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• What Is a Research Design | Types, Guide & Examples

What Is a Research Design | Types, Guide & Examples

Published on June 7, 2021 by Shona McCombes . Revised on November 20, 2023 by Pritha Bhandari.

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• 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 objectives 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, other interesting articles, frequently asked questions about research design.

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• 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 analyzing 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, organizations, 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 generalize 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 generalize 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, behaviors, experiences, and characteristics by asking people directly. There are two main survey methods to choose from: questionnaires and interviews .

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors 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 kinds of 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.

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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 high in reliability and validity.

Operationalization

Some variables, like height or age, are easily measured. But often you’ll be dealing with more abstract concepts, like satisfaction, anxiety, or competence. Operationalization 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 research bias and ensure a representative sample?

Data management

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

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

Keeping your data well-organized will save time when it comes to analyzing it. It can also help other researchers validate and add to your findings (high replicability ).

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

Quantitative data analysis

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

Using descriptive statistics , you can summarize 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 analyzing 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.

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

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

 Statistics

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

Research bias

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

A research design is a strategy for answering your   research question . It defines your overall approach and determines how you will collect and analyze data.

A well-planned research design helps ensure that your methods match your research aims, that you collect high-quality data, and that you use the right kind of analysis to answer your questions, utilizing credible sources . This allows you to draw valid , trustworthy conclusions.

Quantitative research designs can be divided into two main categories:

• Correlational and descriptive designs are used to investigate characteristics, averages, trends, and associations between variables.
• Experimental and quasi-experimental designs are used to test causal relationships .

Qualitative research designs tend to be more flexible. Common types of qualitative design include case study , ethnography , and grounded theory designs.

The priorities of a research design can vary depending on the field, but you usually have to specify:

• Your research questions and/or hypotheses
• Your overall approach (e.g., qualitative or quantitative )
• The type of design you’re using (e.g., a survey , experiment , or case study )
• Your data collection methods (e.g., questionnaires , observations)
• Your data collection procedures (e.g., operationalization , timing and data management)
• Your data analysis methods (e.g., statistical tests  or thematic analysis )

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

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

Operationalization 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, behavioral avoidance of crowded places, or physical anxiety symptoms in social situations.

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

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst… there’s more (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. 

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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

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

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• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

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

Which research method should I choose ?

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

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

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

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

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

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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.

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The Four Types of Research Design — Everything You Need to Know

Updated: December 11, 2023

Published: January 18, 2023

When you conduct research, you need to have a clear idea of what you want to achieve and how to accomplish it. A good research design enables you to collect accurate and reliable data to draw valid conclusions.

In this blog post, we'll outline the key features of the four common types of research design with real-life examples from UnderArmor, Carmex, and more. Then, you can easily choose the right approach for your project.

Table of Contents

What is research design?

The four types of research design, research design examples.

Research design is the process of planning and executing a study to answer specific questions. This process allows you to test hypotheses in the business or scientific fields.

Research design involves choosing the right methodology, selecting the most appropriate data collection methods, and devising a plan (or framework) for analyzing the data. In short, a good research design helps us to structure our research.

Marketers use different types of research design when conducting research .

There are four common types of research design — descriptive, correlational, experimental, and diagnostic designs. Let’s take a look at each in more detail.

Researchers use different designs to accomplish different research objectives. Here, we'll discuss how to choose the right type, the benefits of each, and use cases.

Research can also be classified as quantitative or qualitative at a higher level. Some experiments exhibit both qualitative and quantitative characteristics.

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Experimental

An experimental design is used when the researcher wants to examine how variables interact with each other. The researcher manipulates one variable (the independent variable) and observes the effect on another variable (the dependent variable).

In other words, the researcher wants to test a causal relationship between two or more variables.

In marketing, an example of experimental research would be comparing the effects of a television commercial versus an online advertisement conducted in a controlled environment (e.g. a lab). The objective of the research is to test which advertisement gets more attention among people of different age groups, gender, etc.

Another example is a study of the effect of music on productivity. A researcher assigns participants to one of two groups — those who listen to music while working and those who don't — and measure their productivity.

The main benefit of an experimental design is that it allows the researcher to draw causal relationships between variables.

One limitation: This research requires a great deal of control over the environment and participants, making it difficult to replicate in the real world. In addition, it’s quite costly.

Best for: Testing a cause-and-effect relationship (i.e., the effect of an independent variable on a dependent variable).

Correlational

A correlational design examines the relationship between two or more variables without intervening in the process.

Correlational design allows the analyst to observe natural relationships between variables. This results in data being more reflective of real-world situations.

For example, marketers can use correlational design to examine the relationship between brand loyalty and customer satisfaction. In particular, the researcher would look for patterns or trends in the data to see if there is a relationship between these two entities.

Similarly, you can study the relationship between physical activity and mental health. The analyst here would ask participants to complete surveys about their physical activity levels and mental health status. Data would show how the two variables are related.

Best for: Understanding the extent to which two or more variables are associated with each other in the real world.

Descriptive

Descriptive research refers to a systematic process of observing and describing what a subject does without influencing them.

Methods include surveys, interviews, case studies, and observations. Descriptive research aims to gather an in-depth understanding of a phenomenon and answers when/what/where.

SaaS companies use descriptive design to understand how customers interact with specific features. Findings can be used to spot patterns and roadblocks.

For instance, product managers can use screen recordings by Hotjar to observe in-app user behavior. This way, the team can precisely understand what is happening at a certain stage of the user journey and act accordingly.

Brand24, a social listening tool, tripled its sign-up conversion rate from 2.56% to 7.42%, thanks to locating friction points in the sign-up form through screen recordings.

Carma Laboratories worked with research company MMR to measure customers’ reactions to the lip-care company’s packaging and product . The goal was to find the cause of low sales for a recently launched line extension in Europe.

The team moderated a live, online focus group. Participants were shown w product samples, while AI and NLP natural language processing identified key themes in customer feedback.

This helped uncover key reasons for poor performance and guided changes in packaging.

What is a Research Design? Definition, Types, Methods and Examples

By Nick Jain

Published on: September 8, 2023

Table of Contents

What is a Research Design?

12 types of research design, top 16 research design methods, research design examples.

A research design is defined as the overall plan or structure that guides the process of conducting research. It is a critical component of the research process and serves as a blueprint for how a study will be carried out, including the methods and techniques that will be used to collect and analyze data. A well-designed research study is essential for ensuring that the research objectives are met and that the results are valid and reliable.

Key elements of research design include:

• Research Objectives: Clearly define the goals and objectives of the research study. What is the research trying to achieve or investigate?
• Research Questions or Hypotheses: Formulating specific research questions or hypotheses that address the objectives of the study. These questions guide the research process.
• Data Collection Methods: Determining how data will be collected, whether through surveys, experiments, observations, interviews, archival research, or a combination of these methods.
• Sampling: Deciding on the target population and selecting a sample that represents that population. Sampling methods can vary, such as random sampling, stratified sampling, or convenience sampling.
• Data Collection Instruments: Developing or selecting the tools and instruments needed to collect data, such as questionnaires, surveys, or experimental equipment.
• Data Analysis: Defining the statistical or analytical techniques that will be used to analyze the collected data. This may involve qualitative or quantitative methods , depending on the research goals.
• Time Frame: Establishing a timeline for the research project, including when data will be collected, analyzed, and reported.
• Ethical Considerations: Addressing ethical issues, including obtaining informed consent from participants, ensuring the privacy and confidentiality of data, and adhering to ethical guidelines.
• Resources: Identifying the resources needed for the research , including funding, personnel, equipment, and access to data sources.
• Data Presentation and Reporting: Planning how the research findings will be presented and reported, whether through written reports, presentations, or other formats.

There are various research designs, such as experimental, observational, survey, case study, and longitudinal designs, each suited to different research questions and objectives. The choice of research design depends on the nature of the research and the goals of the study.

A well-constructed research design is crucial because it helps ensure the validity, reliability, and generalizability of research findings, allowing researchers to draw meaningful conclusions and contribute to the body of knowledge in their field.

Understanding the nuances of research design is pivotal in steering your investigation towards success. Delving into various research designs empowers researchers to craft tailored methodologies to address specific queries and attain precise objectives. Here, we unveil a spectrum of research designs, meticulously curated to cater to diverse research pursuits.

1. Experimental Research Design

Randomized Controlled Trial (RCT): Immerse yourself in the realm of experimentation with RCTs. Randomly assigning individuals to either an experimental or control group enables meticulous assessment of interventions or treatments’ efficacy.

2. Quasi-Experimental Research Design

Non-equivalent Group Design: When randomness isn’t viable, non-equivalent group designs offer a pragmatic alternative. Comparison across multiple groups without random assignment ensures ethical and feasible research conduct.

3. Observational Research Design

Cross-Sectional Study: Capture snapshots of data at a single moment with cross-sectional studies, unraveling intricate relationships and disparities between variables.

Longitudinal Study: Embark on a journey through time with longitudinal studies, tracking participants’ trajectories to discern evolving trends and patterns.

4. Descriptive Research Design

Survey Research: Dive into the depths of data collection through surveys, extracting insights into attitudes, characteristics, and opinions.

Case Study: Engage in profound exploration through case studies, dissecting singular individuals, groups, or phenomena to unravel profound insights.

5. Correlational Research Design

Correlational Study: Traverse the realm of correlations, scrutinizing interrelationships between variables while refraining from inferring causality.

6. Ex Post Facto Research Design

Retrospective Exploration: Explore existing conditions and behaviors retrospectively, shedding light on potential causes where variable manipulation isn’t feasible.

7. Exploratory Research Design

Pilot Study: Initiate your research odyssey with pilot studies, laying the groundwork for comprehensive investigations while refining research procedures.

8. Cohort Study

Chronicle of Evolution: Embark on longitudinal expeditions with cohort studies, monitoring cohorts to elucidate the evolution of specific outcomes over time.

9. Action Research

Driving Change: Collaboratively navigate practical challenges with action research, fostering improvements in educational or organizational settings.

10. Meta-Analysis

Synthesizing Insights: Merge insights from multiple studies with meta-analyses, presenting a holistic overview of research findings.

11. Cross-Sequential Design

Bridging the Gap: Seamlessly blend cross-sectional and longitudinal elements to dissect age-related changes across diverse cohorts.

12. Grounded Theory

Rooted Insights: Plunge into the depths of qualitative research with grounded theory, crafting theories grounded in meticulously collected data.

Selecting the optimal research design is akin to sculpting a masterpiece, contingent on the intricacies of the research query, resource availability, ethical considerations, and the desired data intricacies. Researchers adeptly navigate these choices to seamlessly align their methodologies with their research ambitions, ensuring both precision and impact.

Learn more: What is Research?

Research design methods refer to the systematic approaches and techniques used to plan, structure, and conduct a research study. The choice of research design method depends on the research questions, objectives, and the nature of the study. Here are some key research design methods commonly used in various fields:

1. Experimental Method

Controlled Experiments: In controlled experiments, researchers manipulate one or more independent variables and measure their effects on dependent variables while controlling for confounding factors.

2. Observational Method

Naturalistic Observation: Researchers observe and record behavior in its natural setting without intervening. This method is often used in psychology and anthropology.

Structured Observation: Observations are made using a predetermined set of criteria or a structured observation schedule.

3. Survey Method

Questionnaires: Researchers collect data by administering structured questionnaires to participants. This method is widely used for collecting quantitative research data.

Interviews: In interviews, researchers ask questions directly to participants, allowing for more in-depth responses. Interviews can take on structured, semi-structured, or unstructured formats.

4. Case Study Method

Single-Case Study: Focuses on a single individual or entity, providing an in-depth analysis of that case.

Multiple-Case Study: Involves the examination of multiple cases to identify patterns, commonalities, or differences.

5. Content Analysis

Researchers analyze textual, visual, or audio data to identify patterns, themes, and trends. This method is commonly used in media studies and social sciences.

6. Historical Research

Researchers examine historical documents, records, and artifacts to understand past events, trends, and contexts.

7. Action Research

Researchers work collaboratively with practitioners to address practical problems or implement interventions in real-world settings.

8. Ethnographic Research

Researchers immerse themselves in a particular cultural or social group to gain a deep understanding of their behaviors, beliefs, and practices.

9. Cross-sectional and Longitudinal Surveys

Cross-sectional surveys collect data from a sample of participants at a single point in time.

Longitudinal surveys collect data from the same participants over an extended period, allowing for the study of changes over time.

Researchers conduct a quantitative synthesis of data from multiple studies to provide a comprehensive overview of research findings on a particular topic.

11. Mixed-Methods Research

Combines qualitative and quantitative research methods to provide a more holistic understanding of a research problem.

A qualitative research method that aims to develop theories or explanations grounded in the data collected during the research process.

13. Simulation and Modeling

Researchers use mathematical or computational models to simulate real-world phenomena and explore various scenarios.

14. Survey Experiments

Combines elements of surveys and experiments, allowing researchers to manipulate variables within a survey context.

15. Case-Control Studies and Cohort Studies

These epidemiological research methods are used to study the causes and risk factors associated with diseases and health outcomes.

16. Cross-Sequential Design

Combines elements of cross-sectional and longitudinal research to examine both age-related changes and cohort differences.

The selection of a specific research design method should align with the research objectives, the type of data needed, available resources, ethical considerations, and the overall research approach. Researchers often choose methods that best suit the nature of their study and research questions to ensure that they collect relevant and valid data.

Learn more: What is Research Objective?

