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• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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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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• How it works

How to Write a Research Design – Guide with Examples

Published by Alaxendra Bets at August 14th, 2021 , Revised On June 24, 2024

A research design is a structure that combines different components of research. It involves the use of different data collection and data analysis techniques logically to answer the  research questions .

It would be best to make some decisions about addressing the research questions adequately before starting the research process, which is achieved with the help of the research design.

Below are the key aspects of the decision-making process:

• Data type required for research
• Research resources
• Participants required for research
• Hypothesis based upon research question(s)
• Data analysis  methodologies
• Variables (Independent, dependent, and confounding)
• The location and timescale for conducting the data
• The time period required for research

The research design provides the strategy of investigation for your project. Furthermore, it defines the parameters and criteria to compile the data to evaluate results and conclude.

Your project’s validity depends on the data collection and  interpretation techniques.  A strong research design reflects a strong  dissertation , scientific paper, or research proposal .

Step 1: Establish Priorities for Research Design

Before conducting any research study, you must address an important question: “how to create a research design.”

The research design depends on the researcher’s priorities and choices because every research has different priorities. For a complex research study involving multiple methods, you may choose to have more than one research design.

Multimethodology or multimethod research includes using more than one data collection method or research in a research study or set of related studies.

If one research design is weak in one area, then another research design can cover that weakness. For instance, a  dissertation analyzing different situations or cases will have more than one research design.

For example:

• Experimental research involves experimental investigation and laboratory experience, but it does not accurately investigate the real world.
• Quantitative research is good for the  statistical part of the project, but it may not provide an in-depth understanding of the  topic .
• Also, correlational research will not provide experimental results because it is a technique that assesses the statistical relationship between two variables.

While scientific considerations are a fundamental aspect of the research design, It is equally important that the researcher think practically before deciding on its structure. Here are some questions that you should think of;

• Do you have enough time to gather data and complete the write-up?
• Will you be able to collect the necessary data by interviewing a specific person or visiting a specific location?
• Do you have in-depth knowledge about the  different statistical analysis and data collection techniques to address the research questions  or test the  hypothesis ?

If you think that the chosen research design cannot answer the research questions properly, you can refine your research questions to gain better insight.

Step 2: Data Type you Need for Research

Decide on the type of data you need for your research. The type of data you need to collect depends on your research questions or research hypothesis. Two types of research data can be used to answer the research questions:

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

Also, see; Research methods, design, and analysis .

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• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

Once you have selected the type of research to answer your research question, you need to decide where and how to collect the data.

It is time to determine your research method to address the  research problem . Research methods involve procedures, techniques, materials, and tools used for the study.

For instance, a dissertation research design includes the different resources and data collection techniques and helps establish your  dissertation’s structure .

The following table shows the characteristics of the most popularly employed research methods.

Research Methods

Step 4: Procedure of Data Analysis

Use of the  correct data and statistical analysis technique is necessary for the validity of your research. Therefore, you need to be certain about the data type that would best address the research problem. Choosing an appropriate analysis method is the final step for the research design. It can be split into two main categories;

Quantitative Data Analysis

The quantitative data analysis technique involves analyzing the numerical data with the help of different applications such as; SPSS, STATA, Excel, origin lab, etc.

This data analysis strategy tests different variables such as spectrum, frequencies, averages, and more. The research question and the hypothesis must be established to identify the variables for testing.

Qualitative Data Analysis

Qualitative data analysis of figures, themes, and words allows for flexibility and the researcher’s subjective opinions. This means that the researcher’s primary focus will be interpreting patterns, tendencies, and accounts and understanding the implications and social framework.

You should be clear about your research objectives before starting to analyze the data. For example, you should ask yourself whether you need to explain respondents’ experiences and insights or do you also need to evaluate their responses with reference to a certain social framework.

Step 5: Write your Research Proposal

The research design is an important component of a research proposal because it plans the project’s execution. You can share it with the supervisor, who would evaluate the feasibility and capacity of the results  and  conclusion .

Read our guidelines to write a research proposal  if you have already formulated your research design. The research proposal is written in the future tense because you are writing your proposal before conducting research.

The  research methodology  or research design, on the other hand, is generally written in the past tense.

How to Write a Research Design – Conclusion

A research design is the plan, structure, strategy of investigation conceived to answer the research question and test the hypothesis. The dissertation research design can be classified based on the type of data and the type of analysis.

Above mentioned five steps are the answer to how to write a research design. So, follow these steps to  formulate the perfect research design for your dissertation .

ResearchProspect writers have years of experience creating research designs that align with the dissertation’s aim and objectives. If you are struggling with your dissertation methodology chapter, you might want to look at our dissertation part-writing service.

Our dissertation writers can also help you with the full dissertation paper . No matter how urgent or complex your need may be, ResearchProspect can help. We also offer PhD level research paper writing services.

Frequently Asked Questions

What is research design.

Research design is a systematic plan that guides the research process, outlining the methodology and procedures for collecting and analysing data. It determines the structure of the study, ensuring the research question is answered effectively, reliably, and validly. It serves as the blueprint for the entire research project.

How to write a research design?

To write a research design, define your research question, identify the research method (qualitative, quantitative, or mixed), choose data collection techniques (e.g., surveys, interviews), determine the sample size and sampling method, outline data analysis procedures, and highlight potential limitations and ethical considerations for the study.

How to write the design section of a research paper?

In the design section of a research paper, describe the research methodology chosen and justify its selection. Outline the data collection methods, participants or samples, instruments used, and procedures followed. Detail any experimental controls, if applicable. Ensure clarity and precision to enable replication of the study by other researchers.

How to write a research design in methodology?

To write a research design in methodology, clearly outline the research strategy (e.g., experimental, survey, case study). Describe the sampling technique, participants, and data collection methods. Detail the procedures for data collection and analysis. Justify choices by linking them to research objectives, addressing reliability and validity.

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Let’s briefly examine the concept of research paradigms, their pillars, purposes, types, examples, and how they can be combined.

Make sure that your selected topic is intriguing, manageable, and relevant. Here are some guidelines to help understand how to find a good dissertation topic.

How to write a hypothesis for dissertation,? A hypothesis is a statement that can be tested with the help of experimental or theoretical research.

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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

13 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

This post is really helpful.

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?

This post has been very useful to me. Confusing areas have been cleared

This is very helpful and very useful!

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How to Synthesize Written Information from Multiple Sources

Shona McCombes

Content Manager

B.A., English Literature, University of Glasgow

Shona McCombes is the content manager at Scribbr, Netherlands.

Learn about our Editorial Process

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

On This Page:

When you write a literature review or essay, you have to go beyond just summarizing the articles you’ve read – you need to synthesize the literature to show how it all fits together (and how your own research fits in).

Synthesizing simply means combining. Instead of summarizing the main points of each source in turn, you put together the ideas and findings of multiple sources in order to make an overall point.

At the most basic level, this involves looking for similarities and differences between your sources. Your synthesis should show the reader where the sources overlap and where they diverge.

Unsynthesized Example

Franz (2008) studied undergraduate online students. He looked at 17 females and 18 males and found that none of them liked APA. According to Franz, the evidence suggested that all students are reluctant to learn citations style. Perez (2010) also studies undergraduate students. She looked at 42 females and 50 males and found that males were significantly more inclined to use citation software ( p < .05). Findings suggest that females might graduate sooner. Goldstein (2012) looked at British undergraduates. Among a sample of 50, all females, all confident in their abilities to cite and were eager to write their dissertations.

Synthesized Example

Studies of undergraduate students reveal conflicting conclusions regarding relationships between advanced scholarly study and citation efficacy. Although Franz (2008) found that no participants enjoyed learning citation style, Goldstein (2012) determined in a larger study that all participants watched felt comfortable citing sources, suggesting that variables among participant and control group populations must be examined more closely. Although Perez (2010) expanded on Franz’s original study with a larger, more diverse sample…

Step 1: Organize your sources

After collecting the relevant literature, you’ve got a lot of information to work through, and no clear idea of how it all fits together.

Before you can start writing, you need to organize your notes in a way that allows you to see the relationships between sources.

One way to begin synthesizing the literature is to put your notes into a table. Depending on your topic and the type of literature you’re dealing with, there are a couple of different ways you can organize this.

Summary table

A summary table collates the key points of each source under consistent headings. This is a good approach if your sources tend to have a similar structure – for instance, if they’re all empirical papers.

Each row in the table lists one source, and each column identifies a specific part of the source. You can decide which headings to include based on what’s most relevant to the literature you’re dealing with.

For example, you might include columns for things like aims, methods, variables, population, sample size, and conclusion.

For each study, you briefly summarize each of these aspects. You can also include columns for your own evaluation and analysis.

The summary table gives you a quick overview of the key points of each source. This allows you to group sources by relevant similarities, as well as noticing important differences or contradictions in their findings.

Synthesis matrix

A synthesis matrix is useful when your sources are more varied in their purpose and structure – for example, when you’re dealing with books and essays making various different arguments about a topic.

Each column in the table lists one source. Each row is labeled with a specific concept, topic or theme that recurs across all or most of the sources.

Then, for each source, you summarize the main points or arguments related to the theme.

The purposes of the table is to identify the common points that connect the sources, as well as identifying points where they diverge or disagree.

Step 2: Outline your structure

Now you should have a clear overview of the main connections and differences between the sources you’ve read. Next, you need to decide how you’ll group them together and the order in which you’ll discuss them.

For shorter papers, your outline can just identify the focus of each paragraph; for longer papers, you might want to divide it into sections with headings.

There are a few different approaches you can take to help you structure your synthesis.

If your sources cover a broad time period, and you found patterns in how researchers approached the topic over time, you can organize your discussion chronologically .

That doesn’t mean you just summarize each paper in chronological order; instead, you should group articles into time periods and identify what they have in common, as well as signalling important turning points or developments in the literature.

If the literature covers various different topics, you can organize it thematically .

That means that each paragraph or section focuses on a specific theme and explains how that theme is approached in the literature.

Source Used with Permission: The Chicago School

If you’re drawing on literature from various different fields or they use a wide variety of research methods, you can organize your sources methodologically .

That means grouping together studies based on the type of research they did and discussing the findings that emerged from each method.

If your topic involves a debate between different schools of thought, you can organize it theoretically .

That means comparing the different theories that have been developed and grouping together papers based on the position or perspective they take on the topic, as well as evaluating which arguments are most convincing.

Step 3: Write paragraphs with topic sentences

What sets a synthesis apart from a summary is that it combines various sources. The easiest way to think about this is that each paragraph should discuss a few different sources, and you should be able to condense the overall point of the paragraph into one sentence.

