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The Importance of Research Design: A Comprehensive Guide

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

Table of Contents

Understanding research design.

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

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

Definition and Purpose of Research Design

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

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

Key Components of Research Design

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

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

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

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

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

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

Types of Research Design

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

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

Experimental Design

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

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

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

Free 44-page Experimental Design Guide

For Beginners and Intermediates

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

Non-Experimental Design

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

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

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

Quasi-Experimental Design

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

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

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

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

The Role of Research Design in Scientific Inquiry

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

Ensuring Validity and Reliability

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

Facilitating Replication of Studies

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

Steps in Developing a Research Design

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

Identifying Research Questions

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

Selecting Appropriate Design Type

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

Determining Data Collection Methods

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

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

Enhancing Research Design with iMotions and Biosensors

Introduction to enhanced research design.

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

Integrating iMotions in Research Design

Imotions software: a key to multimodal data integration.

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

Biosensors: Gateways to Deeper Insights

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

Application in Different Research Designs

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

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

Streamlining Research Design with iMotions

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

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

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

Challenges in Research Design

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

Ethical Considerations

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

Practical Limitations

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

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

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

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Research Design 101

Everything You Need To Get Started (With Examples)

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

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

Overview: Research Design 101

What is research design.

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

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

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

The problem with defining research design…

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

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

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

Research Design: Quantitative Studies

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

Descriptive Research Design

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

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

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

Correlational Research Design

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

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

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

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

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Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

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10 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

how to cite this page

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

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

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Study designs: Part 1 – An overview and classification

Priya ranganathan.

Department of Anaesthesiology, Tata Memorial Centre, Mumbai, Maharashtra, India

Rakesh Aggarwal

1 Department of Gastroenterology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, Uttar Pradesh, India

There are several types of research study designs, each with its inherent strengths and flaws. The study design used to answer a particular research question depends on the nature of the question and the availability of resources. In this article, which is the first part of a series on “study designs,” we provide an overview of research study designs and their classification. The subsequent articles will focus on individual designs.

INTRODUCTION

Research study design is a framework, or the set of methods and procedures used to collect and analyze data on variables specified in a particular research problem.

Research study designs are of many types, each with its advantages and limitations. The type of study design used to answer a particular research question is determined by the nature of question, the goal of research, and the availability of resources. Since the design of a study can affect the validity of its results, it is important to understand the different types of study designs and their strengths and limitations.

There are some terms that are used frequently while classifying study designs which are described in the following sections.

A variable represents a measurable attribute that varies across study units, for example, individual participants in a study, or at times even when measured in an individual person over time. Some examples of variables include age, sex, weight, height, health status, alive/dead, diseased/healthy, annual income, smoking yes/no, and treated/untreated.

Exposure (or intervention) and outcome variables

A large proportion of research studies assess the relationship between two variables. Here, the question is whether one variable is associated with or responsible for change in the value of the other variable. Exposure (or intervention) refers to the risk factor whose effect is being studied. It is also referred to as the independent or the predictor variable. The outcome (or predicted or dependent) variable develops as a consequence of the exposure (or intervention). Typically, the term “exposure” is used when the “causative” variable is naturally determined (as in observational studies – examples include age, sex, smoking, and educational status), and the term “intervention” is preferred where the researcher assigns some or all participants to receive a particular treatment for the purpose of the study (experimental studies – e.g., administration of a drug). If a drug had been started in some individuals but not in the others, before the study started, this counts as exposure, and not as intervention – since the drug was not started specifically for the study.

Observational versus interventional (or experimental) studies

Observational studies are those where the researcher is documenting a naturally occurring relationship between the exposure and the outcome that he/she is studying. The researcher does not do any active intervention in any individual, and the exposure has already been decided naturally or by some other factor. For example, looking at the incidence of lung cancer in smokers versus nonsmokers, or comparing the antenatal dietary habits of mothers with normal and low-birth babies. In these studies, the investigator did not play any role in determining the smoking or dietary habit in individuals.

For an exposure to determine the outcome, it must precede the latter. Any variable that occurs simultaneously with or following the outcome cannot be causative, and hence is not considered as an “exposure.”

Observational studies can be either descriptive (nonanalytical) or analytical (inferential) – this is discussed later in this article.

Interventional studies are experiments where the researcher actively performs an intervention in some or all members of a group of participants. This intervention could take many forms – for example, administration of a drug or vaccine, performance of a diagnostic or therapeutic procedure, and introduction of an educational tool. For example, a study could randomly assign persons to receive aspirin or placebo for a specific duration and assess the effect on the risk of developing cerebrovascular events.

Descriptive versus analytical studies

Descriptive (or nonanalytical) studies, as the name suggests, merely try to describe the data on one or more characteristics of a group of individuals. These do not try to answer questions or establish relationships between variables. Examples of descriptive studies include case reports, case series, and cross-sectional surveys (please note that cross-sectional surveys may be analytical studies as well – this will be discussed in the next article in this series). Examples of descriptive studies include a survey of dietary habits among pregnant women or a case series of patients with an unusual reaction to a drug.

Analytical studies attempt to test a hypothesis and establish causal relationships between variables. In these studies, the researcher assesses the effect of an exposure (or intervention) on an outcome. As described earlier, analytical studies can be observational (if the exposure is naturally determined) or interventional (if the researcher actively administers the intervention).

Directionality of study designs

Based on the direction of inquiry, study designs may be classified as forward-direction or backward-direction. In forward-direction studies, the researcher starts with determining the exposure to a risk factor and then assesses whether the outcome occurs at a future time point. This design is known as a cohort study. For example, a researcher can follow a group of smokers and a group of nonsmokers to determine the incidence of lung cancer in each. In backward-direction studies, the researcher begins by determining whether the outcome is present (cases vs. noncases [also called controls]) and then traces the presence of prior exposure to a risk factor. These are known as case–control studies. For example, a researcher identifies a group of normal-weight babies and a group of low-birth weight babies and then asks the mothers about their dietary habits during the index pregnancy.

Prospective versus retrospective study designs

The terms “prospective” and “retrospective” refer to the timing of the research in relation to the development of the outcome. In retrospective studies, the outcome of interest has already occurred (or not occurred – e.g., in controls) in each individual by the time s/he is enrolled, and the data are collected either from records or by asking participants to recall exposures. There is no follow-up of participants. By contrast, in prospective studies, the outcome (and sometimes even the exposure or intervention) has not occurred when the study starts and participants are followed up over a period of time to determine the occurrence of outcomes. Typically, most cohort studies are prospective studies (though there may be retrospective cohorts), whereas case–control studies are retrospective studies. An interventional study has to be, by definition, a prospective study since the investigator determines the exposure for each study participant and then follows them to observe outcomes.

The terms “prospective” versus “retrospective” studies can be confusing. Let us think of an investigator who starts a case–control study. To him/her, the process of enrolling cases and controls over a period of several months appears prospective. Hence, the use of these terms is best avoided. Or, at the very least, one must be clear that the terms relate to work flow for each individual study participant, and not to the study as a whole.

Classification of study designs

Figure 1 depicts a simple classification of research study designs. The Centre for Evidence-based Medicine has put forward a useful three-point algorithm which can help determine the design of a research study from its methods section:[ 1 ]

Classification of research study designs

• Does the study describe the characteristics of a sample or does it attempt to analyze (or draw inferences about) the relationship between two variables? – If no, then it is a descriptive study, and if yes, it is an analytical (inferential) study
• If analytical, did the investigator determine the exposure? – If no, it is an observational study, and if yes, it is an experimental study
• If observational, when was the outcome determined? – at the start of the study (case–control study), at the end of a period of follow-up (cohort study), or simultaneously (cross sectional).

In the next few pieces in the series, we will discuss various study designs in greater detail.

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

By Nick Jain

Published on: September 8, 2023

Table of Contents

10 Types of Research Design

Top 16 research design methods, research design examples, what is a research design.

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

Key elements of research design include:

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

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

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

Understanding the intricate tapestry of research design is pivotal for steering your investigations toward unparalleled success. Dive deep into the realm of methodologies, where precision meets impact, and craft tailored approaches to illuminate every research endeavor.

1. Experimental Research Design: Mastering Controlled Trials

Delve into the heart of experimentation with Randomized Controlled Trials (RCTs). By randomizing participants into experimental and control groups, RCTs meticulously assess the efficacy of interventions or treatments, establishing clear cause-and-effect relationships.

2. Quasi-Experimental Research Design: Bridging the Gap Ethically

When randomness isn’t feasible, embrace the pragmatic alternative of Non-equivalent Group Designs. These designs allow ethical comparison across multiple groups without random assignment, ensuring robust research conduct.

3. Observational Research Design: Capturing Real-world Dynamics

Capture snapshots of reality with Cross-Sectional Studies, unraveling intricate relationships and disparities between variables in a single moment. Embark on longitudinal journeys with Longitudinal Studies, tracking evolving trends and patterns over time.

4. Descriptive Research Design: Unveiling Insights Through Data

Plunge into the depths of data collection with Survey Research, extracting insights into attitudes, characteristics, and opinions. Engage in profound exploration through Case Studies, dissecting singular phenomena to unveil profound insights.

5. Correlational Research Design: Navigating Interrelationships

Traverse the realm of correlations with Correlational Studies, scrutinizing interrelationships between variables without inferring causality. Uncover insights into the dynamic web of connections shaping research landscapes.

6. Ex Post Facto Research Design: Retroactive Revelations

Explore existing conditions retrospectively with Retrospective Exploration, shedding light on potential causes where variable manipulation isn’t feasible. Uncover hidden insights through meticulous retrospective analysis.

7. Exploratory Research Design: Pioneering New Frontiers

Initiate your research odyssey with Pilot Studies, laying the groundwork for comprehensive investigations while refining research procedures. Blaze trails into uncharted territories and unearth groundbreaking discoveries.

8. Cohort Study: Chronicling Evolution

Embark on longitudinal expeditions with Cohort Studies, monitoring cohorts to elucidate the evolution of specific outcomes over time. Witness the unfolding narrative of change and transformation.

9. Action Research: Driving Practical Solutions

Collaboratively navigate challenges with Action Research, fostering improvements in educational or organizational settings. Drive meaningful change through actionable insights derived from collaborative endeavors.

10. Meta-Analysis: Synthesizing Knowledge

Combine perspectives gleaned from various studies through Meta-Analyses, providing a comprehensive panorama of research discoveries.

By honing in on the nuances of each research design and aligning your content with strategic SEO principles, you can ascend to the zenith of search engine rankings and establish your authority in the domain of research methodology.

Learn more: What is Research?

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

1. Experimental Method

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

2. Observational Method

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

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

3. Survey Method

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

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

4. Case Study Method

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

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

5. Content Analysis

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

6. Historical Research

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

7. Action Research

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

8. Ethnographic Research

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

9. Cross-sectional and Longitudinal Surveys

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

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

10. Meta-Analysis

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

11. Mixed-Methods Research

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

12. Grounded Theory

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

13. Simulation and Modeling

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

14. Survey Experiments

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

15. Case-Control Studies and Cohort Studies

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

16. Cross-Sequential Design

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

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

Learn more: What is Research Objective?

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

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

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

Learn more: What is Competitive Research?

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What is Research Design? Understand Types of Research Design, with Examples

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Are you unsure about the research design elements or which of the different types of research design best suit your study? Don’t worry! In this article, we’ve got you covered!   