Research designs can vary significantly depending on the research questions and objectives. Here are some examples of research designs across different disciplines:

• Experimental Design: A pharmaceutical company conducts a randomized controlled trial (RCT) to test the efficacy of a new drug. Participants are randomly assigned to two groups: one receiving the new drug and the other a placebo. The company measures the health outcomes of both groups over a specific period.
• Observational Design: An ecologist observes the behavior of a particular bird species in its natural habitat to understand its feeding patterns, mating rituals, and migration habits.
• Survey Design: A market research firm conducts a survey to gather data on consumer preferences for a new product. They distribute a questionnaire to a representative sample of the target population and analyze the responses.
• Case Study Design: A psychologist conducts a case study on an individual with a rare psychological disorder to gain insights into the causes, symptoms, and potential treatments of the condition.
• Content Analysis: Researchers analyze a large dataset of social media posts to identify trends in public opinion and sentiment during a political election campaign.
• Historical Research: A historian examines primary sources such as letters, diaries, and official documents to reconstruct the events and circumstances leading up to a significant historical event.
• Action Research: A school teacher collaborates with colleagues to implement a new teaching method in their classrooms and assess its impact on student learning outcomes through continuous reflection and adjustment.
• Ethnographic Research: An anthropologist lives with and observes an indigenous community for an extended period to understand their culture, social structures, and daily lives.
• Cross-Sectional Survey: A public health agency conducts a cross-sectional survey to assess the prevalence of smoking among different age groups in a specific region during a particular year.
• Longitudinal Study: A developmental psychologist follows a group of children from infancy through adolescence to study their cognitive, emotional, and social development over time.
• Meta-Analysis: Researchers aggregate and analyze the results of multiple studies on the effectiveness of a specific type of therapy to provide a comprehensive overview of its outcomes.
• Mixed-Methods Research: A sociologist combines surveys and in-depth interviews to study the impact of a community development program on residents’ quality of life.
• Grounded Theory: A sociologist conducts interviews with homeless individuals to develop a theory explaining the factors that contribute to homelessness and the strategies they use to cope.
• Simulation and Modeling: Climate scientists use computer models to simulate the effects of various greenhouse gas emission scenarios on global temperatures and sea levels.
• Case-Control Study: Epidemiologists investigate a disease outbreak by comparing a group of individuals who contracted the disease (cases) with a group of individuals who did not (controls) to identify potential risk factors.

These examples demonstrate the diversity of research designs used in different fields to address a wide range of research questions and objectives. Researchers select the most appropriate design based on the specific context and goals of their study.

Learn more: What is Competitive Research?

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What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

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Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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• Webinar: How to Use Generative AI Tools Ethically in Your Academic Writing
• Research Outlines: How to Write An Introduction Section in Minutes with Paperpal Copilot

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Types of Research Design: Process and Elements

• Updated on  
• Nov 25, 2023

Types of Research Design : Be it science and technology , art and culture, media studies, geography , mathematics , and other subjects, research has always been the route towards finding the unknown. In the circumstances when Coronavirus shattered the world, a vast amount of research was being carried out to find vaccines for its treatment. In this blog, we will understand what are the various types of research design and their related components. 

This Blog Includes:

Descriptive research design, experimental research design, correlational research design, diagnostic research design, explanatory research design, process of research design, what is research design, elements of research design, quantitative research design, qualitative research design, quantitative vs. qualitative research design, fixed vs. flexible research design, how to write research design, cohort study, cross-sectional study, longitudinal study, cross-sequential study, types of research design pdf, research design ppt, benefits of research.

Also Read: Research Institutes in India

Types of Research Designs

Now that we know the broadly classified types of research, Quantitative and Qualitative Research can be divided into the following 4 major types of Research Designs:

✏️ Descriptive Research Design ✏️ Case Study ✏️ Correlational Research Design ✏️ Experimental Research Design ✏️ Diagnostic Research Design ✏️ Explanatory Research Design ✏️ Historical research design ✏️ Cohort research design ✏️ Sequential Research Design ✏️ Action Research Design ✏️ Survey ✏️ Causal Research Design

These types of Research Designs mentioned below are considered the closest and exact to true experiments and are preferred in terms of accuracy, relevance as well as quality.

In Descriptive Research Design, the scholar explains/describes the situation or case in depth in their research materials. This type of research design is purely on a theoretical basis where the individual collects data, analyses, prepares and then presents it in an understandable manner. It is the most generalised form of research design. To explore one or more variables, a descriptive design might employ a wide range of research approaches. Unlike in experimental research, the researcher does not control or change any of the variables in a descriptive research design; instead, he or she just observes and measures them.  In other words, while qualitative research may also be utilised for descriptive reasons, a descriptive method of research design is typically regarded as a sort of quantitative research. To guarantee that the results are legitimate and dependable, the study design should be properly constructed. Here are some examples of the descriptive design of the research type:

• How has the Delhi housing market changed over the past 20 years?
• Do customers of Company A prefer Product C or Product D?
• What are the main genetic, behavioural and morphological differences between Indian wild cows and hybrid cows?
• How prevalent is disease 1 in population Z?

Experimental research is a type of research design in which the study is carried out utilising a scientific approach and two sets of variables. The first set serves as a constant against which the variations in the second set are measured. Experimentation is used in quantitative research methodologies, for example. If you lack sufficient evidence to back your conclusions, you must first establish the facts. Experimental research collects data to assist you in making better judgments. Experimentation is used in any research undertaken in scientifically appropriate settings. The effectiveness of experimental investigations is dependent on researchers verifying that a variable change is due only to modification of the constant variable. The study should identify a noticeable cause and effect. The traditional definition of experimental design is “the strategies employed to collect data in experimental investigations.” There are three types of experimental designs:

• Pre-experimental research design
• True experimental research design
• Quasi-experimental research design

A correlational research design looks into correlations between variables without allowing the researcher to control or manipulate any of them. Correlational studies reveal the magnitude and/or direction of a link between two (or more) variables. Correlational studies or correlational study designs might have either a positive, negative or zero.

Correlational research design is great for swiftly collecting data from natural settings. This allows you to apply your results to real-world circumstances in an externally legitimate manner. Correlational studies research is a viable choice in a few scenarios like:

• To investigate non-causal relationships
• To explore causal relationships between variables
• To test new measurement tools

Recommended Read: Scope of Operation Research

Diagnostic research design is a type of research design that tries to investigate the underlying cause of a certain condition or phenomenon. It can assist you in learning more about the elements that contribute to certain difficulties or challenges that your clients may be experiencing. This design typically consists of three research stages, which are as follows:

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

Explanatory research is a method established to explore phenomena that have not before been researched or adequately explained. Its primary goal is to notify us about where we may get a modest bit of information. With this strategy, the researcher obtains a broad notion and uses research as a tool to direct them more quickly to concerns that may be addressed in the future. Its purpose is to discover the why and what of a subject under investigation. In short, it is a type of research design that is responsible for finding the  why  of the events through the establishment of cause-effect relationships. The most popular methods of explanatory research are:

• Literature research
• In-depth interview
• Focus groups
• Case studies

Is it possible to conduct research without a plan? Most likely not. Research design is a topic we cover while discussing a plan for gathering, analyzing, and interpreting data. This design solves issues and produces a coherent and consistent data analysis model. Let’s study up on it.

A methodical and planned technique for conducting research is the research design process. To make sure the study is legitimate, trustworthy, and yields insightful data, the procedure is crucial. One should keep the points in mind while preparing for research.

✅ Think about your goals and strategies : Establish the study’s theoretical framework, methods, and research questions and objectives. ✅ Select a kind of study design : Based on the research questions and objectives, choose the best research design, such as experimental, correlational, survey, case study, or ethnographic. ✅ Decide on your sample technique and population : Establish the sample size and target population before selecting a sampling strategy, such as convenience, stratified, or random sampling. ✅ Select the techniques you’ll use to collect data : Choose the right instruments or tools for data collection and decide on the methodologies, such as surveys, interviews, observations, or experiments. ✅ Arrange the steps you’ll take to collect data : Create a plan for gathering data that takes ethics into account and specifies the time, place, and people involved. ✅ Choose your data analysis techniques : Plan how to interpret the findings after choosing the relevant data analysis methods, such as statistical, content, or discourse analysis.

Also Read: 10 Types of Qualitative Research Methods & Examples

By the term ‘ research ‘, we can understand that it’s a collection of data that includes critical information by taking research methodologies into consideration. In other words, it is a compilation of information or data explored by setting a hypothesis and consequently coming up with substantive findings in an organised way. Research can be done on an academic as well as a scientific basis as well. Let’s first understand what a research design actually means.

A Research Design is simply a structural framework of various research methods as well as techniques that are utilised by a researcher.

The research design helps a researcher to pursue their journey into the unknown but with a systematic approach by their side. The way an engineer or architect frames a design for a structure, likewise the researcher picks the design from various approaches in order to check which type of research to be carried out. 

Here are the most important elements of a research design- 

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

Must Read: What does a Research Assistant do?

Get to know about the characteristics of Research Design through the infographic given below.

2 Major Types of Research Design 

Keeping its dynamics into consideration, the research design is categorised into two different perspectives, i.e. Quantitative Research Design and Qualitative Research Design . Further, there are four main characteristics of research design which include Reliability, Neutrality, Validity as well as Generalization. Further, a researcher should have a clear understanding of how their project can be implemented in the research design. Let’s explore what Quantitative and Qualitative Research Designs mean:

In Quantitative Research Design, a researcher examines the various variables while including numbers as well as statistics in a project to analyze its findings. The use of graphics, figures, and pie charts is the main form of data collection measurement and meta-analysis (it is information about the data by the data).

This type of research is quite contrary to the quantitative research design. It is explanatory in nature and always seeks answers to “What’s” and “How’s”. It mainly focuses on why a specific theory exists and what would be the respondent’s answer to it. This allows a researcher to draw a conclusion with proper findings. Case studies are mainly used in Qualitative Research Design in order to understand various social complexities. 

Know All About Business Research!

Following is the difference between Quantitative vs. Qualitative Research Design

A contrast between fixed and flexible research design can also be drawn. Quantitative (fixed design) and qualitative (flexible design) data gathering are frequently associated with these two study design categories. The research design is pre-determined and understood with a set study design even before you begin collecting data. Flexible designs, on the other hand, provide for more flexibility in data collection — for example, you don’t provide fixed answer alternatives, so respondents must put in their own responses.

Let’s learn how to create and write a research design!

Research Design Types by Grouping

Another classification of study design types is based on how participants are categorized. In most situations, grouping is determined by the research premise and the method used to sample individuals. There is generally at least one experimental and one control group in a typical study based on experimental research design.

In medical research, for example, one group can be given therapy while the other receives none. You get my drift. We can differentiate four types of study designs based on participant grouping:

A cohort study is a sort of longitudinal research that takes a cross-section of a cohort (a group of people who have a common trait) at predetermined time intervals. It’s a form of panel research in which all of the people in the group have something in common.

In social science, medical research, and biology, a cross-sectional study is prevalent. This study approach examines data from a population or a representative sample of the population at a specific point in time.

A longitudinal study is a type of study in which the same variables are observed repeatedly over a short or long period of time. It’s usually observational research, although it can also take the form of a long-term randomized experiment.

Cross-sequential research design combines longitudinal and cross-sectional research methods, with the goal of compensating for some of the flaws inherent in both.

Since we are dealing with the types of research design, it is imperative to understand how beneficial the practice of doing research is and some of its major advantages are:

• Research helps in getting a deeper understanding of the subject.
• You will learn about its varied aspects as well as its different sources like primary and secondary.
• It helps to resolve complex problems in any field through critical analysis and measurement of unsolved problems. 
• You will also get to know how a hypothesis is created by weighing preserved assumptions.

Also Read: How to Make a Career in Research?  

Research designs can be classified into four main categories: descriptive, correlational, experimental, and diagnostic designs.

The five primary types of study design approaches utilized in research disciplines are explanatory, diagnostic, correlational, experimental, and descriptive research.

Quasi-experimental design is a research design in which the researcher does not have complete control over the independent variable, and therefore cannot establish a cause-and-effect relationship. However, they can still examine the relationship between variables.

Correlational design is a research design in which the researcher examines the relationship between two or more variables, without manipulating any of them.

Certainly, research is the fuel that can potentially drive the solutions to redress all the world’s problems. In order to help to gain a deeper understanding of any subject matter, knowing types of research design plays a critical role in carrying out your thesis. If you are aspiring to pursue your career in the field of research and aim to pursue a PhD , call us at 1800572000 for a free 30-minute career counselling session with our Leverage Edu experts and we will help you find a suitable program and university that fit your aspirations, interests and preferences and can guide you towards a fulfilling career in this domain.

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I was able to made very good understanding on research design types

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Thanks you have being of help to me janees

absolutely good notes thanks

I need for thesis work

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We have a few blogs on thesis work that may help you further- https://leverageedu.com/blog/phd-thesis/ https://leverageedu.com/blog/dphil/

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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

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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

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• Indian J Anaesth
• v.60(9); 2016 Sep

Types of studies and research design

Mukul chandra kapoor.

Department of Anesthesiology, Max Smart Super Specialty Hospital, New Delhi, India

Medical research has evolved, from individual expert described opinions and techniques, to scientifically designed methodology-based studies. Evidence-based medicine (EBM) was established to re-evaluate medical facts and remove various myths in clinical practice. Research methodology is now protocol based with predefined steps. Studies were classified based on the method of collection and evaluation of data. Clinical study methodology now needs to comply to strict ethical, moral, truth, and transparency standards, ensuring that no conflict of interest is involved. A medical research pyramid has been designed to grade the quality of evidence and help physicians determine the value of the research. Randomised controlled trials (RCTs) have become gold standards for quality research. EBM now scales systemic reviews and meta-analyses at a level higher than RCTs to overcome deficiencies in the randomised trials due to errors in methodology and analyses.

