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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Quantitative Research

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Quantitative research methods are concerned with the planning, design, and implementation of strategies to collect and analyze data. Descartes, the seventeenth-century philosopher, suggested that how the results are achieved is often more important than the results themselves, as the journey taken along the research path is a journey of discovery. High-quality quantitative research is characterized by the attention given to the methods and the reliability of the tools used to collect the data. The ability to critique research in a systematic way is an essential component of a health professional’s role in order to deliver high quality, evidence-based healthcare. This chapter is intended to provide a simple overview of the way new researchers and health practitioners can understand and employ quantitative methods. The chapter offers practical, realistic guidance in a learner-friendly way and uses a logical sequence to understand the process of hypothesis development, study design, data collection and handling, and finally data analysis and interpretation.

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Babbie ER. The practice of social research. 14th ed. Belmont: Wadsworth Cengage; 2016.

Google Scholar  

Descartes. Cited in Halverston, W. (1976). In: A concise introduction to philosophy, 3rd ed. New York: Random House; 1637.

Doll R, Hill AB. The mortality of doctors in relation to their smoking habits. BMJ. 1954;328(7455):1529–33. https://doi.org/10.1136/bmj.328.7455.1529 .

Article   Google Scholar  

Liamputtong P. Research methods in health: foundations for evidence-based practice. 3rd ed. Melbourne: Oxford University Press; 2017.

McNabb DE. Research methods in public administration and nonprofit management: quantitative and qualitative approaches. 2nd ed. New York: Armonk; 2007.

Merriam-Webster. Dictionary. http://www.merriam-webster.com . Accessed 20th December 2017.

Olesen Larsen P, von Ins M. The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics. 2010;84(3):575–603.

Pannucci CJ, Wilkins EG. Identifying and avoiding bias in research. Plast Reconstr Surg. 2010;126(2):619–25. https://doi.org/10.1097/PRS.0b013e3181de24bc .

Petrie A, Sabin C. Medical statistics at a glance. 2nd ed. London: Blackwell Publishing; 2005.

Portney LG, Watkins MP. Foundations of clinical research: applications to practice. 3rd ed. New Jersey: Pearson Publishing; 2009.

Sheehan J. Aspects of research methodology. Nurse Educ Today. 1986;6:193–203.

Wilson LA, Black DA. Health, science research and research methods. Sydney: McGraw Hill; 2013.

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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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3.2 Quantitative Research Designs

Quantitive research study designs can be broadly classified into two main groups (observational and experimental) depending on if an intervention is assigned. If an intervention is assigned, then an experimental study design will be considered; however, if no intervention is planned or assigned, then an observational study will be conducted. 3 These broad classes are further subdivided into specific study designs, as shown in Figure 3.1. In practice, quantitative studies usually begin simply as descriptive studies, which could subsequently be progressed to more complex analytic studies and then to experimental studies where appropriate.

Observational studies

Observational studies are research designs that involve observing and measuring the characteristics of a sample or population without intervening, altering or manipulating any variables (Figure 3.1). 3 Observational studies can be further subdivided into descriptive and analytic studies. 3

Descriptive observational studies

Descriptive studies are research designs that describe or measure the characteristics of a specific population or phenomenon. These characteristics include descriptions related to the phenomenon under investigation, the people involved, the place, and the time. 4 These study designs are typically non-experimental and do not involve manipulating variables; rather, they rely on the collection and analysis of numerical data to draw conclusions. Examples of descriptive studies include case reports, case series, ecological studies and cross-sectional (prevalence studies). 2 These are discussed below

• Case Reports and Case series

Case reports and case series are both types of descriptive studies in research. A case report is a detailed account of the medical history, diagnosis, treatment, and outcome of a single patient. 5 On the other hand, case series is a collection of cases with similar clinical features. 5 Case series are frequently used to explain the natural history of a disease, the clinical characteristics, and the health outcomes for a group of patients who underwent a certain treatment. Case series typically involve a larger number of patients than case reports. 5 Both case reports and case series are used to illustrate unusual or atypical features found in patients in practice. 5 In a typical, real-world clinical situation, they are both used to describe the clinical characteristics and outcomes of individual patients or a group of patients with a particular condition. These studies have the potential to generate new research questions and ideas. 5 However, there are drawbacks to both case reports and case series, such as the absence of control groups and the potential for bias. Yet, they can be useful sources of clinical data, particularly when researching uncommon or recently discovered illnesses. 5 An example of a case report is the study by van Tulleken, Tipton and Haper, 2018 which showed that open-water swimming was used as a treatment for major depressive disorder for a 24-year-old female patient. 6 Weekly open (cold) water swimming was trialled, leading to an immediate improvement in mood following each swim. A sustained and gradual reduction in symptoms of depression, and consequently a reduction in, and cessation of, medication was observed. 6 An example of a case series is the article by Chen et al , 2020  which described the epidemiology and clinical characteristics of COVID-19 infection among 12 confirmed cases in Jilin Province, China. 7

• Ecological studies

Ecological studies examine the relationship between exposure and outcome at the population level. Unlike other epidemiological studies focusing on individual-level data, ecological studies use aggregate data to investigate the relationship between exposure and outcome of interest. 8 In ecological studies, data on prevalence and the degree of exposure to a given risk factor within a population are typically collected and analysed to see if exposure and results are related. 8 Ecological studies shed light on the total burden of disease or health-related events within a population and assist in the identification of potential risk factors that might increase the incidence of disease/event. However,  these studies cannot prove causation or take into account characteristics at the individual level that can influence the connection between exposure and result. This implies that ecological findings cannot be interpreted and extrapolated to individuals. 9 For example, the association between urbanisation and Type 2 Diabetes was investigated at the country level, and the role of intermediate variables (physical inactivity, sugar consumption and obesity) was examined. One of the key findings of the study showed that in high-income countries (HIC), physical inactivity and obesity were the main determinants of T2D prevalence. 10 However, it will be wrong to infer that people who are physically inactive and obese in HIC have a higher risk of T2D.

• Cross-sectional Descriptive (Prevalence) studies

A cross-sectional study is an observational study in which the researcher collects data on a group of participants at a single point in time. 11 The goal is to describe the characteristics of the group or to explore relationships between variables. Cross-sectional studies can be either descriptive or analytical (Figure 3.2). 11 Descriptive cross-sectional studies are also known as prevalence studies measuring the proportions of health events or conditions in a given population. 11 Although analytical cross-sectional studies also measure prevalence, however, the relationship between the outcomes and other variables, such as risk factors, is also assessed. 12 The main strength of cross-sectional studies is that they are quick and cost-effective. However, they cannot establish causality and may be vulnerable to bias and confounding ( these concepts will be discussed further later in this chapter under “avoiding error in quantitative research) .  An example of a cross-sectional study is the study by Kim et al., 2020 which examined burnout and job stress among physical and occupational therapists in various Korean hospital settings. 13 Findings of the study showed that burnout and work-related stress differed significantly based on several factors, with hospital size, gender, and age as the main contributory factors. The more vulnerable group consisted of female therapists in their 20s at small- or medium-sized hospitals with lower scores for quality of life. 13

Analytical Observational studies

Analytical observational studies aim to establish an association between exposure and outcome and identify causes of disease (causal relationship). 14 Analytical observational studies include analytical cross-sectional ( discussed above ), case-control and cohort studies. 14 This research method could be prospective(cohort study) or retrospective (case-control study), depending on the direction of the enquiry. 14

• Case-control studies

A case-control study is a retrospective study in which the researcher compares a group of individuals with a specific outcome (cases) to a group of individuals without that outcome (controls) to identify factors associated with the outcome. 15 As shown in Figure 3.3 below, the cases and controls are recruited and asked questions retrospectively (going back in time) about possible risk factors for the outcome under investigation.  A case-control study is relatively efficient in terms of time, money and effort, suited for rare diseases or outcomes with a long latent period, and can examine multiple risk factors. 15 For example, before the cause of lung cancer, was established, a case-control study was conducted by British researchers Richard Doll and Bradford Hill in 1950. 16 Subjects with lung cancer were compared with those who did not have lung cancer, and details about their smoking habits were obtained. 16 The findings from this initial study showed that cancer patients were more frequent and heavy smokers. 16 Over the years, more evidence has been generated implicating tobacco as a significant cause of lung cancer. 17, 18 Case-control studies are, therefore, useful for examining rare outcomes and can be conducted more quickly and with fewer resources than other study designs. Nonetheless, it should be noted that case-control studies are susceptible to bias in selecting cases and controls and may not be representative of the overall population. 15

• Cohort Study

Cohort studies are longitudinal studies in which the researcher follows a group of individuals who share a common characteristic (e.g., age, occupation) over time to monitor the occurrence of a particular health outcome. 19 The study begins with the selection of a group of individuals who are initially free of the disease or health outcome of interest (the “cohort”). The cohort is then divided into two or more groups based on their level of exposure (for example, those who have been exposed to a certain risk factor and those who have not). 19 Participants are then followed up, and their health outcomes are tracked over time. The incidence of the health outcome is compared between exposed and non-exposed groups, and the relationship between exposure and the outcome is quantified using statistical methods. 19 Cohort studies can be prospective or retrospective (Figure 3.4). 20 In a prospective cohort study, the researchers plan the study so that participants are enrolled at the start of the study and followed over time. 20, 21 In a retrospective cohort study, data on exposure and outcome are collected from existing records or databases. The researchers go back in time (via available records) to find a cohort that was initially healthy and “at risk” and assess each participant’s exposure status at the start of the observation period. 20, 21 Cohort studies provide an understanding of disease risk factors based on findings in thousands of individuals over many years and are the foundation of epidemiological research. 19 They are useful for investigating the natural history of a disease, identifying risk factors for a disease, providing strong evidence for causality and estimating the incidence of a disease or health outcome in a population. However, they can be expensive and time-consuming to conduct. 15 An example of a cohort study is the study by Watts et al, 2015 which investigated whether the communication and language skills of children who have a history of stuttering are different from children who do not have a history of stuttering at ages 2–5 years. 22 The findings revealed that children with a history of stuttering, as a group, demonstrated higher scores on early communication and language measures compared to their fluent peers. According to the authors, clinicians can be reassured by the finding that, on average, children who stutter have early communication and language skills that meet developmental expectations. 22

Experimental Study Designs (Interventional studies)

Experimental studies involve manipulating one or more variables in order to measure their effects on one or more outcomes. 23 In this type of study, the researcher assigns individuals to two or more groups that receive or do not receive the intervention. Well-designed and conducted interventional studies are used to establish cause-and-effect relationships between variables. 23  Experimental studies can be broadly classified into two – randomised controlled trials and non-randomised controlled trials. 23 These study designs are discussed below:

• Randomised Controlled Trial

Randomised controlled trials (RCTs) are experimental studies in which participants are randomly assigned to the intervention or control arm of the study. 23 The experimental group receives the intervention, while the control group does not (Figure 3.5). RCTs involve random allocation (not by choice of the participants or investigators) of participants to a control or intervention group (Figure 3.5). 24   Randomization or random allocation minimises bias and offers a rigorous method to analyse cause-and-effect links between an intervention and outcome. 24 Randomization balances participant characteristics (both observed and unobserved) between the groups. 24 This is so that any differences in results can be attributed to the research intervention. 24 The most basic form of randomisation is allocating treatment by tossing a coin. Other methods include using statistical software to generate random number tables and assigning participants by simple randomisation or allocating them sequentially using numbered opaque envelopes containing treatment information. 25 This is why RCTs are often considered the gold standard in research methodology. 24 While RCTs are effective in establishing causality, they are not without limitations. RCTs are expensive to conduct and time-consuming. In addition, ethical considerations may limit the types of interventions that can be tested in RCTs. They may also not be appropriate for rare events or diseases and may not always reflect real-world situations, limiting their application in clinical practice. 24   An example of a randomised controlled trial is the study by Shebib et al., 2019 which investigated the effect of a 12-week digital care program (DCP) on improving lower-back pain. The treatment group (DCP) received the 12-week DCP, consisting of sensor-guided exercise therapy, education, cognitive behavioural therapy, team and individual behavioural coaching, activity tracking, and symptom tracking – all administered remotely via an app. 26 While the control group received three digital education articles only. The findings of the study showed that the DCP resulted in improved health outcomes compared to treatment-as-usual and has the potential to scale personalised evidence-based non-invasive treatment for patients with lower-back pain. 26

• Non-randomised controlled design (Quasi-experimental)

Non-randomised controlled trial (non-RCT) designs are used where randomisation is impossible or difficult to achieve. This type of study design requires allocation of the exposure/intervention by the researcher. 23 In some clinical settings, it is impossible to randomise or blind participants. In such cases, non-randomised designs are employed. 27 Examples include pre-posttest design (with or without controls) and interrupted time series. 27, 28 For the pre-posttest design that involves a control group, participants (subjects) are allocated to intervention or control groups (without randomisation) by the researcher. 28 On the other hand, it could be a single pre-posttest design study where all subjects are assessed at baseline, the intervention is given, and the subjects are re-assessed post-intervention. 28 An example of this type of study was reported by Lamont and Brunero (2018 ), who examined the effect of a workplace violence training program for generalist nurses in the acute hospital setting. The authors found a statistically significant increase in behaviour intention scores and overall confidence in coping with patient aggression post-test. 29 Another type of non-RCT study is the interrupted time series (ITS) in which data are gathered before and after intervention at various evenly spaced time points (such as weekly, monthly, or yearly). 30 Thus, it is crucial to take note of the precise moment an intervention occurred. The primary goal of an interrupted time series is to determine whether the data pattern observed post-intervention differs from that noted prior. 30 Several ITS were conducted to investigate the effectiveness of the different prevention strategies (such as lockdown and border closure) used during the COVID pandemic. 31, 32 Although non-RCT may be more feasible to RCTs, they are more prone to bias than RCTs due to the lack of randomisation and may not be able to control for all the variables that might affect the outcome. 23

Hierarchy of Evidence

While each study design has its unique characteristics and strengths, they are not without weaknesses (as already discussed) that impact the accuracy of the results and research evidence they provide. The hierarchy of evidence is a framework used to rank the evidence provided by different study designs in research evaluating healthcare interventions with respect to the strength of the presented results (i.e., validity and reliability of the findings). 33 Study designs can be ranked in terms of their ability to provide valid evidence on the effectiveness (intervention achieves the intended outcomes), appropriateness (impact of the intervention from the perspective of its recipient) and feasibility (intervention is implementable) of the research results they provide. 33 As shown in Figure 3.6, meta-analyses, systematic reviews, and RCTs provide stronger best-practice evidence and scientific base for clinical practice than descriptive studies as well as case reports and case series. Nonetheless, it is important to note that the research question/ hypothesis determines the study design, and not all questions can be answered using an interventional design. In addition, there are other factors that need to be considered when choosing a study design, such as funding, time constraints, and ethical considerations, and these factors are discussed in detail in chapter 6.