Table of Contents

What is research design?  

Have you been wondering “ what is research design ?” or “what are some research design examples ?” Don’t worry! In this article, we’ve got you covered!  

A research design is the plan or framework used to conduct a research study. It involves outlining the overall approach and methods that will be used to collect and analyze data in order to answer research questions or test hypotheses. A well-designed research study should have a clear and well-defined research question, a detailed plan for collecting data, and a method for analyzing and interpreting the results. A well-thought-out research design addresses all these features.  

Research design elements  

Research design elements include the following:  

• Clear purpose: The research question or hypothesis must be clearly defined and focused.  
• Sampling: This includes decisions about sample size, sampling method, and criteria for inclusion or exclusion. The approach varies for different research design types .  
• Data collection: This research design element involves the process of gathering data or information from the study participants or sources. It includes decisions about what data to collect, how to collect it, and the tools or instruments that will be used.  
• Data analysis: All research design types require analysis and interpretation of the data collected. This research design element includes decisions about the statistical tests or methods that will be used to analyze the data, as well as any potential confounding variables or biases that may need to be addressed.  
• Type of research methodology: This includes decisions about the overall approach for the study.  
• Time frame: An important research design element is the time frame, which includes decisions about the duration of the study, the timeline for data collection and analysis, and follow-up periods.  
• Ethical considerations: The research design must include decisions about ethical considerations such as informed consent, confidentiality, and participant protection.  
• Resources: A good research design takes into account decisions about the budget, staffing, and other resources needed to carry out the study.  

The elements of research design should be carefully planned and executed to ensure the validity and reliability of the study findings. Let’s go deeper into the concepts of research design .    

Characteristics of research design  

Some basic characteristics of research design are common to different research design types . These characteristics of research design are as follows:  

• Neutrality : Right from the study assumptions to setting up the study, a neutral stance must be maintained, free of pre-conceived notions. The researcher’s expectations or beliefs should not color the findings or interpretation of the findings. Accordingly, a good research design should address potential sources of bias and confounding factors to be able to yield unbiased and neutral results.   
•   Reliability : Reliability is one of the characteristics of research design that refers to consistency in measurement over repeated measures and fewer random errors. A reliable research design must allow for results to be consistent, with few errors due to chance.   
•   Validity : Validity refers to the minimization of nonrandom (systematic) errors. A good research design must employ measurement tools that ensure validity of the results.  
•   Generalizability: The outcome of the research design should be applicable to a larger population and not just a small sample . A generalized method means the study can be conducted on any part of a population with similar accuracy.   
•   Flexibility: A research design should allow for changes to be made to the research plan as needed, based on the data collected and the outcomes of the study  

A well-planned research design is critical for conducting a scientifically rigorous study that will generate neutral, reliable, valid, and generalizable results. At the same time, it should allow some level of flexibility.  

Different types of research design  

A research design is essential to systematically investigate, understand, and interpret phenomena of interest. Let’s look at different types of research design and research design examples .  

Broadly, research design types can be divided into qualitative and quantitative research.  

Qualitative research is subjective and exploratory. It determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc.  

Quantitative research is objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research is usually done using surveys and experiments.  

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  .

The following will familiarize you with the research design categories in qualitative research:  

• Grounded theory: This design is used to investigate research questions that have not previously been studied in depth. Also referred to as exploratory design , it creates sequential guidelines, offers strategies for inquiry, and makes data collection and analysis more efficient in qualitative research.   

Example: A researcher wants to study how people adopt a certain app. The researcher collects data through interviews and then analyzes the data to look for patterns. These patterns are used to develop a theory about how people adopt that app.  

•   Thematic analysis: This design is used to compare the data collected in past research to find similar themes in qualitative research.  

Example: A researcher examines an interview transcript to identify common themes, say, topics or patterns emerging repeatedly.  

• Discourse analysis : This research design deals with language or social contexts used in data gathering in qualitative research.   

Example: Identifying ideological frameworks and viewpoints of writers of a series of policies.  

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

• Descriptive research design : This quantitative research design is applied where the aim is to identify characteristics, frequencies, trends, and categories. It may not often begin with a hypothesis. The basis of this research type is a description of an identified variable. This research design type describes the “what,” “when,” “where,” or “how” of phenomena (but not the “why”).   

Example: A study on the different income levels of people who use nutritional supplements regularly.  

• Correlational research design : Correlation reflects the strength and/or direction of the relationship among variables. The direction of a correlation can be positive or negative. Correlational research design helps researchers establish a relationship between two variables without the researcher controlling any of them.  

Example : An example of correlational research design could be studying the correlation between time spent watching crime shows and aggressive behavior in teenagers.  

•   Diagnostic research design : In diagnostic design, the researcher aims to understand the underlying cause of a specific topic or phenomenon (usually an area of improvement) and find the most effective solution. In simpler terms, a researcher seeks an accurate “diagnosis” of a problem and identifies a solution.  

Example : A researcher analyzing customer feedback and reviews to identify areas where an app can be improved.    

• Explanatory research design : In explanatory research design , a researcher uses their ideas and thoughts on a topic to explore their theories in more depth. This design is used to explore a phenomenon when limited information is available. It can help increase current understanding of unexplored aspects of a subject. It is thus a kind of “starting point” for future research.  

Example : Formulating hypotheses to guide future studies on delaying school start times for better mental health in teenagers.  

•   Causal research design : This can be considered a type of explanatory research. Causal research design seeks to define a cause and effect in its data. The researcher does not use a randomly chosen control group but naturally or pre-existing groupings. Importantly, the researcher does not manipulate the independent variable.   

Example : Comparing school dropout levels and possible bullying events.  

•   Experimental research design : This research design is used to study causal relationships . One or more independent variables are manipulated, and their effect on one or more dependent variables is measured.  

Example: Determining the efficacy of a new vaccine plan for influenza.  

Benefits of research design  

 T here are numerous benefits of research design . These are as follows:  

• Clear direction: Among the benefits of research design , the main one is providing direction to the research and guiding the choice of clear objectives, which help the researcher to focus on the specific research questions or hypotheses they want to investigate.  
• Control: Through a proper research design , researchers can control variables, identify potential confounding factors, and use randomization to minimize bias and increase the reliability of their findings.
• Replication: Research designs provide the opportunity for replication. This helps to confirm the findings of a study and ensures that the results are not due to chance or other factors. Thus, a well-chosen research design also eliminates bias and errors.  
• Validity: A research design ensures the validity of the research, i.e., whether the results truly reflect the phenomenon being investigated.  
• Reliability: Benefits of research design also include reducing inaccuracies and ensuring the reliability of the research (i.e., consistency of the research results over time, across different samples, and under different conditions).  
• Efficiency: A strong research design helps increase the efficiency of the research process. Researchers can use a variety of designs to investigate their research questions, choose the most appropriate research design for their study, and use statistical analysis to make the most of their data. By effectively describing the data necessary for an adequate test of the hypotheses and explaining how such data will be obtained, research design saves a researcher’s time.   

Overall, an appropriately chosen and executed research design helps researchers to conduct high-quality research, draw meaningful conclusions, and contribute to the advancement of knowledge in their field.

Frequently Asked Questions (FAQ) on Research Design

Q: What are th e main types of research design?

Broadly speaking there are two basic types of research design –

qualitative and quantitative research. Qualitative research is subjective and exploratory; it determines relationships between collected data and observations. It is usually carried out through interviews with open-ended questions, observations that are described in words, etc. Quantitative research , on the other hand, is more objective and employs statistical approaches. It establishes the cause-and-effect relationship among variables using different statistical and computational methods. This type of research design is usually done using surveys and experiments.

Q: How do I choose the appropriate research design for my study?

Choosing the appropriate research design for your study requires careful consideration of various factors. Start by clarifying your research objectives and the type of data you need to collect. Determine whether your study is exploratory, descriptive, or experimental in nature. Consider the availability of resources, time constraints, and the feasibility of implementing the different research designs. Review existing literature to identify similar studies and their research designs, which can serve as a guide. Ultimately, the chosen research design should align with your research questions, provide the necessary data to answer them, and be feasible given your own specific requirements/constraints.

Q: Can research design be modified during the course of a study?

Yes, research design can be modified during the course of a study based on emerging insights, practical constraints, or unforeseen circumstances. Research is an iterative process and, as new data is collected and analyzed, it may become necessary to adjust or refine the research design. However, any modifications should be made judiciously and with careful consideration of their impact on the study’s integrity and validity. It is advisable to document any changes made to the research design, along with a clear rationale for the modifications, in order to maintain transparency and allow for proper interpretation of the results.

Q: How can I ensure the validity and reliability of my research design?

Validity refers to the accuracy and meaningfulness of your study’s findings, while reliability relates to the consistency and stability of the measurements or observations. To enhance validity, carefully define your research variables, use established measurement scales or protocols, and collect data through appropriate methods. Consider conducting a pilot study to identify and address any potential issues before full implementation. To enhance reliability, use standardized procedures, conduct inter-rater or test-retest reliability checks, and employ appropriate statistical techniques for data analysis. It is also essential to document and report your methodology clearly, allowing for replication and scrutiny by other researchers.

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Research Design: What it is, Elements & Types

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

What is Research Design?

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

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

There are three main types of designs for research:

• Data collection
• Measurement
• Data Analysis

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

The Process of Research Design

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

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

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

Research Design Elements

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

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

Characteristics of Research Design

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

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

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

Research Design Types

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

Qualitative research

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

Quantitative research

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

Qualitative Research vs Quantitative Research

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

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

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

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

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

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

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

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

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

This design has three parts of the research:

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

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

Benefits of Research Design

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

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

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

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

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What is Research Design? Characteristics, Types, Process, & Examples

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Ever felt like a hamster on a research wheel fast, spinning with a million questions but going nowhere? You've got your topic; you're brimming with curiosity, but... what next? Think of it as your roadmap, ensuring you don't end up lost in a sea of confusing data. So, forget the research rut and get your papers! This ultimate guide to "what is research design?" will have you navigating your project like a pro, uncovering answers and avoiding dead ends. Know the features of good research design, what you mean by research design, elements of research design, and more.

What is Research Design?

Before starting with the topic, do you know what is research design in research? Well, research design is the plan that shows how the study will be done. This plan covers everything from how data will be collected to how it will be analysed. A good research design has a clear question to answer, a detailed plan for gathering information, and a way to make sense of the findings. A good research design has three key ingredients:

1. A clear question: What exactly are you trying to learn? ‍

2. Data collection: How will you gather information (surveys, interviews, experiments)?

3. Analysis: How will you make sense of the data you collect?

Elements of Research Design 

Now that you know what is research design, it is important to know the elements. The elements or components of research design help to ensure that it is reliable, valid and can yield meaningful results. They also provide a guide for the research process, helping the researcher from the initial stages of formulating the research question to the final stages of interpreting the findings. 

1. Purpose Statement: This is a clear and concise statement of the research objectives and the specific goals the research aims to achieve.

2. Research Questions: These are the specific questions the research aims to answer.

3. Research Methodology: This refers to the overall approach and specific methods used to collect and analyse data.

4. Data Collection Methods: These are the specific techniques used to gather data for the research.

5. Data Analysis Techniques: These are the methods used to analyse and interpret the collected data.

6. Units of Analysis: These are the specific entities (e.g., individuals, groups, organisations) that the research focuses on.

