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Project Design in Project Management: A Quick Guide

The project design phase is the first step when planning a project. It sets the stage for the project initiation and project planning phase and important documents like the project charter and project plan.

What Is Project Design?

Project design is a brainstorming process where the project management team starts thinking about the project from a high-level perspective, outlining goals, methodologies, resources and success criteria to establish a project approach that’ll be presented to stakeholders to then begin with the project initiation and project planning phases. In this project stage, the decisions about how to manage and govern are made.

After the project design phase, a project proposal, project charter and a project plan can be created. These project documents will then be used to manage the execution phase of the project life cycle.

The thinking that goes on during the project design, however, doesn’t focus on details as much as it works on a higher level in terms of managing the project. Project planning software can help organize both the high-level strategy and the specific details of a project design.

ProjectManager , for instance, has Gantt charts for making detailed schedules, but also kanban boards for easy collaboration for the strategic aspect of project design. Manage your strategy, plan, schedule, execution and reporting in one easy-to-use project management software. Try it for free today.

What Is the Purpose of the Project Design Process?

The project design defines the overall project methodology that’ll be used and an overview of the project. It describes the major deliverables , products or features that will be completed. The project design also roughly estimates the budget and outlines how to monitor and evaluate progress. There can be more than one design presented to stakeholders, who can then choose which they think best suits their needs.

Why Is the Project Design Phase Important?

Project design is a major first step toward a successful project. A project design is a strategic organization of ideas, materials and processes for the purpose of achieving a goal. Project managers rely on a good design to avoid pitfalls and provide parameters to maintain crucial aspects of the project, like the schedule and the budget.

Project Design Process: How to Design a Project Step-By-Step

There are steps to take for defining project designs and developing an implementation strategy, and they’re the most important steps in a project. Therefore, you want to involve your team and stakeholders in the process to ensure you’re covering all the bases. Take the time to complete this stage thoroughly.

1. Define Your Project Vision

What’s your vision for the project? This isn’t some far-fetched hope, but a vision statement, which envisions a problem that needs resolution. That means clarifying the reason for the project. The vision statement is a formal document that states the project’s potential. It’s presented to stakeholders to show the viability of the project and its benefits.

It isn’t a long, detailed document. You can have a short, idealistic vision in terms of the outcome of the project; after all, this is how you sell the project. So, paint a picture of the project’s success, and place it in a larger context.

2. State the Problem Your Project Will Solve

To support that vision document, you need to identify a problem that needs solving. A needs assessment is often required, so you can see the obstacles the business is encountering. This aligns the problem you’re addressing with the organization and its strategy. It’ll also provide you with the necessary data to design an optimal solution for the problem.

To begin, what information are you gathering? What sources are there for that information, and how will you then gather the information? Next, analyze and determine the problems that your project is being created to resolve. Collect those results in a document.

3. Estimate the Project Resources That’ll Be Needed

Next, you need to recognize the necessary resources to get the project done. Resources are anything from people to equipment to the facilities necessary to complete the project successfully.

A good way to determine the resources is the same way journalists approach a news story, with the five W’s: who, what, where, when and why. Who do you need to execute the project, what resource management tools are required, where will the work be done, when will the project start and end and why are these resources needed?

4. Outline Your Project Goals

You can’t achieve your goals if you haven’t identified them first. A goal is something at the end of the project that’s both observable and measurable and it coincides with the resolution of a problem.

Create a goal statement that explains how the goals are addressed in the project. To do this well, apply the SMART method , which stands for specific, measurable, achievable, realistic and time-relevant. Each goal should be defined by these terms.

5. Structure Your Project Strategy

To achieve the project goals, there must be a strategy in place. A strategy is a process to reach the goals of the project within the project constraints , such as its resources, schedule, budget, etc. How can a strategy be created to achieve the project goals?

Consider precedent and look back on similar projects from the past and what they might have shown in terms of the pros and cons of their applied strategies. Best practices for project management are always a good foundation and building a strategy incrementally, creating a pathway to success.

6. Prepare a Contingency Plan

Any project manager knows that very few things proceed as planned. There needs to be a backup plan to respond quickly and rightly to issues as they arise in a project. Therefore, this must be included in your project design.

Look for the negative risks inherent in the project. They’re embedded in various places, such as teams, which might lack skills, have unavoidable absences, turnover, etc. Schedules can be plagued with delays. The scope might have been poorly defined. Costs are underestimated or funds dry up. Have a plan to address these risks.

7. Establish an Evaluation Plan

A project must always be under evaluation. An evaluation plan will help you monitor the project, and maybe even alert you when it starts to veer off track. Use this plan to analyze the components of the project, the outcomes and the impacts.

Outcomes are measurable changes, while impacts are how well the project goals are being achieved. Therefore, the evaluation plan is a detailed document that defines criteria to determine the project’s effectiveness and efficiency by tracking progress on all aspects of the project.

8. Estimate Costs and Create a Project Budget

The budget outlines the financial resources that drive the project. A budget will assign a cost to each of the project’s requirements. Creating a project budget means formalizing financial resources that’ll be allocated to the project. This begins with choosing a way to estimate costs, identify impacts and report on the evaluation.

9. Create a Project Proposal

All of this leads to a project proposal to explain why the project should be executed and what its benefits are. The previous steps are summarized, writing out the vision of the project and a brief description of the problem that it speaks to. Then state the goals of the project and outline the strategy that will be used to achieve those goals.

Project Design Example

Project managers use project management tools such as Gantt charts to structure their project designs. Here’s a simple project design example that shows how the project design ideas are added to this project planning tool.

For this project design example, let’s take a look at a construction project . As you can see in the image below, during the project design phase, project managers can use Gantt charts to add the major tasks and deliverables as well as build the work breakdown structure of a project to outline the phases of the project execution.

ProjectManager’s Gantt charts have two major parts. On the left side, there’s a spreadsheet that allows project managers to enter information that’ll be used to automatically generate a project timeline on the right side. This timeline won’t only show the project tasks but also milestones, task dependencies and due dates for project deliverables.

What ProjectManager Can Do to Help Your Project Design

Designing a project takes a lot of work, but using project management tools facilitates the process of creating an outline that details these various parts of the project. Besides using Gantt charts to organize your project design ideas into a project timeline, you can also use kanban boards to manage workflow using ProjectManager .

Plan Workflows With Kanban Boards

ProjectManager has a kanban feature that was created to visualize workflows. The project design phase involves collaboration among members of the project management team who will need to share files and communicate in real-time, which can be achieved with ProjectManager’s kanban boards that let project teams better communicate and structure the project design.

Track Projects With Real-Time Dashboards

ProjectManager’s real-time dashboards help project managers keep track of project costs, timelines and progress once the project design becomes a reality. These powerful dashboards can be used to track multiple projects in a portfolio. There are six key metrics that automatically update as changes are made across the software, making it easy to stay on track throughout your project or portfolio.

Only robust project management software can handle all the data needed for a good project design. ProjectManager is an online tool that has features, such as the online Gantt chart, to help schedule, as well as others, to assist with budget and resource allocation. See how it can help you by taking this free 30-day trial.

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Research Design 101

Everything You Need To Get Started (With Examples)

By: Derek Jansen (MBA) | Reviewers: Eunice Rautenbach (DTech) & Kerryn Warren (PhD) | April 2023

Navigating the world of research can be daunting, especially if you’re a first-time researcher. One concept you’re bound to run into fairly early in your research journey is that of “ research design ”. Here, we’ll guide you through the basics using practical examples , so that you can approach your research with confidence.

Overview: Research Design 101

What is research design.

• Research design types for quantitative studies
• Video explainer : quantitative research design
• Research design types for qualitative studies
• Video explainer : qualitative research design
• How to choose a research design
• Key takeaways

Research design refers to the overall plan, structure or strategy that guides a research project , from its conception to the final data analysis. A good research design serves as the blueprint for how you, as the researcher, will collect and analyse data while ensuring consistency, reliability and validity throughout your study.

Understanding different types of research designs is essential as helps ensure that your approach is suitable  given your research aims, objectives and questions , as well as the resources you have available to you. Without a clear big-picture view of how you’ll design your research, you run the risk of potentially making misaligned choices in terms of your methodology – especially your sampling , data collection and data analysis decisions.

The problem with defining research design…

One of the reasons students struggle with a clear definition of research design is because the term is used very loosely across the internet, and even within academia.

Some sources claim that the three research design types are qualitative, quantitative and mixed methods , which isn’t quite accurate (these just refer to the type of data that you’ll collect and analyse). Other sources state that research design refers to the sum of all your design choices, suggesting it’s more like a research methodology . Others run off on other less common tangents. No wonder there’s confusion!

In this article, we’ll clear up the confusion. We’ll explain the most common research design types for both qualitative and quantitative research projects, whether that is for a full dissertation or thesis, or a smaller research paper or article.

Research Design: Quantitative Studies

Quantitative research involves collecting and analysing data in a numerical form. Broadly speaking, there are four types of quantitative research designs: descriptive , correlational , experimental , and quasi-experimental . 

Descriptive Research Design

As the name suggests, descriptive research design focuses on describing existing conditions, behaviours, or characteristics by systematically gathering information without manipulating any variables. In other words, there is no intervention on the researcher’s part – only data collection.

For example, if you’re studying smartphone addiction among adolescents in your community, you could deploy a survey to a sample of teens asking them to rate their agreement with certain statements that relate to smartphone addiction. The collected data would then provide insight regarding how widespread the issue may be – in other words, it would describe the situation.

The key defining attribute of this type of research design is that it purely describes the situation . In other words, descriptive research design does not explore potential relationships between different variables or the causes that may underlie those relationships. Therefore, descriptive research is useful for generating insight into a research problem by describing its characteristics . By doing so, it can provide valuable insights and is often used as a precursor to other research design types.

Correlational Research Design

Correlational design is a popular choice for researchers aiming to identify and measure the relationship between two or more variables without manipulating them . In other words, this type of research design is useful when you want to know whether a change in one thing tends to be accompanied by a change in another thing.

For example, if you wanted to explore the relationship between exercise frequency and overall health, you could use a correlational design to help you achieve this. In this case, you might gather data on participants’ exercise habits, as well as records of their health indicators like blood pressure, heart rate, or body mass index. Thereafter, you’d use a statistical test to assess whether there’s a relationship between the two variables (exercise frequency and health).

As you can see, correlational research design is useful when you want to explore potential relationships between variables that cannot be manipulated or controlled for ethical, practical, or logistical reasons. It is particularly helpful in terms of developing predictions , and given that it doesn’t involve the manipulation of variables, it can be implemented at a large scale more easily than experimental designs (which will look at next).

That said, it’s important to keep in mind that correlational research design has limitations – most notably that it cannot be used to establish causality . In other words, correlation does not equal causation . To establish causality, you’ll need to move into the realm of experimental design, coming up next…

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Experimental Research Design

Experimental research design is used to determine if there is a causal relationship between two or more variables . With this type of research design, you, as the researcher, manipulate one variable (the independent variable) while controlling others (dependent variables). Doing so allows you to observe the effect of the former on the latter and draw conclusions about potential causality.

For example, if you wanted to measure if/how different types of fertiliser affect plant growth, you could set up several groups of plants, with each group receiving a different type of fertiliser, as well as one with no fertiliser at all. You could then measure how much each plant group grew (on average) over time and compare the results from the different groups to see which fertiliser was most effective.

Overall, experimental research design provides researchers with a powerful way to identify and measure causal relationships (and the direction of causality) between variables. However, developing a rigorous experimental design can be challenging as it’s not always easy to control all the variables in a study. This often results in smaller sample sizes , which can reduce the statistical power and generalisability of the results.

Moreover, experimental research design requires random assignment . This means that the researcher needs to assign participants to different groups or conditions in a way that each participant has an equal chance of being assigned to any group (note that this is not the same as random sampling ). Doing so helps reduce the potential for bias and confounding variables . This need for random assignment can lead to ethics-related issues . For example, withholding a potentially beneficial medical treatment from a control group may be considered unethical in certain situations.

Quasi-Experimental Research Design

Quasi-experimental research design is used when the research aims involve identifying causal relations , but one cannot (or doesn’t want to) randomly assign participants to different groups (for practical or ethical reasons). Instead, with a quasi-experimental research design, the researcher relies on existing groups or pre-existing conditions to form groups for comparison.

For example, if you were studying the effects of a new teaching method on student achievement in a particular school district, you may be unable to randomly assign students to either group and instead have to choose classes or schools that already use different teaching methods. This way, you still achieve separate groups, without having to assign participants to specific groups yourself.

Naturally, quasi-experimental research designs have limitations when compared to experimental designs. Given that participant assignment is not random, it’s more difficult to confidently establish causality between variables, and, as a researcher, you have less control over other variables that may impact findings.

All that said, quasi-experimental designs can still be valuable in research contexts where random assignment is not possible and can often be undertaken on a much larger scale than experimental research, thus increasing the statistical power of the results. What’s important is that you, as the researcher, understand the limitations of the design and conduct your quasi-experiment as rigorously as possible, paying careful attention to any potential confounding variables .

Research Design: Qualitative Studies

There are many different research design types when it comes to qualitative studies, but here we’ll narrow our focus to explore the “Big 4”. Specifically, we’ll look at phenomenological design, grounded theory design, ethnographic design, and case study design.

Phenomenological Research Design

Phenomenological design involves exploring the meaning of lived experiences and how they are perceived by individuals. This type of research design seeks to understand people’s perspectives , emotions, and behaviours in specific situations. Here, the aim for researchers is to uncover the essence of human experience without making any assumptions or imposing preconceived ideas on their subjects.

For example, you could adopt a phenomenological design to study why cancer survivors have such varied perceptions of their lives after overcoming their disease. This could be achieved by interviewing survivors and then analysing the data using a qualitative analysis method such as thematic analysis to identify commonalities and differences.