INTRODUCTION

Expert opinion, experience, and authoritarian judgement were the norm in clinical medical practice. At scientific meetings, one often heard senior professionals emphatically expressing ‘In my experience,…… what I have said is correct!’ In 1981, articles published by Sackett et al . introduced ‘critical appraisal’ as they felt a need to teach methods of understanding scientific literature and its application at the bedside.[ 1 ] To improve clinical outcomes, clinical expertise must be complemented by the best external evidence.[ 2 ] Conversely, without clinical expertise, good external evidence may be used inappropriately [ Figure 1 ]. Practice gets outdated, if not updated with current evidence, depriving the clientele of the best available therapy.

Triad of evidence-based medicine

EVIDENCE-BASED MEDICINE

In 1971, in his book ‘Effectiveness and Efficiency’, Archibald Cochrane highlighted the lack of reliable evidence behind many accepted health-care interventions.[ 3 ] This triggered re-evaluation of many established ‘supposed’ scientific facts and awakened physicians to the need for evidence in medicine. Evidence-based medicine (EBM) thus evolved, which was defined as ‘the conscientious, explicit and judicious use of the current best evidence in making decisions about the care of individual patients.’[ 2 ]

The goal of EBM was scientific endowment to achieve consistency, efficiency, effectiveness, quality, safety, reduction in dilemma and limitation of idiosyncrasies in clinical practice.[ 4 ] EBM required the physician to diligently assess the therapy, make clinical adjustments using the best available external evidence, ensure awareness of current research and discover clinical pathways to ensure best patient outcomes.[ 5 ]

With widespread internet use, phenomenally large number of publications, training and media resources are available but determining the quality of this literature is difficult for a busy physician. Abstracts are available freely on the internet, but full-text articles require a subscription. To complicate issues, contradictory studies are published making decision-making difficult.[ 6 ] Publication bias, especially against negative studies, makes matters worse.

In 1993, the Cochrane Collaboration was founded by Ian Chalmers and others to create and disseminate up-to-date review of randomised controlled trials (RCTs) to help health-care professionals make informed decisions.[ 7 ] In 1995, the American College of Physicians and the British Medical Journal Publishing Group collaborated to publish the journal ‘Evidence-based medicine’, leading to the evolution of EBM in all spheres of medicine.

MEDICAL RESEARCH

Medical research needs to be conducted to increase knowledge about the human species, its social/natural environment and to combat disease/infirmity in humans. Research should be conducted in a manner conducive to and consistent with dignity and well-being of the participant; in a professional and transparent manner; and ensuring minimal risk.[ 8 ] Research thus must be subjected to careful evaluation at all stages, i.e., research design/experimentation; results and their implications; the objective of the research sought; anticipated benefits/dangers; potential uses/abuses of the experiment and its results; and on ensuring the safety of human life. Table 1 lists the principles any research should follow.[ 8 ]

General principles of medical research

Types of study design

Medical research is classified into primary and secondary research. Clinical/experimental studies are performed in primary research, whereas secondary research consolidates available studies as reviews, systematic reviews and meta-analyses. Three main areas in primary research are basic medical research, clinical research and epidemiological research [ Figure 2 ]. Basic research includes fundamental research in fields shown in Figure 2 . In almost all studies, at least one independent variable is varied, whereas the effects on the dependent variables are investigated. Clinical studies include observational studies and interventional studies and are subclassified as in Figure 2 .

Classification of types of medical research

Interventional clinical study is performed with the purpose of studying or demonstrating clinical or pharmacological properties of drugs/devices, their side effects and to establish their efficacy or safety. They also include studies in which surgical, physical or psychotherapeutic procedures are examined.[ 9 ] Studies on drugs/devices are subject to legal and ethical requirements including the Drug Controller General India (DCGI) directives. They require the approval of DCGI recognized Ethics Committee and must be performed in accordance with the rules of ‘Good Clinical Practice’.[ 10 ] Further details are available under ‘Methodology for research II’ section in this issue of IJA. In 2004, the World Health Organization advised registration of all clinical trials in a public registry. In India, the Clinical Trials Registry of India was launched in 2007 ( www.ctri.nic.in ). The International Committee of Medical Journal Editors (ICMJE) mandates its member journals to publish only registered trials.[ 11 ]

Observational clinical study is a study in which knowledge from treatment of persons with drugs is analysed using epidemiological methods. In these studies, the diagnosis, treatment and monitoring are performed exclusively according to medical practice and not according to a specified study protocol.[ 9 ] They are subclassified as per Figure 2 .

Epidemiological studies have two basic approaches, the interventional and observational. Clinicians are more familiar with interventional research, whereas epidemiologists usually perform observational research.

Interventional studies are experimental in character and are subdivided into field and group studies, for example, iodine supplementation of cooking salt to prevent hypothyroidism. Many interventions are unsuitable for RCTs, as the exposure may be harmful to the subjects.

Observational studies can be subdivided into cohort, case–control, cross-sectional and ecological studies.

• Cohort studies are suited to detect connections between exposure and development of disease. They are normally prospective studies of two healthy groups of subjects observed over time, in which one group is exposed to a specific substance, whereas the other is not. The occurrence of the disease can be determined in the two groups. Cohort studies can also be retrospective
• Case–control studies are retrospective analyses performed to establish the prevalence of a disease in two groups exposed to a factor or disease. The incidence rate cannot be calculated, and there is also a risk of selection bias and faulty recall.

Secondary research

Narrative review.

An expert senior author writes about a particular field, condition or treatment, including an overview, and this information is fortified by his experience. The article is in a narrative format. Its limitation is that one cannot tell whether recommendations are based on author's clinical experience, available literature and why some studies were given more emphasis. It can be biased, with selective citation of reports that reinforce the authors' views of a topic.[ 12 ]

Systematic review

Systematic reviews methodically and comprehensively identify studies focused on a specified topic, appraise their methodology, summate the results, identify key findings and reasons for differences across studies, and cite limitations of current knowledge.[ 13 ] They adhere to reproducible methods and recommended guidelines.[ 14 ] The methods used to compile data are explicit and transparent, allowing the reader to gauge the quality of the review and the potential for bias.[ 15 ]

A systematic review can be presented in text or graphic form. In graphic form, data of different trials can be plotted with the point estimate and 95% confidence interval for each study, presented on an individual line. A properly conducted systematic review presents the best available research evidence for a focused clinical question. The review team may obtain information, not available in the original reports, from the primary authors. This ensures that findings are consistent and generalisable across populations, environment, therapies and groups.[ 12 ] A systematic review attempts to reduce bias identification and studies selection for review, using a comprehensive search strategy and specifying inclusion criteria. The strength of a systematic review lies in the transparency of each phase and highlighting the merits of each decision made, while compiling information.

Meta-analysis

A review team compiles aggregate-level data in each primary study, and in some cases, data are solicited from each of the primary studies.[ 16 , 17 ] Although difficult to perform, individual patient meta-analyses offer advantages over aggregate-level analyses.[ 18 ] These mathematically pooled results are referred to as meta-analysis. Combining data from well-conducted primary studies provide a precise estimate of the “true effect.”[ 19 ] Pooling the samples of individual studies increases overall sample size, enhances statistical analysis power, reduces confidence interval and thereby improves statistical value.

The structured process of Cochrane Collaboration systematic reviews has contributed to the improvement of their quality. For the meta-analysis to be definitive, the primary RCTs should have been conducted methodically. When the existing studies have important scientific and methodological limitations, such as smaller sized samples, the systematic review may identify where gaps exist in the available literature.[ 20 ] RCTs and systematic review of several randomised trials are less likely to mislead us, and thereby help judge whether an intervention is better.[ 2 ] Practice guidelines supported by large RCTs and meta-analyses are considered as ‘gold standard’ in EBM. This issue of IJA is accompanied by an editorial on Importance of EBM on research and practice (Guyat and Sriganesh 471_16).[ 21 ] The EBM pyramid grading the value of different types of research studies is shown in Figure 3 .

The evidence-based medicine pyramid

In the last decade, a number of studies and guidelines brought about path-breaking changes in anaesthesiology and critical care. Some guidelines such as the ‘Surviving Sepsis Guidelines-2004’[ 22 ] were later found to be flawed and biased. A number of large RCTs were rejected as their findings were erroneous. Another classic example is that of ENIGMA-I (Evaluation of Nitrous oxide In the Gas Mixture for Anaesthesia)[ 23 ] which implicated nitrous oxide for poor outcomes, but ENIGMA-II[ 24 , 25 ] conducted later, by the same investigators, declared it as safe. The rise and fall of the ‘tight glucose control’ regimen was similar.[ 26 ]

Although RCTs are considered ‘gold standard’ in research, their status is at crossroads today. RCTs have conflicting interests and thus must be evaluated with careful scrutiny. EBM can promote evidence reflected in RCTs and meta-analyses. However, it cannot promulgate evidence not reflected in RCTs. Flawed RCTs and meta-analyses may bring forth erroneous recommendations. EBM thus should not be restricted to RCTs and meta-analyses but must involve tracking down the best external evidence to answer our clinical questions.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

Utilizing virtual reality before, versus during, the COVID-19 pandemic

• Original Paper
• Open access
• Published: 15 March 2024
• Volume 4 , article number  76 , ( 2024 )

Cite this article

You have full access to this open access article

• Diane Guevara   ORCID: orcid.org/0000-0003-3077-9513 1 &
• Jen Koco 1  

As the COVID-19 pandemic abruptly pushed interior design (ID) instruction online, instructors were challenged to adapt, and students adapted a new method of virtual reality (VR). The VR method before COVID-19 was a Homido V2 VR headset with iPhone viewing 360-degree panorama jpeg, and during COVID-19 a liquid crystal display (LCD) computer monitor viewing 360-degree panorama jpeg. The purpose of this study was, if a statistically significant difference (SSD) in spatial presence was found between the two types of VR, then an argument could be supported to evaluate spatial presence, before VR is implemented into ID curriculum. This study was at one Midwestern United States university with a sample ( N = 52) of ID undergraduate students. The results revealed an SSD in the spatial presence in the aforementioned VR types. This SSD was found in two of the three dependent variables: Spatial Presence: Possible Action (SPPA; U = 772, p < 0.001), example survey question feeling you could jump into the action, and Spatial Presence: Self Location (SPSL; U = 789, p < 0.001), example feeling you are in the middle of the action . The third dependent variable, Spatial Situation Model (SSM; U = 1320, p = 0.834) did not reveal an SSD, example imagining the arrangement of the spaces. To support results, the Virtual Reality Spatial Presence Index (VRSPI) applied scored neutral ( neither strong , nor weak) for spatial presence in both. This study filled research gaps on VR spatial presence measurement, with implications supporting a measurable advantage in ID students using VR headsets and ID curriculum developers evaluating VR before implementation.

Avoid common mistakes on your manuscript.

Introduction

In 2022, an influx of researchers studied the effects, including spatial presence , of virtual reality (VR) use in interior design in both instruction and practice (Kahrl et al. 2021 ; Guevara et al. 2022 ; Jin et al. 2022 ; Kim et al. 2022 ; Mejia-Puig and Chandrasekera 2022 ; Vahdat 2022 ). This topic becomes increasingly important, possibly fueled by the COVID-19 pandemic pushing in-person interior design education causing a shift in the methods of VR and also due to a rapidly changing field of VR. Some researchers have already studied the shift (Ahmad et al. 2020 ; Basil-Mohammed et al. 2021 ; Lili and Jiping 2021 ).

With researchers requesting more studies into spatial presence during VR use and effects of the COVID-19 pandemic on interior design instruction, this study highlights the problem of VR needing to be evaluated before inclusion into interior design curriculum. The pandemic pushed in-person VR headset use, (see Fig. 1 ), to online interior design instruction, where instructors sought an alternative, such as the students using a liquid crystal display (LCD) computer monitor viewing a 360-degree panorama jpeg (see Fig. 2 ). The above mentioned acted as the two independent variables for this study, and the sensation of spatial presence acted as the dependent variable for this study. For the purpose of this study, spatial presence was defined as the user consciously experiencing the sensation of presence based on a cognitive feeling and an unconscious process (Wirth et al. 2003 ).

Homido V2 headset

Liquid crystal display (LCD) computer monitor viewing a 360-degree panorama jpeg

The research shows VR has been established as an important tool in design. For example, in architectural firms, Yulio 360-degree panorama VR, viewed on an iPhone with a Samsung Gear or Homido V2, is currently being used in firms such as Gensler, ALSC Architects, Diamond Schmitt Architects, and Ronen Beckerman (Chan n.d. ). Per Diamond Schmitt Architects, this type of passive VR (mobile Yulio VR) “worked better for us because it gave us the opportunity to communicate through every day, accessible objects like smartphones” (Chan n.d. para. 1). In addition, a peer-reviewed study survey indicated “participants most frequently cited virtual reality (39%)” when asked for the largest growth area of technology use (Huber and Waxman 2019 , p. 14). To tie this to interior design education, an Interior Design Educators Council (IDEC) conference included a panel discussion of trends in interior design education. Of the six interior design programs they all wanted to introduce Virtual Reality (Swearingen 2019 ). In summary, VR is beginning to be used by employers who hire interior design graduates. Jin et al. ( 2022 ) reminded us to consider the limitations of the VR and examined these limitations such as realistic, confusion, blurry, dizziness, among others (p. 47).

