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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.

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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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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 .

Psst... there’s more!

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

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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Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
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• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

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• Last Updated: Aug 21, 2023 10:42 AM

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What is design research methodology and why is it important?

What is design research.

Design research is the process of gathering, analyzing and interpreting data and insights to inspire, guide and provide context for designs. It’s a research discipline that applies both quantitative and qualitative research methods to help make well-informed design decisions.

Not to be confused with user experience research – focused on the usability of primarily digital products and experiences – design research is a broader discipline that informs the entire design process across various design fields. Beyond focusing solely on researching with users, design research can also explore aesthetics, cultural trends, historical context and more.

Design research has become more important in business, as brands place greater emphasis on building high-quality customer experiences as a point of differentiation.

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Design research vs. market research

The two may seem like the same thing at face value, but really they use different methods, serve different purposes and produce different insights.

Design research focuses on understanding user needs, behaviors and experiences to inform and improve product or service design.  Market research , on the other hand, is more concerned with the broader market dynamics, identifying opportunities, and maximizing sales and profitability.

Both are essential for the success of a product or service, but cater to different aspects of its lifecycle.

Design research in action: A mini mock case study

A popular furniture brand, known for its sleek and simple designs, faced an unexpected challenge: dropping sales in some overseas markets. To address this, they turned to design research – using quantitative and qualitative methods – to build a holistic view of the issue.

Company researchers visited homes in these areas to interview members of their target audience and understand local living spaces and preferences. Through these visits, they realized that while the local customers appreciated quality, their choices in furniture were heavily influenced by traditions and regional aesthetics, which the company's portfolio wasn’t addressing.

To further their understanding, the company rolled out surveys, asking people about their favorite materials, colors and furniture functionalities. They discovered a consistent desire for versatile furniture pieces that could serve multiple purposes. Additionally, the preference leaned towards certain regional colors and patterns that echoed local culture.

Armed with these insights, the company took to the drawing board. They worked on combining their minimalist style with the elements people in those markets valued. The result was a refreshed furniture line that seamlessly blended the brand's signature simplicity with local tastes. As this new line hit the market, it resonated deeply with customers in the markets, leading to a notable recovery in sales and even attracting new buyers.

When to use design research

Like most forms of research, design research should be used whenever there are gaps in your understanding of your audience’s needs, behaviors or preferences. It’s most valuable when used throughout the product development and design process.

When differing opinions within a team can derail a design process, design research provides concrete data and evidence-based insights, preventing decisions based on assumptions.

Design research brings value to any product development and design process, but it’s especially important in larger, resource intensive projects to minimize risk and create better outcomes for all.

The benefits of design research

Design research may be perceived as time-consuming, but in reality it’s often a time – and money – saver that can. easily prove to be the difference between strong product-market fit and a product with no real audience.

Deeper customer knowledge

Understanding your audience on a granular level is paramount – without tapping into the nuances of their desires, preferences and pain points, you run the risk of misalignment.

Design research dives deep into these intricacies, ensuring that products and services don't just meet surface level demands. Instead, they can resonate and foster a bond between the user and the brand, building foundations for lasting loyalty .

Efficiency and cost savings

More often than not, designing products or services based on assumptions or gut feelings leads to costly revisions, underwhelming market reception and wasted resources.

Design research offers a safeguard against these pitfalls by grounding decisions in real, tangible insights directly from the target market – streamlining the development process and ensuring that every dollar spent yields maximum value.

New opportunities

Design research often brings to light overlooked customer needs and emerging trends. The insights generated can shift the trajectory of product development, open doors to new and novel solutions, and carve out fresh market niches.

Sometimes it's not just about avoiding mistakes – it can be about illuminating new paths of innovation.

Enhanced competitive edge

In today’s world, one of the most powerful ways to stand out as a business is to be relentlessly user focused. By ensuring that products and services are continuously refined based on user feedback, businesses can maintain a step ahead of competitors.

Whether it’s addressing pain points competitors might overlook, or creating user experiences that are not just satisfactory but delightful, design research can be the foundations for a sharpened competitive edge.

Design research methods

The broad scope of design research means it demands a variety of research tools, with both numbers-driven and people-driven methods coming into play. There are many methods to choose from, so we’ve outlined those that are most common and can have the biggest impact.

This stage is about gathering initial insights to set a clear direction.

Literature review

Simply put, this research method involves investigating existing secondary research, like studies and articles, in your design area. It's a foundational method that helps you understand current knowledge and identify any gaps – think of it like surveying the landscape before navigating through it.

Field observations

By observing people's interactions in real-world settings, we gather genuine insights. Field observations are about connecting the dots between observed behaviors and your design's intended purpose. This method proves invaluable as it can reveal how design choices can impact everyday experiences.

Stakeholder interviews

Talking to those invested in the design's outcome, be it users or experts, is key. These discussions provide first-hand feedback that can clarify user expectations and illuminate the path towards a design that resonates with its audience.

This stage is about delving deeper and starting to shape your design concepts based on what you’ve already discovered.

Design review

This is a closer look at existing designs in the market or other related areas. Design reviews are very valuable because they can provide an understanding of current design trends and standards – helping you see where there's room for innovation or improvement.

Without a design review, you could be at risk of reinventing the wheel.

Persona building

This involves creating detailed profiles representing different groups in your target audience using real data and insights.

Personas help bring to life potential users, ensuring your designs address actual needs and scenarios. By having these "stand-in" users, you can make more informed design choices tailored to specific user experiences.

Putting your evolving design ideas to the test and gauging their effectiveness in the real world.

Usability testing

This is about seeing how real users interact with a design.

In usability testing you observe this process, note where they face difficulties and moments of satisfaction. It's a hands-on way to ensure that the design is intuitive and meets user needs.

Benchmark testing

Benchmark testing is about comparing your design's performance against set standards or competitor products.

Doing this gives a clearer idea of where your design stands in the broader context and highlights areas for improvement or differentiation. With these insights you can make informed decisions to either meet or exceed those benchmarks.

This final stage is about gathering feedback once your design is out in the world, ensuring it stays relevant and effective.

Feedback surveys

After users have interacted with the design for some time, use feedback surveys to gather their thoughts. The results of these surveys will help to ensure that you have your finger on the pulse of user sentiment – enabling iterative improvements.

Remember, simple questions can reveal a lot about what's working and where improvements might be needed.

Focus groups

These are structured, moderator-led discussions with a small group of users . The aim is for the conversation to dive deep into their experiences with the design and extract rich insights – not only capturing what users think but also why.

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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? 
• << Previous: Developing a Researchable Question
• Next: Planning, Data, Writing and Dissemination >>
• Last Updated: May 11, 2022 2:56 PM
• URL: https://libguides.sph.uth.tmc.edu/c.php?g=949457

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Inside Design

4 types of research methods all designers should know

Emily esposito,   •   oct 22, 2018.

R emember that fifth grade science project where you learned about primary research for the first time? Like most things we learned in elementary school, you probably didn’t expect it to creep back into your day-to-day adult life. However, in reality, designers have to conduct research and analyze data all the time.

Design research is a critical step in creating the best user experience. It helps you understand your customers’ behavior and turn it into actionable insights to improve your design.

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Primary research.

Perhaps the most important method in design research, this involves you or your team going directly to the source (your customers) to ask questions and gather data. Most often, the goal is to better understand who you are designing for or to validate your ideas with the actual end user.

Some examples of primary research include:

One-on-one interviews are a great place to start when collecting primary research. There are three main types of interviews: directed, non-directed, and ethnographic. Direct interviews are the most common and follow the standard question and answer format. Non-direct interviews are used when participants may not feel comfortable with direct questions. Instead, this interview is set up as a conversation (with some rough guidelines). Ethnographic interviews involve observing people in their day-to-day environment (very similar to the contextual inquiry method covered below).

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User groups.

Also known as focus groups, these are structured interviews involving three to six participants. A moderator guides the discussion, providing verbal and written feedback through the exercises. This research method is best when you need to get a lot of user insight in a short period of time.

Contextual inquiry

You first ask users a set of standard questions, then observe them in their natural environment as they complete their everyday tasks. It’s not just an interview or an observation—you want to watch people perform tasks as they explain what they are doing and why. This type of research is especially important in the beginning of the design process to learn what is important to users and how they interact with similar tools or services.

Asking users to document their own experience will help you see your product through their eyes.

“Design research helps you understand your customers’ behavior and turn it into actionable insights to improve your design.”

Diary study

Occurring over an extended period of time (from a week to a month, or even longer), participants are asked to keep a diary and log specific information about their activities. In-situ logging is the simplest way to collect data from diaries—users report all details about the activities as they complete them.

Usability testing

Once you’re deeper into the design process and have a prototype to share, usability testing helps you put that design into the wild to gather feedback. Here, you would ask potential or current users to complete a set of tasks using your prototype.

Secondary research

Secondary research is when you use existing data like books, articles, or the internet to validate or support existing research. You may use secondary research to create a stronger case for your design choices and provide additional insight into what you learned during primary research.

Work with existing content, like presentations or articles, to present a strong case for your design choices.

This type of research method is quick and cheap—all you need is internet access or a library card to start. However, some common challenges with secondary research include not being able to find the specific information you need, or battling outdated, low-quality data.Here are some places where you could gather secondary research:

• Internal data, like your company database, sales reports, or historical information
• Government statistics or information from government agencies
• University research centers
• Respected magazines and newspapers

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Generative or exploratory research.

Generative research, also known as exploratory research, focuses on a deeper understanding of user needs and desires. It is usually conducted at the beginning of the design project when you need to answer basic questions like, “What problem are we solving for our customers?” This discovery phase helps you to identify a design hypothesis and validate it with your customers. You won’t always know what the outcome or answers will be, but they will create a strong foundation to make good design decisions going forward.

You’ll see a lot of overlap between generative research and primary research since the whole point of generative research is to get out and talk to your users. Examples of generative research include interviews, user groups, surveys, and contextual inquiries.

Before you start your research, make sure you know what you intend to learn from the results.

Evaluative research.

After gathering your generative research, you’re prepared to design a solution for your customers. Evaluative research allows you to test that solution, giving users the opportunity to “evaluate” your prototype. Your goal is to collect feedback to help refine and improve the design experience. One of the most popular ways to conduct evaluative research is to have people use your product or service as they think out loud (again, a subset of primary research). A perfect example of this research method is usability studies.And, for whichever type of evaluative research you choose, there are two types: summative and formative. Summative emphasizes the outcome more than the process (looking at whether the desired effect is achieved) and formative is used to strengthen idea being tested (monitoring the success of a process).

Keep asking questions

How do you decide which research method to use? It depends on what you’re trying to learn. You may start with primary research and find that more questions arise after getting to know your customers better (and that’s a good thing!). These new questions will help you decide what you need to learn next. When in doubt, always follow the questions.

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by Emily Esposito

Emily has written for some of the top tech companies, covering everything from creative copywriting to UX design. When she's not writing, she's traveling the world (next stop: Japan!), brewing kombucha, and biking through the Pacific Northwest.

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Design Methods and Practices for Research of Project Management

DOI link for Design Methods and Practices for Research of Project Management

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Design Methods and Practices for Research of Project Management is the most comprehensive guide on how to do research of and in project management. Project management as a discipline has experienced near-exponential growth in its application across the business and not-for-profit sectors. This second edition of the authoritative reference book offers a substantial update on the first edition with over 60% new content and so provides both practitioner and student researchers with a fully up-to-date and complete guide to research practice on project management.

In Design Methods and Practices for Research of Project Management , Beverly Pasian and Rodney Turner have brought together 27 original chapters from many of the leading international thinkers in project management research. The collection looks at each step in the research stages, including research strategy, management, methodology (quantitative and qualitative), techniques as well as how to share and publish research findings. The chapters offer an international perspective with examples from a wide range of project management applications; engineering, construction, mega-projects, high-risk environments and social transformation. Each chapter includes tips and exercises for the research student, as well as a complete set of further references.

The book is the go-to text for practitioners undertaking research in companies, and also doctoral and masters students and their supervisors who are involved in research projects in and for universities.

TABLE OF CONTENTS

Chapter | 8  pages, introduction, part | 2  pages, part i foundational issues, chapter 1 | 8  pages, project management research: addressing integrative challenges, chapter 2 | 14  pages, project management research: social dimensions and organisational context, chapter 3 | 10  pages, the paradigm as a steering mechanism for new research endeavours, chapter 4 | 14  pages, finding a way in broceliande forest: the magic domain of project management research, chapter 5 | 10  pages, ontology and epistemology, chapter 6 | 16  pages, the praxeology of applied research in autoethnographical research settings: a case study of a radical learning journey, part ii focusing your research effort, chapter 7 | 10  pages, research methods and success meaning in project management, chapter 8 | 12  pages, the constructive research approach: problem solving for complex projects, chapter 9 | 12  pages, novel or incremental contributions: the construction of research questions, chapter 10 | 10  pages, moving from hunches to a research topic: salient literature and research methods, chapter 11 | 14  pages, moving from ‘hunches’ to an interesting research topic: defining the research topic, chapter 12 | 10  pages, ethical considerations in project management research, chapter 13 | 20  pages, developing a critical literature review for project management research, chapter 14 | 10  pages, critical engagement of previous research, part iii specific data collection and analysis techniques, chapter 15 | 18  pages, interview methods for project management research, chapter 16 | 8  pages, considering case studies in project management, chapter 17 | 14  pages, linking theory and practice in using action-oriented methods, chapter 18 | 16  pages, dual cycle action research: a doctor of project management (dpm) research case study, chapter 19 | 10  pages, an agile approach to the real experience of developing research methodology and methods, chapter 20 | 10  pages, giving voice to the project management practitioner, chapter 21 | 10  pages, enter or not – how to gain and sustain access to research sites, part iv examples of mixed methods strategies, chapter 22 | 14  pages, mixed methods research in project management, chapter 23 | 14  pages, the value of mixed methods, chapter 24 | 14  pages, managing research in large collaborative teams, chapter 25 | 12  pages, applying mixed methods for researching project management in engineering projects, chapter 26 | 14  pages, importance of sequencing in mixed methods research design, chapter 27 | 14  pages, an empirical research method strategy for construction consulting services projects, part v unique environments for project management research, chapter 28 | 12  pages, a practical research method: the netlipse case study, chapter 29 | 12  pages, using multi-case approaches in project management research: the megaproject experience, chapter 30 | 12  pages, project management research in post-conflict societies: challenges and complexities identified in kosovo, chapter 31 | 8  pages, complexities of oil and gas exploration in the middle east, part vi writing as a future researcher, chapter 32 | 14  pages, studying relationships in project management through social network analysis, chapter 33 | 12  pages, social network analysis applied to project management, chapter 34 | 10  pages, the electronic portfolio – a research enabler, part vii benefitting from experience: supervisors and publications, chapter 35 | 6  pages, the voice of experience: an interview with lynn crawford, chapter 36 | 8  pages, supervisors and their sociological (and sometimes seemingly illogical) imagination, chapter 37 | 10  pages, common flaws in project management research reports, chapter 38 | 12  pages, publish or perish transforming your thesis into a tangible product.

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The Complete Guide to UX Research Methods

UX research provides invaluable insight into product users and what they need and value. Not only will research reduce the risk of a miscalculated guess, it will uncover new opportunities for innovation.

By Miklos Philips

Miklos is a UX designer, product design strategist, author, and speaker with more than 18 years of experience in the design field.

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“Empathy is at the heart of design. Without the understanding of what others see, feel, and experience, design is a pointless task.” —Tim Brown, CEO of the innovation and design firm IDEO

User experience (UX) design is the process of designing products that are useful, easy to use, and a pleasure to engage. It’s about enhancing the entire experience people have while interacting with a product and making sure they find value, satisfaction, and delight. If a mountain peak represents that goal, employing various types of UX research is the path UX designers use to get to the top of the mountain.

User experience research is one of the most misunderstood yet critical steps in UX design. Sometimes treated as an afterthought or an unaffordable luxury, UX research, and user testing should inform every design decision.

Every product, service, or user interface designers create in the safety and comfort of their workplaces has to survive and prosper in the real world. Countless people will engage our creations in an unpredictable environment over which designers have no control. UX research is the key to grounding ideas in reality and improving the odds of success, but research can be a scary word. It may sound like money we don’t have, time we can’t spare, and expertise we have to seek.

In order to do UX research effectively—to get a clear picture of what users think and why they do what they do—e.g., to “walk a mile in the user’s shoes” as a favorite UX maxim goes, it is essential that user experience designers and product teams conduct user research often and regularly. Contingent upon time, resources, and budget, the deeper they can dive the better.

What Is UX Research?

