what is purpose of research design

What Is the Purpose of Research Design?

Research design, like any framework of research, has its methods and purpose. The purpose of research design is to provide a clear plan of the research, based on independent and dependent variables, and to consider the cause and effect evoked by these variables.

Methods that constitute research design can include:

• Observations
• Experiments
• Working with archives
• Other methods

As we’ve explained here , the methods of research always back up the purpose and the goals of the research.

Main Goals of Research Design

• The main goal of research design is for a researcher to make sure that the conclusions they’ve come to are justified. It means that the research has to confirm or deny the hypothesis.
• Another purpose of research design is to broaden the researcher’s understanding of the topic, and to make them more conscious about various places, groups, and settings.
• Finally, research design allows the researcher to achieve an accurate understanding of the topic they are working on, and be able to explain the topic to others.

Usually, you cannot accomplish all these three goals at the same time, but you can try to do that by using multi-stage design in your study, which is also known as multi-stage sampling .

Examples of Research Design

One should choose the type of research design corresponding to the particular purpose of their research. Here are some examples to choose from when you’re doing research.

General Structure and Writing Style

The chosen type should help the researcher work on the hypothesis accurately; while collecting data, you have to choose specific evidence, which will show you the differences between the variables. Before conducting research, think about what information you will need and how you're going to use it. Otherwise, you can get lost.

Action Research

When you’ve developed an understanding of the problem, and you’ve begun planning an interventional strategy, action research design helps to bring the deviation to the surface. You make an observation and bookmark the deviation. Then you commit an action, and compare the outcome of having committed the action with the outcome of not having committed the action.

We would use a case study when studying a particular problem and when we take some broad samples and try to narrow them down to subjects that are researchable. This one is also cool when we need to check if a certain theory applies to a certain real-world problem.

Causal Research

If A is X, then B is Y — that's how we can simplify causality. By comparing two or more variables, a researcher can understand the effect they have on each other. This effect can then be measured in terms of causality: i.e., what constitutes the cause and the effect.

Cohort Study

Cohort studies are more often used in medical studies, but are now gaining popularity in social science. They are based on a quantitative framework, where a researcher makes notes on a statistical occurrence within a subgroup, with members of the group having similar features that are related to the study topic, rather than analyzing mathematical occurrences within a custom group. Observation is the main method of data gathering in cohort studies.

How to Study Research Design

You can learn research design:

• At universities
• Through online tutorials
• In online classes
• By reading scientific papers

Research with FlowMapp’s Tools

FlowMapp specializes in strategic website planning and user experience analysis and develops special tools for such analysis. Although all of these products focus on web design, FlowMapp's analysis and planning tools can be used by a wide range of other professionals:

• developers;
• copywriters;
• UX strategists;
• researchers;
• sales managers;
• product managers;
• project managers;

User Flow Tool

User flow is a visual representation of the sequence of actions that users perform to achieve their goal. In fact, User flow tool allow you to look at the interaction between the user and the web product through the eyes of the user.

You can see how this looks like in the following example :

Wireframe tool

The Wireframe tool by FlowMapp is a powerful online tool that allows for the rapid creation of website prototypes. With its extensive library of templates for every website block, this tool enables designers to quickly build hi-fi prototypes. These wireframes can be easily shared via a link as a real webpage.

This tool is useful for quickly analyzing the labor required for website development, visualize future project ideas, and effectively present them to stakeholders.

The Notes tool by FlowMapp is a content gathering system that allows you to store all types of data, files, and ideas in one place, and collaborate on them with others. This tool is particularly useful for creating briefs, reports, documentation, and for storing all project-related data.

During the research phase, the Notes tool is a valuable asset for gathering and organizing ideas, especially when working on UX/UI design, user behavior and user flow analysis, or website development projects.

Remember that the step-by-step approach is the one that will help you most. Also, you can try to implement other research formats from multiple spheres and try some more mixed methods of research. Don’t be afraid to experiment – register at our website and get access to all tools we mentioned above!

Peak-end Rule in UX Design

What Is System Usability Scale (SUS)?

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

A sample is a subset of individuals from a larger population. Sampling means selecting the group that you will actually collect data from in your research.

For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 9 April 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

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…

Need a helping hand?

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 (for free)

This post is part of our dissertation mini-course, which covers everything you need to get started with your dissertation, thesis or research project. 

You Might Also Like:

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 .

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• What is New
• Download Your Software
• Behavioral Research
• Software for Consumer Research
• Software for Human Factors R&D
• Request Live Demo
• Contact Sales

Sensor Hardware

We carry a range of biosensors from the top hardware producers. All compatible with iMotions

iMotions for Higher Education

Imotions for business.

Neuroarchitecture: Designing Spaces with Our Brain in Mind

Sensory and Perceptual

Morten Pedersen

Human Factors in Automotive Human-Machine Interface (HMI) Design

Consumer Insights

News & Events

• iMotions Lab
• iMotions Online
• Eye Tracking
• Eye Tracking Screen Based
• Eye Tracking VR
• Eye Tracking Glasses
• FEA (Facial Expression Analysis)
• Voice Analysis
• EDA/GSR (Electrodermal Activity)
• EEG (Electroencephalography)
• ECG (Electrocardiography)
• EMG (Electromyography)
• Respiration
• iMotions Lab: New features
• iMotions Lab: Developers
• EEG sensors
• Consumer Inights
• Human Factors R&D
• Work Environments, Training and Safety
• Customer Stories
• Published Research Papers
• Document Library
• Customer Support Program
• Help Center
• Release Notes
• Contact Support
• Partnerships
• Mission Statement
• Ownership and Structure
• Executive Management
• Job Opportunities

Publications

• Newsletter Sign Up

The Importance of Research Design: A Comprehensive Guide

Research design plays a crucial role in conducting scientific studies and gaining meaningful insights. A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. This comprehensive guide will provide an in-depth understanding of research design, its key components, different types, and its role in scientific inquiry. Furthermore, it will discuss the necessary steps in developing a research design and highlight some of the challenges that researchers commonly face.

Table of Contents

Understanding research design.

Research design refers to the overall plan or strategy that outlines how a study is conducted. It serves as a blueprint for researchers, guiding them in their investigation, and helps ensure that the study objectives are met. Understanding research design is essential for researchers to effectively gather and analyze data to answer research questions.

When embarking on a research study, researchers must carefully consider the design they will use. The design determines the structure of the study, including the research questions, data collection methods, and analysis techniques. It provides clarity on how the study will be conducted and helps researchers determine the best approach to achieve their research objectives. A well-designed study increases the chances of obtaining valid and reliable results.

Definition and Purpose of Research Design

Research design is the framework that outlines the structure of a study, including the research questions, data collection methods, and analysis techniques. It provides a systematic approach to conducting research and ensures that all aspects of the study are carefully planned and executed.

The purpose of research design is to provide a clear roadmap for researchers to follow. It helps them define the research questions they want to answer and identify the variables they will study. By clearly defining the purpose of the study, researchers can ensure that their research design aligns with their objectives.

Key Components of Research Design

A research design consists of several key components that influence the study’s validity and reliability. These components include the research questions, variables and operational definitions, sampling techniques, data collection methods, and statistical analysis procedures.

The research questions are the foundation of any study. They guide the entire research process and help researchers focus their efforts. By formulating clear and concise research questions, researchers can ensure that their study addresses the specific issues they want to investigate.

Variables and operational definitions are also crucial components of research design. Variables are the concepts or phenomena that researchers want to measure or study. Operational definitions provide a clear and specific description of how these variables will be measured or observed. By clearly defining variables and their operational definitions, researchers can ensure that their study is consistent and replicable.

Sampling techniques play a vital role in research design as well. Researchers must carefully select the participants or samples they will study to ensure that their findings are generalizable to the larger population. Different sampling techniques, such as random sampling or purposive sampling, can be used depending on the research objectives and constraints.

Data collection methods are another important component of research design. Researchers must decide how they will collect data, whether through surveys, interviews, observations, or experiments. The choice of data collection method depends on the research questions and the type of data needed to answer them.

Finally, statistical analysis procedures are used to analyze the collected data and draw meaningful conclusions. Researchers must determine the appropriate statistical tests or techniques to use based on the nature of their data and research questions. The choice of statistical analysis procedures ensures that the data is analyzed accurately and that the results are valid and reliable.

Types of Research Design

Research design encompasses various types that researchers can choose depending on their research goals and the nature of the phenomenon being studied. Understanding the different types of research design is essential for researchers to select the most appropriate approach for their study.

When embarking on a research project, researchers must carefully consider the design they will employ. The design chosen will shape the entire study, from the data collection process to the analysis and interpretation of results. Let’s explore some of the most common types of research design in more detail.

Experimental Design

Experimental design involves manipulating one or more variables to observe their effect on the dependent variable. This type of design allows researchers to establish cause-and-effect relationships between variables by controlling for extraneous factors. Experimental design often relies on random assignment and control groups to minimize biases.

Imagine a group of researchers interested in studying the effects of a new teaching method on student performance. They could randomly assign students to two groups: one group would receive instruction using the new teaching method, while the other group would receive instruction using the traditional method. By comparing the performance of the two groups, the researchers can determine whether the new teaching method has a significant impact on student learning.

Experimental design provides a strong foundation for making causal claims, as it allows researchers to control for confounding variables and isolate the effects of the independent variable. However, it may not always be feasible or ethical to manipulate variables, leading researchers to explore alternative designs.

Free 44-page Experimental Design Guide

For Beginners and Intermediates

• Introduction to experimental methods
• Respondent management with groups and populations
• How to set up stimulus selection and arrangement

Non-Experimental Design

Non-experimental design is used when it is not feasible or ethical to manipulate variables. This design relies on naturally occurring variations in data and focuses on observing and describing relationships between variables. Non-experimental design can be useful for exploratory research or when studying phenomena that cannot be controlled, such as human behavior.

For instance, researchers interested in studying the relationship between socioeconomic status and health outcomes may collect data from a large sample of individuals and analyze the existing differences. By examining the data, they can determine whether there is a correlation between socioeconomic status and health, without manipulating any variables.

Non-experimental design allows researchers to study real-world phenomena in their natural setting, providing valuable insights into complex social, psychological, and economic processes. However, it is important to note that non-experimental designs cannot establish causality, as there may be other variables at play that influence the observed relationships.

Quasi-Experimental Design

Quasi-experimental design resembles experimental design but lacks the element of random assignment. In situations where random assignment is not possible or practical, researchers can utilize quasi-experimental designs to gather data and make inferences. However, caution must be exercised when drawing causal conclusions from quasi-experimental studies.

Consider a scenario where researchers are interested in studying the effects of a new drug on patient recovery time. They cannot randomly assign patients to receive the drug or a placebo due to ethical considerations. Instead, they can compare the recovery times of patients who voluntarily choose to take the drug with those who do not. While this design allows for data collection and analysis, it is important to acknowledge that other factors, such as patient motivation or severity of illness, may influence the observed outcomes.

Quasi-experimental designs are valuable when experimental designs are not feasible or ethical. They provide an opportunity to explore relationships and gather data in real-world contexts. However, researchers must be cautious when interpreting the results, as causal claims may be limited due to the lack of random assignment.

By understanding the different types of research design, researchers can make informed decisions about the most appropriate approach for their study. Each design offers unique advantages and limitations, and the choice depends on the research question, available resources, and ethical considerations. Regardless of the design chosen, rigorous methodology and careful data analysis are crucial for producing reliable and valid research findings.

The Role of Research Design in Scientific Inquiry

A well-designed research study enhances the validity and reliability of the findings. Research design plays a crucial role in ensuring the scientific rigor of a study and facilitates the replication of studies by other researchers. Understanding the role of research design in scientific inquiry is vital for researchers to conduct impactful and robust research.

Ensuring Validity and Reliability

Research design plays a critical role in ensuring the validity and reliability of the study’s findings. Validity refers to the degree to which the study measures what it intends to measure, while reliability pertains to the consistency and stability of the results. Through careful consideration of the research design, researchers can minimize potential biases and increase the accuracy of their measurements.

Facilitating Replication of Studies

A robust research design allows for the replication of studies by other researchers. Replication plays a vital role in the scientific process as it helps confirm the validity and generalizability of research findings. By clearly documenting the research design, researchers enable others to reproduce the study and validate the results, thereby contributing to the cumulative knowledge in a field.

Steps in Developing a Research Design

Developing a research design involves a systematic process that includes several important steps. Researchers need to carefully consider each step to ensure that their study is well-designed and capable of addressing their research questions effectively.

Identifying Research Questions

The first step in developing a research design is to identify and define the research questions or hypotheses. Researchers need to clearly articulate what they aim to investigate and what specific information they want to gather. Clear research questions provide guidance for the subsequent steps in the research design process.

Selecting Appropriate Design Type

Once the research questions are identified, researchers need to select the most appropriate type of research design. The choice of design depends on various factors, including the research goals, the nature of the research questions, and the available resources. Careful consideration of these factors is crucial to ensure that the chosen design aligns with the study objectives.

Determining Data Collection Methods

After selecting the research design, researchers need to determine the most suitable data collection methods. Depending on the research questions and the type of data required, researchers can utilize a range of methods, such as surveys, interviews, observations, or experiments. The chosen methods should align with the research objectives and allow for the collection of high-quality data.

One of the most important considerations when designing a study in human behavior research is participant recruitment. We have written a comprehensive guide on best practices and pitfalls to be aware of when recruiting participants, which can be read here.

Enhancing Research Design with iMotions and Biosensors

Introduction to enhanced research design.

In the realm of scientific studies, especially within human cognitive-behavioral research, the deployment of advanced technologies such as iMotions software and biosensors has revolutionized research design. This chapter delves into how these technologies can be integrated into various research designs, improving the depth, accuracy, and reliability of scientific inquiries.

Integrating iMotions in Research Design

Imotions software: a key to multimodal data integration.

The iMotions platform stands as a pivotal tool in modern research design. It’s designed to integrate data from a plethora of biosensors, providing a comprehensive analysis of human behavior. This software facilitates the synchronizing of physiological, cognitive, and emotional responses with external stimuli, thus enriching the understanding of human behavior in various contexts.

Biosensors: Gateways to Deeper Insights

Biosensors, including eye trackers, EEG, GSR, ECG, and facial expression analysis tools, provide nuanced insights into the subconscious and conscious aspects of human behavior. These tools help researchers in capturing data that is often unattainable through traditional data collection methods like surveys and interviews.

Application in Different Research Designs

• Eye Tracking : In experimental designs, where the impact of visual stimuli is crucial, eye trackers can reveal how subjects interact with these stimuli, thereby offering insights into cognitive processes and attention.
• EEG : EEG biosensors allow researchers to monitor brain activity in response to controlled experimental manipulations, offering a window into cognitive and emotional responses.

• Facial Expression Analysis : In observational studies, analyzing facial expressions can provide objective data on emotional responses in natural settings, complementing subjective self-reports.
• GSR/EDA : These tools measure physiological arousal in real-life scenarios, giving researchers insights into emotional states without the need for intrusive measures.
• EMG : In studies where direct manipulation isn’t feasible, EMG can indicate subtle responses to stimuli, which might be overlooked in traditional observational methods.
• ECG/PPG : These sensors can be used to understand the impact of various interventions on physiological states such as stress or relaxation.

Streamlining Research Design with iMotions

The iMotions platform offers a streamlined process for integrating various biosensors into a research design. Researchers can easily design experiments, collect multimodal data, and analyze results in a unified interface. This reduces the complexity often associated with handling multiple streams of data and ensures a cohesive and comprehensive research approach.

Integrating iMotions software and biosensors into research design opens new horizons for scientific inquiry. This technology enhances the depth and breadth of data collection, paving the way for more nuanced and comprehensive findings.

Whether in experimental, non-experimental, or quasi-experimental designs, iMotions and biosensors offer invaluable tools for researchers aiming to uncover the intricate layers of human behavior and cognitive processes. The future of research design is undeniably intertwined with the advancements in these technologies, leading to more robust, reliable, and insightful scientific discoveries.

Challenges in Research Design

Research design can present several challenges that researchers need to overcome to conduct reliable and valid studies. Being aware of these challenges is essential for researchers to address them effectively and ensure the integrity of their research.

Ethical Considerations

Research design must adhere to ethical guidelines and principles to protect the rights and well-being of participants. Researchers need to obtain informed consent, ensure participant confidentiality, and minimize potential harm or discomfort. Ethical considerations should be carefully integrated into the research design to promote ethical research practices.

Practical Limitations

Researchers often face practical limitations that may impact the design and execution of their studies. Limited resources, time constraints, access to participants or data, and logistical challenges can pose obstacles during the research process. Researchers need to navigate these limitations and make thoughtful choices to ensure the feasibility and quality of their research.

Research design is a vital aspect of conducting scientific studies. It provides a structured framework for researchers to answer their research questions and obtain reliable and valid results. By understanding the different types of research design and following the necessary steps in developing a research design, researchers can enhance the rigor and impact of their studies.

However, researchers must also be mindful of the challenges they may encounter, such as ethical considerations and practical limitations, and take appropriate measures to address them. Ultimately, a well-designed research study contributes to the advancement of knowledge and promotes evidence-based decision-making in various fields.

Last edited

About the author

See what is next in human behavior research

Follow our newsletter to get the latest insights and events send to your inbox.

Related Posts

Can you use HTC VIVE Pro Eye for eye tracking research?

Top 5 Publications of 2023

Laila Mowla

Understanding Cognitive Workload: What Is It and How Does It Affect Us?

The Best Neuroscience Software

You might also like these.

Exploring Human Behavior: Why do We All React in Different Ways?

iMotions EduLabs: Democratizing Biometric Technology

Case Stories

Explore Blog Categories

Best Practice

Collaboration, product guides, product news, research fundamentals, research insights, 🍪 use of cookies.

We are committed to protecting your privacy and only use cookies to improve the user experience.

Chose which third-party services that you will allow to drop cookies. You can always change your cookie settings via the Cookie Settings link in the footer of the website. For more information read our Privacy Policy.

• gtag This tag is from Google and is used to associate user actions with Google Ad campaigns to measure their effectiveness. Enabling this will load the gtag and allow for the website to share information with Google.
• Livechat Livechat provides you with direct access to the experts in our office. The service tracks visitors to the website but does not store any information unless consent is given. This service is essential and can not be disabled.
• Pardot Collects information such as the IP address, browser type, and referring URL. This information is used to create reports on website traffic and track the effectiveness of marketing campaigns.
• Third-party iFrames Allows you to see thirdparty iFrames.

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

• Types of Research Designs Compared | Guide & Examples

Types of Research Designs Compared | Guide & Examples

Published on June 20, 2019 by Shona McCombes . Revised on June 22, 2023.

When you start planning a research project, developing research questions and creating a  research design , you will have to make various decisions about the type of research you want to do.

There are many ways to categorize different types of research. The words you use to describe your research depend on your discipline and field. In general, though, the form your research design takes will be shaped by:

• The type of knowledge you aim to produce
• The type of data you will collect and analyze
• The sampling methods , timescale and location of the research

This article takes a look at some common distinctions made between different types of research and outlines the key differences between them.

Table of contents

Types of research aims, types of research data, types of sampling, timescale, and location, other interesting articles.

The first thing to consider is what kind of knowledge your research aims to contribute.

Prevent plagiarism. Run a free check.

The next thing to consider is what type of data you will collect. Each kind of data is associated with a range of specific research methods and procedures.

Finally, you have to consider three closely related questions: how will you select the subjects or participants of the research? When and how often will you collect data from your subjects? And where will the research take place?

Keep in mind that the methods that you choose bring with them different risk factors and types of research bias . Biases aren’t completely avoidable, but can heavily impact the validity and reliability of your findings if left unchecked.