An Introduction to Research Methods for Undergraduate Health Profession Students Copyright © 2023 by Faith Alele and Bunmi Malau-Aduli is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.

Quantitative Research Design: Four Common Ways to Collect Your Data Efficiently

What is the quantitative research design? Why is the research design needed? What are the four main types of quantitative research designs used by researchers?

This article illustrates research design using an analogy, explains why a research design is needed, describes four main types of research designs, and gives examples of each research design’s application.

In doing your research on whatever goals you have in mind, you make a plan to reach those goals. You spell out the specific items that you want to pursue in your research objectives.

An Analogy of Research Design

Researching to reach a predetermined goal is like building a house. To avoid costly rebuilding, it would be a good idea to make a plan first and consider all the requirements to produce one that appeals to your taste.

You need to engage the architect’s help to draw what you have in mind (your concept), estimate the cost to build it, and list the steps to follow to bring that plan into reality. The architect comes up with a blueprint of the house, detailing the size and quantity of reinforced steel bars, the floor plan, dimensions of the house, and aesthetics.

If your house comprises not only one floor but two, or even three, and you want the house to be sturdy, that could last decades or generations; you will need to engage a structural engineer. He makes sure that the home maintains its integrity and can handle the loads and forces they encounter through time.

And, of course, the electrical connections require the expertise of an electrical engineer. He plans how the electrical circuits are arranged in the entire house to make it convenient for you to access electricity.

To build your dream house, you will need to have a good plan–your design.

Why is a Research Design Needed?

As pointed out earlier, the main reasons for coming up with a research design relate to efficiency and effectiveness. If you have a good research design, you will save time, energy, and cost in doing your research. You have a plan to get the data that you want to answer the research objectives.

Thus, before conducting research, you already have in mind what to expect. And of course, you will know how much that would cost you. If you cannot afford it, then you revise your plan.

Defining the Research Objectives

However, your research design or plan cannot be carried out if you don’t have a clear idea about what you want. The architect cannot design a project based on a simple directive to make a house plan. The outcome may not be to your liking, and you will just be wasting your money and his time. It will be a hit-and-miss approach.

Thus, you will need to define your research objectives based on your topic of interest. What do you want to achieve in your research? Will you be dealing with people, animals, plants, or things?

Will you manipulate some variables? Will you compare different groups? Would you want to know which  variable  causes an effect on other variables? Or will you describe what is there?

It all boils down to  what you want . Be very clear if you’re going to describe things, correlate them, find out if one causes the other, or put up an experiment to test if manipulating one variable can effect a change to another variable.

Now, here are the four quantitative research designs.

The Four Main Types of Quantitative Research Design

Experts classify quantitative research design into four types. These are descriptive, correlational, causal-comparative, and experimental research.

The four quantitative research designs are distinguished from each other in Figure 1. Please note that as you go from left to right; the approach becomes more manipulative. The descriptive research design studies the existing situation, whereas the researcher manipulates variables at the other end, using the experimental method.

Quantitative research design examples are given for each of the four quantitative research designs in the next section.

Examples of the Application of the Different Research Design on the Same Subject

Descriptive research design.

A willingness to pay (WTP) study aimed to determine the vehicle owner’s knowledge about air quality and attitude towards the government’s regulation of requiring emission testing every time the car’s registration is renewed. This investigation will provide information that will show how knowledgeable the respondents are about air quality and reveal patterns of behavior towards the government’s measures to control carbon emissions. It explores the drivers’ willingness to pay for vehicle maintenance costs.

Correlational Research Design

The same study on air quality may be conducted as in Example 1, but this time, the respondent’s awareness about air quality is correlated with their attitude towards emission testing.

The study by Amindrad et al. (2013) on the Relationship Between Awareness, Knowledge and Attitudes Towards Environmental Education Among Secondary School Students in Malaysia exemplifies this research design.

Causal-Comparative Research Design

Still, on the air quality study, you might want to know what causes the respondents to behave positively or negatively towards emission testing. Does attitude have something to do with a person’s educational background? Or perhaps, their capacity to pay for emission testing?

The following video explains this research design further with two examples.

Experimental Research Design

Using still the air quality study, you might now want to test if two groups of drivers behaved differently when one group was required to attend a seminar on air pollution, and the other group was not required to attend.

The two groups’ members were randomly assigned, and all other variables were kept constant, meaning the respondents have similar characteristics where only attendance at the seminar is the difference.

You are interested in finding the difference between a person’s attitude towards emission testing. And what discriminates them from the other is that one group attended a seminar on air pollution while the other group did not.

Final Notes

Note that those listed are not mutually exclusive research designs. We can combine them.

For example, you can undertake a study that uses both a descriptive and a correlational research design. Hence, you describe this approach in your methodology as a descriptive-correlational research design.

That wraps it up.

There are still other types of research designs out there. What is important here is that you are clear about what you want to investigate.

Aminrad, Z., Zakariya, S. Z. B. S., Hadi, A. S., & Sakari, M. (2013). Relationship between awareness, knowledge and attitudes towards environmental education among secondary school students in Malaysia. World Applied Sciences Journal, 22(9), 1326-1333.

© 2020 October 15 P. A. Regoniel, updated 27 November 2021

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Dr. Regoniel, a faculty member of the graduate school, served as consultant to various environmental research and development projects covering issues and concerns on climate change, coral reef resources and management, economic valuation of environmental and natural resources, mining, and waste management and pollution. He has extensive experience on applied statistics, systems modelling and analysis, an avid practitioner of LaTeX, and a multidisciplinary web developer. He leverages pioneering AI-powered content creation tools to produce unique and comprehensive articles in this website.

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2 Types of Quantitative Research Designs

There are three main groups of Research Designs that will be explored in this chapter.

• Experimental
• Quasi-experimental
• Non-experimental

When reviewing each design, the purpose and key features of the design, advantages and disadvantages, and the most commonly used designs within the category will be reviewed.

1. Experimental Design 

Purpose:  Evaluate outcomes in terms of efficacy and/or cost effectiveness

Experimental design features include: 

• Randomization of subjects to groups
• Manipulation of independent variable (e.g., an intervention or treatment)
• ​​Control – the use of a control group and control measures (for controlling extraneous variables )​

Advantages:   

• Most appropriate for testing cause-and-effect relationships (e.g., generalizability is most likely)
• Provides the highest level of evidence (e.g., level II) for single studies

Disadvantages: 

• Attrition especially control group participants or with ‘before-after’ experimental designs
• Feasibility and logistics may be an issue is certain settings (e.g., long-term care homes)

Caution: Not all research questions are amenable to experimental manipulation or randomization

Most Commonly Used Experimental Designs

• True experimental (pre- post-test ) design (also referred to as Randomized Control Trials or RCTs ):

Figure 3. True experimental design (pre-post-test).

• After-only (post-test only) design :

Figure 4. After-only (post-test only) design.

• Solomon four-group design

This design is similar to the true experimental design but has an additional two groups, for a total of four groups. Two groups are experimental, while two groups are control. These “extra” groups do not receive the pre-test, allowing the researchers to evaluate the effect of the pretest on the post-test in the first two groups.

2. Quasi-Experimental Design

Purpose: Similar to experimental design, but used when not all the features of an experimental design can be met:

• Manipulation of the independent variable (e.g., an intervention or treatment)
• Experimental and control groups may not be randomly assigned (no randomization)
• There may or may not be a control group

Advantages: 

• Feasibility and logistics are enhanced, particularly in clinical settings
• Offers some degree of generalizability (e.g., applicable to population of interest)
• May be more adaptable in real-world practice environments

Disadvantages:   

• Generally weaker than experimental designs because groups may not be equal with respect to extraneous variable due to the lack of randomization
• As a result, cause-and-effect relationships are difficult to claim

Options for Quasi-experimental Designs include :

• Non-equivalent control group design 

Figure 5. Classical Quasi-Experimental Design. Adapted from Knowledge for Health

• After-only control group design

Figure 6. Post-Test Only Quasi-Experimental Design. Adapted from Knowledge for Health.

• Time-series design Important note: The time series design is considered quasi-experimental because subjects serve as their ‘own controls’ (same group of people, compared before and after the intervention for changes over time). 

Figure 7. Time-series design. Adapted from Knowledge for Health

• One group pre-test-post-design design In this design there is no control group. The one group, considered the experimental group, is tested pre and post the intervention. The design is still considered quasi-experimental as there is manipulation of the intervention.

3. Non-experimental

Purpose: When the problem to be solved or examined is not amenable to experimentation; used when the researcher wants to:

• Study a phenomenon at one point in time or over a period of time
• Study (and measure) variables as they naturally occur
• Test relationships and differences among variables
• Used when the knowledge base on a phenomenon of interest is limited or when the research question is broad or exploratory in nature
• Appropriate for forecasting or making predictions
• Useful when the features of an experiment (e.g., randomization, control, and manipulation) are not appropriate or possible (e.g., ethical issues)
• Inability to claim cause-and-effect relationships

Options for Non-experimental Designs include:

• Survey studies: descriptive, exploratory, comparative
• Relationship or difference studies: Correlational, developmental
• Cross-sectional studies
• Longitudinal or Prospective studies

Figure 8. Longitudinal or Prospective studies. Adapted from University of Minnesota, Driven for Discover Libraries .

• Retrospective ( Ex Post Facto ) studies

Additional terms to consider when reading research

Learners may find it difficult when reading research to identify the Research Design used. Please consult the table below for more information on terms frequently used in research.

This refers to how the sample is selected. When randomization is used each participant from the desired population has an equal chance of being assigned to the experimental or control group.

These are variable that may interfere with the independent and dependent variables. Also called mediating variables.

The loss of participants from the study.

An Introduction to Quantitative Research Design for Students in Health Sciences Copyright © 2024 by Amy Hallaran and Julie Gaudet is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

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Types Of Quantitative Research Designs And Methods

Quantitative research design uses a variety of empirical methods to assess a phenomenon. The most common method is the experiment,…

Quantitative research design uses a variety of empirical methods to assess a phenomenon. The most common method is the experiment, but there are other types of quantitative research as well, such as correlation studies and case studies.

In contrast with qualitative research, which relies on subjective interpretations and extensive explorations, the various types of quantitative methods use objective analysis to reveal patterns and relations among data points that often have a numerical value. Quantitative research provides a mathematical summary of the results.

Let’s look at quantitative research design, the types of quantitative research methods and their respective strengths and weaknesses.

Types Of Quantitative Research

Components of quantitative research design.

If a researcher is studying a single variable, time, space, or another construct, they’re engaged in qualitative research. However, if that variable is a collection of quantitative data points—such as the number of employees that use a workplace break room compared to the number of employees who use other break rooms—the researcher is engaged in quantitative research.

Here are some methods commonly used in quantitative research design:

1. Experiment

The experiment is perhaps the most common way for quantitative researchers to gather data. In this method, researchers manipulate one variable at a time, while they hold all other variables constant. If a researcher wishes to determine which type of computer mouse is easier for employees to use, they must ensure the employees are experienced with computers, comfortable with their chairs or desks and have no issues with their eyesight. Common methods for this type of research include randomized experiments, non-randomized experiments, clinical trials and field studies.

2. Correlation

Correlation studies come in many forms, from simple correlation diagrams to the analysis of multiple variables. For instance, a researcher examining rates of depression among veterinarians could look at associations between self-perceived social status, salary and depression.

3. Cohort Studies

Cohort studies provide a way to measure the extent of change over a period of time. This type of research can lead to results that are both objective and subjective, depending on the type of study employed. For instance, a cohort study examining police officer salaries could determine what salary a police officer should make in an area. However, this same study could also delve into the subjective question of whether police officers are fairly paid compared to other professions.

Research design is a critical factor in the success of a study.

While there are many types of quantitative research methods that can be employed, the basic parts of all research designs are the same. Here are the principal components:

At the heart of every research project is a well-framed and considered question. Having a clear objective is the most important part of quantitative research design. Some examples of research questions could be:

• Which type of coffee brewing method extracts the most flavor?
• Which books are contributing most to a publisher’s profit?
• Which newspaper is the most widely read in a city?

In quantitative research design, researchers may explore the relationship between variables in a correlation study, or it could mean determining what variables are best in an experiment.

Once the aim is in place, the actual data collection method must be chosen. This will depend on the data needed to answer the research question. Some options are:

• Participant observations
• Experimental data

As long as the data is expressed numerically, it is quantitative data.

The selection process used to choose participants is a critical component of all types of quantitative research designs. Researchers need a well-defined population. This group can be as small as two people, but it could also be thousands of people as well.

Data Analysis

Once the data is collated, a researcher must decide how to analyze it. Some options at their disposal include:

• Descriptive analysis
• Content analysis
• Statistical tests

Once again, it depends on the research question and the goals of the study.

Presentation

This is sometimes referred to as dissemination. How will the research findings be shared with the world? Common choices are:

• Presentations
• Website articles and blogs

A quantitative researcher’s greatest contribution is that their work can be replicated. Because quantitative research relies on numbers, the results of the study can be exactly duplicated by other researchers.