There is a long, comprehensive list of UX design research methods employed by user researchers , but at its center is the user and how they think and behave —their needs and motivations. Typically, UX research does this through observation techniques, task analysis, and other feedback methodologies.

There are two main types of user research: quantitative (statistics: can be calculated and computed; focuses on numbers and mathematical calculations) and qualitative (insights: concerned with descriptions, which can be observed but cannot be computed).

Quantitative research is primarily exploratory research and is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. Some common data collection methods include various forms of surveys – online surveys , paper surveys , mobile surveys and kiosk surveys , longitudinal studies, website interceptors, online polls, and systematic observations.

This user research method may also include analytics, such as Google Analytics .

Google Analytics is part of a suite of interconnected tools that help interpret data on your site’s visitors including Data Studio , a powerful data-visualization tool, and Google Optimize, for running and analyzing dynamic A/B testing.

Quantitative data from analytics platforms should ideally be balanced with qualitative insights gathered from other UX testing methods , such as focus groups or usability testing. The analytical data will show patterns that may be useful for deciding what assumptions to test further.

Qualitative user research is a direct assessment of behavior based on observation. It’s about understanding people’s beliefs and practices on their terms. It can involve several different methods including contextual observation, ethnographic studies, interviews, field studies, and moderated usability tests.

Jakob Nielsen of the Nielsen Norman Group feels that in the case of UX research, it is better to emphasize insights (qualitative research) and that although quant has some advantages, qualitative research breaks down complicated information so it’s easy to understand, and overall delivers better results more cost effectively—in other words, it is much cheaper to find and fix problems during the design phase before you start to build. Often the most important information is not quantifiable, and he goes on to suggest that “quantitative studies are often too narrow to be useful and are sometimes directly misleading.”

Not everything that can be counted counts, and not everything that counts can be counted. William Bruce Cameron

Design research is not typical of traditional science with ethnography being its closest equivalent—effective usability is contextual and depends on a broad understanding of human behavior if it is going to work.

Nevertheless, the types of user research you can or should perform will depend on the type of site, system or app you are developing, your timeline, and your environment.

Top UX Research Methods and When to Use Them

Here are some examples of the types of user research performed at each phase of a project.

Card Sorting : Allows users to group and sort a site’s information into a logical structure that will typically drive navigation and the site’s information architecture. This helps ensure that the site structure matches the way users think.

Contextual Interviews : Enables the observation of users in their natural environment, giving you a better understanding of the way users work.

First Click Testing : A testing method focused on navigation, which can be performed on a functioning website, a prototype, or a wireframe.

Focus Groups : Moderated discussion with a group of users, allowing insight into user attitudes, ideas, and desires.

Heuristic Evaluation/Expert Review : A group of usability experts evaluating a website against a list of established guidelines .

Interviews : One-on-one discussions with users show how a particular user works. They enable you to get detailed information about a user’s attitudes, desires, and experiences.

Parallel Design : A design methodology that involves several designers pursuing the same effort simultaneously but independently, with the intention to combine the best aspects of each for the ultimate solution.

Personas : The creation of a representative user based on available data and user interviews. Though the personal details of the persona may be fictional, the information used to create the user type is not.

Prototyping : Allows the design team to explore ideas before implementing them by creating a mock-up of the site. A prototype can range from a paper mock-up to interactive HTML pages.

Surveys : A series of questions asked to multiple users of your website that help you learn about the people who visit your site.

System Usability Scale (SUS) : SUS is a technology-independent ten-item scale for subjective evaluation of the usability.

Task Analysis : Involves learning about user goals, including what users want to do on your website, and helps you understand the tasks that users will perform on your site.

Usability Testing : Identifies user frustrations and problems with a site through one-on-one sessions where a “real-life” user performs tasks on the site being studied.

Use Cases : Provide a description of how users use a particular feature of your website. They provide a detailed look at how users interact with the site, including the steps users take to accomplish each task.

You can do user research at all stages or whatever stage you are in currently. However, the Nielsen Norman Group advises that most of it be done during the earlier phases when it will have the biggest impact. They also suggest it’s a good idea to save some of your budget for additional research that may become necessary (or helpful) later in the project.

Here is a diagram listing recommended options that can be done as a project moves through the design stages. The process will vary, and may only include a few things on the list during each phase. The most frequently used methods are shown in bold.

Reasons for Doing UX Research

Here are three great reasons for doing user research :

To create a product that is truly relevant to users

• If you don’t have a clear understanding of your users and their mental models, you have no way of knowing whether your design will be relevant. A design that is not relevant to its target audience will never be a success.

To create a product that is easy and pleasurable to use

• A favorite quote from Steve Jobs: “ If the user is having a problem, it’s our problem .” If your user experience is not optimal, chances are that people will move on to another product.

To have the return on investment (ROI) of user experience design validated and be able to show:

• An improvement in performance and credibility
• Increased exposure and sales—growth in customer base
• A reduced burden on resources—more efficient work processes

Aside from the reasons mentioned above, doing user research gives insight into which features to prioritize, and in general, helps develop clarity around a project.

What Results Can I Expect from UX Research?

In the words of Mike Kuniaysky, user research is “ the process of understanding the impact of design on an audience. ”

User research has been essential to the success of behemoths like USAA and Amazon ; Joe Gebbia, CEO of Airbnb is an enthusiastic proponent, testifying that its implementation helped turn things around for the company when it was floundering as an early startup.

Some of the results generated through UX research confirm that improving the usability of a site or app will:

• Increase conversion rates
• Increase sign-ups
• Increase NPS (net promoter score)
• Increase customer satisfaction
• Increase purchase rates
• Boost loyalty to the brand
• Reduce customer service calls

Additionally, and aside from benefiting the overall user experience, the integration of UX research into the development process can:

• Minimize development time
• Reduce production costs
• Uncover valuable insights about your audience
• Give an in-depth view into users’ mental models, pain points, and goals

User research is at the core of every exceptional user experience. As the name suggests, UX is subjective—the experience that a person goes through while using a product. Therefore, it is necessary to understand the needs and goals of potential users, the context, and their tasks which are unique for each product. By selecting appropriate UX research methods and applying them rigorously, designers can shape a product’s design and can come up with products that serve both customers and businesses more effectively.

Further Reading on the Toptal Blog:

• How to Conduct Effective UX Research: A Guide
• The Value of User Research
• UX Research Methods and the Path to User Empathy
• Design Talks: Research in Action with UX Researcher Caitria O'Neill
• Swipe Right: 3 Ways to Boost Safety in Dating App Design
• How to Avoid 5 Types of Cognitive Bias in User Research

Understanding the basics

How do you do user research in ux.

UX research includes two main types: quantitative (statistical data) and qualitative (insights that can be observed but not computed), done through observation techniques, task analysis, and other feedback methodologies. The UX research methods used depend on the type of site, system, or app being developed.

What are UX methods?

There is a long list of methods employed by user research, but at its center is the user and how they think, behave—their needs and motivations. Typically, UX research does this through observation techniques, task analysis, and other UX methodologies.

What is the best research methodology for user experience design?

The type of UX methodology depends on the type of site, system or app being developed, its timeline, and environment. There are 2 main types: quantitative (statistics) and qualitative (insights).

What does a UX researcher do?

A user researcher removes the need for false assumptions and guesswork by using observation techniques, task analysis, and other feedback methodologies to understand a user’s motivation, behavior, and needs.

Why is UX research important?

UX research will help create a product that is relevant to users and is easy and pleasurable to use while boosting a product’s ROI. Aside from these reasons, user research gives insight into which features to prioritize, and in general, helps develop clarity around a project.

• UserResearch

Miklos Philips

London, United Kingdom

Member since May 20, 2016

About the author

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Home » Research Project – Definition, Writing Guide and Ideas

Research Project – Definition, Writing Guide and Ideas

Table of Contents

Research Project

Definition :

Research Project is a planned and systematic investigation into a specific area of interest or problem, with the goal of generating new knowledge, insights, or solutions. It typically involves identifying a research question or hypothesis, designing a study to test it, collecting and analyzing data, and drawing conclusions based on the findings.

Types of Research Project

Types of Research Projects are as follows:

Basic Research

This type of research focuses on advancing knowledge and understanding of a subject area or phenomenon, without any specific application or practical use in mind. The primary goal is to expand scientific or theoretical knowledge in a particular field.

Applied Research

Applied research is aimed at solving practical problems or addressing specific issues. This type of research seeks to develop solutions or improve existing products, services or processes.

Action Research

Action research is conducted by practitioners and aimed at solving specific problems or improving practices in a particular context. It involves collaboration between researchers and practitioners, and often involves iterative cycles of data collection and analysis, with the goal of improving practices.

Quantitative Research

This type of research uses numerical data to investigate relationships between variables or to test hypotheses. It typically involves large-scale data collection through surveys, experiments, or secondary data analysis.

Qualitative Research

Qualitative research focuses on understanding and interpreting phenomena from the perspective of the people involved. It involves collecting and analyzing data in the form of text, images, or other non-numerical forms.

Mixed Methods Research

Mixed methods research combines elements of both quantitative and qualitative research, using multiple data sources and methods to gain a more comprehensive understanding of a phenomenon.

Longitudinal Research

This type of research involves studying a group of individuals or phenomena over an extended period of time, often years or decades. It is useful for understanding changes and developments over time.

Case Study Research

Case study research involves in-depth investigation of a particular case or phenomenon, often within a specific context. It is useful for understanding complex phenomena in their real-life settings.

Participatory Research

Participatory research involves active involvement of the people or communities being studied in the research process. It emphasizes collaboration, empowerment, and the co-production of knowledge.

Research Project Methodology

Research Project Methodology refers to the process of conducting research in an organized and systematic manner to answer a specific research question or to test a hypothesis. A well-designed research project methodology ensures that the research is rigorous, valid, and reliable, and that the findings are meaningful and can be used to inform decision-making.

There are several steps involved in research project methodology, which are described below:

Define the Research Question

The first step in any research project is to clearly define the research question or problem. This involves identifying the purpose of the research, the scope of the research, and the key variables that will be studied.

Develop a Research Plan

Once the research question has been defined, the next step is to develop a research plan. This plan outlines the methodology that will be used to collect and analyze data, including the research design, sampling strategy, data collection methods, and data analysis techniques.

Collect Data

The data collection phase involves gathering information through various methods, such as surveys, interviews, observations, experiments, or secondary data analysis. The data collected should be relevant to the research question and should be of sufficient quantity and quality to enable meaningful analysis.

Analyze Data

Once the data has been collected, it is analyzed using appropriate statistical techniques or other methods. The analysis should be guided by the research question and should aim to identify patterns, trends, relationships, or other insights that can inform the research findings.

Interpret and Report Findings

The final step in the research project methodology is to interpret the findings and report them in a clear and concise manner. This involves summarizing the results, discussing their implications, and drawing conclusions that can be used to inform decision-making.

Research Project Writing Guide

Here are some guidelines to help you in writing a successful research project:

• Choose a topic: Choose a topic that you are interested in and that is relevant to your field of study. It is important to choose a topic that is specific and focused enough to allow for in-depth research and analysis.
• Conduct a literature review : Conduct a thorough review of the existing research on your topic. This will help you to identify gaps in the literature and to develop a research question or hypothesis.
• Develop a research question or hypothesis : Based on your literature review, develop a clear research question or hypothesis that you will investigate in your study.
• Design your study: Choose an appropriate research design and methodology to answer your research question or test your hypothesis. This may include choosing a sample, selecting measures or instruments, and determining data collection methods.
• Collect data: Collect data using your chosen methods and instruments. Be sure to follow ethical guidelines and obtain informed consent from participants if necessary.
• Analyze data: Analyze your data using appropriate statistical or qualitative methods. Be sure to clearly report your findings and provide interpretations based on your research question or hypothesis.
• Discuss your findings : Discuss your findings in the context of the existing literature and your research question or hypothesis. Identify any limitations or implications of your study and suggest directions for future research.
• Write your project: Write your research project in a clear and organized manner, following the appropriate format and style guidelines for your field of study. Be sure to include an introduction, literature review, methodology, results, discussion, and conclusion.
• Revise and edit: Revise and edit your project for clarity, coherence, and accuracy. Be sure to proofread for spelling, grammar, and formatting errors.
• Cite your sources: Cite your sources accurately and appropriately using the appropriate citation style for your field of study.

Examples of Research Projects

Some Examples of Research Projects are as follows:

• Investigating the effects of a new medication on patients with a particular disease or condition.
• Exploring the impact of exercise on mental health and well-being.
• Studying the effectiveness of a new teaching method in improving student learning outcomes.
• Examining the impact of social media on political participation and engagement.
• Investigating the efficacy of a new therapy for a specific mental health disorder.
• Exploring the use of renewable energy sources in reducing carbon emissions and mitigating climate change.
• Studying the effects of a new agricultural technique on crop yields and environmental sustainability.
• Investigating the effectiveness of a new technology in improving business productivity and efficiency.
• Examining the impact of a new public policy on social inequality and access to resources.
• Exploring the factors that influence consumer behavior in a specific market.

Characteristics of Research Project

Here are some of the characteristics that are often associated with research projects:

• Clear objective: A research project is designed to answer a specific question or solve a particular problem. The objective of the research should be clearly defined from the outset.
• Systematic approach: A research project is typically carried out using a structured and systematic approach that involves careful planning, data collection, analysis, and interpretation.
• Rigorous methodology: A research project should employ a rigorous methodology that is appropriate for the research question being investigated. This may involve the use of statistical analysis, surveys, experiments, or other methods.
• Data collection : A research project involves collecting data from a variety of sources, including primary sources (such as surveys or experiments) and secondary sources (such as published literature or databases).
• Analysis and interpretation : Once the data has been collected, it needs to be analyzed and interpreted. This involves using statistical techniques or other methods to identify patterns or relationships in the data.
• Conclusion and implications : A research project should lead to a clear conclusion that answers the research question. It should also identify the implications of the findings for future research or practice.
• Communication: The results of the research project should be communicated clearly and effectively, using appropriate language and visual aids, to a range of audiences, including peers, stakeholders, and the wider public.

Importance of Research Project

Research projects are an essential part of the process of generating new knowledge and advancing our understanding of various fields of study. Here are some of the key reasons why research projects are important:

• Advancing knowledge : Research projects are designed to generate new knowledge and insights into particular topics or questions. This knowledge can be used to inform policies, practices, and decision-making processes across a range of fields.
• Solving problems: Research projects can help to identify solutions to real-world problems by providing a better understanding of the causes and effects of particular issues.
• Developing new technologies: Research projects can lead to the development of new technologies or products that can improve people’s lives or address societal challenges.
• Improving health outcomes: Research projects can contribute to improving health outcomes by identifying new treatments, diagnostic tools, or preventive strategies.
• Enhancing education: Research projects can enhance education by providing new insights into teaching and learning methods, curriculum development, and student learning outcomes.
• Informing public policy : Research projects can inform public policy by providing evidence-based recommendations and guidance on issues related to health, education, environment, social justice, and other areas.
• Enhancing professional development : Research projects can enhance the professional development of researchers by providing opportunities to develop new skills, collaborate with colleagues, and share knowledge with others.

Research Project Ideas

Following are some Research Project Ideas:

Field: Psychology

• Investigating the impact of social support on coping strategies among individuals with chronic illnesses.
• Exploring the relationship between childhood trauma and adult attachment styles.
• Examining the effects of exercise on cognitive function and brain health in older adults.
• Investigating the impact of sleep deprivation on decision making and risk-taking behavior.
• Exploring the relationship between personality traits and leadership styles in the workplace.
• Examining the effectiveness of cognitive-behavioral therapy (CBT) for treating anxiety disorders.
• Investigating the relationship between social comparison and body dissatisfaction in young women.
• Exploring the impact of parenting styles on children’s emotional regulation and behavior.
• Investigating the effectiveness of mindfulness-based interventions for treating depression.
• Examining the relationship between childhood adversity and later-life health outcomes.

Field: Economics

• Analyzing the impact of trade agreements on economic growth in developing countries.
• Examining the effects of tax policy on income distribution and poverty reduction.
• Investigating the relationship between foreign aid and economic development in low-income countries.
• Exploring the impact of globalization on labor markets and job displacement.
• Analyzing the impact of minimum wage laws on employment and income levels.
• Investigating the effectiveness of monetary policy in managing inflation and unemployment.
• Examining the relationship between economic freedom and entrepreneurship.
• Analyzing the impact of income inequality on social mobility and economic opportunity.
• Investigating the role of education in economic development.
• Examining the effectiveness of different healthcare financing systems in promoting health equity.

