skew the sample. For example, if the HR database groups employees by team, and team members are listed in order of seniority, there is a risk that your interval might skip over people in junior roles, resulting in a sample that is skewed towards senior employees.

3. Stratified sampling

Stratified sampling involves dividing the population into subpopulations that may differ in important ways. It allows you draw more precise conclusions by ensuring that every subgroup is properly represented in the sample.

To use this sampling method, you divide the population into subgroups (called strata) based on the relevant characteristic (e.g., gender identity, age range, income bracket, job role).

Based on the overall proportions of the population, you calculate how many people should be sampled from each subgroup. Then you use random or systematic sampling to select a sample from each subgroup.

4. Cluster sampling

Cluster sampling also involves dividing the population into subgroups, but each subgroup should have similar characteristics to the whole sample. Instead of sampling individuals from each subgroup, you randomly select entire subgroups.

If it is practically possible, you might include every individual from each sampled cluster. If the clusters themselves are large, you can also sample individuals from within each cluster using one of the techniques above. This is called multistage sampling .

This method is good for dealing with large and dispersed populations, but there is more risk of error in the sample, as there could be substantial differences between clusters. It’s difficult to guarantee that the sampled clusters are really representative of the whole population.

In a non-probability sample, individuals are selected based on non-random criteria, and not every individual has a chance of being included.

This type of sample is easier and cheaper to access, but it has a higher risk of sampling bias . That means the inferences you can make about the population are weaker than with probability samples, and your conclusions may be more limited. If you use a non-probability sample, you should still aim to make it as representative of the population as possible.

Non-probability sampling techniques are often used in exploratory and qualitative research . In these types of research, the aim is not to test a hypothesis about a broad population, but to develop an initial understanding of a small or under-researched population.

1. Convenience sampling

A convenience sample simply includes the individuals who happen to be most accessible to the researcher.

This is an easy and inexpensive way to gather initial data, but there is no way to tell if the sample is representative of the population, so it can’t produce generalizable results. Convenience samples are at risk for both sampling bias and selection bias .

2. Voluntary response sampling

Similar to a convenience sample, a voluntary response sample is mainly based on ease of access. Instead of the researcher choosing participants and directly contacting them, people volunteer themselves (e.g. by responding to a public online survey).

Voluntary response samples are always at least somewhat biased , as some people will inherently be more likely to volunteer than others, leading to self-selection bias .

3. Purposive sampling

This type of sampling, also known as judgement sampling, involves the researcher using their expertise to select a sample that is most useful to the purposes of the research.

It is often used in qualitative research , where the researcher wants to gain detailed knowledge about a specific phenomenon rather than make statistical inferences, or where the population is very small and specific. An effective purposive sample must have clear criteria and rationale for inclusion. Always make sure to describe your inclusion and exclusion criteria and beware of observer bias affecting your arguments.

4. Snowball sampling

If the population is hard to access, snowball sampling can be used to recruit participants via other participants. The number of people you have access to “snowballs” as you get in contact with more people. The downside here is also representativeness, as you have no way of knowing how representative your sample is due to the reliance on participants recruiting others. This can lead to sampling bias .

5. Quota sampling

Quota sampling relies on the non-random selection of a predetermined number or proportion of units. This is called a quota.

You first divide the population into mutually exclusive subgroups (called strata) and then recruit sample units until you reach your quota. These units share specific characteristics, determined by you prior to forming your strata. The aim of quota sampling is to control what or who makes up your sample.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

Samples are used to make inferences about populations . Samples are easier to collect data from because they are practical, cost-effective, convenient, and manageable.

Probability sampling means that every member of the target population has a known chance of being included in the sample.

Probability sampling methods include simple random sampling , systematic sampling , stratified sampling , and cluster sampling .

In non-probability sampling , the sample is selected based on non-random criteria, and not every member of the population has a chance of being included.