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

Research design refers to the overall strategy or plan for conducting a research study. It outlines the methods and procedures that will be used to collect and analyze data, as well as the goals and objectives of the study. Research design is important because it guides the entire research process and ensures that the study is conducted in a systematic and rigorous manner.

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

This type of research design is used to describe a phenomenon or situation. It involves collecting data through surveys, questionnaires, interviews, and observations. The aim of descriptive research is to provide an accurate and detailed portrayal of a particular group, event, or situation. It can be useful in identifying patterns, trends, and relationships in the data.

Correlational Research Design

Correlational research design is used to determine if there is a relationship between two or more variables. This type of research design involves collecting data from participants and analyzing the relationship between the variables using statistical methods. The aim of correlational research is to identify the strength and direction of the relationship between the variables.

Experimental Research Design

Experimental research design is used to investigate cause-and-effect relationships between variables. This type of research design involves manipulating one variable and measuring the effect on another variable. It usually involves randomly assigning participants to groups and manipulating an independent variable to determine its effect on a dependent variable. The aim of experimental research is to establish causality.

Quasi-experimental Research Design

Quasi-experimental research design is similar to experimental research design, but it lacks one or more of the features of a true experiment. For example, there may not be random assignment to groups or a control group. This type of research design is used when it is not feasible or ethical to conduct a true experiment.

Case Study Research Design

Case study research design is used to investigate a single case or a small number of cases in depth. It involves collecting data through various methods, such as interviews, observations, and document analysis. The aim of case study research is to provide an in-depth understanding of a particular case or situation.

Longitudinal Research Design

Longitudinal research design is used to study changes in a particular phenomenon over time. It involves collecting data at multiple time points and analyzing the changes that occur. The aim of longitudinal research is to provide insights into the development, growth, or decline of a particular phenomenon over time.

Structure of Research Design

The format of a research design typically includes the following sections:

• Introduction : This section provides an overview of the research problem, the research questions, and the importance of the study. It also includes a brief literature review that summarizes previous research on the topic and identifies gaps in the existing knowledge.
• Research Questions or Hypotheses: This section identifies the specific research questions or hypotheses that the study will address. These questions should be clear, specific, and testable.
• Research Methods : This section describes the methods that will be used to collect and analyze data. It includes details about the study design, the sampling strategy, the data collection instruments, and the data analysis techniques.
• Data Collection: This section describes how the data will be collected, including the sample size, data collection procedures, and any ethical considerations.
• Data Analysis: This section describes how the data will be analyzed, including the statistical techniques that will be used to test the research questions or hypotheses.
• Results : This section presents the findings of the study, including descriptive statistics and statistical tests.
• Discussion and Conclusion : This section summarizes the key findings of the study, interprets the results, and discusses the implications of the findings. It also includes recommendations for future research.
• References : This section lists the sources cited in the research design.

Example of Research Design

An Example of Research Design could be:

Research question: Does the use of social media affect the academic performance of high school students?

Research design:

• Research approach : The research approach will be quantitative as it involves collecting numerical data to test the hypothesis.
• Research design : The research design will be a quasi-experimental design, with a pretest-posttest control group design.
• Sample : The sample will be 200 high school students from two schools, with 100 students in the experimental group and 100 students in the control group.
• Data collection : The data will be collected through surveys administered to the students at the beginning and end of the academic year. The surveys will include questions about their social media usage and academic performance.
• Data analysis : The data collected will be analyzed using statistical software. The mean scores of the experimental and control groups will be compared to determine whether there is a significant difference in academic performance between the two groups.
• Limitations : The limitations of the study will be acknowledged, including the fact that social media usage can vary greatly among individuals, and the study only focuses on two schools, which may not be representative of the entire population.
• Ethical considerations: Ethical considerations will be taken into account, such as obtaining informed consent from the participants and ensuring their anonymity and confidentiality.

How to Write Research Design

Writing a research design involves planning and outlining the methodology and approach that will be used to answer a research question or hypothesis. Here are some steps to help you write a research design:

• Define the research question or hypothesis : Before beginning your research design, you should clearly define your research question or hypothesis. This will guide your research design and help you select appropriate methods.
• Select a research design: There are many different research designs to choose from, including experimental, survey, case study, and qualitative designs. Choose a design that best fits your research question and objectives.
• Develop a sampling plan : If your research involves collecting data from a sample, you will need to develop a sampling plan. This should outline how you will select participants and how many participants you will include.
• Define variables: Clearly define the variables you will be measuring or manipulating in your study. This will help ensure that your results are meaningful and relevant to your research question.
• Choose data collection methods : Decide on the data collection methods you will use to gather information. This may include surveys, interviews, observations, experiments, or secondary data sources.
• Create a data analysis plan: Develop a plan for analyzing your data, including the statistical or qualitative techniques you will use.
• Consider ethical concerns : Finally, be sure to consider any ethical concerns related to your research, such as participant confidentiality or potential harm.

When to Write Research Design

Research design should be written before conducting any research study. It is an important planning phase that outlines the research methodology, data collection methods, and data analysis techniques that will be used to investigate a research question or problem. The research design helps to ensure that the research is conducted in a systematic and logical manner, and that the data collected is relevant and reliable.

Ideally, the research design should be developed as early as possible in the research process, before any data is collected. This allows the researcher to carefully consider the research question, identify the most appropriate research methodology, and plan the data collection and analysis procedures in advance. By doing so, the research can be conducted in a more efficient and effective manner, and the results are more likely to be valid and reliable.

Purpose of Research Design

The purpose of research design is to plan and structure a research study in a way that enables the researcher to achieve the desired research goals with accuracy, validity, and reliability. Research design is the blueprint or the framework for conducting a study that outlines the methods, procedures, techniques, and tools for data collection and analysis.

Some of the key purposes of research design include:

• Providing a clear and concise plan of action for the research study.
• Ensuring that the research is conducted ethically and with rigor.
• Maximizing the accuracy and reliability of the research findings.
• Minimizing the possibility of errors, biases, or confounding variables.
• Ensuring that the research is feasible, practical, and cost-effective.
• Determining the appropriate research methodology to answer the research question(s).
• Identifying the sample size, sampling method, and data collection techniques.
• Determining the data analysis method and statistical tests to be used.
• Facilitating the replication of the study by other researchers.
• Enhancing the validity and generalizability of the research findings.

Applications of Research Design

There are numerous applications of research design in various fields, some of which are:

• Social sciences: In fields such as psychology, sociology, and anthropology, research design is used to investigate human behavior and social phenomena. Researchers use various research designs, such as experimental, quasi-experimental, and correlational designs, to study different aspects of social behavior.
• Education : Research design is essential in the field of education to investigate the effectiveness of different teaching methods and learning strategies. Researchers use various designs such as experimental, quasi-experimental, and case study designs to understand how students learn and how to improve teaching practices.
• Health sciences : In the health sciences, research design is used to investigate the causes, prevention, and treatment of diseases. Researchers use various designs, such as randomized controlled trials, cohort studies, and case-control studies, to study different aspects of health and healthcare.
• Business : Research design is used in the field of business to investigate consumer behavior, marketing strategies, and the impact of different business practices. Researchers use various designs, such as survey research, experimental research, and case studies, to study different aspects of the business world.
• Engineering : In the field of engineering, research design is used to investigate the development and implementation of new technologies. Researchers use various designs, such as experimental research and case studies, to study the effectiveness of new technologies and to identify areas for improvement.

Advantages of Research Design

Here are some advantages of research design:

• Systematic and organized approach : A well-designed research plan ensures that the research is conducted in a systematic and organized manner, which makes it easier to manage and analyze the data.
• Clear objectives: The research design helps to clarify the objectives of the study, which makes it easier to identify the variables that need to be measured, and the methods that need to be used to collect and analyze data.
• Minimizes bias: A well-designed research plan minimizes the chances of bias, by ensuring that the data is collected and analyzed objectively, and that the results are not influenced by the researcher’s personal biases or preferences.
• Efficient use of resources: A well-designed research plan helps to ensure that the resources (time, money, and personnel) are used efficiently and effectively, by focusing on the most important variables and methods.
• Replicability: A well-designed research plan makes it easier for other researchers to replicate the study, which enhances the credibility and reliability of the findings.
• Validity: A well-designed research plan helps to ensure that the findings are valid, by ensuring that the methods used to collect and analyze data are appropriate for the research question.
• Generalizability : A well-designed research plan helps to ensure that the findings can be generalized to other populations, settings, or situations, which increases the external validity of the study.

Research Design Vs Research Methodology

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Published: 07 November 2023

Social virtual reality helps to reduce feelings of loneliness and social anxiety during the Covid-19 pandemic

• Keith Kenyon   ORCID: orcid.org/0000-0002-5084-9024 1 ,
• Vitalia Kinakh 2 &
• Jacqui Harrison 1  

Scientific Reports volume  13 , Article number:  19282 ( 2023 ) Cite this article

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• Human behaviour
• Quality of life

Evidence shows that the Covid-19 pandemic caused increased loneliness, anxiety and greater social isolation due to social distancing policies. Virtual reality (VR) provides users with an easy way to become engaged in social activities without leaving the house. This study focused on adults, who were socialising in Altspace VR, a social VR platform, during the Covid-19 pandemic and it explored whether social VR could alleviate feelings of loneliness and social anxiety. A mixed-methods research design was applied. Participants (n = 74), aged 18–75, completed a questionnaire inside the social VR platform to measure levels of loneliness (UCLA 20-item scale) and social anxiety (17-item SPIN scale) in the social VR platform (online condition) and real world (offline condition). Subsequently, a focus group (n = 9) was conducted to gather insights into how and why participants were using the social VR platform. Findings from the questionnaire revealed significantly lower levels of loneliness and social anxiety when in the social VR platform. Lower levels of loneliness and social anxiety were also associated with participants who socialised with a regular group of friends. In addition, findings from the focus group suggested that being part of an online group facilitates stronger feelings of belonging. Social VR can be used as a valuable intervention to reduce feelings of loneliness and social anxiety. Future studies should continue to establish whether social VR can help to encourage group formation and provide people with enhanced social opportunities beyond the COVID-19 pandemic.

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Introduction.

On the 11th March 2020 the World Health Organisation declared the rapidly spreading Corona virus outbreak a pandemic 1 and world governments began to impose enforced social isolation rules. Throughout 2020/2021 the majority of countries imposed lengthy periods of lockdown. The first UK lockdown lasted almost 4 months and during this time only essential travel was permitted and interaction with others from outside the direct household was forbidden 2 . The lock-down caused disruption to daily routines, social activities, education and work. Social distancing measures led to a collapse in social contact. When people experience a reduction in social contact or when the quality of interaction with others is diminished, they can suffer feelings of loneliness. Nearly 7.5 million adults experienced "lockdown loneliness," which is the equivalent to around 14% of the population. 3 Additionally, the percentage of the UK population reporting loneliness increased from 10% in March 2020 to 26% in February 2021 4 .

Social isolation and loneliness

Social isolation and loneliness are different. Social isolation is commonly defined as “the state in which the individual or group expresses a need or desire for contact with others but is unable to make that contact” 5 , p. 731 . Social isolation can occur due to quarantine or physical separation. Due to quarantine measures enforced during lockdown, people faced involuntary social isolation or at least a reduction in their social interactions to the point that their social network was quantitatively diminished 6 . Loneliness is a subjective experience that arises when a person feels that they are isolated and deprived of companionship, lack a sense of belonging, or that their social interactions with others are diminished in either quantity or quality 7 .

Social isolation, loneliness and detrimental implications for physical and mental health

The rise of loneliness during lockdown also increased the prevalence of anxiety 3 and such health problems as depressive symptoms and insomnia, reconfirming findings from earlier research 8 that explored the relationship between social isolation and loneliness and the effect it has on our physical and mental health. Loneliness can lead to stress and high blood pressure, a sedentary or less active lifestyle, and a reduction in cognitive function 9 , 10 , 11 . Loneliness can also lead to less healthy behaviours e.g. an increase in alcohol consumption and smoking 12 , a poor diet 13 and poor sleeping patterns 14 . Loneliness has been found to have an impact on a person’s social wellbeing leading to feelings of low self-esteem and worthlessness as well as increased anxiety and decreased levels of happiness, resulting in depression 11 , 15 , 16 , 17 .

Technology-based interventions to reduce social isolation and loneliness

Within the last decade several systematic reviews have focused on technology-based interventions for people who are experiencing or who are at risk of experiencing loneliness and social isolation 18 , 19 , 20 , 21 . Masi et al. 18 in their meta-analysis, explored the efficacy of technology-based vs non-technology-based interventions across all population groups, notably, the mean size effect for technology-based interventions was − 1.04 (N = 6; 95% CI  − 1.68, − 0.40; p  < 0.01), as opposed to − 0.21 (N = 12; 95% CI  − 0.43, 0.01; p = 0.05) for non-technology-based interventions. Choi et al. 19 reported a significant pooled reduction in loneliness in older adults after implementing technology-based interventions (Z = 2.085, p  = 0.037). Early technology-based interventions consisted of conference calls/video conferencing, text-based Inter Relay Chat and Emails 18 , 19 , 20 . Subsequent systematic reviews 21 , 22 found that video conferencing was able to reduce loneliness in older particpants, however, this technology only helped to facilitate communication between existing, rather than new contacts. These types of intervention are therefore less beneficial for individuals who are socially isolated and struggling to establish connections with others.

During the Covid-19 lockdowns there was no possibility to provide or continue providing face-to-face individual or group interventions for lonely people. Moreover, even non-lonely people found themselves in situations where they could not maintain their social relationships through face-to-face interactions. Thus, the Department of Primary Care and Public Health in England recommended that avenues for mitigating feelings of loneliness should look to include web- and smartphone-based interventions 23 .