Field: Sociology

• Investigating the impact of social media on political polarization and civic engagement.
• Examining the effects of neighborhood characteristics on health outcomes.
• Analyzing the impact of immigration policies on social integration and cultural diversity.
• Investigating the relationship between social support and mental health outcomes in older adults.
• Exploring the impact of income inequality on social cohesion and trust.
• Analyzing the effects of gender and race discrimination on career advancement and pay equity.
• Investigating the relationship between social networks and health behaviors.
• Examining the effectiveness of community-based interventions for reducing crime and violence.
• Analyzing the impact of social class on cultural consumption and taste.
• Investigating the relationship between religious affiliation and social attitudes.

Field: Computer Science

• Developing an algorithm for detecting fake news on social media.
• Investigating the effectiveness of different machine learning algorithms for image recognition.
• Developing a natural language processing tool for sentiment analysis of customer reviews.
• Analyzing the security implications of blockchain technology for online transactions.
• Investigating the effectiveness of different recommendation algorithms for personalized advertising.
• Developing an artificial intelligence chatbot for mental health counseling.
• Investigating the effectiveness of different algorithms for optimizing online advertising campaigns.
• Developing a machine learning model for predicting consumer behavior in online marketplaces.
• Analyzing the privacy implications of different data sharing policies for online platforms.
• Investigating the effectiveness of different algorithms for predicting stock market trends.

Field: Education

• Investigating the impact of teacher-student relationships on academic achievement.
• Analyzing the effectiveness of different pedagogical approaches for promoting student engagement and motivation.
• Examining the effects of school choice policies on academic achievement and social mobility.
• Investigating the impact of technology on learning outcomes and academic achievement.
• Analyzing the effects of school funding disparities on educational equity and achievement gaps.
• Investigating the relationship between school climate and student mental health outcomes.
• Examining the effectiveness of different teaching strategies for promoting critical thinking and problem-solving skills.
• Investigating the impact of social-emotional learning programs on student behavior and academic achievement.
• Analyzing the effects of standardized testing on student motivation and academic achievement.

Field: Environmental Science

• Investigating the impact of climate change on species distribution and biodiversity.
• Analyzing the effectiveness of different renewable energy technologies in reducing carbon emissions.
• Examining the impact of air pollution on human health outcomes.
• Investigating the relationship between urbanization and deforestation in developing countries.
• Analyzing the effects of ocean acidification on marine ecosystems and biodiversity.
• Investigating the impact of land use change on soil fertility and ecosystem services.
• Analyzing the effectiveness of different conservation policies and programs for protecting endangered species and habitats.
• Investigating the relationship between climate change and water resources in arid regions.
• Examining the impact of plastic pollution on marine ecosystems and biodiversity.
• Investigating the effects of different agricultural practices on soil health and nutrient cycling.

Field: Linguistics

• Analyzing the impact of language diversity on social integration and cultural identity.
• Investigating the relationship between language and cognition in bilingual individuals.
• Examining the effects of language contact and language change on linguistic diversity.
• Investigating the role of language in shaping cultural norms and values.
• Analyzing the effectiveness of different language teaching methodologies for second language acquisition.
• Investigating the relationship between language proficiency and academic achievement.
• Examining the impact of language policy on language use and language attitudes.
• Investigating the role of language in shaping gender and social identities.
• Analyzing the effects of dialect contact on language variation and change.
• Investigating the relationship between language and emotion expression.

Field: Political Science

• Analyzing the impact of electoral systems on women’s political representation.
• Investigating the relationship between political ideology and attitudes towards immigration.
• Examining the effects of political polarization on democratic institutions and political stability.
• Investigating the impact of social media on political participation and civic engagement.
• Analyzing the effects of authoritarianism on human rights and civil liberties.
• Investigating the relationship between public opinion and foreign policy decisions.
• Examining the impact of international organizations on global governance and cooperation.
• Investigating the effectiveness of different conflict resolution strategies in resolving ethnic and religious conflicts.
• Analyzing the effects of corruption on economic development and political stability.
• Investigating the role of international law in regulating global governance and human rights.

Field: Medicine

• Investigating the impact of lifestyle factors on chronic disease risk and prevention.
• Examining the effectiveness of different treatment approaches for mental health disorders.
• Investigating the relationship between genetics and disease susceptibility.
• Analyzing the effects of social determinants of health on health outcomes and health disparities.
• Investigating the impact of different healthcare delivery models on patient outcomes and cost effectiveness.
• Examining the effectiveness of different prevention and treatment strategies for infectious diseases.
• Investigating the relationship between healthcare provider communication skills and patient satisfaction and outcomes.
• Analyzing the effects of medical error and patient safety on healthcare quality and outcomes.
• Investigating the impact of different pharmaceutical pricing policies on access to essential medicines.
• Examining the effectiveness of different rehabilitation approaches for improving function and quality of life in individuals with disabilities.

Field: Anthropology

• Analyzing the impact of colonialism on indigenous cultures and identities.
• Investigating the relationship between cultural practices and health outcomes in different populations.
• Examining the effects of globalization on cultural diversity and cultural exchange.
• Investigating the role of language in cultural transmission and preservation.
• Analyzing the effects of cultural contact on cultural change and adaptation.
• Investigating the impact of different migration policies on immigrant integration and acculturation.
• Examining the role of gender and sexuality in cultural norms and values.
• Investigating the impact of cultural heritage preservation on tourism and economic development.
• Analyzing the effects of cultural revitalization movements on indigenous communities.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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World Leaders in Research-Based User Experience

Design thinking in practice: research methodology.

January 10, 2021 2021-01-10

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Project Overview 

Over the last decade, we have seen design thinking gain popularity across industries. Nielsen Norman Group conducted a long-term research project to understand design thinking in practice. The research project included 3 studies involving more than 1000 participants and took place from 2018 to 2020: 

• Intercepts and interviews with 87 participants
• Digital survey with 1067 respondents
• In-depth case study at an institution practicing design thinking 

The primary goals of the project were to investigate the following:

• How do practitioners learn and use design thinking?
• How does design thinking provide value to individuals and organizations?
• What makes design thinking successful or unsuccessful? 

This description of what we did may be useful in helping you interpret our results and apply them to your own design-thinking practice. 

Project Findings

The findings from this research are shared in the following articles and videos:

• What Is Design Thinking, Really? (What Practitioners Say) (Article) 
• How UX Professionals Define Design Thinking in Practice (Video) 
• Design Thinking: The Learner’s Journey (Article)

In This Article:

Study 1: intercepts and interviews , study 2:  digital survey, study 3: case study .

In the first study we investigated how UX and design professionals define design thinking.  

This study consisted of 71 in-person intercepts in Washington DC, San Francisco, Boston, and North Carolina and 16 remote interviews over the phone and via video conferencing. These 87 participants were UX professionals from a diverse range of countries with varying roles and experience.

Intercepts consisted of two questions:

• What do think of when you hear the phrase “design thinking”?
• How would you define design thinking?

Interviews consisted of 10 questions, excluding demographic-related questions:

• What are the first words that come to mind when I say “design thinking”?
• Can you tell me more about [word they supplied in response to question 1]?
• How would you define design thinking? Why?
• What does it mean to practice design thinking?
• What are the positive or negative effects of design thinking?
• Products and services
• Clients/customers
• Using this scale, what is your experience using design thinking?
• Using this same scale, how successful has design thinking been in your experience?
• What could have been better?
• What is good about design thinking? What is bad about design thinking?

Our second study consisted of a qualitative digital survey that ran for two months and had 1067 professional respondents primarily from UX-related fields. The survey had 14 questions, excluding demographic-related questions. An alternative set of 4 questions was shown to those with little to no experience using design thinking.  

• Which of the following best describes your experience with design thinking?
• Where did you learn design thinking?  
• UX maturity 
• Frequency of crossteam collaboration 
• User-centered approach 
• Research-driven decision making
• How often do you, yourself, practice design thinking?
• In your own words, what does it mean to practice design thinking? 
• When do you use design thinking?
• What methods or exercises are used?
• In what situations is each one used and why?
• Which ones are done individually versus as a group?
• How is each exercise executed?
• Gives your organization a competitive advantage
• Drives innovation
• Fosters collaboration
• Provides structure to the organization
• Increases likelihood of success
• Please describe a situation where design thinking positively influenced your organization and why it was successful. 
• Please describe a situation where design thinking may have negatively influenced your organization and why it was negative. 
• Design thinking negatively affects efficiency.
• Design thinking requires a collaborative environment to work well.
• Anyone can learn and practice design thinking.
• Design thinking is rigid.
• Design thinking requires all involved to be human-centered.
• Design thinking takes a lot of time.
• Design thinking has low return on investment.
• Design thinking empowers personal growth.
• Design thinking grows interpersonal relationships.
• Design thinking improves organizational progress.

The 1067 survey participants had diverse backgrounds: they held varying roles across industries and were located across the globe. 94 responses were invalid, so we excluded them from our analysis.  

The majority of participants (33%) were UX designers, followed by UX researchers (13%) and UX consultants (12%). 

Of participants who responded “Other”, the most common response provided was an executive role (n=20). This included roles such as CEO, VP, director, founder, and “head of.” Other mentioned roles included service designer (n=17), manager (n=14), business designer or business analyst (n=11), and educator (including teacher, instructor, and curriculum designer) (n=11).

Geographically, we had respondents from 67 different countries. The majority of survey participants work in the United States (34%), followed by India (8%), United Kingdom (7%), and Canada (5%). 

Our survey participants also represented diverse industries, with the majority in software (22%) and finance or insurance (14%). 

Of participants who responded Other , the most common response provided was agency or consulting (n=26), followed by telecommunications (n=17), marketing (n=8), and tourism (n=7).

Our third and final study consisted of an in-person case study at a large, public ecommerce company. The case study involved 9 interviews with company employees, 6 observation sessions of design-thinking (or related) workshops, and an internal resource and literature audit. 

The interviews were 1-hour long and semistructured. Of the 8 participants, 3 were on the same team but had different roles: 1 UX designer, 1 product manager, and 1 engineer. The other 5 interviewees (3 design leaders and 2 UX designers) worked in different groups across the organization. Each participant completed the same digital survey from the second study prior to interviewing.    

In addition to interviews, we conducted 6 observation sessions: 3 design-thinking workshops, 2 meetings, and 1 lunch-and-learn. After the workshops, all participants were invited to fill out a survey about the workshop. The survey had 5 questions: 

• We achieved our goal of [x]. 
• The time and resources spent to conduct the workshop were worth it.
• What aspects were of greatest value to you, and why? 
• Where there any aspects you felt were not useful, and why?
• Will the workshop or its output impact any of your future work? If so, how?
• What is your role?

Lastly, we conducted a resource and literature audit of the company’s internal resources related to design thinking available to employees.  

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• Design Process Design Process
• Research Methods

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Design Thinking: Top 3 Challenges and Solutions

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Reference management. Clean and simple.

What is research methodology?

The basics of research methodology

Why do you need a research methodology, what needs to be included, why do you need to document your research method, what are the different types of research instruments, qualitative / quantitative / mixed research methodologies, how do you choose the best research methodology for you, frequently asked questions about research methodology, related articles.

When you’re working on your first piece of academic research, there are many different things to focus on, and it can be overwhelming to stay on top of everything. This is especially true of budding or inexperienced researchers.

If you’ve never put together a research proposal before or find yourself in a position where you need to explain your research methodology decisions, there are a few things you need to be aware of.

Once you understand the ins and outs, handling academic research in the future will be less intimidating. We break down the basics below:

A research methodology encompasses the way in which you intend to carry out your research. This includes how you plan to tackle things like collection methods, statistical analysis, participant observations, and more.

You can think of your research methodology as being a formula. One part will be how you plan on putting your research into practice, and another will be why you feel this is the best way to approach it. Your research methodology is ultimately a methodological and systematic plan to resolve your research problem.

In short, you are explaining how you will take your idea and turn it into a study, which in turn will produce valid and reliable results that are in accordance with the aims and objectives of your research. This is true whether your paper plans to make use of qualitative methods or quantitative methods.

The purpose of a research methodology is to explain the reasoning behind your approach to your research - you'll need to support your collection methods, methods of analysis, and other key points of your work.

Think of it like writing a plan or an outline for you what you intend to do.

When carrying out research, it can be easy to go off-track or depart from your standard methodology.

Tip: Having a methodology keeps you accountable and on track with your original aims and objectives, and gives you a suitable and sound plan to keep your project manageable, smooth, and effective.

With all that said, how do you write out your standard approach to a research methodology?

As a general plan, your methodology should include the following information:

• Your research method.  You need to state whether you plan to use quantitative analysis, qualitative analysis, or mixed-method research methods. This will often be determined by what you hope to achieve with your research.
• Explain your reasoning. Why are you taking this methodological approach? Why is this particular methodology the best way to answer your research problem and achieve your objectives?
• Explain your instruments.  This will mainly be about your collection methods. There are varying instruments to use such as interviews, physical surveys, questionnaires, for example. Your methodology will need to detail your reasoning in choosing a particular instrument for your research.
• What will you do with your results?  How are you going to analyze the data once you have gathered it?
• Advise your reader.  If there is anything in your research methodology that your reader might be unfamiliar with, you should explain it in more detail. For example, you should give any background information to your methods that might be relevant or provide your reasoning if you are conducting your research in a non-standard way.
• How will your sampling process go?  What will your sampling procedure be and why? For example, if you will collect data by carrying out semi-structured or unstructured interviews, how will you choose your interviewees and how will you conduct the interviews themselves?
• Any practical limitations?  You should discuss any limitations you foresee being an issue when you’re carrying out your research.

In any dissertation, thesis, or academic journal, you will always find a chapter dedicated to explaining the research methodology of the person who carried out the study, also referred to as the methodology section of the work.

A good research methodology will explain what you are going to do and why, while a poor methodology will lead to a messy or disorganized approach.

You should also be able to justify in this section your reasoning for why you intend to carry out your research in a particular way, especially if it might be a particularly unique method.

Having a sound methodology in place can also help you with the following:

• When another researcher at a later date wishes to try and replicate your research, they will need your explanations and guidelines.
• In the event that you receive any criticism or questioning on the research you carried out at a later point, you will be able to refer back to it and succinctly explain the how and why of your approach.
• It provides you with a plan to follow throughout your research. When you are drafting your methodology approach, you need to be sure that the method you are using is the right one for your goal. This will help you with both explaining and understanding your method.
• It affords you the opportunity to document from the outset what you intend to achieve with your research, from start to finish.

A research instrument is a tool you will use to help you collect, measure and analyze the data you use as part of your research.

The choice of research instrument will usually be yours to make as the researcher and will be whichever best suits your methodology.

There are many different research instruments you can use in collecting data for your research.

Generally, they can be grouped as follows:

• Interviews (either as a group or one-on-one). You can carry out interviews in many different ways. For example, your interview can be structured, semi-structured, or unstructured. The difference between them is how formal the set of questions is that is asked of the interviewee. In a group interview, you may choose to ask the interviewees to give you their opinions or perceptions on certain topics.
• Surveys (online or in-person). In survey research, you are posing questions in which you ask for a response from the person taking the survey. You may wish to have either free-answer questions such as essay-style questions, or you may wish to use closed questions such as multiple choice. You may even wish to make the survey a mixture of both.
• Focus Groups.  Similar to the group interview above, you may wish to ask a focus group to discuss a particular topic or opinion while you make a note of the answers given.
• Observations.  This is a good research instrument to use if you are looking into human behaviors. Different ways of researching this include studying the spontaneous behavior of participants in their everyday life, or something more structured. A structured observation is research conducted at a set time and place where researchers observe behavior as planned and agreed upon with participants.

These are the most common ways of carrying out research, but it is really dependent on your needs as a researcher and what approach you think is best to take.

It is also possible to combine a number of research instruments if this is necessary and appropriate in answering your research problem.

There are three different types of methodologies, and they are distinguished by whether they focus on words, numbers, or both.

➡️ Want to learn more about the differences between qualitative and quantitative research, and how to use both methods? Check out our guide for that!

If you've done your due diligence, you'll have an idea of which methodology approach is best suited to your research.

It’s likely that you will have carried out considerable reading and homework before you reach this point and you may have taken inspiration from other similar studies that have yielded good results.

Still, it is important to consider different options before setting your research in stone. Exploring different options available will help you to explain why the choice you ultimately make is preferable to other methods.

If proving your research problem requires you to gather large volumes of numerical data to test hypotheses, a quantitative research method is likely to provide you with the most usable results.

If instead you’re looking to try and learn more about people, and their perception of events, your methodology is more exploratory in nature and would therefore probably be better served using a qualitative research methodology.

It helps to always bring things back to the question: what do I want to achieve with my research?