This is called a topic sentence , and it usually appears at the start of the paragraph. The topic sentence signals what the whole paragraph is about; every sentence in the paragraph should be clearly related to it.

A topic sentence can be a simple summary of the paragraph’s content:

“Early research on [x] focused heavily on [y].”

For an effective synthesis, you can use topic sentences to link back to the previous paragraph, highlighting a point of debate or critique:

“Several scholars have pointed out the flaws in this approach.” “While recent research has attempted to address the problem, many of these studies have methodological flaws that limit their validity.”

By using topic sentences, you can ensure that your paragraphs are coherent and clearly show the connections between the articles you are discussing.

As you write your paragraphs, avoid quoting directly from sources: use your own words to explain the commonalities and differences that you found in the literature.

Don’t try to cover every single point from every single source – the key to synthesizing is to extract the most important and relevant information and combine it to give your reader an overall picture of the state of knowledge on your topic.

Step 4: Revise, edit and proofread

Like any other piece of academic writing, synthesizing literature doesn’t happen all in one go – it involves redrafting, revising, editing and proofreading your work.

Checklist for Synthesis

•   Do I introduce the paragraph with a clear, focused topic sentence?
•   Do I discuss more than one source in the paragraph?
•   Do I mention only the most relevant findings, rather than describing every part of the studies?
•   Do I discuss the similarities or differences between the sources, rather than summarizing each source in turn?
•   Do I put the findings or arguments of the sources in my own words?
•   Is the paragraph organized around a single idea?
•   Is the paragraph directly relevant to my research question or topic?
•   Is there a logical transition from this paragraph to the next one?

Further Information

How to Synthesise: a Step-by-Step Approach

Help…I”ve Been Asked to Synthesize!

Learn how to Synthesise (combine information from sources)

How to write a Psychology Essay

15 Steps to Good Research

• Define and articulate a research question (formulate a research hypothesis). How to Write a Thesis Statement (Indiana University)
• Identify possible sources of information in many types and formats. Georgetown University Library's Research & Course Guides
• Judge the scope of the project.
• Reevaluate the research question based on the nature and extent of information available and the parameters of the research project.
• Select the most appropriate investigative methods (surveys, interviews, experiments) and research tools (periodical indexes, databases, websites).
• Plan the research project. Writing Anxiety (UNC-Chapel Hill) Strategies for Academic Writing (SUNY Empire State College)
• Retrieve information using a variety of methods (draw on a repertoire of skills).
• Refine the search strategy as necessary.
• Write and organize useful notes and keep track of sources. Taking Notes from Research Reading (University of Toronto) Use a citation manager: Zotero or Refworks
• Evaluate sources using appropriate criteria. Evaluating Internet Sources
• Synthesize, analyze and integrate information sources and prior knowledge. Georgetown University Writing Center
• Revise hypothesis as necessary.
• Use information effectively for a specific purpose.
• Understand such issues as plagiarism, ownership of information (implications of copyright to some extent), and costs of information. Georgetown University Honor Council Copyright Basics (Purdue University) How to Recognize Plagiarism: Tutorials and Tests from Indiana University
• Cite properly and give credit for sources of ideas. MLA Bibliographic Form (7th edition, 2009) MLA Bibliographic Form (8th edition, 2016) Turabian Bibliographic Form: Footnote/Endnote Turabian Bibliographic Form: Parenthetical Reference Use a citation manager: Zotero or Refworks

Adapted from the Association of Colleges and Research Libraries "Objectives for Information Literacy Instruction" , which are more complete and include outcomes. See also the broader "Information Literacy Competency Standards for Higher Education."

How to write a research plan: Step-by-step guide

Last updated

30 January 2024

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Today’s businesses and institutions rely on data and analytics to inform their product and service decisions. These metrics influence how organizations stay competitive and inspire innovation. However, gathering data and insights requires carefully constructed research, and every research project needs a roadmap. This is where a research plan comes into play.

Read this step-by-step guide for writing a detailed research plan that can apply to any project, whether it’s scientific, educational, or business-related.

• What is a research plan?

A research plan is a documented overview of a project in its entirety, from end to end. It details the research efforts, participants, and methods needed, along with any anticipated results. It also outlines the project’s goals and mission, creating layers of steps to achieve those goals within a specified timeline.

Without a research plan, you and your team are flying blind, potentially wasting time and resources to pursue research without structured guidance.

The principal investigator, or PI, is responsible for facilitating the research oversight. They will create the research plan and inform team members and stakeholders of every detail relating to the project. The PI will also use the research plan to inform decision-making throughout the project.

• Why do you need a research plan?

Create a research plan before starting any official research to maximize every effort in pursuing and collecting the research data. Crucially, the plan will model the activities needed at each phase of the research project .

Like any roadmap, a research plan serves as a valuable tool providing direction for those involved in the project—both internally and externally. It will keep you and your immediate team organized and task-focused while also providing necessary definitions and timelines so you can execute your project initiatives with full understanding and transparency.

External stakeholders appreciate a working research plan because it’s a great communication tool, documenting progress and changing dynamics as they arise. Any participants of your planned research sessions will be informed about the purpose of your study, while the exercises will be based on the key messaging outlined in the official plan.

Here are some of the benefits of creating a research plan document for every project:

Project organization and structure

Well-informed participants

All stakeholders and teams align in support of the project

Clearly defined project definitions and purposes

Distractions are eliminated, prioritizing task focus

Timely management of individual task schedules and roles

Costly reworks are avoided

• What should a research plan include?

The different aspects of your research plan will depend on the nature of the project. However, most official research plan documents will include the core elements below. Each aims to define the problem statement , devising an official plan for seeking a solution.

Specific project goals and individual objectives

Ideal strategies or methods for reaching those goals

Required resources

Descriptions of the target audience, sample sizes , demographics, and scopes

Key performance indicators (KPIs)

Project background

Research and testing support

Preliminary studies and progress reporting mechanisms

Cost estimates and change order processes

Depending on the research project’s size and scope, your research plan could be brief—perhaps only a few pages of documented plans. Alternatively, it could be a fully comprehensive report. Either way, it’s an essential first step in dictating your project’s facilitation in the most efficient and effective way.

• How to write a research plan for your project

When you start writing your research plan, aim to be detailed about each step, requirement, and idea. The more time you spend curating your research plan, the more precise your research execution efforts will be.

Account for every potential scenario, and be sure to address each and every aspect of the research.

Consider following this flow to develop a great research plan for your project:

Define your project’s purpose

Start by defining your project’s purpose. Identify what your project aims to accomplish and what you are researching. Remember to use clear language.

Thinking about the project’s purpose will help you set realistic goals and inform how you divide tasks and assign responsibilities. These individual tasks will be your stepping stones to reach your overarching goal.

Additionally, you’ll want to identify the specific problem, the usability metrics needed, and the intended solutions.

Know the following three things about your project’s purpose before you outline anything else:

What you’re doing

Why you’re doing it

What you expect from it

Identify individual objectives

With your overarching project objectives in place, you can identify any individual goals or steps needed to reach those objectives. Break them down into phases or steps. You can work backward from the project goal and identify every process required to facilitate it.

Be mindful to identify each unique task so that you can assign responsibilities to various team members. At this point in your research plan development, you’ll also want to assign priority to those smaller, more manageable steps and phases that require more immediate or dedicated attention.

Select research methods

Once you have outlined your goals, objectives, steps, and tasks, it’s time to drill down on selecting research methods . You’ll want to leverage specific research strategies and processes. When you know what methods will help you reach your goals, you and your teams will have direction to perform and execute your assigned tasks.

Research methods might include any of the following:

User interviews : this is a qualitative research method where researchers engage with participants in one-on-one or group conversations. The aim is to gather insights into their experiences, preferences, and opinions to uncover patterns, trends, and data.

Field studies : this approach allows for a contextual understanding of behaviors, interactions, and processes in real-world settings. It involves the researcher immersing themselves in the field, conducting observations, interviews, or experiments to gather in-depth insights.

Card sorting : participants categorize information by sorting content cards into groups based on their perceived similarities. You might use this process to gain insights into participants’ mental models and preferences when navigating or organizing information on websites, apps, or other systems.

Focus groups : use organized discussions among select groups of participants to provide relevant views and experiences about a particular topic.

Diary studies : ask participants to record their experiences, thoughts, and activities in a diary over a specified period. This method provides a deeper understanding of user experiences, uncovers patterns, and identifies areas for improvement.

Five-second testing: participants are shown a design, such as a web page or interface, for just five seconds. They then answer questions about their initial impressions and recall, allowing you to evaluate the design’s effectiveness.

Surveys : get feedback from participant groups with structured surveys. You can use online forms, telephone interviews, or paper questionnaires to reveal trends, patterns, and correlations.

Tree testing : tree testing involves researching web assets through the lens of findability and navigability. Participants are given a textual representation of the site’s hierarchy (the “tree”) and asked to locate specific information or complete tasks by selecting paths.

Usability testing : ask participants to interact with a product, website, or application to evaluate its ease of use. This method enables you to uncover areas for improvement in digital key feature functionality by observing participants using the product.

Live website testing: research and collect analytics that outlines the design, usability, and performance efficiencies of a website in real time.

There are no limits to the number of research methods you could use within your project. Just make sure your research methods help you determine the following:

What do you plan to do with the research findings?

What decisions will this research inform? How can your stakeholders leverage the research data and results?

Recruit participants and allocate tasks

Next, identify the participants needed to complete the research and the resources required to complete the tasks. Different people will be proficient at different tasks, and having a task allocation plan will allow everything to run smoothly.

Prepare a thorough project summary

Every well-designed research plan will feature a project summary. This official summary will guide your research alongside its communications or messaging. You’ll use the summary while recruiting participants and during stakeholder meetings. It can also be useful when conducting field studies.

Ensure this summary includes all the elements of your research project . Separate the steps into an easily explainable piece of text that includes the following:

An introduction: the message you’ll deliver to participants about the interview, pre-planned questioning, and testing tasks.

Interview questions: prepare questions you intend to ask participants as part of your research study, guiding the sessions from start to finish.

An exit message: draft messaging your teams will use to conclude testing or survey sessions. These should include the next steps and express gratitude for the participant’s time.

Create a realistic timeline

While your project might already have a deadline or a results timeline in place, you’ll need to consider the time needed to execute it effectively.

Realistically outline the time needed to properly execute each supporting phase of research and implementation. And, as you evaluate the necessary schedules, be sure to include additional time for achieving each milestone in case any changes or unexpected delays arise.