Common non-probability sampling methods include convenience sampling , voluntary response sampling, purposive sampling , snowball sampling, and quota sampling .

In multistage sampling , or multistage cluster sampling, you draw a sample from a population using smaller and smaller groups at each stage.

This method is often used to collect data from a large, geographically spread group of people in national surveys, for example. You take advantage of hierarchical groupings (e.g., from state to city to neighborhood) to create a sample that’s less expensive and time-consuming to collect data from.

Sampling bias occurs when some members of a population are systematically more likely to be selected in a sample than others.

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Planning your PhD research: A 3-year PhD timeline example

Planning out a PhD trajectory can be overwhelming. Example PhD timelines can make the task easier and inspire. The following PhD timeline example describes the process and milestones of completing a PhD within 3 years.

Elements to include in a 3-year PhD timeline

The example scenario: completing a phd in 3 years, example: planning year 1 of a 3-year phd, example: planning year 2 of a 3-year phd, example: planning year 3 of a 3-year phd, example of a 3 year phd gantt chart timeline, final reflection.

Every successful PhD project begins with a proper plan. Even if there is a high chance that not everything will work out as planned. Having a well-established timeline will keep your work on track.

What to include in a 3-year PhD timeline depends on the unique characteristics of a PhD project, specific university requirements, agreements with the supervisor/s and the PhD student’s career ambitions.

For instance, some PhD students write a monograph while others complete a PhD based on several journal publications. Both monographs and cumulative dissertations have advantages and disadvantages , and not all universities allow both formats. The thesis type influences the PhD timeline.

Furthermore, PhD students ideally engage in several different activities throughout a PhD trajectory, which link to their career objectives. Regardless of whether they want to pursue a career within or outside of academia. PhD students should create an all-round profile to increase their future chances in the labour market. Think, for example, of activities such as organising a seminar, engaging in public outreach or showcasing leadership in a small grant application.

The most common elements included in a 3-year PhD timeline are the following:

• Data collection (fieldwork, experiments, etc.)
• Data analysis
• Writing of different chapters, or a plan for journal publication
• Conferences
• Additional activities

The whole process is described in more detail in my post on how to develop an awesome PhD timeline step-by-step .

Many (starting) PhD students look for examples of how to plan a PhD in 3 years. Therefore, let’s look at an example scenario of a fictional PhD student. Let’s call her Maria.

Maria is doing a PhD in Social Sciences at a university where it is customary to write a cumulative dissertation, meaning a PhD thesis based on journal publications. Maria’s university regulations require her to write four articles as part of her PhD. In order to graduate, one article has to be published in an international peer-reviewed journal. The other three have to be submitted.

Furthermore, Maria’s cumulative dissertation needs an introduction and conclusion chapter which frame the four individual journal articles, which form the thesis chapters.

In order to complete her PhD programme, Maria also needs to complete coursework and earn 15 credits, or ECTS in her case.

Maria likes the idea of doing a postdoc after her graduation. However, she is aware that the academic job market is tough and therefore wants to keep her options open. She could, for instance, imagine to work for a community or non-profit organisation. Therefore, she wants to place emphasis on collaborating with a community organisation during her PhD.

You may also like: Creating awesome Gantt charts for your PhD timeline

Most PhD students start their first year with a rough idea, but not a well-worked out plan and timeline. Therefore, they usually begin with working on a more elaborate research proposal in the first months of their PhD. This is also the case for our example PhD student Maria.

• Months 1-4: Maria works on a detailed research proposal, defines her research methodology and breaks down her thesis into concrete tasks.
• Month 5 : Maria follows a short intensive course in academic writing to improve her writing skills.
• Months 5-10: Maria works on her first journal paper, which is based on an extensive literature review of her research topic. At the end of Month 10, she submits the manuscript. At the same time, she follows a course connected to her research topic.
• Months 11-12: Maria does her data collection.

Maria completed her first round of data collection according to plan, and starts the second year of her PhD with a lot of material. In her second year, she will focus on turning this data into two journal articles.