Continuing the research of Jin et al. ( 2022 ), the objective of this present study was to determine if there was a statistically significant difference (in the variable spatial presence) between the two types of VR: a Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg and, during COVID-19, a liquid crystal display (LCD) computer monitor viewing the same 360-degree panorama jpeg. The same VR scene was used in both VR formats. To create the VR scene, it was first drawn in a CET Designer, a .cmdwr file, then was rendered as a 360-degree .jpg (cube map), and last imported into Yulio, a 3rd party VR application. This same scene was displayed on both independent variables, the Homido V2 VR headset viewing iPhone and the LCD monitor. This study’s results revealed quantitative data collected from a sample ( N = 52) of interior design undergraduate students who experienced both types of VR. The results sought a statistically significant difference between the two types of VR. The purpose of this study was to determine the difference between spatial presence in two types of VR to determine if VR should be evaluated for spatial presence before implementation into interior design curriculum.

Next, the literature review supported this argument by studying these topics: VR as an important tool in interior design, the COVID-19 pandemic had an effect on interior design education in regard to VR tools, the Council for Interior Design Accreditation’s (CIDA) Standards should be evaluated for if they were compliant during the COVID-19 pandemic, spatial presence, during VR use, can be evaluated with an index to a applies a rating from very strong to very weak.

Literature review

Four topics critical to supporting the measurement of interior design students’ perceived spatial presence while they used virtual reality (VR) were:

interior design with VR equipment use,

interior design and its’ education during the COVID-19 pandemic,

interior design and the Council for Interior Design Accreditation’s (CIDA) required Standard 7b . Human-Centered Design: “Interior designers apply knowledge of human experience and behavior to designing the built environment” (CIDA 2022 , p. II-20). Standard 7b. clarified “This could include natural, built, virtual, and/or technological environments” (p. II-21)., and last,

the link between virtual reality and spatial presence and how it can be evaluated with an index.

The gaps revealed in the literature review were: VR use in interior design has varying results and needs to continue to be studied, the COVID-19 pandemic limited interior design education and researchers are still studying the effects, and how can researchers continue to support the Council for Interior Design Accreditation’s (CIDA) goal to help interior design students design the built environment to support human experience and behavior. The literature review culminated in a compelling argument for the importance of data collection and data analyzation of spatial presence while interior design students are using VR.

Interior design and virtual reality use

Virtual reality (VR) is the term meaning to generate the illusion of being somewhere else. Interior design and VR use can be very helpful in this profession. It can allow designers, as well as clients, to view a space in more depth and give a better visualization of what a space will look like when it is completed. According to Jin et al. ( 2022 ) and their research with VR technologies, VR is powerful, but has limitations needed to be reached and will never replace real experiences. VR technologies are not supposed to replace the real-life experience of viewing a site, but they can assist a client or a student in giving a better representation of what to expect with a finished product.

Kahrl et al. ( 2021 ) did research on Mixed Reality (MR), a very similar concept to VR, however adds an element of your physical space, sometimes referred to as natural space, VR technologies to see which was preferred by participants.

We also found that all of the Mixed Reality (MR) mechanisms were perceived better than providing photos of the living room. We concluded that for tasks like ours, PC-based VR might be most favored by potential users, but because of its high price and low availability in people’s homes, a combination of mobile devices and mobile VR might be most favorable. (p. 246)

VR was preferred by most over MR, but it was harder to get their hands on because of the higher cost of the technologies. Having VR on mobile devices will also allow everyone to view scenes from anywhere, thereby making them more accessible.

An alternative form of VR is what is referred to as a fish tank view (Astle 2022 ). Fish tank view defined as a 360-degree VR view not requiring a headset. Arstle explained the positives and negatives, but supported that fish tank view is a powerful tool for viewing a VR scene. They also stated though it does not add depth and immersion to the experience, it can still be an extremely powerful tool to can make an impact. This point became important to interior design students when the COVID-19 pandemic started.

Interior design and COVID-19

Just like all other students, interior design majors had to find a way to navigate around the challenges of switching to online learning during the COVID-19 pandemic. “The COVID-19 pandemic is making the pedagogy profession rethink education, not only through implementing the known but also by discovering new potentials within interior design education” (Ahmad et al. 2020 , p. 178). The researchers continued to discuss limitations and missed opportunities. Students looking to go into this profession have had to miss out on a lot of real-life experiences and hands-on learning during their semester due to the pandemic shutting down businesses. Mohammed et al. ( 2021 ) agreed with this point of view in saying:

The problem with distance education is brought about when assessing its effectiveness. Does this mean that our students and teachers get the best education experience using this method? Especially for practical courses. Findings taken from the Grade 1 class at TIU prove that students and teachers were not adjusting well to online courses when it came to practical classes as compared to theoretical ones. (p. 195)

In other words, the researchers were observing the effect distance learning was having on interior design students and their instructors. They found both students and instructors did not find online learning as effective in teaching the necessary material as in-person classes were. Interior design students were selected for this study because they do a lot of hands-on work in their studio classes to get a better understanding of how to draw plans and observe all the components going into buildings. On the other hand, Lili and Jiping ( 2021 ) disagreed and found online learning as beneficial to students as they are exposed to new teaching methods pushing students’ thinking. They also believe online teaching, due to the pandemic, will break the traditional mold of in-person classes and allow students to better use the assets given to improve themselves.

Interior design and Council for Interior Design Accreditation (CIDA)

The Council for Interior Design Accreditation (CIDA 2022 ) is a non-profit accrediting program for interior design programs at universities and colleges located in the United States and internationally. Albadi and Zollinger ( 2021 ) did a study among interior design CIDA accredited programs to see how a certain generation (Generation Z) of students see their learning styles:

The most common learning style found was the combination of Concrete Random and Abstract Random (i.e., learners who are emotional and imaginative and enjoy holistic experiences with trial and error approaches and exploration)…The second most common learning style was the unimodal Concrete Sequential (i.e., students who enjoy experiential activities and step-by-step processes). (p. 49)

The researchers did this to assist professors to improve instruction in the classroom setting. This also helps the professors develop lesson plans for student understanding and retention. According to CIDA Professional Standards ( 2022 ), learning expectations for students should include students having awareness of the origin/intent of laws, codes, and standards; students’ demonstrating the understanding of standards and guidelines related to sustainability and wellness, and students implementing regulations and guidelines related to construction, products, and materials. Finally, students work should apply federal, state, and local codes including fire and life safety, and barrier-free and accessibility regulations and guidelines. With the knowledge from Albadi and Zollinger’s research ( 2021 ), professors at universities will be able to use tools to help students achieve these standards.

Relating CIDA’s standards in interior design education, to VR use, an Interior Design Educators Council (IDEC) conference included a panel discussion of trends in interior design education. Of the six interior design programs, “all of the programs wanted to introduce Virtual Reality” (Swearingen 2019 , p. 16). IDEC explores trends in interior design education and also CIDA standards as they relate. One of the standards, 7b, simply put focuses on how the interior design student designs the built environment considering human experience and behavior. This CIDA Standard reads 7b . Human-Centered Design: “Interior designers apply knowledge of human experience and behavior to designing the built environment” (CIDA 2022 , p. II-20). Standard 7b. clarifies “This could include natural, built, virtual, and/or technological environments” (p. II-21).

Virtual reality and spatial presence

Wirth et al. ( 2003 ) denote spatial presence can be defined as the sense of being in an environment, which is an important factor when using VR equipment. VR helps to show a more realistic view of space to get a better representation of what a client’s space will look like once it is finished. However, different things can interfere with how spatial presence is perceived by the user. Denzer et al. ( 2022 ) researched how using bizarre dreamlike states in VR would affect the spatial presence of the participant. “Inducing experience of bizarreness and unreality did not interfere with spatial presence, the ‘feeling of being there’ in the virtual world, such that spatial presence was high and similar in both conditions” (Denzer et al. 2022 , p. 12). It was concluded from their findings when changing the overall experience of the participant it did not affect how “in-depth” they felt in the virtual world.

To measure spatial presence Vorderer et al. ( 2004 ) developed the Measurements, Effects, Conditions Spatial Presence Questionnaire (MEC-SPQ). This survey has Cronbach alpha scores from 0.86 to as high as 0.91, supporting high reliability in the survey. Other researchers (Pérez and Escobar 2019 ; Yildirim et al. 2019 ; Guevara et al. 2022 ) also utilized the MEC-SPQ to measure spatial presence while using a virtual media. Guevara et al. ( 2022 ) also found a statistically significant difference between perceived spatial presence when comparing three formats of VR using this same survey. The MEC-SPQ was incorporated into this study because of its strength in finding variances in quantitative survey results. The questionnaire “was designed for immediate assignment after media exposure” (p. 4), which lent itself to this methodology design where the participants were offered time enough to take the survey after exposure to each VR.

To support the validity of collecting data on spatial presence, researchers (Guevara et al. 2020 ) have developed an index to evaluate a user’s perceived spatial presence while viewing a scene in VR. This index is the Virtual Reality Spatial Presence Index (VRSPI) and has been used in peer-reviewed research to evaluate other formats of VR on a 5-point scale from very strong to very weak. For example, Guevara ( 2022 ) evaluated three formats of VR (viewing the same scene) and found the three to vary from slightly strong (Oculus Rift viewing Unity), to neutral (DLP technology shutter glasses (XPAND Edux3) with VR cube viewing Unity, to slightly weak (Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg). See Fig. 3 .

Virtual Reality Spatial Presence Index (VRSPI) applied to VR study

Literature review conclusion.

Researchers agreed VR is the term used to generate the illusion of being somewhere else and adding VR technology to university education of interior designers is an opportunity to support student-centered learning for 3-dimensional design development. However, analyzing the capabilities of the VR display formats can give insight into which technology is the most effective tool to support students’ human sensory experiences. VR formats have already started to be evaluated and their differences measured in regard to spatial presence. The literature review supported this argument in four ways:

virtual reality (VR) use remains an important tool in interior design education;

the COVID-19 pandemic had an effect on interior design education in regard to VR tools;

the Council for Interior Design Accreditation’s (CIDA) Standards should be evaluated for compliance during the COVID-19 pandemic, to support additional studies such as studies reported on interior design students’ competencies as being tied to CIDA requirements in interior design education (Albadi and Zollinger 2021 ; CIDA 2022 );

an index should be applied to a VR spatial presence measurement, from very strong to very weak.

Since VR is an emerging and ever-changing technology, it is important to supplement the research with an ongoing comparison of VR display formats, in particular as interior design education makes shifts.

Methodology

This methodology includes the research design, the variables, prevention of study threats and recruitment of sample. A methodology summary at the end of this section leads to the subsequent section, which reveals the study results.

Research design

The design of the research utilized was a quantitative study using an exploratory, 5-point Likert-style survey (Appendix A ) on a sample ( N = 52) of interior design undergraduate students. This quantitative research design supported the strength in the relationship between the variables. A quantitative exploratory study was appropriate for this, due to the type and quantity of variables which were required to produce a data set appropriate for the inferential statistical Mann-Whitney Test—i.e., two independent variables (VR display formats: Headset and Monitor) and three dependent sub-variables (spatial presence capabilities: SSM, SPSL, and SPPA). The advantage was that this type of analysis helped seek where and if statistically significant difference were found, so that we could support (or not support) the hypotheses.

The surveyed sample included four levels of interior design undergraduate students at one university and their perceived experience with two VR display formats. These formats were available through the institution and have been used in instructing interior design students on this campus. Interior design students were selected as the study participants because in their current curriculum incorporates independent variable one, the VR display format of Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg. Both VR displayed the same interior design student-developed scene. Hypotheses were:

H1: There will be a statistically significant difference between the interior design students’ perceived spatial presence capabilities of the two VR display formats.

H2: There will be no statistically significant difference between the interior design students’ perceived spatial presence capabilities of the two VR display formats.

The independent variable one was the VR display format of Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg. The independent variable two was a liquid crystal display (LCD) computer monitor viewing the same 360-degree panorama jpeg. This VR scene viewed in both VR formats was created first in a CET Designer .cmdwr file, then was rendered as a 360-degree .jpg (cube map), and last imported into Yulio, a 3rd party VR application. The dependent variable was the VR’s spatial presence capability, as perceived by the sample. The variable held constant was the scene viewed within both independent variables one and two. This view was drawn in the software CET Designer and rendered into a 360-degree panorama jpeg

These independent variables were selected in order to seek if interior design curriculum developers should be concerned about the change in VR curriculum during the COVID-19 pandemic. The dependent variables were selected since these variables showed reliability in revealing a statistically significant difference in both the pilot study (Guevara et al. 2022 ) and another study performed by this author using the same dependent variables (Guevara and Bogedain 2022 ).

Reliability and validity

Reliability was supported by utilizing the pre-validated MEC-SPQ (Vorderer et al. 2004 ). This survey has Cronbach alpha scores as low as 0.86 and as high as 0.91; this equates to high reliability. Since a survey is pre-validated, this assisted the researcher to minimize the threats to the reliability of the study. The important of reliability is that the study can be replicated by other researchers and assured a consistency in the measurements when the study is repeated in the future. “Designed for immediate assignment after media exposure” (Vorderer et al. 2004 , p. 4), the survey was completed immediately after the sample experienced independent variables one and two. The survey was designed with 5-point Likert scale. The survey instrument can be found in Appendix A . Construct validity was supported by displaying the same interior design scene in both independent variables one and two. Since the data collection tool had content validity, the tool measured what it was intended to measure. Also, since the mentioned topics supported both the reliability and validity of this study, then our study objective was met. The objective was to determine if there was a statistically significant difference (in the variable spatial presence) between the two types of VR.

The study was announced one month prior by a non-participant study member. The sample ( N = 52) was interior design undergraduate students from a Midwestern United States university interior design program. The sample came from the following courses, Freshmen Studio 1 ( n = 21), Sophomore Studio 3 ( n = 10), Junior Studio 5 ( n = 7) and Senior Studio 7 ( n = 14). Non-reported variables were race, age, sex, and ethnicity. Consent materials were offered one week prior to data collection, after which students voluntarily participated.