Once you have conducted your research, you need to analyze it. Here are some helpful guides for qualitative data analysis:

➡️  How to do a content analysis

➡️  How to do a thematic analysis

➡️  How to do a rhetorical analysis

Research methodology refers to the techniques used to find and analyze information for a study, ensuring that the results are valid, reliable and that they address the research objective.

Data can typically be organized into four different categories or methods: observational, experimental, simulation, and derived.

Writing a methodology section is a process of introducing your methods and instruments, discussing your analysis, providing more background information, addressing your research limitations, and more.

Your research methodology section will need a clear research question and proposed research approach. You'll need to add a background, introduce your research question, write your methodology and add the works you cited during your data collecting phase.

The research methodology section of your study will indicate how valid your findings are and how well-informed your paper is. It also assists future researchers planning to use the same methodology, who want to cite your study or replicate it.

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• How to Write a Research Proposal | Examples & Templates

How to Write a Research Proposal | Examples & Templates

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

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

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

Introduction

Literature review.

• Research design

Reference list

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

Table of contents

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

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

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

Research proposal length

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

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

Download our research proposal template

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

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

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

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

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

Your introduction should:

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

To guide your introduction , include information about:

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

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

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

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

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

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

For example, your results might have implications for:

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

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

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

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

Download our research schedule template

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

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

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

To determine your budget, think about:

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

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

Methodology

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

 Statistics

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

Research bias

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

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

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

I will compare …

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

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

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

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

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

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

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

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

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

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Job Overview

The Legislative Budget Office (LBO) is seeking to fill an Economist position as part of their tax expenditure evaluation team. This is a full-time benefits eligible position. The economist enjoys rewarding work applying statistical and econometric research methods to support the Minnesota Legislature in evaluating tax expenditure policies including, tax credits, tax exemptions, and preferential rates, among other tax incentives. The Legislative Budget Office will determine the starting salary based on the successful applicant’s qualifications and experience. The full salary range for the position is $91,996 to $166,048. The starting salary offered will be based upon an assessment of the candidate’s qualifications and experience. A complete position description is available on the legislative coordinating commission's employment opportunities webpage or call 651-284-6436 to request a copy. For more information regarding the LBO, visit the LBO website.

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• Contributing to the development of written reports submitted to the Minnesota Tax Expenditure Review Commission.
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• Flood and Coastal Erosion Risk Management (FCERM) research reports

Eel passes – improving design and performance

This project reviewed eel passes in England and considers how to improve them.

SC150001 Eel passes - improving design and performance

Project overview

This project reviewed how different designs of eel passes in England are working. The aim was to identify any problems with installed passes and consider how to improve the design and performance of existing and future passes.

In response to the severe decline in numbers of European eels in England, eel passes were installed at more than 500 Environment Agency river structures such as dams, weirs, and tide gates. These passes aim to make it easier for juvenile eels to migrate upstream to help restore eel populations in freshwater habitats. As some passes were installed several years ago, this gave us the opportunity to learn lessons to improve eel passage.

This project had 2 phases. The first was a literature review on eel pass performance and seasonal eel migration patterns. The second part was a detailed case studies of 2 facilities that have been operating and monitored for more than 10 years: Judas Gap Weir (Essex) and Greylake Sluice (Somerset).  

The final report draws together the phase 1 literature review findings with the phase 2 data on how eel passes have worked. It can be used as a resource to:

• identify any issues with passes that may have been installed for several years
• make sure existing and new eel passes work as efficiently as possible with lessons learned and current guidance
• develop a research programme to improve design and performance of eel passes for all upstream migrating life stages of the European eel

Design considerations

The report’s design considerations for eel passes are for the Environment Agency and other structure owners, such as water companies and private individuals. They use lessons learnt from existing passes to help make sure future ones are designed to protect and secure the survival of the nation’s eel population.

The design considerations cover all aspects of pass installation and design, such as:

• choosing the appropriate type of pass
• selecting the right construction materials
• the operating schedule and maintenance considerations

The report also includes a summary of the characteristics of upstream eel migration that are important when designing and operating passage facilities, for example:

• differences in the physical capabilities of eels at their various migratory life stages, to ensure all sizes of upstream migrating eels can use the passes
• eel migration patterns are strongly seasonal, affected by factors such a temperature, river flow, moon phase and tidal cycle

The project identified areas for future research to further improve eel pass design and performance. These are based on a better understanding of the behaviour and requirements of different life stages of eel and lessons learned. This report was written in 2019 and the future research is underway.

Project Information

Project managers: Juliet de Little, Zora van Leeuwen and Sarah Twohig (FCERM research), Ros Wright (National Monitoring, Fisheries and Ecology)

This project was commissioned by the Environment Agency’s FCRM Directorate, as part of the joint Flood and Coastal Erosion Risk Management Research and Development Programme.

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Computer Science > Computation and Language

Title: a survey of prompt engineering methods in large language models for different nlp tasks.

Abstract: Large language models (LLMs) have shown remarkable performance on many different Natural Language Processing (NLP) tasks. Prompt engineering plays a key role in adding more to the already existing abilities of LLMs to achieve significant performance gains on various NLP tasks. Prompt engineering requires composing natural language instructions called prompts to elicit knowledge from LLMs in a structured way. Unlike previous state-of-the-art (SoTA) models, prompt engineering does not require extensive parameter re-training or fine-tuning based on the given NLP task and thus solely operates on the embedded knowledge of LLMs. Additionally, LLM enthusiasts can intelligently extract LLMs' knowledge through a basic natural language conversational exchange or prompt engineering, allowing more and more people even without deep mathematical machine learning background to experiment with LLMs. With prompt engineering gaining popularity in the last two years, researchers have come up with numerous engineering techniques around designing prompts to improve accuracy of information extraction from the LLMs. In this paper, we summarize different prompting techniques and club them together based on different NLP tasks that they have been used for. We further granularly highlight the performance of these prompting strategies on various datasets belonging to that NLP task, talk about the corresponding LLMs used, present a taxonomy diagram and discuss the possible SoTA for specific datasets. In total, we read and present a survey of 44 research papers which talk about 39 different prompting methods on 29 different NLP tasks of which most of them have been published in the last two years.

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• Published: 24 July 2024

Designing a model of emergency medical services preparedness in response to mass casualty incidents: a mixed-method study

• Vahid Saadatmand 1 ,
• Milad Ahmadi Marzaleh 2 ,
• Nasrin Shokrpour 3 ,
• Hamid Reza Abbasi 4 &
• Mahmoud Reza Peyravi 5  

BMC Emergency Medicine volume  24 , Article number:  127 ( 2024 ) Cite this article

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Metrics details

Emergency medical services preparedness in mass casualty incidents is one of the most important concerns in emergency systems. A mass casualty incident is a sudden event with several injured individuals that overwhelms the local health care system. This study aimed to identify and validate the components of emergency medical services readiness in mass casualty incidents which ultimately led to designing a conceptual model.

This research was an explanatory mixed-method study conducted in five consecutive stages in Iran between November 2021 and September 2023. First, a systematic review was carried out to extract the components of emergency medical services preparedness in mass casualty incidents based on the PRISMA guideline. Second, a qualitative study was designed to explore the preparedness components through in-depth semi-structured interviews and analyzed using the content analysis approach. Third, the integration of the components extracted from the two stages of the systematic review and qualitative study was done by an expert panel. Fourth, the obtained components were validated using the Delphi technique. Two rounds were done in the Delphi phase. Finally, the conceptual model of emergency medical services preparedness in mass casualty incidents was designed by a panel of experts.

10 articles were included in the systematic review stage and sixteen main components were extracted and classified into four categories. In the second stage, thirteen components were extracted from the qualitative study and classified into five categories. Then, the components of the previous two phases were integrated into the panel of experts and 23 components were identified. After validation with the Delphi technique, 22 components were extracted. Lastly, the final components were examined by the panel of experts, and the conceptual schematic of the model was drawn.

Conclusions

It is necessary to have an integrated framework and model of emergency medical service readiness in the planning and management of mass casualty incidents. The components and the final model of this research were obtained after the systematic scientific steps, which can be used as a scheme to improve emergency medical service preparedness in response to mass casualty incidents.

Peer Review reports

Mass casualty incidents (MCIs) are among the critical issues that affect the healthcare system including emergency medical service (EMS). The World Health Organization (WHO) defines MCIs as disasters characterized by quantity, severity, and variety of patients that can immediately overwhelm the capability of local medical resources to deliver comprehensive medical care. MCIs occur when the demand for medical services brought on by a sudden event exceeds the capacity of a healthcare system to supply them [ 1 , 2 ]. MCIs span multiple causes including natural disasters, transport incidents, terrorism, armed conflicts, etc. MCIs are often devastating to societies and have profound mortality and long-term consequences [ 3 ]. EMS is a part of the health system that is assigned to emergency response. The WHO considers EMS systems as an integral part of any operational healthcare system. EMS provides care to people in a variety of medical emergencies. One of the most important responsibilities of EMS is to respond to MCIs. When an EMS response involves large casualties, responsible organizations such as the police and fire and rescue teams must utilize resources to expand operations. In large MCIs, there may be more victims than regional EMS can manage, and additional assistance may be required. When the resources are limited or the EMS is involved in the number of casualties, the disaster happens. In most MCIs, routine operations may be developed to manage increased capacity. In the United States, MCIs are managed using the National Incident Management System (NIMS) and the Incident Command System (ICS). All rescue providers and disaster management workforce should be familiar with and competent in using NIMS and ICS [ 4 , 5 ].

EMS plays a major role in managing all phases of MCIs, including prevention, preparedness, response, and recovery [ 6 ]. These systems require an organized and planned approach with adequate resources so that they can effectively manage MCIs. Such important tasks of this system are life-saving measures, triage, and transfer of the injured to medical centers. The EMS system should increase its level of preparedness in response to such disasters. This requires planning, training, coordinating, and sensitizing managers to pay more attention to this area [ 7 ]. Therefore, EMS systems must design a framework based on standard indices. To this end, this study was conducted to identify the components of EMS readiness in MCIs and design a model that can depict the dimensions of EMS preparedness in response to MCIs.

Conceptual framework

The conceptual framework of this research in line with the study objectives includes three components: emergency medical service (EMS), preparedness, and mass casualty incidents (MCIs).

Conceptual view of emergency medical service (EMS)

An Emergency Medical Service can be defined as "a comprehensive system which provides the organization of staff, facilities, and equipment for the effective, coordinated, and timely delivery of care and emergency services to victims of sudden illness or injury." The ambition of EMS focuses on providing timely care to the victims of sudden and life-threatening injuries or emergencies to prevent undesired mortality or long-term morbidity. The function of EMS can be simplified into four main components: accessing emergency care, care in the community, care en route, and care upon arrival to receiving care at the healthcare facility [ 8 , 9 ]. In addition to the routine responsibility of EMS, one of the important tasks of this system is disaster management [ 2 ].

Conceptual view of preparedness

Preparedness is the knowledge and capacities developed by governments, professional response and relief organizations, communities, and individuals to effectively anticipate, respond to, and recover from the impacts of likely, imminent, or current hazards, events, or situations [ 10 ]. Preparedness is a continuous cycle of planning, organizing, training, equipping, exercising, evaluating, and taking corrective action to ensure effective coordination during the incident response [ 11 ]. Preparedness within the field of emergency management can best be defined as a state of readiness to respond to a disaster, crisis, or any other type of emergency [ 12 , 13 ]

Conceptual view of mass casualty incidents (MCIs)

A Mass Casualty Incident (MCI) can be defined as "an overwhelming event, which generates more victims at a time than locally available resources can manage using routine procedures. It requires exceptional emergency arrangements and additional or extraordinary assistance." MCIs can occur as a consequence of a wide variety of events: disasters (both natural and man-made), terrorist attacks, motor vehicle collisions, etc. Whatever the cause of the event is, the characterizing feature of an MCI is the number of victims large enough to disrupt the normal functioning of healthcare services. MCIs can be classified into different levels according to either the number of potential victims or the entity of the response [ 14 ] .

This research was an explanatory mixed method suggested by Creswell [ 15 ] which was conducted in five consecutive stages in Iran between November 2021 and September 2023. The purpose of this mixed-method study was to identify the main components of EMS preparedness in MCIs in several systematic stages and then design a conceptual model that can represent the essential elements of EMS readiness in MCIs in the form of a framework. The questions of this research were as follows:

What are the main and effective components of EMS preparedness in response to MCIs?

What will be the final model of EMS readiness in response to MCIs?

The research implementation diagram is shown in Fig.  1 .

Research implementation diagram

Systematic review

Eligibility criteria and search strategies.

This study was conducted based on the Preferred Reporting Item for Systematic Reviews and Meta-analyses (PRISMA-ScR) instruction. The systematic review instruction included the background data, review questions, inclusion and exclusion criteria, search strategies, selection of studies, criteria to review the studies, literature review, data extraction, and reporting. A systematic search was performed from January 1970 to February 2022 in peer-reviewed English texts related to the research question—i.e., what the components of EMS preparedness for MCIs are. First, a rapid and general search was conducted in the Cochrane Library database to ensure the lack of duplicate studies. The results indicated that there were no duplicate articles. The electronic databases searched included PubMed, Cochrane Library, Scopus, Science Direct, and ProQuest. The gray literature, such as books, theses, conference articles, and websites, was also searched. The “AND” operator was used to search among the groups of words considered as separate concepts. For synonym words, the “OR” operator was used. The search was conducted on the titles, abstracts, and keywords of the articles. Medical Subject Headings (MeSH) terms were used to find articles in the PubMed database. The search strategy is presented in Table  1 .

The keywords were selected using MeSH and investigating the studies based on the objectives. In the next step, a full list of references for all the articles was prepared, and the titles of the articles were investigated by the researchers to omit the irrelevant ones. The END NOTE software, version X8, was used to manage the resources.

Inclusion criteria

The main keywords were used to explore the studies related to EMS preparedness in MCIs. First, the titles of the articles were evaluated by two independent reviewers. We determined whether titles, keywords, or abstracts were included in the study subject. In the case of disagreement, they were verified by one external reviewer using the consensus method, and any disagreement was resolved. Then, the abstracts were assessed; finally, the full texts of the articles were evaluated based on the validated checklists. The STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) and the COREQ (Consolidated criteria for reporting qualitative research) checklists were used for the quality evaluation of the studies. The included articles were written in the English language. Unpublished articles (gray literature), protocols, conference papers, guidelines, and reports from creditable organizations were reviewed as well. To analyze the risk of bias, two researchers independently performed all quality assessment processes based on the Cochrane Collaboration tools.

Exclusion criteria

The articles in the field of hospital emergency department, those irrelevant to the research questions, guidelines outside the scope of the study, and the articles that did not meet the expected quality according to the validated checklists were excluded from this study.

First, the titles of all the articles extracted from the databases were checked by two independent reviewers. The articles that met the inclusion criteria and were relevant to the research question were selected. In the next step, the author studied the abstracts of the selected articles. Then, those abstracts that were completely in line with the study objectives and the inclusion criteria were extracted, and their full texts were assessed by the authors. Finally, the articles relevant to EMS preparedness in MCIs were selected.

Data extraction

The essential information was extracted after reading the articles carefully based on the summaries and collection forms. This form included the title, corresponding author, research objective, research population, samples, country, time of research, research design, instruments, method, results, and conclusion sections. The summary forms were filled out for each article. These forms were evaluated by two independent researchers after reviewing all the articles; the data were then presented in a table. If there were disagreements in data extraction, an external researcher commented on the conflicting issues, and any disagreement was resolved [ 16 ].

Qualitative study

This qualitative study was conducted using a content analysis approach. Inductive coding was adopted in this approach. Recently, three approaches have been presented in content analysis, including conventional, summative, and directed, which differ in coding and trustworthiness. In the conventional approach, classification codes are extracted directly from the data [ 17 ]. The study adopted the conventional content analysis as well. This study was conducted using the consolidated criteria for reporting qualitative research (COREQ).

Setting and participants

Sampling was done from five provinces (Tehran, Fars, Hormozgan, Hamadan, and Kerman) in Iran from April 2022 to mid-March 2023. The participants were managers and members of the EMS incident command team, EMS field experts, technicians, paramedics, and EMS telecommunicators with rich information who were selected using the purposive sampling method. Sampling continued until data saturation. The inclusion criteria were as follows: (1) having at least a bachelor's degree; (2) having experiences in MCIs context and being motivated to participate in the study; and (4) signing an informed consent form.

Data collection

Data were collected through semi-structured interviews by the researcher (VS), using a pilot-tested interview guide. The interviews were conducted face-to-face, and their follow-up was mostly done using a telephone. The study outline was approved by three members of the research team. Some of the main interview questions were as follows:

In what ways is EMS more vulnerable during MCIs?