For this part of your research plan, you might find it helpful to create visuals to ensure your research team and stakeholders fully understand the information.

Determine how to present your results

A research plan must also describe how you intend to present your results. Depending on the nature of your project and its goals, you might dedicate one team member (the PI) or assume responsibility for communicating the findings yourself.

In this part of the research plan, you’ll articulate how you’ll share the results. Detail any materials you’ll use, such as:

Presentations and slides

A project report booklet

A project findings pamphlet

Documents with key takeaways and statistics

Graphic visuals to support your findings

• Format your research plan

As you create your research plan, you can enjoy a little creative freedom. A plan can assume many forms, so format it how you see fit. Determine the best layout based on your specific project, intended communications, and the preferences of your teams and stakeholders.

Find format inspiration among the following layouts:

Written outlines

Narrative storytelling

Visual mapping

Graphic timelines

Remember, the research plan format you choose will be subject to change and adaptation as your research and findings unfold. However, your final format should ideally outline questions, problems, opportunities, and expectations.

• Research plan example

Imagine you’ve been tasked with finding out how to get more customers to order takeout from an online food delivery platform. The goal is to improve satisfaction and retain existing customers. You set out to discover why more people aren’t ordering and what it is they do want to order or experience. 

You identify the need for a research project that helps you understand what drives customer loyalty . But before you jump in and start calling past customers, you need to develop a research plan—the roadmap that provides focus, clarity, and realistic details to the project.

Here’s an example outline of a research plan you might put together:

Project title

Project members involved in the research plan

Purpose of the project (provide a summary of the research plan’s intent)

Objective 1 (provide a short description for each objective)

Objective 2

Objective 3

Proposed timeline

Audience (detail the group you want to research, such as customers or non-customers)

Budget (how much you think it might cost to do the research)

Risk factors/contingencies (any potential risk factors that may impact the project’s success)

Remember, your research plan doesn’t have to reinvent the wheel—it just needs to fit your project’s unique needs and aims.

Customizing a research plan template

Some companies offer research plan templates to help get you started. However, it may make more sense to develop your own customized plan template. Be sure to include the core elements of a great research plan with your template layout, including the following:

Introductions to participants and stakeholders

Background problems and needs statement

Significance, ethics, and purpose

Research methods, questions, and designs

Preliminary beliefs and expectations

Implications and intended outcomes

Realistic timelines for each phase

Conclusion and presentations

How many pages should a research plan be?

Generally, a research plan can vary in length between 500 to 1,500 words. This is roughly three pages of content. More substantial projects will be 2,000 to 3,500 words, taking up four to seven pages of planning documents.

What is the difference between a research plan and a research proposal?

A research plan is a roadmap to success for research teams. A research proposal, on the other hand, is a dissertation aimed at convincing or earning the support of others. Both are relevant in creating a guide to follow to complete a project goal.

What are the seven steps to developing a research plan?

While each research project is different, it’s best to follow these seven general steps to create your research plan:

Defining the problem

Identifying goals

Choosing research methods

Recruiting participants

Preparing the brief or summary

Establishing task timelines

Defining how you will present the findings

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

Start for free today, add your research, and get to key insights faster

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THTR/SOC/DES/ART/ARCH 090-016: How can we use Design as Activism to affect social change?

Make the most of your course guide, the research process.

• Evaluating Sources
• Finding Sources
• Citing Sources
• Extra (Fun) Sources

Humanities Librarian

Use the tabs in this guide to:

• Learn strategies for evaluating information
• Find interdisciplinary and subject specific sources and how to cite them
• "Geek out" on library resources

• Next: Evaluating Sources >>
• Last Updated: Sep 3, 2024 4:49 PM
• URL: https://libraryguides.lehigh.edu/design-as-activism

• Open access
• Published: 02 September 2024

Clinical supervisor’s experiences of peer group clinical supervision during COVID-19: a mixed methods study

• Owen Doody   ORCID: orcid.org/0000-0002-3708-1647 1 ,
• Kathleen Markey   ORCID: orcid.org/0000-0002-3024-0828 1 ,
• James Turner   ORCID: orcid.org/0000-0002-8360-1420 2 ,
• Claire O. Donnell   ORCID: orcid.org/0000-0003-2386-7048 1 &
• Louise Murphy   ORCID: orcid.org/0000-0003-2381-3963 1  

BMC Nursing volume  23 , Article number:  612 ( 2024 ) Cite this article

Metrics details

Providing positive and supportive environments for nurses and midwives working in ever-changing and complex healthcare services is paramount. Clinical supervision is one approach that nurtures and supports professional guidance, ethical practice, and personal development, which impacts positively on staff morale and standards of care delivery. In the context of this study, peer group clinical supervision provides allocated time to reflect and discuss care provided and facilitated by clinical supervisors who are at the same grade/level as the supervisees.

To explore the clinical supervisor’s experiences of peer group clinical supervision a mixed methods study design was utilised within Irish health services (midwifery, intellectual disability, general, mental health). The Manchester Clinical Supervision Scale was used to survey clinical supervisors ( n  = 36) and semi-structured interviews ( n  = 10) with clinical supervisors were conducted. Survey data were analysed through SPSS and interview data were analysed utilising content analysis. The qualitative and quantitative data’s reporting rigour was guided by the CROSS and SRQR guidelines.

Participants generally had a positive encounter when providing clinical supervision. They highly appreciated the value of clinical supervision and expressed a considerable degree of contentment with the supervision they provided to supervisees. The advantages of peer group clinical supervision encompass aspects related to self (such as confidence, leadership, personal development, and resilience), service and organisation (including a positive working environment, employee retention, and safety), and patient care (involving critical thinking and evaluation, patient safety, adherence to quality standards, and elevated levels of care).

There are many benefits of peer group clinical supervision at an individual, service, organisation, and patient level. Nevertheless, there is a need to address a lack of awareness and misconceptions surrounding clinical supervision to create an environment and culture conducive to realising its full potential. It is crucial that clinical supervision be accessible to nurses and midwives of all grades across all healthcare services, with national planning to address capacity and sustainability.

Peer Review reports

Within a dynamic healthcare system, nurses and midwives face growing demands, underscoring the necessity for ongoing personal and professional development. This is essential to improve the effectiveness and efficiency of care delivery for patients, families, and societies. Despite the increased emphasis on increasing the quality and safety of healthcare services and delivery, there is evidence highlighting declining standards of nursing and midwifery care [ 1 ]. The recent focus on re-affirming and re-committing to core values guiding nursing and midwifery practice is encouraging such as compassion, care and commitment [ 2 ], competence, communication, and courage [ 3 ]. However, imposing value statements in isolation is unlikely to change behaviours and greater consideration needs to be given to ways in which compassion, care, and commitment are nurtured and ultimately applied in daily practice. Furthermore, concerns have been raised about global staff shortages [ 4 ], the evidence suggesting several contributing factors such as poor workforce planning [ 5 ], job dissatisfaction [ 6 ], and healthcare migration [ 7 ]. Without adequate resources and staffing, compromising standards of care and threats to patient safety will be imminent therefore the importance of developing effective strategies for retaining competent registered nurses and midwives is paramount in today’s climate of increased staff shortages [ 4 ]. Clinical supervision serves as a means to facilitate these advancements and has been linked to heightened job satisfaction, enhanced staff retention, improved staff effectiveness, and effective clinical governance, by aiding in quality improvements, risk management, and heightened accountability [ 8 ].

Clinical supervision is a key component of professional practice and while the aim is largely known, there is no universally accepted definition of clinical supervision [ 8 ]. Clinical supervision is a structured process where clinicians are allowed protected time to reflect on their practice within a supportive environment and with the purpose of developing high-quality clinical care [ 9 ]. Recent literature published on clinical supervision [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ] highlights the advantages and merits of clinical supervision. However, there are challenges also identified such as a lack of consensus regarding the meaning and goal, implementation issues, variations in approaches in its operationalisation, and an absence of research evidence on its effectiveness. Duration and experience in clinical supervision link to positive benefits [ 8 ], but there is little evidence of how clinical supervision altered individual behaviours and practices. This is reinforced by Kuhne et al., [ 15 ] who emphasise that satisfaction rather than effectiveness is more commonly examined. It is crucial to emphasise that reviews have pinpointed that clinical supervision lowers the risks of adverse patient outcomes [ 9 ] and demonstrates enhancements in the execution of certain care processes. Peer group clinical supervision is a form of clinical supervision whereby two or more practitioners engage in a supervision or consultation process to improve their professional practice [ 17 ]. There is limited evidence regarding peer group clinical supervision and research on the experiences of peer clinical supervision and stakeholders is needed [ 13 ]. In Ireland, peer group clinical supervision has been recommended and guidelines have been developed [ 18 ]. In the Irish context, peer clinical supervision is where both clinical supervisees and clinical supervisors are peers at the same level/grade. However, greater evidence is required to inform future decisions on the implementation of peer group clinical supervision and the purpose of this study is to explore clinical supervisors’ experiences of peer group clinical supervision. As the focus is on peer group supervisors and utilising mixed methods the experiences of the other stakeholders were investigated and reported separately.

A mixed methods approach was used (survey and semi-structured interviews) to capture clinical supervisor’s experiences of clinical supervision. The study adhered to the Consensus-Based Checklist for Reporting of Survey Studies guidelines [ 19 ] (Supplementary File S1 ) and Standards for Reporting Qualitative Research guidelines [ 20 ] (Supplementary File S2 ).

Participants

This study was conducted with participants who successfully completed a professionally credited award: clinical supervision module run by a university in Ireland (74 clinical supervisors across 5 programmes over 3 years). The specific selection criteria for participants were that they were registered nurses/midwives delivering peer group clinical supervision within the West region of Ireland. The specific exclusion criteria were as follows: (1) nurses and midwives who haven’t finished the clinical supervision module at the University, (2) newly appointed peer group clinical supervisors who have yet to establish their groups and initiate the delivery of peer group clinical supervision.