7. Linking Data to Propositions: This involves connecting the data collected to the research questions or hypotheses.

8. Interpretation of Findings: This involves making sense of the data and drawing conclusions based on the research objectives.

9. Possible Obstacles to the Research: This involves identifying potential challenges or issues that may arise during the research process.

10. Settings for Research Study: This refers to the context or environment in which the research is conducted.

11. Time of the Research Study: This refers to the timeframe of the research, whether it’s cross-sectional (at one specific point in time) or longitudinal (over an extended period).

Characteristics of Research Design

Research design has several key characteristics that contribute to the validity, reliability, and overall success of a research study. To know the answer for what is research design, it is important to know the characteristics. These are-

1. Reliability: A reliable research design ensures that each study’s results are accurate and can be replicated. This means that if the research is conducted again under the same conditions, it should yield similar results.

2. Validity: A valid research design uses appropriate measuring tools to gauge the results according to the research objective. This ensures that the data collected and the conclusions drawn are relevant and accurately reflect the phenomenon being studied.

3. Neutrality: A neutral research design ensures that the assumptions made at the beginning of the research are free from bias. This means that the data collected throughout the research is based on these unbiased assumptions.

4. Generalizability: A good research design draws an outcome that can be applied to a large set of people and is not limited to the sample size or the research group.

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The Process of Research Design

What is research design? A good research helps you do a really good study that gives fair, trustworthy, and useful results. But it's also good to have a bit of wiggle room for changes. If you’re wondering how to conduct a research in just 5 mins , here's a breakdown and examples to work even better.

Step 1: Establish Priorities for Research Design: 

Before conducting any research study, you must address an important question: "what is research design and how to create one?" For example, if you're researching the impact of remote learning on student performance, your priority might be to establish a clear research question and objectives.

Step 2: Choose your Data Type you Need for Research

One of the best features of research design is to decide on the type of data you need for your research. For instance, if you’re studying the effects of a new drug, you might need quantitative data like clinical trial results.

There are lots of ways to answer your research questions. Think about what you want to achieve before you decide how to do your research. The first thing, do you know what is qualitative research design and what is quantitative research design? Here's a quick difference between the two:

What is Research Design in Quantitative Research?

There are 4 main types of quantitative research design- 

What are Research Design Examples?

1. Experimental Research Methods: 

Drug Efficacy Study: A pharmaceutical company wants to test the effectiveness of a new drug. They randomly assign participants to two groups: one group receives the new drug (experimental group), and the other group receives a placebo (control group). The company then measures the health outcomes of the two groups.

2. Quasi-Experimental Research Methods:

Teaching Method Evaluation: A researcher is interested in the impact of a new teaching method. A group of students are taught using the new method, while another group is taught using the traditional method. The researcher then compares the academic performance of the two groups.

3. Descriptive Research Methods:

Consumer Behavior Survey: A company wants to understand the shopping habits of their customers. They conduct a survey asking customers about their shopping frequency, preferred products, and reasons for their preferences.

4. Correlational Research Methods:

Health and Lifestyle Study: A health researcher is interested in the relationship between physical activity levels and heart disease. They collect data on the physical activity levels and heart health of a large group of people over several years. The researcher then analyses the data to see if there is a correlation between physical activity and heart disease

What is Qualitative Research Design?

Qualitative research designs are more flexible and open-ended. They're all about deeply understanding a particular situation or topic, and you have room to be imaginative and adaptable in planning your study. Below, you'll find a list of typical qualitative research designs.

Step 3: Decide your Data Collection Techniques

Now that you understand what is research design in research, you should also know the types of what are the different types of research design techniques. Choose the methods you’ll use to gather your data. If you’re surveying consumer behaviour, for example, you might use questionnaires or interviews.

Survey methods

Surveys are like questionnaires or interviews where you ask people about what they think, do, feel, or are like. They help you gather information straight from the source. So, when you're planning a research project, you can pick either questionnaires or interviews as your main way to get data. Research design is just the plan you make for how you're going to do your research, including what methods you'll use, like surveys.

Observation methods

Observational studies are a way to gather information without bothering anyone. You just watch and note down what you see, like people's actions or how they interact, without asking them directly. You can do this right then and there, jotting down stuff, or you can record videos to check out later. Depending on what you're studying, these observations can focus on describing things or counting them up.

Secondary Data

If you can't gather data yourself, you can use info already collected by other researchers, like from government surveys or past studies. You can then analyse this data to explore new questions. This can broaden your research because you might access bigger and more diverse samples. But, since you didn't collect the data yourself, you can't choose what to measure or how, which limits your conclusions.

In simple terms, research design is about how you plan to gather and analyse data to answer your research questions. If you can't collect data directly, you might use data already gathered by others, known as secondary data, to still answer your questions.

Step 4: Sort Out your Data Analysis

When you find what research design in research, just having a bunch of raw data isn't enough to answer your questions. You also need to figure out how you're going to make sense of that data. This is where research design comes in.

If you're working with quantitative research, you'll probably use statistics to analyse your data. Statistics help you understand things like how your data is spread out, what the average is, and how different groups compare. For example, you might use tests to see if there's a connection between two things or if one group is different from another.

But if you're dealing with more qualitative research, you'll need a different approach. Instead of crunching numbers, you'll be diving deep into your data, looking for patterns and meanings. You might use methods like thematic analysis or discourse analysis to make sense of it all.

Sampling Procedures

Choosing the right way to pick people for your study is important. But it's not just about that. You also need a solid plan for how you'll reach out and get those people to join in.

Here's what you need to think about:

1. How many people do you need to join to make sure your study is good?

2. What rules will you use to decide who can join and who can't?

3. How will you get in touch with them—by mail, online, phone, or meeting them in person?

4. If you're picking people randomly, it's crucial that everyone who gets chosen actually takes part. How can you make sure most of them do?

If you're not picking people randomly, how will you ensure that your study is unbiased and represents different kinds of people? 

Benefits of Research Design

After learning about what is research design and the process, it is important to know the key benefits of a well-structured research design:

1. Minimises Risk of Errors: A good research design minimises the risk of errors and reduces inaccuracy. It ensures that the study is carried out in the right direction and that all the team members are on the same page.

2. Efficient Use of Resources: It facilitates a concrete research plan for the efficient use of time and resources. It helps the researcher better complete all the tasks, even with limited resources.

3. Provides Direction: The purpose of the research design is to enable the researcher to proceed in the right direction without deviating from the tasks. It helps to identify the major and minor tasks of the study.

4. Ensures Validity and Reliability: A well-designed research enhances the validity and reliability of the findings and allows for the replication of studies by other researchers. The main advantage of a good research design is that it provides accuracy, reliability, consistency, and legitimacy to the research.

5. Facilitates Problem-Solving: A researcher can easily frame the objectives of the research work based on the design of experiments (research design). A good research design helps the researcher find the best solution for the research problems.

6. Better Documentation: It helps in better documentation of the various activities while the project work is going on.

That's it! You've explored all the answers for what is research design in research? Remember, it's not just about picking a fancy method – it's about choosing the perfect tool to answer your burning questions. By carefully considering your goals and resources, you can design a research plan that gathers reliable information and helps you reach clear conclusions. 

Frequently Asked Questions

What are the 4 types of research design, what are the important concepts of research design, what are the 5 components of a research, what are different types of research, what are the 4 major elements of a research design.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

• Types of Research Designs
• Purpose of Guide
• Design Flaws to Avoid
• Independent and Dependent Variables
• Glossary of Research Terms
• Reading Research Effectively
• Narrowing a Topic Idea
• Broadening a Topic Idea
• Extending the Timeliness of a Topic Idea
• Academic Writing Style
• Applying Critical Thinking
• Choosing a Title
• Making an Outline
• Paragraph Development
• Research Process Video Series
• Executive Summary
• The C.A.R.S. Model
• Background Information
• The Research Problem/Question
• Theoretical Framework
• Citation Tracking
• Content Alert Services
• Evaluating Sources
• Primary Sources
• Secondary Sources
• Tiertiary Sources
• Scholarly vs. Popular Publications
• Qualitative Methods
• Quantitative Methods
• Insiderness
• Using Non-Textual Elements
• Limitations of the Study
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism
• Footnotes or Endnotes?
• Further Readings
• Generative AI and Writing
• USC Libraries Tutorials and Other Guides
• Bibliography

Introduction

Before beginning your paper, you need to decide how you plan to design the study .

The research design refers to the overall strategy and analytical approach that you have chosen in order to integrate, in a coherent and logical way, the different components of the study, thus ensuring that the research problem will be thoroughly investigated. It constitutes the blueprint for the collection, measurement, and interpretation of information and data. Note that the research problem determines the type of design you choose, not the other way around!

De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Trochim, William M.K. Research Methods Knowledge Base. 2006.

General Structure and Writing Style

The function of a research design is to ensure that the evidence obtained enables you to effectively address the research problem logically and as unambiguously as possible . In social sciences research, obtaining information relevant to the research problem generally entails specifying the type of evidence needed to test the underlying assumptions of a theory, to evaluate a program, or to accurately describe and assess meaning related to an observable phenomenon.

With this in mind, a common mistake made by researchers is that they begin their investigations before they have thought critically about what information is required to address the research problem. Without attending to these design issues beforehand, the overall research problem will not be adequately addressed and any conclusions drawn will run the risk of being weak and unconvincing. As a consequence, the overall validity of the study will be undermined.

The length and complexity of describing the research design in your paper can vary considerably, but any well-developed description will achieve the following :

• Identify the research problem clearly and justify its selection, particularly in relation to any valid alternative designs that could have been used,
• Review and synthesize previously published literature associated with the research problem,
• Clearly and explicitly specify hypotheses [i.e., research questions] central to the problem,
• Effectively describe the information and/or data which will be necessary for an adequate testing of the hypotheses and explain how such information and/or data will be obtained, and
• Describe the methods of analysis to be applied to the data in determining whether or not the hypotheses are true or false.

The research design is usually incorporated into the introduction of your paper . You can obtain an overall sense of what to do by reviewing studies that have utilized the same research design [e.g., using a case study approach]. This can help you develop an outline to follow for your own paper.

NOTE: Use the SAGE Research Methods Online and Cases and the SAGE Research Methods Videos databases to search for scholarly resources on how to apply specific research designs and methods . The Research Methods Online database contains links to more than 175,000 pages of SAGE publisher's book, journal, and reference content on quantitative, qualitative, and mixed research methodologies. Also included is a collection of case studies of social research projects that can be used to help you better understand abstract or complex methodological concepts. The Research Methods Videos database contains hours of tutorials, interviews, video case studies, and mini-documentaries covering the entire research process.

Creswell, John W. and J. David Creswell. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 5th edition. Thousand Oaks, CA: Sage, 2018; De Vaus, D. A. Research Design in Social Research . London: SAGE, 2001; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Leedy, Paul D. and Jeanne Ellis Ormrod. Practical Research: Planning and Design . Tenth edition. Boston, MA: Pearson, 2013; Vogt, W. Paul, Dianna C. Gardner, and Lynne M. Haeffele. When to Use What Research Design . New York: Guilford, 2012.