Phenomenological research design typically involves in-depth interviews or open-ended questionnaires to collect rich, detailed data about participants’ subjective experiences. This richness is one of the key strengths of phenomenological research design but, naturally, it also has limitations. These include potential biases in data collection and interpretation and the lack of generalisability of findings to broader populations.

Grounded Theory Research Design

Grounded theory (also referred to as “GT”) aims to develop theories by continuously and iteratively analysing and comparing data collected from a relatively large number of participants in a study. It takes an inductive (bottom-up) approach, with a focus on letting the data “speak for itself”, without being influenced by preexisting theories or the researcher’s preconceptions.

As an example, let’s assume your research aims involved understanding how people cope with chronic pain from a specific medical condition, with a view to developing a theory around this. In this case, grounded theory design would allow you to explore this concept thoroughly without preconceptions about what coping mechanisms might exist. You may find that some patients prefer cognitive-behavioural therapy (CBT) while others prefer to rely on herbal remedies. Based on multiple, iterative rounds of analysis, you could then develop a theory in this regard, derived directly from the data (as opposed to other preexisting theories and models).

Grounded theory typically involves collecting data through interviews or observations and then analysing it to identify patterns and themes that emerge from the data. These emerging ideas are then validated by collecting more data until a saturation point is reached (i.e., no new information can be squeezed from the data). From that base, a theory can then be developed .

As you can see, grounded theory is ideally suited to studies where the research aims involve theory generation , especially in under-researched areas. Keep in mind though that this type of research design can be quite time-intensive , given the need for multiple rounds of data collection and analysis.

Ethnographic Research Design

Ethnographic design involves observing and studying a culture-sharing group of people in their natural setting to gain insight into their behaviours, beliefs, and values. The focus here is on observing participants in their natural environment (as opposed to a controlled environment). This typically involves the researcher spending an extended period of time with the participants in their environment, carefully observing and taking field notes .

All of this is not to say that ethnographic research design relies purely on observation. On the contrary, this design typically also involves in-depth interviews to explore participants’ views, beliefs, etc. However, unobtrusive observation is a core component of the ethnographic approach.

As an example, an ethnographer may study how different communities celebrate traditional festivals or how individuals from different generations interact with technology differently. This may involve a lengthy period of observation, combined with in-depth interviews to further explore specific areas of interest that emerge as a result of the observations that the researcher has made.

As you can probably imagine, ethnographic research design has the ability to provide rich, contextually embedded insights into the socio-cultural dynamics of human behaviour within a natural, uncontrived setting. Naturally, however, it does come with its own set of challenges, including researcher bias (since the researcher can become quite immersed in the group), participant confidentiality and, predictably, ethical complexities . All of these need to be carefully managed if you choose to adopt this type of research design.

Case Study Design

With case study research design, you, as the researcher, investigate a single individual (or a single group of individuals) to gain an in-depth understanding of their experiences, behaviours or outcomes. Unlike other research designs that are aimed at larger sample sizes, case studies offer a deep dive into the specific circumstances surrounding a person, group of people, event or phenomenon, generally within a bounded setting or context .

As an example, a case study design could be used to explore the factors influencing the success of a specific small business. This would involve diving deeply into the organisation to explore and understand what makes it tick – from marketing to HR to finance. In terms of data collection, this could include interviews with staff and management, review of policy documents and financial statements, surveying customers, etc.

While the above example is focused squarely on one organisation, it’s worth noting that case study research designs can have different variation s, including single-case, multiple-case and longitudinal designs. As you can see in the example, a single-case design involves intensely examining a single entity to understand its unique characteristics and complexities. Conversely, in a multiple-case design , multiple cases are compared and contrasted to identify patterns and commonalities. Lastly, in a longitudinal case design , a single case or multiple cases are studied over an extended period of time to understand how factors develop over time.

As you can see, a case study research design is particularly useful where a deep and contextualised understanding of a specific phenomenon or issue is desired. However, this strength is also its weakness. In other words, you can’t generalise the findings from a case study to the broader population. So, keep this in mind if you’re considering going the case study route.

How To Choose A Research Design

Having worked through all of these potential research designs, you’d be forgiven for feeling a little overwhelmed and wondering, “ But how do I decide which research design to use? ”. While we could write an entire post covering that alone, here are a few factors to consider that will help you choose a suitable research design for your study.

Data type: The first determining factor is naturally the type of data you plan to be collecting – i.e., qualitative or quantitative. This may sound obvious, but we have to be clear about this – don’t try to use a quantitative research design on qualitative data (or vice versa)!

Research aim(s) and question(s): As with all methodological decisions, your research aim and research questions will heavily influence your research design. For example, if your research aims involve developing a theory from qualitative data, grounded theory would be a strong option. Similarly, if your research aims involve identifying and measuring relationships between variables, one of the experimental designs would likely be a better option.

Time: It’s essential that you consider any time constraints you have, as this will impact the type of research design you can choose. For example, if you’ve only got a month to complete your project, a lengthy design such as ethnography wouldn’t be a good fit.

Resources: Take into account the resources realistically available to you, as these need to factor into your research design choice. For example, if you require highly specialised lab equipment to execute an experimental design, you need to be sure that you’ll have access to that before you make a decision.

Keep in mind that when it comes to research, it’s important to manage your risks and play as conservatively as possible. If your entire project relies on you achieving a huge sample, having access to niche equipment or holding interviews with very difficult-to-reach participants, you’re creating risks that could kill your project. So, be sure to think through your choices carefully and make sure that you have backup plans for any existential risks. Remember that a relatively simple methodology executed well generally will typically earn better marks than a highly-complex methodology executed poorly.

Recap: Key Takeaways

We’ve covered a lot of ground here. Let’s recap by looking at the key takeaways:

• Research design refers to the overall plan, structure or strategy that guides a research project, from its conception to the final analysis of data.
• Research designs for quantitative studies include descriptive , correlational , experimental and quasi-experimenta l designs.
• Research designs for qualitative studies include phenomenological , grounded theory , ethnographic and case study designs.
• When choosing a research design, you need to consider a variety of factors, including the type of data you’ll be working with, your research aims and questions, your time and the resources available to you.

If you need a helping hand with your research design (or any other aspect of your research), check out our private coaching services .

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

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10 Comments

Is there any blog article explaining more on Case study research design? Is there a Case study write-up template? Thank you.

Thanks this was quite valuable to clarify such an important concept.

Thanks for this simplified explanations. it is quite very helpful.

This was really helpful. thanks

Thank you for your explanation. I think case study research design and the use of secondary data in researches needs to be talked about more in your videos and articles because there a lot of case studies research design tailored projects out there.

Please is there any template for a case study research design whose data type is a secondary data on your repository?

This post is very clear, comprehensive and has been very helpful to me. It has cleared the confusion I had in regard to research design and methodology.

This post is helpful, easy to understand, and deconstructs what a research design is. Thanks

how to cite this page

Thank you very much for the post. It is wonderful and has cleared many worries in my mind regarding research designs. I really appreciate .

how can I put this blog as my reference(APA style) in bibliography part?

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• The Workstream
• Project management
• Project design

A Guide to Project Design in Project Management

Browse topics.

Project managers often face challenges when managing various project components and resources. However, a well-crafted project design can simplify project management and help teams collaborate. It lays the groundwork for your ideas, resources, and deliverables, setting a clear path before the project’s wheels are even in motion. An effective project design not only streamlines your preparatory work but also paves the way for more efficient project management. By establishing a coherent project design, teams can synchronize their understanding of the project’s vision, expected outcomes, and methodologies. This guide delves into the seven pivotal steps of project design, providing insights on crafting a design with precision using tools like Jira  and Confluence .

What is project design?

Project design is the process of planning a project’s objectives, structure, tasks, and deliverables and deciding on the definition of done. Project managers execute the design process before implementation to align teams on project objectives.  Developing alternative designs is helpful for stakeholders to decide on the best execution plan. A well-managed project design can ensure stakeholder buy-in. It’s important to get stakeholder buy-in sooner than later so you avoid getting their feedback late in the process, which can cause a roadblock.

How project design works

Project design happens early on in the five project management phases as a broad blueprint before any further detailed project planning. A project design uses Gantt charts and flowcharts to provide a project overview. It explains the project’s plan, timeline, and roadmap, establishing a baseline for the team and stakeholders. A project plan template outlines the project design.

Why is project design important?

Project design ensures the viability and success of a project and helps communicate project value to stakeholders. For example, you can demonstrate to stakeholders what your plans are and set expectations for the entire project.  Some other benefits of project design include: 

• Ensuring project success: Without taking stock of a project’s potential scope and resources beforehand, you’ll build a project plan in the dark. 
• Minimizing risks: A project design will take into account potential roadblocks. That way, you can figure out ways to circumvent them ahead of time. 
• Maximizing use of resources: With a project design in place, you’ll know what resources are available and how to utilize them better. 

The design phase of a project determines the resources required and ensures efficient resource utilization.

Steps in the project design process

The seven steps in the project design process are important for Agile teams to simplify the Agile project management process and plan how the project will unfold. For effective project design, teams must follow these seven steps:

Define goals

Project goals should be clear and achievable, never exceeding team members’ abilities.  Goals should align with the project purpose and business objectives. As a project manager, you should consider whether your project’s goals add value to your company’s products and customers. The SMART system —specific, measurable, achievable, relevant, and time-bound—is a proven method for establishing goals. You can use the template in Confluence to help you. Using the template will allow your team to set specific and measurable goals.

Establish outcomes

Outline the required project outcomes and align them with your project goals. Outcomes should resolve product problems for your users, such as functionality and usability. In short, they should further improve your product. For example, an outcome can be to improve the sales flow of the product.  It’s important to note that outcomes are not deliverables or work output. Instead, outcomes determine how customers will use deliverables and their value to customers and the company.  Outlining project outcomes provides a measure of success and a definition of done. From those outcomes, you can better assess their impact.

Identify risks

Risk management is essential to project design. Your job as a project manager is to anticipate problems. You’ll need to look for potential roadblocks, such as development’s current capacity, and determine its impact on the project. Risks include stretched resources, high costs, or scope creep. For example, once you have identified scope creep as a potential risk, you can mitigate this risk by creating clear project parameters and identifying and adhering to deliverables.

Create a project strategy

The strategy is the foundation of the project plan and ensures the team reaches its goals while adhering to project constraints. You should create the strategy at the same time as the project overview to ensure precision in your execution.  To create an excellent project strategy, study similar projects and learn from them. This can help unearth common pitfalls, allowing you to plan for them. Once you have identified several potential strategies, consider the pros and cons of each and apply your research to identify the best strategy. This process is part of continuous improvement .

Set a budget

The next step is to create a budget with the information gathered in the previous steps. The project budget will depend on the required project resources. The free budget template in Jira  is extremely useful for building a project budget.  Creating a budget helps to reduce the likelihood of cost increases and misallocated resources. Stakeholders appreciate project managers who stick to a budget.

Prepare a contingency plan

It’s important to create a contingency plan for all identified risks. For example, you may need to communicate a change to your customers, especially if the change fundamentally affects the product’s primary function. That kind of change could be a risk where you lose users if you don’t communicate the change to them properly. So, your contingency plan could include onboarding and customer success training, so you’re communicating any changes to customers.

Track deliverables

A well-rounded project design includes details on project deliverables. It’s important to monitor and track the progress of deliverables during the project to ensure the team stays on target, remains within budget, and meets deadlines.  Jira  helps project managers track deliverables using Gantt charts or Kanban boards to track project progress.

How to build the best project design

To build an effective product design for your business, there are a few important things to know, such as:

• Communicate effectively. Communication and transparency are critical to project success. Jira includes advanced roadmaps to facilitate communication. These enable teams to collaborate and visualize boards, projects, and filters for insight into project design. You can use Confluence to create the documentation for your project and organize the team. 
• Involve stakeholders. Getting stakeholder buy-in as soon as possible ensures a project will meet their expectations. 
• Adapt to change. Deal with changes promptly by reviewing the contingency plan and quickly resolving issues as they arise. Jira  allows you to execute your plan by highlighting any possible roadblocks ahead of time. Jira integrates with Confluence for a seamless project management experience.

Excel in project design with Jira

Now that you understand the concept behind project design, you can use the process for your next venture. Effective project design is the foundation of a successful project, ensuring projects are delivered on time and within budget. Jira helps you build an effective project design and assists your team with understanding and sharing project goals with other team members. Use Confluence and Jira to track your project and create your documentation.

Project design: Frequently asked questions

How do you define project scope.

The project scope lays out all aspects of a project, including deadlines and deliverables. It describes the project boundaries and helps communicate the exact nature of the project to key stakeholders. The project scope becomes a document that helps team members understand the project outcomes.

What is the difference between project scope and project objectives?

Project scope is the overall outcome of the project. For example, you’ll outline all the tasks and deliverables for this particular project. Project objectives define the project outputs and what the team wants to achieve. For example, an objective might be a deliverable that increases product performance.

How does project design contribute to overall project success?

A well-designed project plan enhances efficiency, minimizes risk, and guides the project toward success. Project design brings project details together for clarity, giving the team confidence to execute the project effectively.

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Project poster template.

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Project Plan Template

Define, scope, and plan milestones for your next project.

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• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

Published on 5 May 2022 by Shona McCombes . Revised on 20 March 2023.

A research design is a strategy for answering your research question  using empirical data. Creating a research design means making decisions about:

• Your overall aims and approach
• The type of research design you’ll use
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

A well-planned research design helps ensure that your methods match your research aims and that you use the right kind of analysis for your data.

Table of contents

Step 1: consider your aims and approach, step 2: choose a type of research design, step 3: identify your population and sampling method, step 4: choose your data collection methods, step 5: plan your data collection procedures, step 6: decide on your data analysis strategies, frequently asked questions.

• Introduction

Before you can start designing your research, you should already have a clear idea of the research question you want to investigate.

There are many different ways you could go about answering this question. Your research design choices should be driven by your aims and priorities – start by thinking carefully about what you want to achieve.

The first choice you need to make is whether you’ll take a qualitative or quantitative approach.