Measures and procedures

The Manchester Clinical Supervision Scale-26 was used to survey participants in February/March 2022 and measure the peer group clinical supervisors’ overall experiences of facilitating peer group clinical supervision. The Manchester Clinical Supervision Scale-26 is a validated 26-item self-report questionnaire with a Likert-type (1–5) scale ranging from strongly disagree (1) to strongly agree (5) [ 21 ]. The Manchester Clinical Supervision Scale-26 measures the efficiency of and satisfaction with supervision, to investigate the skills acquisition aspect of clinical supervision and its effect on the quality of clinical care [ 21 ]. The instrument consists of two main sections to measure three (normative, restorative, and formative) dimensions of clinical supervision utilising six sub-scales: (1) trust and rapport, (2) supervisor advice/support, (3) improved care/skills, (4) importance/value of clinical supervision, (5) finding time, (6) personal issues/reflections and a total score for the Manchester Clinical Supervision Scale-26 is also calculated. Section two consisted of the demographic section of the questionnaire and was tailored to include eight demographic questions concerning the supervisor’s demographics, supervisee characteristics, and characteristics of clinical supervision sessions. There were also two open field questions on the Manchester Clinical Supervision Scale-26 (model of clinical supervision used and any other comments about experience of peer group clinical supervision). The main question about participants’ experiences with peer clinical supervision was “What was your experience of peer clinical supervision?” This was gathered through individual semi-structured interviews lasting between 20 and 45 min, in March/April 2022 (Supplementary file 3 ).

Ethical considerations

Health service institutional review boards of two University hospitals approved this study (Ref: 091/19 and Ref: C.A. 2199). Participants were recruited after receiving a full explanation of the study’s purpose and procedure and all relevant information. Participants were aware of potential risks and benefits and could withdraw from the study, or the survey could be stopped at any time. Informed consent was recorded, and participant identities were protected by using a pseudonym to protect anonymity.

Data analysis method

Survey data was analysed using the data analysis software package Statistical Package for the Social Sciences, version 26 (SPSS Inc., Chicago, Il, USA). Descriptive analysis was undertaken to summarise responses to all items and categorical variables (nominal and ordinal) were analysed using frequencies to detail the number and percentage of responses to each question. Scores on the Manchester Clinical Supervision Scale-26 were reverse scored for 9 items (Q1-Q6, Q8, Q20,21) and total scores for each of the six sub-scales were calculated by adding the scores for each item. Raw scores for the individual sub-scales varied in range from 0 to 20 and these raw scores were then converted to percentages which were used in addition to the raw scores for each sub-scale to describe and summarise the results of the Manchester Clinical Supervision Scale-26. Cronbach’s alpha coefficient was undertaken with the 26 questions included within the Manchester Clinical Supervision Scale-26 and more importantly with each of the dimensions in the Manchester Clinical Supervision Scale-26. The open-ended questions on the Manchester Clinical Supervision Scale-26 and interviews were analysed using content analysis guided by Colorafi and Evans [ 22 ] and categories were generated using their eight steps, (1) creating a coding framework, (2) adding codes and memos, (3) applying the first level of coding, (4) categorising codes and applying the second level of coding, (5) revising and redefining the codes, (6) adding memos, (7) visualising data and (8) representing the data.

Research rigour

To ensure the validity and rigour of this study the researchers utilised the Manchester Clinical Supervision Scale-26 a recognised clinical supervision tool with good reliability and wide usage. Interviews were recorded, transcribed, and verified by four participants, data were collected until no new components appeared, data collection methods and analysis procedures were described, and the authors’ biases were minimised throughout the research process. The Manchester Clinical Supervision Scale-26 instrument internal consistency reliability was assessed which was overall good (α = 0.878) with individual subscale also good e.g., normative domain 0.765, restorative domain 0.864, and formative domain 0.900. Reporting rigour was demonstrated using the Consensus-Based Checklist for Reporting of Survey Studies guidelines [ 19 ] and Standards for Reporting Qualitative Research guidelines [ 20 ].

Quantitative data

Participant and clinical supervision characteristics.

Thirty-six of the fifty-two (69.2%) peer group clinical supervisors working across a particular region of Ireland responded to the Manchester Clinical Supervision Scale-26 survey online via Qualtrics. Table 1 identifies the demographics of the sample who were predominantly female (94.4%) with a mean age of 44.7 years (SD. 7.63).

Peer group clinical supervision session characteristics (Table  2 ) highlight over half of peer group clinical supervisors ( n  = 20, 55.6%) had been delivering peer group clinical supervision for less than one year and were mainly delivered to female supervisees ( n  = 28, 77.8%). Most peer group clinical supervision sessions took place monthly ( n  = 32, 88.9%) for 31–60 min ( n  = 27, 75%).

Manchester Clinical Supervision Scale-26 results

Participants generally viewed peer group clinical supervision as effective (Table  3 ), the total mean Manchester Clinical Supervision Scale-26 score among all peer group clinical supervisors was 76.47 (SD. 12.801) out of 104, Surpassing the clinical supervision threshold score of 73, which was established by the developers of the Manchester Clinical Supervision Scale-26 as the benchmark indicating proficient clinical supervision provision [ 21 ]. Of the three domains; normative, formative, and restorative, the restorative domain scored the highest (mean 28.56, SD. 6.67). The mean scores compare favourably to that of the Manchester Clinical Supervision Scale-26 benchmark data and suggest that the peer group clinical supervisors were satisfied with both the level of support, encouragement, and guidance they provided and the level of trust/rapport they had developed during the peer group clinical supervision sessions. 83.3% ( n  = 30) of peer group clinical supervisors reported being either very satisfied ( n  = 12, 33.3%) or moderately satisfied ( n  = 18, 50%) with the peer group clinical supervision they currently delivered. Within the peer group clinical supervisor’s supervisee related issues ( n  = 17, 47.2%), work environment-related issues ( n  = 16, 44.4%), staff-related issues ( n  = 15, 41.7%) were reported as the most frequent issues, with patient/client related issues being less frequent ( n  = 8, 22.2%). The most identified model used to facilitate peer group clinical supervision was the Proctors model ( n  = 8, 22.22%), which was followed by group ( n  = 2, 5.55%), peer ( n  = 2, 5.55%), and a combination of the seven-eyed model of clinical supervision and Proctors model ( n  = 1, 2.77%) with some not sure what model they used ( n  = 2, 5.553%) and 58.33% ( n  = 21) did not report what model they used.

Survey open-ended question

‘Please enter any additional comments , which are related to your current experience of delivering Peer Group Clinical Supervision.’ There were 22 response comments to this question, which represented 61.1% of the 36 survey respondents, which were analysed using content analysis guided by Colorafi & Evans [ 22 ]. Three categories were generated. These included: personal value/benefit of peer group clinical supervision, challenges with facilitating peer group clinical supervision, and new to peer group clinical supervision.

The first category ‘personal value/benefit of peer group clinical supervision’ highlighted positive experiences of both receiving and providing peer group clinical supervision. Peer group clinical supervisors reported that they enjoyed the sessions and found them both worthwhile and beneficial for both the group and them as peer group clinical supervisors in terms of creating a trusted supportive group environment and motivation to develop. Peer group clinical supervision was highlighted as very important for the peer group clinical supervisors working lives and they hoped that there would be more uptake from all staff. One peer group clinical supervisor expressed that external clinical supervision was a ‘lifeline’ to shaping their supervisory journey to date.

The second category ‘challenges with facilitating peer group clinical supervision’, identified time constraints, lack of buy-in/support from management, staff shortages, lack of commitment by supervisees, and COVID-19 pandemic restrictions and related sick leave, as potential barriers to facilitating peer group clinical supervision. COVID-19 was perceived to have a negative impact on peer group clinical supervision sessions due to staff shortages, which resulted in difficulties for supervisees attending the sessions during work time. Peer group clinical supervisors felt that peer group clinical supervision was not supported by management and there was limited ‘buy-in’ at times. There was also a feeling expressed that peer group clinical supervision was in its infancy, as COVID-19 and its related restrictions impacted on this by either slowing down the process of commencing peer group clinical supervision in certain areas or having to move online. However, more recently improvements in managerial support and supervisee engagement with the peer group clinical supervision process are noted.

The final category ‘new to peer group clinical supervision’ highlighted that some peer group clinical supervisors were new to the process of providing peer group clinical supervision and some felt that this survey was not a true reflection of their experience of delivering peer group clinical supervision, as they were not fully established yet as clinical supervisors due to the impact of COVID-19. Peer group clinical supervisors identified that while they were new to providing peer group clinical supervision, they were enjoying it and that it was a learning curve for them.

Qualitative data

The qualitative phase explored peer group clinical supervisors’ ( n  = 10) own experiences of preparation received and experiences of being a peer group clinical supervisor. Three themes were identified through data analysis, building the foundations, enacting engagement and actions, and realities (Table  4 ).

Building the foundations

This theme highlights the importance of prior knowledge, awareness, and training but also the recruitment process and education in preparing peer group clinical supervisors.

Knowledge and awareness

Participant’s prior knowledge and awareness of peer group clinical supervision was mixed with some reporting having little or no knowledge of clinical supervision.

I’m 20 years plus trained as a nurse , and I had no awareness of clinical supervision beforehand , I really hadn’t got a clue what all of this was about , so it was a very new concept to me (Bernie) .

Others were excited about peer group clinical supervision and while they could see the need they were aware that there may be limited awareness of the value and process of clinical supervision among peers.

I find that there’s great enthusiasm and passion for clinical supervision as it’s a great support mechanism for staff in practice , however , there’s a lack of awareness of clinical supervision (Jane) .

Recruitment

Some participants highlighted that the recruitment process to become a peer group clinical supervisor was vague in some organisations with an unclear and non-transparent process evident where people were chosen by the organisation’s management rather than self-selecting interested parties.

It was just the way the training was put to the people , they were kind of nominated and told they were going and there was a lot of upset over that , so they ended up in some not going at all (Ailbhe) .

In addition, the recruitment process was seen as top loaded where senior grades of staff were chosen, and this limited staff nurse grade opportunities where there was a clear need for peer group clinical supervisors and support.

We haven’t got down to the ground level like you know we’ve done the directors , we’ve done the CNM3s the CNM2s we are at the CNM1s , so we need to get down to the staff nurse level so the nurses at the direct frontline are left out and aren’t receiving supervision because we don’t have them trained (Bernie) .

Training and education

Participants valued the training and education provided but there was a clear sense of ‘imposter syndrome’ for some peer group clinical supervisors starting out. Participants questioned their qualifications, training duration, and confidence to undertake the role of peer group clinical supervisor.

Because it is group supervision and I know that you know they say that we are qualified to do supervision and you know we’re now qualified clinical supervisors but I’m not sure that a three-month module qualifies you to be at the top of your game (Maria) .

Participants when engaged in the peer group clinical supervisor educational programme did find it beneficial and the true benefit was the actual re-engagement in education and published evidence along with the mix of nursing and midwifery practice areas.

I found it very beneficial , I mean I hadn’t been engaged in education here in a while , so it was great to be back in that field and you know with the literature that’s big (Claire) .

Enacting engagement and actions

This theme highlights the importance of forming the groups, getting a clear message out, setting the scene, and grounding the group.