Action Research Design

Definition and Purpose

The essentials of action research design follow a characteristic cycle whereby initially an exploratory stance is adopted, where an understanding of a problem is developed and plans are made for some form of interventionary strategy. Then the intervention is carried out [the "action" in action research] during which time, pertinent observations are collected in various forms. The new interventional strategies are carried out, and this cyclic process repeats, continuing until a sufficient understanding of [or a valid implementation solution for] the problem is achieved. The protocol is iterative or cyclical in nature and is intended to foster deeper understanding of a given situation, starting with conceptualizing and particularizing the problem and moving through several interventions and evaluations.

What do these studies tell you ?

• This is a collaborative and adaptive research design that lends itself to use in work or community situations.
• Design focuses on pragmatic and solution-driven research outcomes rather than testing theories.
• When practitioners use action research, it has the potential to increase the amount they learn consciously from their experience; the action research cycle can be regarded as a learning cycle.
• Action research studies often have direct and obvious relevance to improving practice and advocating for change.
• There are no hidden controls or preemption of direction by the researcher.

What these studies don't tell you ?

• It is harder to do than conducting conventional research because the researcher takes on responsibilities of advocating for change as well as for researching the topic.
• Action research is much harder to write up because it is less likely that you can use a standard format to report your findings effectively [i.e., data is often in the form of stories or observation].
• Personal over-involvement of the researcher may bias research results.
• The cyclic nature of action research to achieve its twin outcomes of action [e.g. change] and research [e.g. understanding] is time-consuming and complex to conduct.
• Advocating for change usually requires buy-in from study participants.

Coghlan, David and Mary Brydon-Miller. The Sage Encyclopedia of Action Research . Thousand Oaks, CA:  Sage, 2014; Efron, Sara Efrat and Ruth Ravid. Action Research in Education: A Practical Guide . New York: Guilford, 2013; Gall, Meredith. Educational Research: An Introduction . Chapter 18, Action Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Gorard, Stephen. Research Design: Creating Robust Approaches for the Social Sciences . Thousand Oaks, CA: Sage, 2013; Kemmis, Stephen and Robin McTaggart. “Participatory Action Research.” In Handbook of Qualitative Research . Norman Denzin and Yvonna S. Lincoln, eds. 2nd ed. (Thousand Oaks, CA: SAGE, 2000), pp. 567-605; McNiff, Jean. Writing and Doing Action Research . London: Sage, 2014; Reason, Peter and Hilary Bradbury. Handbook of Action Research: Participative Inquiry and Practice . Thousand Oaks, CA: SAGE, 2001.

Case Study Design

A case study is an in-depth study of a particular research problem rather than a sweeping statistical survey or comprehensive comparative inquiry. It is often used to narrow down a very broad field of research into one or a few easily researchable examples. The case study research design is also useful for testing whether a specific theory and model actually applies to phenomena in the real world. It is a useful design when not much is known about an issue or phenomenon.

• Approach excels at bringing us to an understanding of a complex issue through detailed contextual analysis of a limited number of events or conditions and their relationships.
• A researcher using a case study design can apply a variety of methodologies and rely on a variety of sources to investigate a research problem.
• Design can extend experience or add strength to what is already known through previous research.
• Social scientists, in particular, make wide use of this research design to examine contemporary real-life situations and provide the basis for the application of concepts and theories and the extension of methodologies.
• The design can provide detailed descriptions of specific and rare cases.
• A single or small number of cases offers little basis for establishing reliability or to generalize the findings to a wider population of people, places, or things.
• Intense exposure to the study of a case may bias a researcher's interpretation of the findings.
• Design does not facilitate assessment of cause and effect relationships.
• Vital information may be missing, making the case hard to interpret.
• The case may not be representative or typical of the larger problem being investigated.
• If the criteria for selecting a case is because it represents a very unusual or unique phenomenon or problem for study, then your interpretation of the findings can only apply to that particular case.

Case Studies. Writing@CSU. Colorado State University; Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 4, Flexible Methods: Case Study Design. 2nd ed. New York: Columbia University Press, 1999; Gerring, John. “What Is a Case Study and What Is It Good for?” American Political Science Review 98 (May 2004): 341-354; Greenhalgh, Trisha, editor. Case Study Evaluation: Past, Present and Future Challenges . Bingley, UK: Emerald Group Publishing, 2015; Mills, Albert J. , Gabrielle Durepos, and Eiden Wiebe, editors. Encyclopedia of Case Study Research . Thousand Oaks, CA: SAGE Publications, 2010; Stake, Robert E. The Art of Case Study Research . Thousand Oaks, CA: SAGE, 1995; Yin, Robert K. Case Study Research: Design and Theory . Applied Social Research Methods Series, no. 5. 3rd ed. Thousand Oaks, CA: SAGE, 2003.

Causal Design

Causality studies may be thought of as understanding a phenomenon in terms of conditional statements in the form, “If X, then Y.” This type of research is used to measure what impact a specific change will have on existing norms and assumptions. Most social scientists seek causal explanations that reflect tests of hypotheses. Causal effect (nomothetic perspective) occurs when variation in one phenomenon, an independent variable, leads to or results, on average, in variation in another phenomenon, the dependent variable.

Conditions necessary for determining causality:

• Empirical association -- a valid conclusion is based on finding an association between the independent variable and the dependent variable.
• Appropriate time order -- to conclude that causation was involved, one must see that cases were exposed to variation in the independent variable before variation in the dependent variable.
• Nonspuriousness -- a relationship between two variables that is not due to variation in a third variable.
• Causality research designs assist researchers in understanding why the world works the way it does through the process of proving a causal link between variables and by the process of eliminating other possibilities.
• Replication is possible.
• There is greater confidence the study has internal validity due to the systematic subject selection and equity of groups being compared.
• Not all relationships are causal! The possibility always exists that, by sheer coincidence, two unrelated events appear to be related [e.g., Punxatawney Phil could accurately predict the duration of Winter for five consecutive years but, the fact remains, he's just a big, furry rodent].
• Conclusions about causal relationships are difficult to determine due to a variety of extraneous and confounding variables that exist in a social environment. This means causality can only be inferred, never proven.
• If two variables are correlated, the cause must come before the effect. However, even though two variables might be causally related, it can sometimes be difficult to determine which variable comes first and, therefore, to establish which variable is the actual cause and which is the  actual effect.

Beach, Derek and Rasmus Brun Pedersen. Causal Case Study Methods: Foundations and Guidelines for Comparing, Matching, and Tracing . Ann Arbor, MI: University of Michigan Press, 2016; Bachman, Ronet. The Practice of Research in Criminology and Criminal Justice . Chapter 5, Causation and Research Designs. 3rd ed. Thousand Oaks, CA: Pine Forge Press, 2007; Brewer, Ernest W. and Jennifer Kubn. “Causal-Comparative Design.” In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 125-132; Causal Research Design: Experimentation. Anonymous SlideShare Presentation; Gall, Meredith. Educational Research: An Introduction . Chapter 11, Nonexperimental Research: Correlational Designs. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007; Trochim, William M.K. Research Methods Knowledge Base. 2006.

Cohort Design

Often used in the medical sciences, but also found in the applied social sciences, a cohort study generally refers to a study conducted over a period of time involving members of a population which the subject or representative member comes from, and who are united by some commonality or similarity. Using a quantitative framework, a cohort study makes note of statistical occurrence within a specialized subgroup, united by same or similar characteristics that are relevant to the research problem being investigated, rather than studying statistical occurrence within the general population. Using a qualitative framework, cohort studies generally gather data using methods of observation. Cohorts can be either "open" or "closed."

• Open Cohort Studies [dynamic populations, such as the population of Los Angeles] involve a population that is defined just by the state of being a part of the study in question (and being monitored for the outcome). Date of entry and exit from the study is individually defined, therefore, the size of the study population is not constant. In open cohort studies, researchers can only calculate rate based data, such as, incidence rates and variants thereof.
• Closed Cohort Studies [static populations, such as patients entered into a clinical trial] involve participants who enter into the study at one defining point in time and where it is presumed that no new participants can enter the cohort. Given this, the number of study participants remains constant (or can only decrease).
• The use of cohorts is often mandatory because a randomized control study may be unethical. For example, you cannot deliberately expose people to asbestos, you can only study its effects on those who have already been exposed. Research that measures risk factors often relies upon cohort designs.
• Because cohort studies measure potential causes before the outcome has occurred, they can demonstrate that these “causes” preceded the outcome, thereby avoiding the debate as to which is the cause and which is the effect.
• Cohort analysis is highly flexible and can provide insight into effects over time and related to a variety of different types of changes [e.g., social, cultural, political, economic, etc.].
• Either original data or secondary data can be used in this design.
• In cases where a comparative analysis of two cohorts is made [e.g., studying the effects of one group exposed to asbestos and one that has not], a researcher cannot control for all other factors that might differ between the two groups. These factors are known as confounding variables.
• Cohort studies can end up taking a long time to complete if the researcher must wait for the conditions of interest to develop within the group. This also increases the chance that key variables change during the course of the study, potentially impacting the validity of the findings.
• Due to the lack of randominization in the cohort design, its external validity is lower than that of study designs where the researcher randomly assigns participants.

Healy P, Devane D. “Methodological Considerations in Cohort Study Designs.” Nurse Researcher 18 (2011): 32-36; Glenn, Norval D, editor. Cohort Analysis . 2nd edition. Thousand Oaks, CA: Sage, 2005; Levin, Kate Ann. Study Design IV: Cohort Studies. Evidence-Based Dentistry 7 (2003): 51–52; Payne, Geoff. “Cohort Study.” In The SAGE Dictionary of Social Research Methods . Victor Jupp, editor. (Thousand Oaks, CA: Sage, 2006), pp. 31-33; Study Design 101. Himmelfarb Health Sciences Library. George Washington University, November 2011; Cohort Study. Wikipedia.

Cross-Sectional Design

Cross-sectional research designs have three distinctive features: no time dimension; a reliance on existing differences rather than change following intervention; and, groups are selected based on existing differences rather than random allocation. The cross-sectional design can only measure differences between or from among a variety of people, subjects, or phenomena rather than a process of change. As such, researchers using this design can only employ a relatively passive approach to making causal inferences based on findings.

• Cross-sectional studies provide a clear 'snapshot' of the outcome and the characteristics associated with it, at a specific point in time.
• Unlike an experimental design, where there is an active intervention by the researcher to produce and measure change or to create differences, cross-sectional designs focus on studying and drawing inferences from existing differences between people, subjects, or phenomena.
• Entails collecting data at and concerning one point in time. While longitudinal studies involve taking multiple measures over an extended period of time, cross-sectional research is focused on finding relationships between variables at one moment in time.
• Groups identified for study are purposely selected based upon existing differences in the sample rather than seeking random sampling.
• Cross-section studies are capable of using data from a large number of subjects and, unlike observational studies, is not geographically bound.
• Can estimate prevalence of an outcome of interest because the sample is usually taken from the whole population.
• Because cross-sectional designs generally use survey techniques to gather data, they are relatively inexpensive and take up little time to conduct.
• Finding people, subjects, or phenomena to study that are very similar except in one specific variable can be difficult.
• Results are static and time bound and, therefore, give no indication of a sequence of events or reveal historical or temporal contexts.
• Studies cannot be utilized to establish cause and effect relationships.
• This design only provides a snapshot of analysis so there is always the possibility that a study could have differing results if another time-frame had been chosen.
• There is no follow up to the findings.