Qualitative research designs tend to be more flexible and inductive , allowing you to adjust your approach based on what you find throughout the research process.

Quantitative research designs tend to be more fixed and deductive , with variables and hypotheses clearly defined in advance of data collection.

It’s also possible to use a mixed methods design that integrates aspects of both approaches. By combining qualitative and quantitative insights, you can gain a more complete picture of the problem you’re studying and strengthen the credibility of your conclusions.

Practical and ethical considerations when designing research

As well as scientific considerations, you need to think practically when designing your research. If your research involves people or animals, you also need to consider research ethics .

• How much time do you have to collect data and write up the research?
• Will you be able to gain access to the data you need (e.g., by travelling to a specific location or contacting specific people)?
• Do you have the necessary research skills (e.g., statistical analysis or interview techniques)?
• Will you need ethical approval ?

At each stage of the research design process, make sure that your choices are practically feasible.

Prevent plagiarism, run a free check.

Within both qualitative and quantitative approaches, there are several types of research design to choose from. Each type provides a framework for the overall shape of your research.

Types of quantitative research designs

Quantitative designs can be split into four main types. Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships, while descriptive and correlational designs allow you to measure variables and describe relationships between them.

With descriptive and correlational designs, you can get a clear picture of characteristics, trends, and relationships as they exist in the real world. However, you can’t draw conclusions about cause and effect (because correlation doesn’t imply causation ).

Experiments are the strongest way to test cause-and-effect relationships without the risk of other variables influencing the results. However, their controlled conditions may not always reflect how things work in the real world. They’re often also more difficult and expensive to implement.

Types of qualitative research designs

Qualitative designs are less strictly defined. This approach is about gaining a rich, detailed understanding of a specific context or phenomenon, and you can often be more creative and flexible in designing your research.

The table below shows some common types of qualitative design. They often have similar approaches in terms of data collection, but focus on different aspects when analysing the data.

Your research design should clearly define who or what your research will focus on, and how you’ll go about choosing your participants or subjects.

In research, a population is the entire group that you want to draw conclusions about, while a sample is the smaller group of individuals you’ll actually collect data from.

Defining the population

A population can be made up of anything you want to study – plants, animals, organisations, texts, countries, etc. In the social sciences, it most often refers to a group of people.

For example, will you focus on people from a specific demographic, region, or background? Are you interested in people with a certain job or medical condition, or users of a particular product?

The more precisely you define your population, the easier it will be to gather a representative sample.

Sampling methods

Even with a narrowly defined population, it’s rarely possible to collect data from every individual. Instead, you’ll collect data from a sample.

To select a sample, there are two main approaches: probability sampling and non-probability sampling . The sampling method you use affects how confidently you can generalise your results to the population as a whole.

Probability sampling is the most statistically valid option, but it’s often difficult to achieve unless you’re dealing with a very small and accessible population.

For practical reasons, many studies use non-probability sampling, but it’s important to be aware of the limitations and carefully consider potential biases. You should always make an effort to gather a sample that’s as representative as possible of the population.

Case selection in qualitative research

In some types of qualitative designs, sampling may not be relevant.

For example, in an ethnography or a case study, your aim is to deeply understand a specific context, not to generalise to a population. Instead of sampling, you may simply aim to collect as much data as possible about the context you are studying.

In these types of design, you still have to carefully consider your choice of case or community. You should have a clear rationale for why this particular case is suitable for answering your research question.

For example, you might choose a case study that reveals an unusual or neglected aspect of your research problem, or you might choose several very similar or very different cases in order to compare them.

Data collection methods are ways of directly measuring variables and gathering information. They allow you to gain first-hand knowledge and original insights into your research problem.

You can choose just one data collection method, or use several methods in the same study.

Survey methods

Surveys allow you to collect data about opinions, behaviours, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews.

Observation methods

Observations allow you to collect data unobtrusively, observing characteristics, behaviours, or social interactions without relying on self-reporting.

Observations may be conducted in real time, taking notes as you observe, or you might make audiovisual recordings for later analysis. They can be qualitative or quantitative.

Other methods of data collection

There are many other ways you might collect data depending on your field and topic.

If you’re not sure which methods will work best for your research design, try reading some papers in your field to see what data collection methods they used.

Secondary data

If you don’t have the time or resources to collect data from the population you’re interested in, you can also choose to use secondary data that other researchers already collected – for example, datasets from government surveys or previous studies on your topic.

With this raw data, you can do your own analysis to answer new research questions that weren’t addressed by the original study.

Using secondary data can expand the scope of your research, as you may be able to access much larger and more varied samples than you could collect yourself.

However, it also means you don’t have any control over which variables to measure or how to measure them, so the conclusions you can draw may be limited.

As well as deciding on your methods, you need to plan exactly how you’ll use these methods to collect data that’s consistent, accurate, and unbiased.

Planning systematic procedures is especially important in quantitative research, where you need to precisely define your variables and ensure your measurements are reliable and valid.

Operationalisation

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalisation means turning these fuzzy ideas into measurable indicators.

If you’re using observations , which events or actions will you count?

If you’re using surveys , which questions will you ask and what range of responses will be offered?

You may also choose to use or adapt existing materials designed to measure the concept you’re interested in – for example, questionnaires or inventories whose reliability and validity has already been established.

Reliability and validity

Reliability means your results can be consistently reproduced , while validity means that you’re actually measuring the concept you’re interested in.

For valid and reliable results, your measurement materials should be thoroughly researched and carefully designed. Plan your procedures to make sure you carry out the same steps in the same way for each participant.

If you’re developing a new questionnaire or other instrument to measure a specific concept, running a pilot study allows you to check its validity and reliability in advance.

Sampling procedures

As well as choosing an appropriate sampling method, you need a concrete plan for how you’ll actually contact and recruit your selected sample.

That means making decisions about things like:

• How many participants do you need for an adequate sample size?
• What inclusion and exclusion criteria will you use to identify eligible participants?
• How will you contact your sample – by mail, online, by phone, or in person?

If you’re using a probability sampling method, it’s important that everyone who is randomly selected actually participates in the study. How will you ensure a high response rate?

If you’re using a non-probability method, how will you avoid bias and ensure a representative sample?

Data management

It’s also important to create a data management plan for organising and storing your data.

Will you need to transcribe interviews or perform data entry for observations? You should anonymise and safeguard any sensitive data, and make sure it’s backed up regularly.

Keeping your data well organised will save time when it comes to analysing them. It can also help other researchers validate and add to your findings.

On their own, raw data can’t answer your research question. The last step of designing your research is planning how you’ll analyse the data.

Quantitative data analysis

In quantitative research, you’ll most likely use some form of statistical analysis . With statistics, you can summarise your sample data, make estimates, and test hypotheses.

Using descriptive statistics , you can summarise your sample data in terms of:

• The distribution of the data (e.g., the frequency of each score on a test)
• The central tendency of the data (e.g., the mean to describe the average score)
• The variability of the data (e.g., the standard deviation to describe how spread out the scores are)

The specific calculations you can do depend on the level of measurement of your variables.

Using inferential statistics , you can:

• Make estimates about the population based on your sample data.
• Test hypotheses about a relationship between variables.

Regression and correlation tests look for associations between two or more variables, while comparison tests (such as t tests and ANOVAs ) look for differences in the outcomes of different groups.

Your choice of statistical test depends on various aspects of your research design, including the types of variables you’re dealing with and the distribution of your data.

Qualitative data analysis

In qualitative research, your data will usually be very dense with information and ideas. Instead of summing it up in numbers, you’ll need to comb through the data in detail, interpret its meanings, identify patterns, and extract the parts that are most relevant to your research question.

Two of the most common approaches to doing this are thematic analysis and discourse analysis .

There are many other ways of analysing qualitative data depending on the aims of your research. To get a sense of potential approaches, try reading some qualitative research papers in your field.

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.

Statistical sampling allows you to test a hypothesis about the characteristics of a population. There are various sampling methods you can use to ensure that your sample is representative of the population as a whole.

Operationalisation means turning abstract conceptual ideas into measurable observations.

For example, the concept of social anxiety isn’t directly observable, but it can be operationally defined in terms of self-rating scores, behavioural avoidance of crowded places, or physical anxiety symptoms in social situations.

Before collecting data , it’s important to consider how you will operationalise the variables that you want to measure.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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Design of Research Projects

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This chapter takes its starting point in the definition of research designs that I presented in the introduction: A research design is a plan for how to carry out a research project. This plan or structure has two parts. One is the purpose of the project, in other words what knowledge we want to develop about what. I distinguish between different purposes that can govern the research interest in a research project: Theory testing, theory development, theoretical interpretation, and lastly an intervention orientation. The other element in the design has to do with how we construct data to answer our questions. Here I distinguish between variable-centred and case-centred strategies as two main forms of data construction and show how they lead to different approaches in the production and analysis of data. Used for different research purposes, these two basic strategies for data construction constitute a number of design variants discussed in the next two chapters about variable-centred designs and case designs. We can also combine the main strategies for design in various ways. Depending on the purpose, such combinations are the basis of integrated designs, comparative designs, and intervention-oriented designs, all of which are introduced in the following chapters.

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This totality of perspective on and techniques of data production is sometimes called a research methodology. This concept is used to distinguish between a complete methodology for data construction, and the technical questions belonging to the method (Gobo, 2008 ). I prefer the concept of strategy for the simple reason that it is more easily kept apart from the concept of method, and because a common use of the strategy concept is to regard strategy as an approach to realise a given goal.

Altogether, Gobo categorises six types of cognitive modes: the listening, the inquiring, the observing, the reading, the operative, and the reflective modes. He relates them to six corresponding methodologies: the discursive, the survey, the ethnographic, the documenting, the transformative, and the speculative methodologies. Except for the survey methodology, he believes that the methodologies can be used for structured as well as unstructured data. However, his philosophy is complicated and it is not easy to identify specific research designs outside his special area, which is ethnography. For instance, it is not easy to understand that the listening, inquiring, and reading ways of assessing data should lead to different methodological choices on a more fundamental level. In practice, Gobo’s detailed classifications reduce methodology to an approach for data collection.

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Project Planning for the Beginner: Research Design

• Defining a Topic
• Reviewing the Literature
• Developing a Researchable Question

Research Design

• Planning, Data, Writing and Dissemination

What Is a Research Plan?

This refers to the overall plan for your research, and will be used by you and your supervisor to indicate your intentions for your research and the method(s) you’ll use to carry it out. It includes:

• A specification of your research questions

• An outline of your proposed research methods

• A timetable for doing the work

What Is Research Design?

The term “ research design “ is usually used in reference to experimental research, and refers to the design of your experiment. However, you will also see the term “research design” used in other types of research. Below is a list of possible research designs you might encounter or adopt for your research:

• Descriptive or exploratory (e.g., case study , naturalistic observation )

• Correlational (e.g., case-control study, observational study )

• Quasi-experimental (e.g., field experiment , quasi-experiment )

• Experimental (experiment with random allocation and a control and test group )

• Review (e.g. literature review , systematic review )

• Meta-analytic (e.g. meta-analysis )

Research Design Choices

How do i match my research method to my research question.

The method(s) you use must be capable of answering the research questions you have set. Here are some things you may have to consider:

• Often questions can be answered in different ways using different methods

• You may be working with multiple methods

• Methods can answer different sorts of questions

• Questions can be answered in different ways.

The matching of method(s) to questions always matters . Some methods work better for particular sorts of questions.

If your question is a hypothesis which must be falsifiable, you can answer it using the following possible methods:

• An experimental method using statistical methods to test your hypothesis.

• Survey data (either generated by you or secondary data) using statistical methods to test your hypothesis.

If your question requires you to describe a social context and/or process, then you can answer it using the following possible methods:

• You can use data from your own surveys and/or secondary data to carry out descriptive statistics and numerical taxonomy methods for classification .

• You can use qualitative material derived from:

• Documentary research

• Qualitative interviews

• Focus groups

• Visual research

• Ethnographic methods

• Any combination of the above may be deployed.

If your question(s) require you to make causal statements about how certain things have come to be as they are, then you might consider using the following:

• You can build quantitative causal models using techniques which derive from statistical regression analysis and seeing if the models “fit” your quantitative data set.

• You can do this through building simulations .

• You can do this by using figurational methods, particularly qualitative comparative analysis , which start either with the construction of quantitative descriptions of cases from qualitative accounts of those cases, or with an existing data set which contains quantitative descriptions of cases. 

• You can combine both approaches.

If your question(s) require you to produce interpretive accounts of human social actions with a focus on the meanings actors have attached to those actions, then you might consider using the following:

• You can use documentary resources which include accounts of action(s) and the meanings actors have attached to those actions. This is a key approach in historical research.

• You can conduct qualitative interviews .

• You can hold focus groups .

• You can do this using ethnographic observation .

• You can combine any or all of above approaches.

If your question(s) are evaluative, this could mean that you have to find out if some intervention has worked, how it has worked if it has, and why it didn’t work if it didn’t. You might then consider using the following:

• Any combination of quantitative and qualitative methods which fit the data you have.

• You should always use process tracing to generate a careful historical account of the intervention and its context(s). 

Checklist: Question to Ask When Deciding On a Method

Here are seven questions you should be able to answer about the methods you have chosen for your research. 

• Does your method/do your methods fit the research question(s)?
• Do you understand how the methods relate to your methodological position?
• Do you know how to use the method(s)  ?  If not, can you learn how to use the method(s)?
• Do you have the resources you need to use the methods? For example:

• statistical software

• qualitative data analysis software

• an adequate computer

• access to secondary data sets

• audio-visual equipment

• language training

• transport You need to work through this list and add anything else that you need.