What measures are needed to be taken in EMS before MCIs?

What features should EMS systems have to respond to MCIs?

What elements affect EMS readiness in MCIs?

All the interviews were completed and recorded by the main researcher (VS). The interviews lasted about 30–90 min, and the participants responded to all the questions according to the research outline. Before the interview, the necessary arrangements were made with the participants. The interviews were conducted in a quiet and uninterrupted atmosphere. The researcher refrained from using negative, judgmental, and forgiving statements and attitudes. Data collection continued until saturation, and the researcher stopped sampling when he realized that no new data was obtained and that there were a lot of duplicated data.

Data analysis

A qualitative method was used for data analysis. After each interview, the recordings were transcribed, and the main researcher (VS) used the content analysis method to analyze and summarize the data. The steps were: (1) familiarization: the text was read repeatedly to familiarize the subjects with the qualitative data; (2) coding: the open coding method was used to analyze the data line by line, and essential words and phrases (unit meaning) were recorded in the margins of the content; and (3) integration: each important meaning unit was described in a descriptive code, and codes with the same meaning were integrated [ 18 ]. MAXQDA was used for data organization and coding. After the analysis, two other researchers, who were familiar with the telephone records, performed a peer review to ensure the validity of the analysis results.

Long-term engagement with participants, member and peer checking, presentation of rich descriptions of data, and data analysis were used for rigor [ 19 ]. Besides long-term communication with the participants, the research team spent enough time on data collection and follow-up with the interviewees. Transcriptions along with coding and categories were shown to two qualitative researchers for peer check. Their confirmation was obtained after applying their comments. A brief report of the interviews and the extracted codes was given to the interviewees, who approved it to examine the members. There was also an attempt to consider the maximum diversity in sampling for transferability [ 20 ].

Integrating the results of the systematic review and qualitative study

The integration of the results was done using joint display technique in the expert panel [ 21 ]. The members present in the panel consisted of 10 experts in the field of EMS and MCIs area who were selected purposefully. To integrate the findings of two qualitative stages and systematic review, we first entered a table with three columns into Word software. On the left side, the findings of the systematic review were entered, and on the right column, those of the qualitative study were entered. A column in the middle was considered empty to enter the integrated findings. Before entering the findings into the table, the research team did a content analysis on the findings of the systematic review, and new concepts were obtained from the data extracted from this stage. Two expert researchers in the field of EMS independently reviewed the results of this content analysis, and any disagreement was resolved. This content analysis was done because, firstly, the components of the systematic review were unique in terms of content and did not overlap with each other. Secondly, the components of the two stages should have a closer affinity for integration. It is worth mentioning that two researchers in the field of EMS and the people present in the panel were very familiar with the setting of EMS and this selection of people was done purposefully. After entering the final results of each step into the table, the panel members reviewed all the findings of both studies separately. Then, the components were read one by one from the left side by the researcher and the members were asked to determine the similarity of this finding with that of the right column by drawing a line through critical thinking. To reach a consensus at this stage, voting by raising hands was used. This process was done for all the findings in two consecutive panel sessions until the completion of the integration stage. Table 2  shows integration of components using the display joint method.

Validation of the components using the Delphi technique

The Delphi technique was used to validate the integrated components. It is a systematic approach to elicit opinions from a group of experts on a specific topic [ 22 ]. Targeted sampling was used to select the experts who were working in EMS systems at this stage. The research population consisted of experts in the field of health in disasters and emergencies, emergency medicine specialists, and managers of the EMS system of Iran (Fars, Hormozgan, Tehran, Hamedan, and Kerman provinces). A questionnaire containing extracted components was created by the research team and sent to 29 participants who were selected purposefully. This questionnaire included 23 questions scored using a five-point Likert scale, and the scored ranged from completely agree (score 5); I agree (score 4); I have no opinion (score 3); I disagree (score 2), and completely disagree (score 1). In the questionnaire form, the purpose of this step was explained, and the consent form was signed by the participants. Out of 29 people, 22 completed the questionnaire. SPSS version 16 software was used for data analysis. Descriptive statistics were used to interpret the data. Mean and standard deviation indicators were used to describe the percentage of the participants' agreement on each component. In the first round, the components whose average score was less than 50% were eliminated. A percentage of agreement greater than 75% for each component means that it is accepted. The components whose percentage of agreement was between 50 and 75% were included in the second round, and if more than 75% agreement was obtained, they were approved [ 23 ].

Model design

A panel of experts was used to design the model. This panel was held with the presence of 7 disaster context specialists and EMS experts who selected purposefully. For this purpose, the objectives of the research were first described to the experts. Then, the components obtained from the previous steps were explained to them so that they could express their opinions and ideas about the role and placement of the variables, and schematic of the model. Since in this model the cause-effect relationship of the variables was not considered, an attempt was made to design a simple and expressive model by considering the international standards. Therefore, to examine the models, we searched internationally recognized EMS agencies and systems such as the Federal Emergency Management Agency (FEMA), Pan American Health Organization (PAHO), and other related databases. After reviewing and discussing, a model that was close to our goals was selected finally. However, minor changes were made to this model to make it easier to understand. This model was introduced by Adam Tager. Adam Tager serves as the disaster preparedness program manager in the FEMA mission support executive office. In this role, he leads preparedness and response attempts on behalf of the associate administrator for mission support. Before this, he served as a field operations analyst and a consultant supporting FEMA and the Department of Defense. He has worked with national and state emergency management programs and has had roles in all-hazards event response and recovery, development and conduct of training exercises, and development and writing after-action reports [ 24 ]. Therefore, this model was selected, and after some changes was made in it, the schematic of the model was drawn.

Findings of the systematic review

After searching the databases, 20,499 articles were identified; of them, 201 were in Direct Science, 1417 in PubMed, 16,192 in Scopus, 2677 in ProQuest, and 12 in the Cochrane Library. After screening and evaluating the quality of the articles, finally, 10 articles were included in the study. The components of the EMS preparedness in MCIs were identified after reviewing the articles. The 16 main components including education and training, skills and experience, relationships and psychological factors, management planning, constant improvement, coordination, manpower, equipment and ambulance, support processes, command and control, incident scene, triage, evacuation and gathering, treatment, communication, distribution, transport, and follow-up of the injured were extracted. These were extracted by the research team through summarizing the findings of the articles. The components were classified into four categories, including individual improvement, group improvement, resources, and operations through thematic analysis [ 16 ].

Findings of the qualitative study

Thirty-six participants (24 men and 12 women) were included in the study. Thirteen components were extracted from the study and classified into five categories including strengthening management and organization, individual and group empowerment, capacity expansion, technology and infrastructure development, and operational response measures [ 20 ], as shown in Fig. 2 .

Findings of the qualitative stage (this printed table is the result of our article published in reference 20)

Findings related to the integration of the results of the systematic review and qualitative study

23 components were extracted from combining the results of the two stages of systematic review and qualitative study in the panel of experts. The integrated components are presented in Table  3 .

Findings of the Delphi phase

In the first round of Delphi, 22 out of 28 participants completed the questionnaire. In the second round of Delphi, out of 28 subjects who had received the questionnaires again, 22 experts filled out the questionnaires sent. Descriptive statistics showed that 22 subjects entered the Delphi stage, of whom 16 were men and 6 were women in the age range of 32–55 years and with a work experience of 5–25 years. The results of the statistical analysis showed that out of 23 components, only "Establishment of EMS decontamination teams in CBRNE" scored less than 2.5 (with an average score of 2.27, which means less than 50% of the acceptable score), so it was eliminated during the first round.

According to the results, the average of most of the components was more than 3.75 (more than 75% agreement), and only two components, "Common triage and treatment" and "Unified emergency contact number", had an average score of more than 2.5 and less than 3.75 (i.e. agreement between 50 and 75 percent) and were sent to the second round of Delphi. In the second round of Delphi, the two components mentioned were again given to 22 participants and the scores were analyzed. The results showed that the average agreement score of these components was more than 3.75 (i.e. more than 75% agreement).

After the completion of the Delphi phase, it was found that 22 out of the 23 components finally got the necessary points; thus, they were approved for review in the next phase which was related to the panel of experts. All the Delphi rounds and the final scores are shown in Table  4 .

The design of the schematic was based on the model proposed by Adam Tager. According to the acceptability and validation of this model in the panel of experts, with some modifications in line with the study objectives, this model was selected and finally the schematic form of the model was designed. This pattern consists of classes that are placed in a cycle, while the main subject of the model is in the center, and the subclasses are placed outside the cycle in their partitions. After content analysis in the panel, all components were placed in 5 main categories including "Command", "Operations", "Resources", "Communications and Information", and "Group and Individual Empowerment". The final model of EMS preparedness in response to MCIs is designed in Fig.  3 .

The model of EMS preparedness in MCIs

After several consecutive steps in this research, the components of EMS preparedness in MCIs which were the main objectives of the study were finally determined. One of the reasons why the research team decided to do a mixed-method work was the importance of the area. Maybe, if we had a single study approach to achieve the main goal, we could not confidently trust the obtained result. Therefore, the mixed method was adopted. Systematic review studies contain rich results, and we have made comprehensive findings in this phase. The findings of the systematic review included the experiences of EMS systems around the world in response to MCIs. Most of the results of this phase, according to the selected articles, were taken from the real experiences of EMS systems, and this was a turning point in the findings obtained in this research. Considering that this study was in a special context (the field of emergencies and disasters), it would be better to consider complementary methods to achieve the goals. Therefore, we decided to look for unwritten experiences in disaster context, in addition to the published documents. For this reason, we chose a qualitative approach to obtain the experiences of people who spend their lives in EMS systems. The purpose of conducting a qualitative study was to enrich the results in this mixed method. A qualitative study was conducted with in-depth semi-structured interviews in Iran, and the results obtained were mostly consistent with those of the systematic review phase. After passing through these two steps, we integrated the findings in a systematic way to obtain more comprehensive results. After several consecutive stages, 22 main components were finalized, which were drawn in a conceptual model in 5 main levels including command, operation, resources, communication and information, and group and individual empowerment for better understanding and implementation. Although the known components in this research have many similarities with the studies done, the differences mostly originate from regional and national structures and policies. One of the highlights of this study was the removal of the component of "EMS decontamination teams in CBRNE" in the first round of Delphi. Although this component was extracted from the systematic review [ 16 ], it was not accepted in the remaining stages, including the qualitative study [ 20 ], Delphi stage, and panel of experts. One of the reasons this issue arose in the qualitative study and expert panel was that EMS has complex tasks in triage, treatment, transfer, and other incident management processes [ 25 ], and decontamination is a sensitive operation that requires specialized logistics and equipment that is beyond the scope of most EMS systems. In most emergency systems, the duty of decontamination (according to the type of the risk) is the responsibility of the fire department, army, safety units of refineries and factories, and so on. However, this component must be accepted in some EMS systems due to the existence of resources, equipment, integration of rescue teams, and regional policies [ 26 ]. Due to the complexity of CBRNE incidents, regional policies are essential, and this component may not be accepted globally in an EMS setting. However, this issue depends on national and regional policies.

In addition, components such as a common emergency contact number, common triage and treatment system, and tactical teams that are used in many emergency systems still have pros and cons in some countries. However, the rest of the components, including the unified command, strengthening of communication, education and training, physical and psychological factors, and so on, are present in most EMS systems as the main indicators of readiness although there may be differences in how they are implemented. Nonetheless, in this research, the components of EMS preparedness in MCIs were explored in several stages so that a model with reliable indicators for planning and policymaking can be developed.

Various methods have been used in developing EMS preparedness and response models in MCIs. Among these, dynamic modeling methods, computer modeling, conceptual modeling, and modeling with cause-effect relationships can be mentioned.

A study by Lee aimed to simulate the distribution of emergency relief equipment for disaster response operations. In this study, the concern was that in the event of disasters such as storms, earthquakes, and terrorism, there is a need to distribute emergency relief equipment to the victims to protect their health and lives. The research team developed a modeling framework for disaster response in which the supply chain of relief supplies and distribution operations were simulated and analyzed to test the optimal transportation of relief supplies to various distribution points. The disaster response simulation model included the modeling of the relief resource supply chain and operations at the distribution point. The results showed that the model could evaluate a wide range of disaster scenarios, evaluate disaster response plans and policies, and identify better approaches for government agencies and first responders [ 27 ].

In another study, a simulated model of EMS services in MCIs was designed by Su in Taiwan. In this study, object-oriented simulation software was used to improve EMS care. In this research, a computer virtual simulated model was designed. The results showed that the most efficient part of this model in caring for the injured is when the integrated deployment of EMS is launched along with the increase of emergency networks and specialized life-saving protocols [ 28 ].

Another research was conducted by Pasupathy in Belgium to design a simulated model of casualty management in disasters and emergencies. In this study, a profile of the real injured was drawn by the elites of the pre-hospital field. These profiles were drawn in a medical emergency model where a single response was given by the system to real casualties. The medical emergency response model focused on emergency services operations including triage, evacuation, and medical procedures. Medical decisions such as whether to evacuate or treat at the scene were based on the victim’s breathing, heart rate, and motor response. Finally, a simulated model was designed that was related to road accidents and showed how much resources could affect the prognosis in these incidents [ 29 ].

In China, a model entitled EMS response dynamic model was designed in response to MCIs. This study was conducted to find out the EMS-MCI modeling in Shanghai, improving rescue efficiency in MCIs and providing a possible method for quick decision making in these incidents. This model was designed using the Vensim DSS program and intervention scenarios by adjusting the scales of accidents, ambulance allocation, emergency medical staff allocation and the efficiency of organization and command. The results showed that by increasing the number of ambulances and improving the efficiency of the organization and command, the mortality rate decreased significantly [ 30 ].

Tseng in China designed a theoretical model of EMS response in tunnel-related traffic incidents as a scenario-based computer simulation model. In this study, based on a theory, data related to the general characteristics and components of MCIs were collected in order to create a simulation model based on the method of emergency response plans. In this method, a disaster response simulation model was presented using realistic accidents taken from previous experiences. In this study, the main variables included EMS response components in MCIs, pre-hospital time indicators, the ability to save and preserve hospital life, and the level of organizational and command efficiency. This model is called causal curve diagram and included 5 main parameters with 102 variables, which were connected in the form of curvilinear flows and based on one-way or two-way relation. In addition, the subsystems of this model include 5 items: MCIs, hospital rescue and life preservation operations, organization and control, emergency center, and finally prognosis of the injured which were connected through input and output variables [ 31 ].

The results of this mixed method indicate that EMS systems need to strengthen specific components to increase their readiness in response to MCIs. The components extracted from this study were identified in depth with several approaches which were finally drawn in the form of a simple model. Due to the deep and scientific view in the methodology of this research and the extraction of validated findings, these results can be theoretically used in EMS planning and policies in the field of disasters. In addition, EMS systems and Partner organizations can use the results of this research in practice and personnel training. Due to the importance of the mixed method in achieving rich results in the field of disasters, the method of this study can be used more than single approaches. Also, the methodology and techniques of this research can be used as a pattern in the design of future studies in important areas such as disasters where it is necessary to achieve valid and vital results.

Due to the complexity of management and operational planning in most MCIs, it would be better to draw the preparedness plans in a simple and comprehensible way so that a quick and effective response can be delivered. In this research, the goal was to create a simple conceptual model that represents the main components of EMS preparedness in MCIs. The most important distinguishing feature of the model designed in this study was that simulation, computer and special software methods were not used as in the above-mentioned studies and the main attempt was to design a model to cover the important readiness indicators of EMS systems in response to MCIs so that they can be used easily. Although most of the components were identified and validated in this study, there may be other components that can be unique to states and regional policies. For example, some specific indicators were related to the regions with specific climatic and geographical conditions that overshadow the EMS response. However, in this research the components of EMS preparedness in MCIs were only introduced and presented in the form of a model, and the effectiveness of this model was not measured practically. Therefore, it is recommended that the effectiveness of this model in the management of MCIs in practical exercises, simulations and real incidents should be measured.

Limitations

In the systematic review phase, one of the limitations was the lack of access to some electronic databases such as Web of science (WOS), for searching articles. Unfortunately, due to economic and political sanctions in Iran, some databases were not available at the time of the search and we had to ignore them. This issue worried us because we might have missed some studies. However, we tried to use other reliable electronic databases such as PubMed, Scopus, Science direct, ProQuest, and Cochrane.