Human subjects approval and consent

A human subjects approval was secured three months prior to data collection. Study variables, methodology and constraints were included in the human subjects approval. To ensure human subject privacy, no names were collected in the study. See consent in Appendix B . See Institutional Review Board for approval in Appendix C .

Instrument for data collection

A quantitative data collection instrument was utilized in this study (Appendix A ). The researcher collected quantitative data from the participants with the digital survey software Qualtrics, with an electronic copy of the MEC-SPQ survey with five Likert-scale responses from strongly agree assigned a score of 1, up to strongly disagree assigned a score of 5. To ease data analysis, the following three sub-variables were used:

Spatial Situation Model (SSM) for Questions 2 through 9.

Spatial Presence: Self Location (SPSL) for Questions 10 through 17.

Spatial Presence: Possible Actions (SPPA) for Questions 18 through 25.

Collection of data

During August of 2022 to September 2022, setting up the data collection instrument was completed, student assistants were trained, and recruitment of subjects was completed.

Setup of data collection instrument

Both VR display formats, independent variables one and two, both showed the same interior design scene, completed prior to the study by an interior design student. This scene was created first in a CET Designer .cmdwr file, then was rendered as a 360-degree .jpg (cube map), and last imported into Yulio, a 3rd party VR application. Data collection was assigned to one room on campus, which was convenient to access. Pre-selected student assistants moderated each VR display format. In the case of a study participant experiencing motion sickness, one student assistant was available to assist, though no participant required assistance.

Set-up of variables

Independent variable one.

One row of desks in the data collection room held independent variable one, the Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg. The jpeg resolution was 1536 × 1536 (Chan n.d. ). No audio effect was included.

Independent variable two

The second row of desks consisted of desktop computers in the designated research room, and held independent variable two, liquid crystal display (LCD) computer monitor viewing the same 360-degree panorama jpeg. Each computer used the same monitor and showed the identical scene in the VR headset. Audio effects were not included.

Consent form

All student assistants collected signed consent forms prior to study participants participation.

Study recruitment and participation

Study recruitment began with the school director contacting the instructors of the courses to ask for permission to introduce the study. With prior approval from the instructors and school director, two student assistants verbally presented the research methodology, along with the consent. Participants chose to volunteer, signed the paper consent forms. As part of the consent process, participants were reminded that if motion sickness occurred, they should ask to end their intervention and they would not be negatively impacted by the decision.

The intervention spanned over a course of three months, with participants escorted to and from their class to the data collection room housing independent variables one and two. Upon arriving, participants would take a seat in the room, divided equally between the two rows. The participants viewed the first VR display format for 45 s. To signal the end of the viewing, the moderator tapped the participant on the shoulder to designate the 45 s end and directed the participant to complete the electronic survey. Participants were asked to finish incomplete surveys and then proceeded to the second VR display format. This process repeated until the participant completed both displays and both surveys. Once participants had finished viewing the displays and being surveyed, the participants were excused.

Pilot study

A pilot study (Guevara et al. 2022 ) was reviewed and reported to support the validity of this study. The pilot study was performed prior to this study. The pilot study pre-tested the variables with a smaller sample ( N = 33). The pilot study also used the data collection tool the Measurements, Effects, Conditions Spatial Presence Questionnaire (MEC-SPQ; Vorderer et al. 2004 ). The MEC-SPQ survey gathered data from the sample on perceived spatial awareness while experiencing a virtual environment. The same three dependent sub-variables were used in both the pilot study and this study. Though the independent variables were different (VR type). The pilot study results found that the independent variables of VR types A, B, and C did have statistically significant differences with each of the three dependent sub-variables.

Methodology summary

This quantitative methodology utilized the strength of a pre-validated instrument with a high Cronbach alpha score, while supporting both reliability and validity. The human subjects’ approval and consent assured the safety and privacy of the 52 participants. In addition, training of the student assistants ensured accuracy in the data collection process as well as the safety of the study participants.

Results and data analysis

The results section includes and reviews the descriptive statistics, the data analysis process and the data analysis results.

Descriptive statistics

The study sampled ( N = 52) interior design undergraduate students from four levels of interior design studio courses. Participants, by course level, were Freshmen Studio 1 ( n = 21), Sophomore Studio 3 ( n = 10), Junior Studio 5 ( n = 7) and Senior Studio 7 ( n = 14). See Table 1 .

The study sampled the 52 interior design undergraduate students for their perceived experience while viewing the same interior design scene, but viewed in two different VR display formats. The first VR format was the Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg. The second VR format was the liquid crystal display (LCD) computer monitor viewing the same 360-degree panorama jpeg. When applying the VRSPI to this study, the study participants ( N = 52) evaluated the first format (headset) with a combined mean score of 99.62. The second format (Monitor) was evaluated with a combined mean score of 87.04. See Fig. 4 .

Headset and Monitor: Combined Mean Score

As mentioned in the literature review, the Virtual Reality Spatial Presence Index (VRSPI; Guevara et al. 2020 ) is an effective way to measure and evaluate the overall perceived spatial presence of a VR format when a comparison to another VR format is needed. This was the case in this study, where we needed to compare the VR utilized prior to the COVID-19 pandemic to the VR used during the COVID-19 pandemic, in interior design instruction. Applying the VRSPI to this study, both VR formats would be assigned VRSPI = 3 neutral. Refer back to Fig. 3 .

Inferential statistics interpretation

Inferential statistics were analyzed in two steps, the Mean Rank and the Mann-Whitney Test. The first step reported and analyzed the Mean Rank. The group with the highest Mean Rank was the Headset group in SPSL and Headset group in SPPA, scoring 4.32 vs. 4.10 respectively. See Fig. 5 and Table 2 .

Mean rank scores for each variable: SSM, SPSL, SPPA

The resulting data set was appropriate for the inferential statistical Mann-Whitney Test with two independent variables (VR display formats: Headset and Monitor) and three dependent sub-variables (spatial presence capabilities: SSM, SPSL, and SPPA).

The Mann-Whitney Test was conducted to determine whether there is a difference in SSM, SPSL, and SPPA scores between Headset/Monitor. The results indicated statistically significant differences between Headset/Monitor for SPSL ( U = 789, p < 0.001) and SPPA ( U = 772, p < 0.001) groups, but not for SSM groups ( U = 1320, p = 0.834). Simply put, there was a difference in how the user felt items in the scene surrounded them and how much a part of the scene the user feels (SPSL), as well as, there was a difference in whether the user feels they could jump into the action and how well the user felt they could be active in the scene (SPPA). In contrast, there was not a difference in how well the user can remember the scene and how well the user can understand how far apart items are in the scene (SSM). See Table 3 .

Inferential statistics interpretation conclusion

Simply put, the results revealed three major conclusions.

There was a difference in how present one feels, with stronger spatial presence felt while viewing the same scene in the headset, versus viewing the scene in a 360-degree view. This stronger spatial presence was felt in the headset in Spatial Presence: Self Location (SPSL). One example survey question was “I had the feeling that I was in the middle of the action rather than merely observing ” (Vorderer et al. 2004 , p. 8). This stronger spatial presence was also felt in the headset in Spatial Presence: Possible Action (SPPA). One example survey question was “ I felt like I could jump into the action ” (p. 9).

The variable with no spatial presence difference was Spatial Situation Model (SSM). One example survey question was “ I was able to imagine the arrangement of the spaces presented in the scene very well ” (p. 7).

When the VRSPI (Guevara et al. 2020 , p. 259) was assigned to Headset and Monitor, both VR formats were assigned VRSPI = 3 neutral. Simply put, neither the headset nor the monitor had strong , nor weak spatial presence.

To support the validity of this study, a previous study also found the same Homido V2 VR headset with an iPhone viewing 360-degree panorama jpeg. with a VRSPI = 2 slightly weak spatial presence (p. 261). The same study, however, compared and found the Oculus Rift with a VRSPI = 4 slightly strong spatial presence (p. 261).

Discussion/conclusion

The key findings of this study supported the headset utilized for VR does in fact provide increased spatial presences for the user for two of the dependent variables Spatial Presence: Self Location (SPSL) and Spatial Presence: Possible Action (SPPA). Example survey questions “ I had the feeling that I was in the middle of the action rather than merely observing ” (Vorderer et al. 2004 , p. 8) and “ I felt like I could jump into the action ” (p. 9), respectively. This study followed the shift in VR taught before and during the COVID-19 pandemic, and its effect on spatial presence felt. The sample ( N = 52) of interior design Midwestern United States university undergraduate students provided the data which revealed the results, that in fact, supported the objective of this study. The results revealed a significant difference in the spatial presence felt in two out of the three dependent variables; Spatial Presence: Possible Action (SPPA) ( U = 772, p < 0.001) and Spatial Presence: Self Location ( U = 789, p < 0.001). Example questions being feeling you could jump into the action and feeling you are in the middle of the action , respectively. The third dependent variable, Spatial Situation Model ( U = 1320, p = 0.834) did not reveal a difference. Example question being [I am] imagining the arrangement of the spaces . The researchers speculated that the reason that no significant difference was found in the third dependent variable is that the study participants being interior design students, are already adept at imaging the arrangement of spaces.

In addition, to support the results, the Virtual Reality Spatial Presence Index (VRSPI) was applied and both VR formats scored neutral ( neither strong , nor weak) for spatial presence . Since the purpose of this study was if a statistically significant different spatial presence was found between the two types of VR, then an argument can be supported before VR is introduced into interior design curriculum, and it should be evaluated for perceived spatial presence . The implications of the findings that supported that VR headset use by interior design students has a measurable advantage in learnings over VR use without headset use. For future research, interior design curriculum developers should evaluate the type of VR prior to implementing into curriculum. This could guide future interior design curriculum development and how it could guide instructional strategies.

This study filled the gap of research needed on spatial presence measurement during VR use. Before a new technology is introduced into instruction and curriculum, typically there is a driving force such as the industry progressing and the curriculum developers meeting the need of the changing industry. In the case of the Covid-19 pandemic, interior design instruction had no choice but to shift to online instruction, so instructors sought a method of teaching how to utilize VR.

Study limitations must be revealed in all studies. The limitations in this study were, but not limited to:

It is unknown which study participants previously (prior to this study) utilized either of the independent variables (VR).

It is unknown if study participants discussed their opinions with other participants.

Since interior design instruction includes virtual environments, this study examined the research question: is there a statistically significant difference between the virtual environments utilized prior to the COVID-19 pandemic, headset, and the interior design instructor’s solution to “virtual” during the COVID-19 pandemic, liquid crystal display (LCD) computer monitor viewing the same 360-degree panorama jpeg? Spatial presence, simply meaning how present do you feel while you are in a virtual environment? Wirth et al. ( 2003 ), defined spatial presence as the user consciously experiencing the sensation of presence, based on a cognitive feeling and an unconscious process.

The direct impact this study’ results could directly relate to interior design students’ future ability to improve their learnings. VR assists students, any type of student, to retain information, comprehend information and spatial presence has been shown to increase learnings. When evaluating VR, researchers can assist interior design educators determine which VR to add to their curriculum, as well as professional already in practice. If educators start incorporating this measurement now, then current students will benefit. The current student will eventually be professionals taking this knowledge into professional practice.

Spatial presence is an essential concept for interior designers when using technology to view a project or a space. This allows both the students in their learning journey, as well as designers in the field, to get more knowledge of how the space feels without the need to be on site; however, technologies change, education delivery changes and human experience and behaviors change. This research is the platform of how we are educating the future generation of interior designers. To support this study, future researchers could identify additional interior design VR user perceptions, such fatigue, relaxation or how close the initial VR experience matches the interior design end in the physical world.

Data availability

Data supporting reported results can be retrieved by request from [email protected] or at this data link Qualtrics Qsupport V2 Combined Monitor and Headset.

Abbreviations

Mixed reality

• Virtual reality

Desktop environment

VR environment

Hybrid design environment

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Contributions

Conceptualization, D.G.; methodology, D.G.; software, D.G. and J.K.; validation, D.G.; formal analysis, D.G.; investigation, D.G. and J.K.; resources, D.G.; data curation, D.G. and J.K.; writing—original draft preparation, D.G. and J.K.; writing—review and editing, D.G. and J.K.; visualization, D.G.; supervision, D.G.; project administration, D.G. and J.K. All authors have read and agreed to the published version of the manuscript.

Corresponding author

Correspondence to Diane Guevara .

Ethics declarations

Ethical approval.

(a) University Human Subjects Review Committee Approval (UHSRC) approval on July 7, 2022 for conducting human subject research, as defined by the Federal Government. The UHSRC consists of faculty members who volunteer their time and service. The UHSRC is composed of members from every College on campus as well as representation from the community (i.e., members who are not affiliated with EMU). UHSRC members serve 3-year terms with the option of renewal. All high-risk studies will be reviewed by the UHSRC. Human Subjects Protections-IRB - Research | Eastern Michigan University ( emich.edu ). (b) Research performed in accordance with the Federal Government. (c) No exemption granted.

Informed consent

Informed consent was obtained from all participants for participation in the study. Informed consent displayed in Appendix B .

Competing interests

The authors declare no conflict of interest, nor competing interests.

Appendix A: Survey

○ CHOOSE ONE (5)

○ Freshman Studio 1 (1)

○ Sophomore Studio 3 (2)

○ Junior Studio 5 (3)

○ Senior Studio 7 (4)

Q2 I was able to imagine the arrangement of the spaces presented in it very well.