Choosing between all these different research types is part of the process of creating your research design , which determines exactly how your research will be conducted. But the type of research is only the first step: next, you have to make more concrete decisions about your research methods and the details of the study.

Read more about creating a research design

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Ecological validity

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, June 22). Types of Research Designs Compared | Guide & Examples. Scribbr. Retrieved April 9, 2024, from https://www.scribbr.com/methodology/types-of-research/

Is this article helpful?

Shona McCombes

Other students also liked, what is a research design | types, guide & examples, qualitative vs. quantitative research | differences, examples & methods, what is a research methodology | steps & tips, what is your plagiarism score.

Research Design

• First Online: 13 April 2022

Cite this chapter

• Yanmei Li 3 &
• Sumei Zhang 4  

835 Accesses

This chapter introduces methods to design the research. Research design is the blueprint of how to conduct research from conception to completion. It requires careful crafts to ensure success. The initial step of research design is to theorize key concepts of the research questions, operationalize the variables used to measure the key concepts, and carefully identify the levels of measurements for all the key variables. After theorization of the key concepts, a thorough literature search and synthetization is imperative to explore extant studies related to the research questions. The purpose of literature review is to retrieve ideas, replicate studies, or fill the gap for issues and theories that extant research has (or has not) investigated.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as PDF
• Read on any device
• Instant download
• Own it forever
• Available as EPUB and PDF
• Compact, lightweight edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info
• Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Borrego, M., Douglas, E. P., & Amelink, C. T. (2009). Quantitative, qualitative, and mixed research methods in engineering education. Journal of Engineering Education, 98 (1), 53–66.

Article   Google Scholar  

Creswell, J. W., Plano Clark, V. L., & Garrett, A. L. (2008). Methodological issues in conducting mixed methods research design. In M. M. Bergman (Ed.), Advances in mixed methods research: Theories and application (pp. 66–83). Sage.

Google Scholar  

Li, Y., & Walter, R. (2013). Single-family housing market segmentation, post-foreclosure resale duration, and neighborhood attributes. Housing Policy Debate, 23 (4), 643–665. https://doi.org/10.1080/10511482.2013.835331

Opoku, A., Ahmed, V., & Akotia, J. (2016). Choosing an appropriate research methodology and method. In V. Ahmed, A. Opoku, & Z. Aziz (Eds.), Research methodology in the built environment: A selection of case studies . Routledge.

Pickering, C., Johnson, M., & Byrne, J. (2021). Using systematic quantitative literature reviews for urban analysis. In S. Baum (Ed.). Methods in Urban Analysis (Cities Research Series) (pp. 29–49) . Singapore: Springer.

Download references

Author information

Authors and affiliations.

Florida Atlantic University, Boca Raton, FL, USA

University of Louisville, Louisville, KY, USA

Sumei Zhang

You can also search for this author in PubMed   Google Scholar

Electronic Supplementary Material

(docx 13 kb), rights and permissions.

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this chapter

Li, Y., Zhang, S. (2022). Research Design. In: Applied Research Methods in Urban and Regional Planning. Springer, Cham. https://doi.org/10.1007/978-3-030-93574-0_3

Download citation

DOI : https://doi.org/10.1007/978-3-030-93574-0_3

Published : 13 April 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-93573-3

Online ISBN : 978-3-030-93574-0

eBook Packages : Mathematics and Statistics Mathematics and Statistics (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Skip to main content
• Skip to primary sidebar
• Skip to footer
• QuestionPro

• Solutions Industries Gaming Automotive Sports and events Education Government Travel & Hospitality Financial Services Healthcare Cannabis Technology Use Case NPS+ Communities Audience Contactless surveys Mobile LivePolls Member Experience GDPR Positive People Science 360 Feedback Surveys
• Resources Blog eBooks Survey Templates Case Studies Training Help center

Home Market Research Research Tools and Apps

Research Design: What it is, Elements & Types

Can you imagine doing research without a plan? Probably not. When we discuss a strategy to collect, study, and evaluate data, we talk about research design. This design addresses problems and creates a consistent and logical model for data analysis. Let’s learn more about it.

What is Research Design?

Research design is the framework of research methods and techniques chosen by a researcher to conduct a study. The design allows researchers to sharpen the research methods suitable for the subject matter and set up their studies for success.

Creating a research topic explains the type of research (experimental,  survey research ,  correlational , semi-experimental, review) and its sub-type (experimental design, research problem , descriptive case-study). 

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

The research problem an organization faces will determine the design, not vice-versa. The design phase of a study determines which tools to use and how they are used.

The Process of Research Design

The research design process is a systematic and structured approach to conducting research. The process is essential to ensure that the study is valid, reliable, and produces meaningful results.

• Consider your aims and approaches: Determine the research questions and objectives, and identify the theoretical framework and methodology for the study.
• Choose a type of Research Design: Select the appropriate research design, such as experimental, correlational, survey, case study, or ethnographic, based on the research questions and objectives.
• Identify your population and sampling method: Determine the target population and sample size, and choose the sampling method, such as random , stratified random sampling , or convenience sampling.
• Choose your data collection methods: Decide on the methods, such as surveys, interviews, observations, or experiments, and select the appropriate instruments or tools for collecting data.
• Plan your data collection procedures: Develop a plan for data collection, including the timeframe, location, and personnel involved, and ensure ethical considerations.
• Decide on your data analysis strategies: Select the appropriate data analysis techniques, such as statistical analysis , content analysis, or discourse analysis, and plan how to interpret the results.

The process of research design is a critical step in conducting research. By following the steps of research design, researchers can ensure that their study is well-planned, ethical, and rigorous.

Research Design Elements

Impactful research usually creates a minimum bias in data and increases trust in the accuracy of collected data. A design that produces the slightest margin of error in experimental research is generally considered the desired outcome. The essential elements are:

• Accurate purpose statement
• Techniques to be implemented for collecting and analyzing research
• The method applied for analyzing collected details
• Type of research methodology
• Probable objections to research
• Settings for the research study
• Measurement of analysis

Characteristics of Research Design

A proper design sets your study up for success. Successful research studies provide insights that are accurate and unbiased. You’ll need to create a survey that meets all of the main characteristics of a design. There are four key characteristics:

• Neutrality: When you set up your study, you may have to make assumptions about the data you expect to collect. The results projected in the research should be free from research bias and neutral. Understand opinions about the final evaluated scores and conclusions from multiple individuals and consider those who agree with the results.
• Reliability: With regularly conducted research, the researcher expects similar results every time. You’ll only be able to reach the desired results if your design is reliable. Your plan should indicate how to form research questions to ensure the standard of results.
• Validity: There are multiple measuring tools available. However, the only correct measuring tools are those which help a researcher in gauging results according to the objective of the research. The  questionnaire  developed from this design will then be valid.
• Generalization:  The outcome of your design should apply to a population and not just a restricted sample . A generalized method implies that your survey can be conducted on any part of a population with similar accuracy.

The above factors affect how respondents answer the research questions, so they should balance all the above characteristics in a good design. If you want, you can also learn about Selection Bias through our blog.

Research Design Types

A researcher must clearly understand the various types to select which model to implement for a study. Like the research itself, the design of your analysis can be broadly classified into quantitative and qualitative.

Qualitative research

Qualitative research determines relationships between collected data and observations based on mathematical calculations. Statistical methods can prove or disprove theories related to a naturally existing phenomenon. Researchers rely on qualitative observation research methods that conclude “why” a particular theory exists and “what” respondents have to say about it.

Quantitative research

Quantitative research is for cases where statistical conclusions to collect actionable insights are essential. Numbers provide a better perspective for making critical business decisions. Quantitative research methods are necessary for the growth of any organization. Insights drawn from complex numerical data and analysis prove to be highly effective when making decisions about the business’s future.

Qualitative Research vs Quantitative Research

Here is a chart that highlights the major differences between qualitative and quantitative research:

In summary or analysis , the step of qualitative research is more exploratory and focuses on understanding the subjective experiences of individuals, while quantitative research is more focused on objective data and statistical analysis.

You can further break down the types of research design into five categories:

1. Descriptive: In a descriptive composition, a researcher is solely interested in describing the situation or case under their research study. It is a theory-based design method created by gathering, analyzing, and presenting collected data. This allows a researcher to provide insights into the why and how of research. Descriptive design helps others better understand the need for the research. If the problem statement is not clear, you can conduct exploratory research. 

2. Experimental: Experimental research establishes a relationship between the cause and effect of a situation. It is a causal research design where one observes the impact caused by the independent variable on the dependent variable. For example, one monitors the influence of an independent variable such as a price on a dependent variable such as customer satisfaction or brand loyalty. It is an efficient research method as it contributes to solving a problem.

The independent variables are manipulated to monitor the change it has on the dependent variable. Social sciences often use it to observe human behavior by analyzing two groups. Researchers can have participants change their actions and study how the people around them react to understand social psychology better.

3. Correlational research: Correlational research  is a non-experimental research technique. It helps researchers establish a relationship between two closely connected variables. There is no assumption while evaluating a relationship between two other variables, and statistical analysis techniques calculate the relationship between them. This type of research requires two different groups.

A correlation coefficient determines the correlation between two variables whose values range between -1 and +1. If the correlation coefficient is towards +1, it indicates a positive relationship between the variables, and -1 means a negative relationship between the two variables. 

4. Diagnostic research: In diagnostic design, the researcher is looking to evaluate the underlying cause of a specific topic or phenomenon. This method helps one learn more about the factors that create troublesome situations. 

This design has three parts of the research:

• Inception of the issue
• Diagnosis of the issue
• Solution for the issue

5. Explanatory research : Explanatory design uses a researcher’s ideas and thoughts on a subject to further explore their theories. The study explains unexplored aspects of a subject and details the research questions’ what, how, and why.

Benefits of Research Design

There are several benefits of having a well-designed research plan. Including:

• Clarity of research objectives: Research design provides a clear understanding of the research objectives and the desired outcomes.
• Increased validity and reliability: To ensure the validity and reliability of results, research design help to minimize the risk of bias and helps to control extraneous variables.
• Improved data collection: Research design helps to ensure that the proper data is collected and data is collected systematically and consistently.
• Better data analysis: Research design helps ensure that the collected data can be analyzed effectively, providing meaningful insights and conclusions.
• Improved communication: A well-designed research helps ensure the results are clean and influential within the research team and external stakeholders.
• Efficient use of resources: reducing the risk of waste and maximizing the impact of the research, research design helps to ensure that resources are used efficiently.

A well-designed research plan is essential for successful research, providing clear and meaningful insights and ensuring that resources are practical.

QuestionPro offers a comprehensive solution for researchers looking to conduct research. With its user-friendly interface, robust data collection and analysis tools, and the ability to integrate results from multiple sources, QuestionPro provides a versatile platform for designing and executing research projects.

Our robust suite of research tools provides you with all you need to derive research results. Our online survey platform includes custom point-and-click logic and advanced question types. Uncover the insights that matter the most.

FREE TRIAL         LEARN MORE

MORE LIKE THIS

Government Customer Experience: Impact on Government Service

Apr 11, 2024

Employee Engagement App: Top 11 For Workforce Improvement 

Apr 10, 2024

Top 15 Employee Evaluation Software to Enhance Performance

Event Feedback Software: Top 11 Best in 2024

Apr 9, 2024

Other categories

• Academic Research
• Artificial Intelligence
• Assessments
• Brand Awareness
• Case Studies
• Communities
• Consumer Insights
• Customer effort score
• Customer Engagement
• Customer Experience
• Customer Loyalty
• Customer Research
• Customer Satisfaction
• Employee Benefits
• Employee Engagement
• Employee Retention
• Friday Five
• General Data Protection Regulation
• Insights Hub
• Life@QuestionPro
• Market Research
• Mobile diaries
• Mobile Surveys
• New Features
• Online Communities
• Question Types
• Questionnaire
• QuestionPro Products
• Release Notes
• Research Tools and Apps
• Revenue at Risk
• Survey Templates
• Training Tips
• Uncategorized
• Video Learning Series
• What’s Coming Up
• Workforce Intelligence

What is Research Design?

Crafting a well-defined research design is essential for guiding the entire project, ensuring coherence in methodology and analysis, and upholding the validity and reproducibility of outcomes in the complex landscape of research.

Updated on March 8, 2024

Diving into any new project necessitates a solid plan, a blueprint for navigating the very complex research process. It requires a framework that illustrates how all the principal components of the project are intended to work together to address your central research questions - the research design .

This research design is crucial not only for guiding your entire project, from methodology to analysis, but also for ensuring the validity and reproducibility of its outcomes. Let’s take a closer look at research design by focusing on some of its benefits and core elements.

Why do researchers need a research design?

By taking a deliberate approach to research design, you ensure your chosen methods realistically match the project’s objectives. For example:

• If your project seeks to find out how a certain group of people was influenced by a natural disaster, you could use interviews as methods for gathering data. Then, inductive or deductive coding may be used for analysis.
• On the other hand, if your project asks how drinking water was affected by that same natural disaster, you would conduct an experiment to measure certain variables. Inferential or descriptive statistical analysis might then be used to assess the data.

Attention to robust research design helps the project run smoothly and efficiently by reducing both errors and unnecessary busywork. Good research design possesses these specific characteristics :

• Neutrality : Stick to only the facts throughout, creating a plan based on relevant research methods and analysis. Use it as an opportunity to identify possible sources of bias.
• Reliability : Include reliable methods that support the consistent measurement of project variables. Not only does it improve the legitimacy of your conclusions but also improves the possibility of replication.
• Validity : Apply measurement tools that minimize systematic errors. Show the straightforward connection between your project results and research hypothesis.
• Generalizability : Verify that research outcomes are applicable to a larger population beyond the sample studied for your project. Employ sensible methods and processes that easily adapt to variations in the population.
• Flexibility : Consider alternative measures for adjusting to unexpected data or outcomes. Veer away from rigid procedures and requirements and plan for adaptability.

When you make the effort to focus on these characteristics while developing a research design, the process itself weeds out many potential challenges. It illuminates the relationships between the project’s multiple elements and allows for modifications from the start. 

What makes up a research design?

As the overarching strategy for your entire project, the research design outlines the plans, considerations, and feasibility of every facet. To make this task less daunting, divide it into logical sections by asking yourself these questions:

• What is your general approach for the study?
• What type of design will you employ?
• How will you choose the population and sampling methods?
• Which data collection methods will you use?
• How will the data be analyzed?

The answers to these questions depend on your research questions and hypothesis. Before starting your research design, make certain that these elements are well thought out, basically solidified, and truly represent your intentions for the project.

When considering the overall approach for your project, decide what kind of data is needed to answer the research questions. Start by asking yourself:

• Do I want to establish a cause-and-effect relationship, test a hypothesis, or identify patterns in data? If yes, use quantitative methodologies.
• Or, am I seeking non-numerical textual information, like human beliefs, cultural experiences, or individual behaviors? If so, use qualitative methods.

Quantitative research methods offer a systematic means of investigating complex phenomena by measuring, describing, and testing relationships between variables. On the other hand, the qualitative approach explores subjective experiences and concepts within their natural settings. Here are some key characteristics of both approaches:

Approach : Basis

Quantitative : The research begins with the formulation of specific research questions or hypotheses that can be tested empirically using numerical data.

Qualitative : The exploratory and flexible nature allows researchers to delve deeply into the subject matter and generate insights.

Approach : Data collection

Quantitative : Typically involves collecting numerical data through methods such as surveys, experiments, structured observations, or existing datasets.

Qualitative : To collect detailed, contextually rich information directly from participants, researchers use methods such as interviews, focus groups, participant observation, and document analysis.

Approach : Data analysis

Quantitative : Quantitative data are analyzed using statistical techniques.

Qualitative : Data analysis in qualitative research involves systematic techniques for organizing, coding, and interpreting textual or visual data. 

Approach : Interpretation of findings

Quantitative : Researchers interpret the results of the statistical analysis in relation to the research questions or hypotheses.

Qualitative : By paying close attention to context, qualitative researchers focus on interpreting the meanings, patterns, and themes that emerge from the data. 

Approach : Reporting results

Quantitative : Reported in a structured format, often including tables, charts, and graphs to present the data visually.

Qualitative : Contributes to theory building and exploration by generating new insights, challenging existing theories, and uncovering unexpected findings.

Approach : Types

Quantitative :

• Experimental
• Quasi-experimental
• Correlational
• Descriptive

Qualitative :

• Ethnography
• Grounded theory
• Phenomenology

Population and sampling method

In research, the population, or target population, encompasses all individuals, objects, or events that share the specific attributes you’ve decided are relevant to the study’s objectives. As it is impractical to investigate every individual of this broad population, you will need to choose a subset, or sample.

Starting with a comprehensive understanding of the target population is crucial for selecting a sample that will assure the generalizability of your study’s results. However, drawing a truly random sample can be challenging, often resulting in some degree of sampling bias in most studies.

Sampling strategies vary across research fields, but are generally subdivided into these two categories:

• Probability Sampling : accurately measurable probability for each member of the target population to have a chance of being included in the sample.
• Non-probability sampling : selection is non-systematic and does not offer an equal chance for those in the target population to be selected for the sample.

There are several specific sampling methods that fall under these two broad headings:

Probability Sampling Examples

• Simple random sampling: Each individual is chosen entirely by chance from a population, ensuring equal probability of selection. 
• Convenience sampling: Participants are selected based on availability and willingness to participate.
• Systematic sampling: Individuals are selected at regular intervals from the sampling frame based on a systematic rule.
• Quota sampling: Interviewers are given quotas of specific subjects to recruit.

Non-probability Sampling Examples

• Stratified sampling: The population is divided into homogenous subgroups based on shared characteristics, then used for a random sample.
• Judgmental sampling: Researchers select participants based on their judgment or specific criteria.
• Clustered sampling: Subgroups, or clusters, of the population are determined and then randomly selected for inclusion.
• Snowball sampling: Existing subjects nominate further subjects known to them, allowing for sampling of hard-to-reach groups.

While they are often resource intensive, probability sampling methods have the advantage of providing representative samples with reduced biases. Non-probability sampling methods, on the other hand, are more cost-effective and convenient, yet lack representativeness and are prone to bias.

Data collection

Throughout the research process, you'll employ a variety of sources to gather, record, and organize information that is relevant to your study or project. Achieving results that hold validity and significance requires the skillful use of efficient data collection methods.

Primary and secondary data collection methods are two distinct approaches to consider when gathering information for your project. Let's take a look at these methods and their associated techniques:

Primary data collection : involves gathering original data directly from the source or through direct interaction with respondents. 

• Surveys and Questionnaires: collecting data from individuals or groups through face-to-face interviews, telephone calls, mail, or online platforms.
• Interviews: direct interaction between the researcher and the respondent, conducted in person, over the phone, or through video conferencing.
• Observations: researchers observe and record behaviors, actions, or events in their natural setting.
• Experiments: manipulating variables to observe their impact on outcomes. 
• Focus Groups: small groups of individuals discuss specific topics in a moderated setting.

Secondary data collection: entails collecting and analyzing existing data already collected by someone else for a different purpose.

• Published sources: books, academic journals, magazines, newspapers, government reports, and other published materials that contain relevant data.
• Online sources: databases, websites, repositories, and other platforms available for consuming and downloading from the internet. 
• Government and institutional sources: records, statistics, and other pertinent information to access and purchase.
• Publicly available data: shared by individuals, organizations, or communities on public stages, websites, or social media.
• Past research: studies and results available through libraries, educational institutions, and other communal archives. 

Though primary methods offer significant control over data collection, they can be time-consuming, costly, and susceptible to biases. Secondary methods, in contrast, provide cost-effective and time-saving alternatives but offer reduced control over the data collection process.

Data analysis

To extract maximum value from your collected data, it's essential to engage in purposeful evaluation and interpretation. This process of data analysis involves thorough examination, meticulous cleaning, and insightful modeling to reveal patterns pertinent to your research questions.