Bethlehem, Jelke. "7: Cross-sectional Research." In Research Methodology in the Social, Behavioural and Life Sciences . Herman J Adèr and Gideon J Mellenbergh, editors. (London, England: Sage, 1999), pp. 110-43; Bourque, Linda B. “Cross-Sectional Design.” In  The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman, and Tim Futing Liao. (Thousand Oaks, CA: 2004), pp. 230-231; Hall, John. “Cross-Sectional Survey Design.” In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 173-174; Helen Barratt, Maria Kirwan. Cross-Sectional Studies: Design Application, Strengths and Weaknesses of Cross-Sectional Studies. Healthknowledge, 2009. Cross-Sectional Study. Wikipedia.

Descriptive Design

Descriptive research designs help provide answers to the questions of who, what, when, where, and how associated with a particular research problem; a descriptive study cannot conclusively ascertain answers to why. Descriptive research is used to obtain information concerning the current status of the phenomena and to describe "what exists" with respect to variables or conditions in a situation.

• The subject is being observed in a completely natural and unchanged natural environment. True experiments, whilst giving analyzable data, often adversely influence the normal behavior of the subject [a.k.a., the Heisenberg effect whereby measurements of certain systems cannot be made without affecting the systems].
• Descriptive research is often used as a pre-cursor to more quantitative research designs with the general overview giving some valuable pointers as to what variables are worth testing quantitatively.
• If the limitations are understood, they can be a useful tool in developing a more focused study.
• Descriptive studies can yield rich data that lead to important recommendations in practice.
• Appoach collects a large amount of data for detailed analysis.
• The results from a descriptive research cannot be used to discover a definitive answer or to disprove a hypothesis.
• Because descriptive designs often utilize observational methods [as opposed to quantitative methods], the results cannot be replicated.
• The descriptive function of research is heavily dependent on instrumentation for measurement and observation.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 5, Flexible Methods: Descriptive Research. 2nd ed. New York: Columbia University Press, 1999; Given, Lisa M. "Descriptive Research." In Encyclopedia of Measurement and Statistics . Neil J. Salkind and Kristin Rasmussen, editors. (Thousand Oaks, CA: Sage, 2007), pp. 251-254; McNabb, Connie. Descriptive Research Methodologies. Powerpoint Presentation; Shuttleworth, Martyn. Descriptive Research Design, September 26, 2008; Erickson, G. Scott. "Descriptive Research Design." In New Methods of Market Research and Analysis . (Northampton, MA: Edward Elgar Publishing, 2017), pp. 51-77; Sahin, Sagufta, and Jayanta Mete. "A Brief Study on Descriptive Research: Its Nature and Application in Social Science." International Journal of Research and Analysis in Humanities 1 (2021): 11; K. Swatzell and P. Jennings. “Descriptive Research: The Nuts and Bolts.” Journal of the American Academy of Physician Assistants 20 (2007), pp. 55-56; Kane, E. Doing Your Own Research: Basic Descriptive Research in the Social Sciences and Humanities . London: Marion Boyars, 1985.

Experimental Design

A blueprint of the procedure that enables the researcher to maintain control over all factors that may affect the result of an experiment. In doing this, the researcher attempts to determine or predict what may occur. Experimental research is often used where there is time priority in a causal relationship (cause precedes effect), there is consistency in a causal relationship (a cause will always lead to the same effect), and the magnitude of the correlation is great. The classic experimental design specifies an experimental group and a control group. The independent variable is administered to the experimental group and not to the control group, and both groups are measured on the same dependent variable. Subsequent experimental designs have used more groups and more measurements over longer periods. True experiments must have control, randomization, and manipulation.

• Experimental research allows the researcher to control the situation. In so doing, it allows researchers to answer the question, “What causes something to occur?”
• Permits the researcher to identify cause and effect relationships between variables and to distinguish placebo effects from treatment effects.
• Experimental research designs support the ability to limit alternative explanations and to infer direct causal relationships in the study.
• Approach provides the highest level of evidence for single studies.
• The design is artificial, and results may not generalize well to the real world.
• The artificial settings of experiments may alter the behaviors or responses of participants.
• Experimental designs can be costly if special equipment or facilities are needed.
• Some research problems cannot be studied using an experiment because of ethical or technical reasons.
• Difficult to apply ethnographic and other qualitative methods to experimentally designed studies.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 7, Flexible Methods: Experimental Research. 2nd ed. New York: Columbia University Press, 1999; Chapter 2: Research Design, Experimental Designs. School of Psychology, University of New England, 2000; Chow, Siu L. "Experimental Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 448-453; "Experimental Design." In Social Research Methods . Nicholas Walliman, editor. (London, England: Sage, 2006), pp, 101-110; Experimental Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Kirk, Roger E. Experimental Design: Procedures for the Behavioral Sciences . 4th edition. Thousand Oaks, CA: Sage, 2013; Trochim, William M.K. Experimental Design. Research Methods Knowledge Base. 2006; Rasool, Shafqat. Experimental Research. Slideshare presentation.

Exploratory Design

An exploratory design is conducted about a research problem when there are few or no earlier studies to refer to or rely upon to predict an outcome . The focus is on gaining insights and familiarity for later investigation or undertaken when research problems are in a preliminary stage of investigation. Exploratory designs are often used to establish an understanding of how best to proceed in studying an issue or what methodology would effectively apply to gathering information about the issue.

The goals of exploratory research are intended to produce the following possible insights:

• Familiarity with basic details, settings, and concerns.
• Well grounded picture of the situation being developed.
• Generation of new ideas and assumptions.
• Development of tentative theories or hypotheses.
• Determination about whether a study is feasible in the future.
• Issues get refined for more systematic investigation and formulation of new research questions.
• Direction for future research and techniques get developed.
• Design is a useful approach for gaining background information on a particular topic.
• Exploratory research is flexible and can address research questions of all types (what, why, how).
• Provides an opportunity to define new terms and clarify existing concepts.
• Exploratory research is often used to generate formal hypotheses and develop more precise research problems.
• In the policy arena or applied to practice, exploratory studies help establish research priorities and where resources should be allocated.
• Exploratory research generally utilizes small sample sizes and, thus, findings are typically not generalizable to the population at large.
• The exploratory nature of the research inhibits an ability to make definitive conclusions about the findings. They provide insight but not definitive conclusions.
• The research process underpinning exploratory studies is flexible but often unstructured, leading to only tentative results that have limited value to decision-makers.
• Design lacks rigorous standards applied to methods of data gathering and analysis because one of the areas for exploration could be to determine what method or methodologies could best fit the research problem.

Cuthill, Michael. “Exploratory Research: Citizen Participation, Local Government, and Sustainable Development in Australia.” Sustainable Development 10 (2002): 79-89; Streb, Christoph K. "Exploratory Case Study." In Encyclopedia of Case Study Research . Albert J. Mills, Gabrielle Durepos and Eiden Wiebe, editors. (Thousand Oaks, CA: Sage, 2010), pp. 372-374; Taylor, P. J., G. Catalano, and D.R.F. Walker. “Exploratory Analysis of the World City Network.” Urban Studies 39 (December 2002): 2377-2394; Exploratory Research. Wikipedia.

Field Research Design

Sometimes referred to as ethnography or participant observation, designs around field research encompass a variety of interpretative procedures [e.g., observation and interviews] rooted in qualitative approaches to studying people individually or in groups while inhabiting their natural environment as opposed to using survey instruments or other forms of impersonal methods of data gathering. Information acquired from observational research takes the form of “ field notes ” that involves documenting what the researcher actually sees and hears while in the field. Findings do not consist of conclusive statements derived from numbers and statistics because field research involves analysis of words and observations of behavior. Conclusions, therefore, are developed from an interpretation of findings that reveal overriding themes, concepts, and ideas. More information can be found HERE .

• Field research is often necessary to fill gaps in understanding the research problem applied to local conditions or to specific groups of people that cannot be ascertained from existing data.
• The research helps contextualize already known information about a research problem, thereby facilitating ways to assess the origins, scope, and scale of a problem and to gage the causes, consequences, and means to resolve an issue based on deliberate interaction with people in their natural inhabited spaces.
• Enables the researcher to corroborate or confirm data by gathering additional information that supports or refutes findings reported in prior studies of the topic.
• Because the researcher in embedded in the field, they are better able to make observations or ask questions that reflect the specific cultural context of the setting being investigated.
• Observing the local reality offers the opportunity to gain new perspectives or obtain unique data that challenges existing theoretical propositions or long-standing assumptions found in the literature.

What these studies don't tell you

• A field research study requires extensive time and resources to carry out the multiple steps involved with preparing for the gathering of information, including for example, examining background information about the study site, obtaining permission to access the study site, and building trust and rapport with subjects.
• Requires a commitment to staying engaged in the field to ensure that you can adequately document events and behaviors as they unfold.
• The unpredictable nature of fieldwork means that researchers can never fully control the process of data gathering. They must maintain a flexible approach to studying the setting because events and circumstances can change quickly or unexpectedly.
• Findings can be difficult to interpret and verify without access to documents and other source materials that help to enhance the credibility of information obtained from the field  [i.e., the act of triangulating the data].
• Linking the research problem to the selection of study participants inhabiting their natural environment is critical. However, this specificity limits the ability to generalize findings to different situations or in other contexts or to infer courses of action applied to other settings or groups of people.
• The reporting of findings must take into account how the researcher themselves may have inadvertently affected respondents and their behaviors.

Historical Design

The purpose of a historical research design is to collect, verify, and synthesize evidence from the past to establish facts that defend or refute a hypothesis. It uses secondary sources and a variety of primary documentary evidence, such as, diaries, official records, reports, archives, and non-textual information [maps, pictures, audio and visual recordings]. The limitation is that the sources must be both authentic and valid.

• The historical research design is unobtrusive; the act of research does not affect the results of the study.
• The historical approach is well suited for trend analysis.
• Historical records can add important contextual background required to more fully understand and interpret a research problem.
• There is often no possibility of researcher-subject interaction that could affect the findings.
• Historical sources can be used over and over to study different research problems or to replicate a previous study.
• The ability to fulfill the aims of your research are directly related to the amount and quality of documentation available to understand the research problem.
• Since historical research relies on data from the past, there is no way to manipulate it to control for contemporary contexts.
• Interpreting historical sources can be very time consuming.
• The sources of historical materials must be archived consistently to ensure access. This may especially challenging for digital or online-only sources.
• Original authors bring their own perspectives and biases to the interpretation of past events and these biases are more difficult to ascertain in historical resources.
• Due to the lack of control over external variables, historical research is very weak with regard to the demands of internal validity.
• It is rare that the entirety of historical documentation needed to fully address a research problem is available for interpretation, therefore, gaps need to be acknowledged.

Howell, Martha C. and Walter Prevenier. From Reliable Sources: An Introduction to Historical Methods . Ithaca, NY: Cornell University Press, 2001; Lundy, Karen Saucier. "Historical Research." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor. (Thousand Oaks, CA: Sage, 2008), pp. 396-400; Marius, Richard. and Melvin E. Page. A Short Guide to Writing about History . 9th edition. Boston, MA: Pearson, 2015; Savitt, Ronald. “Historical Research in Marketing.” Journal of Marketing 44 (Autumn, 1980): 52-58;  Gall, Meredith. Educational Research: An Introduction . Chapter 16, Historical Research. 8th ed. Boston, MA: Pearson/Allyn and Bacon, 2007.