• Months 1-2: Maria works on her data analysis.
• Months 3-7: Maria works on her second journal paper.
• Month 7: Maria attends her first conference, and presents the results of her literature-review paper.
• Month 8: Maria received ‘major revisions’ on her first manuscript submission, and implements the changes in Month 8 before resubmitting her first journal paper for publication.
• Month 9: Maria follows a course on research valorisation to learn strategies to increase the societal impact of her thesis.
• Months 9-12: Maria works on her third journal paper. She uses the same data that she collected for the previous paper, which is why she is able to complete the third manuscript a bit faster than the previous one.

Time flies, and Maria finds herself in the last year of her PhD. There is still a lot of work to be done, but she sticks to the plan and does her best to complete her PhD.

• Month 1: Maria starts a second round of data collection, this time in collaboration with a community organisation. Together, they develop and host several focus groups with Maria’s target audience.
• Month 2: Maria starts to analyse the material of the focus group and develops the argumentation for her fourth journal paper.
• Month 3: Maria presents the results of her second journal paper at an international conference. Furthermore, she helps out her supervisor with a grant application. They apply for funding to run a small project that is thematically connected to her PhD.
• Months 4-9: Maria writes her fourth and final journal article that is required for her PhD.
• Month 10: Maria writes her thesis introduction .
• Month 11: Maria works on her thesis conclusion.
• Month 12 : Maria works on the final edits and proof-reading of her thesis before submitting it.

Combining the 3-year planning for our example PhD student Maria, it results in the following PhD timeline:

Creating these PhD timelines, also called Gantt charts, is easy. You can find instructions and templates here.

Completing a PhD in 3 years is not an easy task. The example of our fictional PhD student Maria shows how packed her timeline is, and how little time there is for things to go wrong.

In fact, in real life, many PhD students spend four years full-time to complete a PhD based on four papers, instead of three. Some extend their studies even longer.

Furthermore, plan in some time for thesis editing, which is a legitimate practice and can bring your writing to the next level. Finding a reputable thesis editor can be challenging, so make sure you make an informed choice.

Finishing a PhD in 3 years is not impossible, but it surely is not easy. So be kind to yourself if things don’t work out entirely as planned, and make use of all the help you can get.

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10 amazing benefits of getting a PhD later in life

How to prepare your viva opening speech, related articles.

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How to create a work plan (examples and template)

As a kid, I watched John “Hannibal” Smith from The A-Team often say, “I love when a plan comes together.” The four action heroes appeared to be able to create elaborate plans to solve their challenge of the week and execute them on the spot. Well, I never thought that as an adult product manager, I would be saying the same words.

In practice, my planning comes with way more hurdles than the TV action heroes faced. However, both the A-team and the product teams still need a work plan to achieve success!

What is a work plan?

Now, what is a work plan in product management, especially in an agile product management world? A work plan is a high-level document that helps you gather any initiative’s goals, timelines, risks, and other aspects for a future update. It helps you coordinate and monitor these details to make sure the work is going accordingly.

“Wait a minute!” – you may shout – “That sounds like project management!”

Well, I need to agree here. But, this work plan is not there for you to get timelines from your team and monitor them with a stopwatch.

This is a communication tool. This is how you help yourself, your team members, and your stakeholders get a common understanding of upcoming challenges. When you work with your team on the plan, the right people get their chance to voice their concerns and ideas.

Also, in the product management world, it is not about setting everything in stone. It’s about being able to create clarity where possible (say, the problem you want to fix with the new initiative) and identify the moving elements (i.e., where will the development go once a certain aspect is A/B tested).

What are the 3 steps of a work plan?

To understand a tool work plan for a product manager, let’s divide it into three stages:

• Preparation

Let’s take a look at them one by one!