Virtual reality (VR) using a head mounted display (HMD) is considered qualitatively different from other technologies in that it has the ability to provide a sensation of immersiveness or ‘being there’ 24 . VR technologies are becoming more accessible and comfortable with the creation of lighter more portable HMDs at a more affordable cost. This allows the technology to be used by a greater range of adults and members of vulnerable groups, e.g. adults with mobility impairments and older adults with age-related impairments. VR users, often represented as avatars, are able to meet and communicate in real-time with each other within a range of different scenarios. People are able to participate in social activities with new people, e.g. venturing off into new and exciting worlds (with nature scenes) 24 , travelling to different destinations around the world 25 , 26 without leaving their homes and escaping their confined realties or engaging in horticultural therapeutic interactions 27 . Older adults are able to engage in social networking activities, including playing games with other people and attending family events through VR, users spoke very positively and expressed visible signs of enjoyment about their experience 28 , 29 , 30 . Virtual gaming is very popular among younger users with 31 , 32 reporting that players experience significantly lower levels of loneliness and social anxiety when playing VR games compared within the real world.

Users taking part in VR interventions report being less socailly isolated, show less signs of depression, and demonstrate greater levels of overal well-being 24 , 25 , 26 , 27 , 33 , 34 . Widow(er)s in a VR support group showed a significant improvement during an 8-week intervention 35 . While both systematic reviews 33 , 34 reported useful insights regarding the positive impact of VR technology on loneliness, most studies on VR environments included a small number of participants from specific populations, thus the reported findings have limited generalisability.

When VR is used as an intervention to reduce social and public speaking anxiety, it is found to be most effective as a mode of delivery for alternative therapeutic interventions such as Acceptance and Commitment Therapy 36 . Furthermore, Kim et al. 37 found that patients with Social Anxiety Disorder (SAD) benefitted from the use of VR as an intervention, evidenced by short-term neuronal changes during exposure. They concluded that VR is useful as a first intervention for SAD patients who are unable to access formal treatment.

Various social VR platforms have emerged since 2013, e.g. VRChat, Altspace VR and RecRoom, however, the use of social VR as an intervention for reducing social isolation and loneliness is still a relatively new and unexplored field. Therefore, whilst there is research to support the effectiveness of VR as a tool to deliver therapeutic interventions and improve social well-being, there is limited research on the use of social VR as an online mechanism to decrease social isolation and improve group belonging.

Innovation and contributions of this study

The current study is a cross-sectional study of the general population, socially isolated during the Covid-19 pandemic and who were using social VR platforms to interact with each other. This study addresses the limitations of previous studies, which have focused exclusively on specific groups within the population, i.e. older adults or VR gamers, or explored general well-being rather that loneliness and social anxiety. In previous studies the HMDs were often provided by the research team, meaning that there was a time restrain (frequency or length) in relation to the use of the VR technology by participants. This study is novel as it explores the effects of loneliness and social anxiety on a wider demographic of people, who have unrestricted access to HMDs and have been socialising in Altspace VR during the Covid-19 pandemic. This study is of an international character and utilises a mixed methods approach to explore the benefits of social VR to help reduce feelings of loneliness and social anxiety and to provide additional means by which social contact can be enhanced for vulnerable populations who may remain isolated post-pandemic.

Research hypotheses

The following hypotheses were explored:

Lower levels of loneliness and social anxiety are experienced when participants are in the social VR platform (online) compared with in the real-world condition (offline).

Lower levels of loneliness and social anxiety are experienced by participants who are part of a group in social VR, i.e. members of a Virtual Social Group (VSG), than those who are not.

Lower levels of loneliness and social anxiety are experienced by participants who have a group of friends in the social VR in comparison with those who do not.

Lower levels of loneliness and social anxiety are experienced by participants who spend greater amounts of time in social VR.

The study used a convergent parallel mixed-methods research design 38 to collect both diverse quantitative and qualitative data (see Fig.  1 ). The study complied will relevant ethical regulations and was approved by the Research Ethics Committee of the University of Bolton, UK. Written informed consent was obtained from all participants.

A convergent parallel mixed-methods model of the current research.

Collection of quantitative data

Participants.

Participants were required to be English speaking, over the age of 18 and users of Altspace VR. A message of invitation was posted on different Discord community channels/message boards: Official Altspace VR; Educators In VR; Spatial Network; Humanism; Computer Science in VR; VR Church. 87 participants were recruited via an opportunity sampling method.

Materials and measures

A private research room was created inside Altspace VR to ensure that participants were able to complete the questionnaire undisturbed (see Fig.  5 ). The online questionnaire was created in Qualtrics XM and could be accessed across multiple devices: Oculus Quest, Oculus GO, Oculus Rift, HTC Vive and PC. The online questionnaire included sections about demographics, details of Altspace VR usage and sections assessing participant’s subjective feelings of loneliness and social anxiety. Measures of loneliness and social anxiety were collected for both conditions—real world (offline condition), followed by social VR (online condition).

The UCLA Loneliness Scale version 3 39 was used to measure the subjective level of loneliness. This 20-item self-reporting questionnaire uses a four-point Likert scale, with 0 = “Never”, 1 = “Rarely”, 2 = “Sometimes”, 3 = “Often”. The loneliness score for each participant (range from 0 to 60) was determined as the sum of responses to all 20 items—higher scores reflecting greater loneliness. The UCLA Loneliness scale was adapted to include the word Altspace in the online condition as it was felt that this would further help participants to focus specifically on the online experience. No further adaptations were made to this questionnaire. The Social Phobia Inventory (SPIN) scale 40 was used to measure the subjective level of social anxiety as it is effective in measuring the severity of social anxiety. This 17-item self-reporting questionnaire uses a five-point Likert scale, with 0 = “Not at all”, 1 = "A little”, 2 = “Somewhat”, 3 = “Very much”, 4 = “Extremely”. Adding the scores from each item produced a SPIN score for each participant. A higher SPIN score indicates more severe symptoms of social anxiety. No adaptations were made to the SPIN questionnaire.

Participants who were interested in taking part in the survey were taken to the research room inside Altspace VR where they were sent a message with a link to the online questionnaire. Participants who clicked on the link were then presented with a browser window inside the room that only they could see. Participants who opened the questionnaire were first presented with the participant information sheet giving full details of the study. Information regarding withdrawal from the study and a list of additional support services were also provided in line with the University of Bolton’s ethical guidelines. After reading the study information sheet, participants were presented with the consent form for which full consent was required before they were able to move onto the survey.

The strategy for dealing with incomplete cases was to remove any participants who did not answer all of the questions, thus analysis was conducted on 74 participants. Exported data from the Qualtrics system was imported into the Statistical Package for Social Sciences (IBM SPSS, version 25). A Kolmogorov–Smirnov test ( p  > 0.5) was carried out to test for a normal distribution and histograms, nominal Q-Q plots and box plots were used to identify any outliers. Two outliers were found in the data for Social Anxiety in the offline condition and these were replaced with the mean of 17.54 .

Characteristics of the sample

Of the total sample (n = 74), 46 were males and 28 females. The age range of respondents was 18–75 years (the split of valid participants is shown in Table 1 ). Participants were recruited globally (the geographical demographic is shown in Fig.  2 ). Out of these 74 participants, 31 participants (15 males, 16 females) were new to Altspace VR, having joined Altspace VR during the Covid-19 pandemic. 43 participants indicated that they had used Altspace VR before the outbreak of Covid-19.

Participant’s location.

Change in loneliness and social anxiety

Figure  3 shows the breakdown of social anxiety scores in both the online and offline conditions. The data shows that the severity of social anxiety is higher in the offline condition, whereas participant’s levels of anxiety reduce when they are online.

Participant’s SPIN Scores.

The UCLA loneliness scale uses continuous scoring and so it is not possible to provide a similar breakdown for participant’s levels of loneliness. The effect that social VR has on the participant will be discussed in greater detail later.

It was anticipated that during the Covid-19 pandemic and as a direct result of social distancing rules being imposed that general usage in Altspace VR would increase. Figure  4 shows that 76% of participants felt that their usage had increased and after calculating the average difference in usage (before and during Covid-19) an average increase per user of 11 h per week was reported.

Participants usage of Altspace VR since Covid-19.

Hypothesis 1

Hypothesis 1 predicted lower levels of loneliness and social anxiety are experienced when participants are in social VR (online) compared with in the real-world condition (offline) A paired-samples t-test was carried out to compare online (inside social VR) and offline (real-world) conditions for both loneliness and social anxiety. The results in Table 2 demonstrate a statistically significant decrease in the scores for loneliness from the offline condition (M = 20.53, SD = 14.80) to the online condition (M = 16.32, SD = 11.04), t  = − 2.573, p  < 0.05. A statistically significant decrease in social anxiety was found in the offline condition (M = 23.01, SD = 16.65) compared to the online condition (M = 16.34, SD = 13.09), t  = − 5.80, p  < 0.05. A small to moderate effect size 41 was found for both variables (i.e. d loneliness = 0.32 and d social anxiety = 0.45).

Hypotheses 2, 3 and 4

H2 predicted that lower levels of loneliness and social anxiety are experienced by participants who are part of a group in social VR than those who are not.

Being a member of a VSG means that the participant meets with a group or number of groups on a regular basis to take part in scheduled events, e.g. regular church services for members of VR Church; discussions around education each week for members of Educators in VR; mediation and relaxation sessions for members of the EvolVR group; and discussions on a whole range of matters relating to life in the Humanism group. 75.7% of participants (n = 56) indicated that they were a member of a VSG and 24.3% (n = 18) were not affiliated with any groups.

A one-way between participants ANOVA was carried out to compare the effect of being a member of a VSG separately for each of the dependent variables. No significant effect was found for loneliness in both the online condition F(1,72) = 0.17, p  = 0.68 and offline condition F(1,72) = 1.63, p  = 0.20. No significant effect was found for social anxiety in the online condition F(1,72) = 2.22, p  = 0.14, however, a significant effect was found for social anxiety in the offline condition F(1,72) = 4.23, p  < 0.05, η 2  = 0.06 (a medium effect size). This finding suggests that participants who are part of a VSG experience less social anxiety (M = 20.80, SD = 15.64) than those who are not (M = 29.89, SD = 18.26) when in the real world (offline) condition.

H3 predicted that lower levels of loneliness and social anxiety are experienced by participants who have a group of friends in social VR in comparison with those who do not. This differs from Hypothesis 2 in that having friends in Altspace VR is seen as a deeper connection than simply taking part in group events where connections may not have been formed. Participants were grouped on whether they have a circle of friends in social VR with whom they regularly socialise with (52.7%, n = 39) and not (47.3%, n = 35).

A one-way between participants ANOVA was carried out to compare the effect of having a circle of friends separately for each of the dependent variables. A significant effect was found for loneliness in the online condition F(1,72) = 6.75, p  < 0.05, η 2  = 0.08 (a medium effect size), whereas no significant effect was found for loneliness in the offline condition F(1,72) = 0.03, p  = 0.86. This suggests that participants who have a circle of online friends experience less loneliness (M = 13.28, SD = 11.02) than those who do not (M = 19.71, SD = 10.17). A significant effect was found for social anxiety in both the online condition F(1,72) = 6.82, p  < 0.05, η 2  = 0.09 (a medium effect size) and offline condition F(1,72) = 9.18, p  < 0.01, η 2  = 0.11 (a large effect size). This suggests that participants who have a circle of online friends experience less social anxiety (M = 12.72, SD = 12.64) than those who do not (M = 20.37, SD = 12.54) in both online and offline conditions.

H4 predicted that lower levels of loneliness and social anxiety are experienced by participants who spend greater amounts of time in social VR. There was a reasonable balance of participants who have been members of Altspace VR for more than 6 months prior to (n = 43) and who joined during (n = 31) the Covid-19 pandemic.

A one-way between participants ANOVA shows a significant effect for loneliness in the online condition F(1,72) = 4.68, p  < 0.05, η 2  = 0.06 (a medium effect size), whereas no significant effect was found for loneliness in the offline condition F(1,72) = 0.08, p  = 0.93. This suggests that participants who have been members of Altspace VR for more than 6 months experienced less loneliness (M = 14.02, SD = 11.63) than those who joined during the Covid-19 pandemic (M = 19.52, SD = 09.43). No significant effect was found for social anxiety in the online condition F(1,72) = 2.13, p  = 0.15, however, a significant effect was found for social anxiety in the offline condition F(1,72) = 4.77, p  < 0.05, η 2  = 0.06 (a medium effect size). This suggests that participants who have been members of Altspace VR for more than 6 months experienced less social anxiety (M = 19.51, SD = 16.82) than those who recently joined (M = 27.87, SD = 15.38).

Discussion of quantitative results

Research into the use of web-based technologies and virtual worlds has consistently demonstrated positive effects of such interventions on an individual’s subjective feelings of loneliness and social anxiety. Hypothesis 1 of this study is therefore supported and is consistent with the earlier findings 31 , 32 , 42 , 43 and a recent review 44 .

The results of this study in relation to hypothesis 2 were unable to support the assumption that being part of a VSG will reduce feelings of loneliness. The study was therefore unable to support findings from 32 which reported that VR gamers who played as part of a guild were less likely to experience feelings of loneliness. Social identity theory 45 provides a possible explanation for this. Teaming up with a specific VR gaming guild with the common purpose of defeating an enemy for example exerts a stronger sense of identity and group attachment compared to belonging to multiple virtual social groups, where an individual could have several social identities, thus group attachment is less salient. Furthermore, group attachment takes time to develop and within Altspace VR new VSGs are being created all the time. Future studies should look to explore the relationship between the membership duration and the strength of group attachment and the effect this has on subjective feelings of loneliness.

The results of this study support hypothesis 3 in that participants, who have a circle of friends with who they regularly socialise in social VR, experience lower levels of loneliness and social anxiety. This is consistent with the findings of 32 who found that playing with known people helps to reduce feelings of loneliness and social anxiety. This also further supports the findings of 46 who found that half of participants considered their gamer friends to be comparable to their real-life friends. As pointed out by 47 in the Need to Belong Theory, people need frequent and meaningful interactions to feel fulfilled. The ability to form positive social interactions with people with which we feel most connected, i.e. a circle of friends that share our goals or with which we have a common purpose, promotes greater levels of satisfaction and generates greater feelings of belonginess, which in turn reduces our feelings of loneliness and social anxiety 48 .