Longitudinal Design

A longitudinal study follows the same sample over time and makes repeated observations. For example, with longitudinal surveys, the same group of people is interviewed at regular intervals, enabling researchers to track changes over time and to relate them to variables that might explain why the changes occur. Longitudinal research designs describe patterns of change and help establish the direction and magnitude of causal relationships. Measurements are taken on each variable over two or more distinct time periods. This allows the researcher to measure change in variables over time. It is a type of observational study sometimes referred to as a panel study.

• Longitudinal data facilitate the analysis of the duration of a particular phenomenon.
• Enables survey researchers to get close to the kinds of causal explanations usually attainable only with experiments.
• The design permits the measurement of differences or change in a variable from one period to another [i.e., the description of patterns of change over time].
• Longitudinal studies facilitate the prediction of future outcomes based upon earlier factors.
• The data collection method may change over time.
• Maintaining the integrity of the original sample can be difficult over an extended period of time.
• It can be difficult to show more than one variable at a time.
• This design often needs qualitative research data to explain fluctuations in the results.
• A longitudinal research design assumes present trends will continue unchanged.
• It can take a long period of time to gather results.
• There is a need to have a large sample size and accurate sampling to reach representativness.

Anastas, Jeane W. Research Design for Social Work and the Human Services . Chapter 6, Flexible Methods: Relational and Longitudinal Research. 2nd ed. New York: Columbia University Press, 1999; Forgues, Bernard, and Isabelle Vandangeon-Derumez. "Longitudinal Analyses." In Doing Management Research . Raymond-Alain Thiétart and Samantha Wauchope, editors. (London, England: Sage, 2001), pp. 332-351; Kalaian, Sema A. and Rafa M. Kasim. "Longitudinal Studies." In Encyclopedia of Survey Research Methods . Paul J. Lavrakas, ed. (Thousand Oaks, CA: Sage, 2008), pp. 440-441; Menard, Scott, editor. Longitudinal Research . Thousand Oaks, CA: Sage, 2002; Ployhart, Robert E. and Robert J. Vandenberg. "Longitudinal Research: The Theory, Design, and Analysis of Change.” Journal of Management 36 (January 2010): 94-120; Longitudinal Study. Wikipedia.

Meta-Analysis Design

Meta-analysis is an analytical methodology designed to systematically evaluate and summarize the results from a number of individual studies, thereby, increasing the overall sample size and the ability of the researcher to study effects of interest. The purpose is to not simply summarize existing knowledge, but to develop a new understanding of a research problem using synoptic reasoning. The main objectives of meta-analysis include analyzing differences in the results among studies and increasing the precision by which effects are estimated. A well-designed meta-analysis depends upon strict adherence to the criteria used for selecting studies and the availability of information in each study to properly analyze their findings. Lack of information can severely limit the type of analyzes and conclusions that can be reached. In addition, the more dissimilarity there is in the results among individual studies [heterogeneity], the more difficult it is to justify interpretations that govern a valid synopsis of results. A meta-analysis needs to fulfill the following requirements to ensure the validity of your findings:

• Clearly defined description of objectives, including precise definitions of the variables and outcomes that are being evaluated;
• A well-reasoned and well-documented justification for identification and selection of the studies;
• Assessment and explicit acknowledgment of any researcher bias in the identification and selection of those studies;
• Description and evaluation of the degree of heterogeneity among the sample size of studies reviewed; and,
• Justification of the techniques used to evaluate the studies.
• Can be an effective strategy for determining gaps in the literature.
• Provides a means of reviewing research published about a particular topic over an extended period of time and from a variety of sources.
• Is useful in clarifying what policy or programmatic actions can be justified on the basis of analyzing research results from multiple studies.
• Provides a method for overcoming small sample sizes in individual studies that previously may have had little relationship to each other.
• Can be used to generate new hypotheses or highlight research problems for future studies.
• Small violations in defining the criteria used for content analysis can lead to difficult to interpret and/or meaningless findings.
• A large sample size can yield reliable, but not necessarily valid, results.
• A lack of uniformity regarding, for example, the type of literature reviewed, how methods are applied, and how findings are measured within the sample of studies you are analyzing, can make the process of synthesis difficult to perform.
• Depending on the sample size, the process of reviewing and synthesizing multiple studies can be very time consuming.

Beck, Lewis W. "The Synoptic Method." The Journal of Philosophy 36 (1939): 337-345; Cooper, Harris, Larry V. Hedges, and Jeffrey C. Valentine, eds. The Handbook of Research Synthesis and Meta-Analysis . 2nd edition. New York: Russell Sage Foundation, 2009; Guzzo, Richard A., Susan E. Jackson and Raymond A. Katzell. “Meta-Analysis Analysis.” In Research in Organizational Behavior , Volume 9. (Greenwich, CT: JAI Press, 1987), pp 407-442; Lipsey, Mark W. and David B. Wilson. Practical Meta-Analysis . Thousand Oaks, CA: Sage Publications, 2001; Study Design 101. Meta-Analysis. The Himmelfarb Health Sciences Library, George Washington University; Timulak, Ladislav. “Qualitative Meta-Analysis.” In The SAGE Handbook of Qualitative Data Analysis . Uwe Flick, editor. (Los Angeles, CA: Sage, 2013), pp. 481-495; Walker, Esteban, Adrian V. Hernandez, and Micheal W. Kattan. "Meta-Analysis: It's Strengths and Limitations." Cleveland Clinic Journal of Medicine 75 (June 2008): 431-439.

Mixed-Method Design

• Narrative and non-textual information can add meaning to numeric data, while numeric data can add precision to narrative and non-textual information.
• Can utilize existing data while at the same time generating and testing a grounded theory approach to describe and explain the phenomenon under study.
• A broader, more complex research problem can be investigated because the researcher is not constrained by using only one method.
• The strengths of one method can be used to overcome the inherent weaknesses of another method.
• Can provide stronger, more robust evidence to support a conclusion or set of recommendations.
• May generate new knowledge new insights or uncover hidden insights, patterns, or relationships that a single methodological approach might not reveal.
• Produces more complete knowledge and understanding of the research problem that can be used to increase the generalizability of findings applied to theory or practice.
• A researcher must be proficient in understanding how to apply multiple methods to investigating a research problem as well as be proficient in optimizing how to design a study that coherently melds them together.
• Can increase the likelihood of conflicting results or ambiguous findings that inhibit drawing a valid conclusion or setting forth a recommended course of action [e.g., sample interview responses do not support existing statistical data].
• Because the research design can be very complex, reporting the findings requires a well-organized narrative, clear writing style, and precise word choice.
• Design invites collaboration among experts. However, merging different investigative approaches and writing styles requires more attention to the overall research process than studies conducted using only one methodological paradigm.
• Concurrent merging of quantitative and qualitative research requires greater attention to having adequate sample sizes, using comparable samples, and applying a consistent unit of analysis. For sequential designs where one phase of qualitative research builds on the quantitative phase or vice versa, decisions about what results from the first phase to use in the next phase, the choice of samples and estimating reasonable sample sizes for both phases, and the interpretation of results from both phases can be difficult.
• Due to multiple forms of data being collected and analyzed, this design requires extensive time and resources to carry out the multiple steps involved in data gathering and interpretation.

Burch, Patricia and Carolyn J. Heinrich. Mixed Methods for Policy Research and Program Evaluation . Thousand Oaks, CA: Sage, 2016; Creswell, John w. et al. Best Practices for Mixed Methods Research in the Health Sciences . Bethesda, MD: Office of Behavioral and Social Sciences Research, National Institutes of Health, 2010Creswell, John W. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches . 4th edition. Thousand Oaks, CA: Sage Publications, 2014; Domínguez, Silvia, editor. Mixed Methods Social Networks Research . Cambridge, UK: Cambridge University Press, 2014; Hesse-Biber, Sharlene Nagy. Mixed Methods Research: Merging Theory with Practice . New York: Guilford Press, 2010; Niglas, Katrin. “How the Novice Researcher Can Make Sense of Mixed Methods Designs.” International Journal of Multiple Research Approaches 3 (2009): 34-46; Onwuegbuzie, Anthony J. and Nancy L. Leech. “Linking Research Questions to Mixed Methods Data Analysis Procedures.” The Qualitative Report 11 (September 2006): 474-498; Tashakorri, Abbas and John W. Creswell. “The New Era of Mixed Methods.” Journal of Mixed Methods Research 1 (January 2007): 3-7; Zhanga, Wanqing. “Mixed Methods Application in Health Intervention Research: A Multiple Case Study.” International Journal of Multiple Research Approaches 8 (2014): 24-35 .

Observational Design

This type of research design draws a conclusion by comparing subjects against a control group, in cases where the researcher has no control over the experiment. There are two general types of observational designs. In direct observations, people know that you are watching them. Unobtrusive measures involve any method for studying behavior where individuals do not know they are being observed. An observational study allows a useful insight into a phenomenon and avoids the ethical and practical difficulties of setting up a large and cumbersome research project.

• Observational studies are usually flexible and do not necessarily need to be structured around a hypothesis about what you expect to observe [data is emergent rather than pre-existing].
• The researcher is able to collect in-depth information about a particular behavior.
• Can reveal interrelationships among multifaceted dimensions of group interactions.
• You can generalize your results to real life situations.
• Observational research is useful for discovering what variables may be important before applying other methods like experiments.
• Observation research designs account for the complexity of group behaviors.
• Reliability of data is low because seeing behaviors occur over and over again may be a time consuming task and are difficult to replicate.
• In observational research, findings may only reflect a unique sample population and, thus, cannot be generalized to other groups.
• There can be problems with bias as the researcher may only "see what they want to see."
• There is no possibility to determine "cause and effect" relationships since nothing is manipulated.
• Sources or subjects may not all be equally credible.
• Any group that is knowingly studied is altered to some degree by the presence of the researcher, therefore, potentially skewing any data collected.

Atkinson, Paul and Martyn Hammersley. “Ethnography and Participant Observation.” In Handbook of Qualitative Research . Norman K. Denzin and Yvonna S. Lincoln, eds. (Thousand Oaks, CA: Sage, 1994), pp. 248-261; Observational Research. Research Methods by Dummies. Department of Psychology. California State University, Fresno, 2006; Patton Michael Quinn. Qualitiative Research and Evaluation Methods . Chapter 6, Fieldwork Strategies and Observational Methods. 3rd ed. Thousand Oaks, CA: Sage, 2002; Payne, Geoff and Judy Payne. "Observation." In Key Concepts in Social Research . The SAGE Key Concepts series. (London, England: Sage, 2004), pp. 158-162; Rosenbaum, Paul R. Design of Observational Studies . New York: Springer, 2010;Williams, J. Patrick. "Nonparticipant Observation." In The Sage Encyclopedia of Qualitative Research Methods . Lisa M. Given, editor.(Thousand Oaks, CA: Sage, 2008), pp. 562-563.