○ Strongly agree (1)

○ Slightly agree (2)

○ Neutral (3)

○ Slightly disagree (4)

○ Strongly agree (5)

Q3 I had a precise idea of the spatial surroundings presented it.

○ Strongly disagree (5)

Q4 In my mind’s eye, I was able to clearly see the arrangement of the objects presented.

○ Slightly disagree (5)

Q5 I was able to make a good estimate of the size of the presented space.

Q6 I was able to make a good estimate of how far apart things were from each other.

Q7 Even now, I still have a concrete mental image of the spatial environment.

○ Strongly disagree (4)

Q8 Even now, I could still draw a plan of the spatial environment in the presentation.

Q9 Even now, I could still find my way around the spatial environment in the presentation.

Q10 I had the feeling I was in the middle of the action rather than observing.

Q11 I felt I was a part of the environment in the presentation.

Q12 I felt like I was actually there in the environment of the presentation

Q13 I felt like the objects in the presentation surrounded me.

Q14 It was as though my true location had shifted into the environment.

Q15 It seemed as though myself was present in the environment in the presentation.

Q16 I felt as though I was physically present in the environment in the presentation.

Q17 It seemed as though I actually took park in the action of the presentation.

Q18 I felt I could jump into the action.

Q19 I had the impression that I could act in the environment of the presentation.

Q20 I had the impressing I could be active in the environment of the presentation.

Q21 I felt I could move among the objects in the presentation.

Q22 The objects in the presentation gave me the feeling that I could do things with them.

Q23 I had the impression that I could reach for the objects in the presentation.

Q24 It seemed to me that I could have some effect on things in the presentation, as I do in real life.

Q25 It seemed to me that I could do whatever I wanted in the environment of the presentation.

Appendix B: Informed consent form

Appendix C: Institutional review board approval

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Guevara, D., Koco, J. Utilizing virtual reality before, versus during, the COVID-19 pandemic. SN Soc Sci 4 , 76 (2024). https://doi.org/10.1007/s43545-024-00870-4

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Can the Reboot coaching programme support critical care nurses in coping with stressful clinical events? A mixed-methods evaluation assessing resilience, burnout, depression and turnover intentions

• K. S. Vogt 1 , 2 , 8 ,
• J. Johnson 1 , 2 , 3 ,
• R. Coleman 1 , 7 ,
• R. Simms-Ellis 1 , 2 ,
• R. Harrison 3 , 4 ,
• N. Shearman 5 , 11 ,
• J. Marran 1 ,
• L. Budworth 1 , 6 , 10 ,
• C. Horsfield 9 ,
• R. Lawton 1 , 2 &
• A. Grange 1  

BMC Health Services Research volume  24 , Article number:  343 ( 2024 ) Cite this article

Metrics details

Critical care nurses (CCNs) are routinely exposed to highly stressful situations, and at high-risk of suffering from work-related stress and developing burnout. Thus, supporting CCN wellbeing is crucial. One approach for delivering this support is by preparing CCNs for situations they may encounter, drawing on evidence-based techniques to strengthen psychological coping strategies. The current study tailored a Resilience-boosting psychological coaching programme [Reboot] to CCNs. Other healthcare staff receiving Reboot have reported improvements in confidence in coping with stressful clinical events and increased psychological resilience. The current study tailored Reboot for online, remote delivery to CCNs (as it had not previously been delivered to nurses, or in remote format), to (1) assess the feasibility of delivering Reboot remotely, and to (2) provide a preliminary assessment of whether Reboot could increase resilience, confidence in coping with adverse events and burnout.

A single-arm mixed-methods (questionnaires, interviews) before-after feasibility study design was used. Feasibility was measured via demand, recruitment, and retention (recruitment goal: 80 CCNs, retention goal: 70% of recruited CCNs). Potential efficacy was measured via questionnaires at five timepoints; measures included confidence in coping with adverse events (Confidence scale), Resilience (Brief Resilience Scale), depression (PHQ-9) and burnout (Oldenburg-Burnout-Inventory). Intention to leave (current role, nursing more generally) was measured post-intervention. Interviews were analysed using Reflexive Thematic Analysis.

Results suggest that delivering Reboot remotely is feasible and acceptable. Seventy-seven nurses were recruited, 81% of whom completed the 8-week intervention. Thus, the retention rate was over 10% higher than the target. Regarding preliminary efficacy, follow-up measures showed significant increases in resilience, confidence in coping with adverse events and reductions in depression, burnout, and intention to leave. Qualitative analysis suggested that CCNs found the psychological techniques helpful and particularly valued practical exercises that could be translated into everyday practice.

This study demonstrates the feasibility of remote delivery of Reboot and potential efficacy for CCNs. Results are limited due to the single-arm feasibility design; thus, a larger trial with a control group is needed.

Peer Review reports

The healthcare professions are seen as some of the most stressful occupations, due to the close human contact, involvement with illness, death and dying, quick decision-making, risk of making errors and the involvement in adverse events they entail [ 1 , 2 , 3 , 4 , 5 , 6 ]. This stress and the demands on health care professionals (HCPs) have been exacerbated by the Covid-19 pandemic. Over the past 3 years, HCPs have had to cope with extreme emotional and physical stress, which has included redeployment, insufficient provision of medical supplies and personal protective equipment (PPE) and witnessing a record number of deaths among patients and colleagues. They have also been under pressure to adhere to ever-evolving infection control measures and have experienced anxiety about their personal health (as well as that of their families) [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Out of all areas of healthcare, Critical Care has been the most significantly affected clinical area by Covid-19 [ 17 , 18 , 19 ]. This has had detrimental effects to the psychological wellbeing of staff working in critical care units and is especially true for critical care nurses (CCNs) [ 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 ].

The international literature has consistently identified CCNs as having the worst outcomes on psychological wellbeing measures, such as depression, burnout, and post- traumatic stress disorder (PTSD) both during, and since the pandemic, and both compared to other critical care HCPs, such as physicians, and compared to non-critical care HCPs [ 21 ]. Two studies that illustrate the impact of working as a critical care nurse during the pandemic were conducted by Greenberg et al. [ 22 ] and Moll et al. [ 21 ]. In the United Kingdom (UK), Greenberg et al. surveyed 709 HCPs working in Critical Care on nine intensive care units (ICUs). Out of the three groups (doctors, nurses and ‘other’ ), CCNs ( n  = 344; 49% of the sample) were significantly more likely to screen positive for depression (moderate, and severe), PTSD and anxiety (moderate, and severe). Further,19% of these nurses reported suicidal ideation [ 21 ]. In the US, Moll et al. compared the burnout scores of healthcare professionals working on critical care units between 2017 ( n  = 572, nurses n  = 323) and 2020 ( n  = 710, nurses n  = 372). Nurses were found to have the sharpest increase in burnout, despite increases in burnout across all professions surveyed. Taken together, these findings show that CCN wellbeing has been significantly impacted by the pandemic. Therefore, it essential that HCPs are supported in their wellbeing, and that they can draw on evidence-based techniques to recover from stressful events, without suffering negative psychological consequences.

Furthermore, poor CCNs wellbeing, the development of burnout and PTSD have been linked with intention to leave critical care nursing, and nursing altogether [ 15 , 23 , 27 , 28 , 29 ]. Thus, supporting CCNs’ wellbeing is not only a priority at an individual level (for individual CCNs), but must also be a priority at organizational level, to avoid further staff shortages [ 20 ]. One of the protective factors against the development of PTSD and burnout is psychological resilience [ 30 , 31 , 32 , 33 ]. Resilience refers to someone’s ability to maintain an emotional equilibrium during difficult experiences [ 34 ]. There is now increasing evidence that resilience can be increased with the help of psychological interventions [ 35 , 36 ]. The Recovery-boosting [“Reboot”] coaching programme evaluated in the current study seeks to enhance HCP resilience by providing them with evidence-based psychological tools to prepare and recover from stressful clinical events. Reboot aims to develop psychological constructs known to confer resilience, including higher self-esteem, greater mental flexibility, and a more positive explanatory style for negative events [ 19 , 37 , 38 ]. Reboot was first developed and piloted in 66 HCPs and healthcare students; groups included paediatric doctors, midwives, and physician associate students [ 37 ]. It consisted of one 4-hour workshop and one 1-hour coaching phone call. At follow-up, participants showed significantly higher levels of psychological resilience and confidence in coping with adverse events; suggesting the intervention was feasible and acceptable to participants and potentially effective for increasing resilience. Although these results were promising, nurses were not included in the study [ 37 ]. Therefore, the feasibility of Reboot for nurses remains to be established through further research [ 19 ].

The COVID-19 pandemic generated an increased need for psychological support for nurses, particularly CCNs, given their significant distress and worse psychological outcomes than other critical care professionals [ 22 , 25 ]. However, the pandemic also drastically reduced the feasibility of delivering in-person psychological interventions. Thus, the current study aimed to adapt the pre-existing Reboot programme for remote delivery for CCNs [ 19 ].

The primary objective was to assess the feasibility of delivering Reboot via online, remote delivery to CCNs. This was measured via demand, recruitment, and programme retention statistics.

The secondary objective was to provide a preliminary assessment of whether Reboot was associated with increases in self-reported psychological resilience and confidence in coping with adverse events, and decreases in depression and burnout, via analysis of questionnaires and interviews.

A more detailed report of the methods can be found in the open-access study protocol paper [ 19 ].

Study design & settings

A single-arm before-after feasibility study design was used; with a mixed-methods evaluation. Participants were invited to complete online questionnaires at five time points, which were Baseline (Time 1), following completion of two group workshops (Time 2), following completion of two individual coaching calls (Time 3), at 2-week follow-up post the final coaching call (Time 4). A fifth timepoint (Time 5) was added in May 2022 to investigate participants’ intention to leave nursing.

Online interviews were conducted with 25% of participants [who were randomly selected], after completion of the intervention. The Kirkpatrick model for assessing training interventions [ 39 ] was used, and four levels of outcome data were collected (Reaction, Learning, Behaviour, Results), as per Johnson et al. [ 37 ].

Ethics approval

Ethics approval was granted by the School of Psychology, University of Leeds Ethics committee (approved on 25-08-2021, PSYC-302; an ethics amendment was approved on 09-05-2022, PSYC-535). The study adheres to both the British Psychological Society’s Code of Ethics and Conduct, as well as Declaration of Helsinki.

Adaptation to online, remote delivery

Reboot was adapted for online delivery from a previous in-person group delivery method. This is reported in-depth in the study protocol [ 19 ]. The original intervention consisted of one 4-hour in-person group workshop and one 1-hour individual coaching phone call; and was delivered by a Clinical Psychologist (JJ) and an Occupational Health Psychologist (RSE) [ 37 ]. Adaptation for online, remote delivery involved changing this to two 2-hour online group workshops hosted via Zoom (each pair of workshops was termed a ‘cycle’), and two 1-hour individual coaching calls and was delivered by a Cognitive-behavioural (CBT) therapist (RC).

Participants

The recruitment target was 80 CCNs working in the National Health Service (NHS) in the UK. Full inclusion/exclusion criteria, and sample size justification can be found in the protocol paper [ 19 ].

Primary feasibility outcomes

As per protocol, feasibility outcomes were measured via demand [how many CCNs signed up], recruitment [how many CCNs consented and attended the first workshop], and retention [a) how many participants completed both workshops, b) how many participants completed both workshops and coaching calls, and c) how many completed the final follow-up questionnaires]. Using results of the in-person version of Reboot delivered by, feasibility success for the current study was met, if the following criteria were met:

at least 80 CCNs signed up to the study (demand)

at least 80 CCNs consented to taking part in the study and attend the first workshop (recruitment).

at least 90% of recruited CCNs complete both workshops

at least 70% completed both workshops and coaching calls, and

at least 50% of recruited CCNs complete all follow-up measures, up to Time 4.

Secondary outcomes

Secondary outcomes were resilience [measured via the Brief Resilience Scale (BRS)] [ 40 ]], confidence in coping with adverse events [measured via Confidence in Coping with Adverse Events Questionnaire [ 37 ]] , knowledge of resilience [measured via Knowledge Assessment [ 37 ] , Burnout [ measured via an abbreviated version of the Oldenburg Burnout Inventory (OLBI) [ 41 ]], and depression [measured via the Patient Health Questionnaire (PHQ-9) [ 42 ]]. Feedback and reactions to the Reboot workshops [assessed via “Feedback” questionnaire [ 37 ] were also assessed. Internal reliability coefficients for the measures are reported in the ‘ Results ’ section of this paper.

• Intention to leave

In addition to the above outcomes, an amendment was made to the original protocol to include a measure of intention to leave. All participants who completed the programme (both workshops, both coaching calls) were asked to answer an extra questionnaire as part of an additional follow-up survey. Participants were firstly asked to answer a set of questions measuring their turnover intentions as they recalled them prior to participating in Reboot (“ Think back to two weeks before you attended your first Reboot workshop, how were you feeling …?” and then a set of questions about their current turnover intentions (“How are you feeling now …?”). More specifically, intention to leave was measured via three items for the two time points (“I was/am planning to leave critical care nursing for another type of nursing”, “ I was/am planning to leave nursing altogether” and “ I was/am planning to continue working as a critical care nurse ” [reverse coded]) and answered on a scale from Strongly agree (1) to strongly disagree (5), with lower scores indicating lower intention to leave in critical care nursing.