The results of this study in relation to hypothesis 4 support the assumption that the longer a person has been in social VR the lower will be their feelings of loneliness. There was a significant reduction in feelings of loneliness in the online condition, but not in the offline condition. The explanation for the divergence is that both new and existing Altspace VR users were experiencing similarly high levels of loneliness in the real-world condition, due to the sudden enforced period of lockdown that was imposed upon them, and that whilst being in social VR for a longer period of time showed a greater reduction in feelings of loneliness, in the real world the length of time they had been using social VR was not significant. A possible explanation for this is that when returning to the real world a person is again faced with the challenges of the imposed social isolation and will therefore continue to experience greater levels of loneliness. The reverse situation was found for social anxiety with a significant reduction in social anxiety being found in the offline condition for participants who had been using social VR for longer. This is a useful finding because it shows that using social VR for longer periods of time can help to reduce feelings of social anxiety in the real world. As is suggested by 42 social VR can be used to build up social capital and thereby help to improve a person’s social skills in the real world.

Focus group

Nine participants (6 male, 3 female) who took part in the online questionnaire were later recruited to take part in a focus group. The demographics of this group are shown in Table 3 . The focus group was made up of a wide mix of people from around the world. Participants were a mix of educators, students, developers and other professionals. Four of the participants were new to Altspace VR, having joined during the Covid-19 pandemic, whilst five had been in Altspace VR for more than 6 months. All the participants had previously attended at least one Educators in VR research event.

The focus group study took place in a private research room inside of Altspace VR (see Fig.  5 ), purposely created by the researcher. Only selected participants were able to join this room via a portal link provided by the researcher. The interview was recorded using OBS screen recording software on the researcher’s computer.

Virtual research room.

Prompts were kept to a minimum and questions were open-ended to elicit rich responses from participants. The focus group was later transcribed verbatim by the researcher. The transcript was analysed using a thematic data analysis approach as per the Braun and Clarke framework 49 . Thematic analysis is a suitable analytic approach to systematically establish patterns of meaning within qualitative data sets 50 . Microsoft Word was used to facilitate data management and the coding of themes. Participants’ responses were coded and themes identified.

Qualitative results

Four superordinate themes with several subordinate themes were identified (see Table 4 ).

Theme 1. Why the participant visits the social VR platform

Participants spoke freely about how they got involved in Altspace VR and what they believe to be the main reason they visit Altspace VR. Three sub-themes were discovered, although from the discussions it was clear that most, if not all, participants, valued the group interaction and attendance at events very highly.

Socialising in VR

What was interesting about the group of participants in the focus group was that they were all connected due to their involvement with the Educators in VR community and not through friendship ties. Some participants highlighted that they initially joined Altspace VR to meet new people and then started building a network of professional relationships.

Participant quotes from the transcripts are given within the results section for each subordinate theme. For confidentiality purposes quotes from participants will be referenced as: Participant (P), followed by a number 1–9 and the participant’s gender M (male), F (female) e.g. “P1M”.

“In VR I hang out with friends and of course the [Educators in VR] research team, but I don’t hang out around the campfire as much anymore” (31-33,P3F).

The campfire in Altspace VR is a meeting place for new users to mingle, chat and make friends. New users to Altspace VR tend to levitate towards the campfire until they establish friendship groups and events in which to take part in. This participant has already established a network of meaningful friendships and they are now spending less unstructured time in social zones.

All participants highlighted that they had seen an increase in their usage during the Covid-19 pandemic. The imposed restrictions on physical meetups led to several participants using social VR to meet with real-world friends to satisfy their social needs.

“During this pandemic I have probably come in an hour or two more per day. Part of that was to connect with some of my friends. I got some friends to start coming into Altspace VR so we were able actually hang out in Altspace” (52-55,P5F). “more recently, in the last month or so, because I work in the VR community and a lot of my personal friends have VR headsets, the people that I work with at the university, The people that are in my groups and in my sphere so to speak at the university are some of my best friends and so we have started having social meet-ups in VR for nothing other than social, like just for social meet-ups” (125-132,P1M)

Attending community events and learning new skills

All of the focus group participants recognised the value of taking part in regular events in social VR. In particular, participants were positive about the opportunities that exists within Altspace VR to collaborate with others to expand and learn new skills. Community involvement within Altspace VR generates a strong sense of belonging thus reducing feelings of loneliness and social anxiety.

“I got inspired by the Covid situation to host events, so it inspired me to bring people together. I think if the Covid situation did not happen I wouldn’t have organised these research meetings to be honest, so it was pretty much the catalyst to hosting events” (161-165,P3F) “One thing I love about the Altspace environment is the Educators forum because I have joined philosophy classes, I’ve done Psychology classes, I’ve really interacted. In fact, I started a talk show, [ ] my own event, and that’s one thing that I love about Altspace, so I do love this place” (72-78,P7M)

Sharing ideas with professionals and like-minded people

Altspace VR allows users to create their own events and to share knowledge with other users. There are a wide range of different interest groups within Altspace VR. Establishing common interests with others is a cornerstone to forming positive and meaningful relationships. Establishing a network of contacts is also beneficial by encouraging, giving advice and supporting each other in difficult times 51 . Several of the participants commented that social VR is a useful tool not least during periods of enforced social isolation, but also to those who find themselves unable to form such relationships within their existing real-world social networks.

“I entered Altspace mainly for the Educators in VR conference and after that, during the Covid crisis obviously I stayed because it is a perfect place to find people that have a similar interest with mine” (62-64,P6F). “It’s almost impossible where I live to find people with similar interests like mine, so this is probably the only way for me to find people with similar interests” (188-190,P6F) “I love coming here because there are so many truly brilliant people with so much to learn and so many interesting things to hear and see” (105-107,P9M)

Theme 2. How the participant sees their current situation

Although participants were not specifically asked, they took it upon themselves to reflect how they see the current situation and their specific circumstance in terms of being socially isolated. Participants felt that they were socially isolated and less social for several reasons. These have been broken down into the following sub-themes.

Introverted/anti-social

Several participants stated that they are socially inhibited and anxious individuals, who find socialising in the real world more challenging, whereas social VR offers a less intimidating way for them to meet and make friends.

“If you struggle with social interaction, VR is a little less intimidating, I would say. I really think these platforms are a great way to connect and less intimidating as well” (240-245,P3F) “Prior to Covid I was actually pretty like unsocial, I still kind of am unsocial, but it seems as though now society is kind of like bending towards introverts so in a sense it’s like the market’s benefiting my type so like in a sense I’m becoming increasingly more social” (18-22,P2M).

Socially isolated due to remote location and work/life balance

Some participants lamented that their geographic location or work/life balance in the real world made it very difficult for them to meet and to have frequent interactions with people with similar interests to theirs. This aspect makes them at a greater risk of loneliness to others. Social interaction within social VR is not restricted by geographic location and so these participants feel that this has helped to enhance their social interaction with others.

“I use VR to socialise because I live in a little village so for me it’s the only way to meet people, to communicate with people etc because normally I don’t meet people in the real life. With my friends and with my brother etc so I use the VR to socialise okay” (40-43,P4M) “I went on sabbatical in September this academic year I spent my entire summer, last year outside hiking and camping and all of that and then all of a sudden I was inside doing research and I was isolated from my community. I feel like my work community is my community, you know, and I felt like I lost my community and I felt like I found a new one in Altspace” (259-265,P1M)

Theme 3. How the participant sees the social VR platform

Several participants elaborated in detail on how they felt that social VR helped them to connect with people in ways that were better than alternative digital communication methods such as video conferencing, text chat or social media.

Greater immersion/presence

Immersion and presence are important characteristics within VR because the aim after all is to replicate, to some degree, the feelings of being within the real world. The more this is made possible the more useful VR will be in combating feelings of loneliness and social anxiety during periods of prolonged isolation in the real world.

“I’ve been in here with students for tutorials and […] students have said that they feel more presence with other students in this environment” (108-111,P9M) “I’m a perceptual psychologist so I even think about it from the view of like it feels like some of the spaces that I go into now in Altspace really regularly feel in my head like real spaces that I go to so when I feel like I go to a couple of events in the afternoon in Altspace and then I take the headset off it kind of feels like I left my house and I went out and did something and then came back, it doesn’t feel like I was in my house the whole time” (154-160,P1M)

More ways to connect

In addition to the greater immersion and presence that VR can create, Altspace VR also gives individuals the ability to control and create their own environments for social interaction. It is not possible within the real world for most of us to simply create our own hang-outs or to control our environments so easily. This allows people to therefore interact in ways that up until now have not been possible. Several participants linked the ability to create stimulating and exciting environments in the Altspace VR to something that they can feel proud of, and this gives them social capital over other users with less advanced skills in world creation. This in turn helps to improve their ability to socialise and build further friendships in social VR that they would not have been able to build in the real world.

“I made a beach environment, a beach world and there are other ones out there, but I made a custom private one for me and my friends to meet in and so we meet in there and other places and we bounce around and look at different places but we often find somewhere like a private room where we can actually have a nice private conversation and we don’t have to worry about anyone interfering and everyone said its fantastic it really allows us to connect in ways, you know like those personal chats you have with close friends that it’s hard to do in any other medium, it feels a little more natural in VR to do that and so it’s been fantastic, we’ve been really enjoying it” (132-142,P1M) “Since coming in here now [my friends] are like world building and have created some really awesome spaces in here and so we go in and check out the space that they just created and so I’m still kind of doing project oriented hang-outs as far as like we will be like oh that lighting needs to be a little different and stuff like that but it’s been a really fun way to hang out with people that I already may have been friends with before all this happened but now that this happened they are starting to come into this space so we can connect even more often” (214-222,P5F)

Theme 4. How social VR is helping during the Covid-19 pandemic

In the second part of the focus group, participants were asked to think about how they thought Altspace VR was helping them specifically during the Covid-19 pandemic and whether they thought that others could benefit from this experience too. The responses were very positive and provided a great deal of insight into how Altspace VR is helping them to deal with loneliness and social anxiety during Covid-19. A number of key sub-themes emerged from this category.

Helps people feel less lonely

Several participants said that social VR helps them to feel connected with a circle of friends and that this helps to reduce feelings of loneliness and depression.

“I feel it really does help me in social isolation. I have been on sabbatical this last year so my whole year has been about isolation even before Covid-19, I’ve been working a lot on my own and that sort of thing so yeah becoming part of the community in Altspace, collectively in the different ways that I have has had a huge impact on my mental health. I was getting a little depressed in the fall and having this community has really felt like that it brought me out of it a bit” (147-154,P1M) “By the second semester I only had like one course and we were like really concentrating on a specific project and everything and it was like really limiting me to go outside and do some other stuff. Even though I’m an introvert but I do feel like I really wanted to go outside and have some fun. I really like to see other stuff around me and doing all this stuff here in VR kept me really engaged with the communities” (191-197,P8M)

Helps to motivate and provide structure

Having a purpose and being occupied with an interesting project and subsequently conversing about its progress/issues with others in social VR were perceived as motivational factors, which helped them to deal with the imposed social isolation.

“Events really motivated me to keep busy also when I was in social isolation for two months. Yeah, two months is a long time you know to not get out of your house so that was great I created some sense of purpose and it was really heart-warming to see everybody come together and really interesting people as well. Everybody has something cool to share and was very helpful so that gave me some energy, you know to just keep on going and make the best out of the situation” (166-173,P3F) “I finally have a structure for a project that I have been thinking about for over a year now and having these interactions in here and talking to people allowed me to bring a clear picture of how I can start a project I have been thinking about and start building it inside Altspace, so that’s a big plus for me” (178-182,P6F)

Helps people to be less anti-social and reduced social anxiety

Several participants explained that social VR is “a great way to connect and less intimidating as well” for socially anxious, i.e. “unsocial” and “introverted” people, who as a result often feel lonely. In addition, social VR is a convenient tool for social interactions as it brings people closer “especially during these situations, but not only during like pandemics”. (240–243,P3F)

“In my case the Covid increased my social interaction with people because I’m a pretty anti-social person in real life so for me this has increased ten-fold my social interaction in general” (174-176,P6F). “Covid pushed people inside spaces like VR and made my social interactions far easier to have” (186-188,P6F). “I am in sort of a group, let’s say of people who have problems with connecting with people, this is awesome. This is definitely a big plus and I would like more of this” (322-324,P6F) “I was, I guess, somewhat socially isolated before coming in Altspace I tend to just like to work on projects and stay at home or be at work, but since coming in Altspace I’ve definitely started experiencing more of the social aspect of living like making connections with other people in ways that aren’t strictly like a project that I’m working on and so that’s been nice” (202-208,P5F). “I do think that VR can help us, those of us who are socially isolated or have social anxieties of some sort. It does make it more accessible for us to be able to go into a space and interact with people. For instance in real life, if you were to have social anxiety and you start feeling almost like a panic attack coming on, that would prevent you from going into a real life space, whereas in VR you […] can say, oh I have to go really easily and you’re back in your home and you can work through whatever may have come up with social anxiety. So I do think it makes social interactions more accessible in those cases” (307-316,P5F)

Helps to socialise with real life friends during lock-down

Another idea that surfaced among the participants is the potential to use social VR as a mode of interaction/engagement with real-life friends/family members who live afar. Participants expressed the view that the current restriction on face-to-face contact could to some extent be counterbalanced by inviting real-world friends into social VR to socialise.

“The fully social part of VR has happened because of the Covid-19 situation, because I used to go for dinners with people like every month, […] and we can’t do the real world social, so we are trying to do the VR social” (142-146,P1M) “Once everyone went into social isolation for Covid I actually started hanging out with a friend that lives 3 hours away from me more than before because before it would be a 3 hour drive, but then once all this happened, I actually convinced them to come into Altspace” (208-212,P5F) “It’s been a really fun way to hang out with people that I already may have been friends with before all this happened but now that this happened they are starting to come into this space so we can connect even more often. (218-222,P5F).