The choice of methods depends on the specific research objectives, data characteristics, and analytical requirements of your particular project. Here are a few examples of the diverse range of methods you can use for data analysis:

Descriptive statistics : Summarizes key features of the data, like central tendency, spread, and variability. 

Inferential statistics : Draws conclusions about populations based on sample data to test relationships and make predictions.

Qualitative analysis : Considers non-numerical transcripts to identify themes, patterns, and connections.

Causal analysis : Looks at the cause and effect of relationships between variables to test correlations.

Survey and questionnaire analysis : Transforms responses into usable data through processes like cross-tabulation and benchmarking.

Machine learning and data mining : Employs algorithms and computational techniques to discover patterns and insights from large datasets.

By integrating various data analysis tools, you can approach research questions from multiple perspectives to enhance the depth and breadth of your analysis.

Considerations for research design

A meticulous and thorough research design is essential to maintain the quality, reliability, and overall value of your study results. Consider these tips:

Do : Clearly define research questions

Don’t : Rush through the design process

Do : Choose appropriate methods

Don’t : Overlook ethical considerations

Do : Ensure data reliability and validity

Don’t : Neglect practical constraints

Do : Mitigate biases and confounding factors

Don’t : Use overly complex designs

Do : Pilot test the research design

Don’t : Ignore feedback from peers and experts

Do : Document the research design

Don’t : Assume the design is flawless

Final thoughts

A robust research design is undeniably crucial. It sets the framework for data collection, analysis, and interpretation throughout the entire research process. 

Because vagueness and assumptions can jeopardize the success of your project, you must prioritize clarity, make informed choices, and pay meticulous attention to detail. By embracing these strategies, your valuable research has the best chance of making its maximum impact on the world.

Charla Viera, MS

See our "Privacy Policy"

• Interesting
• Scholarships
• UGC-CARE Journals

What is a Research Design? Importance and Types

Why Research Design is Important for a Researcher?

Table of contents

• What is a Research Design in Research Methodology?

Importance of Research Design

Considerations in selecting the research design, types of research design.

A research design is a systematic procedure or an idea to carry out different tasks of the research study. It is important to know the research design and its types for the researcher to carry out the work in a proper way.

The purpose of research design is that enable the researcher to proceed in the right direction without any deviation from the tasks. It is an overall detailed strategy of the research process.

The design of experiments is a very important aspect of a research study. A poor research design may collapse the entire research project in terms of time, manpower, and money.

7 Importance of Research Design – iLovePhD

What is a Research Design in Research Methodology ?

A research design is a plan or framework for conducting research. It includes a set of plans and procedures that aim to produce reliable and valid data. The research design must be appropriate to the type of research question being asked and the type of data being collected.

A typical research design is a detailed methodology or a roadmap for the successful completion of any research work. ilovephd.com

A Good research design consists of the following important points:

• Formulating a research design helps the researcher to make correct decisions in each and every step of the study.
• It helps to identify the major and minor tasks of the study.
• It makes the research study effective and interesting by providing minute details at each step of the research process.
• Based on the design of experiments (research design), a researcher can easily frame the objectives of the research work.
• A good research design helps the researcher to complete the objectives of the study in a given time and facilitates getting the best solution for the research problems .
• It helps the researcher to complete all the tasks even with limited resources in a better way.
• The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

How to Create a Research Design?                      

According to Thyer, the research design has the following components:

• A researcher begins the study by framing the problem statement of the research work.
• Then, the researcher has to identify the sampling points, the number of samples, the sample size, and the location.
• The next step is to identify the operating variables or parameters of the study and detail how the variables are to be measured.
• The final step is the collection, interpretation, and dissemination of results.

The researchers should know the various types of research designs and their applicability. The selection of a research design can only be made after a careful understanding of the different research design types . The factors to be considered in choosing a research design are

• Qualitative Vs quantitative
• Basic Vs applied
• Empirical Vs Non-empirical

There are four main types of research designs: experimental, observational, quasi-experimental, and descriptive.

• Experimental designs: are used to test cause-and-effect relationships. In an experiment, the researcher manipulates one or more independent variables and observes the effect on a dependent variable.
• Observational designs are used to study behavior without manipulating any variables. The researcher simply observes and records the behavior.
• Quasi-experimental designs are used when it is not possible to manipulate the independent variable. The researcher uses a naturally occurring independent variable and controls for other variables.
• Descriptive designs are used to describe a behavior or phenomenon. The researcher does not manipulate any variables, but simply observes and records the behavior.

I hope, this article would help you to know about what is research design, the types of research design, and what are the important points to be considered in carrying out the research work.

• classification of research design
• experimental research design
• research design
• research design and methodology
• research design and methods
• research design example
• research design explained
• research design in hindi
• research design lecture
• research design meaning
• research design types
• Research Methodology
• research methods
• types of research design
• what is research design

Indian Council of Social Science Research Calls for Collaborative Research Project

How to download free ieee dataset – 2024, leave a reply cancel reply.

Notify me of follow-up comments by email.

Notify me of new posts by email.

Email Subscription

iLovePhD is a research education website to know updated research-related information. It helps researchers to find top journals for publishing research articles and get an easy manual for research tools. The main aim of this website is to help Ph.D. scholars who are working in various domains to get more valuable ideas to carry out their research. Learn the current groundbreaking research activities around the world, love the process of getting a Ph.D.

WhatsApp Channel

Join iLovePhD WhatsApp Channel Now!

Contact us: [email protected]

Copyright © 2019-2024 - iLovePhD

• Artificial intelligence

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Research design: the methodology for interdisciplinary research framework

1 Biometris, Wageningen University and Research, PO Box 16, 6700 AA Wageningen, The Netherlands

Jarl K. Kampen

2 Statua, Dept. of Epidemiology and Medical Statistics, Antwerp University, Venusstraat 35, 2000 Antwerp, Belgium

Many of today’s global scientific challenges require the joint involvement of researchers from different disciplinary backgrounds (social sciences, environmental sciences, climatology, medicine, etc.). Such interdisciplinary research teams face many challenges resulting from differences in training and scientific culture. Interdisciplinary education programs are required to train truly interdisciplinary scientists with respect to the critical factor skills and competences. For that purpose this paper presents the Methodology for Interdisciplinary Research (MIR) framework. The MIR framework was developed to help cross disciplinary borders, especially those between the natural sciences and the social sciences. The framework has been specifically constructed to facilitate the design of interdisciplinary scientific research, and can be applied in an educational program, as a reference for monitoring the phases of interdisciplinary research, and as a tool to design such research in a process approach. It is suitable for research projects of different sizes and levels of complexity, and it allows for a range of methods’ combinations (case study, mixed methods, etc.). The different phases of designing interdisciplinary research in the MIR framework are described and illustrated by real-life applications in teaching and research. We further discuss the framework’s utility in research design in landscape architecture, mixed methods research, and provide an outlook to the framework’s potential in inclusive interdisciplinary research, and last but not least, research integrity.

Introduction

Current challenges, e.g., energy, water, food security, one world health and urbanization, involve the interaction between humans and their environment. A (mono)disciplinary approach, be it a psychological, economical or technical one, is too limited to capture any one of these challenges. The study of the interaction between humans and their environment requires knowledge, ideas and research methodology from different disciplines (e.g., ecology or chemistry in the natural sciences, psychology or economy in the social sciences). So collaboration between natural and social sciences is called for (Walsh et al. 1975 ).

Over the past decades, different forms of collaboration have been distinguished although the terminology used is diverse and ambiguous. For the present paper, the term interdisciplinary research is used for (Aboelela et al. 2007 , p. 341):

any study or group of studies undertaken by scholars from two or more distinct scientific disciplines. The research is based upon a conceptual model that links or integrates theoretical frameworks from those disciplines, uses study design and methodology that is not limited to any one field, and requires the use of perspectives and skills of the involved disciplines throughout multiple phases of the research process.

Scientific disciplines (e.g., ecology, chemistry, biology, psychology, sociology, economy, philosophy, linguistics, etc.) are categorized into distinct scientific cultures: the natural sciences, the social sciences and the humanities (Kagan 2009 ). Interdisciplinary research may involve different disciplines within a single scientific culture, and it can also cross cultural boundaries as in the study of humans and their environment.

A systematic review of the literature on natural-social science collaboration (Fischer et al. 2011 ) confirmed the general impression of this collaboration to be a challenge. The nearly 100 papers in their analytic set mentioned more instances of barriers than of opportunities (72 and 46, respectively). Four critical factors for success or failure in natural-social science collaboration were identified: the paradigms or epistemologies in the current (mono-disciplinary) sciences, the skills and competences of the scientists involved, the institutional context of the research, and the organization of collaborations (Fischer et al. 2011 ). The so-called “paradigm war” between neopositivist versus constructivists within the social and behavioral sciences (Onwuegbuzie and Leech 2005 ) may complicate pragmatic collaboration further.

It has been argued that interdisciplinary education programs are required to train truly interdisciplinary scientists with respect to the critical factor skills and competences (Frischknecht 2000 ) and accordingly, some interdisciplinary programs have been developed since (Baker and Little 2006 ; Spelt et al. 2009 ). The overall effect of interdisciplinary programs can be expected to be small as most programs are mono-disciplinary and based on a single paradigm (positivist-constructivist, qualitative-quantitative; see e.g., Onwuegbuzie and Leech 2005 ). We saw in our methodology teaching, consultancy and research practices working with heterogeneous groups of students and staff, that most had received mono-disciplinary training with a minority that had received multidisciplinary training, with few exceptions within the same paradigm. During our teaching and consultancy for heterogeneous groups of students and staff aimed at designing interdisciplinary research, we built the framework for methodology in interdisciplinary research (MIR). With the MIR framework, we aspire to contribute to the critical factors skills and competences (Fischer et al. 2011 ) for social and natural sciences collaboration. Note that the scale of interdisciplinary research projects we have in mind may vary from comparably modest ones (e.g., finding a link between noise reducing asphalt and quality of life; Vuye et al. 2016 ) to very large projects (finding a link between anthropogenic greenhouse gas emissions, climate change, and food security; IPCC 2015 ).

In the following section of this paper we describe the MIR framework and elaborate on its components. The third section gives two examples of the application of the MIR framework. The paper concludes with a discussion of the MIR framework in the broader contexts of mixed methods research, inclusive research, and other promising strains of research.

The methodology in interdisciplinary research framework

Research as a process in the methodology in interdisciplinary research framework.

The Methodology for Interdisciplinary Research (MIR) framework was built on the process approach (Kumar 1999 ), because in the process approach, the research question or hypothesis is leading for all decisions in the various stages of research. That means that it helps the MIR framework to put the common goal of the researchers at the center, instead of the diversity of their respective backgrounds. The MIR framework also introduces an agenda: the research team needs to carefully think through different parts of the design of their study before starting its execution (Fig.  1 ). First, the team discusses the conceptual design of their study which contains the ‘why’ and ‘what’ of the research. Second, the team discusses the technical design of the study which contains the ‘how’ of the research. Only after the team agrees that the complete research design is sufficiently crystalized, the execution of the work (including fieldwork) starts.

The Methodology of Interdisciplinary Research framework

Whereas the conceptual and technical designs are by definition interdisciplinary team work, the respective team members may do their (mono)disciplinary parts of fieldwork and data analysis on a modular basis (see Bruns et al. 2017 : p. 21). Finally, when all evidence is collected, an interdisciplinary synthesis of analyses follows which conclusions are input for the final report. This implies that the MIR framework allows for a range of scales of research projects, e.g., a mixed methods project and its smaller qualitative and quantitative modules, or a multi-national sustainability project and its national sociological, economic and ecological modules.

The conceptual design

Interdisciplinary research design starts with the “conceptual design” which addresses the ‘why’ and ‘what’ of a research project at a conceptual level to ascertain the common goals pivotal to interdisciplinary collaboration (Fischer et al. 2011 ). The conceptual design includes mostly activities such as thinking, exchanging interdisciplinary knowledge, reading and discussing. The product of the conceptual design is called the “conceptual frame work” which comprises of the research objective (what is to be achieved by the research), the theory or theories that are central in the research project, the research questions (what knowledge is to be produced), and the (partial) operationalization of constructs and concepts that will be measured or recorded during execution. While the members of the interdisciplinary team and the commissioner of the research must reach a consensus about the research objective, the ‘why’, the focus in research design must be the production of the knowledge required to achieve that objective the ‘what’.

With respect to the ‘why’ of a research project, an interdisciplinary team typically starts with a general aim as requested by the commissioner or funding agency, and a set of theories to formulate a research objective. This role of theory is not always obvious to students from the natural sciences, who tend to think in terms of ‘models’ with directly observable variables. On the other hand, students from the social sciences tend to think in theories with little attention to observable variables. In the MIR framework, models as simplified descriptions or explanations of what is studied in the natural sciences play the same role in informing research design, raising research questions, and informing how a concept is understood, as do theories in social science.

Research questions concern concepts, i.e. general notions or ideas based on theory or common sense that are multifaceted and not directly visible or measurable. For example, neither food security (with its many different facets) nor a person’s attitude towards food storage may be directly observed. The operationalization of concepts, the transformation of concepts into observable indicators, in interdisciplinary research requires multiple steps, each informed by theory. For instance, in line with particular theoretical frameworks, sustainability and food security may be seen as the composite of a social, an economic and an ecological dimension (e.g., Godfray et al. 2010 ).

As the concept of interest is multi-disciplinary and multi-dimensional, the interdisciplinary team will need to read, discuss and decide on how these dimensions and their indicators are weighted to measure the composite interdisciplinary concept to get the required interdisciplinary measurements. The resulting measure or measures for the interdisciplinary concept may be of the nominal, ordinal, interval and ratio level, or a combination thereof. This operationalization procedure is known as the port-folio approach to widely defined measurements (Tobi 2014 ). Only after the research team has finalized the operationalization of the concepts under study, the research questions and hypotheses can be made operational. For example, a module with descriptive research questions may now be turned into an operational one like, what are the means and variances of X1, X2, and X3 in a given population? A causal research question may take on the form, is X (a composite of X1, X2 and X3) a plausible cause for the presence or absence of Y? A typical qualitative module could study, how do people talk about X1, X2 and X3 in their everyday lives?

The technical design

Members of an interdisciplinary team usually have had different training with respect to research methods, which makes discussing and deciding on the technical design more challenging but also potentially more creative than in a mono-disciplinary team. The technical design addresses the issues ‘how, where and when will research units be studied’ (study design), ‘how will measurement proceed’ (instrument selection or design), ‘how and how many research units will be recruited’ (sampling plan), and ‘how will collected data be analyzed and synthesized’ (analysis plan). The MIR framework provides the team a set of topics and their relationships to one another and to generally accepted quality criteria (see Fig.  1 ), which helps in designing this part of the project.

Interdisciplinary teams need be pragmatic as the research questions agreed on are leading in decisions on the data collection set-up (e.g., a cross-sectional study of inhabitants of a region, a laboratory experiment, a cohort study, a case control study, etc.), the so-called “study design” (e.g., Kumar 2014 ; De Vaus 2001 ; Adler and Clark 2011 ; Tobi and van den Brink 2017 ) instead of traditional ‘pet’ approaches. Typical study designs for descriptive research questions and research questions on associations are the cross-sectional study design. Longitudinal study designs are required to investigate development over time and cause-effect relationships ideally are studied in experiments (e.g., Kumar 2014 ; Shipley 2016 ). Phenomenological questions concern a phenomenon about which little is known and which has to be studied in the environment where it takes place, which calls for a case study design (e.g., Adler and Clark 2011 : p. 178). For each module, the study design is to be further explicated by the number of data collection waves, the level of control by the researcher and its reference period (e.g., Kumar 2014 ) to ensure the teams common understanding.

Then, decisions about the way data is to be collected, e.g., by means of certified instruments, observation, interviews, questionnaires, queries on existing data bases, or a combination of these are to be made. It is especially important to discuss the role of the observer (researcher) as this is often a source of misunderstanding in interdisciplinary teams. In the sciences, the observer is usually considered a neutral outsider when reading a standardized measurement instrument (e.g., a pyranometer to measure incoming solar radiation). In contrast, in the social sciences, the observer may be (part of) the measurement instrument, for example in participant observation or when doing in-depth interviews. After all, in participant observation the researcher observes from a member’s perspective and influences what is observed owing to the researcher’s participation (Flick 2006 : p. 220). Similarly in interviews, by which we mean “a conversation that has a structure and a purpose determined by the one party—the interviewer” (Kvale 2007 : p. 7), the interviewer and the interviewee are part of the measurement instrument (Kvale and Brinkmann 2009 : p. 2). In on-line and mail questionnaires the interviewer is eliminated as part of the instrument by standardizing the questions and answer options. Queries on existing data bases refer to the use of secondary data or secondary analysis. Different disciplines tend to use different bibliographic data bases (e.g., CAB Abstracts, ABI/INFORM or ERIC) and different data repositories (e.g., the European Social Survey at europeansocialsurvey.org or the International Council for Science data repository hosted by www.pangaea.de ).

Depending on whether or not the available, existing, measurement instruments tally with the interdisciplinary operationalisations from the conceptual design, the research team may or may not need to design instruments. Note that in some cases the social scientists’ instinct may be to rely on a questionnaire whereas the collaboration with another discipline may result in more objective possibilities (e.g., compare asking people about what they do with surplus medication, versus measuring chemical components from their input into the sewer system). Instrument design may take on different forms, such as the design of a device (e.g., pyranometer), a questionnaire (Dillman 2007 ) or a part thereof (e.g., a scale see DeVellis 2012 ; Danner et al. 2016 ), an interview guide with topics or questions for the interviewees, or a data extraction form in the context of secondary analysis and literature review (e.g., the Cochrane Collaboration aiming at health and medical sciences or the Campbell Collaboration aiming at evidence based policies).

Researchers from different disciplines are inclined to think of different research objects (e.g., animals, humans or plots), which is where the (specific) research questions come in as these identify the (possibly different) research objects unambiguously. In general, research questions that aim at making an inventory, whether it is an inventory of biodiversity or of lodging, call for a random sampling design. Both in the biodiversity and lodging example, one may opt for random sampling of geographic areas by means of a list of coordinates. Studies that aim to explain a particular phenomenon in a particular context would call for a purposive sampling design (non-random selection). Because studies of biodiversity and housing obey the same laws in terms of appropriate sampling design for similar research questions, individual students and researchers are sensitized to commonalities of their respective (mono)disciplines. For example, a research team interested in the effects of landslides on a socio-ecological system may select for their study one village that suffered from landslides and one village that did not suffer from landslides that have other characteristics in common (e.g., kind of soil, land use, land property legislation, family structure, income distribution, et cetera).

The data analysis plan describes how data will be analysed, for each of the separate modules and for the project at large. In the context of a multi-disciplinary quantitative research project, the data analysis plan will list the intended uni-, bi- and multivariate analyses such as measures for distributions (e.g., means and variances), measures for association (e.g., Pearson Chi square or Kendall Tau) and data reduction and modelling techniques (e.g., factor analysis and multiple linear regression or structural equation modelling) for each of the research modules using the data collected. When applicable, it will describe interim analyses and follow-up rules. In addition to the plans at modular level, the data analysis plan must describe how the input from the separate modules, i.e. different analyses, will be synthesized to answer the overall research question. In case of mixed methods research, the particular type of mixed methods design chosen describes how, when, and to what extent the team will synthesize the results from the different modules.