• If you are using multiple methods, do you know how you are going to combine them to carry out the research?
• If you are using multiple methods, do you know how you are going to combine the  products of using them when writing up your research? 
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• Volume 34, Issue 1
• Stages in the development of a research project: putting the idea together
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• Roger G Eston ,
• Ann V Rowlands
• School of Sport, Health and Exercise Sciences, University of Wales, Bangor LL57 2EN, Wales, United Kingdom
• Correspondence to: Dr R G Eston.

https://doi.org/10.1136/bjsm.34.1.59

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The purpose of this paper is to provide an overview of the process and stages involved in developing a research idea from its inception to realisation. It is not designed to be an all encompassing summary of the research process. It fprovides a brief guide to the most common sequence of stages involved in the development of a research idea into a viable research proposal. Useful references for further reading on important issues that are beyond the scope of this article are also provided.

Familiarity with the topic

Reviewing the literature.

Most literature searches begin with one of the many electronic databases available, using the author's name or a combination of carefully selected keywords. Individual databases are limited as to which journals are listed. Within sports and exercise science, Sports Discus and Medline are widely used. To avoid missing pertinent articles, it is suggested that a number of electronic databases are searched. Medline covers biomedically orientated journals, Sports Discus covers sport and exercise orientated sources, and the Science Citation Index covers scientific articles in general. In addition, the National Sports Medicine Institute has recently released the Sports Medicine and Related Topics (SMART) database of journal articles, which covers all aspects of sports and exercise science and medicine from 1985. Addresses for these databases on the internet are: for Medline, http://www.ncbi.nlm.nih.gov/PubMed/medline.html; for the Science Citation Index (United Kingdom higher education institutions only), http://www.bids.ac.uk/; for SMART, http://smart.nsmi.org.uk .

Most universities offering courses in sports and exercise science courses have access to Sports Discus available on a centrally accessed CD-ROM. Further relevant papers may be obtained by studying their reference lists. Papers that provide a critical review of the topic are also very useful. Many journals such as Sports Medicine specialise in reviews, although these can also be found in other mainstream journals such as the British Journal of Sports Medicine, Medicine and Science in Sports and Exercise , and Journal of Sports Sciences . If the library does not hold a particular journal, it should be possible to obtain issues through the university's interlibrary loan service, although this is expensive and may limit the number of papers obtained.

When writing a review paper or conducting a more sophisticated empirically based review, such as a meta-analysis, it is important to endeavour to obtain all papers asking a given research question, 2 whether they are published or not. Although this may not be practically possible, the researcher should be careful not to select only those papers that are easiest to obtain as this may bias the review. Hence, further steps should be taken to obtain more elusive papers and/or obtain a random selection of relevant papers. Rosenthal 3 has written a comprehensive paper on bibliographic retrieval for researchers wishing to conduct a meta-analysis.

The research hypothesis and rationale

The typical empirical journal article and research proposal commences with a brief literature review to provide the background and rationale for the research. Questions that remain unanswered, or findings that need clarifying, are often highlighted here. This leads into explicit statements about the importance and necessity of the planned research.

Once the research problem has been stated, the hypothesis is normally presented. 4 However, often, hypotheses are not stated in research papers, primarily because of authors' assumptions that the reader can implicitly determine the hypotheses being tested from the description of the purpose or statement of the problem, which is most commonly stated at the end of the introduction. 1

The expected results form the research hypothesis. For example, it may be hypothesised that the mean cholesterol levels of trained men are lower than those of sedentary men. This is a research hypothesis, as it states what the results are expected to be. Conversely, the null hypothesis often states what the researcher does not expect to be the case. Its purpose is for use in the statistical test of reliability of results. It usually, although not always, 1 states that there are no differences between treatments, or that there is no relation between variables. 4 For example, the null hypothesis for the above study may state that there is no difference between the mean cholesterol levels of trained and sedentary men. If the null hypothesis were true, any observed differences would be due to chance alone, and the statistically non-significant differences that existed in the sample would not be inferred to exist in the population. Huck and Cormier 1 (chapters 7 and 8) give a detailed description of the different forms of hypotheses and the stages of hypothesis testing.

The design of the study

A study should be designed to answer the research question being asked. A thorough evaluation of the literature can help the researcher avoid repeating design mistakes that have been made in the past. Theoretically, research studies should become better and better with time as past mistakes are rectified and studies become more and more robust. However, this is generally not the case as in reality each study is a new and novel endeavour. 5

TYPES OF STUDY

As in medical research, broadly speaking the types of study used in sports science research can be split into two groups: observational and experimental. 5

Observational design

In observational studies, the participants receive no treatment or experimental manipulation. As the name suggests, the variables of interest are recorded from the participant with no attempt to influence the variables in any way. This is a descriptive study. In observational studies, the researcher analyses the data with the aim of determining differences or relations between variables and reasons why they do or do not exist. In experimental studies, the effect of treatment or manipulation of the independent variable is examined. 5 Examples of observational studies include those that have recorded skinfold levels and other estimates of body fat, 6– 9 or the measurement of body fat and physical activity in children to assess whether there is a relation between the two. 10, 11 In the latter examples, if the participants had received an aerobic training programme to assess the effects on body fat, the independent variable in the study would have been directly manipulated and the study would be experimental in nature. 12

Experimental design

If the study has an experimental design—that is, one of the independent variables is to be manipulated, it is important to be sure that any observed changes in the dependent variable—for example, power output—are due to the experimental treatment—for example, creatine ingestion—and not due to chance, growth, learning, or other extraneous factors. For example, in a study to investigate the effects of creatine supplementation on maximal anaerobic capacity—for example, that of Worth et al 13 —a control group was necessary to separate the treatment effect from any other causes that may have improved performance. A placebo group was also included in the above study to determine whether any improvement in performance was due to the creatine supplementation or to a psychological effect. 4

There are situations in which the inclusion of a placebo is not possible. For example, in studies in which the treatment is obvious to the participant (and the investigator). An example of this is a study to examine the effects of cryotherapy on exercise induced muscle damage and the soreness that accompanies it—for example, the study of Eston and Peters. 14 In this study, the control group had no treatment for the symptoms of delayed onset muscle soreness, while the treatment group received cryotherapy by immersing the damaged arm in cool water for a limited period of time on several occasions after the eccentric exercise bout. It was not possible to have a placebo group in this study because it would be very clear to the participants what treatment they were receiving.

Repeated measures and independent groups design

Experimental studies can be conducted using separate groups for treatment, control, and placebo conditions (independent groups design) or by using the same group for all conditions (repeated measures design). The option chosen depends on the design of the experiment. There are advantages and disadvantages to each method.

REPEATED MEASURES DESIGN

In a repeated measures design, the same group is tested under all conditions. The experiment is more powerful, as the within group variability due to individual differences is removed 15 and thus the number of participants (n) in each condition can be smaller than if separate groups are required for each condition. However, the commitment required from each participant is greater. In addition, there may need to be a large gap between conditions because there may be long lasting effects that may remain during the subsequent condition. For example, if the treatment is a drug, it may remain in the participant's system after the drug course has finished. It is important that the drug is completely flushed out from the system or it may affect the results from the control or placebo condition. An example of this type of study is that of Head et al . 16 In this study, all participants received two types of β-blockers and a placebo for five days in a double blind randomised cross over design. A minimum of two days was allowed for wash out.

For many studies a repeated measures design is the best tool for tackling the research question. In a study of this nature, in which the same group of participants are exposed to several conditions, it is essential that the order in which they are exposed to the conditions is randomised. 4 This helps control for any learning effect or acclimatisation related to the testing procedure. For example, when the effects of practice in using ratings of perceived exertion (RPE) to regulate exercise intensity were assessed, healthy 17 and blind participants 18 performed bouts of exercise at randomised RPEs.

INDEPENDENT GROUPS DESIGN

If two or more independent groups are used in a study, the groups should be similar except for the factor that is being investigated. For example, if the treatment group is comprised of young men, the control group should also consist of young men, not older men or young women. Ideally the participants should only differ with respect to the variable of interest. The method of allocating participants to groups must not be affected by the characteristics of the participants, therefore each participant should have an equal chance of being in any group. Bland 5 (chapter 2) describes various methods of randomly allocating participants to groups. There are numerous examples of random assignment of participants to independent groups—for example, Doyle and Parfitt 19 and Ehrlich and Haber. 20

The equivalence of the groups with respect to various measures can be checked before treatment by simple independent groups t tests or, in the case of more than two groups, by a one way analysis of variance. Provided that there is sufficient power to detect differences that are meaningful, these tests can provide an assurance of the equivalence of the groups. Alternatively, but less commonly, the investigators may adjust the scores after treatment on the basis of differences in the groups' scores before the test by using analysis of covariance procedures—for example, the study of Eston et al . 21 In this study, scores on muscle strength after treatment were adjusted for each group using the score obtained before the test as the covariate. 22 This reduced the possibility of the scores obtained after treatment being influenced by initial group differences.

When independent groups are used, the commitment required from the participant is less. Normally, he/she will experience the procedure only once. The time taken is therefore less, as all groups may be studied simultaneously. However, the design is less powerful, as the within group variability is greater because of individual differences between groups. 15 This implies that more participants per group are necessary (in comparison with a repeated measures design) for the design to have sufficient power.

MIXED MODEL DESIGN

Perhaps the most commonly used experimental design in sports and exercise science research is the mixed model analysis of variance. This contains at least one repeated measures factor and one independent groups factor. A typical example of this would be an experimental study that compares effects before and after treatment. For example, the effects of aerobics training on peak oxygen uptake and submaximal heart rate measures in girls, 23 or the effects of a prophylactic anti-inflammatory drug on muscle soreness after strenuous eccentric exercise—for example, the study of Semark et al . 24 In both of these studies, the participants were randomly assigned to an experimental group and a control group. In the latter study, the control group received a placebo. There are many other examples of the mixed model type of study.

Blind/double blind studies

In a single blind study, participants do not know whether they are receiving the placebo or the experimental treatment. A double blind study is when the tester also does not know what treatment the participant is receiving. This strengthens the design as it also reduces the tester's potential influence on the participants' results. Hence, neither the participant's nor the tester's expectations of the effects of the treatment should affect the outcome of the study. This is obviously important in studies to determine the effects of orally administered substances on performance, such as in the study by Head et al , 16 which assessed the effects of two different types of β-blocker on exercise metabolism, or in studies to assess the effects of oral creatine supplementation on anaerobic capacity. 13

Power of the study

There is increasing criticism about the lack of statistical power of papers published in sports and exercise science and psychology journals. 25 – 27 Statistical power refers to the probability of rejecting the null hypothesis—that is, the probability that the study will lead to significant results. 26 If the null hypothesis is false but not rejected, a type 2 error is incurred. Cohen 26 suggested that a power of 0.80 is adequate when an alpha is set at 0.05—that is, the risk of type 1 error, which is rejection of the null hypothesis when it is true, is 0.05. This means that the risk of a type 2 error is 0.20.

An important consideration in relation to the statistical power of the study is the magnitude of the relation or treatment effect. This is known as the effect size. When calculated a priori, this quantifies the degree to which the investigator believes the null hypothesis to be false. 26 Each statistical test has an effect size index, which ranges from zero upwards and is scale free. 26 For example, the effect size index for a correlation is simply r ; no conversion is necessary. For assessment of the difference between two sample means, Cohen's d , Hedges g , or Glass's Δ can be used. These divide the difference between two means by a standard deviation (see Rosenthal, 28 p 35). Formulae are available for converting other test statistics—for example, t test, one way analysis of variance, and χ 2 results—into effect size indexes (see Rosenthal, 28 p 19).

To evaluate an effect size, some idea of its scale is needed. 26 Effect sizes are often described as small, medium, and large. Correlations ( r ) equalling 0.1, 0.3, and 0.5 and Cohen's d equalling 0.2, 0.5, and 0.8 equate to small, medium, and large effect sizes respectively. A table detailing the magnitude of other effect size indexes equal to small, medium, and large effect sizes is presented in Cohen. 26 The smaller the expected effect size, the larger the sample size necessary if the study is to have sufficient power to detect that effect size.

An example of a study in which the effect size may be medium, could be one to assess the effects of habitual physical activity on body fat in children—for example, that of Rowlands et al . 10 In this study, there was a moderate correlation between habitual physical activity and body fat, corresponding to a medium effect size. A large effect size may be expected in a study to assess the effects of a very low energy diet on body fat in overweight women; an example is the study of Eston et al . 29 In this study, a greatly reduced energy intake (daily intake 1695 kJ a day for six weeks) resulted in a substantial decrease in total body mass and percentage body fat.

The effect size should be estimated during the design stage of a study. This allows the determination of the sample size required to give adequate power for a given alpha. Hence, the study can be designed to ensure it has sufficient power to detect the effect of interest—that is, minimising type 2 error. A simple table detailing sample sizes necessary to detect small, medium, and large effect sizes, with a power of 0.80 and an alpha of 0.05, is presented in Cohen. 26 This table covers eight statistical tests including the difference between independent means, product-moment correlation, χ 2 , and one way analysis of variance. More detailed descriptions of power analysis and methods for determining the sample size necessary in more complex tests can be found in the texts by Cohen 30 and Stevens. 15 Power calculations can also be carried out on interactive sites on the internet—for example, http://members.aol.com/johnp71/javastat.html#Power .

When empirical data are available, this can sometimes be used to estimate the effect size for a study. However, for some research questions it is difficult to find enough information to estimate the expected effect size. Here, the expected effect size may be difficult to calculate because of the limited number of studies that provide empirical information on the topic, or there may be insufficient detail provided in the results of the relevant studies. To enable comparison of effect sizes from studies that differ in sample size, it is recommended that, in addition to reporting the test statistic and p value, the appropriate effect size index is also reported.

A review of 108 articles published in the Australian Journal of Science and Medicine in Sport (AJMS; now The Journal of Science and Medicine in Sport ) in 1996 or 1997 showed that the median power to detect small, medium, and large effect sizes was 0.10, 0.46, and 0.84 respectively. 27 No study had adequate power to detect a small effect size, 38% had adequate power to detect a medium effect size, and 75% had adequate power to detect a large effect size. It is clear that, as recently as two to three years ago, the power of studies was often not being considered at the design stage of a study, if at all.