Philosophical Design

Understood more as an broad approach to examining a research problem than a methodological design, philosophical analysis and argumentation is intended to challenge deeply embedded, often intractable, assumptions underpinning an area of study. This approach uses the tools of argumentation derived from philosophical traditions, concepts, models, and theories to critically explore and challenge, for example, the relevance of logic and evidence in academic debates, to analyze arguments about fundamental issues, or to discuss the root of existing discourse about a research problem. These overarching tools of analysis can be framed in three ways:

• Ontology -- the study that describes the nature of reality; for example, what is real and what is not, what is fundamental and what is derivative?
• Epistemology -- the study that explores the nature of knowledge; for example, by what means does knowledge and understanding depend upon and how can we be certain of what we know?
• Axiology -- the study of values; for example, what values does an individual or group hold and why? How are values related to interest, desire, will, experience, and means-to-end? And, what is the difference between a matter of fact and a matter of value?
• Can provide a basis for applying ethical decision-making to practice.
• Functions as a means of gaining greater self-understanding and self-knowledge about the purposes of research.
• Brings clarity to general guiding practices and principles of an individual or group.
• Philosophy informs methodology.
• Refine concepts and theories that are invoked in relatively unreflective modes of thought and discourse.
• Beyond methodology, philosophy also informs critical thinking about epistemology and the structure of reality (metaphysics).
• Offers clarity and definition to the practical and theoretical uses of terms, concepts, and ideas.
• Limited application to specific research problems [answering the "So What?" question in social science research].
• Analysis can be abstract, argumentative, and limited in its practical application to real-life issues.
• While a philosophical analysis may render problematic that which was once simple or taken-for-granted, the writing can be dense and subject to unnecessary jargon, overstatement, and/or excessive quotation and documentation.
• There are limitations in the use of metaphor as a vehicle of philosophical analysis.
• There can be analytical difficulties in moving from philosophy to advocacy and between abstract thought and application to the phenomenal world.

Burton, Dawn. "Part I, Philosophy of the Social Sciences." In Research Training for Social Scientists . (London, England: Sage, 2000), pp. 1-5; Chapter 4, Research Methodology and Design. Unisa Institutional Repository (UnisaIR), University of South Africa; Jarvie, Ian C., and Jesús Zamora-Bonilla, editors. The SAGE Handbook of the Philosophy of Social Sciences . London: Sage, 2011; Labaree, Robert V. and Ross Scimeca. “The Philosophical Problem of Truth in Librarianship.” The Library Quarterly 78 (January 2008): 43-70; Maykut, Pamela S. Beginning Qualitative Research: A Philosophic and Practical Guide . Washington, DC: Falmer Press, 1994; McLaughlin, Hugh. "The Philosophy of Social Research." In Understanding Social Work Research . 2nd edition. (London: SAGE Publications Ltd., 2012), pp. 24-47; Stanford Encyclopedia of Philosophy . Metaphysics Research Lab, CSLI, Stanford University, 2013.

Sequential Design

• The researcher has a limitless option when it comes to sample size and the sampling schedule.
• Due to the repetitive nature of this research design, minor changes and adjustments can be done during the initial parts of the study to correct and hone the research method.
• This is a useful design for exploratory studies.
• There is very little effort on the part of the researcher when performing this technique. It is generally not expensive, time consuming, or workforce intensive.
• Because the study is conducted serially, the results of one sample are known before the next sample is taken and analyzed. This provides opportunities for continuous improvement of sampling and methods of analysis.
• The sampling method is not representative of the entire population. The only possibility of approaching representativeness is when the researcher chooses to use a very large sample size significant enough to represent a significant portion of the entire population. In this case, moving on to study a second or more specific sample can be difficult.
• The design cannot be used to create conclusions and interpretations that pertain to an entire population because the sampling technique is not randomized. Generalizability from findings is, therefore, limited.
• Difficult to account for and interpret variation from one sample to another over time, particularly when using qualitative methods of data collection.

Betensky, Rebecca. Harvard University, Course Lecture Note slides; Bovaird, James A. and Kevin A. Kupzyk. "Sequential Design." In Encyclopedia of Research Design . Neil J. Salkind, editor. (Thousand Oaks, CA: Sage, 2010), pp. 1347-1352; Cresswell, John W. Et al. “Advanced Mixed-Methods Research Designs.” In Handbook of Mixed Methods in Social and Behavioral Research . Abbas Tashakkori and Charles Teddle, eds. (Thousand Oaks, CA: Sage, 2003), pp. 209-240; Henry, Gary T. "Sequential Sampling." In The SAGE Encyclopedia of Social Science Research Methods . Michael S. Lewis-Beck, Alan Bryman and Tim Futing Liao, editors. (Thousand Oaks, CA: Sage, 2004), pp. 1027-1028; Nataliya V. Ivankova. “Using Mixed-Methods Sequential Explanatory Design: From Theory to Practice.” Field Methods 18 (February 2006): 3-20; Bovaird, James A. and Kevin A. Kupzyk. “Sequential Design.” In Encyclopedia of Research Design . Neil J. Salkind, ed. Thousand Oaks, CA: Sage, 2010; Sequential Analysis. Wikipedia.

Systematic Review

• A systematic review synthesizes the findings of multiple studies related to each other by incorporating strategies of analysis and interpretation intended to reduce biases and random errors.
• The application of critical exploration, evaluation, and synthesis methods separates insignificant, unsound, or redundant research from the most salient and relevant studies worthy of reflection.
• They can be use to identify, justify, and refine hypotheses, recognize and avoid hidden problems in prior studies, and explain data inconsistencies and conflicts in data.
• Systematic reviews can be used to help policy makers formulate evidence-based guidelines and regulations.
• The use of strict, explicit, and pre-determined methods of synthesis, when applied appropriately, provide reliable estimates about the effects of interventions, evaluations, and effects related to the overarching research problem investigated by each study under review.
• Systematic reviews illuminate where knowledge or thorough understanding of a research problem is lacking and, therefore, can then be used to guide future research.
• The accepted inclusion of unpublished studies [i.e., grey literature] ensures the broadest possible way to analyze and interpret research on a topic.
• Results of the synthesis can be generalized and the findings extrapolated into the general population with more validity than most other types of studies .
• Systematic reviews do not create new knowledge per se; they are a method for synthesizing existing studies about a research problem in order to gain new insights and determine gaps in the literature.
• The way researchers have carried out their investigations [e.g., the period of time covered, number of participants, sources of data analyzed, etc.] can make it difficult to effectively synthesize studies.
• The inclusion of unpublished studies can introduce bias into the review because they may not have undergone a rigorous peer-review process prior to publication. Examples may include conference presentations or proceedings, publications from government agencies, white papers, working papers, and internal documents from organizations, and doctoral dissertations and Master's theses.

Denyer, David and David Tranfield. "Producing a Systematic Review." In The Sage Handbook of Organizational Research Methods .  David A. Buchanan and Alan Bryman, editors. ( Thousand Oaks, CA: Sage Publications, 2009), pp. 671-689; Foster, Margaret J. and Sarah T. Jewell, editors. Assembling the Pieces of a Systematic Review: A Guide for Librarians . Lanham, MD: Rowman and Littlefield, 2017; Gough, David, Sandy Oliver, James Thomas, editors. Introduction to Systematic Reviews . 2nd edition. Los Angeles, CA: Sage Publications, 2017; Gopalakrishnan, S. and P. Ganeshkumar. “Systematic Reviews and Meta-analysis: Understanding the Best Evidence in Primary Healthcare.” Journal of Family Medicine and Primary Care 2 (2013): 9-14; Gough, David, James Thomas, and Sandy Oliver. "Clarifying Differences between Review Designs and Methods." Systematic Reviews 1 (2012): 1-9; Khan, Khalid S., Regina Kunz, Jos Kleijnen, and Gerd Antes. “Five Steps to Conducting a Systematic Review.” Journal of the Royal Society of Medicine 96 (2003): 118-121; Mulrow, C. D. “Systematic Reviews: Rationale for Systematic Reviews.” BMJ 309:597 (September 1994); O'Dwyer, Linda C., and Q. Eileen Wafford. "Addressing Challenges with Systematic Review Teams through Effective Communication: A Case Report." Journal of the Medical Library Association 109 (October 2021): 643-647; Okoli, Chitu, and Kira Schabram. "A Guide to Conducting a Systematic Literature Review of Information Systems Research."  Sprouts: Working Papers on Information Systems 10 (2010); Siddaway, Andy P., Alex M. Wood, and Larry V. Hedges. "How to Do a Systematic Review: A Best Practice Guide for Conducting and Reporting Narrative Reviews, Meta-analyses, and Meta-syntheses." Annual Review of Psychology 70 (2019): 747-770; Torgerson, Carole J. “Publication Bias: The Achilles’ Heel of Systematic Reviews?” British Journal of Educational Studies 54 (March 2006): 89-102; Torgerson, Carole. Systematic Reviews . New York: Continuum, 2003.

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Experimental Research Design — 6 mistakes you should never make!

Since school days’ students perform scientific experiments that provide results that define and prove the laws and theorems in science. These experiments are laid on a strong foundation of experimental research designs.

An experimental research design helps researchers execute their research objectives with more clarity and transparency.

In this article, we will not only discuss the key aspects of experimental research designs but also the issues to avoid and problems to resolve while designing your research study.

Table of Contents

What Is Experimental Research Design?

Experimental research design is a framework of protocols and procedures created to conduct experimental research with a scientific approach using two sets of variables. Herein, the first set of variables acts as a constant, used to measure the differences of the second set. The best example of experimental research methods is quantitative research .

Experimental research helps a researcher gather the necessary data for making better research decisions and determining the facts of a research study.

When Can a Researcher Conduct Experimental Research?

A researcher can conduct experimental research in the following situations —

• When time is an important factor in establishing a relationship between the cause and effect.
• When there is an invariable or never-changing behavior between the cause and effect.
• Finally, when the researcher wishes to understand the importance of the cause and effect.

Importance of Experimental Research Design

To publish significant results, choosing a quality research design forms the foundation to build the research study. Moreover, effective research design helps establish quality decision-making procedures, structures the research to lead to easier data analysis, and addresses the main research question. Therefore, it is essential to cater undivided attention and time to create an experimental research design before beginning the practical experiment.

By creating a research design, a researcher is also giving oneself time to organize the research, set up relevant boundaries for the study, and increase the reliability of the results. Through all these efforts, one could also avoid inconclusive results. If any part of the research design is flawed, it will reflect on the quality of the results derived.

Types of Experimental Research Designs

Based on the methods used to collect data in experimental studies, the experimental research designs are of three primary types:

1. Pre-experimental Research Design

A research study could conduct pre-experimental research design when a group or many groups are under observation after implementing factors of cause and effect of the research. The pre-experimental design will help researchers understand whether further investigation is necessary for the groups under observation.

Pre-experimental research is of three types —

• One-shot Case Study Research Design
• One-group Pretest-posttest Research Design
• Static-group Comparison

2. True Experimental Research Design

A true experimental research design relies on statistical analysis to prove or disprove a researcher’s hypothesis. It is one of the most accurate forms of research because it provides specific scientific evidence. Furthermore, out of all the types of experimental designs, only a true experimental design can establish a cause-effect relationship within a group. However, in a true experiment, a researcher must satisfy these three factors —

• There is a control group that is not subjected to changes and an experimental group that will experience the changed variables
• A variable that can be manipulated by the researcher
• Random distribution of the variables

This type of experimental research is commonly observed in the physical sciences.

3. Quasi-experimental Research Design

The word “Quasi” means similarity. A quasi-experimental design is similar to a true experimental design. However, the difference between the two is the assignment of the control group. In this research design, an independent variable is manipulated, but the participants of a group are not randomly assigned. This type of research design is used in field settings where random assignment is either irrelevant or not required.

The classification of the research subjects, conditions, or groups determines the type of research design to be used.