Discussion of qualitative findings

Overall, participants’ commentaries to Theme 1 reconfirm that their usage of social VR has increased during the period of imposed social isolation and restrictions on physical meetups due to the Covid-19 pandemic. They were using social VR to meet with real-world friends to satisfy their social needs and continue to receive support from people they are close to; or to mix socially with other users who they meet either at a “campfire” or whilst taking part in regular events inside of the social VR platform, thus expanding their social network of non-intimate contacts. As a result, they felt less lonely online (whilst being in Altspace VR) as they felt like they were in the same space together. Interestingly, participants noted that they also benefited emotionally from meeting like-minded people/professionals and sharing ideas with them, getting support and advice, and working together in real-time. This is a new explanation why people use VR technology, which did not surface in the earlier research studies. Nonetheless this reason ties with the Need to Belong Theory 47 . This is useful to help us to understand why users visit Altspace VR in general and during the enforced social isolation period.

In theme 2 participants’ responses reiterate what has already been explained in the literature that shy, socially inhibited and anxious individuals find online anonymity liberating and less inhibited than the real world 52 . Moreover, in Altspace VR it is also possible to make use of non-verbal communication such as emojis or emoticons (see Fig.  6 ).

Use of emojis to communicate in Altspace VR.

Some participants commented that their geographic location or work/life balance in the real world made it very difficult for them to meet people with similar interests. The social internet, e.g. Facebook 53 and video conferencing 54 have long been used to socialise with friends and family and have been found to be an affective intervention for reducing loneliness. Theme 3 considers that social VR could be regarded as the latest endeavour within this field as individuals are able to create their own exciting hangouts, e.g. a beach or a city from Ancient Greece. Furthermore users are able to easily control environments and restrict entry. This allows people to interact in ways that up until now have not been possible.

Findings in Theme 4 give a clear indication that social VR helps to reduce feelings of loneliness, and this further supports the findings of 32 . Social interactions in social VR are also particularly attractive to those who are lonely or shy/socially anxious/self-conscious or have poor social skills, etc. as they feel more in control of their online interactions and feel that they have a broader range of topics that they are able to discuss compared with in the real world 55 . Lonelier people also feel that they can be more themselves in online social interactions than in the real world 56 .

General discussion

People use social VR for many different reasons: to socialise with new and existing friends; to join social interest groups; to learn new skills and generally to be part of a larger community of people (including other professionals) than those that they are part of in the real world. Social VR attracts a wide range of people because of the ease in which people can meet people with similar interests to their own, although it could be argued that up until the recent Covid-19 pandemic social VR tended to attract a greater amount of people who found real-life social interaction difficult. The results of this study show a reduction in social anxiety in individuals with moderate, severe and very severe social anxiety in the online condition, i.e. when using social VR. The increase in availability of VR headsets in recent years has led to an expansion in usage of social VR and the recent Covid-19 pandemic and subsequent social distancing rules led to more people and organisations making a greater use of VR to communicate and carry out their daily business and routines during the prolonged period of social isolation. Social VR also enables people to collaborate in ways not possible within the real world, reducing geographic restrictions and breaking through communication barriers by using visually stimulating content creation tools to enhance the process of human interaction through world-building and event hosting.

The main objective of this study was to explore whether social VR could be used to help reduce feelings of loneliness and social anxiety amongst people confined to their homes and away from their regular friendship groups and social connections, i.e. when the quantity and quality of their social network is gravely affected. Overall, the synthesised results of the present study show that participants experience a statistically significant reduction in loneliness and social anxiety when in social VR than in the real world during prolonged periods of imposed social isolation. Qualitative findings support/validate the quantitative results for H1. Thus, the evidence shows that social VR can decrease the sense of loneliness and social anxiety with users and have an overall positive effect on their emotional and social wellbeing.

The qualitative data diverges from the quantitative results presented for H2 that addressed the effect of being part of a VSG separately for loneliness and social anxiety. The quantitative results showed no significant effect for loneliness in the online and the offline conditions, whereas participants’ views showed that being a member of a VSG created a sense of belongingness and helped them to feel less lonely and depressed. Quantitative data showed no significant effect for social anxiety when an individual is a member of a VSG or not; but revealed a medium effect for social anxiety in the offline condition indicating that users, who are part of a VSG and subsequently take part in regular group events, experience less social anxiety in real world (i.e. offline), than those who are not part of a VSG. Participants who are part of a VSG were positive about the possibilities of social VR and being part of a VSG, because this setup helped shy and socially inhibited individuals to observe conversations, use emojis to show emotions rather than speak, use the online anonymity to get over the discomfort of social interactions and gradually become more connected and accepted by other members of the VSG. This prepares socially anxious individuals to handle being out there (in online and the real world).

Qualitative findings are in line with the quantitative results for H3 in that the degree of loneliness and social anxiety is also further reduced by factors such as having a circle of online friends. Social VR allows people to meet others who share similar interests, this is more difficult within the real world for people who struggle with social anxiety or who live in remote locations for example, or as was the case with this study, people who were confined to their homes due to social distancing rules during a pandemic. The qualitative data helps to produce a better understanding in relation to ‘online friends’ as these include individuals who were met in social VR and real-life friends who currently live afar and were invited to join the social VR platform.

The qualitative findings somewhat converge with quantitative results for H4 in that online loneliness reduces with the length of time the participant has been using social VR, i.e. participants who had been using social VR for greater than 6 months experienced less loneliness than those who joined during the Covid-19 pandemic. The length of time the participant had been using social VR had no effect on their feelings of loneliness in the real world. Comments from participants who have been members of Altspace VR for more than 6 months revealed that finding a new (online) community that supports their need to belong and provides meaningful and positive social interactions acted as an antidote to the loneliness that they experience in the real world. Individuals who struggle to build meaningful relationships in the real world due to social anxiety and other social phobias turn to social VR as it provides a less confrontational way in which to form and maintain social relationships with others and therefore help to reduce feelings of loneliness and social anxiety.

Research limitations and implications

The heterogeneity of the sample for the quantitative survey enabled conclusions to be drawn regarding the participant experience in Altspace VR, their subjective feelings of loneliness and social during the Covid-19 pandemic. However, in interpreting the views of participants in the focus group it should be stressed that the sample of participants was solely recruited from the Educators in VR research event and that this may not represent the views of others who do not take part in such events. Although the reported themes were clearly identified, there remains a possibility that additional themes would be detected should the views of participants from a wider pool be collected.

It is the researcher’s understanding that this is the first study that has exclusively focused on participant’s feelings of loneliness and social anxiety during a period of enforced prolonged isolation whereby social VR has been utilized as an intervention to help reduce such feelings. The results offered here, should therefore be taken as a starting point upon which further empirical studies could be built. Longitudinal investigations could be carried out to further assess the suitability of social VR as an intervention to help reduce loneliness and social anxiety amongst specific communities, e.g. remote learners/workers, people living alone or in care, the less physically able, prisoners and other sub-groups of people facing loneliness and social anxiety whereby their ability to socialise with other is in some way restricted. Future research would also need to provide accurate estimates of the prevalence of loneliness and social anxiety in these sub-groups.

The COVID-19 pandemic forced people to change the way in which they connected with others during lockdown. Social VR helped to improve social connectedness during the COVID-19 pandemic and reduce “lockdown loneliness”. Post-pandemic it is necessary to recognise the additional needs that face society, especially vulnerable people and those struggling with mental health issues resulting from lockdown. Social VR can, therefore, be a way of further supporting people facing social isolation, loneliness and social anxiety. Social VR platforms may be virtual, but the relationships we build in them are very real.

Data availability

All data generated or analysed during this study are included in this published article or in the accompanying Supplementary Information file.

WHO. WHO Director-General’s opening remarks at the media briefing on COVID-19- 11 March 2020. www.who.int . https://www.who.int/dg/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing-on-covid-19---11-march-2020 (2020).

Gov.UK. PM Address to the Nation on coronavirus: 23 March 2020. GOV.UK. https://www.gov.uk/government/speeches/pm-address-to-the-nation-on-coronavirus-23-march-2020 (2020).

Office for national statistics. Mapping loneliness during the coronavirus pandemic. onsgovuk. https://www.ons.gov.uk/peoplepopulationandcommunity/wellbeing/articles/mappinglonelinessduringthecoronaviruspandemic/2021-04-07 (2021).

Pandemic one year on: Landmark mental health study reveals mixed picture. www.mentalhealth.org.uk . Available from: https://www.mentalhealth.org.uk/about-us/news/pandemic-one-year-mental-health-study

Schuch, R. Nursing diagnosis-application to clinical practice. Gastroenterol. Nurs. 14 (5), 275 (1992).

Article   Google Scholar  

Victor, C. R. & Yang, K. The prevalence of loneliness among adults: A case study of the United Kingdom. J. Psychol. 146 (1–2), 85–104. https://doi.org/10.1080/00223980.2011.613875 (2012).

Article   PubMed   Google Scholar  

Mansfield, L., Daykin, N., Meads, C., Tomlinson, A., Gray, K., Lane, J. et al. A conceptual review of loneliness across the adult life course (16+ years). https://whatworkswellbeing.org/wp-content/uploads/2020/02/V3-FINAL-Loneliness-conceptual-review.pdf (2019).

Torales, J., O’Higgins, M., Castaldelli-Maia, J. M. & Ventriglio, A. The outbreak of COVID-19 coronavirus and its impact on global mental health. Int. J. Soc. Psychiatry 66 (4), 317–320. https://doi.org/10.1177/0020764020915212 (2020).

Hawkley, L. C., Masi, C. M., Berry, J. D. & Cacioppo, J. T. Loneliness is a unique predictor of age-related differences in systolic blood pressure. Psychol. Aging 21 (1), 152–164 (2006).

Hawkley, L. C., Thisted, R. A., Masi, C. M. & Cacioppo, J. T. Loneliness predicts increased blood pressure: 5-year cross-lagged analyses in middle-aged and older adults. Psychol. Aging 25 (1), 132–141 (2010).

Article   PubMed   PubMed Central   Google Scholar  

British Library. www.bl.uk . Available from: https://www.bl.uk/collection-items/loneliness-and-social-isolationamong-older-people-in-north-yorkshire-project-commissioned-by-north-yorkshireolder-peoples-partnership-board-executive-summary . Accessed 9 May 2023

Liffe, S. et al. Health risk appraisal in older people 1: Are older people living alone an “at-risk” group?. Br. J. Gen. Pract. 57 (537), 277–282 (2007).

Google Scholar  

Locher, J. L. et al. Social isolation, support, and capital and nutritional risk in an older sample: Ethnic and gender differences. Soc. Sci. Med. 60 (4), 747–761 (2005).

Matthews, T. et al. Sleeping with one eye open: Loneliness and sleep quality in young adults. Psychol. Med. 47 (12), 2177–2186 (2017).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cacioppo, J. T. et al. Loneliness within a nomological net: An evolutionary perspective. J. Res. Pers. 40 (6), 1054–1085 (2006).

Wei, M., Russell, D. W. & Zakalik, R. A. Adult attachment, social self-efficacy, self-disclosure, loneliness, and subsequent depression for freshman college students: A longitudinal study. J. Couns. Psychol. 52 (4), 602–614 (2005).

Coyle, C. E. & Dugan, E. Social Isolation, loneliness and health among older adults. J. Aging Health 24 (8), 1346–1363 (2012).

Masi, C. M., Chen, H. Y., Hawkley, L. C. & Cacioppo, J. T. A meta-analysis of interventions to reduce loneliness. Personal. Soc. Psychol. Rev. 15 (3), 219–266 (2010).

Choi, M., Kong, S. & Jung, D. Computer and internet interventions for loneliness and depression in older adults: A meta-analysis. Healthc. Inform. Res. 18 (3), 191 (2012).

Hagan, R., Manktelow, R., Taylor, B. J. & Mallett, J. Reducing loneliness amongst older people: A systematic search and narrative review. Aging Ment. Health 18 (6), 683–693 (2014).

Morris, M. E. et al. Smart technologies to enhance social connectedness in older people who live at home. Australas J. Ageing 33 (3), 142–152 (2014).

Poscia, A. et al. Interventions targeting loneliness and social isolation among the older people: An update systematic review. Exp. Gerontol. 102 , 133–144 (2018).

BMJ. The effects of isolation on the physical and mental health of older adults. The BMJ . https://blogs.bmj.com/bmj/2020/04/09/the-effects-of-isolation-on-the-physical-and-mental-health-of-older-adults (2020).

Appel, L. et al. Older adults with cognitive and/or physical impairments can benefit from immersive virtual reality experiences: A feasibility study. Front. Med. 15 , 6 (2020).

Tussyadiah, I. P., Wang, D., Jung, T. H. & Dieck, M. C. Virtual reality, presence, and attitude change: Empirical evidence from tourism. Tour. Manag. 66 , 140–154 (2018).

Lin, C. X., Lee, C., Lally, D. & Coughlin, J. Impact of Virtual Reality (VR) Experience on Older Adults’ Well-Being. In Human Aspects of IT for the Aged Population. Applications in Health, Assistance, and Entertainment Vol. 10927 (eds Zhou, J. & Salvendy, G.) (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-319-92037-5_8 .

Chapter   Google Scholar  

Lin, T. Y. et al. Effects of a combination of three-dimensional virtual reality and hands-on horticultural therapy on institutionalized older adults’ physical and mental health: Quasi-experimental design. J. Med. Internet Res. 22 (11), e19002 (2020).

Brown, J. A. An exploration of virtual reality use and application among older adult populations. Gerontol. Geriatr. Med. 5 , 233372141988528. https://doi.org/10.1177/2333721419885287 (2019).

Baker, S. et al. Interrogating social virtual reality as a communication medium for older adults. Proc. ACM Human-Comput. Interact. 3 (CSCW), 1–24 (2019).