Forming the groups

Recruitment for the group was of key importance to the peer group clinical supervisor and they all sent out a general invitation to form their group. Some supervisors used invitation letters or posters in addition to a general email and this was effective in recruiting supervisees.

You’re reaching out to people , I linked in with the ADoN and I put together a poster and circulated that I wasn’t ‘cherry picking , and I set up a meeting through Webex so people could get a sense of what it was if they were on the fence about it or unsure if it was for them (Karen) .

In forming the peer clinical supervision groups consideration needs to be given to the actual number of supervisees and participants reported four to six supervisees as ideal but that number can alter due to attendance.

The ideal is having five or six consistent people and that they all come on board and that you get the dynamics of the group and everything working (Claire) .

Getting a clear message out

Within the recruitment process, it was evident that there was a limited and often misguided understanding or perception of peer group clinical supervision.

Greater awareness of what actually clinical supervision is , people misjudge it as a supervision where someone is appraising you , when in fact it is more of a support mechanism , I think peer support is the key element that needs to be brought out (Jane) .

Given the lack of clarity and understanding regarding peer group clinical supervision, the participants felt strongly that further clarity is needed and that the focus needs to be on the support it offers to self, practice, and the profession.

Clinical supervision to me is clinical leadership (Jane) .

Setting the scene and grounding the group

In the initial phase of the group coming together the aspect of setting the scene and grounding the group was seen as important. A key aspect of this process was establishing the ground rules which not only set the boundaries and gave structure but also ensured the adoption of principles of trust, confidentiality, and safety.

We start with the ground rules , they give us structure it’s our contract setting out the commitment the expectation for us all , and the confidentiality as that’s so important to the trust and safety and building the relationships (Brid) .

Awareness of group dynamics is important in this process along with awareness of the group members (supervisees) as to their role and expectations.

I reiterate the role of each person in relation to confidentiality and the relationship that they would have with each other within the group and the group is very much aware that it is based on respect for each person’s point of view people may have a fear of contributing to the group and setting the ground rules is important (Jane) .

To ground the group, peer group clinical supervisors saw the importance of being present and allowing oneself to be in the room. This was evident in the time allocated at the start of each session to allow ‘grounding’ to occur in the form of techniques such as a short meditation, relaxation, or deep breathing.

At the start , I do a bit of relaxation and deep breathing , and I saw that with our own external supervisor how she settled us into place so very much about connecting with your body and you’ve arrived , then always come in with the contract in my first sentence , remember today you know we’re in a confidential space , of course , you can take away information , but the only information you will take from today is your own information and then the respect aspect (Mary Rose) .

This settling in and grounding was seen as necessary for people to feel comfortable and engage in the peer group clinical supervision process where they could focus, be open, converse, and be aware of their role and the role of peer group clinical supervision.

People have to be open, open about their practice and be willing to learn and this can only occur by sharing, clinical supervision gives us the space to do it in a space where we know we will be respected, and we can trust (Claire) .

This theme highlights the importance of the peer group clinical supervisors’ past experiences, delivering peer group clinical supervision sessions, responding to COVID-19, personal and professional development, and future opportunities.

Past experiences

Past experiences of peer group clinical supervisors were not always positive and for one participant this related to the lack of ground rules or focus of the sessions and the fact it was facilitated by a non-nurse.

In the past , I suppose I would have found it very frustrating as a participant because I just found that it was going round in circles , people moaning and you know it wasn’t very solution focused so I came from my situation where I was very frustrated with clinical supervision , it was facilitated by somebody that was non-nursing then it wasn’t very , there wasn’t the ground rules , it was very loose (Caroline) .

However, many did not have prior experience of peer group clinical supervision. Nonetheless, through the education and preparation received, there was a sense of commitment to embrace the concept, practice, and philosophy.

I did not really have any exposure or really much information on clinical supervision , but it has opened my eyes , and as one might say I am now a believer (Brid) .

Delivering peer group clinical supervision

In delivering peer group clinical supervision, participants felt supervisees were wary, as they did not know what peer group clinical supervision was, and they had focused more on the word supervision which was misleading to them. Nonetheless, the process was challenging, and buy-in was questioned at an individual and managerial level.

Buy-in wasn’t great I think now of course people will blame the pandemic , but this all happened before the pandemic , there didn’t seem to be you know , the same support from management that I would have expected so I kind of understood it in a way because then there wasn’t the same real respect from the practitioners either (Mary Rose) .

From the peer group clinical supervisor’s perspective, they were all novices in delivering/facilitating peer group clinical supervision sessions, and the support of the external clinical supervisors, and their own peer group clinical supervision sessions were invaluable along with a clinical supervision model.

Having supervision myself was key and something that is vital and needed , we all need to look at our practice and how we work it’s no good just facilitating others without being part of the process yourself but for me I would say the three principles of clinical supervision , you know the normative , formative and restorative , I keep hammering that home and bring that in regularly and revisit the contract and I have to do that often you know (Claire) .

All peer group clinical supervisors commented on the preparation for their peer group clinical supervision sessions and the importance of them having the right frame of mind and that often they needed to read over their course work and published evidence.

I want everybody to have a shared voice and you know that if one person , there is something that somebody feels very strongly and wants to talk about it that they e-mail in advance like we don’t have a set agenda but that’s agreed from the participant at the start (Caroline) .

To assist this, the peer group clinical supervisors noted the importance of their own peer group clinical supervision, the support of their peers, and external clinical supervisors. This preparation in an unpredictable situation can be difficult but drawing on one’s experience and the experience within the group can assist in navigating beyond unexpected situations.

I utilise the models of clinical supervision and this helps guide me , I am more of a facilitator of the group we are experts in our own area and our own role but you can only be an expert if you take the time to examine your practice and how you operate in your role (Brid) .

All clinical supervisors noted that the early sessions can be superficial, and the focus can be on other practice or management issues, but as time moves on and people become more engaged and involved it becomes easier as their understanding of supervision becomes clearer. In addition, there may be hesitancy and people may have difficulty opening up with certain people in the group and this is a reality that can put people off.

Initially there was so much managerial bashing and I think through supervision , I began to kind of think , I need the pillars of supervision , the governance , bringing more knowledge and it shifted everything in the room , trying to marry it with all the tensions that people have (Mary Rose) .

For some clinical supervisors, there were expected and unexpected challenges for them as clinical supervisors in terms of the discussions veering off course and expectations of their own ability.

The other big challenge is when they go off , how do you bring him back , you know when they veer off and you’re expected to be a peer , but you have to try and recoil that you have to get the balance with that right (Mary Rose) .

While peer group clinical supervision is accepted and seen as a valuable process by the peer group clinical supervisors, facilitating peer group supervision with people known to you can be difficult and may affect the process.

I’d love to supervise a group where I actually don’t know the people , I don’t know the dynamics within the group , and I’d love to see what it would be like in a group (Bernie) .

Of concern to clinical supervisors was the aspect of non-attendance and while there may be valid reasons such as COVID-19 the absence of a supervisee for several sessions can affect the group dynamics, especially if the supervisee has only engaged with early group sessions.

One of the ones that couldn’t attend because of COVID and whatever , but she’s coming to the next one and I just feel there’s a lot of issues in her area and I suppose I’m mindful that I don’t want that sort of thing to seep in , so I suppose it’s just for me just to keep reiterating the ground rules and the boundaries , that’s something I just have to manage as a facilitator , but what if they don’t attend how far will the group have progressed before she attends (Caroline) .

Responding to COVID-19

The advent of COVID-19 forced peer group clinical supervisors to find alternative means of providing peer group clinical supervision sessions which saw the move from face-to-face to online sessions. The online transition was seen as seamless for many established groups while others struggled to deliver sessions.

With COVID we did online for us it was fine because we were already formed (Corina) .

While the transition may have been positive many clinical supervisors came across issues because they were using an online format that would not be present in the face-to-face session.

We did have a session where somebody was in the main office and they have a really loud booming voice and they were saying stuff that was not appropriate to say outside of clinical supervision and I was like are you in the office can you lower it down a bit can you put your headphones on (Maria) .

However, two peer group clinical supervisors ceased or hasted the progress of rolling out peer group clinical supervision sessions mainly due to redeployment and staff availability.

With COVID it just had to be canceled here , it’s just the whole thing was canceled so it was very , very difficult for people (Mary Rose) .

It was clear from clinical supervisors that online sessions were appropriate but that they felt they were only appropriate for existing established groups that have had the opportunity to build relationships, develop trust, embed the ground rules, and create the space for open communication and once established a combined approach would be appropriate.

Since we weren’t as established as a group , not everybody knew each other it would be difficult to establish that so we would hold off/reschedule , obviously COVID is a major one but also I suppose if you have an established group now , and again , you could go to a remote one , but I felt like since we weren’t established as a group it would be difficult to develop it in that way (Karen) .

Within practice COVID-19 took priority and other aspects such as peer group clinical supervision moved lower down on the priority list for managers but not for the clinical supervisors even where redeployment occurred.

With COVID all the practical side , if one of the managers is dealing with an outbreak , they won’t be attending clinical supervision , because that has to be prioritised , whereas we’ve prioritised clinical supervision (Maria) .

The valuing of peer group clinical supervision was seen as important by clinical supervisors, and they saw it as particularly needed during COVID-19 as staff were dealing with many personal and professional issues.

During the height of COVID , we had to take a bit of a break for four months as things were so demanding at work for people but then I realised that clinical supervision was needed and started back up and they all wanted to come back (Brid) .

Having peer group clinical supervision during COVID-19 supported staff and enabled the group to form supportive relationships.

COVID has impacted over the last two years in every shape and they needed the supervision and the opportunity to have a safe supportive space and it gelled the group I think as we all were there for each other (Claire) .

While COVID-19 posed many challenges it also afforded clinical supervisors and supervisees the opportunity for change and to consider alternative means of running peer group clinical supervision sessions. This change resulted in online delivery and in reflecting on both forms of delivery (face-to-face and online) clinical supervisors saw the benefit in both. Face-to-face was seen as being needed to form the group and then the group could move online once the group was established with an occasional periodic face-to-face session to maintain motivation commitment and reinforce relationships and support.

Online formats can be effective if the group is already established or the group has gone through the storming and forming phase and the ground rules have been set and trust built , then I don’t see any problem with a blended online version of clinical supervision , and I think it will be effective (Jane) .

Personal and professional development

Growth and development were evident from peer group clinical supervisors’ experiences and this growth and development occurred at a personal, professional, and patient/client level. This development also produced an awakening and valuing of one’s passion for self and their profession.