1. Preparation

Every change starts with an idea. However, the idea is simply not enough to convince anyone that it’s a good one. Thus, here comes the bulk of, often lonely, work of a product manager. In this stage, you should:

Create an initiative document

Establish a user story or similar, verify how the idea fits the current product fundamentals and focus, choose the product metrics to address, perform research and early product discovery, identify the internal stakeholders.

• Create a ticket in your product management software

Plan a project kick-off meeting

This is not your Jira ticket — not yet. This will be your work plan’s main document, where you will gather all the notes and key information needed. For now, this can be a private document, but be aware that you should be able to share it with anyone at any point.

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It’s not your notepad, but a document collecting all aspects of the initiative. This document will later be super handy when crafting any other communication or updates connected to this initiative. Be sure to record the information you collect from the following points in this document.

While the book approach dictates that you start with a problem to solve, let’s face it, we usually work on solutions. However, a solution looking for a problem is most likely nothing more than a waste of time.

Thus, provide the right context for your initiative, the intended user group and the value the idea delivers. This will set a solid foundation for the rest of the work you are going to do. Thus, start by filling in this sentence:

As a [description of user], I want [functionality], so that [benefit].

Every product idea needs a good context. It has to fit the current product vision and strategy, and address current goals and OKRs . Without it, it will be a distraction from the current direction. Doesn’t mean the idea is bad, but timing is also important. You can still plan it! Duh! A well-crafted work plan can be a basis to define the next period OKRs!

If you verified the context, it’s time to link your solution to your product metrics and goals. It’s OK to have ideas that don’t address any metrics directly, but build product quality to increase long-term retention. However, it will be far harder to sell such work to your stakeholders.

No worries, you just need to adjust your pitching strategy. Maybe the idea is worth pursuing as a side project or hackathon initiative. Anyway, make sure your flagship initiatives at least plan to improve some of the key product metrics. It’s best to also include that in a written product change hypothesis.

While it’s not always possible to get all the information you need, try to research the answers to the following questions:

• Was this solution tested in other areas of the product or by competitors or semi-competitors? For example, the “stories” feature worked out great on Instagram, but it was a disaster on Skype
• Is there a science paper that evaluated the idea in a controlled environment? For example, have scientists evaluated the impact of gamification on users’ behavior?
• Are you able to estimate the impact of the initiative on product metrics or are you performing a pure “guesstimation”?
• Which user personas will benefit from the update?
• What are the other ways to solve the same problem (this is just a sanity check step to see if you can come up with even a better idea)

From developers, researchers, business analysts, and other product managers to high-level executives. This will help you down the road in making sure all the right parties are up to speed!

Create a ticket in your product management software (most likely Jira)

At this stage, you should be able to create your epic and copy-paste relevant information from the initiative’s document. It’s a formality at this stage, as you basically have everything to create an exceptional epic.

Once you did all that due diligence, it’s time to call in a meeting and get all the needed stakeholders in one room to get everyone on the same page. However, the main goal will be to create a more tangible product plan and identify the unknown risks and gaps that could prevent the idea from becoming a reality.

2. Kick off

Now that all the product manager’s preparation work is completed, the kick-off meeting can be treated as sort of a handoff meeting. While the PM doesn’t move on to researching another idea right after this meeting, the responsibility of making it happen is essentially passed on to the development team. Or at least it should be like this.

If you are a product manager that also performs the project manager/development team leader duties, you will have to see all the work through until the end.