Unfortunately, in our experience, when some of the research modules rely on a qualitative approach, teams tend to refrain from designing a data analysis plan before starting the field work. While absence of a data analysis plan may be regarded acceptable in fields that rely exclusively on qualitative research (e.g., ethnography), failure to communicate how data will be analysed and what potential evidence will be produced posits a deathblow to interdisciplinarity. For many researchers not familiar with qualitative research, the black box presented as “qualitative data analysis” is a big hurdle, and a transparent and systematic plan is a sine qua non for any scientific collaboration. The absence of a data analysis plan for all modules results in an absence of synthesis of perspectives and skills of the disciplines involved, and in separate (disciplinary) research papers or separate chapters in the research report without an answer to the overall research question. So, although researchers may find it hard to write the data analysis plan for qualitative data, it is pivotal in interdisciplinary research teams.

Similar to the quantitative data analysis plan, the qualitative data analysis plan presents the description of how the researcher will get acquainted with the data collected (e.g., by constructing a narrative summary per interviewee or a paired-comparison of essays). Additionally, the rules to decide on data saturation need be presented. Finally, the types of qualitative analyses are to be described in the data analysis plan. Because there is little or no standardized terminology in qualitative data analysis, it is important to include a precise description as well as references to the works that describe the method intended (e.g., domain analysis as described by Spradley 1979 ; or grounded theory by means of constant-comparison as described by Boeije 2009 ).

Integration

To benefit optimally from the research being interdisciplinary the modules need to be brought together in the integration stage. The modules may be mono- or interdisciplinary and may rely on quantitative, qualitative or mixed methods approaches. So the MIR framework fits the view that distinguishes three multimethods approaches (quali–quali, quanti–quanti, and quali–quant).

Although the MIR framework has not been designed with the intention to promote mixed methods research, it is suitable for the design of mixed methods research as the kind of research that calls for both quantitative and qualitative components (Creswell and Piano Clark 2011 ). Indeed, just like the pioneers in mixed methods research (Creswell and Piano Clark 2011 : p. 2), the MIR framework deconstructs the package deals of paradigm and data to be collected. The synthesis of the different mono or interdisciplinary modules may benefit from research done on “the unique challenges and possibilities of integration of qualitative and quantitative approaches” (Fetters and Molina-Azorin 2017 : p. 5). We distinguish (sub) sets of modules being designed as convergent, sequential or embedded (adapted from mixed methods design e.g., Creswell and Piano Clark 2011 : pp. 69–70). Convergent modules, whether mono or interdisciplinary, may be done parallel and are integrated after completion. Sequential modules are done after one another and the first modules inform the latter ones (this includes transformative and multiphase mixed methods design). Embedded modules are intertwined. Here, modules depend on one another for data collection and analysis, and synthesis may be planned both during and after completion of the embedded modules.

Scientific quality and ethical considerations in the design of interdisciplinary research

A minimum set of jargon related to the assessment of scientific quality of research (e.g., triangulation, validity, reliability, saturation, etc.) can be found scattered in Fig.  1 . Some terms are reserved by particular paradigms, others may be seen in several paradigms with more or less subtle differences in meaning. In the latter case, it is important that team members are prepared to explain and share ownership of the term and respect the different meanings. By paying explicit attention to the quality concepts, researchers from different disciplines learn to appreciate each other’s concerns for good quality research and recognize commonalities. For example, the team may discuss measurement validity of both a standardized quantitative instrument and that of an interview and discover that the calibration of the machine serves a similar purpose as the confirmation of the guarantee of anonymity at the start of an interview.

Throughout the process of research design, ethics require explicit discussion among all stakeholders in the project. Ethical issues run through all components in the MIR framework in Fig.  1 . Where social and medical scientists may be more sensitive to ethical issues related to humans (e.g., the 1979 Belmont Report criteria of beneficence, justice, and respect), others may be more sensitive to issues related to animal welfare, ecology, legislation, the funding agency (e.g., implications for policy), data and information sharing (e.g., open access publishing), sloppy research practices, or long term consequences of the research. This is why ethics are an issue for the entire interdisciplinary team and cannot be discussed on project module level only.

The MIR framework in practice: two examples

Teaching research methodology to heterogeneous groups of students, institutional context and background of the mir framework.

Wageningen University and Research (WUR) advocates in its teaching and research an interdisciplinary approach to the study of global issues related to the motto “To explore the potential of nature to improve the quality of life.” Wageningen University’s student population is multidisciplinary and international (e.g., Tobi and Kampen 2013 ). Traditionally, this challenge of diversity in one classroom is met by covering a width of methodological topics and examples from different disciplines. However, when students of various programmes received methodological education in mixed classes, students of some disciplines would regard with disinterest or even disdain methods and techniques of the other disciplines. Different disciplines, especially from the qualitative respectively quantitative tradition in the social sciences (Onwuegbuzie and Leech 2005 : p. 273), claim certain study designs, methods of data collection and analysis as their territory, a claim reflected in many textbooks. We found that students from a qualitative tradition would not be interested, and would not even study, content like the design of experiments and quantitative data collection; and students from a quantitative tradition would ignore case study design and qualitative data collection. These students assumed they didn’t need any knowledge about ‘the other tradition’ for their future careers, despite the call for interdisciplinarity.

To enhance interdisciplinarity, WUR provides an MSc course mandatory for most students, in which multi-disciplinary teams do research for a commissioner. Students reported difficulties similar to the ones found in the literature: miscommunication due to talking different scientific languages and feelings of distrust and disrespect due to prejudice. This suggested that research methodology courses ought help prepare for interdisciplinary collaboration by introducing a single methodological framework that 1) creates sensitivity to the pros and challenges of interdisciplinary research by means of a common vocabulary and fosters respect for other disciplines, 2) starts from the research questions as pivotal in decision making on research methods instead of tradition or ontology, and 3) allows available methodologies and methods to be potentially applicable to any scientific research problem.

Teaching with MIR—the conceptual framework

As a first step, we replaced textbooks by ones refusing the idea that any scientific tradition has exclusive ownership of any methodological approach or method. The MIR framework further guides our methodology teaching in two ways. First, it presents a logical sequence of topics (first conceptual design, then technical design; first research question(s) or hypotheses, then study design; etc.). Second, it allows for a conceptual separation of topics (e.g., study design from instrument design). Educational programmes at Wageningen University and Research consistently stress the vital importance of good research design. In fact, 50% of the mark in most BSc and MSc courses in research methodology is based on the assessment of a research proposal that students design in small (2-4 students) and heterogeneous (discipline, gender and nationality) groups. The research proposal must describe a project which can be executed in practice, and which limitations (measurement, internal, and external validity) are carefully discussed.

Groups start by selecting a general research topic. They discuss together previously attained courses from a range of programs to identify personal and group interests, with the aim to reach an initial research objective and a general research question as input for the conceptual design. Often, their initial research objective and research question are too broad to be researchable (e.g., Kumar 2014 : p. 64; Adler and Clark 2011 : p. 71). In plenary sessions, the (basics of) critical assessment of empirical research papers is taught with special attention to the ‘what’ and ‘why’ section of research papers. During tutorials students generate research questions until the group agrees on a research objective, with one general research question that consists of a small set of specific research questions. Each of the specific research questions may stem from a different discipline, whereas answering the general research question requires integrating the answers to all specific research questions.

The group then identifies the key concepts in their research questions, while exchanging thoughts on possible attributes based on what they have learnt from previous courses (theories) and literature. When doing so they may judge the research question as too broad, in which case they will turn to the question strategies toolbox again. Once they agree on the formulation of the research questions and the choice of concepts, tasks are divided. In general, each student turns to the literature he/she is most familiar with or interested in, for the operationalization of the concept into measurable attributes and writes a paragraph or two about it. In the next meeting, the groups read and discuss the input and decide on the set-up and division of tasks with respect to the technical design.

Teaching with MIR—the technical framework

The technical part of research design distinguishes between study design, instrument design, sampling design, and the data analysis plan. In class, we first present students with a range of study designs (cross sectional, experimental, etc.). Student groups select an appropriate study design by comparing the demands made by the research questions with criteria for internal validity. When a (specific) research question calls for a study design that is not seen as practically feasible or ethically possible, they will rephrase the research question until the demands of the research question tally with the characteristics of at least one ethical, feasible and internally valid study design.

While following plenary sessions during which different random and non-random sampling or selection strategies are taught, groups start working on their sampling design. The groups make two decisions informed by their research question: the population(s) of research units, and the requirements of the sampling strategy for each population. Like many other aspects in research design, this can be an iterative process. For example, suppose the research question mentioned “local policy makers,” which is too vague for a sampling design. Then the decision may be to limit the study to “policy makers at the municipality level in the Netherlands” and adapt the general and the specific research questions accordingly. Next, the group identifies whether a sample design needs to focus on diversity (e.g., when the objective is to make an inventory of possible local policies), representativeness (e.g., when the objective is to estimate prevalence of types of local policies), or people with particular information (e.g., when the objective is to study people having experience with a given local policy). When a sample has to representative, the students must produce an assessment of external validity, whereas when the aim is to map diversity the students must discuss possible ways of source triangulation. Finally, in conjunction with the data analysis plan, students decide on the sample size and/or the saturation criteria.

When the group has agreed on their population(s) and the strategy for recruiting research units, the next step is to finalize the technical aspects of operationalisation i.e. addressing the issue of exactly how information will be extracted from the research units. Depending on what is practically feasible qua measurement, the choice of a data collection instrument may be a standardised (e.g., a spectrograph, a questionnaire) or less standardised (e.g., semi-structured interviews, visual inspection) one. The students have to discuss the possibilities of method triangulation, and explain the possible weaknesses of their data collection plan in terms of measurement validity and reliability.

Recent developments

Presently little attention is payed to the data analysis plan, procedures for synthesis and reporting because the programmes differ on their offer in data analysis courses, and because execution of the research is not part of the BSc and MSc methodology courses. Recently, we have designed one course for an interdisciplinary BSc program in which the research question is put central in learning and deciding on statistics and qualitative data analysis. Nonetheless, during the past years the number of methodology courses for graduate students that supported the MIR framework have been expanded, e.g., a course “From Topic to Proposal”; separate training modules on questionnaire construction, interviewing, and observation; and optional courses on quantitative and qualitative data analysis. These courses are open to (and attended by) PhD students regardless of their program. In Flanders (Belgium), the Flemish Training Network for Statistics and Methodology (FLAMES) has for the last four years successfully applied the approach outlined in Fig.  1 in its courses for research design and data collection methods. The division of the research process in terms of a conceptual design, technical design, operationalisation, analysis plan, and sampling plan, has proved to be appealing for students of disciplines ranging from linguistics to bioengineering.

Researching with MIR: noise reducing asphalt layers and quality of life

Research objective and research question.

This example of the application of the MIR framework comes from a study about the effects of “noise reducing asphalt layers” on the quality of life (Vuye et al. 2016 ), a project commissioned by the City of Antwerp in 2015 and executed by a multidisciplinary research team of Antwerp University (Belgium). The principal researcher was an engineer from the Faculty of Applied Engineering (dept. Construction), supported by two researchers from the Faculty of Medicine and Health Sciences (dept. of Epidemiology and Social Statistics), one with a background in qualitative and one with a background in quantitative research methods. A number of meetings were held where the research team and the commissioners discussed the research objective (the ‘what’ and ‘why’).The research objective was in part dictated by the European Noise Directive 2002/49/EC, which forces all EU member states to draft noise action plans, and the challenge in this study was to produce evidence of a link between the acoustic and mechanical properties of different types of asphalt, and the quality of life of people living in the vicinity of the treated roads. While there was literature available about the effects of road surface on sound, and other studies had studied the link between noise and health, no study was found that produced evidence simultaneously about noise levels of roads and quality of life. The team therefore decided to test the hypothesis that traffic noise reduction has a beneficial effect on the quality of life of people into the central research. The general research question was, “to what extent does the placing of noise reducing asphalt layers increase the quality of life of the residents?”

Study design

In order to test the effect of types of asphalt, initially a pretest–posttest experiment was designed, which was expanded by several added experimental (change of road surface) and control (no change of road surface) groups. The research team gradually became aware that quality of life may not be instantly affected by lower noise levels, and that a time lag is involved. A second posttest aimed to follow up on this effect although it could only be implemented in a selection of experimental sites.

Instrument selection and design

Sound pressure levels were measured by an ISO-standardized procedure called the Statistical Pass-By (SPB) method. A detailed description of the method is in Vuye et al. ( 2016 ). No such objective procedure is available for measuring quality of life, which can only be assessed by self-reports of the residents. Some time was needed for the research team to accept that measuring a multidimensional concept like quality of life is more complicated than just having people rate their “quality of life” on a 10 point scale. For instance, questions had to be phrased in a way that gave not away the purpose of the research (Hawthorne effect), leading to the inclusion of questions about more nuisances than traffic noise alone. This led to the design of a self-administered questionnaire, with questions of Flanders Survey on Living Environment (Departement Leefmilieu, Natuur & Energie 2013 ) appended by new questions. Among other things, the questionnaire probed for experienced nuisance by sound, quality of sleep, effort to concentrate, effort to have a conversation inside or outside the home, physical complaints such as headaches, etc.

Sampling design

The selected sites needed to accommodate both types of measurements: that of noise from traffic and quality of life of residents. This was a complicating factor that required several rounds of deliberation. While countrywide only certain roads were available for changing the road surface, these roads had to be mutually comparable in terms of the composition of the population, type of residential area (e.g., reports from the top floor of a tall apartment building cannot be compared to those at ground level), average volume of traffic, vicinity of hospitals, railroads and airports, etc. At the level of roads therefore, targeted sampling was applied, whereas at the level of residents the aim was to realize a census of all households within a given perimeter from the treated road surfaces. Considerations about the reliability of applied instruments were guiding decisions with respect to sampling. While the measurements of the SPB method were sufficiently reliable to allow for relatively few measurements, the questionnaire suffered from considerable nonresponse which hampered statistical power. It was therefore decided to increase the power of the study by adding control groups in areas where the road surface was not replaced. This way, detecting an effect of the intervention did not solely depend on the turnout of the pre and the post-test.

Data analysis plan

The statistical analysis had to account for the fact that data were collected at two different levels: the level of the residents filling out the questionnaires, and the level of the roads which surface was changed. Because survey participation was confidential, results of the pre- and posttest could only be compared at aggregate (street) level. The analysis had to control for confounding variables (e.g., sample composition, variety in traffic volume, etc.), experimental factors (varieties in experimental conditions, and controls), and non-normal dependent variables. The statistical model appropriate for analysis of such data is a Generalised Linear Mixed Model.

Data were collected during the course of 2015, 2016 and 2017 and are awaiting final analysis in Spring 2017. Intermediate analyses resulted in several MSc theses, conference presentations, and working papers that reported on parts of the research.

In this paper we presented the Methodology in Interdisciplinary Research framework that we developed over the past decade building on our experience as lecturers, consultants and researchers. The MIR framework recognizes research methodology and methods as important content in the critical factor skills and competences. It approaches research and collaboration as a process that needs to be designed with the sole purpose to answer the general research question. For the conceptual design the team members have to discuss and agree on the objective of their communal efforts without squeezing it into one single discipline and, thus, ignoring complexity. The specific research questions, when formulated, contribute to (self) respect in collaboration as they represent and stand witness of the need for interdisciplinarity. In the technical design, different parts were distinguished to stimulate researchers to think and design research out of their respective disciplinary boxes and consider, for example, an experimental design with qualitative data collection, or a case study design based on quantitative information.

In our teaching and consultancy, we first developed a MIR framework for social sciences, economics, health and environmental sciences interdisciplinarity. It was challenged to include research in the design discipline of landscape architecture. What characterizes research in landscape architecture and other design principles, is that the design product as well as the design process may be the object of study. Lenzholder et al. ( 2017 ) therefore distinguish three kinds of research in landscape architecture. The first kind, “Research into design” studies the design product post hoc and the MIR framework suits the interdisciplinary study of such a product. In contrast, “Research for design” generates knowledge that feeds into the noun and the verb ‘design’, which means it precedes the design(ing). The third kind, Research through Design(ing) employs designing as a research method. At first, just like Deming and Swaffield ( 2011 ), we were a bit skeptical about “designing” as a research method. Lenzholder et al. ( 2017 ) pose that the meaning of research through design has evolved through a (neo)positivist, constructivist and transformative paradigm to include a pragmatic stance that resembles the pragmatic stance assumed in the MIR framework. We learned that, because landscape architecture is such an interdisciplinary field, the process approach and the distinction between a conceptual and technical research design was considered very helpful and embraced by researchers in landscape architecture (Tobi and van den Brink 2017 ).

Mixed methods research (MMR) has been considered to study topics as diverse as education (e.g., Powell et al. 2008 ), environmental management (e.g., Molina-Azorin and Lopez-Gamero 2016 ), health psychology (e.g., Bishop 2015 ) and information systems (e.g., Venkatesh et al. 2013 ). Nonetheless, the MIR framework is the first to put MMR in the context of integrating disciplines beyond social inquiry (Greene 2008 ). The splitting of the research into modules stimulates the identification and recognition of the contribution of both distinct and collaborating disciplines irrespective of whether they contribute qualitative and/or quantitative research in the interdisciplinary research design. As mentioned in Sect.  2.4 the integration of the different research modules in one interdisciplinary project design may follow one of the mixed methods designs. For example, we witnessed at several occasions the integration of social and health sciences in interdisciplinary teams opting for sequential modules in a sequential exploratory mixed methods fashion (e.g., Adamson 2005 : 234). In sustainability science research, we have seen the design of concurrent modules for a concurrent nested mixed methods strategy (ibid) in research integrating the social and natural sciences and economics.

The limitations of the MIR framework are those of any kind of collaboration: it cannot work wonders in the absence of awareness of the necessity and it requires the willingness to work, learn, and research together. We developed MIR framework in and alongside our own teaching, consultancy and research, it has not been formally evaluated and compared in an experiment with teaching, consultancy and research with, for example, the regulative cycle for problem solving (van Strien 1986 ), or the wheel of science from Babbie ( 2013 ). In fact, although we wrote “developed” in the previous sentence, we are fully aware of the need to further develop and refine the framework as is.

The importance of the MIR framework lies in the complex, multifaceted nature of issues like sustainability, food security and one world health. For progress in the study of these pressing issues the understanding, construction and quality of interdisciplinary portfolio measurements (Tobi 2014 ) are pivotal and require further study as well as procedures facilitating the integration across different disciplines.

Another important strain of further research relates to the continuum of Responsible Conduct of Research (RCR), Questionable Research Practices (QRP), and deliberate misconduct (Steneck 2006 ). QRP includes failing to report all of a study’s conditions, stopping collecting data earlier than planned because one found the result one had been looking for, etc. (e.g., John et al. 2012 ; Simmons et al. 2011 ; Kampen and Tamás 2014 ). A meta-analysis on selfreports obtained through surveys revealed that about 2% of researchers had admitted to research misconduct at least once, whereas up to 33% admitted to QRPs (Fanelli 2009 ). While the frequency of QRPs may easily eclipse that of deliberate fraud (John et al. 2012 ) these practices have received less attention than deliberate misconduct. Claimed research findings may often be accurate measures of the prevailing biases and methodological rigor in a research field (Fanelli and Ioannidis 2013 ; Fanelli 2010 ). If research misconduct and QRP are to be understood then the disciplinary context must be grasped as a locus of both legitimate and illegitimate activity (Fox 1990 ). It would be valuable to investigate how working in interdisciplinary teams and, consequently, exposure to other standards of QRP and RCR influence research integrity as the appropriate research behavior from the perspective of different professional standards (Steneck 2006 : p. 56). These differences in scientific cultures concern criteria for quality in design and execution of research, reporting (e.g., criteria for authorship of a paper, preferred publication outlets, citation practices, etc.), archiving and sharing of data, and so on.