Baker, S. et al. Evaluating the use of interactive virtual reality technology with older adults living in residential aged care. Inf. Proc. Manag. 57 (3), 102105 (2020).

Mandryk, R. L., Frommel, J., Armstrong, A. & Johnson, D. How passion for playing world of warcraft predicts in-game social capital, loneliness, and wellbeing. Front. Psychol. 11 , 2165 (2020).

Martončik, M. & Lokša, J. Do World of Warcraft (MMORPG) players experience less loneliness and social anxiety in online world (virtual environment) than in real world (offline)?. Comput. Human Behav. 56 , 127–134 (2016).

Miller, K. J. et al. Effectiveness and feasibility of virtual reality and gaming system use at home by older adults for enabling physical activity to improve health-related domains: A systematic review. Age Ageing 43 (2), 188–195 (2013).

Lee, L. N., Kim, M. J. & Hwang, W. J. Potential of augmented reality and virtual reality technologies to promote wellbeing in older adults. Appl. Sci. 9 (17), 3556 (2019).

Knowles, L. M., Stelzer, E. M., Jovel, K. S. & O’Connor, M. F. A pilot study of virtual support for grief: Feasibility, acceptability, and preliminary outcomes. Comput. Human Behav. 73 , 650–658 (2017).

Gorinelli, S., Gallego, A., Lappalainen, P. & Lappalainen, R. Virtual reality acceptance and commitment therapy intervention for social and public speaking anxiety: A randomized controlled trial. J. Context. Behav. Sci. 28 , 289–299 (2023).

Kim, M. K., Eom, H., Kwon, J. H., Kyeong, S. & Kim, J. J. Neural effects of a short-term virtual reality self-training program to reduce social anxiety. Psychol. Med. 52 (7), 1296–1305 (2022).

Creswell, J. W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches 4th edn. (Sage Publications Ltd, 2014).

Russell, D. W. UCLA loneliness scale (Version 3): Reliability, validity, and factor structure. J. Personal. Assess. 66 (1), 20–40. https://doi.org/10.1207/s15327752jpa6601_2 (1996).

Article   CAS   Google Scholar  

Connor, K. M. et al. Psychometric properties of the social phobia inventory (SPIN). Br. J. Psychiatry 176 (4), 379–386 (2000).

Article   CAS   PubMed   Google Scholar  

Cohen, J. Statistical Power Analysis for the Behavioral Sciences 2nd edn. (Routledge, 1988).

MATH   Google Scholar  

Nowland, R., Necka, E. A. & Cacioppo, J. T. Loneliness and social internet use: Pathways to reconnection in a digital world?. Perspect. Psychol. Sci. 13 (1), 70–87. https://doi.org/10.1177/1745691617713052 (2017).

Hwang, T. J., Rabheru, K., Peisah, C., Reichman, W. & Ikeda, M. Loneliness and social isolation during the COVID-19 pandemic. Int. Psychogeriatr. 32 (10), 1–15 (2020).

Döring, N. et al. Can communication technologies reduce loneliness and social isolation in older people? A scoping review of reviews. Int. J. Environ. Res. Public Health 19 (18), 11310 (2022).

Tajfel, H. & Turner, J. C. An Integrative Theory of Inter-Group Conflict. In The Social Psychology of Inter-Group Relations (eds Austin, W. G. & Worchel, S.) 33–47 (Brooks/Cole, 1979).

Cole, H. & Griffiths, M. D. Social interactions in massively multiplayer online role-playing gamers. Cyberpsyhol. Behav. 10 (4), 575–583 (2007).

Baumeister, R. F. & Leary, M. R. The need to belong: Desire for interpersonal attachments as a fundamental human motivation. Psychol. Bull. 117 (3), 497–529 (1995).

de Gierveld, J. J., van Tilburg, T. G. & Dykstra, P. A. New Ways of Theorizing and Conducting Research in the Field of Loneliness and Social Isolation. In The Cambridge Handbook of Personal Relationships (eds Vangelisti, A. L. & Perlman, D.) 391–404 (Cambridge University Press, 2018).

Braun, V. & Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol. 3 (2), 77–101. https://doi.org/10.1191/1478088706qp063oa (2006).

Braun, V. & Clarke. V. Thematic analysis . APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological 2 (2), 57–71 (2012).

Ernst CPH. The Influence of Perceived Belonging on Massively Multiplayer Online Role-Playing Games. Proceedings of the 50th Hawaii International Conference on System Sciences (2017).

Morahan-Martin, J. & Schumacher, P. Loneliness and social uses of the Internet. Comput. Human Behav. 19 (6), 659–671 (2003).

Teppers, E., Luyckx, K. A., Klimstra, T. & Goossens, L. Loneliness and Facebook motives in adolescence: A longitudinal inquiry into directionality of effect. J. Adolesc. 37 (5), 691–699 (2014).

Tsai, H. H. & Tsai, Y. F. Changes in depressive symptoms, social support, and loneliness over 1 year after a minimum 3-month videoconference program for older nursing home residents. J. Med. Internet Res. 13 (4), e93 (2011).

Valkenburg, P. M. & Peter, J. Preadolescents’ and adolescents’ online communication and their closeness to friends. Dev. Psychol. 43 (2), 267–277 (2007).

Leung, L. Loneliness, social support, and preference for online social interaction: The mediating effects of identity experimentation online among children and adolescents. Chin. J. Commun. 4 (4), 381–399 (2011).

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Kenyon, K., Kinakh, V. & Harrison, J. Social virtual reality helps to reduce feelings of loneliness and social anxiety during the Covid-19 pandemic. Sci Rep 13 , 19282 (2023). https://doi.org/10.1038/s41598-023-46494-1

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Shikuan Zhang, Songmao Wang, Fang Lu, Lingzi Bie, Yongjiang Luo, Jiahe Sun, Yang Zhang, Yi Wang, Yaou Zhang, Qing Rex Lyu, LncRNAway: a web-based sgRNA design tool for precise and effective suppression of long noncoding RNAs, Nucleic Acids Research , 2024;, gkae383, https://doi.org/10.1093/nar/gkae383

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Thousands of long noncoding RNAs (lncRNAs) have been annotated via high-throughput RNA sequencing, yet only a small fraction have been functionally investigated. Genomic knockout is the mainstream strategy for studying the biological function of protein-coding genes and lncRNAs, whereas the complexity of the lncRNA locus, especially the natural antisense lncRNAs (NAT-lncRNAs), presents great challenges. Knocking out lncRNAs often results in unintended disruptions of neighboring protein-coding genes and small RNAs, leading to ambiguity in observing phenotypes and interpreting biological function. To address this issue, we launched LncRNAway , a user-friendly web tool based on the BESST (branchpoint to 3’ splicing site targeting) method, to design sgRNAs for lncRNA knockout. LncRNAway not only provides specific and effective lncRNA knockout guidelines but also integrates genotyping primers and quantitative PCR primers designing, thereby streamlining experimental procedures of lncRNA function study. LncRNAway is freely available at https://www.lncrnaway.com .

Long noncoding RNA is defined as RNA transcripts larger than 200 nucleotides, which typically do not have protein-coding capability ( 1 ). There are 16 193 long noncoding RNA genes, and 30 369 long noncoding RNA loci transcripts were annotated according to the GENECODE V30 release ( 2 ). Studies in the past decade have revealed the essential and versatile roles of lncRNAs in various biological and pathological processes ( 3 ). Only a fraction of these annotated lncRNAs were investigated functionally ( 4 ).

LncRNAs share many similarities with protein-coding messenger RNAs (mRNAs): transcribed by RNA polymerase II, 3’ poly adenylation, and intron excision ( 5 ). However, unlike mRNAs exported to the cytoplasm for protein synthesis, lncRNAs can play roles in both the cytoplasm and nucleus ( 3 ). This dual localization poses a challenge in loss-of-function (LOF) studies. Transient knockdown using small interference RNA (siRNA) and antisense oligonucleotide (ASO) can achieve target RNA knockdown in the cytoplasm and nucleus, respectively ( 6 , 7 ). However, the efficacy of these methods declines over time as the nucleotides are degraded intracellularly, compromising their suppression efficiency ( 8 ). Cutting-edge CRISPR-Cas9 genome editing is undoubtedly the best approach to acquiring a stable and high-efficiency knockout of target RNAs ( 9 ). While frameshift mutations, excision of functional domains, and promoter removal are commonly effective in protein-coding gene knockout, they often prove inadequate for lncRNA knockout due to the complexity of lncRNA genomic loci and their adjacency with neighboring genes ( 10 ).

Several web-based sgRNA designer tools have been launched in the past few years, such as CRISPOR ( 11 ), E-CRISP ( 12 ), CRISPick ( 13 , 14 ), CRISPR-ERA ( 15 ) and CHOPCHOP ( 16 ). These web tools are dedicated to calculating sgRNAs for CRISPR knock-out/knock-in, CRISPR repression/inactivation (CRISPRi), and CRISPR activation (CRISPRa) with different protospacer adjacent motif (PAM) sequences from various Cas proteins. However, their primary focus lies in optimizing sgRNAs for single-target applications using different RNA-guided endonucleases. While tools like CRISPR-ERA and CRISPick can generate sgRNAs for lncRNA repression, they employ algorithms similar to those for targeting protein-coding genes. This approach may lead to non-specific knockouts due to the removal of large DNA fragments.

When considering lncRNA functional knockout, scientists seek high repression efficiency of target lncRNA transcripts, less non-specific impact on neighboring genes resulting from genome manipulation, minimal disturbance to genomic DNA, and ease of execution. In our previous study, we introduced a novel lncRNA knockout strategy by removing the branch point to the 3’ splicing site of the last intron of target lncRNA (BESST), achieving exceptional knockout efficiency and specificity comparing to the common-used promoter-exon 1 (PE1) deletion methods ( 17 , 18 ). The BESST approach can achieve an average of 72% efficiency in repressing lncRNA by removing as few as ∼37 bp from genomic DNA ( 18 ). However, the PE1 method remains suitable for knocking out long intergenic noncoding RNA (lincRNA) because it is distant from neighboring genes and, importantly, prevents the generation of any RNA transcripts ( 19 ).

To facilitate lncRNA functional knockout in vitro and in vivo , we developed LncRNAway ( https://www.lncrnaway.com ), a web tool integrating the BESST and PE1 algorithms. This tool aims to streamline the generation of research plans before wet lab experiments (Figure 1 ). The user can choose human or mouse lncRNAs from the Ensembl database (Homo sapiens.GRch38.104.gtf or Mus musculus.GRCm39.104.gtf) or input the novel unannotated lncRNA sequence in GFF format. Users can also select sgRNA design tools including CRISPOR ( 11 ), DeepHF ( 20 ), E-CRISP ( 12 ), sgRNA Scorer 2.0 ( 21 ) and Cas-Designer ( 22 ), and choose different Cas proteins (SpCas9, Nme2Cas9, SaCas9) ( 23–25 ). LncRNAway web server not only calculates sgRNA sequence in real-time following BESST or PE1 algorithm but also offers optimized primers for conducting quantitative PCR and genotyping PCR. Therefore, users can effortlessly set up the lncRNA knockout experimental plan in minutes, expediting the lncRNA functional study.

The workflow of lncRNA web server.

Sequence input

There are two different ways to start the sgRNA designing process. Firstly, for annotated lncRNAs present in a database, users simply need to input the official gene symbol or Ensembl gene ID. Subsequently, the webpage will automatically display transcript variants of the target lncRNA for selection (Figure 2 ) ( 26 ). Alternatively, LncRNAway offers the option to input newly discovered lncRNAs not yet cataloged in the database. In such instances, users are required to upload the RNA sequence in GFF format, which includes positional information for the transcript and exons.

The front page for selecting RNA transcript, method, tool, and algorithm.

SgRNA design strategy selection

BESST is a novel lncRNA/mRNA knockout strategy, especially for natural antisense lncRNAs (NAT-lncRNAs) ( 18 ). By removing the branchpoint to the 3’ splicing site of the last intron with two sgRNAs, BESST offers a high specificity and comparable repressive efficiency as other knockout strategies ( 18 ). Within the LncRNAway web tool, we have designated BESST as the default method for lncRNA knockout. The last intron of the provided lncRNA is identified to generate sgRNAs using the BESST algorithm. Subsequently, a window spans from −250 to −50 bp upstream of the 3’ splicing site (3’SS) of the last intron is utilized to calculate the upstream sgRNA (5’ sgRNA, or sgRNA#1) using five different algorithms including CRISPOR ( 11 ), DeepHF ( 20 ), E-CRISP ( 12 ), sgRNA Scorer 2.0 ( 21 ) and Cas-Designer ( 22 ). Following this, a second window extending from the first nucleotide of the last exon to 200 bp downstream is employed to generate the downstream sgRNA (3’ sgRNA, or sgRNA#2). Of note, the BESST algorithm is only applicable to multi-exon lncRNAs/genes. Single exon lncRNAs bypass this step and directly utilize the PE1 algorithm.

The promoter-Exon1 (PE1) algorithm is a conventional approach to knockout lncRNAs and protein-coding genes. Generally, PE1 removes the DNA sequence from the transcription start site (TSS) to the upstream gene promoter to abolish the transcription process and suppress its expression level ( 27 ). For single-exon lncRNAs/genes, DNA sequences from −1200 to −1000 bp upstream of TSS and +250 to +750 bp downstream of TSS are input into sgRNA designing tools, such as CRISPOR , for sgRNA calculation. For multi-exon lncRNAs/genes, −1200 to −1000 bp upstream of TSS and +0 to +200 bp downstream of the first exon are used for sgRNA designing. PE1 can eliminate the transcription process of the target lncRNA/gene; however, the removal of larger DNA fragments may lead to undesired perturbations in the genome.