I suppose clinical supervision is about development I can see a lot of development for me and my supervisees , you know personally and professionally , it’s the support really , clinical supervision can reinvigorate it’s very exciting and a great opportunity for nursing to support each other and in care provision (Claire) .

A key to the peer group clinical supervisor’s development was the aspect of transferable skills and the confidence they gained in fulfilling their role.

All of these skills that you learn are transferable and I am a better manager because of clinical supervision (Maria) .

The confidence and skills gained translated into the clinical supervisor’s own practice as a clinical practitioner and clinical supervisor but they were also realistic in predicting the impact on others.

I have empowered my staff , I empower them to use their voice and I give my supervisees a voice and hope they take that with them (Corina) .

Fundamental to the development process was the impact on care itself and while this cannot always be measured or identified, the clinical supervisors could see that care and support of the individual practitioner (supervisee) translated into better care for the patient/client.

Care is only as good as the person delivering it and what they know , how they function and what energy and passion they have , and clinical supervision gives the person support to begin to understand their practice and how and why they do things in a certain way and when they do that they can begin to question and even change their way of doing something (Brid) .

Future opportunities

Based on the clinical supervisor’s experiences there was a clear need identified regarding valuing and embedded peer group clinical supervision within nursing/midwifery practice.

There has to be an emphasis placed on supervision it needs to be part of the fabric of a service and valued by all in that service , we should be asking why is it not available if it’s not there but there is some work first on promoting it and people knowing what it actually is and address the misconceptions (Claire) .

While such valuing and buy-in are important, it is not to say that all staff need to have peer group clinical supervision so as to allow for personal choice. In addition, to value peer group clinical supervision it needs to be evident across all staffing grades and one could question where the best starting point is.

While we should not mandate that all staff do clinical supervision it should become embedded within practice more and I suppose really to become part of our custom and practice and be across all levels of staff (Brid) .

When peer group clinical supervision is embedded within practice then it should be custom and practice, where it is included in all staff orientations and is nationally driven.

I suppose we need to be driving it forward at the coal face at induction , at orientation and any development for the future will have to be driven by the NMPDUs or nationally (Ailbhe) .

A formalised process needs to address the release of peer group clinical supervisors but also the necessity to consider the number of peer group clinical supervisors at a particular grade.

The issue is release and the timeframe as they have a group but they also have their external supervision so you have to really work out how much time you’re talking about (Maria) .

Vital within the process of peer group clinical supervision is receiving peer group clinical supervision and peer support and this needs to underpin good peer group clinical supervision practice.

Receiving peer group supervision helps me , there are times where I would doubt myself , it’s good to have the other group that I can go to and put it out there to my own group and say , look at this , this is what we did , or this is what came up and this is how (Bernie) .

For future roll out to staff nurse/midwife grade resourcing needs to be considered as peer group clinical supervisors who were managers could see the impact of having several peer group clinical supervisors in their practice area may have on care delivery.

Facilitating groups is an issue and needs to be looked at in terms of the bigger picture because while I might be able to do a second group the question is how I would be supported and released to do so (Maria) .

While there was ambiguity regarding peer group clinical supervision there was an awareness of other disciplines availing of peer group clinical supervision, raising questions about the equality of supports available for all disciplines.

I always heard other disciplines like social workers would always have been very good saying I can’t meet you I have supervision that day and I used to think my God what’s this fabulous hour that these disciplines are getting and as a nursing staff it just wasn’t there and available (Bernie) .

To address this equity issue and the aspect of low numbers of certain grades an interdisciplinary approach within nursing and midwifery could be used or a broader interdisciplinary approach across all healthcare professionals. An interdisciplinary or across-services approach was seen as potentially fruitful.

I think the value of interprofessional or interdisciplinary learning is key it addresses problem-solving from different perspectives that mix within the group is important for cross-fertilisation and embedding the learning and developing the experience for each participant within the group (Jane) .

As we move beyond COVID-19 and into the future there is a need to actively promote peer group clinical supervision and this would clarify what peer group clinical supervision actually is, its uptake and stimulate interest.

I’d say it’s like promoting vaccinations if you could do a roadshow with people , I think that would be very beneficial , and to launch it , like you have a launch an official launch behind it (Mary Rose) .

The advantages of peer group clinical supervision highlighted in this study pertain to self-enhancement (confidence, leadership, personal development, resilience), organisational and service-related aspects (positive work environment, staff retention, safety), and professional patient care (critical thinking and evaluation, patient safety, adherence to quality standards, elevated care standards). These findings align with broader literature that acknowledges various areas, including self-confidence and facilitation [ 23 ], leadership [ 24 ], personal development [ 25 ], resilience [ 26 ], positive/supportive working environment [ 27 ], staff retention [ 28 ], sense of safety [ 29 ], critical thinking and evaluation [ 30 ], patient safety [ 31 ], quality standards [ 32 ] and increased standards of care [ 33 ].

In this study, peer group clinical supervision appeared to contribute to the alleviation of stress and anxiety. Participants recognised the significance of these sessions, where they could openly discuss and reflect on professional situations both emotionally and rationally. Central to these discussions was the creation of a safe, trustworthy, and collegial environment, aligning with evidence in the literature [ 34 ]. Clinical supervision provided a platform to share resources (information, knowledge, and skills) and address issues while offering mutual support [ 35 ]. The emergence of COVID-19 has stressed the significance of peer group clinical supervision and support for the nursing/midwifery workforce [ 36 ], highlighting the need to help nurses/midwifes preserve their well-being and participate in collaborative problem-solving. COVID-19 impacted and disrupted clinical supervision frequency, duration and access [ 37 ]. What was evident during COVID-19 was the stress and need for support for staff and given the restorative or supportive functions of clinical supervision it is a mechanism of support. However, clinical supervisors need support themselves to be able to better meet the supervisee’s needs [ 38 ].

The value of peer group clinical supervision in nurturing a conducive working environment cannot be overstated, as it indorses the understanding and adherence to workplace policies by empowering supervisees to understand the importance and rationale behind these policies [ 39 ]. This becomes vital in a continuously changing healthcare landscape, where guidelines and policies may be subject to change, especially in response to situations such as COVID-19. In an era characterised by international workforce mobility and a shortage of healthcare professionals, a supportive and positive working environment through the provision of peer group clinical supervision can positively influence staff retention [ 40 ], enhance job satisfaction [ 41 ], and mitigate burnout [ 42 ]. A critical aspect of the peer group clinical supervision process concerns providing staff the opportunity to reflect, step back, problem-solve and generate solutions. This, in turn, ensures critical thinking and evaluation within clinical supervision, focusing on understanding the issues and context, and problem-solving to draw constructive lessons for the future [ 30 ]. Research has determined a link between clinical supervision and improvements in the quality and standards of care [ 31 ]. Therefore, peer group clinical supervision plays a critical role in enhancing patient safety by nurturing improved communication among staff, facilitating reflection, promoting greater self-awareness, promoting the exchange of ideas, problem-solving, and facilitating collective learning from shared experiences.

Starting a group arose as a foundational aspect emphasised in this study. The creation of the environment through establishing ground rules, building relationships, fostering trust, displaying respect, and upholding confidentiality was evident. Vital to this process is the recruitment of clinical supervisees and deciding the suitable group size, with a specific emphasis on addressing individuals’ inclination to engage, their knowledge and understanding of peer group clinical supervision, and dissipating any lack of awareness or misconceptions regarding peer group supervision. Furthermore, the educational training of peer group clinical supervisors and the support from external clinical supervisors played a vital role in the rollout and formation of peer group clinical supervision. The evidence stresses the significance of an open and safe environment, wherein supervisees feel secure and trust their supervisor. In such an environment, they can effectively reflect on practice and related issues [ 41 ]. This study emphasises that the effectiveness of peer group supervision is more influenced by the process than the content. Clinical supervisors utilised the process to structure their sessions, fostering energy and interest to support their peers and cultivate new insights. For peer group clinical supervision to be effective, regularity is essential. Meetings should be scheduled in advance, allocate protected time, and take place in a private space [ 35 ]. While it is widely acknowledged that clinical supervisors need to be experts in their professional field to be credible, this study highlights that the crucial aspects of supervision lie in the quality of the relationship with the supervisor. The clinical supervisor should be supportive, caring, open, collaborative, sensitive, flexible, helpful, non-judgmental, and focused on tacit knowledge, experiential learning, and providing real-time feedback.

Critical to the success of peer group clinical supervision is the endorsement and support from management, considering the organisational culture and attitudes towards the practice of clinical supervision as an essential factor [ 43 ]. This support and buy-in are necessary at both the management and individual levels [ 28 ]. The primary obstacles to effective supervision often revolve around a lack of time and heavy workloads [ 44 ]. Clinical supervisors frequently struggle to find time amidst busy environments, impacting the flexibility and quality of the sessions [ 45 ]. Time constraints also limit the opportunity for reflection within clinical supervision sessions, leaving supervisees feeling compelled to resolve issues on their own without adequate support [ 45 ]. Nevertheless, time-related challenges are not unexpected, prompting a crucial question about the value placed on clinical supervision and its integration into the culture and fabric of the organisation or profession to make it a customary practice. Learning from experiences like those during the COVID-19 pandemic has introduced alternative ways of working, and the use of technology (such as Zoom, Microsoft Teams, Skype) may serve as a means to address time, resource, and travel issues associated with clinical supervision.

Despite clinical supervision having a long international history, persistent misconceptions require attention. Some of these include not considering clinical supervision a priority [ 46 ], perceiving it as a luxury [ 41 ], deeming it self-indulgent [ 47 ], or viewing it as mere casual conversation during work hours [ 48 ]. A significant challenge lies in the lack of a shared understanding regarding the role and purpose of clinical supervision, with past perceptions associating it with surveillance and being monitored [ 48 ]. These negative connotations often result in a lack of engagement [ 41 ]. Without encouragement and recognition of the importance of clinical supervision from management or the organisation, it is unlikely to become embedded in the organisational culture, impeding its normalisation [ 39 ].

In this study, some peer group clinical supervisors expressed feelings of being impostors and believed they lacked the knowledge, skills, and training to effectively fulfil their roles. While a deficiency in skills and competence are possible obstacles to providing effective clinical supervision [ 49 ], the peer group clinical supervisors in this study did not report such issues. Instead, their concerns were more about questioning their ability to function in the role of a peer group clinical supervisor, especially after a brief training program. The literature acknowledges a lack of training where clinical supervisors may feel unprepared and ill-equipped for their role [ 41 ]. To address these challenges, clinical supervisors need to be well-versed in professional guidelines and ethical standards, have clear roles, and understand the scope of practice and responsibilities associated with being a clinical supervisor [ 41 ].