Other strains of research include interdisciplinary collaboration and negotiation, where we expect contributions from the “science of team science” (Falk-Krzesinski et al. 2010 ); and compatibility of the MIR framework with new research paradigms such as “inclusive research” (a mode of research involving people with intellectual disabilities as more than just objects of research; e.g., Walmsley and Johnson 2003 ). Because of the complexity and novelty of inclusive health research a consensus statement was developed on how to conduct health research inclusively (Frankena et al., under review). The eight attributes of inclusive health research identified may also be taken as guiding attributes in the design of inclusive research according to the MIR framework. For starters, there is the possibility of inclusiveness in the conceptual framework, particularly in determining research objectives, and in discussing possible theoretical frameworks with team members with an intellectual disability which Frankena et al. labelled the “Designing the study” attribute. There are also opportunities for inclusiveness in the technical design, and in execution. For example, the inclusiveness attribute “generating data” overlaps with the operationalization and measurement instrument design/selection and the attribute “analyzing data” aligns with the data analysis plan in the technical design.

On a final note, we hope to have aroused the reader’s interest in, and to have demonstrated the need for, a methodology for interdisciplinary research design. We further hope that the MIR framework proposed and explained in this article helps those involved in designing an interdisciplinary research project to get a clearer view of the various processes that must be secured during the project’s design and execution. And we look forward to further collaboration with scientists from all cultures to contribute to improving the MIR framework and make interdisciplinary collaborations successful.

Acknowledgements

The MIR framework is the result of many discussions with students, researchers and colleagues, with special thanks to Peter Tamás, Jennifer Barrett, Loes Maas, Giel Dik, Ruud Zaalberg, Jurian Meijering, Vanessa Torres van Grinsven, Matthijs Brink, Gerda Casimir, and, last but not least, Jenneken Naaldenberg.

• Aboelela SW, Larson E, Bakken S, Carrasquillo O, Formicola A, Glied SA, Gebbie KM. Defining interdisciplinary research: conclusions from a critical review of the literature. Health Serv. Res. 2007; 42 (1):329–346. doi: 10.1111/j.1475-6773.2006.00621.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Adamson J. Combined qualitative and quantitative designs. In: Bowling A, Ebrahim S, editors. Handbook of Health Research Methods: Investigation, Measurement and Analysis. Maidenhead: Open University Press; 2005. pp. 230–245. [ Google Scholar ]
• Adler ES, Clark R. An Invitation to Social Research: How it’s Done. 4. London: Sage; 2011. [ Google Scholar ]
• Babbie ER. The Practice of Social Research. 13. Belmont Ca: Wadsworth Cengage Learning; 2013. [ Google Scholar ]
• Baker GH, Little RG. Enhancing homeland security: development of a course on critical infrastructure systems. J. Homel. Secur. Emerg. Manag. 2006 [ Google Scholar ]
• Bishop FL. Using mixed methods research designs in health psychology: an illustrated discussion from a pragmatist perspective. Br. J. Health. Psychol. 2015; 20 (1):5–20. doi: 10.1111/bjhp.12122. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Boeije HR. Analysis in Qualitative Research. London: Sage; 2009. [ Google Scholar ]
• Bruns D, van den Brink A, Tobi H, Bell S. Advancing landscape architecture research. In: van den Brink A, Bruns D, Tobi H, Bell S, editors. Research in Landscape Architecture: Methods And Methodology. New York: Routledge; 2017. pp. 11–23. [ Google Scholar ]
• Creswell JW, Piano Clark VL. Designing and Conducting Mixed Methods Research. 2. Los Angeles: Sage; 2011. [ Google Scholar ]
• Danner D, Blasius J, Breyer B, Eifler S, Menold N, Paulhus DL, Ziegler M. Current challenges, new developments, and future directions in scale construction. Eur. J. Psychol. Assess. 2016; 32 (3):175–180. doi: 10.1027/1015-5759/a000375. [ CrossRef ] [ Google Scholar ]
• Deming ME, Swaffield S. Landscape Architecture Research. Hoboken: Wiley; 2011. [ Google Scholar ]
• Departement Leefmilieu, Natuur en Energie: Uitvoeren van een uitgebreide schriftelijke enquête en een beperkte CAWI-enquête ter bepaling van het percentage gehinderden door geur, geluid en licht in Vlaanderen–SLO-3. Leuven: Market Analysis & Synthesis. www.lne.be/sites/default/files/atoms/files/lne-slo-3-eindrapport.pdf (2013). Accessed 8 March 2017
• De Vaus D. Research Design in Social Research. London: Sage; 2001. [ Google Scholar ]
• DeVellis RF. Scale Development: Theory and Applications. 3. Los Angeles: Sage; 2012. [ Google Scholar ]
• Dillman DA. Mail and Internet Surveys. 2. Hobroken: Wiley; 2007. [ Google Scholar ]
• Falk-Krzesinski HJ, Borner K, Contractor N, Fiore SM, Hall KL, Keyton J, Uzzi B, et al. Advancing the science of team science. CTS Clin. Transl. Sci. 2010; 3 (5):263–266. doi: 10.1111/j.1752-8062.2010.00223.x. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fanelli D. How many scientists fabricate and falsify research? A systematic review and metaanalysis of survey data. PLoS ONE. 2009 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fanelli D. Positive results increase down the hierarchy of the sciences. PLoS ONE. 2010 [ PMC free article ] [ PubMed ] [ Google Scholar ]
• Fanelli D, Ioannidis JPA. US studies may overestimate effect sizes in softer research. Proc. Natl. Acad. Sci. USA. 2013; 110 (37):15031–15036. doi: 10.1073/pnas.1302997110. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fetters MD, Molina-Azorin JF. The journal of mixed methods research starts a new decade: principles for bringing in the new and divesting of the old language of the field. J. Mixed Methods Res. 2017; 11 (1):3–10. doi: 10.1177/1558689816682092. [ CrossRef ] [ Google Scholar ]
• Fischer ARH, Tobi H, Ronteltap A. When natural met social: a review of collaboration between the natural and social sciences. Interdiscip. Sci. Rev. 2011; 36 (4):341–358. doi: 10.1179/030801811X13160755918688. [ CrossRef ] [ Google Scholar ]
• Flick U. An Introduction to Qualitative Research. 3. London: Sage; 2006. [ Google Scholar ]
• Fox MF. Fraud, ethics, and the disciplinary contexts of science and scholarship. Am. Sociol. 1990; 21 (1):67–71. doi: 10.1007/BF02691783. [ CrossRef ] [ Google Scholar ]
• Frischknecht PM. Environmental science education at the Swiss Federal Institute of Technology (ETH) Water Sci. Technol. 2000; 41 (2):31–36. [ PubMed ] [ Google Scholar ]
• Godfray HCJ, Beddington JR, Crute IR, Haddad L, Lawrence D, Muir JF, Pretty J, Robinson S, Thomas SM, Toulmin C. Food security: the challenge of feeding 9 billion people. Science. 2010; 327 (5967):812–818. doi: 10.1126/science.1185383. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Greene JC. Is mixed methods social inquiry a distinctive methodology? J. Mixed Methods Res. 2008; 2 (1):7–22. doi: 10.1177/1558689807309969. [ CrossRef ] [ Google Scholar ]
• IPCC.: Climate Change 2014 Synthesis Report. Geneva: Intergovernmental Panel on Climate Change. www.ipcc.ch/pdf/assessment-report/ar5/syr/SYR_AR5_FINAL_full_wcover.pdf (2015) Accessed 8 March 2017
• John LK, Loewenstein G, Prelec D. Measuring the prevalence of questionable research practices with incentives for truth telling. Psychol. Sci. 2012; 23 (5):524–532. doi: 10.1177/0956797611430953. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Kagan J. The Three Cultures: Natural Sciences, Social Sciences and the Humanities in the 21st Century. Cambridge: Cambridge University Press; 2009. [ Google Scholar ]
• Kampen JK, Tamás P. Should I take this seriously? A simple checklist for calling bullshit on policy supporting research. Qual. Quant. 2014; 48 :1213–1223. doi: 10.1007/s11135-013-9830-8. [ CrossRef ] [ Google Scholar ]
• Kumar R. Research Methodology: A Step-by-Step Guide for Beginners. 1. Los Angeles: Sage; 1999. [ Google Scholar ]
• Kumar R. Research Methodology: A Step-by-Step Guide for Beginners. 4. Los Angeles: Sage; 2014. [ Google Scholar ]
• Kvale S. Doing Interviews. London: Sage; 2007. [ Google Scholar ]
• Kvale S, Brinkmann S. Interviews: Learning the Craft of Qualitative Interviews. 2. London: Sage; 2009. [ Google Scholar ]
• Lenzholder S, Duchhart I, van den Brink A. The relationship between research design. In: van den Brink A, Bruns D, Tobi H, Bell S, editors. Research in Landscape Architecture: Methods and Methodology. New York: Routledge; 2017. pp. 54–64. [ Google Scholar ]
• Molina-Azorin JF, Lopez-Gamero MD. Mixed methods studies in environmental management research: prevalence, purposes and designs. Bus. Strateg. Environ. 2016; 25 (2):134–148. doi: 10.1002/bse.1862. [ CrossRef ] [ Google Scholar ]
• Onwuegbuzie AJ, Leech NL. Taking the “Q” out of research: teaching research methodology courses without the divide between quantitative and qualitative paradigms. Qual. Quant. 2005; 39 (3):267–296. doi: 10.1007/s11135-004-1670-0. [ CrossRef ] [ Google Scholar ]
• Powell H, Mihalas S, Onwuegbuzie AJ, Suldo S, Daley CE. Mixed methods research in school psychology: a mixed methods investigation of trends in the literature. Psychol. Sch. 2008; 45 (4):291–309. doi: 10.1002/pits.20296. [ CrossRef ] [ Google Scholar ]
• Shipley B. Cause and Correlation in Biology. 2. Cambridge: Cambridge University Press; 2016. [ Google Scholar ]
• Simmons JP, Nelson LD, Simonsohn U. False positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 2011; 22 :1359–1366. doi: 10.1177/0956797611417632. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Spelt EJH, Biemans HJA, Tobi H, Luning PA, Mulder M. Teaching and learning in interdisciplinary higher education: a systematic review. Educ. Psychol. Rev. 2009; 21 (4):365–378. doi: 10.1007/s10648-009-9113-z. [ CrossRef ] [ Google Scholar ]
• Spradley JP. The Ethnographic Interview. New York: Holt, Rinehart and Winston; 1979. [ Google Scholar ]
• Steneck NH. Fostering integrity in research: definitions, current knowledge, and future directions. Sci. Eng. Eth. 2006; 12 (1):53–74. doi: 10.1007/s11948-006-0006-y. [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tobi H. Measurement in interdisciplinary research: the contributions of widely-defined measurement and portfolio representations. Measurement. 2014; 48 :228–231. doi: 10.1016/j.measurement.2013.11.013. [ CrossRef ] [ Google Scholar ]
• Tobi H, Kampen JK. Survey error in an international context: an empirical assessment of crosscultural differences regarding scale effects. Qual. Quant. 2013; 47 (1):553–559. doi: 10.1007/s11135-011-9476-3. [ CrossRef ] [ Google Scholar ]
• Tobi H, van den Brink A. A process approach to research in landscape architecture. In: van den Brink A, Bruns D, Tobi H, Bell S, editors. Research in Landscape Architecture: Methods and Methodology. New York: Routledge; 2017. pp. 24–34. [ Google Scholar ]
• van Strien PJ. Praktijk als wetenschap: Methodologie van het sociaal-wetenschappelijk handelen [Practice as science. Methodology of social scientific acting.] Assen: Van Gorcum; 1986. [ Google Scholar ]
• Venkatesh V, Brown SA, Bala H. Bridging the qualitative-quantitative divide: guidelines for conducting mixed methods research in information systems. MIS Q. 2013; 37 (1):21–54. doi: 10.25300/MISQ/2013/37.1.02. [ CrossRef ] [ Google Scholar ]
• Vuye C, Bergiers A, Vanhooreweder B. The acoustical durability of thin noise reducing asphalt layers. Coatings. 2016 [ Google Scholar ]
• Walmsley J, Johnson K. Inclusive Research with People with Learning Disabilities: Past, Present and Futures. London: Jessica Kingsley; 2003. [ Google Scholar ]
• Walsh WB, Smith GL, London M. Developing an interface between engineering and social sciences- interdisciplinary team-approach to solving societal problems. Am. Psychol. 1975; 30 (11):1067–1071. doi: 10.1037/0003-066X.30.11.1067. [ CrossRef ] [ Google Scholar ]

General purpose of research designs

• PMID: 7369224

The purposes and criteria for formulating a design of research, conditions for judging causality, and use of research design as a control of variance are discussed. The purpose of a research design is to provide a plan of study that permits accurate assessment of cause and effect relationships between independent and dependent variables. The classic controlled experiment is an ideal example of good research design. Factors that jeopardize the evaluation of the effect of experimental treatment (internal validity) and the generalizations derived from it (external validity) are identified. Sources of variance can be controlled by eliminating a variable, randomization, matching, or including a variable as part of the design. A research project should be so designed that (1) it answers the questions being investigated, (2) extraneous factors are controlled, and (3) the degree of generalization that can be made is valid.

• Analysis of Variance
• Research Design* / standards
• Statistics as Topic

• Privacy Policy

Buy Me a Coffee

Home » Descriptive Research Design – Types, Methods and Examples

Descriptive Research Design – Types, Methods and Examples

Table of Contents

Descriptive Research Design

Definition:

Descriptive research design is a type of research methodology that aims to describe or document the characteristics, behaviors, attitudes, opinions, or perceptions of a group or population being studied.

Descriptive research design does not attempt to establish cause-and-effect relationships between variables or make predictions about future outcomes. Instead, it focuses on providing a detailed and accurate representation of the data collected, which can be useful for generating hypotheses, exploring trends, and identifying patterns in the data.

Types of Descriptive Research Design

Types of Descriptive Research Design are as follows:

Cross-sectional Study

This involves collecting data at a single point in time from a sample or population to describe their characteristics or behaviors. For example, a researcher may conduct a cross-sectional study to investigate the prevalence of certain health conditions among a population, or to describe the attitudes and beliefs of a particular group.

Longitudinal Study

This involves collecting data over an extended period of time, often through repeated observations or surveys of the same group or population. Longitudinal studies can be used to track changes in attitudes, behaviors, or outcomes over time, or to investigate the effects of interventions or treatments.

This involves an in-depth examination of a single individual, group, or situation to gain a detailed understanding of its characteristics or dynamics. Case studies are often used in psychology, sociology, and business to explore complex phenomena or to generate hypotheses for further research.

Survey Research

This involves collecting data from a sample or population through standardized questionnaires or interviews. Surveys can be used to describe attitudes, opinions, behaviors, or demographic characteristics of a group, and can be conducted in person, by phone, or online.

Observational Research

This involves observing and documenting the behavior or interactions of individuals or groups in a natural or controlled setting. Observational studies can be used to describe social, cultural, or environmental phenomena, or to investigate the effects of interventions or treatments.

Correlational Research

This involves examining the relationships between two or more variables to describe their patterns or associations. Correlational studies can be used to identify potential causal relationships or to explore the strength and direction of relationships between variables.

Data Analysis Methods

Descriptive research design data analysis methods depend on the type of data collected and the research question being addressed. Here are some common methods of data analysis for descriptive research:

Descriptive Statistics

This method involves analyzing data to summarize and describe the key features of a sample or population. Descriptive statistics can include measures of central tendency (e.g., mean, median, mode) and measures of variability (e.g., range, standard deviation).

Cross-tabulation

This method involves analyzing data by creating a table that shows the frequency of two or more variables together. Cross-tabulation can help identify patterns or relationships between variables.

Content Analysis

This method involves analyzing qualitative data (e.g., text, images, audio) to identify themes, patterns, or trends. Content analysis can be used to describe the characteristics of a sample or population, or to identify factors that influence attitudes or behaviors.

Qualitative Coding

This method involves analyzing qualitative data by assigning codes to segments of data based on their meaning or content. Qualitative coding can be used to identify common themes, patterns, or categories within the data.

Visualization

This method involves creating graphs or charts to represent data visually. Visualization can help identify patterns or relationships between variables and make it easier to communicate findings to others.

Comparative Analysis

This method involves comparing data across different groups or time periods to identify similarities and differences. Comparative analysis can help describe changes in attitudes or behaviors over time or differences between subgroups within a population.

Applications of Descriptive Research Design

Descriptive research design has numerous applications in various fields. Some of the common applications of descriptive research design are:

• Market research: Descriptive research design is widely used in market research to understand consumer preferences, behavior, and attitudes. This helps companies to develop new products and services, improve marketing strategies, and increase customer satisfaction.
• Health research: Descriptive research design is used in health research to describe the prevalence and distribution of a disease or health condition in a population. This helps healthcare providers to develop prevention and treatment strategies.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs. This helps educators to improve teaching methods and develop effective educational programs.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs. This helps researchers to understand social behavior and develop effective policies.
• Public opinion research: Descriptive research design is used in public opinion research to understand the opinions and attitudes of the general public on various issues. This helps policymakers to develop effective policies that are aligned with public opinion.
• Environmental research: Descriptive research design is used in environmental research to describe the environmental conditions of a particular region or ecosystem. This helps policymakers and environmentalists to develop effective conservation and preservation strategies.

Descriptive Research Design Examples

Here are some real-time examples of descriptive research designs:

• A restaurant chain wants to understand the demographics and attitudes of its customers. They conduct a survey asking customers about their age, gender, income, frequency of visits, favorite menu items, and overall satisfaction. The survey data is analyzed using descriptive statistics and cross-tabulation to describe the characteristics of their customer base.
• A medical researcher wants to describe the prevalence and risk factors of a particular disease in a population. They conduct a cross-sectional study in which they collect data from a sample of individuals using a standardized questionnaire. The data is analyzed using descriptive statistics and cross-tabulation to identify patterns in the prevalence and risk factors of the disease.
• An education researcher wants to describe the learning outcomes of students in a particular school district. They collect test scores from a representative sample of students in the district and use descriptive statistics to calculate the mean, median, and standard deviation of the scores. They also create visualizations such as histograms and box plots to show the distribution of scores.
• A marketing team wants to understand the attitudes and behaviors of consumers towards a new product. They conduct a series of focus groups and use qualitative coding to identify common themes and patterns in the data. They also create visualizations such as word clouds to show the most frequently mentioned topics.
• An environmental scientist wants to describe the biodiversity of a particular ecosystem. They conduct an observational study in which they collect data on the species and abundance of plants and animals in the ecosystem. The data is analyzed using descriptive statistics to describe the diversity and richness of the ecosystem.

How to Conduct Descriptive Research Design

To conduct a descriptive research design, you can follow these general steps:

• Define your research question: Clearly define the research question or problem that you want to address. Your research question should be specific and focused to guide your data collection and analysis.
• Choose your research method: Select the most appropriate research method for your research question. As discussed earlier, common research methods for descriptive research include surveys, case studies, observational studies, cross-sectional studies, and longitudinal studies.
• Design your study: Plan the details of your study, including the sampling strategy, data collection methods, and data analysis plan. Determine the sample size and sampling method, decide on the data collection tools (such as questionnaires, interviews, or observations), and outline your data analysis plan.
• Collect data: Collect data from your sample or population using the data collection tools you have chosen. Ensure that you follow ethical guidelines for research and obtain informed consent from participants.
• Analyze data: Use appropriate statistical or qualitative analysis methods to analyze your data. As discussed earlier, common data analysis methods for descriptive research include descriptive statistics, cross-tabulation, content analysis, qualitative coding, visualization, and comparative analysis.
• I nterpret results: Interpret your findings in light of your research question and objectives. Identify patterns, trends, and relationships in the data, and describe the characteristics of your sample or population.
• Draw conclusions and report results: Draw conclusions based on your analysis and interpretation of the data. Report your results in a clear and concise manner, using appropriate tables, graphs, or figures to present your findings. Ensure that your report follows accepted research standards and guidelines.