Anyway, regardless of your role, here are the elements you need to tackle during the kick-off meeting. Remember to record everything in the initiative document! The meeting here should consist of the following elements:

• Product manager presenting their findings
• Discussion on those findings to make sure all meeting members are on the same page
• Planning out the high-level technical elements needed plus the timeline
• Agreeing on the potential risks and brainstorming solutions to mitigate those
• Seeking an MVP that can be implemented to test the product hypothesis as early as possible. This can mean different things, from a product discovery process where a mock of the update is presented to random people, to a full-blown working update with perhaps only a selected pool of intended functionalities
• Drawing expected timeline and check-in points
• Finalize the design – Mart – by the 14th of August
• Build the API – Donna – by end of August
• Prepare the tracking specification – Lucas – early August
• Clearing the necessary architecture changes with team Astro – Monica – ASAP
• Expected MVP release – Jane – second sprint of September
• Agreeing on the requirements. You can come up with a draft of the update’s requirements for the Jira ticket, but it’s best to run them with the stakeholders and include their input
• Final Q&A session to make sure everyone leaves the room at least in peace, if not optimistic and confident

Now, as I mentioned earlier, what happens next depends on how your organization defines your product manager role. This might be a point after which you only check in with the team to share your opinion on the deliverables and collect status updates for your communication. However, you may also be the person who needs to change this into a full-on project management role, with full oversight, daily reports, and addressing any hurdles the team should face.

Sidenote: honestly, if you are closer to the second possibility, please consider changing employers. You won’t be growing as a product manager if project management takes the bulk of your job.

Assuming you work in an agile organization, the plan is now being realized and adjusted on the fly based on what will happen. The timeline can be pushed back for multiple reasons, but that’s OK.

Timely delivery is not about setting dates and deliverable outcomes in stone — it’s about using agile scrum to deliver the optimal value to the user and knowing how to do it transparently. It’s a neat compromise between chaotic-on-the-fly decision-making and a complex predefined structure project execution plan as presented by, i.e., PRINCE2 framework.

As you are, hopefully, an agile product manager, let’s look at the final stage of the plan:

3. Evaluation

This is a bit fuzzy part where you are meant to reflect on how the plan is coming together. While you will be making your decisions based on the MVP and following releases’ performance, it will be really impressive if you are already prepared for certain outcomes.

Try to answer the following questions:

• What happens next after the MVP is successful (or not)?
• What parts of a successful MVP should be improved first and foremost?
• Does the result of this initiative impact your roadmap going forward? How? (i.e., when your experiment is done as a small side project, but is so successful it promises way better results than other roadmap items planned)
• Can other parts of the product/company have any stake in this update? (i.e., If a new feature introduced in the mobile app is successful, perhaps the web browser version of the product should follow suit?)
• Are there any opportunities this update unlocks?

Generally, sit down and speculate what you should do next. Don’t wait until the update is released to do that, as that will be a waste of time. If you find a successful result, you should pursue it immediately – every day you don’t build on your previous success is a day lost!

Let’s make it easy for you: Template with an example

Hope you liked the framework above and would like to give it a go! If so, this article comes with both a work plan template and a filled-in example of a work plan for you to use.

I hope you will use it and it will be easier for you to deliver your next initiative to the kick-off meeting stage. Good luck!

Conclusion: “I love when a plan comes together”

I believe some of you were skeptical when you saw the beginnings of this article. A work plan for an agile product manager does seem to contradict the basic foundations of how the role should be executed.

I hope however that now you see that “agile” doesn’t mean “without a plan.” On the contrary, being agile is not about working without one — it’s about having a solid understanding and foundations to be able to adjust the plan on the go while maintaining the right vision and ability to still attain any pursued goal.

I hope this article will help you achieve those!

Dr. Bart Jaworski, Senior Product Manager at Stepstone, ex-Microsoft

Follow me on LinkedIn!

Check out my product management resources: drbartpm.com

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FREE 4+ Research Proposal Action Plan Samples in PDF

It is no doubt that research is and has always been the hallmark of progress. Every global milestone humanity has achieved, always began with research. It enhances society by advancing knowledge through development of scientific theories, ideas, and concepts. To simply put it, research is the process of discovering knowledge , from the development of new concepts to the advancement of existing ones. It’s how the world moves forward, through a series of research leading to new discoveries. For the betterment and convenience of us all.