Ethical considerations

A further consideration in the design of a study involves the ethics of the testing procedures. Some journals will not accept papers unless the study has had ethics approval from a recognised ethics committee. The ethical implications of the study are dependent on the procedures to be undertaken and the nature of the participants. For example, the British Association of Sport and Exercise Sciences (BASES) recommend that ethical clearance should be obtained before imposing unusual or severe psychological or physiological stress, administering any ergogenic aid, working with clients with disabilities, or using biopsy or venepuncture techniques. 31 The above list is not complete, and where there is any doubt cases should be looked at individually. Certain procedures that may be approved for adult participants may not be approved for children. Children are recognised as a vulnerable group with a limited comprehension capacity. 32 Consequently, they are unable legally to give consent. However, it is generally accepted that parents/guardians can give parental permission, and children who are old enough can choose whether or not to participate. Rowland 32 (chapter 5) presents a thorough discussion on the ethical aspects of research with children.

Whether the participants are children or not, the relevance of each of the measures or treatments should be considered during the design stage of the research. There should be a clear and justifiable rationale for the necessity of invasive procedures, particularly if there are valid alternative and non-invasive measures available. The frequency of the invasive procedures and the effect this has on the participants should be considered.

A further ethical consideration involves the denial of potentially beneficial treatment. For example, in an experimental design there may be one group of participants who receive the treatment, one group who receive the placebo, and one group who receive the control treatment. Those who receive the placebo will, by definition, think they are receiving the treatment. In this case, both the placebo and control group have been denied the treatment. It is important to consider the ethics of denying these groups the treatment, particularly if the treatment is expected to be beneficial. This does not arise in a repeated measures design as all participants are exposed to all treatments.

A possible solution is to offer all groups the treatment after the study. This would not be possible with some studies—for example, when the purpose of the treatment is to reduce the symptoms of delayed onset muscle soreness, because the symptoms would have dissipated by the end of the study. However, if the effects of an ergogenic aid were studied, the participants may volunteer because they believe they will have the opportunity to benefit from the ergogenic aid. In this situation it would be possible to offer the aid to all participants after the study. Ethically, this may be preferable to withholding the treatment from two thirds of the volunteers. It may also prevent participants from withdrawing from a study which they consider is providing little or no benefit to them.

This issue becomes increasingly important if the treatment is for a medical condition or for rehabilitation. This is a common scenario in clinical trials. It has been argued that withholding a potentially beneficial treatment from patients is ethically justified, as any biologically active treatment is also potentially harmful. Hence, the benefits need to be conclusively demonstrated in properly controlled trials before general administration. 5 The ethics of withholding treatment clearly depend on the type of treatment and participants involved.

Before a study is embarked on, it is necessary to ensure that the study is viable. This involves making a realistic assessment of the costs, time, and availability of the participants. If there is an application for funding, these details have to be approved by the host institution and the funding body. Costs will be related to the sample size, duration of the study, equipment needed, consumables, research assistants or other staff, travel, conference presentations, and institutional overheads.

tk;3The facilities available for conducting the research also need to be considered. If the study is laboratory based, it may be necessary to book laboratory time relatively early, as many people typically share laboratory facilities. This can only be achieved if a reasonably accurate estimate of the laboratory time needed to conduct the research is known. A pilot study can help answer these questions, identify problems, and prevent or limit methodological faults in the main study. Piloting procedures are an essential part of preparing a study. 4

Most studies within sports science and sports medicine require human participants. It is important to consider how these participants will be obtained and how representative of the relevant population they are. It may be necessary to advertise or send letters to request participation. If this is so, consideration should be given as to where to advertise, or where to obtain addresses of potential participants. For example, an advertisement in a leisure centre is likely to attract a different type of participant from a similar advertisement in a doctors surgery or outpatients clinic. The methods of obtaining participants will be largely determined by the population the sample is supposed to represent. For some studies, it is acceptable to use the most convenient sample of sports and exercise science undergraduates, but this is not appropriate for all proposed research.

Expected outcomes

During the planning stages of the study, the potential benefits should be considered. The expected outcomes are strongly linked with the literature review, hypothesis, and rationale. A useful exercise is to plot a graph of the expected outcomes for each group. This also helps to identify the most appropriate statistical analysis of the prospective data. An assessment of the expected outcomes and the potential value of these outcomes will help show whether or not the study is worth while.

We have considered some of the most important factors involved in designing a viable study that will adequately address the research question. Although we do not profess to be experts in all aspects of the above, we have learned through experience that attention to many of the above points will help to avoid frustration during the experimental process and when the study is presented for external review and subsequent presentation and publication. Good luck in your research.

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Capstone Components

12 Research Design

The story continues….

“So, how do we go about answering our research questions?” asked Harry.

Physicus explained that they will have to analyze their questions to see what types of answers are required. Knowing this will guide their decisions about how to design the needs assessment to answer their questions.

“There are two basic types of answers to research questions, quantitative and qualitative. The types of answers the questions require tell us what type of research design we need,” said Physicus.

“I guess if I ask how we decide which type of research design we should choose, you will say, ‘It depends?'” uttered Harry.

Physicus’ face brightened as he blurted out, “Absolutely not! Negative!” Physicus continued, “If the research questions are stated well, there will only be two ways in which they can be answered. The research questions are king; they make all the decisions.”

“How come?” Harry appeared confused.

“Well, let us see. Think about our first question. How many mice will Pickles attack at one time? What type of answer does this question require? It requires a numeric answer, correct?” Physicus asked.

“Yes, that is correct,” Harry said.

Physicus continued, “Good. So, does our second question also require a numeric answer?”

“The second question is also answered with a number,” replied Harry

Physicus blurted, “Correct! This means we need to use a quantitative research design!”

Physicus continued, “Now if we had research questions that could not be answered with numbers, we would need to use a qualitative research design to answer our questions with words or phrases instead.”

Harry now appeared relieved, “I get it. So in designing a research project, we simply look for a way to answer the research questions. That’s easy!”

“Well, it depends,” answered Physicus smiling.

Interpreting the Story

There are qualitative, quantitative, mixed methods, and applied research designs. Based on the research questions, the research design will be obvious. Physicus led Harry in determining their investigation would need a quantitative design, because they only needed numerical data to answer their research questions. If Harry’s questions could only be answered with words or phrases, then a qualitative design would be needed. If the friends had questions needing to be answered with numbers and phrases, then either a mixed methods or an applied research design would have been the choice.

Research Design

The Research Design explains what type of research is being conducted in the needs assessment. The writing in this heading also explains why this type of research is needed to obtain the answers to the research or guiding questions for the project. The design provides a blueprint for the methodology. Articulating the nature of the research design is critical for explaining the Methodology (see the next chapter).

There are four categories of research designs used in educational research and a variety of specific research designs in each category. The first step in determining which category to use is to identify what type of data will answer the research questions. As in our story, Harry and Physicus had research questions that required quantitative answers, so the category of their research design is quantitative.

The next step in finding the specific research design is to consider the purpose (goal) of the research project. The research design must support the purpose. In our story, Harry and Physicus need a quantitative research design that supports their goal of determining the effect of the number of mice Pickles encounters at one time on his behavior.  A causal-comparative or quasi-experimental research design is the best choice for the friends because these are specific quantitative designs used to find a cause-and-effect relationship.

Quantitative Research Designs

Quantitative research designs seek results based on statistical analyses of the collected numerical data. The primary quantitative designs used in educational research include descriptive, correlational, causal-comparative, and quasi-experimental designs. Numerical data are collected and analyzed using statistical calculations appropriate for the design. For example, analyses like mean, median, mode, range, etc. are used to describe or explain a phenomenon observed in a descriptive research design. A correlational research design uses statistics, such as correlation coefficient or regression analyses to explain how two phenomena are related. Causal-comparative and quasi-experimental designs use analyses needed to establish causal relationships, such as pre-post testing, or behavior change (like in our story).

The use of numerical data guides both the methodology and the analysis protocols. The design also guides and limits how the results are interpreted. Examples of quantitative data found in educational research include test scores, grade point averages, and dropout rates.

Qualitative Research Designs

Qualitative research designs involve obtaining verbal, perspective, and/or visual results using code-based analyses of collected data. Typical qualitative designs used in educational research include the case study, phenomenological, grounded theory, and ethnography. These designs involve exploring behaviors, perceptions/feelings, and social/cultural phenomena found in educational settings.

Qualitative designs result in a written description of the findings. Data collection strategies include observations, interviews, focus groups, surveys, and documentation reviews. The data are recorded as words, phrases, sentences, and paragraphs. Data are then grouped together to form themes. The process of grouping data to form themes is called coding. The labeled themes become the “code” used to interpret the data. The coding can be determined ahead of time before data are collected, or the coding emerges from the collected data. Data collection strategies often include media such as video and audio recordings. These recordings are transcribed into words to allow for the coding analysis.

The use of qualitative data guides both the methodology and the analysis protocols. The “squishy” nature of qualitative data (words vs. numbers) and the data coding analysis limits the interpretation and conclusions made from the results. It is important to explain the coding analysis used to provide clear reasoning for the themes and how these relate to the research questions.

Mixed Method Designs

Mixed Methods research designs are used when the research questions must be answered with results that are both quantitative and qualitative. These designs integrate the data results to arrive at conclusions. A mixed method design is used when there are greater benefits to using multiple data types, sources, and analyses. Examples of typical mixed methods design approaches in education include convergent, explanatory, exploratory, and embedded designs. Using mixed methods approaches in educational research allows the researcher to triangulate, complement, or expand understanding using multiple types of data.

The use of mixed methods data guides the methodology, analysis, and interpretation of the results. Using both qualitative (quant) and quantitative (qual) data analyses provides a clearer or more balanced picture of the results. Data are analyzed sequentially or concurrently depending on the design. While the quantitative and qualitative data are analyzed independently, the results are interpreted integratively. The findings are a synthesis of the quantitative and qualitative analyses.

Applied Research Designs

Applied research designs seek both quantitative and qualitative results to address issues of educational practice. Applied research designs include evaluation, design and development, and action research. The purposes of applied research are to identify best practices, to innovate or improve current practices or policies, to test pedagogy, and to evaluate effectiveness. The results of applied research designs provide practical solutions to problems in educational practice.

Applied designs use both theoretical and empirical data. Theoretical data are collected from published theories or other research. Empirical data are obtained by conducting a needs assessment or other data collection methods. Data analyses include both quantitative and qualitative procedures. The findings are interpreted integratively as in mixed methods approaches, and then “applied” to the problem to form a solution.

Telling the research story

The Research Design in a research project tells the story of what direction the plot of the story will take.  The writing in this heading sets the stage for the rising action of the plot in the research story. The Research Design describes the journey that is about to take place. It functions to guide the reader in understanding the type of path the story will follow. The Research Design is the overall direction of the research story and is determined before deciding on the specific steps to take in obtaining and analyzing the data.

The Research Design heading appears in Chapter 2 of a capstone project. In the capstone project, the Research Design explains the type of design used for conducting the needs assessment.

Capstone Projects in Education: Learning the Research Story Copyright © 2023 by Kimberly Chappell and Greg I. Voykhansky is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.)

Public involvement in research: assessing impact through a realist evaluation.

Chapter 2 project design and methodology.

• Introduction

This project was initially designed by a group of academic researchers and research partners drawn from the Service User and Carer Involvement in Research (SUCIR) group at the University of the West of England (UWE). An outline application was submitted in May 2010 to a joint funding call for proposals from the NIHR Health Services and Delivery Research programme (HS&DR) and INVOLVE on public involvement in research (see Research brief ). The team were invited to submit a full proposal, which was submitted in October 2010 and approved in February 2011. The HS&DR Board asked the team to consider three points relating to the economic evaluation, the involvement of children and young people and the total number of interviews to be conducted, and changes were made in response in redrafting the project protocol in July 2011 prior to submission for ethical review. A second version of the protocol was drafted once the research fellow was in post in November 2011 and recruitment of case studies had been completed. The overall design and methodology remained similar, but some minor changes were made. In particular, the research fellow appointed was an anthropologist who argued the need for more informal visits at the beginning of the study and observation of case study processes. Because of the time between application, approval and commencing the project other factors became apparent that required additional minor modifications of the planned design. In particular the timescales for public involvement in the agreed case studies were not always optimal for our planned data collection processes and timescales and, as discussed below, research governance processes led to some delays in our starting data collection in some case studies.

Research brief

The NIHR HS&DR Programme and INVOLVE jointly invited proposals in 2010 to address the gap in evidence around the impact of public involvement in research. 15 A background paper summarised the evidence then available, particularly drawing on the reviews by Staley 11 and Brett et al. 12 The invitation expressed three key aims for the research: to collect evidence on the impact of public involvement in research, to identify methods of evaluating this involvement, and to identify effective ways of involving the public in research (implementation). The call was open to a range of methodological approaches.

• Methodology

Realist evaluation framework

Our research design was based on the application of realist theory of evaluation, particularly drawing on the work of Ray Pawson, 13 , 16 , 17 which argues that social programmes (in this case public involvement in research) are driven by an underlying vision of change – a ‘programme theory’ of how the programme is supposed to work. The role of the evaluator is to compare the theory and the practice: ‘It is the realist evaluator’s task, and the added value of social science, to identify and explain the precise circumstances under which each theory holds.’ 17 Moreover, the outcomes of social programmes can be understood by identifying regularities of CMO. Thus the key question for the evaluator is ‘What works for whom in what circumstances . . . and why?’ 17 The realist approach is increasingly used in the evaluation of complex health programmes, and producing useful analyses. 18 , 19 After our study began, Staley et al. published a paper calling for the application of realist evaluation to the study of the impact of public involvement in research. 20 The development of our realist theory of public involvement in research over the course of our study is described in more detail in Chapter 3 .