Study information was circulated to the CCNs via Critical Care Networks and social media, via flyers, tweets, websites, and emails. A QR code could be used to access a website containing study information and a sign-up link. During sign-up, participants provided their details, and selected dates for workshops 1 and 2 from a list of cycles. Confirmation of dates was confirmed by email. Seven days prior to workshop 1, participants received an email with a questionnaire link, containing 1) consent form, 2) baseline survey, 3) a video to watch prior to attending the first workshop and 4) online links to access their workshops. Around the same time, participants also received a booklet in the post to use in the workshops, as well as a welcome phone call from the therapist facilitating the workshops. Both workshops took place via Zoom. At the end of the second workshop, the therapist asked participants to complete the Time-2 questionnaire and booked participants in for their two coaching calls. The coaching calls took place via phone or video call, depending on preference. After coaching call 2, participants completed Time-3 questionnaires, which they were sent by the therapist. Two to three weeks after the second coaching call, participants were emailed Time-4 questionnaires, and were invited to take part in an interview if selected (see Appendix 1 for Interview Guide). Interviewees were selected via random number generation from 0 to 100, numbers were assigned to participants in order of sign-up.

In May 2022, participants received a further questionnaire, assessing intention to leave critical care nursing. This questionnaire was an amendment to the protocol. This was added due to several stakeholder groups and the research literature indicating that measures of intention to leave are paramount to evaluation and implementation of interventions, and especially salient considering the current international healthcare workforce crisis. The questionnaire was distributed via email to all 62 nurses who completed the whole programme (thus, both workshops and both coaching calls). A £5 voucher was offered to all as an incentive to participate.

Analysis plan

Quantitative analyses.

A more detailed report of the analysis can be found in the study protocol paper [ 19 ]. Data were analysed with both R and SPSS. Multilevel (random intercepts for participants) regression models for each outcome included a timepoint coefficient, and were unadjusted, or sequentially adjusted for gender, age, and experience (years in profession). Holm-corrected t- tests further assessed between timepoint differences in outcomes.

Qualitative analyses

As per protocol, reflexive thematic analysis (RTA) [ 43 ] was used to analyse the interviews. RTA does not require a pre-determined ontological or epistemological framework; and is therefore commonly used in applied health research. KSV coded all interviews; and RSE coded a subset of these [ n  = 3, 20%]. Similarities and differences in coding was discussed between the researchers; however, the researchers generally agreed on the use of codes and salient aspects to code.

Participant characteristics

A total of 84 participants consented to participate in the study. Most participants were female [86%], and their mean age was 39.7 [SD = 9.2; range: 22-60; missing n  = 3]. Participants’ years of experience as registered nurses ranged from 0 [i.e., less than 1 year’s experience] to 39, with a mean of 13.9 [SD = 9.0, missing n  = 3]; while their years of experience as registered nurses in critical care ranged from 0 to 35, with a mean of 10.7 [SD = 8.8, missing n  = 3]. Three CCNs indicated that they were off work with stress when they completed the baseline questionnaire; however, when/if those nurses return to work was not followed up. At baseline, two CCNs were also taking part in other workplace wellbeing initiatives, and four others indicated that they had taken part in workplace wellbeing initiatives in the past. Fifteen interviews were conducted by KV (24% of participants), one of which was a pilot interview to trial the interview schedule, so is not included in the analysis.

Intervention delivery

Twenty-five workshop cycles were offered to participants. Nineteen cycles were chosen by participants, 6 cycles were cancelled and participant numbers in each ranged from two to six participants.

A total of 102 UK CCNs signed-up to the study by booking a place; thus, the target of recruiting at least 80 CCNs was met.

Recruitment

A total of 84 CCNs consented to participate in the study. Out of the 84, 77 attended the first workshop [91.7%], thus the objective of recruiting at least 80 CCNs was not met, but this was within 5% of the goal figure.

Programme retention: online, remote delivery of reboot

Of the 102 sign-ups, there were 62 completions, 15 dropped-out during the programme and 25 signed up but did not attend or cancelled their first workshop. Out of the 77 who attended the first workshop, 62 completed both workshops and both coaching calls; thus, 80.5% of those who attended the first workshop completed the programme – this means that the objective of achieving a (participation) retention rate of ≥70% was met.

Retention: feasibility of evaluation of online, remote delivery of reboot

Out of the 77 who completed the first workshop, 58.4% completed final, time-4 measures. Thus, the objective of ≥50% completion rates for the final follow-up questionnaire was met.

The secondary objective was to provide a preliminary assessment of whether Reboot could potentially significantly increase both self-reported psychological resilience and confidence in coping with adverse events, via analysis of questionnaires and interviews.

Quantitative results

Descriptive statistics are presented in Tables  1 , 2 and 3 ; and model-fit and results are presented in Tables  4 and 5 (4 for unadjusted models, 5 for adjusted models). All analyses indicated considerable clustering, supporting the use of random intercepts. The proportion of variance explained by all indicator variables was sizeable across measures, however, a higher proportion of variance was explained by fixed time points explained plus random effects. Adjusting the model for gender, age and experience did not alter model fit, thus are not reported here but results can be viewed in Table  4 .

Confidence scores increased significantly, compared to pre-intervention (Time 1) [Time 2: unadjusted β = 0.80, CI: 0.66 - 0.94, p  < .001, d = .81; Time 3: unadjusted R 2  = 0.75, CI: 0.59 - 0.91, p  < .001, d = 0.78; Time 4: unadjusted R 2  = 0.85, CI: 0.68 - 1.01, p  < .001, d = 0.80]. Post-hoc t-tests comparing timepoint means showed no further increase in confidence when comparing between T2, T3 and T4, indicating that initial increases were maintained and remained stable [range p holm .977; adjusted for multiple comparisons]. Cronbach’s α for the confidence measure ranged from 0.64 - 0.87 across timepoints.

Knowledge scores increased significantly between Time 1 and Time 2 [unadjusted β = 0.48, CI: 0.16-0.30, p  < .001, d = 0.79].

Descriptive statistic present sums of items, whereas models used the mean of items.

Resilience scores increased significantly between Time 1 and Time 3 [unadjusted β = 0.39, CI: 0.23 - 0.56, p  < .001, d = 0.43] as well as between Time 1 and Time 4 [unadjusted β = 0.42, CI: 0.26 - 0.59, p  < .001, d = 0.49]. Post-hoc tests comparing timepoint means indicated that there was no further increase in resilience between Time 3 and Time 4 ( p holm  = .75), suggesting that initial gains remained stable. Cronbach’s alpha for the three time points it was used at, was between .80-.83; thus indicating good reliability.

Burnout scores decreased significantly between Time 1 and Time 3 [unadjusted β = −.037, CI: − 0.50- (− 0.25), p  < .001, d = − 0.51 and between Time 1 and Time 4 [unadjusted β = −.039, CI-0.52 – (− 0.26), p  < .001, d = − 0.56]. Post-hoc tests showed no significant difference was found on the BRS between T2 and T4; p holm  = .82, indicating that decreases were maintained and remained stable. Reliability for the questionnaire was good, with Cronbach’s alpha ranging from .76-.84 for the three time points.

Scores on the PHQ-9 indicated a significant decrease in depression from both Time 1 to Time 3 [unadjusted β = −.045, CI: − 0.59 – - 0.32, p  < .001 as well as from Time 1 to Time 4 [unadjusted β = −.048, CI: − 0.6 – (− 0.35); p  < .001].

Post-hoc tests showed no significant difference on PHQ-9 scores between T3 and T4; p holm  = .73, indicating that reductions were maintained and remained stable. Cronbach’s alpha for the PHQ-9 for the three time points it was used ranged from .83-.88, thus indicating good reliability. Out of the 39 participants who completed the PHQ-9 at both baseline and Time 4, almost 80% of participants screened for the presence of mild or severe depression at baseline (PHQ-9 score of 4 or above), whereas at Time 4, only 31.8% did.

Feedback/reactions

Feedback and reactions to the Reboot workshop were overwhelmingly positive (Tables  2 and 3 ). Most participants agreed or strongly agreed that it was relevant for their professional group; they learned useful skills and felt the workshops were adequate in length and were engaging. The majority also indicated that they would react differently if they were involved in a stressful workplace event after attending the workshop. Only a minority ( n  = 5) indicated that there were aspects of the workshops that they did not find useful. Seventy-two CCNs answered the question as to whether they would recommend the workshops to other HCPs; 71 indicated that yes, they would, whereas one participant said they would not.

Thirty-two out of the 62 nurses who completed the full programme responded to the invitation to complete an additional questionnaire in May 2022 (response rate: 51.6%). Participants were asked to answer a set of questions measuring their turnover intentions as they recalled them prior to participating in Reboot (pre-Reboot), and as they are now (post-Reboot). Higher scores indicate lower intention to leave. Using a paired-samples t-test, a significant difference in intention to leave between pre-Reboot (mean = 11.50, SD =2.64) to post-Reboot (mean = 13.56, SD = 1.63) was found [t (31) = 4.93, p  < .001, d = 0.94], showing that nurses reported significantly lower intention to leave critical care nursing after completing the programme than before. Cronbach’s α for post-Reboot was 0.73 and 0.78. for pre-Reboot.

Qualitative results

From the 15 interviews, two themes were developed. These were: “The value and impact of Reboot for participants and beyond ” and “Online delivery and content”. Both themes had subthemes, illustrated in Fig.  1 .

Graphic representation of qualitative findings

Theme 1: the value and impact of reboot for participants and beyond

The value and impact of Reboot was described as “priceless ” (Interview 14) for participants themselves, for their peers with whom they were able to share the psychological tools and knowledge with, and for organisations.

The value and impact of reboot for participants

Specific benefits that participants identified for themselves as a result of attending Reboot were better understanding of their own thought processes and emotions, better understanding of why errors happen at work, having a “ tool kit ” (Interview 15) of simple, psychological tools that they can draw on in times of stress (both at work and outside of work), being able to better manage mental and physical stress, and being able to better compartmentalise work and life outside of work.

Participants also specifically identified increases in wellbeing, confidence and knowledge about resilience, and decreases in burnout and intention to leave (Table  6 ). In addition, CCNs expressed that the group workshops made them feel validated in their feelings of stress, and that it was helpful to meet other professionals outside of their organisation who had similar experiences:

“… but actually speaking to other people who'd been through a similar experience, who they wish they'd done some things differently as well you know… made you kind of realize we are human, we tried our best and hindsight is a wonderful thing, and experience is a wonderful thing…” (Interview 4)

The value and impact of reboot for peers of participants

Five participants, who were predominantly senior CCNs, also expressed that after Reboot, they were “ also able now to help other people bounce back “(Interview 14) by sharing knowledge and tools learnt during Reboot.

“I was sat with them one of my nurses who, who thought they'd made an error. I'm not sure they did but they were being really hard on themselves, they were ruminating and going over and over and over, to a really unhealthy extent. So, I brought in some of what we've done at the workshop, and I said you know this is what you're doing and it's not healthy and these are ways that you can you know, you need to try and break the cycle.” (Interview 4)

The value and impact of reboot for organisations

Being offered training around psychological tools to cope with stress and how to boost resilience was described as essential by CCNs, as otherwise they would not be able to do their jobs. Thus, CCNs drew links between accessing programs like Reboot and the sustainability of the workforce in critical care.

“I'm sure that it will give me the staying power because things are going - always going to come up at work I think that are challenging” (Interview 13)

“you've got to have a lot of resilience to be able to even want to turn up ” (Interview 2)

All participants said that Reboot should be offered to nurses early on in their nursing career, especially within the first year of working in critical care.

“everybody else had ought to be going” (Interview 7)

“I think really early on in their career, to be able… to know how to approach negative thinking habits… to stop the rumination;… the amount of time I have ruminated on situations and blamed myself for things… they really do play on your mind for weeks sometimes. I think having these tools, so just to have them really early on in your career, so you know how to, how to approach those situations…” (Interview 15)

One participant suggested that while the value of Reboot lay in its focus on the acute, stressful situations that occur in intensive care settings, it does not address more long-term problems, such as issues with turnover and short staffing which are also affecting staff wellbeing.

“I think it's a bit more difficult with everything that's happening due to Covid and staffing at the moment with us, because we've got a lot of turnover of staff because, I guess, people just aren’t happy, but in acute situations, definitely.” (Interview 2)

Theme 2: online delivery and content

This theme incorporates narratives around the delivery and content of the workshops, coaching calls and suggestions for improvement.

Delivery and content of the workshops

Participants spoke positively about the online delivery of Reboot. The workshops were perceived as “ delivered at the right pitch” (Interview 14) and “comfortable ” (Interview 4), with no problems with internet connectivity. Participants liked the presented background to the interventions, the opportunity to share their experiences with other CCNs and the practical content of the workshops. Some participants commented on the fact that the ‘ homework’ set between the first and second workshop was helpful as it made them more conscious of what they were doing and feeling. Participants liked having the workbook to accompany the sessions, and to have as a point of reference for the future.

‘I liked the activities that you did and it was quite personal to you, so you could bring your own experiences, and use them and we went through them, shared with each person how you could use strategies to help you. That was good as well.” -Interview 2

“Really enjoyed and it was very active, not like one speaker is speaking and someone else just listening in - no, really, everything was really practical, in a realistic way… It was really a natural, realistic knowledgeable feeling.” - Interview 11

Delivery and content of the coaching calls

The coaching calls following the workshops were described as deepening understanding, empowering, helpful, professional, and relaxed. Participants spoke very highly of the CBT therapist delivering the intervention, praising their kindness and helpfulness. CCNs felt understood, and appreciated the individualised support, which often included specific materials being sent to them by email following the coaching calls.