MCIs are complex conditions that can seriously overwhelm the EMS function. Improving EMS readiness in MCIs is multifactorial and influenced by regional and national conditions. Designing a model with different methods in improving EMS readiness in MCIs can have a significant impact on better understanding of the plans and policies in simulated environments and real incidents. The designed model can be used as a framework for implementing EMS management strategies in MCIs.

Availability of data and materials

The data are not publicly available due to privacy or ethical restrictions, but the data that support the findings of this study are available on reasonable request from the corresponding author.

Abbreviations

Mass Casualty Incident

Emergency Medical Service

World Health Organization

Emergency Operation Center

Chemical, Biologic, Radioactive, Nuclear, Explosive

National Incident Management System

Incident Command System

Preferred Research Items for Systematic review and Meta-Analysis

Federal Emergency Management Agency

Pan American Health Organization

Consolidated criteria for reporting qualitative research

Strengthening the Reporting of Observational Studies in Epidemiology

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Acknowledgements

The research team would like to thank the participants for their valuable time and the research deputy of Shiraz University of Medical Sciences.

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Department of Medical Emergencies, School of Paramedicine, Faculty of Jahrom University of Medical Science, Jahrom, Iran

Vahid Saadatmand

Department of Health in Disasters and Emergencies, School of Health Management and Medical Information Sciences, Faculty of Shiraz University of Medical Sciences, Shiraz, Iran

Milad Ahmadi Marzaleh

English Department, Faculty of Paramedical Sciences, Shiraz, Iran

Nasrin Shokrpour

Department of Surgery, School of Medicine, Faculty of Shiraz University of Medical Sciences, Shiraz, Iran

Hamid Reza Abbasi

Department of Health in Disasters and Emergencies, School of Health Management and Medical Information Sciences, Faculty of Shiraz University of Medical Sciences, Alley 29, Qasrodasht Ave, Shiraz, 71336-54361, Iran

Mahmoud Reza Peyravi

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Contributions

Conceptualization, VS, M AM, MR P; methodology, VS, MR P; Investigation and formal analysis, VS, HR A, MR P; writing—original draft preparation, VS, N SH, M AM; writing—review and editing, VS, N SH. All authors have read and approved the final version of the manuscript. The corresponding author had full access to all of the data in this study and takes complete responsibility for the integrity of the data and the accuracy of the data analysis.

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Correspondence to Mahmoud Reza Peyravi .

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The study was approved by the ethics committee of Shiraz University of Medical Sciences with the code of IR.SUMS.NUMIMG.REG.1401.020, and consent was obtained from all participants and experts. The subjects were volunteers and were assured that the data would be strictly confidential, and the principle of anonymity would be adhered to. Additionally, the participants and experts could withdraw from the study any time they wished.

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Saadatmand, V., Ahmadi Marzaleh, M., Shokrpour, N. et al. Designing a model of emergency medical services preparedness in response to mass casualty incidents: a mixed-method study. BMC Emerg Med 24 , 127 (2024). https://doi.org/10.1186/s12873-024-01055-1

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Received : 13 March 2024

Accepted : 17 July 2024

Published : 24 July 2024

DOI : https://doi.org/10.1186/s12873-024-01055-1

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• Published: 26 July 2024

Potential assessment of CO 2 source/sink and its matching research during CCS process of deep unworkable seam

• Huihuang Fang 1 , 2 , 3 ,
• Yujie Wang 1 , 2 ,
• Shuxun Sang 4 , 5 , 6 ,
• Shua Yu 1 , 2 ,
• Huihu Liu 1 , 2 ,
• Jinran Guo 1 , 2 &
• Zhangfei Wang 1 , 2  

Scientific Reports volume  14 , Article number:  17206 ( 2024 ) Cite this article

Metrics details

• Energy and society
• Environmental economics
• Solid Earth sciences
• Stratigraphy

It is of great significance for the engineering popularization of CO 2 -ECBM technology to evaluate the potential of CCUS source and sink and study the matching of pipeline network of deep unworkable seam. In this study, the deep unworkable seam was taken as the research object. Firstly, the evaluation method of CO 2 storage potential in deep unworkable seam was discussed. Secondly, the CO 2 storage potential was analyzed. Then, the matching research of CO 2 source and sink was carried out, and the pipe network design was optimized. Finally, suggestions for the design of pipe network are put forward from the perspective of time and space scale. The results show that the average annual CO 2 emissions of coal-fired power plants vary greatly, and the total emissions are 58.76 million tons. The CO 2 storage potential in deep unworkable seam is huge with a total amount of 762 million tons, which can store CO 2 for 12.97 years. During the 10-year period, the deep unworkable seam can store 587.6 million tons of CO 2 , and the cumulative length of pipeline is 251.61 km with requiring a cumulative capital of $ 4.26 × 10 10 . In the process of CO 2 source-sink matching, the cumulative saving mileage of carbon sink is 98.75 km, and the cumulative saving cost is $ 25.669 billion with accounting for 39.25% and 60.26% of the total mileage and cost, respectively. Based on the three-step approach, the whole line of CO 2 source and sink in Huainan coalfield can be completed by stages and regions, and all CO 2 transportation and storage can be realized. CO 2 pipelines include gas collection and distribution branch lines, intra-regional trunk lines, and interregional trunk lines. Based on the reasonable layout of CO 2 pipelines, a variety of CCS applications can be simultaneously carried out, intra-regional and inter-regional CO 2 transport network demonstrations can be built, and integrated business models of CO 2 transport and storage can be simultaneously built on land and sea. The research results can provide reference for the evaluation of CO 2 sequestration potential of China's coal bases, and lay a foundation for the deployment of CCUS clusters.

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Introduction.

CCUS stands for CO 2 Capture, Utilization and Storage 1 . On the one hand, CCUS technology can reduce CO 2 emissions in the atmosphere and reduce the concentration of greenhouse gases 2 , 3 . On the other hand, it can help industries with CO 2 high-emission achieve low-carbon development and promote economic transformation 4 , 5 . Therefore, CCUS technology has broad application prospects in the field of global energy and environment. CO 2 emissions from coal are the largest source of carbon emissions in China, and it will take a long time for China to transform its energy situation 6 , 7 . Therefore, based on CCUS technology, it is of profound significance to reduce CO 2 emissions from coal, and can promote the realization of China's dual-carbon strategy.

CO 2 geological sequestration, a core component of CCUS, is an effective way to achieve large-scale de-carbonization 8 , 9 . Scientific evaluation of CO 2 storage potential in sedimentary basin and realization of source-sink matching are the basis of CCUS cluster deployment 10 , 11 . Major sedimentary basin in China have great potential for CO 2 storage, and the storage forms are diverse 12 . However, due to the lack of unified methods for CO 2 storage potential in sedimentary basin in China, the assessment of CO 2 storage potential greatly varies 13 . The CO 2 sequestration potential of geological body in China, such as oil and gas fields, deep unrecoverable seam, production and closed mines and goaf areas, is unclear and needs to be evaluated in detail.

Carbon emission sources in China's coal base are concentrated, and CO 2 emission sources and CO 2 storage sinks are highly overlapping 14 , which provides favorable conditions for CCUS cluster deployment. CCUS technology is the only way for coal base to achieve near zero for CO 2 emission in the future, and the deployment of “Coal base + CCUS” cluster has scale and agglomeration effects 15 . The geographical proximity of CO 2 sources and sinks can save more costs for CO 2 transportation, and the geographical concentration of a large number of CO 2 sources and sinks is also conducive to large-scale and clustered layout engineering practices. Geological body, such as deep unrecoverable seam, is the most typical forms of CO 2 storage in coal bases 16 , 17 . However, its CO 2 geological storage is still in the exploration stage, and there are few studies on its CO 2 storage potential 18 . Therefore, it is necessary to establish potential assessment methods suitable for the characteristics of China’s coal bases.

The CO 2 sequestration process can be simplified as the reverse process of the CBM extraction process, and its core mechanism is the dynamic process of CO 2 adsorption and displacement of CBM 19 , 20 . Therefore, the mechanism of CO 2 geological storage in unworkable seam is mainly about the mechanism of CO 2 adsorption and desorption in coal seam 21 . The coal resource in Huainan and Huaibei coalfields account for 97.7% of the total resources in the province, and the distribution is concentrated 22 , 23 , 24 . Therefore, the Huainan coalfield is determined as the estimation coalfield for CO 2 storage in this study. Due to the limitation of technical and geological conditions, the buried depth of coal mining in Anhui province is limited to less than 1000 m at present stage, and the coal seam with 1000–2000 m is the resource amount, which will be exploited in the next stage, and belongs to the deep unworkable seam at the present stage 16 , that is, the geological reserves with burial depth of 1000–2000 m are used to estimate the CO 2 storage potential in Anhui province.

In this study, the deep unworkable seam in Huainan coalfield was taken as the research object. Firstly, the evaluation method of CO 2 storage potential in deep unworkable seam was discussed. Secondly, the CO 2 geological storage potential was analyzed. Then, based on the lowest cost objective function and improved mileage saving method, the matching research of CO 2 source and sink for CO 2 geological storage was carried out, and the pipe network design was optimized. Finally, from the perspective of time and space scale, suggestions on the design of network planning of CCS source and sink are put forward in Huainan coalfield. The research innovations are described as follows: (1) Evaluation method of CO 2 storage potential in deep unworkable seam is discussed; (2) Matching problem of CO 2 source and sink is studied, and its pipe network design is optimized; (3) Design idea of network planning of CCS source and sink is systematically proposed. The results can provide reference for the evaluation of CO 2 sequestration potential of coal bases in China, and lay a foundation for CCUS cluster deployment.

Geological setting and analysis method

Geological background of the study area.

Based on regional structural analysis, the Huainan coalfield is located at the southern margin of North China Plate. In the west–east direction, the coal field boundary lies between the Kouziji-Nanzhaoji faults and the Xinchengkou-Changfeng faults. From north to south, the coalfield boundary lies between the Shangtangming-Longshan faults and Yingshang-Dingyuan faults (Fig.  1 ) 25 , 26 . The coalfield is a near east–west hedge tectonic basin with imbricate fan composed of nappe structures on both sides of the basin and simple synclinic structure in the interior (Fig.  1 ).

Geological background of Huainan coalfield and distribution of CO 2 source-sink geological points in deep unrecoverable coal seams.

The coal-bearing strata are Taiyuan formation of upper Carboniferous series, Shanxi formation and Xiashihezi formation of lower Permian series, and Shangshihezi formation of upper Permian series, with a total thickness of about 900 m and about 40 layers of coal seams 27 , 28 . In the coal-bearing strata, there are 9–18 coal layers with a single layer thickness greater than 0.7 m on average, the maximum thickness is 12 m, and the total thickness is 23–36 m, which are distributed in Shanxi formation, Xiashihezi formation and lower part of Shangshihezi formation. In this study, the CO 2 emission sources were 10 coal-fired power plants in the coalfield with numbered D1-D10, respectively. Deep unworkable seams are CO 2 storage sinks, which are bounded by faults and numbered B1-B15, respectively (Fig.  1 ).

Evaluation method of CO 2 geological storage potential

In deep unworkable seam, CO 2 geological storage is mainly in adsorbed, dissolved and free states 29 , and adsorption storage is the main storage form of coal seam 30 . Considering the storage differences of different phase of CO 2 , the following potential assessment model of CO 2 storage can be adopted 16 , 31 :

where \(M_{{{\text{CO}}_{2} }}\) is CO 2 storage capacity, t; ρ CO2 is the CO 2 density, kg/m 3 ; M coal is proved coal reserves, t; m ab , m d and m f are the stored quantity of CO 2 adsorbed, dissolved and free states in coal per unit mass, m 3 /t.

In the unit mass coal, the storage potential of CO 2 adsorbed state in deep unworkable seam can be characterized by the following formula 16 , 31 :

where P is the reservoir pressure, which is also CO 2 adsorption pressure, MPa; T c is CO 2 critical temperature, K; Z is the CO 2 compression coefficient; p c is CO 2 critical pressure, MPa; T is the reservoir temperature, which also CO 2 adsorption temperature, K; and m ex is the CO 2 excess adsorption amount per unit mass of coal, m 3 /t, which can be calculated using the following D-R adsorption model 16 , 31 :

where m 0 is the maximum CO 2 adsorption capacity of coal per unit mass tested by adsorption experiment, m 3 /t; ρ f and ρ a are the densities of free and adsorbed CO 2 under the real temperature and pressure conditions, kg/m 3 ; D is the adsorption constant, and k is the constant associated with Henry's Law.

In coal reservoir, CO 2 density is a function of pressure and temperature, which can be expressed as ρ f  =  f(p, T) , and can be further characterized as follows 16 , 31 , 32 :

where δ  =  ρ c /ρ f is the CO 2 reduced density; ρ c is the CO 2 critical density, kg/m 3 ; τ  =  T c /T is the reduced temperature; and ϕ(δ,τ) is the Helmholtz free energy, which can be controlled by temperature and density 16 , 31 , 32 :

where ϕ o (δ, τ) is the Helmholtz free energy of ideal fluid, and ϕ r (δ, τ) is the Helmholtz free energy of the residual fluid.

In deep unworkable seam, the storage potential of dissolved CO 2 per unit mass of coal is a function of coal porosity, water saturation, coal density and CO 2 solubility, which can be characterized as follows 16 , 31 :

where φ is the coal porosity, %; S w is the water saturation, %; \(S_{{{\text{CO}}_{2} }}\) is the CO 2 solubility, and ρ coal is the coal density, kg/m 3 .

According to Boyle-Mariotte law, the free CO 2 storage potential per unit mass of coal in deep unworkable seam can be characterized as follows 16 , 31 :

where S g is the gas saturation, %; P 0 is the standard atmospheric pressure, MPa; T 0 is the temperature under the standard condition, K; and ρ visual is the coal apparent density, kg/m 3 .

Construction of matching model of CO 2 source-sink

Co 2 source and sink matching.

CO 2 source-sink matching is the basis of CCUS cluster deployment and its pipe network design and construction, with the goal of minimizing CO 2 transportation cost and maximizing carbon removal. Its essence is the optimization planning of CCUS cluster system 33 , 34 . Based on CO 2 emission source, storage sink, storage geological process, transport network connecting source and sink and corresponding parameter data, the dynamic optimal matching between CO 2 source and sink can be achieved in terms of target quantity, continuity and economic efficiency (Fig.  2 ).

Schematic diagram of connotation of CO 2 source and sink matching.

The matching of CO 2 source and sink is mainly based on the characteristics of large number, different types and scattered locations of CO 2 emission sources (i.e., thermal power, steel, cement, chemical industry, etc.) and storage sinks (i.e., saltwater layer, CO 2 -ECBM, CO 2 -EOR, MCO 2 -ILU, CO 2 -SDR, etc.). Based on the discussion of constraint conditions and determination of objective function, the influence of regional geographical conditions, traffic, population density, transportation cost and transportation mode on CO 2 transport between emission sources and storage sinks is fully considered in the CCUS system. The optimal matching of CO 2 emission sources, storage sinks and transportation parameters was realized, so as to determine scientific and reasonable CO 2 source and sink matching schemes (Fig.  2 ).

Objective functions

Based on the theory of network analysis in operations research, theoretical models of CO 2 source-sink matching within CCUS technology can be constructed in Huainan coalfield by using the minimum support tree method. The construction of theoretical models should meet the following basic assumptions: (1) Source and sink with the lowest cost should be firstly matched; (2) Allow the matching of one source with multi sinks or one sink with multi sources; (3) Sequestration sink must meet the requirement of CCUS planning period.

In this study, the lowest total cost of matching of CO 2 source-sink in CCS technology is taken as the objective function, namely:

where i refers to the i th CO 2 source; j means the j th CO 2 sink; m indicates the number of CO 2 sources and the value is 10, and n indicates the number of CO 2 sinks with the value of 15.

CO 2 capture cost (i.e., C C )

Based on the analysis of the industrial sources report published by the National Energy Technology Laboratory of the United States, the average capture cost of CO 2 source in coal-fired power plants is $ 64.35 /t 30 , 35 . Therefore, the capture cost of CO 2 source in Huainan coalfield can be characterized as follows:

where \(\omega_{ij}\) represents the CO 2 capture cost in the i coal-fired power plant, $/t; and X ij represents CO 2 transport amount from the i coal-fired power plant to the j sequestration sink, t.

CO 2 transportation cost (i.e., C T )

CO 2 transport is most common by pipeline, ship and tanker, and pipeline transportation is suitable for directional transportation with large capacity, long distance and stable load, which mainly includes construction cost and operation and maintenance cost. The operation and maintenance cost accounts for about 1.5% of the construction cost 35 , which can be calculated according to formula 10 and 11 , respectively.

where L is the distance of pipeline transportation, km.

where N represents the transportation cycle of the pipeline, year.