Our realist theory of public involvement in research was based on the two recently published literature reviews, 11 , 12 which allowed us to identify a number of contextual factors and mechanisms that we believe were intended by policy-makers and other stakeholders in research policy to enable desired outcomes to be achieved. There has not previously been a robust testing of the underlying ‘programme theory’ of public involvement in research; our study was designed to allow an independent prospective testing of this underlying programme theory for the first time. We included an economic evaluation, designed to complement a realist evaluation design, estimating the resources used for public involvement across eight case studies.

Case study sampling

The setting for this project was within organisations hosting health and social care-related research studies (i.e. universities, NHS trusts and third-sector organisations) in the west of England. Our aim was to recruit a methodologically diverse sample of eight case studies which would have significant elements of public involvement during the period January to December 2012. There was no existing database or other source of routinely available data that enabled such upcoming studies to be identified. To meet our aims the studies needed only to be diverse, not representative, so we took the pragmatic decision to sample through our existing knowledge of studies with public involvement in the west of England and to ‘snowball’ through our existing networks, including the People and Research West of England consortium.

We developed a pro forma to identify from network stakeholders upcoming studies they were involved in or aware of with what they identified as ‘significant’ elements of public involvement. Our key inclusion criterion was evidence of some ongoing public involvement in key stages of the research process (design, recruitment, data collection, analysis, dissemination). A key exclusion criterion was that no study would be included unless both the principal investigator (PI) and at least one research partner agreed to take part. In order to identify generalisable regularities of CMO for public involvement in research, we wanted to identify a maximum variety sample of studies in terms of study type, stages of the research process and public involved. In a relatively small-scale study such as this, however, we knew we would not be able to achieve full diversity in all three dimensions. We therefore prioritised diversity of study type, as different study types can drive very different priorities for public involvement (e.g. emphasis on participant information and recruitment in clinical trials). We also prioritised including some studies involving young people and families with children because they make up a substantial minority of health service users but are underrepresented in the literature on public involvement in research. Our case studies are described in Chapter 4 .

In deciding the number of case studies to undertake we recognise that there is always a trade-off between the depth of exploration (which suggests a small number of case studies) and identification of regularities (which benefits from a larger number). There are many ways to categorise research studies (e.g. basic science vs. applied, qualitative vs. quantitative, pilot studies vs. full trials, clinical vs. epidemiological, primary vs. secondary data, action research, translational research) and we could not hope to cover the full diversity in our case studies. From previous experience of case study research, 21 we believed that eight case studies would enable us both to examine the CMO regularities in depth and to look for generalisable regularities across the case studies. This number of case studies did not enable us to examine all potential types of research study, but did enable us to include the most common, for example qualitative, mixed methods, feasibility and clinical trials. We received agreement from four PIs with appropriate funded studies taking place in the west of England at the application stage of our study, and the final four between approval and the early months of our study.

Case study data collection

The first stage of data collection involved initial mapping of the eight case studies through informal visits, encompassing observation of research settings and team meetings (where possible) and unstructured interviews.

Intelligence from the informal visits, together with previous findings from the two literature reviews and the CMO configuration, was then used to design an interview guide for semistructured interviews with case study project stakeholders. For each case study we aimed to carry out semistructured interviews with approximately five stakeholders (PIs, other researchers, research managers and two research partners) on three occasions over the course of the year of data collection, January–December 2012 (three interviews × five participants × eight case studies = 120 interviews in total during the year). Potential interviewees were identified in discussion with PIs and invitations forwarded via the PI or an administrative member of the PI’s team.

Interviews were broadly structured around our CMO hypothesis. Data collected include measurable elements (e.g. resources allocated for supporting public involvement and actual spend) and stakeholder perceptions (e.g. respective views of researchers and research partners on whether research partner contributions influenced project decisions). In addition, some of the stakeholders were given a resource log to record over 2 weeks, chosen at random, the amount of time spent contributing to a range of activities linked to public involvement in each case study. These were then costed using prices taken from published or recognised sources (see Chapter 7 ). Interviews were intended to take place at three broadly evenly spaced times over the 12-month data collection period.

In practice the number and timing of interviews varied widely across the case studies for a variety of reasons including delays in research governance approvals, illness among case study participants and research team members, delays in one research project commencing, and general logistical issues. In two case studies it was possible to carry out only two rounds of interviews rather than three, and the total number of interviews completed was 88 with 42 participants rather than the 120 with 40 participants initially envisaged. Table 1 summarises the total number of interviews (and research partner interviews) undertaken in each round across the case studies.

Total number of interviews (research partners) conducted per case study per round

Case study 4 was exceptional in that, unexpectedly, no public involvement activity took place during the year. Thus the PI, a research manager and one research partner were interviewed initially and only the research manager at the end of the year. The other case studies where numbers of interviews were relatively low were case study 6, which started much later in the year than expected and where only one research partner chose to participate, and case study 7, where the research team was relatively small, there was relatively little involvement activity, and illness prevented final interviews with the two research partners. The relatively low completion rates on the initially planned 120 interviews were not a problem in themselves, so much as a symptom of lack of involvement activity for long periods in some of the case studies.

Given the small numbers of research partners overall, and the fact that some case studies targeted particular socioeconomic or age groups, we do not believe it would be meaningful to present demographic data on these participants. Our perception was, however, that our experience echoed other reports that those members of the public who choose to get involved in research tend to have attained a higher educational level than the population as a whole.

We recognise that a few interviewees do not fall easily into the categories of researcher, research manager or research partner, but we have kept to a limited number of categories to ensure anonymity.

Each case study was intended to be conducted by pairing an academic researcher and a research partner, under the overall supervision of the PI and co-ordinated by the research fellow. In one case, for logistical reasons, the research fellow undertook the data collection on his own. In the other cases, interviews were conducted by both an academic and research partner, usually separately but on occasion interviewing together.

The first round of interviews focused particularly on understanding the context of the case study and the mechanisms for public involvement planned for the remainder of the year. The second round of interviews was ‘light touch’, intended to capture developments in public involvement since the first round and to identify members of the research team able to nominate at least one research partner per case study who could be approached to complete the resource logs for economic costing. The final round of interviews focused on capturing outcomes and learning from the year, to enable us to assess how the researchers’ initial intentions and aspirations for public involvement turned out in practice. In addition to the semistructured interviews, a flexible approach to capturing data included observation of meetings where possible and/or other group tasks directly related to public involvement, and collection of project documents related to involvement processes. Observations were carried out in case studies 1, 2, 5, 7 and 8 but were not possible in case studies 3, 4 or 6, either because of internal case study project considerations or because no public involvement activity took place during the study period.

Developing a methodology for the economic costing of public involvement

There has been minimal exploration and there is little evidence for the costs and benefits of public involvement in research. El Ansari and Andersson conclude that analysis of the costs and benefits of participatory activities should form part of an overall evaluation of public participation. 22 They state that, for participation to move forward as a field, a broader ‘set of analytical frameworks is required, which captures the richness and unique qualities of participation, [and] that recognises and values the different perspectives that led to its initial development.’ 22

Our work here is an attempt to develop an analytical framework of how to assess the economic costs of involvement in research. Planning the budget for public involvement in research at the outset is crucial. The budget needs to include all planned research involvement work to be completed by research partners (for example participating in patient advisory groups or undertaking data analysis) as well as time for academics to facilitate research partners. Two key aspects of budgeting for public involvement are the researcher and research partner relationship and contingency planning. For example, research partners may be asked to contribute their expertise to respond to problems arising during a research project, for example poor recruitment of participants to a study (there were several examples of this among our case studies). These contributions generally arose during the research process and were not foreseen.

Payment and reward issues have generally proved controversial. At our second consensus event our case study participants debated the nature of payment and reward for public involvement vigorously, revealing a wide range of strongly held views on this subject. INVOLVE has developed guidelines on payments for involvement research work to respond to these issues. A recent document outlines the issues to bear in mind in paying research partners, gives examples of payments and provides general tips about issues connected with payment and ‘payments in kind’ that need to be carefully considered by project managers. 23 A range of pay rates for different research activities connected with public involvement are mentioned in INVOLVE documents, including a flat rate payment of £19.40 per hour. 23

Our economic analysis aimed to collect data from each case study team, in order to:

• identify all activities relating to public involvement
• measure the amounts of activities using a resource log
• value or put a price on these activities using prices from published or established sources.

Identifying and measuring involvement activity

To gather data from our eight case studies for our economic analysis, we asked selected members of the case study teams (researchers, research managers and research partners) to log all the resources that were used in public involvement work/activities over a snapshot 2-week period. During the 2 weeks each person recorded/logged:

• all involvement-related activities
• length of time spent on each activity.

We asked them to include all activities (or inputs) that were undertaken as contributing to or enabling the central objective (or output) of public involvement in research. A sample log sheet for research partners on 1 day is in Appendix 1 . Our ethical approval letter stipulated that research staff within each case study were to nominate research partners to provide our data, so we were dependent on these nominations being made successfully from within our case studies, as we were not able to make direct contact with research partners.

We issued user-friendly guidance for completing our resource log, and supported respondents over a 2-week period by e-mail and providing a telephone helpline. Our guidance document for research partners to complete resource logs is in Appendix 2 . Our contact and ongoing dialogue with the academics and research partners who used our guidance and completed our resource logs enabled us to become familiar with how involvement activities were working within each case study from the point of view of both academics and their nominated research partners. These exchanges helped us gain a rounded understanding of the nature and diversity of involvement activity and the relationships and issues within each case study.

Economic valuation/costing of involvement activities

We translated the knowledge we had accrued of each case study into some working assumptions about each one. These assumptions are significant but complex, so we have detailed them in Appendices 3 – 5 .

We then used the completed 2-week resource logs to estimate involvement costs for a projected 12-month period. From there we scaled up the 12-month projected costs to the length of each case study. This enabled us to compare the actual budgeted costs from each grant with the projected costs on a like-for-like basis.

We followed a standard economic approach to treat resource use and prices separately to arrive at a cost.

For example:

Ideally the price applied should come from a published source or the next best alternative, a recognised or established source. There are illustrative examples within INVOLVE guidelines of a range of prices for different research activities connected with public involvement. In our own project we had previously paid research partners at a ‘meeting rate’ of £19.77 per hour, but early in this project it became obvious that most work was being done outside meetings, so a lower ‘research associate’ rate of £14.02 per hour was agreed. Research partners kept records of all their work for the project (including e-mails, collecting and analysing data, and writing) and submitted claim forms regularly. Our project did not have a means of costing researcher time for public involvement activities, as working alongside research partners was a continuous process during our project.

A new set of guidelines from INVOLVE to budget for involvement was incomplete at the time of our analysis, but we saw the draft document, which again gave the example of the flat rate payment of £19.40 per hour for public involvement participation, so we used this price when costing research partners’ activities for our case studies. 23

Reflective practice

Data were captured on our reflective learning on the impact of public involvement in our own study. This was done by facilitating and audio-recording short reflective sessions during team meetings on our own experiences as a project team of academic researchers and research partners working together.

Consensus events

Two consensus workshops were organised as part of our plans to develop and test a theory of public involvement in research. Initially we aimed to hold the first event prior to the first round of data collection to inform the interview schedules for this round. As the project developed, however, we realised that this would not be practical in terms of the length of time research governance approval was taking from some NHS trusts and, more importantly, that an event after the first round would be more fruitful in terms of theory development. Thus, the decision was taken to hold the first consensus event between the first and second rounds of data collection.

At the first workshop we presented an overview of our initial findings from our first round of interviews and visits in the eight case studies. The overview was in the form of 12 statements drawn from our initial mapping of the case studies. The statements identify key contextual factors and mechanisms for public involvement in research that we hypothesised were regularly linked to positive impacts on research design and delivery.

The aim of the workshop was to test these statements with case study participants and steering group members, drawing on their experiences and insights regarding public involvement in research, in order to refine or replace the statements, to inform the next phase of data collection and analysis. The workshop was limited to one afternoon in the hope that this relatively short time commitment would make it more feasible for case study participants to attend.

Nineteen participants took part in the first consensus workshop. Six of the eight case studies were represented. The intention had been that all case studies would be represented by both research staff and research partners in their projects, but it was not possible to achieve this because of participants’ other commitments and some last-minute illness.

Participants first voted electronically on the 12 statements with the choices ‘agree’, ‘disagree’ or ‘abstain’. Participants were then divided into three groups, with each group asked to look in depth at four of the statements, discuss and revise them as necessary and identify any omissions, connections or other comments. The groups then fed back to a plenary session and participated in a final discussion.

A second half-day consensus workshop was held at the end of the third round of data collection. On this occasion all eight case studies were represented with a total of 29 participants. Our emerging theory of public involvement was presented in graphic form in a set of four slides covering different aspects of CMO regularities (field of research, leadership and culture, relationships and structures of involvement). Participants were asked to discuss, amend and comment on A1 printed versions of the slides. The output of the workshop was amended slides with marginal commentary, which were further analysed by the project team and used to form the basis of the analysis of findings presented in Chapter 5 and the development of our theory of public involvement as described in Chapter 8 , Our revised theory of public involvement in research .

Case study data analysis

All interview data were transcribed and entered into an NVivo 10 database (QSR International, Warrington, Cheshire, UK). A key team discussion was how to most fully involve our research partners in the analysis of these data given that only one of them had experience of using any version of the NVivo software. The decision was made to offer NVivo 10 training to research partners but not to require this, as some did not feel confident of learning and using the programme effectively in the time available. A manual coding alternative was therefore made available. In order to make this practical, we limited the number of codes we identified to a minimum necessary to allow meaningful analysis. Those team members coding in NVivo 10 were supplied with this coding framework. Those coding manually were given a numerical code to use with transcripts and the coding was entered into the NVivo 10 database by the project research associate. For each case study, at least one transcript was coded independently by a researcher and research partner, and any divergence discussed and a joint approach agreed.