“they [the coaching calls] were probably the most helpful” -Interview 2

“Touched in every corner… whenever I got a doubt, I was suddenly sharing with [therapist name] and she was listening and giving some kind of tools and… it was really touching it.” – Interview 11

“I thought, I thought she was brilliant… and really kind, and she listened… I was really thankful.” – Interview 13

One of the participants described the coaching calls as “ invaluable ” (Interview 14) and liked the fact that the coaching calls gave her opportunity to discuss what she was struggling with and tools to solve the problems for herself, with support of the therapist, rather than being given a solution to a problem.

“Quite invaluable and as a supported tool… because it wasn't like, …“this this is the problem… okay well, here's the answer”, it wasn’t that.. it was a “right, well that is the problem, let’s look at some tools you can use to help and support you to find a way through that yourself”, which was really empowering…. It's not sort of… “this the problem I had…this is how you fix it… this is what you've got to do. It wasn't that - it's “here's some tools, work through those tools, see what you think when we come back on the next coaching call” … very empowering, so I had to sit there and do that myself, which was great.” – Interview 14

Suggestions and recommendations

Participants made a small number of suggestions to improve Reboot, which included a slightly longer workbook with more content, to ensure that content is not forgotten about, more coaching calls and delivering some ‘refreshers’ on content later on. One participant also suggested including an example about long-term stressors, such as sustained short staffing.

Unlike with the workshops, participants did not receive reminders about their coaching calls from the research team or therapist, which meant that some participants forgot they had booked their coaching calls. Some participants suggested reminders about upcoming coaching calls would be beneficial, to ensure they remember and attended.

The current study sought to assess the feasibility of delivering Reboot via online, remote delivery to CCNs, and to provide a preliminary assessment of whether Reboot could potentially increase resilience and confidence in coping with adverse events and decrease burnout, depression, and intention to leave. The results suggested that it is feasible to deliver Reboot via online, remote delivery to CCNs, and found significant increases in resilience and confidence in coping with adverse events and decreases in burnout and depression. Retrospective recall also indicated that nurses believed they had reduced intention to leave after participating in the programme. The qualitative findings echoed the quantitative findings, with CCNs particularly valuing the practical exercises that could be translated into everyday practice.

These findings support those of previous studies indicating that Reboot may be a valuable intervention for HCPs [ 37 , 38 , 44 ], but also extend this in four main ways.

First, the current results were the first to indicate that Reboot may have value in a post-pandemic context. Pre-pandemic, there were already around a third of doctors and nurses suffering from burnout and significant increases reported for work-related stress among healthcare staff [ 37 , 45 ]. However, rates have increased internationally following the onset of the pandemic [ 46 , 47 ]. In the UK, the General Medical Council (GMC) has been running its annual workforce burnout survey since 2018, making it the largest and most comprehensive annual workforce survey in the UK. In 2022, the burnout risk for doctors was at its highest since 2018. In 2021, 46,793 UK medical trainees completed the survey; 43% said that they found their work emotionally exhausting to a high or very high degree, and 33% indicated that they were feeling burnt out from work to either a high or very high degree [ 48 ]. A year later, in 2022, the numbers worsened as 39% (a 6% increase) of trainees indicated that they were feeling burnt out to either a high or very high degree, and 51% of trainees (8% increase) indicated that they found their work emotionally exhausting to a high or very high degree [ 49 ]. While, unfortunately, there is no equivalent study of this scale and magnitude for nurses, this survey, alongside reports from the Nursing and Midwifery Council, show just how extreme the situation has become in healthcare in the UK [ 50 ], and that HCPs desperately need support. While it was possible that these increases in burnout across healthcare professions may have rendered Reboot unworkable or irrelevant, this study shows that Reboot is still feasible and potentially effective, even in the context of psychological changes within the healthcare workforce.

Second, the current results extend the existing literature by showing that Reboot is feasible and potentially effective for CCNs in particular. To date, there are no systematic reviews or meta-analyses that assess the efficacy of intervention to increase resilience or decrease burnout in CCNs. There are, however, reviews that either assess the efficacy of interventions on reducing burnout, or increasing resilience, in physicians and nurses concomitantly [ 35 , 51 , 52 ], or the efficacy of resilience or interventions more generally [ 36 , 53 ]. Overall, these reviews conclude that online programmes and internet-based interventions, as well as psychosocial training interventions, are among the interventions that have a positive effect on burnout and resilience, and that CBT-based resilience interventions and mixed-methods most effective at increasing resilience [ 36 , 51 , 53 ]. However, one major criticism of existing interventions is that they are generic and lack relevance for the work stresses different types of HCPs, or even nurses, are facing. Reboot overcomes this by the fact that it can be tailored to each disciplinary group, including critical care nurses or other specialist areas of nursing, ensuring relevance and saliency of the material for specific discipline groups, rather than for HCPs more generally. For example, CCNs will require different content to be included in a resilience and burnout intervention that is salient and acceptable to them, compared to trainee doctors, surgeons or midwives [ 37 ] but also compared to other nurses. CCNs tend to have different psychological profiles, compared to non-CCNs. For example, nurses working on either orthopaedic or dialysis wards have been found to have much lower burnout scores, compared to nurses working on critical care units [ 54 ] – a difference that has likely been further exacerbated by the pandemic, and effective resilience interventions must take this into account. This will also be a challenge for implementation into practice, as delivery of Reboot would need to be planned and tailored in advance for each HCP group.

Third, the current results also add to the wealth of evidence for the efficacy of person-directed interventions [ 36 , 51 ]. Person-directed interventions can be defined as those which aim to improve an individual’s capacity to cope with the demands of their job, which is often achieved via mindfulness or CBT programmes. While the quantitative findings highlight that Reboot is feasible and potentially effective for CCNs, the qualitative findings add important knowledge to the aspects of person-directed interventions which CCNs found valuable. For example, participants especially valued the practical applications of the programme which helped them, and by proxy, their peers, cope with the demands of their critical care nursing. In this context, it is not possible to suggest that Reboot can be considered superior to other existing interventions for nurses but as one of several candidate interventions which should be tested using more rigorous research designs. However, it should be noted that Reboot has some unique features not shared with other existing interventions: for example, it involves a mixed-modality format, ensuring the benefit of both peer support and one-to-one confidential space with a therapist.

It is clear that interventions, such as Reboot, cannot compensate for organisational failings; and should be used alongside, rather than in place of, organisational interventions [ 19 , 37 , 55 ]. However, organisational changes are often decided at a regional or national level and influenced by political and economic factors. As such they can be challenging to implement. In this context, person-directed interventions are often appealing to organisations as they are within their decision-making latitude/capability to select and deliver. At the same time though, staff often do not currently have the time to attend, and engage with, wellbeing programmes on offer, leading to lack of uptake and furthering intention to leave among NHS employees [ 56 ]. Thus, organisational changes that allow the attendance of, and engagement with, wellbeing programs are desperately needed, alongside changes that have been associated with reduced burnout in nursing, such as higher pay, more work flexibility, higher autonomy and fewer/better working hours [ 57 , 58 , 59 , 60 ].

Fourth, the present study also contributes to a growing literature which is focused on the prevention rather than amelioration of work-related mental distress. Research is starting to highlight the importance of higher levels of resilience as protective factors against burnout and the development of post-traumatic-stress disorder (PTSD) for CCNs, and beyond [ 30 , 36 , 61 , 62 , 63 ]. For example, a 2021 study conducted in Poland [ 63 ] found that higher levels of resilience were associated with lower levels of burnout and secondary traumatic stress, while exposure to secondary traumatic stress was positively related to burnout. This supports the development, and implementation, of prophylactic resilience interventions for healthcare staff, rather than ameliorative burnout or PTSD interventions.

Limitations

While strengths of the current study include its mixed-methods design, which can elucidate not just the potential impact of the intervention but also the mechanisms underlying this, there are a number of limitations.

Firstly, the current uncontrolled study design means that causal associations between Reboot and the outcomes measured cannot be assumed. Higher quality evidence, perhaps in the form of a wait-list control design or randomised controlled trial, is now needed.

Secondly, intention to leave scores were collected retrospectively for pre- and post-Reboot, thus future work should include intention to leave measures from baseline.

Thirdly, the majority of workshop and coaching sessions were delivered by the same therapist, a future trial should ensure the inclusion of multiple therapists. The therapist also encouraged the completion of outcome measures (especially at Time 2, post completion of second workshop), which means the therapist was not entirely independent of the evaluation.

Fourth, non-completers were not further followed up or invited to interview. Future research should consider their perspectives too.

Fifth, due to the study focus on CCNs, generalization to nurses working outside of critical care is not possible.

The current results suggest that it is feasible to deliver Reboot via online delivery to CCNs, and that it is associated with self-reported increases in resilience and confidence in coping with adverse events and decreases in burnout and depression. Participants also reported that their intention to leave reduced following the programme. The qualitative findings echoed the quantitative findings, with CCNs particularly valuing the practical exercises that could be translated into everyday practice. These findings, alongside those of the previously investigated in-person (rather than remote) version support the evidence-base and efficacy of Reboot. However, a randomised controlled trial design is now needed to more fully and robustly ascertain the efficacy of Reboot.

Availability of data and materials

Anonymised behavioural data and statistical analysis may be requested via email from Dr. KS Vogt, after data collection and publication of results.

Abbreviations

Brief Resilience Scale

Cognitive Behavioural Therapy

Critical Care Nurse(s)

Healthcare professional(s)

UK National Health Service

Patient Health Questionnaire

Post-traumatic stress disorder

Recovery boosting coaching programme

Senior Research Fellow

United Kingdom

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Acknowledgements

The authors would like to thank the following individuals for their contributions to this study: Sobia Bibi and Lucy Chapman, our Industrial placement students Ryan Carter and Rameen Haq, our Steering Group and the CC3N Network, for their support of the study.

This work is funded by the Burdett Trust for Nursing [Grant code SB/ZA/101010662/632762, Funding Stream: Covid-19: Supporting Resilience in the Nursing Workforce] and supported by the NIHR Yorkshire and Humber Patient Safety Translational Research Centre [under grant PSTRC-2016-006]. The sponsor is Bradford Teaching Hospitals [contact details for sponsor: [email protected] ]. Neither study sponsor nor funder have no role in the study design, the collection of data, the management of data, the analysis thereof, or its interpretation.

This report is independent research supported by National Institute for Health and Care Research Yorkshire and Humber ARC [under grant NIHR200166]. The views expressed in this publication are those of the authors and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care.

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This work is based on previous work conducted by JJ and RSE. For this project, RSE, JJ, JM, RL, AG, RL, HR and LB conceived the study and initiated the adaptation. NS and CH contributed to the design of the study. Adaptation to online, remote delivery was led by JJ and RSE. The Principal Investigator [and grant holder] is AG. JJ, RSE, AG and KSV worked on the ethical approvals. JJ and RC delivered the workshops, RC delivered the coaching calls. JJ provided regular supervision to RC. KSV led on recruitment, supported by CH, and conducted all data collection [quantitative via online questionnaire and qualitative interviews]. LB and KSV conducted statistical analyses; KSV the qualitative analysis. JJ, AG, RSE and JM assisted with data analysis, when needed. KSV wrote the first version of this paper, all authors have had opportunity to read and contribute to the manuscript prior to submission.

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Correspondence to K. S. Vogt .

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Vogt, K.S., Johnson, J., Coleman, R. et al. Can the Reboot coaching programme support critical care nurses in coping with stressful clinical events? A mixed-methods evaluation assessing resilience, burnout, depression and turnover intentions. BMC Health Serv Res 24 , 343 (2024). https://doi.org/10.1186/s12913-023-10468-w

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Received : 05 April 2023

Accepted : 12 December 2023

Published : 15 March 2024

DOI : https://doi.org/10.1186/s12913-023-10468-w

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Women In Science: Materials 2023

Stainless steel selection tool for water application: Pitting Engineering Diagrams Provisionally Accepted

• 1 Outokumpu Stainless AB - Avesta Research Centre, Sweden
• 2 Krefeld Research Centre, Outokumpu Nirosta GmbH, Germany

The final, formatted version of the article will be published soon.

This work systematically investigates the effect of chloride level, temperature, and the water system's oxidative power on the pitting corrosion performance of stainless steels in pH-neutral environments. Two test programs were set to a) develop a robust method for constructing the pitting engineering diagrams and b) construct the pitting engineering diagrams based on the obtained method from the first test program. The various electrochemical techniques were selected to permit and understand factors that affect the corrosion behavior of stainless steel. Extensive testing has been performed with shortterm electrochemical measurements and long-term immersion tests. The obtained result demonstrates that the electrochemical methods were sufficient to define pitting diagrams showing the boundaries between pitting and no pitting as a function of chloride concentration, temperature, and the water system's oxidation potential. The laboratory long-term electrochemical test results correspond the best to real applications and clearly underline the importance of an induction time for pit initiation. Two different types of pitting engineering diagrams have been constructed based on the water system's oxidation potential. The open circuit potential (EOCP) of 150 mV vs saturated calomel electrode (SCE) corresponds to simulating sterile tap water, whereas EOCP of 400 mV vs SCE corresponds to slightly chlorinated water or water with some biological activity. Pitting engineering diagrams are a very useful tool to aid material selection. However, it is important to realize that additional factors, such as different surface conditions and the presence of other environmental species, crevice design, or weld will affect the exact position of the boundaries between pitting and no pitting.

Keywords: Stainless Steel, Pitting corrosion, Chloride ion concentration, temperature, oxidation potential of water system

Received: 11 Dec 2023; Accepted: 15 Mar 2024.

Copyright: © 2024 Hägg Mameng, Wegrelius and Hosseinpour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Sukanya Hägg Mameng, Outokumpu Stainless AB - Avesta Research Centre, Avesta, Sweden Dr. Saman Hosseinpour, Krefeld Research Centre, Outokumpu Nirosta GmbH, Krefeld, Germany

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