Selection of endonuclease

Streptococcus pyogenes Cas9 (SpCas9) is the most prevailing RNA-guided DNA endonuclease used in genome editing with a versatile PAM sequence of “NGG” ( 28 ). However, the off-targeting caused by SpCas9 also cannot be overlooked ( 29 ). To address this issue, we included two widely used and associated adenovirus (AAV) compatible Cas proteins, Nme2Cas9 and SaCas9. Nme2Cas9 endonuclease recognizes pyrimidine-rich PAMs, like N 4 CN, and shows high gene editing accuracy ( 23 ). SaCas9 endonuclease recognizes PAM ‘NNGRRT’ with higher specificity ( 24 ). LncRNAway allows users to pick Cas protein among these three common endonucleases based on the experiment's aim and the context of the DNA sequence.

Guide RNA selection

After users submit their tasks, sgRNA sequences are automatically processed in the background using a selected sgRNA designer. Subsequently, the top 5 sgRNAs for each target are displayed on the ‘Guide RNA selection’ page. The web tool highlights a recommended sgRNA pair in orange on this page. Users also have the flexibility to select the sgRNA pair from each column to proceed to the next step. A recommended sgRNA pair is determined by meeting a CFD specificity score of 70% and limiting the number of off-targets to 30% of the CRISPOR score. For the BESST method, the distance between cutting sites targeted by both sgRNAs ranges from 30 to approximately 200 bp, depending on the specific PAM sequences. In contrast, the length can extend up to 1−2 kb for the PE1 method.

siRNA design

RNA interference is an effective method to transiently reduce target cytoplasmic lncRNA/mRNA levels in vitro and in vivo ( 30 ). To generate siRNAs for target lncRNA/gene, LncRNAway uses transcript sequence fetching from the database and calculates siRNA sequence using the combinatorial algorithm we named “ siRNAexplorer ”. The algorithms include: (i) optimized GC content between 40% and 60%; (ii) number of A/U in the 3’ terminus; (iii) a at position 3 and 19; (iv) U at position 10; (v) no G at position 13; (vi) no G/C at position 19; (vii) no RNA secondary structure ( 31 ). RNAfold predicts the RNA secondary structure from the Vienna RNA package ( 32 ).

Primers for genotyping and quantitative determination

To provide a streamlined sgRNA design and validation experience for users, LncRNAway designs genotyping PCR primers and qPCR primers. The genotyping PCR primers spanned the DNA excision area, enabling rapid detection of knockout strains using agarose gel electrophoresis. The qPCR primers are preferentially generated using the DNA sequence from the last exon of target cDNA, and amplicon size is optimized to 100–200 bp. All primer sequences are calculated using Primer 3 ( 33 ).

Result page

The result page is structured into three sections following BESST, PE1 and RNAi algorithms. For BESST and PE1 algorithms, LncRNAway displays diagrams illustrating each approach, featuring tracks showcasing the position, direction, and PAM sequence of sgRNAs, along with primer sets utilized for genotyping and qPCR detection. Users can interact with the elements on the webpage to access corresponding sequences or scroll along the track axis to obtain a comprehensive view of the adjacent genomic context of the target lncRNA/gene. In the case of the RNAi method, the sense strand sequence of the siRNA duplex, score matrix, and RNA interloop free energy are presented. Also, LncRNAway offers a comprehensive report of sgRNA sequence, primer sequence, and siRNA sequences, relative position on the transcript (Figure 3 ). Additionally, we offer a gateway to export .txt and .pdf format full reports for users' convenience in further processes.

The result page that includes schematic of gene loci, sgRNA sequences, and primer information.

Many web-based tools for designing single-guide RNAs (sgRNAs) are currently available ( 11 , 16 ). However, most of these tools emphasize the sgRNA calculation algorithm, aiming to achieve high specificity and minimize off-target effects for a single input DNA sequence, such as CRISPOR ( 11 ). While these tools adequately facilitate the design of sgRNAs targeting protein-coding genes, they may not fully address the requirements for studying functional knockouts of lncRNAs. As previously discussed, achieving an effective lncRNA knockout entails considering additional factors due to the distinct features of lncRNAs compared to mRNAs ( 17 ). The launch of LncRNAway will help scientists, especially those focused on lncRNA function study, by providing a streamlined lncRNA knockout/knockdown guidance. BESST and PE1 algorithms tested the lncRNA suppression efficiency in cultured cell lines ( 18 ). Therefore, the LncRNAway web tool could be used to design sgRNA pools for screening functional lncRNA in a defined biological or pathological process.

In summary, LncRNAway offers a platform for the accurate and rapid design of sgRNAs, enabling the implementation of CRISPR-Cas9-based genome editing and functional knockout of lncRNAs.

LncRNAway is freely available and does not require registration or login at https://www.lncrnaway.com .

National Natural Science Foundation of China [82070486, 82270908]; Intramural funding from Chongqing General Hospital, Chongqing University. Funding for open access charge: National Natural Science Foundation of China [82070486].

Conflict of interest statement . None declared.

Kopp   F. , Mendell   J.T.   Functional classification and experimental dissection of long noncoding RNAs . Cell . 2018 ; 172 : 393 – 407 .

Google Scholar

Frankish   A. , Diekhans   M. , Jungreis   I. , Lagarde   J. , Loveland   J.E. , Mudge   J.M. , Sisu   C. , Wright   J.C. , Armstrong   J. , Barnes   I.  et al. .   Gencode 2021 . Nucleic Acids Res.   2021 ; 49 : D916 – D923 .

Yao   R.W. , Wang   Y. , Chen   L.L.   Cellular functions of long noncoding RNAs . Nat. Cell Biol.   2019 ; 21 : 542 – 551 .

Kim   J. , Piao   H.L. , Kim   B.J. , Yao   F. , Han   Z. , Wang   Y. , Xiao   Z. , Siverly   A.N. , Lawhon   S.E. , Ton   B.N.  et al. .   Long noncoding RNA MALAT1 suppresses breast cancer metastasis . Nat. Genet.   2018 ; 50 : 1705 – 1715 .

Guo   C.J. , Xu   G. , Chen   L.L.   Mechanisms of long noncoding RNA nuclear retention . Trends Biochem. Sci.   2020 ; 45 : 947 – 960 .

Elbashir   S.M. , Harborth   J. , Lendeckel   W. , Yalcin   A. , Weber   K. , Tuschl   T.   Duplexes of 21-nucleotide RNAs mediate RNA interference in cultured mammalian cells . Nature . 2001 ; 411 : 494 – 498 .

Bennett   C.F.   Therapeutic antisense oligonucleotides are coming of age . Annu. Rev. Med.   2019 ; 70 : 307 – 321 .

Lennox   K.A. , Behlke   M.A.   Cellular localization of long non-coding RNAs affects silencing by RNAi more than by antisense oligonucleotides . Nucleic Acids Res.   2016 ; 44 : 863 – 877 .

Shalem   O. , Sanjana   N.E. , Hartenian   E. , Shi   X. , Scott   D.A. , Mikkelson   T. , Heckl   D. , Ebert   B.L. , Root   D.E. , Doench   J.G.  et al. .   Genome-scale CRISPR-Cas9 knockout screening in human cells . Science . 2014 ; 343 : 84 – 87 .

Ho   T.T. , Zhou   N. , Huang   J. , Koirala   P. , Xu   M. , Fung   R. , Wu   F. , Mo   Y.Y.   Targeting non-coding RNAs with the CRISPR/Cas9 system in human cell lines . Nucleic Acids Res.   2015 ; 43 : e17 .

Concordet   J.P. , Haeussler   M.   CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens . Nucleic Acids Res.   2018 ; 46 : W242 – W245 .

Heigwer   F. , Kerr   G. , Boutros   M.   E-CRISP: fast CRISPR target site identification . Nat. Methods . 2014 ; 11 : 122 – 123 .

Kim   H.K. , Min   S. , Song   M. , Jung   S. , Choi   J.W. , Kim   Y. , Lee   S. , Yoon   S. , Kim   H.H.   Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity . Nat. Biotechnol . 2018 ; 36 : 239 – 241 .

Doench   J.G. , Fusi   N. , Sullender   M. , Hegde   M. , Vaimberg   E.W. , Donovan   K.F. , Smith   I. , Tothova   Z. , Wilen   C. , Orchard   R.  et al. .   Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9 . Nat. Biotechnol.   2016 ; 34 : 184 – 191 .

Liu   H. , Wei   Z. , Dominguez   A. , Li   Y. , Wang   X. , Qi   L.S.   CRISPR-ERA: a comprehensive design tool for CRISPR-mediated gene editing, repression and activation . Bioinformatics . 2015 ; 31 : 3676 – 3678 .

Labun   K. , Montague   T.G. , Krause   M. , Torres Cleuren   Y.N. , Tjeldnes   H. , Valen   E.   CHOPCHOP v3: expanding the CRISPR web toolbox beyond genome editing . Nucleic Acids Res.   2019 ; 47 : W171 – W174 .

Lyu   Q.R. , Zhang   S. , Zhang   Z. , Tang   Z.   Functional knockout of long non-coding RNAs with genome editing . Front Genet.   2023 ; 14 : 1242129 .

Zhang   S. , Chen   Y. , Dong   K. , Zhao   Y. , Wang   Y. , Wang   S. , Qu   C. , Xu   N. , Xie   W. , Zeng   C.  et al. .   BESST: a novel LncRNA knockout strategy with less genome perturbance . Nucleic Acids Res.   2023 ; 51 : e49 .

McDonel   P. , Guttman   M.   Approaches for understanding the mechanisms of long noncoding RNA regulation of gene expression . Cold Spring Harb. Perspect Biol.   2019 ; 11 : a032151 .

Wang   D. , Zhang   C. , Wang   B. , Li   B. , Wang   Q. , Liu   D. , Wang   H. , Zhou   Y. , Shi   L. , Lan   F.  et al. .   Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning . Nat. Commun.   2019 ; 10 : 4284 .

Chari   R. , Yeo   N.C. , Chavez   A. , Church   G.M.   sgRNA Scorer 2.0: a species-independent model to predict CRISPR/Cas9 activity . ACS Synth. Biol.   2017 ; 6 : 902 – 904 .

Park   J. , Bae   S. , Kim   J.S.   Cas-Designer: a web-based tool for choice of CRISPR-Cas9 target sites . Bioinformatics . 2015 ; 31 : 4014 – 4016 .

Huang   T.P. , Heins   Z.J. , Miller   S.M. , Wong   B.G. , Balivada   P.A. , Wang   T. , Khalil   A.S. , Liu   D.R.   High-throughput continuous evolution of compact Cas9 variants targeting single-nucleotide-pyrimidine PAMs . Nat. Biotechnol.   2023 ; 41 : 96 – 107 .

Nishimasu   H. , Cong   L. , Yan   W.X. , Ran   F.A. , Zetsche   B. , Li   Y. , Kurabayashi   A. , Ishitani   R. , Zhang   F. , Nureki   O.   Crystal Structure of Staphylococcus aureus Cas9 . Cell . 2015 ; 162 : 1113 – 1126 .

Casini   A. , Olivieri   M. , Petris   G. , Montagna   C. , Reginato   G. , Maule   G. , Lorenzin   F. , Prandi   D. , Romanel   A. , Demichelis   F.  et al. .   A highly specific SpCas9 variant is identified by in vivo screening in yeast . Nat. Biotechnol.   2018 ; 36 : 265 – 271 .

Khan   M.R. , Avino   M. , Wellinger   R.J. , Laurent   B.   Distinct regulatory functions and biological roles of lncRNA splice variants . Mol. Ther. Nucleic Acids . 2023 ; 32 : 127 – 143 .

Zhen   S. , Hua   L. , Liu   Y.H. , Sun   X.M. , Jiang   M.M. , Chen   W. , Zhao   L. , Li   X.   Inhibition of long non-coding RNA UCA1 by CRISPR/Cas9 attenuated malignant phenotypes of bladder cancer . Oncotarget . 2017 ; 8 : 9634 – 9646 .

Jinek   M. , Chylinski   K. , Fonfara   I. , Hauer   M. , Doudna   J.A. , Charpentier   E.   A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity . Science . 2012 ; 337 : 816 – 821 .

Fu   Y. , Foden   J.A. , Khayter   C. , Maeder   M.L. , Reyon   D. , Joung   J.K. , Sander   J.D.   High-frequency off-target mutagenesis induced by CRISPR-Cas nucleases in human cells . Nat. Biotechnol.   2013 ; 31 : 822 – 826 .

Mello   C.C. , Conte   D.  Jr   Revealing the world of RNA interference . Nature . 2004 ; 431 : 338 – 342 .

Reynolds   A. , Leake   D. , Boese   Q. , Scaringe   S. , Marshall   W.S. , Khvorova   A.   Rational siRNA design for RNA interference . Nat. Biotechnol.   2004 ; 22 : 326 – 330 .

Lorenz   R. , Bernhart   S.H. , Höner Zu Siederdissen   C. , Tafer   H. , Flamm   C. , Stadler   P.F. , Hofacker   I.L.   ViennaRNA Package 2.0 . Algorithms Mol. Biol.   2011 ; 6 : 26 .

Untergasser   A. , Cutcutache   I. , Koressaar   T. , Ye   J. , Faircloth   B.C. , Remm   M. , Rozen   S.G.   Primer3–new capabilities and interfaces . Nucleic Acids Res.   2012 ; 40 : e115 .

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Calls for Research Proposals

The IDB Group’s Gender and Diversity Knowledge Initiative (GDLab) seeks to advance knowledge development regarding the existing inequalities between men and women, as well as inequalities faced by Indigenous Peoples, people of African descent, people with disabilities, and LGBTQ+ people through promoting competitive research calls. The sixth edition of this call focuses on the inclusion of people with disabilities . Download the brochure and application form to learn more about this call and the application process. The deadline to submit a proposal is May 12, 2024 (11:59 P.M. ET) .

The IDB Group’s Gender and Diversity Knowledge Initiative (GDLab) is responsible for leading and funding high-impact research aimed at reducing gender and diversity gaps and addressing challenges to overcome existing inequalities between men and women, as well as those faced by indigenous peoples, Afro-descendants, people with disabilities, and LGBTQ+ people.

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