When to Use Descriptive Research Design

Descriptive research design is used in situations where the researcher wants to describe a population or phenomenon in detail. It is used to gather information about the current status or condition of a group or phenomenon without making any causal inferences. Descriptive research design is useful in the following situations:

• Exploratory research: Descriptive research design is often used in exploratory research to gain an initial understanding of a phenomenon or population.
• Identifying trends: Descriptive research design can be used to identify trends or patterns in a population, such as changes in consumer behavior or attitudes over time.
• Market research: Descriptive research design is commonly used in market research to understand consumer preferences, behavior, and attitudes.
• Health research: Descriptive research design is useful in health research to describe the prevalence and distribution of a disease or health condition in a population.
• Social science research: Descriptive research design is used in social science research to describe social phenomena such as cultural norms, values, and beliefs.
• Educational research: Descriptive research design is used in educational research to describe the performance of students, schools, or educational programs.

Purpose of Descriptive Research Design

The main purpose of descriptive research design is to describe and measure the characteristics of a population or phenomenon in a systematic and objective manner. It involves collecting data that describe the current status or condition of the population or phenomenon of interest, without manipulating or altering any variables.

The purpose of descriptive research design can be summarized as follows:

• To provide an accurate description of a population or phenomenon: Descriptive research design aims to provide a comprehensive and accurate description of a population or phenomenon of interest. This can help researchers to develop a better understanding of the characteristics of the population or phenomenon.
• To identify trends and patterns: Descriptive research design can help researchers to identify trends and patterns in the data, such as changes in behavior or attitudes over time. This can be useful for making predictions and developing strategies.
• To generate hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• To establish a baseline: Descriptive research design can establish a baseline or starting point for future research. This can be useful for comparing data from different time periods or populations.

Characteristics of Descriptive Research Design

Descriptive research design has several key characteristics that distinguish it from other research designs. Some of the main characteristics of descriptive research design are:

• Objective : Descriptive research design is objective in nature, which means that it focuses on collecting factual and accurate data without any personal bias. The researcher aims to report the data objectively without any personal interpretation.
• Non-experimental: Descriptive research design is non-experimental, which means that the researcher does not manipulate any variables. The researcher simply observes and records the behavior or characteristics of the population or phenomenon of interest.
• Quantitative : Descriptive research design is quantitative in nature, which means that it involves collecting numerical data that can be analyzed using statistical techniques. This helps to provide a more precise and accurate description of the population or phenomenon.
• Cross-sectional: Descriptive research design is often cross-sectional, which means that the data is collected at a single point in time. This can be useful for understanding the current state of the population or phenomenon, but it may not provide information about changes over time.
• Large sample size: Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Systematic and structured: Descriptive research design involves a systematic and structured approach to data collection, which helps to ensure that the data is accurate and reliable. This involves using standardized procedures for data collection, such as surveys, questionnaires, or observation checklists.

Advantages of Descriptive Research Design

Descriptive research design has several advantages that make it a popular choice for researchers. Some of the main advantages of descriptive research design are:

• Provides an accurate description: Descriptive research design is focused on accurately describing the characteristics of a population or phenomenon. This can help researchers to develop a better understanding of the subject of interest.
• Easy to conduct: Descriptive research design is relatively easy to conduct and requires minimal resources compared to other research designs. It can be conducted quickly and efficiently, and data can be collected through surveys, questionnaires, or observations.
• Useful for generating hypotheses: Descriptive research design can be used to generate hypotheses or research questions that can be tested in future studies. For example, if a descriptive study finds a correlation between two variables, this could lead to the development of a hypothesis about the causal relationship between the variables.
• Large sample size : Descriptive research design typically involves a large sample size, which helps to ensure that the data is representative of the population of interest. A large sample size also helps to increase the reliability and validity of the data.
• Can be used to monitor changes : Descriptive research design can be used to monitor changes over time in a population or phenomenon. This can be useful for identifying trends and patterns, and for making predictions about future behavior or attitudes.
• Can be used in a variety of fields : Descriptive research design can be used in a variety of fields, including social sciences, healthcare, business, and education.

Limitation of Descriptive Research Design

Descriptive research design also has some limitations that researchers should consider before using this design. Some of the main limitations of descriptive research design are:

• Cannot establish cause and effect: Descriptive research design cannot establish cause and effect relationships between variables. It only provides a description of the characteristics of the population or phenomenon of interest.
• Limited generalizability: The results of a descriptive study may not be generalizable to other populations or situations. This is because descriptive research design often involves a specific sample or situation, which may not be representative of the broader population.
• Potential for bias: Descriptive research design can be subject to bias, particularly if the researcher is not objective in their data collection or interpretation. This can lead to inaccurate or incomplete descriptions of the population or phenomenon of interest.
• Limited depth: Descriptive research design may provide a superficial description of the population or phenomenon of interest. It does not delve into the underlying causes or mechanisms behind the observed behavior or characteristics.
• Limited utility for theory development: Descriptive research design may not be useful for developing theories about the relationship between variables. It only provides a description of the variables themselves.
• Relies on self-report data: Descriptive research design often relies on self-report data, such as surveys or questionnaires. This type of data may be subject to biases, such as social desirability bias or recall bias.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Questionnaire – Definition, Types, and Examples

Case Study – Methods, Examples and Guide

Observational Research – Methods and Guide

Quantitative Research – Methods, Types and...

Qualitative Research Methods

Explanatory Research – Types, Methods, Guide

• Open access
• Published: 12 April 2024

Healthcare team resilience during COVID-19: a qualitative study

• John W. Ambrose 1 ,
• Ken Catchpole 2 ,
• Heather L. Evans 3 ,
• Lynne S. Nemeth 1 ,
• Diana M. Layne 1 &
• Michelle Nichols 1  

BMC Health Services Research volume  24 , Article number:  459 ( 2024 ) Cite this article

Metrics details

Resilience, in the field of Resilience Engineering, has been identified as the ability to maintain the safety and the performance of healthcare systems and is aligned with the resilience potentials of anticipation, monitoring, adaptation, and learning. In early 2020, the COVID-19 pandemic challenged the resilience of US healthcare systems due to the lack of equipment, supply interruptions, and a shortage of personnel. The purpose of this qualitative research was to describe resilience in the healthcare team during the COVID-19 pandemic with the healthcare team situated as a cognizant, singular source of knowledge and defined by its collective identity, purpose, competence, and actions, versus the resilience of an individual or an organization.

We developed a descriptive model which considered the healthcare team as a unified cognizant entity within a system designed for safe patient care. This model combined elements from the Patient Systems Engineering Initiative for Patient Safety (SEIPS) and the Advanced Team Decision Making (ADTM) models. Using a qualitative descriptive design and guided by our adapted model, we conducted individual interviews with healthcare team members across the United States. Data were analyzed using thematic analysis and extracted codes were organized within the adapted model framework.

Five themes were identified from the interviews with acute care professionals across the US ( N  = 22): teamwork in a pressure cooker , consistent with working in a high stress environment; healthcare team cohesion , applying past lessons to present challenges , congruent with transferring past skills to current situations; knowledge gaps , and altruistic behaviors , aligned with sense of duty and personal responsibility to the team. Participants’ described how their ability to adapt to their environment was negatively impacted by uncertainty, inconsistent communication of information, and emotions of anxiety, fear, frustration, and stress. Cohesion with co-workers, transferability of skills, and altruistic behavior enhanced healthcare team performance.

Working within the extreme unprecedented circumstances of COVID-19 affected the ability of the healthcare team to anticipate and adapt to the rapidly changing environment. Both team cohesion and altruistic behavior promoted resilience. Our research contributes to a growing understanding of the importance of resilience in the healthcare team. And provides a bridge between individual and organizational resilience.

Peer Review reports

Introduction

The COVID-19 pandemic highlighted the complexity and dynamic nature of healthcare systems. It also created a unique opportunity to look at the concept of resilience through the lens of the healthcare team versus the more common approach of situating the concept within the individual or the organization. The early phase of the pandemic was marked by challenges, such as limited access to personal protective equipment, personnel shortages, drug shortages, and increased risks of infection [ 1 , 2 ]. Ensuring patient safety and proper functioning requires coordination and adaptation of the healthcare team and various processes across the health system infrastructure [ 3 , 4 ]. Resilience results from adaptive coordination which enables healthcare systems to maintain routine function in the face of all conditions [ 5 , 6 ].

Resilience in healthcare has been operationalized through resilience engineering, an interdisciplinary aspect of systems engineering focused on promotingpatient safety through the design, implementation, and management of healthcare systems [ 7 , 8 , 9 ] (e.g., how healthcare systems adapt and adjust to maneuver through the daily complexities and challenges to identify effective practices, prevent errors and maintain resilient performance) [ 6 , 8 , 9 , 10 , 11 ]. Resilient performance in healthcare is proposed to be the net result of reaching the threshold of four resilience capabilities within the system: anticipation, the ability to expect and prepare for the unexpected; monitoring, the ability to observe threats to daily system performance; responding, the ability to adapt how the performance is enacted; and learning, the ability to learn from present and past accomplishments within the system [ 12 ]. At present, there is a paucity of research on the resilience of the healthcare team as a cohesive, singular conscious source of knowledge in a highly complex healthcare system. While the resilience of both healthcare systems [ 11 , 13 ] and healthcare workers [ 14 ] has been investigated, there is a gap in knowledge specific to the resilience of the healthcare team as a unified singular consciousness. The circumstances surrounding the COVID-19 pandemic presented a unique opportunity to understand the resilience of the healthcare team in a highly complex system as a singular aware entity within the system; how it acknowledges itself, defines its purpose, and performs under extenuating circumstances. This shifts the emphasis of individual and organization resilience to the resilience in the interconnected healthcare team that extends beyond the boundary of any single person.

The adapted model situates the healthcare team as a cohesive singlular conscious source of knowledge within an intricate and highly complex system [ 15 ]. This model was designed as a bridge between resilience found in individuals within the healthcare system and the organization to emphasize the healthcare team as an aware, unified whole. Our model [ 15 ] combines the existing Systems Engineering Initiative for Patient Safety (SEIPS) model [ 16 ] (version 1), which is based on five domains (organization, person, tasks, technologies, and tools), and environment and the Advanced Team Decision Making Model [ 17 ], which includes components for team performance [ 17 , 18 , 19 ]. Team performance is comprised of team identity, team cognition, team competency, and team metacognition [ 17 , 18 , 19 ]. Team identity describes how the team identifies their purpose to help one another [ 17 ]. Team cognition describes the state of mind of the team, their focus, and common goals [ 17 ]. Team competency describes how well the team accomplishes tasks, and team metacognition describes problem solving and responsibility [ 17 , 19 ], Fig.  1 .

Healthcare Team as a cohesive, singular conscious source of knowledge in a highly complex system. The continuous variegated border represents the singularity and connectedness of the healthcare team within the system. The gears represent the processes, people, technology, and tasks within this highly dynamic healthcare system

The purpose of this qualitative research was to describe resilience in the healthcare team during the COVID-19 pandemic with the healthcare team situated as a singular conscious source of knowledge defined by its collective identity, purpose, competence, and actions. Additionally, we sought to identify factors that may facilitate or hinder the healthcare team from achieving the necessary capabilities to monitor, anticipate, adapt, and learn to meet the standard for resilient performance.

Methodology

A qualitative descriptive design [ 20 , 21 ] was employed. The interview guide was framed using the adapted model to explore various aspects of healthcare team performance (identity, purpose, competence, and cognition). These questions were pilot tested on the first 3 participants and no further changes were needed. Specifically, we aimed to investigate resilience capabilities, decision-making processes, and overall healthcare team performance.

Sampling strategy

A purposive snowball sample was used to identify healthcare team members who worked in U.S. acute care settings between January 2020–December 2020. This sampling method was used to ensure recruitment of participants most likely to have insight into the phenomenon of resilience in the acute care setting.

Inclusion criteria

To explore a wide range of interprofessional experience, participants were recruited across geographic regions and professional roles through personal contacts and social media [ 22 , 23 , 24 , 25 ]. Eligible participants included English-speaking individuals ages 20 and older with a valid personal email address, internet access, and the ability to participate in an online video interview. Potential participants had to be employed full or part-time for any period from January 2020–December 2020 in any of the following acute healthcare environments: emergency room (ER), intensive care unit (ICU), COVID- 19 ICU, COVID-19 floor, gastroenterology inpatient unit, endoscopy suite, operating room (OR), post anesthesia recovery room (PACU), pre-operative holding area, hospital administration, or inpatient medical and/or surgical patient care unit.

Exclusion criteria

Healthcare team members who did not complete the pre-screening survey or failed to schedule an interview were not enrolled.

National recruitment in the U.S

Upon approval by MUSC Institutional Review Board (IRB), registered under Pro00100917, fliers, social media posts on Twitter TM (version 9.34 IOS, San Francisco, California) and Facebook TM (version 390.1 IOS, Menlo Park, CA), and word of mouth were used to initiate recruitment efforts. Interested participants were sent a link to an electronic screening survey explaining the purpose of the study and verifying the respondents’ eligibility to participate. Informed consent was obtained from all subjects.

Data collection

Data were collected via an initial screening questionnaire to determine eligibility. Data were managed using REDCap™ (version 11.2.2) electronic data capture tools hosted at MUSC. Demographic data included age, sex, race, professional role, years of experience, geographic region, patient population served, practice specialty area, and deployment status during the pandemic. Deployment refers to the reassignment of personnel from their primary clinical area to another area to meet the demands of another clinical area without regard for the participant’s clinical expertise. Qualitative data were collected through semi-structured audio video recorded interviews to understand the healthcare team in their natural environment. Recorded interviews were conducted via Microsoft® Teams (version 1.5.00.17261, Microsoft Corporation) from the PIs private office to mitigate the risk of COVID-19 transmission and promote participation across the U.S.

Data monitoring and safety

The quality of the demographic data was monitored to ensure completeness. Potential participants who submitted incomplete responses on the questionnaire were excluded. Interviews were transcribed using software, transcriptions were reviewed and verified for accuracy, and then uploaded to MAXQDA Analytics Pro, Version 2022 (VERBI software) to facilitate data analysis. Transcripts were not returned to the participants. Qualitative codebooks, institutional review board (IRB) logs, and other study records were stored on a secure university server, with access limited to authorized study personnel. Adherence to Consolidated Criteria for Reporting Qualitative Research (COREQ) standards were maintained throughout the study and analysis [ 26 ].

Data analysis

Quantitative analysis.

Demographic data were analyzed using SPSS Statistics for MAC, version 28 (IBM). Both descriptive statistics for the continuous variables of age and years of experience (mean, standard deviation) and frequency tables (age, sex, race, role, geographic region, population served, deployment status) were analyzed.

Qualitative analysis

The Principal Investigator (PI) (JA) and senior mentor (MN) independently coded the interview transcripts. Open coding method was used to identify the categories of data [ 22 , 27 ]. Both a reflexive journal and audit trail were maintained. Codes were identified through induction from participant experiences and verified through weekly consensus meetings, while theoretical deductive analysis was guided by the adapted model and the four resilience capabilities (anticipation, monitoring, responding, learning [ 12 ]. Reflexive thematic analysis (TA) [ 28 , 29 , 30 , 31 ] was used to analyze the coded data and generate themes. Data were collected and categorized into the codebook until no further codes were identified by the PI and research mentor [ 22 , 27 ]. Participant checking was not employed.

Demographics

The eligibility pool was established based on survey completion. Eighty-nine healthcare team members opened the online screening survey; 21 were incomplete and eliminated from the dataset, which left a pool of 68 potential eligible participants. Eligible participants (100%) were contacted by email and phone to determine their interest in completing the study interview. Twenty-two participants completed screening surveys and study interviews between May–September 2021, equating to a 32.5% enrollment rate. Participant interviews lasted between 21 and 91 min with an average of 43 min. None of the interviews were repeated. Participant demographics, including descriptive statistic and role key, are noted in Tables  1 and 2 , respectively.

Five themes were identified: team work in a pressure cooker , healthcare team cohesion , applying past lessons to present challenges , knowledge gaps , and altruistic behaviors .

Teamwork in a pressure cooker

The theme teamwork in a pressure cooker describes the relentless pressures and emotional stressors (e.g., fear, anxiety, frustration, and stress) experienced by the healthcare team from the risks and potential threats associated with COVID-19 contamination and infection. Factors associated with these pressures included risk of COVID-19 exposure, lack of COVID-19 testing, rapid changes to policies and procedures from the standard, personnel shortages, limited physical space, and limited supplies. Exemplary quotes highlighting participant descriptions of these pressures or subthemes are noted in Table  3 .

The healthcare team described an unprecedented level of stress in the workplace as the healthcare team had to adjust to rapidly changing protocols. The lack of protective equipment, shortage of providers to perform patient care and a lack of a familiar clinical routine saturated them in overwhelming pressure and emotions that stuck to them as they navigated uncharted territory. Exemplary quotes highlighting the healthcare team’s descriptions of these emotions are noted in Table  4 .

“It was…uncharted territory for me.” (P1, DIR) “You were stuck in a situation you never— you didn’t know when it was going to end.” (P4, RN PACU) “They have not enough staff—they can’t do it—they—I don’t know what we’re going to do.” (P6, DIR). “When we deployed—trying to get re-accustomed to the changes—with the needs that had to be met was very difficult.” (P10, RN ENDO) “I wasn’t about to sign up for extra time working in under those stressful conditions.” (P17, RN PACU)

The fear of the unknown, combined with the constant need to adapt to rapidly changing circumstances, led to widespread stress, frustration, anxiety, and exhaustion within the healthcare team. This theme was characterized by the constant pressure both inside and outside of work experienced by the healthcare team.

“Driving to the hospital, crying, driving back from the hospital, crying, still doesn’t sum it up— surrounded by people who were just dying. And what could you do?” (P6, DIR) “It was constant. It was terrible. I couldn’t sleep at night. I’d wake up worried.” (P8, ER MD) “It was kind of like just keep sending the Calvary forward—and when one drops, you just walk over them.” (P17, RN PACU) “It was always there—COVID here, COVID there—you never could just completely get away from it. It was basically the center of everybody’s conversation everywhere you went or if you were on the phone with somebody.” (P18, RN COVID ICU) “I was having to call my parents before I’d leave my apartment to go into work— to vent to them and cry— to let out my frustration and my anxiety—and have them essentially convince me to go into work.” (P19, RN ICU). “Working so much— COVID was all that was on my brain—and it was a lot of pressure.” (P22, MGR)

Working during COVID-19 challenged the resilience of the healthcare team in the face of constant fear and uncertainty. The pressure to maintain team performance, while dealing with constant fear associated with the pandemic effected the healthcare team’s resilience.

“I have to tell you that after being in hospital—I don’t feel resilient right now— doing all the things I’ve done—I just want to be out of the hospital— [crying] I can tell you that it will stay with me the rest of my life— It will always stay with me.” (P6, DIR) “I feel like my team has used up all of their resilience. I don’t think there’s much left.” (P8, ER MD)

However, one team member stood out as an exception. They reported the pressures from the environment helped them to make decisions. This demonstrates that environmental pressures affect members of the healthcare team differently. They reported that the pressure and intensity of the situation sharpened their focus and allowed them to make choices more quickly and effectively.

“I make better decisions when I’m under pressure.” (P22, MGR)

Healthcare team cohesion

The theme healthcare team cohesion describes the unique experience of working together during the pandemic that created a means among the healthcare team to form close relationships and unite. This bond was characterized by the emergence of strong interpersonal connections among healthcare professionals during the COVID-19 pandemic. These connections shaped healthcare team relationships and were a factor in the collaborative decision-making processes within healthcare team for their day-to day functions. This cohesive bonding was fueled by the stress and uncertainty of the situation, which brought the healthcare team together illustrated by their solidarity, camaraderie, trust, and empowerment.