Therefore, CO 2 transport cost can be characterized as follows:

CO 2 sequestration cost (i.e., C S )

The cost of CO 2 geological storage is closely related to the amount of CO 2 storage and the type of storage site, and the average storage cost coefficient is $ 5.59 /t 30 , 35 . Therefore, the cost of CO 2 geological storage in coal reservoir can be characterized as follows:

where \(\varepsilon_{ij}\) is the sequestration cost factor of transporting CO 2 from coal-fired power plant i to sequestration sink j , $/t.

In summary, by substituting formulas ( 9 ), ( 12 ) and ( 13 ) into formula ( 8 ), the minimum objective function of total cost of CO 2 source-sink matching in CCS technology can be obtained:

Constraint conditions

Based on the basic assumptions of theoretical model, in the planning process of matching pipe network of CO 2 source-sink with CCS technology, the constraint conditions of the lowest total cost objective function are as follows:

The total amount of CO 2 captured from all CO 2 emission sources is equal to the total amount of pipeline transport, that is:

where a i is the CO 2 capture amount of the i th coal-fired power plant.

The CO 2 content transported by the pipeline to the storage site shall not exceed the storage capacity of the storage sink, that is:

where b j is the storage capacity of the j th storage sink.

The amount of CO 2 captured in all coal-fired power plants must not exceed the total capacity of all potential sequestration sinks, that is:

Non-negative constraint: the pipeline of CO 2 transport content is non-negative, that is:

Optimization of matching pipe network of CO 2 source-sink

The core idea of the mileage saving algorithm is to merge two transportation loops into one loop to reduce the transportation distance in the merging process, and keep cycling until the limit condition is reached, thus reducing the transportation cost. Specifically, three points, A, B and C, transport goods from A to B and C, where the distance from A to B is L AB (unit: km), the distance from A to C is L AC (unit: km), and the distance from B to C is L BC (unit: km), if the transportation from A to B and A to C is separately completed, the transportation distance is 2 × (L AB  + L AC ) with including the round trip process (Fig.  3 a). If from A to B, then from B to C, and finally from C back to A, then the transport distance is L AB  + L AC  + L BC (Fig.  3 a), then the distance saved is 2 × (L AB  + L AC ) − (L AB  + L AC  + L BC ) = L AB  + L AC  − L BC  > 0.

Optimization of CCUS source-sink matching pipe network. ( a ) Traditional mileage saving methods; ( b ) Improvement of the mileage saving method.

In CO 2 source-sink matching, each sink is taken as the distribution center and distributed with the connected source points. The basic principle is similar to the mileage saving method, except that there is only a transportation network from the source to the sink, and there is no return pipeline. Based on this, the idea of mileage saving method is introduced in this study, and it is improved to meet the needs of CO 2 source-sink matching and transportation network optimization. As shown in Fig.  3 b, the CO 2 emitted from points B and C is transported to the storage sink A for storage. The most direct way is from B to A, and then from C to A, with a transport distance of L AB  + L AC (Fig.  3 b). If it is transported from B to C and then from C to A or from C to B and then from B to A (Fig.  3 b), the transport distance is L AC  + L BC or L AB  + L BC . L AB and L AC need to be compared to choose a route with a smaller distance for connection. If L BC  < L AB /L AC , then L AB (L AC ) − L BC is the savings; if L BC  > L AB /L AC , then L AB /L AC  − L BC is negative, which means no savings (Fig.  3 b).

CO 2 source and sink characteristics

Characteristics of co 2 sources.

In Huainan coalfield, CO 2 emission sources are 10 coal-fired power plants within the coalfield, of which 9 have been put into operation, 1 has finished commissioning and plans to put into operation. According to the “Greenhouse Gas Emission Accounting Methods and Reporting Guidelines for Chinese Power Generation Enterprises (Trial)” and related methods, the carbon emission intensity of the coal-fired power plants was calculated, and on this basis, the average annual CO 2 emissions of each coal-fired power plant were estimated. The installed capacity of China's coal-fired power plants is mainly 300 WM, 600 WM and 1000 WM, and the CO 2 emission intensity of which is 0.845 t/MW/h, 0.807 t/MW/h and 0.768 t/MW/h, respectively, and in this study, the mean value is taken as the basis for estimation 36 , 37 . Based on the average annual power generation statistics of each power plant, the average annual CO 2 emissions of each coal-fired power plant can be analyzed (Table 1 ).

As can be seen from Table 1 , the average annual CO 2 emissions of coal-fired power plants vary greatly with ranging from 0.36 million tons to 17.12 million tons. Among them, the average annual CO 2 emissions of D7 power plant reach 17.12 million tons, accounting for about 30% of the total annual CO 2 emissions. The total annual CO 2 emissions of all coal-fired power plants are 58.76 million tons, which includes 5.28 million tons of emissions from the proposed D6 power plant (Table 1 ).

Assessment of CO 2 sink

The core parameters of potential assessment of CO 2 geological storage are mainly derived from engineering data, test data, experimental data and scientific research papers (Table 2 ) 16 , 31 , 38 , 39 . In this study, for deep unworkable seam in Huainan coalfield, the proved reserves with burial depth ≤ 1500 m are obtained from coal exploration, and the proved reserves with burial depth > 1500 m are predicted reserves by the resource management department. The geothermal gradient is 3.10 °C/100 m. When the depth of coal seam is less than 1000 m, the pressure gradient is 0.95 MPa/100 m. When the depth of coal seam is more than 1000 m, the pressure gradient is 1.08 MPa/100 m 16 , 31 . The core parameters of CO 2 geological storage potential assessment can be detailed in Table 2 16 , 31 , 38 , 39 .

The CO 2 geological storage potential of deep unworkable seam in Huainan coalfield is huge, and the total amount is 762 million tons. The adsorbed, free and dissolved CO 2 can be stored 685 million tons, 53 million tons and 24 million tons, respectively. The CO 2 geological storage with adsorbed state in deep unworkable seam is the most dominant, accounting for 89.895% of the total storage. When the buried depth of coal seam is ≤ 1500 m and > 1500 m, the total CO 2 geological storage is 253 million tons and 510 million tons, with accounting for 33.17% and 66.83% of the total storage, respectively. Regardless of the state in which CO 2 is stored, the total amount of CO 2 stored when the buried depth is greater than 1500 m is greater than that under the same state when the buried depth is less than 1500 m (Table 3 ).

When the buried depth of coal seam is > 1500 m and ≤ 1500 m, the proved coal reserves are 4.03 billion tons and 1.99 billion tons, respectively, with a ratio of 2.025. For the total amount of CO 2 geologic storage and its adsorption, free and dissolved state, the ratio of coal seam buried depth > 1500 m and ≤ 1500 m is 2.016, 1.996, 2.312 and 2.000, respectively. The main reason why the ratio of total CO 2 geological storage and total adsorption state is lower than 2.025 is that although the CO 2 geological storage potential of deep unworkable seam is positively correlated with the proved coal reserves, the maximum CO 2 adsorption capacity at the depth ≤ 1500 m is much higher than that at the depth > 1500 m. With the increase of burial depth, the reservoir pressure gradually increases, and the CO 2 storage potential in free state in pore structure gradually increases, which will make the free CO 2 ratio far greater than 2.025.

Matching characteristics of CO 2 source-sink

Plane distribution characteristics of co 2 sinks.

The total CO 2 storage potential of deep unworkable seam in Huainan coalfield is 762 million tons (Table 3 ). For the average annual CO 2 emissions of the 10 coal-fired power plants, it can be stored for 12.97 years. The deep unworkable seam is the most potential body for CO 2 storage in Huainan coalfield. The unrecoverable coal seam with buried depth ≤ 1500 m can meet the CO 2 geological storage requirements of coal-fired power plants for 4.31 years. Considering the technical challenges and implementation costs of CO 2 storage in coal seam with different burial depths, the unworkable coal seam with burial depths ≤ 1500 m should be the main target reservoir for the implementation of CO 2 -ECBM technology in the next five years.

With fault structure as the boundary, the deep unworkable seam can be divided into 15 CO 2 storage blocks, and the comparative analysis of the plane distribution of CO 2 storage sinks can be carried out according to the plane area size (Fig.  4 ). The main blocks of CO 2 geological storage are B9, B12, B8 and B5, and their sealable stocks are 124 million tons, 114 million tons, 97 million tons and 85 million tons, respectively, among which the largest two blocks, B9 and B12, can store the CO 2 emissions of 10 coal-fired power plants for nearly four years. The four blocks with larger area are also the main blocks of the CO 2 source-sink matching.

Plane distribution of CO 2 storage sink in unrecoverable coal seams of Huainan coalfield.

According to the preliminary potential assessment analysis, for the average annual CO 2 emissions of the 10 coal-fired power plants in Huainan coalfield, the deep unworkable seam can be stored for 12.97 years. Therefore, in this study, the matching study of CO 2 source-sink was conducted based on the cumulative CO 2 emissions of 10 coal-fired power plants in Huainan coalfield in 10 years for deep unworkable seam (Fig.  5 ).

CCS source and sink matching of cumulative CO 2 emissions from 10 coal-fired power plants in Huainan coalfield during the 10-year cycle.

Based on the matching results of CO 2 source and sink during the 10-year cycle in Huainan coalfield, it can be seen that the coal-fired power plant of D1 can be mainly stored in blocks of B2, B3, B4 and B7, with the stored stocks of 20.2 million tons, 19.7 million tons, 30.9 million tons and 10.8 million tons, respectively. Coal-power plant of D2 is mainly stored in block of B5, and the stored stock is 3.6 million tons. The coal-power plant of D3 is mainly stored in blocks of B7 and B10, with a stored stock of 25.8 million tons and 51 million tons, respectively. Coal-fired power plant of D4 is mainly stored in blocks of B8 and B9, with a storage capacity of 10.9 million tons and 12.3 million tons, respectively. Coal-fired power plant of D5 is mainly stored in block of B9, with a stored stock of 58.9 million tons. Coal-fired power plant of D6 is mainly stored in block of B9, and the stored stock is 52.8 million tons. Coal-fired power plant of D7 is mainly stored in blocks of B8, B12 and B14, with stored stocks of 61.1 million tons, 58.3 million tons and 51.8 million tons, respectively. Coal-fired power plant of D8 is mainly stored in block B8, with a stored stock of 15.5 million tons. Coal-fired power plant of D9 is mainly stored in block of B13, and the stored stock is 48.2 million tons. The coal-fired power plant of D10 is mainly stored in block of B12, with a stored stock of 56.0 million tons (Fig.  5 ). During the 10-year cycle, the CO 2 in deep unworkable seam can be stored up to 587.6 million tons, and the cumulative planned pipeline is 251.61 km, which will require a cumulative capital of $ 4.26 × 10 10 .

Discussions

Analysis of matching pipe network of co 2 source-sink.

Based on the analysis of matching pipe network of CO 2 source-sink in deep unworkable seam, it can be seen that the transportation routes of pipelines of 9, 4, 16, 5 and 8 are relatively long, which accounts for 53.65% of the total transportation route length (Fig.  6 ). Because the transportation cost is proportional to the route, it is important to optimize the line length of pipelines of 9, 4, 16, 5 and 8 to reduce the total cost.

Analysis of the number, length and proportion of CO 2 source-sink matching pipe network in deep unrecoverable coal seams.

Based on the analysis of CO 2 storage and transport costs and their proportion in deep unworkable seam, it can be seen that the transport costs of blocks of 8, 7, 12 and 13 are the highest, which accounts for 36.96%, 14.01%, 11.60% and 11.86% of the total CO 2 storage and transport costs, respectively. The transportation cost of four CO 2 storage sinks accounted for 74.43% of the total cost. Therefore, blocks of 8, 7, 12 and 13 of deep unworkable seam will be the focus of optimization of matching pipe network of CO 2 source-sink. Blocks of 1, 6, 11 and 15 do not need to bear CO 2 geological storage for the time being, which can be used as alternative blocks for CO 2 storage (Figs.  5 and 7 ).

Transportation cost and proportion of CO 2 storage sinks matched by CO 2 source and sink in deep unrecoverable coal seams.

Based on the improved mileage saving method, the optimization results of matching pipe network of CO 2 source-sink in deep unworkable seam can be obtained (Fig.  8 ).The unchanged pipe network paths are D1–B4, D1–B7, D3–B10, D4–B8, D4–B9 and D7–B13 (Fig.  8 ), and the routes among other source-sink take the minimum total transportation cost as the objective function, and the pipe network optimization is carried out according to the constraints of the emission source and the storage capacity (Fig.  8 ).

Optimization results of CO 2 source-sink matching pipe network in Huainan coalfield.

Based on the optimization results of matching pipe network of CO 2 source-sink in Huainan coalfield, it can be seen that the accumulated mileage saved is 98.75 km, and the accumulated cost saved is $ 25.669 billion, which accounts for 39.25% and 60.26% of the total mileage and cost of pipeline, respectively (Table 4 ). Among them, the mileage and cost savings of 13 and 14 blocks in deep unworkable seam are more obvious, which accounts for 10.43% and 10.10% of the total mileage and 16.20% and 16.01% of the total cost, respectively (Table 4 ).

Planning and design of matching pipe network of CO 2 source-sink

Pipeline network planning on a time scale.

By analyzing the optimization results of matching pipe network of CO 2 source-sink in Huainan coalfield and the amount of CO 2 transported by each pipe network line, it can be seen that the entire pipe network is centrally distributed in the east and west regions, and it is obvious that the transport amount of the eastern pipe network is significantly greater than that of the western one (Fig.  9 ). The thicker the lines of the route, the greater the traffic amount (Fig.  9 ). The planning and design of matching pipe network of CO 2 source-sink should refer to the thickness of the transportation line, that is, the amount of CO 2 transported (Figs. 10 , 11 , 12 ). The planning and design of matching pipe network of CO 2 source-sink in Huainan coalfield is proposed in accordance with three steps:

CO 2 transport statistics of CCS source-sink matching pipe networks in Huainan coalfield.

Three-step planning and design of CO 2 source-sink matching pipe network in Huainan coalfield (First step).

First step: It is recommended to preferentially plan the pipeline route of D9–D8–D7–B12–D6–D4–B8 in the eastern region, and the D3–B10 and D1–B4 in the western region. This planned pipeline can effectively connect the coal-fired power plants of D9, D8, D7, D6 and D4, and unworkable blocks of B12, B8, B10 and B4 of Huainan coalfield (Fig.  10 ). At this step, the total amount of CO 2 that can be transported by the pipeline network is 6.65 billion tons, and the total amount of CO 2 that can be stored is 2.27 billion tons, which accounts for 56.99% and 38.74% of the total transportation and storage stock of CO 2 , respectively.

Second step: It is recommended to further plan the pipeline lines of D10–D9, D7–B13, D7–B14, D4–B9, D5–B9, B10–B7, and B4–B3–B2, which can further effectively connect the deep unworkable seam in the east, middle and west areas (Fig.  11 ). After the pipeline network planning at this step, the total amount of CO 2 transported can be 10.345 billion tons, and the total amount of CO 2 stored can be 5.84 billion tons, which accounts for 88.66% and 99.39% of the total CO 2 transport and storage, respectively.

Three-step planning and design of CO 2 source-sink matching pipe network in Huainan coalfield (Second step).

Third step: Complete the design of all remaining pipelines to connect the deep unworkable seam in the east and west of the study area. It is suggested to add the design of B3 and B4 pipelines, so as to run through all CO 2 emission sources and CO 2 storage sinks in Huainan coalfield, so as to realize all CO 2 transportation and geological storage (Fig.  12 ).

Three-step planning and design of CO 2 source-sink matching pipe network in Huainan coalfield (Third step).

Pipeline network planning at the spatial scale

In this study, the location of each point in deep unworkable seam is determined by taking the center location of each region (Fig.  1 ), but in the actual well location layout, the regional center location is often not the only consideration. Therefore, the analysis of the type of CCS pipeline within each region and the planning of CCS pipeline network between each region are very important (Fig.  13 ).

Schematic diagram of four types of CO 2 pipelines connecting carbon sources and carbon sinks.

According to the location and use of CO 2 pipelines in the pipe network, CO 2 pipelines can be defined as the following four types (Fig.  13 ): (1) Gas collection branch, that is, the pipeline that communicates CO 2 source and transfer point, and the transport phase is determined according to its economy; (2) Distribution branch, that is, the pipeline from the end of the communication pipeline to the carbon sequestration point; (3) Intra-regional trunk lines, that is, trunk pipelines from the transfer point to the carbon sequestration point in the region; (4) Interregional trunk lines, that is, shared pipelines connecting regions. As far as Huainan coalfield is concerned, in terms of spatial scale, priority should be given to planning intra-regional pipe networks in various regions within unworkable seam bounded by faults, that is, the pipe networks in various regions within B1–B15 (Fig.  13 ).