Research Proposal Action Plan

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It is true that research can be done by anyone from any field. You could do research right now, if you wish. But research is usually done to broaden knowledge in a specific field. Physical , biological, and social worlds. It can range from wanting to know why certain materials work, to asking certain people about their political views. There are a lot of things that’s left to be discovered in our world, and research is the way to explore those things. So yeah, you could definitely do research right now if you want. It’s for the curious mind, and if you’re curious and brave enough, then why not venture into it. First things first though, you’ll need to have a research proposal . Research proposals can help you gather support from the academe that you belong to and helps make sure that your research goes the way you want it to. And to make sure that your proposal is well done and is actually effective, you’ll need a certain document to help you. Yes it’s a whole process. That particular document is what is known as a research proposal action plan . Research proposal action plans mitigate noise, making sure that the focus of the research stays on the purpose of the research. To learn more about a research action plan and how it’s structured, and just to have a better idea of what they are, check out these research action plan samples listed below. You can then use these further as a guide or even as a template if you want to create your own action plan for your research.

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Basically, action plans are documents that contain detailed and strategic outlines for any sort of project and project implementation. It can be used for the completion of any project in all sorts of scale, shape, and size. If you want your project to reach the goals you’ve set pre-emptively, then developing an action plan is the best step you can take.

A research proposal action plan is an example of such document. It’s a written document that enumerate and describe the steps that the researchers take to make sure that the research proposal and even the research itself goes the way that they want it to. The document is prepared prior to the development of the research proposal to establish steps and guidelines that the researchers have to follow. It’s an action plan, so it dictates what action the researchers should take and do for the fulfillment of not just the proposal, but also for the research itself.

It may seem like a fairly easy task, to develop a research action plan. But you’d be right not to underestimate the writing process. There is significant challenge in getting to know what you want to see as an end result and to explain and visualize it concisely for your team members and the panel that you will be presenting it to to understand. The fate of your research and your proposal rests upon this document, so at least try to make sure to do it well. There are key factors that you have to consider when writing your own research action plan, these factors will be discussed in more detail below.

• Clarity You and your team members have to be clear on the purpose of your research. Knowing your goal is an invaluable piece of information that should keep you on the right track in achieving it. An action plan should prompt the individual to reflect upon the things that they want to achieve. A poorly set goal makes it hard to reach, and there is no definitive way to know whether progress has been made or not.
• Desire You’ve set your goal, now you have to have a burning desire to achieve it. Reflect and improve upon your methods to reach your goals. A desire set in stone will lead you to take and follow through the steps in your research proposal action plan.
• Support Us human beings are social creatures. We constantly look for interaction and validation from others. Although we don’t show it, but we constantly yearn for it. It doesn’t even have to be intimate. Any casual interaction will do. Teams, organizations, businesses, these are living organisms that constantly change. Filled with life brought by people’s constant and countless interactions in the environment. People require help from others, and it’s the same thing with working on a team. A good support system with each other can drastically make things easier and can even give you the motivation you need to push forward.
• Action Now that we’ve identified the what, why, and who, it’s time to deal with the how. What actions will the team be willing to take and how will it adapt to different circumstances? Try and think about going through your action plan. Are the actions tangible? Doable? What sacrifices are you willing to make to ensure your success? These are the sort of questions that you have to ask yourself, and ultimately reflect on your action plan. Think about anything and everything, take every possibility into account. An action plan should keep your prepared, should tell you what to do.

A training plan is a document that can be used to communicate to the management and other departments several important details of a proposed training program. An authorized training plan should give the team for proper resource management and development, and of course, program implementation.

Specific. Measurable. Achievable. Realistic. Timed.

• Background and rationale
• Research questions
• Research methodology
• Time & work schedule
• Bibliography

The main purpose of a research is to further find out what is known, what is unknown, and what we can develop further. Through this professionals can develop new theories, ideas, and products that ultimately shape our society and our everyday lives. That’s how heavy research is, and that’s why it takes such a long process to make sure that it goes the right way.

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