“All those decisions, important decisions were made together.” (P7, CRNA) “Everyone felt like they were they were, you know, in a in a battle zone and on the same side—and so that kind of brought people together.” (P8, ER MD) “I think our team worked as one.” (P11, CEO)

Solidarity described the sense of unity evident among the members of the healthcare team. This was characterized by connectedness and a sense of reliance on one another that promoted teamwork and resilience within the team from support both given and received. The sub-theme camaraderie described the close personal connection and support between the healthcare team that went beyond normal social interactions prior to the pandemic. These connections were filled with trust and respect for other healthcare team members.

“I think we were all trying to do the best we could do and help each other do the best they could do—I think early on just camaraderie helped a lot within the department and, you know, just relying on each other for support.” (P8, ER MD) “We knew that we can depend on each other and we all had different skill sets— I think that that was very important—that made us feel secure— rather than going alone.” (P10, RN ENDO) “We [The ICU Nurses] developed a sense of camaraderie that I mean, it’s nothing I’ve ever felt before, like we had to trust each other with our licenses, with our own health—my resiliency came from my coworkers.” (P14, CHG RN) “One of the things that I think the pandemic did in a positive—was—I believe that the teams that I worked for really started to solidify. We leaned on each other. I felt more of a team environment than I had had pre-pandemic—I felt that people were a bit better together. We all needed each other, and we all leaned on each other, and we gave each other support—more so than before COVID- 19.” (P15, CRNA) ”The nurses on the unit were always there for me—they became my friends— my family.” (P19, RN ICU)

The sub theme of empowerment referred to the ability of the healthcare team to confidently make decisions and assume responsibility for their actions within the healthcare setting. This process involved a sense of authority and the ability to exercise agency in decision-making together to respond and adapt to the demands the healthcare team experienced. The combination of solidarity, camaraderie, trust, and empowerment resulted in a strong sense of cohesion within the healthcare team which led to improved relationships and enhanced resilience in their performance.

“I felt that I felt that the team—we all needed each other and we all leaned on each other and we gave each other support—more so than before COVID.” (P15, CRNA) “How do you want to handle this? What’s the plan?—and we collaborated in the true sense of collaboration.” (P15, CRNA) “We just knew that we could count on each other—we knew that we could count on each other at any time if we had questions, because we all worked so closely together during this. We really became a really tight knit group, and it was great.” (P22, MGR)

The benefits of the cohesion found in the healthcare team were significant and apparent during the COVID-19 pandemic. The strengthened relationships and increased resilience allowed for improved communication and collaboration, leading to better patient care and outcomes. Despite these advantages, it was noted by one participant that the relationships developed were not sustained beyond the peak of the pandemic.

“Now that COVID is kind of at bay in our area, it’s kind of gone back to the same way it was— it has not stuck.” (P15, CRNA)

Applying past lessons to present challenges

The theme applying past lessons to present challenges describes how the knowledge and understanding gained from prior participant experiences was used to adapt to the novel clinical and infrastructural challenges faced during the pandemic. Past experiences facilitated the healthcare team to strategize ways to meet the demands of the healthcare system during this time.

Participants described two strategies the healthcare team used to improve the system’s ability to adapt and function effectively: changing roles and deploying personnel. The process of changing roles involved assigning new responsibilities to individuals based on priority-based initiatives, while deployment involved transferring clinical staff from areas with lower patient care needs to those with higher needs to optimize their utilization. Eleven participants (50%) were affected by these strategies. Of these, 73% were assigned to clinical areas for direct patient care, while the remaining 27% underwent a role change to support the operational needs of the system. The participants’ preexisting work relationships, specialized clinical expertise, and leadership abilities helped them adapt to their new clinical and non-clinical roles, which in turn enhanced the resilience of the healthcare team.

“We wanted to make sure that we were putting people into the right area where their skill set could be used the best.” (P1, DIR) “I’m known for moving people forward—I’m also well known for speaking up when I don’t think it is right and there was a lot of stuff that I didn’t think was right— and not only speaking up, I’m also going to come with the solution.” (P6, DIR)

Participants indicated the lessons learned from prior experience positively impacted team performance and improved patient care outcomes. There were two significant examples in the data: the perspective of a nurse who was redeployed to work in an obstetrics unit (P5, ENDO RN) and the perspective of a nursing director (P6, DIR) whose role was changed to develop a program to ensure adequate staffing.

“Because we [the team of interprofessionals] were all very familiar with what we had to do at the task, at handit [the experience of the provision of care] was very fluid—I think it’s because of our years of experience and working with each other for so long that it just worked out very well ”. (P5, ENDO RN) “Staff believed in me when I said I would do something— I could galvanize people because of my reputation of caring for staff, so I was chosen specifically because of my ability to move people forward in spite of things.” (P6, DIR)

Participants identified being assigned to unfamiliar clinical areas or working with unfamiliar patient populations as a barrier that hindered their ability to adapt to clinical situations. The lack of clinical competence among some personnel led to an increase in workload for other healthcare team members, who had to provide additional instruction and guidance on how to complete the task. Decision-makers who deployed nursing staff to a clinical area with higher staffing needs may have believed that the individual nurse had specific clinical skills that would be helpful in that area, and this was not the case.

“She [the patient] felt like it was that he [the new nurse]—really didn’t know what he was doing—not only were we kind of reintroduced to that role of caring for patients where we haven’t been recently, but we’re also in a teaching mode, too, for the new nurses—we had to prioritize how sick the patients were, from basic vital signs to wound dressings to respiratory, and help those new nurses know which to attend to first.” (P10, RN ENDO) “Nurses weren’t really put in a place with enough support and enough resources to be able to do a job, and to do a job that maybe they haven’t done for a few years.” (P10, RN ENDO)

The participants indicated that clinical competencies of a healthcare provider in one patient population may not necessarily be applicable to another patient group. For instance, a neonatal intensive care unit (NICU) nurse who has experience in managing Extra Corporeal Membranous Oxygen (ECMO) in newborns may not have the necessary skills to care for adult ECMO patients in an adult COVID-19 intensive care unit.

“The ECMO nurse was a NICU nurse, so she really could not help me.” (P14, CHG RN)

Knowledge gaps

The theme knowledge gaps refers to the disparity between the existing knowledge of the healthcare team and the knowledge required for the team to effectively respond and adapt to the needs of the healthcare system. The lack of COVID-19 specific knowledge led to gaps in the healthcare team’s understanding, while the lack of communication made it difficult for necessary information to be effectively conveyed and received (e.g., medical logistics, human resources, and other operational policies and procedures). This knowledge gap created a barrier to healthcare team resilience as their capacities to surveil, anticipate, and respond were diminished from the lack of knowledge.

“That [information] is pretty fundamental to how you [the healthcare team] function.” (P17, RN PACU) “I don’t think any amount of preparation could have actually prepared us for how bad COVID was—but we were very, very, very unprepared.” (P18, RN COVID ICU) “It was confusing, it was disruptive to the patients that we had there. They sensed that. And that’s— OK—screw with me, screw with my colleagues, but don’t screw with the patient.” (P21, RN ENDO)

All the participants in leadership roles during the COVID-19 pandemic emphasized the importance of having a thorough understanding of the information and effectively communicating it to the frontline healthcare team members most involved in providing patient care.

“There’s nothing worse than having to learn something in the moment and not being prepared for it.” (P1, DIR) “That made us communicate in multiple ways throughout a day because we all know people learn and adapt it could be in print. It could be in person; it could be a video. We tried to have multiple ways of getting messages out and knowing we needed to repeat messages because this was so unknown, and people were so stressed.” (P11, CEO)

One team member (P13, CRNA), highlighted areas where there were gaps in knowledge in greater detail.

“It was as if the unit was being run by all these sort of substitute teachers that were called in at the last minute. Nobody knew where stuff was—nobody knew what the protocol was—the communication was terrible.” (P13, CRNA)

The cumulative effect from the knowledge gaps contributed to the lack of a practical working knowledge for the healthcare team and affected the healthcare team’s ability to anticipate what needed to be done and adapt their performance to accomplish it. Despite knowledge gaps, healthcare team members reported their capability to learn was facilitated by incremental gains in practical knowledge through their experience over time.

“—people got to be experts at protecting patients and keeping themselves safe.” (P8, ER MD) “I think it kind of was like on the job training at that point, I felt like we were all just trying to survive—learning was like—you went out —then you came back, and you would share how things went.” (P15, CRNA) “You tried to educate yourself so you could be safe.” (P17, RN PACU)

The participant responses received from the leadership (CNO, Directors, and Manager) and front-line personnel (administrative staff, nurses, and physicians) regarding the importance of communication highlighted a difference in perspective. Leadership exhibited a strong commitment toward effective communication and made efforts to ensure all healthcare team members were well informed. On the other hand, the frontline participants indicated instances where communication strategies were not perceived as effective.

“I wasn’t contacted by a manager from the unit or anything to be able to reassure, reassure me that things were being followed through and it should be okay, so that was tough.” (P10, RN ENDO) “It really seemed like there was no communication between—like staffing and the floor—we would get up to the floor and they would say, who are you? What are you doing here? What are we supposed to do with you?” (P20, RN OR)

Altruistic behaviors

The theme altruistic behaviors , encompasses the participants’ perception of their obligation and accountability to their patients and healthcare team, and their steadfastness in supporting the healthcare team even if it meant facing personal or professional repercussions. This readiness to aid the healthcare team and accept consequences showcased their altruism and commitment to the healthcare team. The team’s dedication to both their patients and each other was a primary focus driven by a strong sense of responsibility and obligation.

“I want to be able to look myself in the mirror and feel like I did the right thing—.” (P6, DIR) “My resiliency came from my coworkers. I wanted to come back to work to help them.” (P14, RN COVID ICU) “People really looked out for each other—and people were really kind and compassionate to each other—we all were in this together.” (P15, CRNA) “I’m grateful for the experience that I had and all of the different patients that I was able to help in my time there definitely solidified that being a nurse is what I needed to do—and why I chose the profession is exactly what I should have been doing.” (P19, RN ICU) “You just have to go with what seems right—.” (P22, MGR)

A defining characteristic of this theme was a willingness to endure consequences for the benefit of the healthcare team. These consequences varied from contracting the virus, facing criticism from the healthcare team, to foregoing financial incentives, and even job loss.

“I felt like I was punished for speaking up and I was punished for doing the right thing for patients.” (P6, DIR) “I mean, I literally broke the law so many times. Do you know how many times I started pressors [vasoactive drugs to increase blood pressure] on patients that I had no orders for [because a physician would not enter the ICU]?” (P14, CHG RN)

We identified five key themes based on the coded data; namely teamwork in a pressure cooker , healthcare team cohesion , applying past lessons to present challenges , knowledge gaps , and altruistic behaviors . The researchers propose that stressors arising from the COVID-19 pandemic had an impact on the healthcare team’s resilience. In addition, strong healthcare team cohesion, selfless behaviors among the healthcare team, shared knowledge, and job competence within the healthcare team, enhanced resilient performance.

The healthcare team experienced significant stress and uncertainty, due to the COVID-19 pandemic. This is consistent with previous research that has shown that the unprecedented nature of the pandemic led to challenging working conditions, limited resources, lack of information, and concerns about infecting loved ones [ 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 ]. The collective global impact of COVID-19 on healthcare systems is likely a contributing factor to these stressors [ 45 , 46 , 47 , 48 ].

Our study, along with those conducted by Anjara et al. (2021)[ 49 ] and Kaye-Kauderer et al. (2022) [ 50 ], found that solidarity and camaraderie among healthcare professionals improve resilience. Specifically, Anjara et al. observed increased collaboration among the healthcare professionals they studied in Ireland during the COVID-19 pandemic, while Kaye-Kauderer et al. identified team camaraderie among their sample of front-line healthcare workers from New York. Kinsella et al. (2023) [ 51 ] reported that COVID − 19 offered frontline workers in the UK the opportunity to work together toward a common goal. Potential explanations for these findings align with the concepts of social capital proposed by Coleman [ 52 ] and social identification with other as proposed by Drury [ 54 ]. Coleman suggests an individual’s skills and capabilities are enhanced through their interdependent relationships with others [ 52 ]. Drury found in communities affected by disasters, mutual aid and support emerged from a shared social identity, which serves to strengthen the community [ 53 ]. Brooks et al. (2021) [ 54 ] conducted a study with healthcare, police, and commercial sectors in England. They found it was important for these individuals to receive support from and provide support to their colleagues to mitigate the psychological impact of disaster exposure [ 54 ]. In addition, like our findings, Aufegger and colleague’s 2019 systematic review [ 55 ] found that social support in acute care healthcare teams creates a supportive atmosphere where team members help each other communicate problems, fulfill needs, and deal with stress.

Our results are consistent with those of Liu et al. (2020) [ 32 ] and Banerjee et al. (2021) [ 44 ] who each found that healthcare professionals frequently feel a sense of personal responsibility to overcome challenges. One potential explanation for this may be the influence of collectivism in their cultures. Similarly, our study suggests the sense of camaraderie among healthcare professionals may also contribute to a sense of responsibility and increased altruistic behavior. However, other studies have highlighted different perspectives on healthcare professionals’ sense of responsibility and duty. Godkin and Markwell’s (2003) [ 56 ] revealed that healthcare professionals’ sense of responsibility during the Severe Acute Respiratory Syndrome (SARS) outbreak was dependent on the protective measures and support offered by the healthcare system where most SARS infected patients were hospitalized. More recently, Gray et al. (2021) reported that nurses’ sense of responsibility stems from their ethical obligations, regardless of potential personal or familial risks [ 57 ].

The altruistic behaviors described by our participants helped maintain the performance of the healthcare team. It is too soon to see the long-term impact from working in this high-pressure environment; however, past research by Liu et al. (2012) [ 58 ] and Wu (2009) [ 59 ] demonstrated that “altruistic-risk acceptance” during the SARS outbreak was shown to decrease depressive symptoms among hospital employees in China.

Our research on resilience has important implications for healthcare organizations and professionals. In order to ready themselves for forthcoming events, healthcare systems must emphasize the significance of shared knowledge and its influence on the healthcare team’s ability to foresee and monitor effectively. This knowledge can help the healthcare organization function as a unified entity, rather than as individuals in separate roles or clusters within the organization to improve healthcare team preparedness. Establishing a cohesive, clinically competent healthcare team benefits the organization and the patients served. Measures to enhance social support, improve communication and ensure clinical competence maintain healthcare team resilience.

There are several limitations to consider when interpreting the results of this study. First, the sample was obtained using purposive snowball sampling, which may have introduced sampling bias and may not accurately represent the larger population of healthcare team members who worked during the COVID-19, as 95% of the sample were white. Second, our study did not have equal representation of all interprofessional team members. It is possible that a more heterogenous sample regarding role, race and gender may have introduced additional codes. Additionally, the PI (JA) worked as a Certified Registered Nurse Anesthesiologist (CRNA) in acute care during the pandemic and personal experience may have introduced confirmation bias. Also, the focus of our research was to fill a gap in the existing knowledge of what is known about healthcare team resilience in pandemic disasters, and help to answer if and how it intersects with individual and organizational resilience. It is possible this novel conceptualization of healthcare team as a cohesive singular conscious source of knowledge did not adequately address this.

Steps to ensure rigor and mitigate any potential shortcomings of qualitative data analysis were the maintenance of a reflexive journal, a willingness of the PI to let go of unsupported ideas and constant verification of codes and themes with the research mentor (MN) for coherence and consistency within the coded data, selected methodology and research questions.

Overall, the extracted themes of teamwork in a pressure cooker; healthcare team cohesion; applying past lessons to present challenges; knowledge gaps; and altruistic behaviors illustrate comparable experiences within the healthcare team. As healthcare professionals and organizations continue to navigate the challenges of the COVID-19 pandemic and other crises, our findings provide valuable insights into how team cohesion, along with altruistic behaviors, may enhance resilience capabilities to create and maintain a unified resilient healthcare team.

Data availability

The data for this study are confidential as required by the IRB approval. To protect the anonymity of the participants, the data are not publicly available. Additional information about the research method, Interview questions, informant data, and the study in general can be requested from the corresponding author, J.A.

Berlin G, Singhal S, Lapointe M, Schulz J. Challenges emerge for the US healthcare system as COVID-19 cases rise. 2020;9.

Stevens JP, O’Donoghue A, Horng S, Tabb K. Healthcare’s earthquake: Lessons from complex adaptive systems to develop Covid-19-responsive measures and models. 2020.

Kopach-Konrad R, Lawley M, Criswell M, Hasan I, Chakraborty S, Pekny J, et al. Applying systems Engineering principles in improving Health Care Delivery. J Gen Intern Med. 2007;22(S3):431–7.

Article   PubMed   PubMed Central   Google Scholar  

Compton WD, Fanjiang G, Grossman JH, Reid PP. Institute of Medicine (U.S.), National Academy of Engineering. Building a better delivery system: a new engineering/health care partnership [Internet]. Washington, D.C.: National Academies Press; 2005 [cited 2021 Feb 12]. http://public.ebookcentral.proquest.com/choice/publicfullrecord.aspx?p=3378176 .

Hollnagel E, Woods DD. Resilience Engineering concepts and precepts. 1st ed. Boca Raton, FL: CRC Press/Routledge/Taylor & Francis Group; 2006. p. 416.

Google Scholar  

Wiig S, O’Hara JK. Resilient and responsive healthcare services and systems: challenges and opportunities in a changing world. BMC Health Serv Res. 2021;21(1):1037.

Nemeth C, Wears RL, Patel S, Rosen G, Cook R. Resilience is not control: healthcare, crisis management, and ICT. Cogn Tech Work. 2011;13(3):189–202.

Article   Google Scholar  

Hollnagel E. Safety-II in Practice: Developing the Resilience Potentials [Internet]. 1st ed. Routledge; 2017 [cited 2022 May 7]. https://www.taylorfrancis.com/books/9781351780766 .

Braithwaite J, Wears RL, Hollnagel E. Resilient health care: turning patient safety on its head. Int J Qual Health Care. 2015;27(5):418–20.

Article   PubMed   Google Scholar  

Madni AM, Jackson S. Towards a conceptual Framework for Resilience Engineering. IEEE Syst J. 2009;3(2):181–91.

Carthey J. Institutional resilience in healthcare systems. Qual Health Care. 2001;10(1):29–32.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Hollnagel E. The Four cornerstones of Resilience Engineering. In: Dekker, editor. Resilience engineering perspcetives. E. Hollnagel&S. Ashgate: Farnham, UK; 2009. pp. 117–33.

Fridell M, Edwin S, von Schreeb J, Saulnier DD. Health System Resilience: what are we talking about? A scoping review mapping characteristics and keywords. Int J Health Policy Manag. 2019;9(1):6–16.

Article   PubMed Central   Google Scholar  

Curtin M, Richards HL, Fortune DG. Resilience among health care workers while working during a pandemic: a systematic review and meta synthesis of qualitative studies. Clin Psychol Rev. 2022;95:102173.

Ambrose JW, Layne DM, Catchpole K, Evans H, Nemeth LS. A qualitative protocol to examine Resilience Culture in Healthcare teams during COVID-19. Healthcare. 2021;9(9):1168.

Carayon P, Hundt AS, Karsh B, Gurses AP, Alvarado CJ, Smith M, et al. Work system design for patient safety: the SEIPS model. Qual Saf Health Care. 2006;15(Suppl 1):i50–8.

Thordsen ML, Kyne MM, Klein G, A Model of Advanced Team Decision Making and Performance.: Summary Report: [Internet]. Fort Belvoir, VA: Defense Technical Information Center; 1994 Sep [cited 2021 Feb 13]. http://www.dtic.mil/docs/citations/ADA400497 .