The support provided by external clinical supervisors and the peer group clinical supervision sessions played a pivotal role in helping peer group clinical supervisors ease into their roles, gain experiential learning, and enhance their facilitation skills within a supportive structure. Educating clinical supervisors is an investment, but it should not be a one-time occurrence. Ongoing external clinical supervision for clinical supervisors [ 50 ] and continuous professional development [ 51 ] are crucial, as they contribute to the likelihood of clinical supervisors remaining in their roles. However, it is important to interpret the results of this study with caution due to the small sample size in the survey. Generalising the study results should be approached with care, particularly as the study was limited to two regions in Ireland. However, the addition of qualitative data in this mixed-methods study may have helped offset this limitation.

This study highlights the numerous advantages of peer group clinical supervision at individual, service, organisational, and patient/client levels. Success hinges on addressing the initial lack of awareness and misconceptions about peer group clinical supervision by creating the right environment and establishing ground rules. To unlock the full potential of peer group clinical supervision, it is imperative to secure management and organisational support for staff release. More crucially, there is a need for valuing and integrating peer group clinical supervision into nursing and midwifery education and practice. Making peer group clinical supervision accessible to all grades of nurses and midwives across various healthcare services is essential, necessitating strategic planning to tackle capacity and sustainability challenges.

Data availability

Data are available from the corresponding author upon request owing to privacy or ethical restrictions.

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Acknowledgements

The research team would like to thank all participants for their collaboration, the HSE steering group members and Carmel Hoey, NMPDU Director, HSE West Mid West, Dr Patrick Glackin, NMPD Area Director, HSE West, Annette Cuddy, Director, Centre of Nurse and Midwifery Education Mayo/Roscommon; Ms Ruth Hoban, Assistant Director of Nursing and Midwifery (Prescribing), HSE West; Ms Annette Connolly, NMPD Officer, NMPDU HSE West Mid West.

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Doody, O., Markey, K., Turner, J. et al. Clinical supervisor’s experiences of peer group clinical supervision during COVID-19: a mixed methods study. BMC Nurs 23 , 612 (2024). https://doi.org/10.1186/s12912-024-02283-3

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• Questionnaire Design | Methods, Question Types & Examples

Questionnaire Design | Methods, Question Types & Examples

Published on July 15, 2021 by Pritha Bhandari . Revised on June 22, 2023.

A questionnaire is a list of questions or items used to gather data from respondents about their attitudes, experiences, or opinions. Questionnaires can be used to collect quantitative and/or qualitative information.

Questionnaires are commonly used in market research as well as in the social and health sciences. For example, a company may ask for feedback about a recent customer service experience, or psychology researchers may investigate health risk perceptions using questionnaires.

Table of contents

Questionnaires vs. surveys, questionnaire methods, open-ended vs. closed-ended questions, question wording, question order, step-by-step guide to design, other interesting articles, frequently asked questions about questionnaire design.

A survey is a research method where you collect and analyze data from a group of people. A questionnaire is a specific tool or instrument for collecting the data.

Designing a questionnaire means creating valid and reliable questions that address your research objectives , placing them in a useful order, and selecting an appropriate method for administration.

But designing a questionnaire is only one component of survey research. Survey research also involves defining the population you’re interested in, choosing an appropriate sampling method , administering questionnaires, data cleansing and analysis, and interpretation.

Sampling is important in survey research because you’ll often aim to generalize your results to the population. Gather data from a sample that represents the range of views in the population for externally valid results. There will always be some differences between the population and the sample, but minimizing these will help you avoid several types of research bias , including sampling bias , ascertainment bias , and undercoverage bias .

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Questionnaires can be self-administered or researcher-administered . Self-administered questionnaires are more common because they are easy to implement and inexpensive, but researcher-administered questionnaires allow deeper insights.

Self-administered questionnaires

Self-administered questionnaires can be delivered online or in paper-and-pen formats, in person or through mail. All questions are standardized so that all respondents receive the same questions with identical wording.

Self-administered questionnaires can be:

• cost-effective
• easy to administer for small and large groups
• anonymous and suitable for sensitive topics

But they may also be:

• unsuitable for people with limited literacy or verbal skills
• susceptible to a nonresponse bias (most people invited may not complete the questionnaire)
• biased towards people who volunteer because impersonal survey requests often go ignored.

Researcher-administered questionnaires

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents.

Researcher-administered questionnaires can:

• help you ensure the respondents are representative of your target audience
• allow clarifications of ambiguous or unclear questions and answers
• have high response rates because it’s harder to refuse an interview when personal attention is given to respondents

But researcher-administered questionnaires can be limiting in terms of resources. They are:

• costly and time-consuming to perform
• more difficult to analyze if you have qualitative responses
• likely to contain experimenter bias or demand characteristics
• likely to encourage social desirability bias in responses because of a lack of anonymity

Your questionnaire can include open-ended or closed-ended questions or a combination of both.

Using closed-ended questions limits your responses, while open-ended questions enable a broad range of answers. You’ll need to balance these considerations with your available time and resources.

Closed-ended questions

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. Closed-ended questions are best for collecting data on categorical or quantitative variables.

Categorical variables can be nominal or ordinal. Quantitative variables can be interval or ratio. Understanding the type of variable and level of measurement means you can perform appropriate statistical analyses for generalizable results.

Examples of closed-ended questions for different variables

Nominal variables include categories that can’t be ranked, such as race or ethnicity. This includes binary or dichotomous categories.

It’s best to include categories that cover all possible answers and are mutually exclusive. There should be no overlap between response items.

In binary or dichotomous questions, you’ll give respondents only two options to choose from.

White Black or African American American Indian or Alaska Native Asian Native Hawaiian or Other Pacific Islander

Ordinal variables include categories that can be ranked. Consider how wide or narrow a range you’ll include in your response items, and their relevance to your respondents.

Likert scale questions collect ordinal data using rating scales with 5 or 7 points.

When you have four or more Likert-type questions, you can treat the composite data as quantitative data on an interval scale . Intelligence tests, psychological scales, and personality inventories use multiple Likert-type questions to collect interval data.

With interval or ratio scales , you can apply strong statistical hypothesis tests to address your research aims.

Pros and cons of closed-ended questions

Well-designed closed-ended questions are easy to understand and can be answered quickly. However, you might still miss important answers that are relevant to respondents. An incomplete set of response items may force some respondents to pick the closest alternative to their true answer. These types of questions may also miss out on valuable detail.

To solve these problems, you can make questions partially closed-ended, and include an open-ended option where respondents can fill in their own answer.

Open-ended questions

Open-ended, or long-form, questions allow respondents to give answers in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered. For example, respondents may want to answer “multiracial” for the question on race rather than selecting from a restricted list.

• How do you feel about open science?
• How would you describe your personality?
• In your opinion, what is the biggest obstacle for productivity in remote work?

Open-ended questions have a few downsides.

They require more time and effort from respondents, which may deter them from completing the questionnaire.

For researchers, understanding and summarizing responses to these questions can take a lot of time and resources. You’ll need to develop a systematic coding scheme to categorize answers, and you may also need to involve other researchers in data analysis for high reliability .

Question wording can influence your respondents’ answers, especially if the language is unclear, ambiguous, or biased. Good questions need to be understood by all respondents in the same way ( reliable ) and measure exactly what you’re interested in ( valid ).

Use clear language

You should design questions with your target audience in mind. Consider their familiarity with your questionnaire topics and language and tailor your questions to them.

For readability and clarity, avoid jargon or overly complex language. Don’t use double negatives because they can be harder to understand.

Use balanced framing

Respondents often answer in different ways depending on the question framing. Positive frames are interpreted as more neutral than negative frames and may encourage more socially desirable answers.

Use a mix of both positive and negative frames to avoid research bias , and ensure that your question wording is balanced wherever possible.

Unbalanced questions focus on only one side of an argument. Respondents may be less likely to oppose the question if it is framed in a particular direction. It’s best practice to provide a counter argument within the question as well.

Avoid leading questions

Leading questions guide respondents towards answering in specific ways, even if that’s not how they truly feel, by explicitly or implicitly providing them with extra information.

It’s best to keep your questions short and specific to your topic of interest.

• The average daily work commute in the US takes 54.2 minutes and costs $29 per day. Since 2020, working from home has saved many employees time and money. Do you favor flexible work-from-home policies even after it’s safe to return to offices?
• Experts agree that a well-balanced diet provides sufficient vitamins and minerals, and multivitamins and supplements are not necessary or effective. Do you agree or disagree that multivitamins are helpful for balanced nutrition?

Keep your questions focused

Ask about only one idea at a time and avoid double-barreled questions. Double-barreled questions ask about more than one item at a time, which can confuse respondents.

This question could be difficult to answer for respondents who feel strongly about the right to clean drinking water but not high-speed internet. They might only answer about the topic they feel passionate about or provide a neutral answer instead – but neither of these options capture their true answers.

Instead, you should ask two separate questions to gauge respondents’ opinions.

Strongly Agree Agree Undecided Disagree Strongly Disagree

Do you agree or disagree that the government should be responsible for providing high-speed internet to everyone?

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You can organize the questions logically, with a clear progression from simple to complex. Alternatively, you can randomize the question order between respondents.

Logical flow

Using a logical flow to your question order means starting with simple questions, such as behavioral or opinion questions, and ending with more complex, sensitive, or controversial questions.

The question order that you use can significantly affect the responses by priming them in specific directions. Question order effects, or context effects, occur when earlier questions influence the responses to later questions, reducing the validity of your questionnaire.

While demographic questions are usually unaffected by order effects, questions about opinions and attitudes are more susceptible to them.

• How knowledgeable are you about Joe Biden’s executive orders in his first 100 days?
• Are you satisfied or dissatisfied with the way Joe Biden is managing the economy?
• Do you approve or disapprove of the way Joe Biden is handling his job as president?

It’s important to minimize order effects because they can be a source of systematic error or bias in your study.

Randomization

Randomization involves presenting individual respondents with the same questionnaire but with different question orders.

When you use randomization, order effects will be minimized in your dataset. But a randomized order may also make it harder for respondents to process your questionnaire. Some questions may need more cognitive effort, while others are easier to answer, so a random order could require more time or mental capacity for respondents to switch between questions.

Step 1: Define your goals and objectives

The first step of designing a questionnaire is determining your aims.

• What topics or experiences are you studying?
• What specifically do you want to find out?
• Is a self-report questionnaire an appropriate tool for investigating this topic?