Data analysis focused on identifying CMO regularities across our case studies. From the initial CMO configuration identified in the proposal, with amendments from the first consensus workshop, a coding framework was devised with 38 codes (see Appendix 6 ) organised into six broad themes: relationships, leadership and culture, field of research, structures of involvement, resources and outcomes. We agreed as a team that the codes were the primary unit of analysis and the themes were provisional. Following coding of data, team discussion lead to the codes being reordered in terms of hypothesised CMO regularities presented in Chapter 5 . This coding framework was then validated by one academic team member not involved in the case studies, who independently undertook a framework analysis 24 of a sample of transcripts and compared her emerging framework with that drawn from the CMO configuration.

Verification of coding

Initial data were analysed by team members identifying codes within interviews. NVivo 10 software was used for data storage, retrieval, coding, analysis, memo writing and theme building across the CMO approach. This was useful in that data coding and development of findings was a collective team activity but an overall verification process was also established. One senior team member, who had not been party to the initial coding discussions, undertook a second verification to ensure consistency and rigor. The coding verification consisted of five transcripts randomly chosen across the case studies and involved two activities: naive reading and structured coding review analysis.

First, transcripts were repeatedly read, blindly from assigned codes, by the independent reviewer, with memo writing with regard to potential codes. Next, data were coded line by line, and each sentence or group of sentences was given a code using the direct meaning of the text. The second reviewer then read the transcripts with the allocated codes assigned by the initial coder. Similarities and differences were recorded. Comparing and contrasting meanings across and within transcripts through the use of memos was used. There was very high agreement found between the coder and second reviewer, which was a very positive result.

Narrative review

There were many issues of agreement in the broader level of understanding. For example, leadership emerged in all five transcripts, as did common terms such as culture (team and organisational), PI beliefs or senior lead issues. Feeling valued, trust and interpersonal relationships and other ‘emotional’-type codes were allocated by coders in all five transcripts. The second reviewer found similar patterns and this showed good overall broad agreement of coders. Power emerged in three transcripts more clearly and repeatedly but was in all five transcripts in some form.

There were minor issues of differences in coding where coders had consistently coded information in a similar way in terms of the value of public involvement. There was only one research partner who coded public involvement not just in terms of value but in terms of impact .

The thematic analysis of each code across the case studies was then used as the basis for the thematic analysis used to test our theory of public involvement in research as presented in Chapter 5 .

Research ethics and governance

The study team took the importance of ethical practice extremely seriously and considered whether it raised any substantive ethical issues. As the study was primarily qualitative and did not involve questions around particularly personal health status or behaviours, we came to the view that it was relatively low risk. However, we recognised that, in asking researchers and research partners from the same studies about what was working and not working in terms of public involvement, we could potentially be raising some sensitive interpersonal relationship issues. We therefore sought to address these issues in our study design, participant information sheets and processes for ensuring confidentiality and anonymity. Following the screening questions on the Integrated Research Application System form, our study was identified as eligible for proportionate review. Ethical approval for the study was therefore sought from the County Durham & Tees Valley Research Ethics Committee prior to the commencement of the study and approval was given with minor conditions in August 2011. An application for a substantive amendment was made in December 2011 to include observation of case study meetings, which had not been included in the original application. Approval was given in February 2012.

Research governance approval, which was sought from the three NHS trusts hosting the four NHS-based case studies, proved much more time-consuming and problematic to obtain than expected. This was particularly because of our desire to enable research partners to participate fully in data collection, which required taking them through the NHS research passport system, something that neither the university nor the NHS trust human resources departments appeared familiar with. Final research governance approval for all three NHS trusts was not obtained until late March 2012, thus delaying our planned date for data collection in some case studies by around 3 months.

Throughout the study we sought to adhere to our ethical and research governance approvals by ensuring informed consent for all participants and fully complying with all conditions of our approvals. All case study participants were sent copies of the report in draft form and invited to comment on how their data had been used and any inaccuracies or other comments on their case studies.

Public involvement in our team

Our aim throughout this project has been to model good practice in our own research while studying the impact of public involvement in our case studies. The project was developed by the SUCIR group at UWE, which had strong service user representation. One research partner co-applicant was the cochair of SUCIR. Three other research partner co-applicants had previously worked with the PI and other academic co-applicants on developing the SUCIR scheme and/or on other research projects.

The four research partner co-applicants were involved in all aspects of the project including design, data collection, analysis and dissemination. The case studies were designed to be undertaken by four subteams, each consisting of one academic researcher and one research partner working together on two case studies. The research partners also formed a separate research partner reference group meeting bimonthly.

The initial intention was for two academic co-applicants with extensive experience in working with young people to recruit and support a young persons’ advisory group to work on the two case studies where participants were young people. In the end it did not prove feasible to recruit such a group and a decision was made to develop an alternative model of involving young people in the project. Two young people, one of whom who had worked on a previous study, were recruited to join the project as research partners. Over time they came to play a similar role to the original four research partners, attending team meetings, research partner meetings and other events, and participating in data collection and analysis in their two case studies.

Research partners were involved in our team’s reflective process on what worked well and what did not work well in terms of our own processes around public involvement. A period of approximately 15 minutes was set aside at the beginning of each team meeting and research partner meeting to share reflections and learning about public involvement in our own project. Research partners have co-authored and presented our outputs at the INVOLVE conference and elsewhere, and have contributed to ensuring that this final report is as user-friendly as possible, and that our wider dissemination plans include outputs specifically designed to be accessible to a wide public. The plain English summary of this report was drafted by research partners. Chapter 6 of this report includes the synthesis, led by one research partner, of the shared reflections on public involvement in our project by both the academic researchers and the research partners.

Included under terms of UK Non-commercial Government License .

• Cite this Page Evans D, Coad J, Cottrell K, et al. Public involvement in research: assessing impact through a realist evaluation. Southampton (UK): NIHR Journals Library; 2014 Oct. (Health Services and Delivery Research, No. 2.36.) Chapter 2, Project design and methodology.
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Case studies in design: open call to study projects designed in community.

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Case Studies in Design is a new effort to create opportunities for community and design leaders to think together about ways to catalyze transformational design, planning, and place-keeping from the ground up. The goals are to learn from ambitious projects designed in community, to share knowledge and experience through dialogue and a public library of case studies, and to train ourselves for new practices of creative, collective action. We hope to build conversation among thinkers and doers in community organizations, movements, public agencies, schools, and the architecture, landscape, planning, heritage and art fields.  

Projects designed in community Case Studies in Design will support the development of 5 case studies per year, over 3 years (15 case studies prepared by 15 people in total). The aim is to study projects where design and planning helped build community power, and where community-led processes produced new forms of design agency through:

1. Deep conversation to shape the nature and time horizon of the project 2. Openness and deference to rooted leadership 3. Reciprocal (not extractive) processes, creatively designed 4. Collaboration and resource-sharing 5. New alliances to achieve leverage.

Collective writing and thinking project Case Studies in Design is coordinated by PennPraxis , a center at Weitzman School of Design at the University of Pennsylvania that is dedicated to the translation of theory into praxis (or action). Our aspiration is to bring together people from a wide geography and set of perspectives on diverse change efforts. Case study projects could range from outstanding examples of community- engaged design practice to more radical roles and results of design, planning or place-keeping. To propel this collective writing and thinking project, we are seeking applications from people who would like to research and author a case study.

We will support 5 case study writers in 2024 with a fee and expense allowance of $50,000 per author to research, write, and curate or create illustrations for a case study over a period of 8 months. Fees for community members participating in interviews, travel and other expenses will be managed by authors within the resources of the $50,000 lump sum for fee and expense. (Two people may apply to work together on a case project, sharing the fee.)

Public library and action—oriented summit PennPraxis will publish the case studies and create an online public library to disseminate them. We will organize a variety of forums from the classroom to gatherings with policymakers and funders to propagate strategies that increase community conversation and influence in the built environment. In the third year of the effort, with 15 case studies in print, we will organize a summit for community leaders, policymakers, students, practitioners, and thinkers to probe more deeply into methods and to shape lessons learned for key audiences. The aim of the summit is to create culture-shifting dialogue between disciplines, spheres of action, governments, funders and community leaders, practice and theory.

Case study method of conversation and analysis Most design “case studies” are project descriptions and images that focus on the what, not the how—the built project and perhaps its reception and performance. Designers are skilled at presenting the thesis, appearance and materials of their projects; so much so, that it can be difficult to understand whether the project outcomes and process measure up to the image for those who will live with them. The statement of the designer rarely conveys how the project was made, or the perspectives of community leaders, policymakers, scientists and other participants in the process.

Our case studies will place focus on the process—the many collaborators and contingencies—and offer insight into how communities and interdisciplinary teams have attempted to traverse the “valley of death” between ideas and implementation.

We aim to create a case study method that invites analysis and requires participants to shape their own values and strategies—active learning for would-be activist designers and community leaders interested taking on complex challenges. Similar to teaching case studies developed in policy and business schools, we are interested in supporting the creation of case studies that are intentionally open-ended presentations of a compelling situation that carries some conflict and uncertainty, with many different viewpoints included in the reporting, rather than critical essays that offer the authors’ conclusions or a how-to guide. The purpose is to cultivate the users capacity for critical analysis, bias recognition, collaboration, leadership, decision-making and action on challenging issues and projects. We believe that, done well, case research and discussion can help us develop theory from practice, and apply new theory to practice.

Applying to study and document a project Individuals (or pairs) can apply to develop a case for a fee of $50,000 by submitting the following material via this webform in a single pdf file of no more than 20MB:

1) a writing sample—past work that demonstrates capacity for narrative and analytical writing 2) CV or résumé with current contact information 3 a) 2,000 to 3,000-word description of a project that you think would make an interesting case study in community-engaged design, planning or place-keeping, and why (project images are optional) AND / OR 3 b) 500 to 2,000-word response to our outline of the intent of the case study program, including any critique that you think would make it stronger.

An applicant who does not apply with an interest in a particular project (outlined in response to 3a above) may be invited to document a project suggested by someone else. The length of your résumé is less important than your perspective on projects designed in community and capacity to enrich the knowledge base through the medium of case study writing and illistration.

Suggesting a project if you are not applying to write a case study We also welcome suggestions of exemplary projects worthy of deep analysis from colleagues in community and indigenous organizations, movements, public agencies, design and planning practices, and foundations. You can submit a project suggestion (with or without images) via this webform . Recommendations can be of any length, even just a project name and location or link. We can accept file sizes up to 20MB. Please include your contact information in case we would like to reach out to learn more.

Send any questions to [email protected] .

Timeline Applications will be reviewed as they come in until the deadline at 12pm on June 30, 2024. We aim to award all contracts by July 28, 2024, and may award some contracts for early applicants prior to that date. Authors will have 8 months to submit a completed case study (April 1, 2025), with an interim review at roughly 4 months.  

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£12 million for UK projects to address rapid AI advances

A series of breakthrough projects has been awarded £12 million to address the challenges of rapid advances in artificial intelligence (AI).

Three initiatives in the UK will look to tackle emerging concerns of generative and other forms of AI currently being built and deployed across society.

The projects cover the health and social care sectors, law enforcement and financial services.

An additional two projects, funded by UK Research and Innovation (UKRI), are looking at both how responsible AI can help drive productivity and how public voices can be amplified in the design and deployment of these technologies.

Funding has been awarded by Responsible AI UK (RAI UK) and form the pillars of its £31 million programme that will run for four years. RAI UK is led from the University of Southampton and backed by UKRI, through the UKRI Technology Missions Fund and the Engineering and Physical Sciences Research Council (EPSRC). UKRI has also committed an additional £4 million of funding to further support these initiatives.

Addressing complex socio-technical challenges

Professor of AI Gopal Ramchurn, from the University of Southampton and CEO of RAI UK, said the projects are multidisciplinary and bring together computer and social scientists, alongside other specialists.

These projects are the keystones of the Responsible AI UK programme and have been chosen because they address the most pressing challenges that society faces with the rapid advances in AI. The projects will deliver interdisciplinary research that looks to address the complex socio-technical challenges that already exist or are emerging with the use of generative AI and other forms of AI deployed in the real-world. The concerns around AI are not just for governments and industry to deal with – it is important that AI experts engage with researchers and policymakers to ensure we can better anticipate the issues that will be caused by AI.

Turning the UK into a powerhouse for future AI development

Since its launch last year, RAI UK has delivered £13 million of research funding. It is developing its own research programme to support ongoing work across major initiatives such as the AI Safety Institute, the Alan Turing Institute, and Bridging Responsible AI Divides UK.

RAI UK is supported by UKRI, the largest public funder of research and innovation, as part of government plans to turn the UK into a powerhouse for future AI development.

Dr Kedar Pandya, UKRI Technology Missions Fund Senior Responsible Owner and Executive Director at EPSRC said:

AI has great potential to drive positive impacts across both our society and economy. This £4 million of funding through the UKRI Technology Missions Fund will support projects that are considering the responsible use of AI within specific contexts. These projects showcase strong features of the responsible AI ecosystem we have within the UK and will build partnerships across a diverse set of organisations working on shared challenges. These investments complement UKRI’s £1 billion portfolio of investments in AI research and innovation, and will help strengthen public trust in AI, maximising the value of this transformative technology.

Using AI to support police and courts

The £10.5 million awarded to the keystone projects was allocated from the UKRI’s Technology Missions Fund investment at the inception of RAI UK last year.

This includes nearly £3.5 million for the PROBabLE Futures project, which is focusing on the uncertainties of using AI for law enforcement.

Its lead Professor Marion Oswald MBE, from Northumbria University, said that AI can help police and the courts to tackle digital data overload, unknown risks and increase operational efficiencies.

The key problem is that AI tools take inputs from one part of the law enforcement system but their outputs have real-world, possibly life changing, effects in another part – a miscarriage of justice is only a matter of time. Our project works alongside law enforcement and partners to develop a framework that understands the implications of uncertainty and builds confidence in future probabilistic AI, with the interests of justice and responsibility at its heart.

Limited trust in large language models

Around £3.5 million has also been awarded to a project addressing the limitations of large language models, known as LLMs, for medical and social computers.