Zsambok CE. Advanced Team Decision Making: A Model and Training Implications.

Klein GA. Sources of power: how people make decisions. MIT Press; 1988.

Doyle L, McCabe C, Keogh B, Brady A, McCann M. An overview of the qualitative descriptive design within nursing research. J Res Nurs. 2020;25(5):443–55.

Siedlecki SL. Understanding descriptive research designs and methods. Clin Nurse Spec. 2020;34(1):8–12.

Crabtree BF, Miller WL. Doing qualitative research. Second. Thousand Oaks, CA: Sage; 1999. p. 406.

Bradley EH, Curry LA, Devers KJ. Qualitative Data Analysis for Health Services Research: developing taxonomy, themes, and theory. Health Serv Res. 2007;42(4):1758–72.

Gale NK, Heath G, Cameron E, Rashid S, Redwood S. Using the framework method for the analysis of qualitative data in multi-disciplinary health research. BMC Med Res Methodol. 2013;13(1):117.

Lincoln Y, Guba E. Naturalistic Inquiry. California: Sage; 1985.

Book   Google Scholar  

Tong A, Sainsbury P, Craig J. Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care. 2007;19(6):349–57.

Saldaña J. The coding manual for qualitative rearchers. Los Angeles, USA: Sage; 2021.

Boyatzis RE. Transforming qualitative information: thematic analysis and code development. Thousand Oaks, CA: SAGE Publications Ltd; 1998.

Braun V, Clarke V. What can thematic analysis offer health and wellbeing researchers? Int J Qualitative Stud Health Well-being. 2014;9(1):26152.

Braun V, Clarke V. Thematic analysis. In: Cooper H, Camic PM, Long DL, Panter AT, Rindskopf D, Sher KJ, editors. APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological [Internet]. Washington: American Psychological Association; 2012 [cited 2022 May 15]. pp. 57–71. http://content.apa.org/books/13620-004 .

Braun V, Clarke V. Conceptual and design thinking for thematic analysis. Qualitative Psychol. 2022;9(1):3–26.

Liu Y, Zhai Z, Han Y, Liu Y, Liu F, Hu D. Experiences of front-line nurses combating coronavirus disease‐2019 in China: a qualitative analysis. Public Health Nurs. 2020;37(5):757–63.

Catania G, Zanini M, Hayter M, Timmins F, Dasso N, Ottonello G, et al. Lessons from Italian front-line nurses’ experiences during the COVID‐19 pandemic: a qualitative descriptive study. J Nurs Manag. 2021;29(3):404–11.

Croghan IT, Chesak SS, Adusumalli J, Fischer KM, Beck EW, Patel SR et al. Stress, Resilience, and Coping of Healthcare Workers during the COVID-19 Pandemic. Journal of Primary Care and Community Health [Internet]. 2021;12. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85104122303&doi=10.1177%2f21501327211008448&partnerID=40&md5=96ad0164880c9725ce14d534e3c3117

Arnetz JE, Goetz CM, Arnetz BB, Arble E. Nurse reports of stressful situations during the COVID-19 pandemic: qualitative analysis of survey responses. IJERPH. 2020;17(21):8126.

Dagyaran I, Risom SS, Berg SK, Højskov IE, Heiden M, Bernild C, et al. Like soldiers on the front– a qualitative study understanding the frontline healthcare professionals’ experience of treating and caring for patients with COVID-19. BMC Health Serv Res. 2021;21(1):666.

Goh Y, Ow Yong QYJ, Chen TH, Ho SHC, Chee YIC, Chee TT. The impact of COVID-19 on nurses working in a University Health System in Singapore: a qualitative descriptive study. Int J Mental Health Nurs. 2021;30(3):643–52.

LoGiudice JA, Bartos S. Experiences of nurses during the COVID-19 pandemic: a mixed-methods study. AACN Adv Crit Care. 2021;32(1):14–26.

O’Brien JM, Bae FA, Kawchuk J, Reimche E, Abramyk CA, Kitts C et al. We were treading water. Experiences of healthcare providers in Canadian ICUs during COVID-19 visitor restrictions: a qualitative descriptive study.

Perraud F, Ecarnot F, Loiseau M, Laurent A, Fournier A, Lheureux F, et al. A qualitative study of reinforcement workers’ perceptions and experiences of working in intensive care during the COVID-19 pandemic: a PsyCOVID-ICU substudy. Sharma GA, editor. PLoS ONE. 2022;17(3):e0264287.

Shanafelt T, Ripp J, Trockel M. Understanding and addressing sources of anxiety among Health Care professionals during the COVID-19 pandemic. JAMA. 2020;323(21):2133.

Article   CAS   PubMed   Google Scholar  

Speroni KG, Seibert DJ, Mallinson RK. Nurses’ perceptions on Ebola Care in the United States, Part 2: a qualitative analysis. JONA: J Nurs Adm. 2015;45(11):544–50.

Sonis J, Pathman DE, Read S, Gaynes BN, Canter C, Curran P, et al. Effects of Healthcare Organization Actions and policies related to COVID-19 on Perceived Organizational Support among U.S. internists: a National Study. J Healthc Manag. 2022;67(3):192–205.

PubMed   Google Scholar  

Banerjee D, Sathyanarayana Rao TS, Kallivayalil RA, Javed A. Psychosocial Framework of Resilience: navigating needs and adversities during the pandemic, a qualitative exploration in the Indian Frontline Physicians. Front Psychol. 2021;12:622132.

Freudenberg LS, Paez D, Giammarile F, Cerci J, Modiselle M, Pascual TNB, et al. Global impact of COVID-19 on Nuclear Medicine departments: an International Survey in April 2020. J Nucl Med. 2020;61(9):1278–83.

Haldane V, Morgan GT. From resilient to transilient health systems: the deep transformation of health systems in response to the COVID-19 pandemic. Health Policy Plann. 2021;36(1):134–5.

Shrestha N, Shad MY, Ulvi O, Khan MH, Karamehic-Muratovic A, Nguyen USDT, et al. The impact of COVID-19 on globalization. One Health. 2020;11:100180.

Jean WC, Ironside NT, Sack KD, Felbaum DR, Syed HR. The impact of COVID-19 on neurosurgeons and the strategy for triaging non-emergent operations: a global neurosurgery study. Acta Neurochir. 2020;162(6):1229–40.

Anjara S, Fox R, Rogers L, De Brún A, McAuliffe E. Teamworking in Healthcare during the COVID-19 pandemic: a mixed-method study. IJERPH. 2021;18(19):10371.

Kaye-Kauderer H, Loo G, Murrough JW, Feingold JH, Feder A, Peccoralo L, et al. Effects of Sleep, Exercise, and Leadership Support on Resilience in Frontline Healthcare workers during the COVID-19 pandemic. J Occup Environ Med. 2022;64(5):416–20.

Kinsella EL, Muldoon OT, Lemon S, Stonebridge N, Hughes S, Sumner RC. In it together? Exploring solidarity with frontline workers in the United Kingdom and Ireland during COVID-19. Br J Social Psychol. 2023;62(1):241–63.

Coleman JS. Social Capital in the creation of Human Capital. Am J Sociol. 1988;94:S95–120.

Drury J, Carter H, Cocking C, Ntontis E, Tekin Guven S, Amlôt R. Facilitating collective psychosocial resilience in the Public in emergencies: twelve recommendations based on the Social Identity Approach. Front Public Health. 2019;7:141.

Brooks SK, Dunn R, Amlôt R, Rubin GJ, Greenberg N. Protecting the psychological wellbeing of staff exposed to disaster or emergency at work: a qualitative study. BMC Psychol. 2019;7(1):78.

Aufegger L, Shariq O, Bicknell C, Ashrafian H, Darzi A. Can shared leadership enhance clinical team management? A systematic review. LHS. 2019;32(2):309–35.

Godkin D, Markwell H. The Duty to Care of Healthcare Professionals: Ethical Issues and Guidelines for Policy Development. Submitted to SARS Expert Panel Secretariat.:23.

Gray K, Dorney P, Hoffman L, Crawford A. Nurses’ pandemic lives: a mixed-methods study of experiences during COVID-19. Appl Nurs Res. 2021;60:151437.

Liu X, Kakade M, Fuller CJ, Fan B, Fang Y, Kong J, et al. Depression after exposure to stressful events: lessons learned from the severe acute respiratory syndrome epidemic. Compr Psychiatr. 2012;53(1):15–23.

Wu P, Fang Y, Guan Z, Fan B, Kong J, Yao Z, et al. The psychological impact of the SARS Epidemic on Hospital employees in China: exposure, risk perception, and Altruistic Acceptance of Risk. Can J Psychiatry. 2009;54(5):302–11.

Download references

Acknowledgements

The authors want to thank all the interviewed healthcare team participants for their time and sharing their personal stories and for their continued service during the COVID-19 pandemic. We would also like to acknowledge Ayaba Logan, the Research and Education Informationist, Mohan Madisetti, the MUSC College of Nursing Director of Research, the staff of the MUSC Center for Academic Excellence and the reviewers of this journal for their constructive criticism.

This research (software, transcription services, etc.) was solely funded by the Principal Investigator, J.A.

Author information

Authors and affiliations.

College of Nursing, Medical University of South Carolina, Charleston, SC, USA

John W. Ambrose, Lynne S. Nemeth, Diana M. Layne & Michelle Nichols

Department of Anesthesia and Perioperative Medicine, College of Medicine, Medical University of South Carolina, Charleston, SC, USA

Ken Catchpole

Department of Surgery, College of Medicine, Medical University of South Carolina, Charleston, SC, USA

Heather L. Evans

You can also search for this author in PubMed   Google Scholar

Contributions

Conceptualization J.A., K.C., L.N., D.L., H.E., and M.N.; methodology J.A. and M.N.; J.A. led the study, recruited the interviewees, conducted interviews, led the data analysis, and drafted the manuscript. J.A., and M.N. conducted the data analyses; review and editing K.C., H.E., D.L., and M.N.; supervision M.N.; research project administration J.A. and M.N.; funding acquisition J.A. All authors reviewed the manuscript.

Corresponding author

Correspondence to John W. Ambrose .

Ethics declarations

Ethics approval and consent to participate.

This study presented no greater than minimal risk to participants and met exempt status per regulatory criteria established by 45 CFR 46.104 and 21 CFR 56.104. The study protocol and all materials were approved by the MUSC Institutional Review Board (IRB), [Pro00100917 ]. All study procedures were followed in accordance with these standards.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Ambrose, J.W., Catchpole, K., Evans, H.L. et al. Healthcare team resilience during COVID-19: a qualitative study. BMC Health Serv Res 24 , 459 (2024). https://doi.org/10.1186/s12913-024-10895-3

Download citation

Received : 25 February 2023

Accepted : 25 March 2024

Published : 12 April 2024

DOI : https://doi.org/10.1186/s12913-024-10895-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Resilience Engineering
• Healthcare System
• Healthcare Administration
• Healthcare Team
• Thematic Analysis
• Qualitative Research

BMC Health Services Research

ISSN: 1472-6963

• General enquiries: [email protected]

General Enquiries : Tel : + 27 12 841 2911 | Email :  [email protected]

• The CSIR in brief
• Our vision, mission and values
• Organisational structure
• History and highlights
• Facts and figures
• Collaborations and partnerships
• CSIR Code of Conduct
• CSIR Ethics Statement
• Partner with us
• Industry innovation support
• Specialised services
• Collaborate on technology commercialisation
• Contract R&D
• Alumni Programme
• CSIR International Convention Centre
• Students and graduates
• Professionals
• CSIR career guide
• Recruitment Agency Vacancies
• Multimedia Gallery
• Media releases
• Advanced Agriculture and Food
• Future production: Chemicals
• NextGen Health
• Future production: Manufacturing
• Future production: Mining
• Defence and security
• NextGen Enterprises and Institutions
• Smart mobility
• Smart places
• Scientific infrastructure
• CSIR Energy Research Centre
• CSIR Emerging Digital Technologies for 4IR Research Centre
• CSIR Information and Cybersecurity Research Centre
• CSIR Photonics Centre
• CSIR Robotics and Production Centre
• CSIR Water Research Centre
• Research groups
• Waste RDI Roadmap
• C4IR South Africa
• Annual report
• Corporate brochures
• eNewsletter
• Research publications
• ScienceScope
• Sector-specific publications
• Shareholder's compact
• Events for the Science Community
• CSIR upcoming conferences and events
• CSIR past conferences and events

EOI Appointment of a panel of experts relating to infrastructure design, construction, costing and management for the purpose of supporting research, development and innovation projects in the CSIR Smart Places cluster for 36 months period

Copyright © CSIR 2024. All Rights Reserved.

Multi-Purpose Design of Optimized Current Controller for Bridgeless Totem-Pole Power Factor Correction Converters

Ieee account.

• Change Username/Password
• Update Address

Purchase Details

• Payment Options
• Order History
• View Purchased Documents

Profile Information

• Communications Preferences
• Profession and Education
• Technical Interests
• US & Canada: +1 800 678 4333
• Worldwide: +1 732 981 0060
• Contact & Support
• About IEEE Xplore
• Accessibility
• Terms of Use
• Nondiscrimination Policy
• Privacy & Opting Out of Cookies

A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity. © Copyright 2024 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.

An official website of the United States government Here's how you know

Official websites use .gov A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS A lock ( Lock A locked padlock ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

FHWA National Scenic Byways Program (NSBP) Technical Assistance Services Program

Date Modified: March 20, 2024

Disclaimer: Title V, of Public Law 100-656 requires that Federal agencies make available its Procurement Forecast to the Small Business Administration (SBA) and to interested business owners. The forecast listing is not intended to be all-inclusive. The Forecast is one of many tools available to small businesses. All projected procurements are subject to revision or cancellation. Final decisions on the extent of competition, small business participation, estimated value, or any aspect of the procurement action will not be made until each procurement is initiated. The forecast data is for planning purposes, does not represent a pre-solicitation synopsis, does not constitute an invitation for bid or request for proposal, and is not a commitment by the government to purchase the desired products and services. Actual solicitation notices will be posted on beta.SAM.gov as prescribed by the Federal Acquisition Regulation (FAR)

• Login/Settings

Student Design Assistant-Dr Dorafshan

The University of North Dakota is currently not hiring remote employees in the following states: AR, CA, CO, HI, MD, ME, OR, PA, WA, and any country outside of the United States.

Categories: Student Summer Term

Department: CEM Research Institute

• Grand Forks, North Dakota, United States
• Institutional Student
• Closing on: May 31 2024

Salary/Position Classification

• $18.00-20.00 Hourly Depending on Experience Hourly, Non-Exempt (Eligible for overtime)
• up to 40 in the summer hours per week, 10-20 during the school year
• 100% Remote Work Availability: No
• Hybrid Work Availability: No

Purpose of Position

This is a part-time, non-benefited position. Only UND students are eligible to be hired in this position. The Student Design Center (SDC), in partnership with the North Dakota Department of Transportation (NDDOT), the Transportation Technical Research Initiative (TTRI), and UND College of Engineering & Mines, puts engineering students to work on real-world transportation designs and engineering issues. As a Student Design Assistant at SDC, students gain hands-on training as they work under the direction of NDDOT engineers to prepare studies, plans, and estimates for NDDOT projects. Student Design Assistants are in demand by the NDDOT as well as other agencies, consultants, and employers as they recognize SDC students have a head start in skills to join the workforce.

Duties & Responsibilities

• Student Design Assistants are educated on current Design & Construction practices and learn geometric highway design through NDDOT projects. This on-the-job training will consist of the following job duties:
• Engineering Calculations: Geometrics, cost estimates, & quantities
• Technical Writing: Write reports for preliminary engineering and environmental documentation that explore options for projects and help upper management make decisions on how projects should proceed
• CADD drafting: Training provided to learn Microstation/ORD design software and utilize it in plan sheet development and 3D modeling
• Communication: Work with supervisor and other Student Design Assistants to communicate ideas clearly, both orally and in writing, to coordinate the progress, completion, and review of project tasks and meet pre-determined deadlines and expectations
• Other job duties that are not required but are potential opportunities based on student's interests: Participation in field reviews, public input meetings, and other design review meetings; job shadowing a field engineer and touring active construction projects; visiting other Divisions and Districts within the NDDOT; networking with other professionals in the transportation industry; create resources and/or presentations on specific design tasks

Required Competencies

• Must have a strong interest in Civil Engineering, transportation, or related engineering disciplines.
• Ideal candidates would possess leadership, prioritization, and time management skills; they would be highly motivated, detail-oriented, and possess logical decision-making and analytical skills.
• Previous internship experience is not required.

Minimum Requirements

• Must be a student at UND in good academic standing; must be able to work in an office setting; any necessary travel for projects will be provided from UND
• Successful completion of a Criminal History Background Check

In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the US and to complete the required employment eligibility verification form upon hire.  This position does not support visa sponsorship for continued employment.

For full consideration, applications must be received by the closing date and include the following materials: •Resume & Cover Letter Please include in the application if you are currently or have in the past 12 months been employed with the University of North Dakota, the North Dakota University System or any other North Dakota State agency. If so, include which agency/department, as well as how many hours you work a week. Career Services is here to help students looking for student employment positions at UND by offering individual sessions that include: resume, cover letter reviews, and interview preparation. Please schedule an appointment through Starfish or email us at [email protected].

For full consideration, all application materials must be fully submitted by 11:55PM CST on the closing date. Student job openings are posted for a minimum of 3 business days.

Want to be notified of similar opportunities?

Thank you for your interest in applying to the University of North Dakota

Other UND Career Openings

Research assistant-dr. dorafshan, research construction assistant-dr. dorafshan, office assistant, additional information.

Find out why Grand Forks is Cooler .

All information listed in this position announcement will be used by Human Resources, the Hiring Department, and EO/Title IX for screening, interviewing and selection purposes.

Please email the Human Resources Department at  [email protected] or contact us by phone at 701-777-4226. If you anticipate needing any type of accommodation to participate in any portion of the University's employment process, including completion of the online application process, please contact our office in advance of your participation or visit. 

Veteran’s Preference

Veterans claiming preference must submit all proof of eligibility by the closing date. Proof of eligibility includes a copy of NGB 22 from National Guard or Reserve (with a unit located in ND) or certification from the applicant's unit command that the individual is expected to be discharged or released from active duty in the uniformed services under other than dishonorable conditions not later than one hundred twenty days after the date of the submission of the certification. If claiming disabled status, proof of eligibility includes a DD-214 and a current letter of disability. 

Confidentiality of Application Materials

Pursuant to NDCC 44-04-18.27, applications and any records related to the applications that identify an applicant are confidential, except records related to the finalists of the position, which are open to the public after the search committee has identified the top three or more finalists who will be invited to campus.

EEO Statement

The University of North Dakota is an Affirmative Action/Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability or other protected characteristic. Women, minorities, veterans, individuals with disabilities, and members of other underrepresented groups are especially encouraged to apply. Applicants are invited to provide information regarding their gender, race and/or ethnicity, veteran’s status and disability status as part of the application process. This information will remain confidential and separate from your application.

Clery Statement

In compliance with the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act, the University of North Dakota publishes an Annual Security and Fire Safety Report. The report includes the university’s policies, procedures, and programs concerning safety and security, as well as three years of crime statistics for our campus. As a prospective employee, you are entitled to a copy of this report. The report and statistical data can be found online at UND.edu. You may also request a paper copy of the report from the UND Police Department located at 3851 Campus Road, Grand Forks, ND, 58202.

This website uses cookies.

We use cookies to personalize content such as job recommendations, and to analyze our traffic. You consent to our cookies if you click "I Accept". If you click on "I Do Not Accept", then we will not use cookies but you may have a deteriorated user experience. You can change your settings by clicking on the Settings link on the top right of the device