Once you’ve specified your research aims, you can operationalize your variables of interest into questionnaire items. Operationalizing concepts means turning them from abstract ideas into concrete measurements. Every question needs to address a defined need and have a clear purpose.

Step 2: Use questions that are suitable for your sample

Create appropriate questions by taking the perspective of your respondents. Consider their language proficiency and available time and energy when designing your questionnaire.

• Are the respondents familiar with the language and terms used in your questions?
• Would any of the questions insult, confuse, or embarrass them?
• Do the response items for any closed-ended questions capture all possible answers?
• Are the response items mutually exclusive?
• Do the respondents have time to respond to open-ended questions?

Consider all possible options for responses to closed-ended questions. From a respondent’s perspective, a lack of response options reflecting their point of view or true answer may make them feel alienated or excluded. In turn, they’ll become disengaged or inattentive to the rest of the questionnaire.

Step 3: Decide on your questionnaire length and question order

Once you have your questions, make sure that the length and order of your questions are appropriate for your sample.

If respondents are not being incentivized or compensated, keep your questionnaire short and easy to answer. Otherwise, your sample may be biased with only highly motivated respondents completing the questionnaire.

Decide on your question order based on your aims and resources. Use a logical flow if your respondents have limited time or if you cannot randomize questions. Randomizing questions helps you avoid bias, but it can take more complex statistical analysis to interpret your data.

Step 4: Pretest your questionnaire

When you have a complete list of questions, you’ll need to pretest it to make sure what you’re asking is always clear and unambiguous. Pretesting helps you catch any errors or points of confusion before performing your study.

Ask friends, classmates, or members of your target audience to complete your questionnaire using the same method you’ll use for your research. Find out if any questions were particularly difficult to answer or if the directions were unclear or inconsistent, and make changes as necessary.

If you have the resources, running a pilot study will help you test the validity and reliability of your questionnaire. A pilot study is a practice run of the full study, and it includes sampling, data collection , and analysis. You can find out whether your procedures are unfeasible or susceptible to bias and make changes in time, but you can’t test a hypothesis with this type of study because it’s usually statistically underpowered .

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

Closed-ended, or restricted-choice, questions offer respondents a fixed set of choices to select from. These questions are easier to answer quickly.

Open-ended or long-form questions allow respondents to answer in their own words. Because there are no restrictions on their choices, respondents can answer in ways that researchers may not have otherwise considered.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

You can organize the questions logically, with a clear progression from simple to complex, or randomly between respondents. A logical flow helps respondents process the questionnaire easier and quicker, but it may lead to bias. Randomization can minimize the bias from order effects.

Questionnaires can be self-administered or researcher-administered.

Researcher-administered questionnaires are interviews that take place by phone, in-person, or online between researchers and respondents. You can gain deeper insights by clarifying questions for respondents or asking follow-up questions.

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Bhandari, P. (2023, June 22). Questionnaire Design | Methods, Question Types & Examples. Scribbr. Retrieved September 3, 2024, from https://www.scribbr.com/methodology/questionnaire/

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Written by Syed Balkhi

3 September, 2024

Do you ever wonder how websites and apps seem to know exactly what you want? The truth is, this is in large part due to AI in UX design.

There’s no question that AI has fundamentally changed how companies across all industries operate. According to Forbes, the AI industry is expected to hit $407 billion by 2027! For more context, research shows that around 73% of online businesses in the U.S. are already using AI in some way. 

And it’s no wonder why. AI saves marketers about 2.5 hours a day – that’s 25 whole days a year!

But if you want to get the most from AI, you need to start thinking about how you can use it in UX research. With the right knowledge and resources, you can continuously streamline your design process, which will ultimately lead to more engagement, clicks, and, most importantly, sales. 

Today, we are going to show you how AI is improving UX research and design and how you can use it to your advantage. 

Let’s get started!

How AI Enhances UX Research and the Design Process

Let’s start by exploring a few key benefits of adding AI to your existing design process. 

• Speed Things Up – AI tools can crunch data faster than any human. With the right tool, you can quickly and accurately gather actionable data about your target audience and campaigns. 
• Make Smarter Choices – Instead of guessing or “going with your gut,” you can use hard data to make decisions that line up with your visitors’ needs, goals, and pain points.
• Get Personal – People love it when things feel made just for them. In fact, 80% of people prefer to engage with brands that personalize content and offers. Since you now know what your visitors like, it’s easy to understand how AI can help you build rapport and get to know your audience.
• Include Everyone – AI can spot accessibility issues that humans might miss. Automated accessibility checks ensure that designs are inclusive, which will broaden your audience and improve user experiences for all.
• Save Money – By reducing the need for extensive manual testing and analysis, AI helps businesses optimize their UX research budget and allocate resources more effectively.

7 Practical AI Applications in User Testing and Research

Now that you know a little more about the benefits, let’s talk about some of the specific, practical applications for AI in both user testing and research. 

1. AI-Driven User Testing and Analytics

Automated user behavior analysis makes it easier to see how your audience interacts with your product. AI can track and analyze every click, scroll, and hover, things that would take humans much longer to uncover. Since all of this information is at your fingertips in minutes, you can quickly identify patterns and take action. 

It can handle a whole lot of data at once, which makes large-scale user testing more manageable than ever before. Traditional user testing often limits the number of participants due to logistical constraints. But with AI, you can test with thousands or even millions of users, which means your data will be statistically significant and reliable.

By finding subtle trends and anomalies in the big picture, AI helps you understand user behavior and needs better, which means you can make data-driven decisions that improve user satisfaction and overall experience.

2. Intelligent A/B Testing

A/B testing is a proven way to see which version of a web page or app performs better based on a number of different metrics. This strategy is also called split-testing, and it’s been around since long before AI. 

But there’s no question that machine learning makes this process even better by optimizing it in new and exciting ways. For example, AI algorithms are very good at analyzing different versions of a landing page or offer, which will help you find the best ways to connect with your audience.

Predictive analytics is another element of A/B testing that you should know about. By using historical data and learned patterns, AI can predict which version will perform better even before the testing is complete. This means faster decision-making and better version implementation, so you waste less time and resources on testing. 

Ultimately, AB testing helps you deliver the best possible experience based on data, not guesswork or intuition. AI can make it even better. 

3. AI-Assisted Prototyping

Brainstorming for new designs is important but time-consuming. Instead of spending hours at meetings, you can use AI to speed up this process.

Basically, you can ‘feed’ your program different designs and campaigns that worked well in the past. The AI-powered software can look through this information and pull out key elements that it can then use to build new campaigns. 

Doing this can help you generate a library of great-looking templates and concepts that you can discuss with your design team. Much like traditional brainstorming, not every idea will be a winner. But there’s no question that you’ll save a significant chunk of time by using AI to come up with a pool of ideas.

Rapid iteration on an existing campaign is also possible with AI-driven prototyping, which actually leads back to A/B testing. Instead of spending weeks or months on one prototype, AI tools can generate multiple versions in minutes that you can experiment with on your site or social media. 

Trying out different concepts is important for creative growth, and AI makes it possible to experiment more. By making it easy to test and discard many ideas, AI creates an environment where prototype testing can flourish. 

4. Automated Accessibility Compliance

Making your product accessible to all users is not only the right thing to do, but it also dramatically expands your audience.

AI-driven accessibility checks can automatically scan your design for issues that will hinder users with disabilities. For example, there are tests to determine if a site is color-blind friendly, with different versions for each type of colorblindness. In my experience, this proactive approach helps you identify and fix problems early in the design process.

There’s no doubt that AI can check for accessibility issues faster and more accurately than manual testing. Since AI is working on this part of your site, the design team can focus on other important parts of the user experience while knowing accessibility is being taken care of.

Using AI for periodic checks makes sure no one is left out. It promotes a welcoming design approach so all users, regardless of ability, can have a good experience with your product. This not only increases user satisfaction but also your brand’s reputation for inclusivity .

5. Personalized User Experiences

Did you know that 74% of shoppers say they’ve felt frustrated by a shopping experience because the content didn’t match their interests? This is exactly why personalization is the key to user engagement. People are more likely to take action on your site if you show them things that are relevant to their interests.

AI can supercharge this concept by automatically adapting content and promotions to each user’s preferences. It can do this by analyzing user behavior, determining what content users will find most interesting based on their actions, and tailoring the experience accordingly. This will create a more engaging experience for every single person who lands on your site.

Cool, right?

AI-driven personalization at scale is especially useful for growing businesses. As your user base grows, it admittedly gets hard to add a personal touch to everything you do. AI is able to process huge amounts of data, which means it can hone in and deliver personalized experiences to millions of users without compromising on quality.

The bottom line is people will engage with content that feels tailored to them, which means a big boost in retention and satisfaction. When you add AI to the mix, it creates a win-win situation for you and your customers. 

6. Predictive UX Modeling

Knowing what users want and need is a powerful way to improve the user experience. With the right data, AI can predict what someone might want or do next by looking at past behavior and trends. This foresight will help you design interfaces and interactions that are more intuitive and meet each customer’s expectations. 

For example, a streaming service like Max will show each person shows and movies they might like based on their watch history. 

In the old days, you’d have to wait weeks, sometimes months, and read through tons of user feedback before you could make actionable changes to your campaigns. With AI, you can make these changes essentially in real time based on how people are engaging with your content or offers. 

By knowing what they need before they even realize they have a problem, you can offer solutions that add value to their lives. This proactive approach builds loyalty as users feel understood and valued by your brand.

7. AI-Enhanced Design Systems

Consistency is a massive part of creating a positive user experience. People want to feel comfortable with a brand, whether they’re browsing their blog or buying their products. 

AI can help with this by generating style guides across your entire brand, including products, landing pages, and social media profiles. As a result, all the different parts of your product will have an original look and feel that your customers will recognize.

In this instance, AI will save you a lot of time. Instead of manually checking each component, like color hex codes, against the style guide, AI can do this for you. This reduces the chances of inconsistencies and makes sure every part of the product aligns with your brand. 

Final Thoughts: The Human Touch in an AI World

It’s more clear than ever before that AI is having a huge impact on UX research and design. It’s making things faster, smarter, and more personal.

One of the misconceptions I see around this topic is that it’s meant to replace humans. The truth is, that tools like this actually give designers and researchers better tools to do their job.

The best UX still comes from a mix of AI smarts and human creativity. AI can crunch the numbers and spot patterns, but it takes human insight to turn those insights into experiences that truly connect with users.If you’re ready to bring AI design and research tools to your business, there’s no time like right now to start. Artificial intelligence is getting more helpful every day, so why not take advantage of this remarkable and effective tool today?

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