Professor in Natural Language Processing Maria Liakata, from Queen Mary, University of London, said:

LLMs are being rapidly adopted without forethought for repercussion. For instance, UK judges are allowed to use LLMs to summarise court cases and, on the medical side, public medical question answering services are being rolled out. Our vision addresses the socio-technical limitations of LLMs that challenge their responsible and trustworthy use, particularly in medical and legal use cases.

Power back in hands of people who understand AI

The remaining £3.5 million is for the Participatory Harm Auditing Workbenches and Methodologies project led from the University of Glasgow.

According to principle investigator Dr Simone Stumpf, its aim is to maximise the potential benefits of predictive and generative AI while minimising potential for harm arising from bias and ‘hallucinations’, where AI tools present false or invented information as fact.

Our project will put auditing power back in the hands of people who best understand the potential impact in the four fields these AI systems are operating in. By the project’s conclusion, we will have developed a fully featured workbench of tools to enable people without a background in artificial intelligence to participate in audits, make informed decisions, and shape the next generation of AI.

Read more about the three AI projects and RAI UK .

Including public voices in Responsible AI

UKRI have invested an additional £4 million of support through the UKRI Technology Missions Fund to both support the keystone projects and additional satellite projects.

£750,000 has been awarded to the Digital Good Network, The Alan Turing Institute and the Ada Lovelace Institute to ensure that public voices are attended to in AI research, development and policy.

The project will synthesise, review, build and share knowledge about public views on AI and engaging diverse publics in AI research, development and policy. A key aim of the project will be to drive equity-driven approaches to AI development, amplifying the voices of underrepresented groups.

Project lead, Professor Helen Kennedy, said:

Public voices need to inform AI research, development and policy much more than they currently do. It brings together some of the best public voice thinkers and practitioners in the UK, and we’re excited to work with them to realise the project’s aims.

Understanding the Responsible AI landscape

A further £650,000 has been awarded to The Productivity Institute to gain insights on how the uptake of responsible AI can be in incentivised through incentive structures, business models and regulatory frameworks.

The institute wishes to better understand how responsible AI can drive productivity and ensure the technologies are deployed responsibly across society and enhance the UK’s prosperity.

Project lead Professor Diane Coyle said:

This is an opportunity for the UK to drive forward research globally at the intersection of technical and social science disciplines, particularly where there has been relatively little interdisciplinary research to date. We are keen to enhance connections between the research communities and businesses and policymakers.

Further information

Professor gopal ramchurn.

Professor of AI Gopal Ramchurn from the University of Southampton is principal investigator for the project and the CEO and director of RAI UK. His research at Southampton focuses on the design of responsible AI across energy and disaster management. He is also the CEO of Empati limited, a climate tech start-up, and Chairman of Sentient Sports, a Sports-AI start-up.

RAI UK is connecting UK research into responsible AI to leading research centres and institutions around the world, delivering world-leading best practices for how to design, evaluate, regulate, and operate AI-systems in ways that benefit people, society and the nation.

UKRI Technology Missions Fund

The UKRI Technology Missions Fund is designed to exploit the UK’s global leadership in transformative technologies to help solve specific problems, whilst also helping cement that leading position. Overall, UKRI is investing £250 million in Technology Missions to enable new and existing capabilities and capacity in artificial intelligence, quantum technologies and engineering biology in the years 2023 to 2025 and beyond. A further £70 million has been announced to support future telecommunications.

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Evolution Design showcases Viennese Design for research and development client PULS Vario ‘s expanded offices .

Project overview:.

• Design Firm: Evolution Design
• Client: PULS Vario
• Completion Date: 2024
• Location: Vienna, Austria
• Size: 2000 sq m (21,527 sq ft)

Designed specifically to stimulate innovative thinking, the expanded offices of PULS Vario are a showcase of a bold use of colors in workplace design.

The Vienna-based research and development company specializes in tailor-made power supply solutions and product development. In direct response to the client’s requirement to establish a dynamic and versatile work environment that would stimulate innovation, increase creativity, and improve productivity for the team of 60 engineers, Evolution Design crafted a design concept that employs a striking color palette.

The interior concept follows four stages of the innovation process directly reflected in the physical space: dialogue, create, share, and retreat , creating a highly functional workplace bathed in natural light and rich hues that fully supports employee choice.

Two spiral staircases, seamlessly connecting two floors, facilitate easy access between the different workshop zones, high-tech labs, prototyping areas, a broadcast studio, individual workspaces, and a magnetic hub for exchange and interaction designed in a genuine Viennese café style.

Thanks to a blend of soft textures, natural materials, ambient lighting, and comfortable furniture, this work environment feels more like home than a traditional office, while also incorporating innovative working methods and technology to achieve a truly hybrid workspace .

Project Planning

Just as in the project’s initial phase back in 2018, both the company’s leadership and the entire team of engineers and developers were intricately involved in the planning and execution of the project. Evolution Design places significant emphasis on fostering close collaboration with clients. In this case, the project thrived thanks to the strong partnership that was established between the design team and the entire PULS Vario team. By engaging engineers and developers and fostering open communication channels, the architects seamlessly aligned the interior concept with PULS’s objectives, culminating in a successful implementation.

Among the notable achievements was the integration of Austrian and Viennese cultural references into the interior design.

This was accomplished through a vibrant color scheme in the meeting rooms, drawing inspiration from the region’s distinctive features. For example, deep blues and subtle grays reminiscent of alpine flowers such as gentian and edelweiss were employed to evoke the tranquil ambiance of mountain landscapes. Meanwhile, the choice of poppy red was inspired by the vibrant poppy blossoms adorning fields outside Vienna. Furthermore, the crawling vines adorning the pergola in the focus work area pay homage to Austria’s rich winemaking tradition.

Project Details

The health and wellness dimensions of this project are fundamental components woven into the overarching design philosophy, aligned with the previously mentioned four stages of the innovation process: retreat, dialogue, create, and share. For instance, through the establishment of retreat areas such as dedicated spaces for focused work, employees are provided with opportunities for reflection, time for individual research and deep thinking, and concentrated work without interruptions – an intentional response to the understanding that chronic stress can lead to illness. Creating customized spaces for different activities, like breakout areas, casual meeting spots, formal meeting rooms, AV booths, and brainstorming areas, is crucial in empowering people to perform optimally in their roles.

Another standout feature of the project is the integration of branding within the interiors. Consistent with the design direction set in 2018, the interiors showcase personalized wall graphics showcasing the client’s flagship power supply products.

Key Products  

Arco Ease, chair Arco Sketch work middle, chair Bene Timba, stool Cascando Trunk, space divider / whiteboard Four Design Four Real Flake, table Four Design FourSure 77, stackable chair Freifrau Ona, chair / bar stool Johanson Design Stroll 65 with wheels, stool Johanson Design Pelikan 03 Johanson Design Peak 72, table Pedrali Blume, chair Pedrali Inox 4402, table (laminate with satin brass edge) Pedrali Modus MDL, modular sofa Sancal Tea, high chair Sedus Turn around, high chair Wilkhahn Timetable, foldable conference table

BuzziSpace BuzziPleat + Trio Globe Light, pendant light BuzziSpace BuzziDome, pendant light Flos IC Lights S1 Messing, pendant & wall lights Karboxx /Quadrifoglio Group Triangle / Lightsound, pendant light Lodes A-Tube Nano, pendant lights LZF Cuad, pendant light LZF Cosmos, standing light Prolicht Glorious, pendant light Secto Design Owalo 7000, pendant light Toscot Novecento Aste, pendant lights (light bulbs)

Camira Blazer (Cuz1N), curtain Object Carpet Poodle 1421, deep pile carpet

Room in Room

Cap Solutions Mike XL, AV Pod

Floor Finishes

Aparici Stracciatella, porcelain stoneware tiles Interface Touch & Tones 103, carpet Weitzer Parkett WP Quadra, parquett

Wall Finishes

Tarimatec Aris Cadence, wall panels Aistec Ecopanel Origami, acoustic wall panels Impact Acoustic Fon, wall panels

Overall Project Results

Overall, PULS Vario offices serve as an inspiring and nurturing work environment, dedicated to fostering an innovative mindset, building informal team spirit, and promoting the growth of this innovation-driven engineering company.

Contributors:

Leo Schulmeister, joinery

Schreinerei Ganslmeier, joinery

Trowal, metal work  

Bürofreunde, furniture supplier

Lichtprojekt, lighting consultant and supplier

Lindner Group, partitions

Design Team

Claudia Berkefeld, Katarzyna Kosciuk, Natalia Maciejowska, Dariusz Florczak, Balazs Götz

Photography

Andreas Buchberger ©Evolution Design

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The role of smart furniture in achieving healthier office spaces, creating a resilient and positive workplace through design, will ai pave the way for a three-day workweek.

DOE to Invest $160M in Microelectronics Research & Development Projects

The Department of Energy will invest $160 million over four years in research projects led by national laboratories to advance microelectronics development to support energy innovation in the U.S.

DOE said Wednesday the research investment initiative will implement the Microelectronics Research for Energy Innovation Act and support the establishment of Microelectronics Science Research Centers focused on energy efficiency and extreme environments.

The department is seeking research proposals in four research areas: new or improved materials, surface processing and control, chemistry, synthesis and fabrication; advanced computing paradigms and architectures; integrated sensing, edge computing and communication; and processing in extreme environments, radiation, radiation transport and materials interaction.

The funding opportunity is open to DOE’s national laboratories and other institutions to act as subcontractors.

“This funding will ensure our labs are all in on the Biden-Harris Administration’s whole of government effort to drive the future of innovation in chips and deliver the highly efficient computing capabilities we need to power frontier AI for many years to come,” said Geraldine Richmond , undersecretary for science and innovation in DOE.

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Aging Well in Asia: Asian Development Policy Report 2024.

Over the next 30 years, the number of persons aged 60 years or older who are living in Asia is expected to more than double, from 606 million in 2020 to 1.3 billion by 2050. Asia is aging but the region remains unprepared to secure old-age well-being.

Working with the Asian Development Bank in partnership with the University of Gothenburg. Research and contributions to

Aging Well in Asia: Asian Development Policy Report 2024 https://www.adb.org/sites/default/files/institutional-document/964861/adpr2024bp-universal-health-coverage-ageing-developing-asia.pdf

through a background paper

Kowal P, Hoang T, Ng N. Universal Health Coverage and Ageing in Developing Asia. Asian Development Bank. 2024. https://www.adb.org/sites/default/files/institutional-document/964861/adpr2024bp-universal-health-coverage-ageing-developing-asia.pdf

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University of Gothenburg, Professor Nawi Ng

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• Kowal P, Hoang T, Ng N. Universal Health Coverage and Ageing in Developing Asia. ( PDF , 1.24 MB )
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Mechanical Engineering Seniors Design Equipment for B-52s at Barksdale AFB

BATON ROUGE, LA – As part of their senior capstone project, six LSU Mechanical Engineering seniors have been working with Barksdale Air Force Base in Bossier City, La., to design an apparatus that will enable airmen to more easily work on or change out the engines on their B-52 bombers. Considering Barksdale AFB houses 26 B-52s, and each B-52 has eight engines, the students’ design should definitely come in handy.

Hannah Beene of Cyber Innovation Center, an innovation arm of the Air Force Global Strike Command Office of the Chief Scientist headquartered at Barksdale AFB, has been the point of contact between the LSU students and Barksdale AFB.

“Our role is to further AFGSC’s mission by fostering innovation and collaboration between industry, academia, business, and government to meet technology challenges,” Beene said.

When ME seniors Vaughn Bell of Ponchatoula, La.; Seth Chiasson of Denham Springs, La.; Matthew Day of Slaughter, La.; Sydney Gambino of Madisonville, La.; Stephen Freemen of Katy, Texas; and Ryan Purvis of Mandeville, La.; saw they had an opportunity to work on a project involving a B-52, they jumped at the chance.

“When it came time to choose my capstone project, I was excited as soon as I saw B-52 on the project list and immediately knew that was my top choice,” Bell said.

In October 2023, the students traveled to Barksdale AFB to see the B-52 engine in person and take measurements and photos to help them design their engine stand. The Air Force’s request was that they design something lighter and more compact than their current stand, which resembles a boat trailer and takes up much-needed room on the C-17 plane that flies alongside the B-52, carrying necessary cargo such as two spare engines.

The students’ design consists of four 3-ft.-high jack-stands that form a 4.5-ft. by 10.5-ft. rectangle supporting four aluminum I-beams on each corner and two long steel tubes that will hold two 80-lb. adapters a little more than 10 ft. apart that ultimately touch and support the engine. The whole stand holds up to 6,000 lbs., the estimated weight of a single B-52 engine.

The students tested their stand at Reeb Rigging in Baton Rouge, where they were able to load 6,000 lbs. on their stand. The students also worked with Brock Group in Port Allen, which did some welding on the stand while the students themselves cut the materials to size in the LSU Advanced Manufacturing and Machining Facility (AAMF) across from Patrick F. Taylor Hall.

The team also performed a timed test to make sure its stand could be assembled in 15 minutes or less. When disassembled, the entire stand fits into a 24x60x24-inch D box that goes on the C-17 plane to hold a damaged engine if it needs to be replaced. While the C-17 carries a spare engine for the left and right side of the B-52, the B-52 itself carries eight engines at a time (up to 48,000 lbs.) in pods supported by four pylons beneath the wings’ leading edge. Their placement allows them to work as wing fences and delay the onset of a stall.

What makes the B-52 so unique is its size and function. The 185-ft. wide plane weighs 185,000 lbs. and was designed and built by Boeing for the U.S. Air Force in the 1950s. Its purpose was to carry nuclear weapons during the Cold War era, though NASA used it for 40 years as an air-launch and testbed aircraft. The B-52’s ability to stay in the air for more than 72 hours at a time, along with its excellent performance at high subsonic speeds, are why the USAF still uses the B-52 today.

“What I’ve enjoyed most about this project is getting the chance to say I’ve designed and worked on something for the B-52 engine,” Freeman said. “I don’t know who wouldn’t find that interesting.”

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