Whether it is a small area of Huainan coalfield or the whole large area of China, the CCS pipe network layout should follow the following ideas. First of all, small-scale carbon sources in the region should be transferred to main pipelines through gas collection branch lines, and commercial CO 2 pipeline demonstration projects can be built. Secondly, the collection and distribution pipelines of regional carbon sources can be planned within the basin to form a backbone sharing pipeline, and a variety of CCS carbon sequestration applications can be simultaneously carried out to build an interregional transport network demonstration. Then, for areas that do not have the conditions for storage, inter-regional trunk pipelines should be built to gradually form a cross-regional carbon network on land to fully meet the matching transport of source and sink. Offshore CO 2 storage resources should be developed, suitable coastal injection points should be selected, marine transport pipelines and ship transport should be simultaneously carried out, and integrated business models of transport and storage based on land and sea should be built (Fig.  13 ).

Conclusions

In this study, the deep unworkable seam in Huainan coalfield was taken as the research object. Firstly, the evaluation method of CO 2 storage potential in deep unworkable seam was discussed. Secondly, the CO 2 geological storage potential was analyzed. Then, the matching research of CO 2 source and sink for CO 2 geological storage was carried out, and the pipe network design was optimized. Finally, suggestions on the design of network planning of CCS source and sink are put forward in Huainan coalfield. The main conclusions are as follows:

The total annual CO 2 emissions of each coal-fired power plant are 58.76 million tons, and the average annual CO 2 emissions of each coal-fired power plant vary greatly with ranging from 0.356 million tons to 17.12 million tons. The CO 2 geological storage potential of deep unworkable seam is huge, and the total amount is 762 million tons. It can store 685 million tons, 53 million tons and 24 million tons of CO 2 in adsorbed, free and dissolved states, respectively. For the average annual CO 2 emissions of coal-fired power plants, deep unworkable seam can be stored for 12.97 years. During the 10-year period, the deep unworkable coal seam can store 587.6 million tons, and the cumulative planning pipeline is 251.61 km, requiring a cumulative capital of $ 4.26 × 10 10 .

The main blocks of CO 2 geological storage are B9, B12, B8 and B5, with stored stocks of 124 million tons, 114 million tons, 97 million tons and 85 million tons, respectively. The matching of CO 2 source and sink saved 98.75 km, and saved $ 25.67 billion, accounting for 39.25% and 60.26% of the total mileage and cost, respectively. The mileage and cost savings in 13 and 14 blocks are more obvious, which accounts for 10.43%, 10.10% and 16.20% and 16.01% of the total mileage and cost, respectively.

Based on the three-step approach, the whole line of CO 2 emission sources and CO 2 storage sinks in Huainan coalfield can be completed by stages and regions, and all CO 2 transportation and storage can be realized. CO 2 pipelines include gas collection branch lines, gas distribution branch lines, intra-regional trunk lines, and interregional trunk lines. Based on the reasonable layout of various types of CO 2 pipelines, a variety of CCS carbon sequestration applications can be simultaneously carried out, the intra-regional and inter-regional network demonstration for CO 2 transport can be built, and integrated business models of CO 2 transport and storage can be built simultaneously on land and sea.

Data availability

All data generated or analysed during this study are included in this published article (Please refer to the manuscript that has been uploaded).

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Acknowledgements

We would like to express our gratitude to the anonymous reviewers for offering their constructive suggestions and comments which improved this manuscript in many aspects. This work was financially supported by the Natural Science Research Project of Anhui Educational Committee (2023AH040154), the Anhui Provincial Natural Science Foundation (2308085Y30), the Anhui Provincial Key Research and Development Project (2023z04020001), the National Natural Science Foundation of China (No. 42102217; 42277483), and the University Synergy Innovation Program of Anhui Province (No. GXXT-2021-018).

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Huihuang Fang, Yujie Wang, Shua Yu, Huihu Liu, Jinran Guo & Zhangfei Wang

Institute of Energy, Hefei Comprehensive National Science Center, Hefei, 230000, China

Department of Geological Sciences, University of Saskatchewan, Saskatoon, SK, S7N 5E2, Canada

Huihuang Fang

Carbon Neutrality Institute, China University of Mining and Technology, Xuzhou, 221008, China

Shuxun Sang

School of Resources and Geosciences, China University of Mining and Technology, Xuzhou, 221116, China

Jiangsu Key Laboratory of Coal-based Greenhouse Gas Control and Utilization, China University of Mining and Technology, Xuzhou, 221008, China

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H.F. and S.S.: The conception and design of the study, revising it critically for important intellectual content, final approval of the version to be submitted. H.F. and Y.W.: Drafting the article. J.G. and H.L.: Drawing of all figures. H.F. and Z.W.: Collection and analysis of the field data. S.Y.: Derivation of mathematical models.

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Fang, H., Wang, Y., Sang, S. et al. Potential assessment of CO 2 source/sink and its matching research during CCS process of deep unworkable seam. Sci Rep 14 , 17206 (2024). https://doi.org/10.1038/s41598-024-67968-w

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Architects Tackling Carbon: Good for Business and the Environment

11 min read

Climate change is a wicked problem architects can and should play a significant role in addressing. Buildings are responsible for nearly 40% of carbon emissions globally. Opportunities abound through design, education, development and support of policy, research, and more. 

With the need to draw down carbon emissions sooner rather than later and the current and forthcoming policies worldwide, the ethical and business case for design firms focusing on carbon is easy to make. 

“We have to draw the carbon back down as fast as we can, and buildings have a huge role to play in that effort,” writes Chris Magwood and Bruce King in their book Build Beyond Zero.  

Furthermore, as the late Lance Hosey eloquently expressed in his book The Shape of Green , design—or aesthetics—and sustainability do not have to be mutually exclusive. This idea is celebrated by the AIA’s Committee on the Environment (COTE) Top Ten awards recognizing beautiful buildings that have implemented many of the measures defined in the AIA Framework for Design Excellence.  

We are not all starting from the same place in the journey to mitigate climate change. If any of this is new to you, the AIA and Carbon Leadership Forum (CLF) have developed an Embodied Carbon Toolkit for Architects which is a great place to start. Another great “getting started” resource is the Lake|Flato white paper on embodied carbon, discussed later in this article.

As we are all in this together, we believe that sharing methods and practices can be a good way to solve the climate change problem. In this article, I am going to tell you a bit about Lake|Flato’s story.

“Design can save the world…” wrote Corey Squire, architect, author, and sustainability expert, next to his name when signing my copy of his new book People Planet Design . Understanding the opportunities, policies, and tools goes a long way in making meaningful impacts on carbon pollution reductions through design.

I recently worked with a small team of about six people to update the Architects Are Environmentally Responsible section of the AIA Public Policies and Position Statements . There are three sections related to carbon: Operational, Reuse, and Embodied. Architects, especially AIA members, should read this and understand the obligation to “consider with their clients the environmental effects of their project decisions” per the AIA Code of Ethics and Professional Conduct .

More pointedly, governments are implementing policies that can leave design firms empty-handed when it comes to being awarded commissions if they are not prepared to design and deliver low-carbon projects, specifications, and required reports and documentation. Current examples abound, from the Embodied Carbon Guidelines in Vancouver, Canada, to the LCA requirements in Minnesota’s B3 Guidelines, to policies in unexpected places, such as the deconstruction ordinance in San Antonio, Texas. 

AIA 2030 Commitment

As design firms become more invested in operation and embodied carbon, a good next step is making a firm-wide tracking commitment. The AIA 2030 Commitment supports a firm culture of high-performance design because, like municipal policy, the firm has obligated itself (although, not legally) to portfolio-wide tracking of operational carbon with optional embodied carbon and light power density (LPD) metrics.  

“There is no better resource to track your impact on climate change through measurement of the project performance than the AIA 2030 Commitment and the corresponding Design Data Exchange (DDx). Now is the time to make the commitment, measure to improve, and help solve climate change.” – Ashley Mulhall, AIA, LEED AP, BD+C, Orcutt|Winslow Architects

Every year, the AIA publishes a report, 2030 By the Numbers , to show how the collective group of reporting firms is tracking towards the net zero carbon goal by 2030. Lake|Flato has been a 2030 Commitment signatory since the program’s inception 14 years ago. 

Architecture firm Mahlum recently presented at the 2024 AIA Conference on Architecture in Washington D.C. on a Tableau dashboard they created to better visualize their downloaded AIA 2030 Design Data Exchange (DDx) data. They have made this dashboard freely available via this link: Tableau 2030 Dashboard by Mahlum Architects . 

A key component of becoming a 2030 commitment signatory is creating a firmwide Sustainability Action Plan (SAP). An SAP creates actionable goals and strategies that help firms progress even when projects, business, and life compete for valuable time and resources. 

If your firm does not have an SAP and needs inspiration, the AIA maintains a Dropbox repository of firm SAPs you can download, compare, and be inspired by. We would love to inspire you— so if you are interested, Lake|Flato recently updated its SAP, which is easy to find in the AIA Dropbox folder. Our document is divided into Practice, People, and Planet. The base and reach goals for operational and embodied carbon are shown below. At the very least, we strive to consider each base goal on every project. 

Tools and Workflows

Well-defined strategies and tested tools are critical in verifying assumptions and validating results. For example, the Path to Zero graphic below is often presented internally, to clients, and at conferences to reinforce the importance of timing and decision-making within the design process. We also highlight the tools our firm uses during new hire orientations, studio huddles, design charettes, with clients, and at conferences.

Another tool, Autodesk Forma is a powerful analysis tool designers can use to understand microclimate conditions, daylight, wind, and noise for one or more design solutions. A newer feature is the ability to develop a carbon budget or benchmark/baseline – more on the backstory for this later in this article. You can check out my YouTube playlist, sponsored by Autodesk, on Forma: Autodesk Forma Series by Daniel John Stine . 

An embodied carbon benchmark/baseline has traditionally been much more challenging to understand than operational carbon, given the years of development and post-occupancy evaluations not to mention robust codes such as ASHRAE 90.1, which list EUIs by building type and climate zone. Great work is being done on the embodied carbon front by the Carbon Leadership Forum, Rocky Mountain Institute, and others. 

Due to the limited benchmark data for embodied carbon and the fact that the number and size of columns and beams are not 1:1 between a steel/concrete structure and mass timber, on one project Lake|Flato paid the structural engineer to do a basic structural design for the baseline design for a more accurate comparison. You can also see an example of Lake|Flato’s use of Forma in this Autodesk customer story and video: Architecture firm Lake|Flato’s adaptive-reuse project embodies sustainability  

The image below shows the embodied carbon results of three options in Forma. Using AI, simple massing, and a handful of inputs about the project, we can arrive at some helpful early metrics to use as a guidepost (or budget) throughout the project. Fun fact: For the concrete structure option, Forma uses carbon data that is accurate to the zip code level in the US.

A popular Life Cycle Analysis (LCA) tool for architects is TallyLCA. This is an add-in for Revit, which allows Tally material definitions to be assigned to objects and materials within Revit. In addition to Tally, Lake|Flato also uses OneClick LCA . 

The image below features an LCA I performed on a boutique hotel in Austin, Texas – Hotel Magdalena . The building has a mass timber structure, which is primarily a Dowel Laminated Timber (DLT) system. Notably, the DLT system does not have glue in it like CLT so the global warming potential was even lower! The result from a Tally LCA is a spreadsheet (shown in the image below) that itemizes and tallies the global warming potential as well as biogenic carbon, eutrophication, and acidification.

Autodesk also offers two robust tools for understanding the predictive energy use intensity (pEUI) of a project: Insight and Systems Analysis. The first primarily works in the cloud, and the latter works locally using OpenStudio (NREL) and EnergyPlus (US DOE). In addition to being able to develop a highly accurate envelope, the image below shows the internal loads being specified at the room level, including set points, LPD, schedules, and more. Notice the OpenStudio/EnergyPlus results are even seen within Revit. 

Autodesk Total Carbon

A new tool Autodesk has been working on, and one I have been attending customer-facing sprint reviews for, is the cloud-based Total Carbon. Using the same Revit model preparation for operation carbon/energy the model can be studied for embodied carbon impacts. Not to reinvent the wheel and for more transparency and credibility, Autodesk uses the open source EPD data by EC3 from Building Transparency. 

Once configured in the cloud, a project dashboard with custom factors can be created as shown in the image below.

Another example of Autodesk Total Carbon in action can be seen in the image below. This is a healthcare project in Australia by Warren and Mahoney . Sustainability Lead and Low Carbon Design and Research Specialist, Emily Newmarch gave a compelling presentation at Autodesk University 2023 titled The Carbon Legacy of Building Portfolios: Time Bombs for Future Generations . In this session, she shares her firm’s staunch commitment to historic preservation, circular economy, and minimizing embodied carbon throughout the lifecycle of a building.

EPIC and C.Scale by EHDD Architects

The embodied carbon engine added to Forma was created by EHDD Architects . This is a wonderful example of practice and software working together to address this wicked problem of solving climate change! EHDD has a free standalone tool called EPIC that uses an AI/ML engine (they also created) under the hood called C.Scale. EHDD used their own tool in the interview and during the design of the national AIA Headquarters project in Washington D.C.

While working on the Autodesk Forma series I created, I had the opportunity to sit down with Jack Rusk, Climate Strategist at EHDD, and talk about their tool and its integration into Forma. You can see that video here: Talking Climate Strategy with Jack Rusk from EHDD . 

Whether embedded in a project, a partnership with academia, or an independent effort, research can significantly impact a firm’s understanding of carbon, especially as it relates to its specific project types and location in the world. Engaging in research also enhances staff/firm knowledge, efficiency, and can start to develop a thought leadership position within the industry. 

I lead the research program at Lake|Flato called Investigations. It is a self-funded program that allows designers to submit proposals for projects that, if selected, offer dedicated billable time to focus on the topic they are interested in. The deliverable is a whitepaper and an officewide presentation. They also often result in presentations, recent examples include the national AIA conference, AIA/ACSA Intersections, and Environmental Design and Research Association (EDRA) conferences. 

You can learn more about the Lake|Flato research program . In recent months, we have also submitted for grants with academic institutions to further some of our research on carbon and circularity and design for deconstruction. 

Embodied Carbon White Paper

You can learn more about the research on embodied carbon by Kate Sector, Design Performance Manager at Lake|Flato in “ Embodied Carbon: Exploring Global Warming Potential Using Life Cycle Assessments .” The featured case studies include Hotel Magdalena and a project currently under construction at the University of Pennsylvania (UPenn). 

Let me conclude by saying architects are crucial in combating climate change. Architects can significantly reduce carbon pollution by integrating innovative design, education, supportive policies, and research. The ethical and business incentives for focusing on carbon reduction are clear, especially with emerging global policies.  

Aesthetic and sustainable design can coexist, as evidenced by the AIA COTE Top Ten awards. Resources like the AIA’s Embodied Carbon Toolkit, and Lake|Flato’s white paper provide essential guidance. Policy advancements will continue to demand low-carbon projects, and the AIA 2030 Commitment supports firms in tracking and improving their carbon footprint. Tools like Autodesk Forma and Tally help analyze and reduce carbon impacts. 

Research initiatives, such as Lake|Flato’s Investigations program, enhance personal, firm, and industry knowledge and efficiency. By embracing sustainable design, adhering to policies, making commitments, and engaging in research, architects can take charge of reducing carbon emissions, benefiting both the environment and their businesses. 

Dan Stine, AIA, IES, CSI, CDT, Well AP. Dan is the Director of Design Technology and leads the internal research program, Investigations, at the top-ranked architecture firm Lake|Flato, in San Antonio, Texas. He is a registered architect (WI), educator, author, blogger, and international speaker.

In addition to teaching graduate architecture students at NDSU, he has written 19 textbooks, including the #1 Revit book in North America, which is used extensively in the academic market. Committed to climate action, Dan was on a six-person team commissioned by the American Institute of Architects (AIA) to write the AIA Climate Action Business Playbook. He also serves on the national AIA Committee on the Environment (COTE) Leadership Group. He chairs a national Illuminating Engineering Society (IES) committee.

Dedicated to furthering the design profession, Dan has given presentations on building performance and design technology in North America, Europe, Singapore, and Australia. He has also presented at AIA conferences (National, TX, MN), AIA-COTE working groups (Philly, New Orleans, San Antonio, LA), universities (Penn State, Pratt, University of MN, UTSA, and more), lightfair, NVIDIA GTC, Autodesk University, and more.

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