Advantages of Experimental Research

Experimental research allows you to test your idea in a controlled environment before taking the research to clinical trials. Moreover, it provides the best method to test your theory because of the following advantages:

• Researchers have firm control over variables to obtain results.
• The subject does not impact the effectiveness of experimental research. Anyone can implement it for research purposes.
• The results are specific.
• Post results analysis, research findings from the same dataset can be repurposed for similar research ideas.
• Researchers can identify the cause and effect of the hypothesis and further analyze this relationship to determine in-depth ideas.
• Experimental research makes an ideal starting point. The collected data could be used as a foundation to build new research ideas for further studies.

6 Mistakes to Avoid While Designing Your Research

There is no order to this list, and any one of these issues can seriously compromise the quality of your research. You could refer to the list as a checklist of what to avoid while designing your research.

1. Invalid Theoretical Framework

Usually, researchers miss out on checking if their hypothesis is logical to be tested. If your research design does not have basic assumptions or postulates, then it is fundamentally flawed and you need to rework on your research framework.

2. Inadequate Literature Study

Without a comprehensive research literature review , it is difficult to identify and fill the knowledge and information gaps. Furthermore, you need to clearly state how your research will contribute to the research field, either by adding value to the pertinent literature or challenging previous findings and assumptions.

3. Insufficient or Incorrect Statistical Analysis

Statistical results are one of the most trusted scientific evidence. The ultimate goal of a research experiment is to gain valid and sustainable evidence. Therefore, incorrect statistical analysis could affect the quality of any quantitative research.

4. Undefined Research Problem

This is one of the most basic aspects of research design. The research problem statement must be clear and to do that, you must set the framework for the development of research questions that address the core problems.

5. Research Limitations

Every study has some type of limitations . You should anticipate and incorporate those limitations into your conclusion, as well as the basic research design. Include a statement in your manuscript about any perceived limitations, and how you considered them while designing your experiment and drawing the conclusion.

6. Ethical Implications

The most important yet less talked about topic is the ethical issue. Your research design must include ways to minimize any risk for your participants and also address the research problem or question at hand. If you cannot manage the ethical norms along with your research study, your research objectives and validity could be questioned.

Experimental Research Design Example

In an experimental design, a researcher gathers plant samples and then randomly assigns half the samples to photosynthesize in sunlight and the other half to be kept in a dark box without sunlight, while controlling all the other variables (nutrients, water, soil, etc.)

By comparing their outcomes in biochemical tests, the researcher can confirm that the changes in the plants were due to the sunlight and not the other variables.

Experimental research is often the final form of a study conducted in the research process which is considered to provide conclusive and specific results. But it is not meant for every research. It involves a lot of resources, time, and money and is not easy to conduct, unless a foundation of research is built. Yet it is widely used in research institutes and commercial industries, for its most conclusive results in the scientific approach.

Have you worked on research designs? How was your experience creating an experimental design? What difficulties did you face? Do write to us or comment below and share your insights on experimental research designs!

Frequently Asked Questions

Randomization is important in an experimental research because it ensures unbiased results of the experiment. It also measures the cause-effect relationship on a particular group of interest.

Experimental research design lay the foundation of a research and structures the research to establish quality decision making process.

There are 3 types of experimental research designs. These are pre-experimental research design, true experimental research design, and quasi experimental research design.

The difference between an experimental and a quasi-experimental design are: 1. The assignment of the control group in quasi experimental research is non-random, unlike true experimental design, which is randomly assigned. 2. Experimental research group always has a control group; on the other hand, it may not be always present in quasi experimental research.

Experimental research establishes a cause-effect relationship by testing a theory or hypothesis using experimental groups or control variables. In contrast, descriptive research describes a study or a topic by defining the variables under it and answering the questions related to the same.

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What is design research methodology and why is it important?

What is design research.

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

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

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

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Design research vs. market research

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

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

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

Design research in action: A mini mock case study

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

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

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

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

When to use design research

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

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

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

The benefits of design research

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

Deeper customer knowledge

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

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

Efficiency and cost savings

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

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

New opportunities

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

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

Enhanced competitive edge

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

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

Design research methods

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

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

Literature review

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

Field observations

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

Stakeholder interviews

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

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

Design review

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

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

Persona building

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

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

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

Usability testing

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

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

Benchmark testing

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

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

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

Feedback surveys

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

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

Focus groups

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

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What is a Research Design? Importance and Types

Why Research Design is Important for a Researcher?

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

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

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

7 Importance of Research Design – iLovePhD

What is a Research Design in Research Methodology ?

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

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

Importance of Research Design

A Good research design consists of the following important points:

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

How to Create a Research Design?                      

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

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

Considerations in selecting the research design

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

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

Types of Research Design?

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

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

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

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Next generation multiple access for IMT towards 2030 and beyond

• Perspective
• Published: 22 May 2024
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• Zhiguo Ding 1 , 2 ,
• Robert Schober 3 ,
• Pingzhi Fan 4 &
• H. Vincent Poor 5  

NOMA assisted NGMA has been envisioned in the recently published IMT-2030 Framework. This perspective has outlined three important features of NOMA assisted NGMA, namely multi-domain utilization, multi-mode compatibility, and multi-dimensional optimality, where important directions for future research into the design of NOMA assisted NGMA have also been discussed.

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You X H, Wang C X, Huang J, et al. Towards 6G wireless communication networks: vision, enabling technologies, and new paradigm shifts. Sci China Inf Sci, 2021, 64: 110301

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ITU. Framework and Overall Objectives of the Future Development of IMT for 2030 and Beyond. Technical Report ITU-R M.2160-0, 2023

Islam S M R, Avazov N, Dobre O A, et al. Power-domain non-orthogonal multiple access (NOMA) in 5G systems: potentials and challenges. IEEE Commun Surv Tut, 2017, 19: 721–742

Ding Z G, R Schober, Poor H V. Design of downlink hybrid NOMA transmission. 2024. ArXiv:2401.16965

Zhu J, Wan Z, Dai L, et al. Electromagnetic information theory: fundamentals, modeling, applications, and open problems. IEEE Wireless Commun, 2024. doi: https://doi.org/10.1109/MWC.019.2200602

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Sutton R S, Barto A G. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 1998

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Acknowledgements

Zhiguo DING was supported by UK EP-SRC (Grant Nos. EP/W034522/1, H2020-MSCA-RISE-2020, 101006411). Robert SCHOBER was supported by German Research Foundation (DFG) under Project SFB 1483 (Project-ID 442419336 Empkins) and BMBF under the Program of “Souveran. Digital. Vernetzt.” Joint Project 6G-RIC (Project-ID 16KISK023). Pingzhi FAN was supported by National Natural Science Foundation of China (Grant No. 62020106001). H. Vincent POOR was supported by U.S National Science Foundation (Grant Nos. CNS-2128448, ECCS-2335876).

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Department of Computer and Information Engineering, Khalifa University, Abu Dhabi, UAE

Zhiguo Ding

Department of Electrical and Electronic Engineering, University of Manchester, Manchester, M1 9BB, UK

Institute for Digital Communications, Friedrich Alexander-University Erlangen-Nurnberg (FAU), Erlangen, 91054, Germany

Robert Schober

CSNMT International Cooperation Research Center (MoST), Southwest Jiaotong University, Chengdu, 610032, China

Pingzhi Fan

Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, 08544, USA

H. Vincent Poor

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Ding, Z., Schober, R., Fan, P. et al. Next generation multiple access for IMT towards 2030 and beyond. Sci. China Inf. Sci. 67 , 166301 (2024). https://doi.org/10.1007/s11432-024-4014-x

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Accepted : 22 April 2024

Published : 22 May 2024

DOI : https://doi.org/10.1007/s11432-024-4014-x

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Preparedness for a first clinical placement in nursing: a descriptive qualitative study

• Philippa H. M. Marriott 1 ,
• Jennifer M. Weller-Newton 2   nAff3 &
• Katharine J. Reid 4  

BMC Nursing volume  23 , Article number:  345 ( 2024 ) Cite this article

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A first clinical placement for nursing students is a challenging period involving translation of theoretical knowledge and development of an identity within the healthcare setting; it is often a time of emotional vulnerability. It can be a pivotal moment for ambivalent nursing students to decide whether to continue their professional training. To date, student expectations prior to their first clinical placement have been explored in advance of the experience or gathered following the placement experience. However, there is a significant gap in understanding how nursing students’ perspectives about their first clinical placement might change or remain consistent following their placement experiences. Thus, the study aimed to explore first-year nursing students’ emotional responses towards and perceptions of their preparedness for their first clinical placement and to examine whether initial perceptions remain consistent or change during the placement experience.

The research utilised a pre-post qualitative descriptive design. Six focus groups were undertaken before the first clinical placement (with up to four participants in each group) and follow-up individual interviews ( n  = 10) were undertaken towards the end of the first clinical placement with first-year entry-to-practice postgraduate nursing students. Data were analysed thematically.

Three main themes emerged: (1) adjusting and managing a raft of feelings, encapsulating participants’ feelings about learning in a new environment and progressing from academia to clinical practice; (2) sinking or swimming, comprising students’ expectations before their first clinical placement and how these perceptions are altered through their clinical placement experience; and (3) navigating placement, describing relationships between healthcare staff, patients, and peers.

Conclusions

This unique study of first-year postgraduate entry-to-practice nursing students’ perspectives of their first clinical placement adds to the extant knowledge. By examining student experience prior to and during their first clinical placement experience, it is possible to explore the consistency and change in students’ narratives over the course of an impactful experience. Researching the narratives of nursing students embarking on their first clinical placement provides tertiary education institutions with insights into preparing students for this critical experience.

Peer Review reports

First clinical placements enable nursing students to develop their professional identity through initial socialisation, and where successful, first clinical placement experiences can motivate nursing students to persist with their studies [ 1 , 2 , 3 , 4 ]. Where the transition from the tertiary environment to learning in the healthcare workplace is turbulent, it may impact nursing students’ learning, their confidence and potentially increase attrition rates from educational programs [ 2 , 5 , 6 ]. Attrition from preregistration nursing courses is a global concern, with the COVID-19 pandemic further straining the nursing workforce; thus, the supply of nursing professionals is unlikely to meet demand [ 7 ]. The COVID-19 pandemic has also impacted nursing education, with student nurses augmenting the diminishing nursing workforce [ 7 , 8 ].

The first clinical placement often triggers immense anxiety and fear for nursing students [ 9 , 10 ]. Research suggests that among nursing students, anxiety arises from perceived knowledge deficiencies, role ambiguity, the working environment, caring for ‘real’ people, potentially causing harm, exposure to nudity and death, and ‘not fitting in’ [ 2 , 3 , 11 ]. These stressors are reported internationally and often relate to inadequate preparation for entering the clinical environment [ 2 , 10 , 12 ]. Previous research suggests that high anxiety before the first clinical placement can be related to factors likely to affect patient outcomes, such as self-confidence and efficacy [ 13 ]. High anxiety during clinical placement may impair students’ capacity to learn, thus compromising the value of the clinical environment for learning [ 10 ].

The first clinical placement often occurs soon after commencing nursing training and can challenge students’ beliefs, philosophies, and preconceived ideas about nursing. An experience of cultural or ‘reality’ shock often arises when entering the healthcare setting, creating dissonance between reality and expectations [ 6 , 14 ]. These experiences may be exacerbated by tertiary education providers teaching of ‘ideal’ clinical practice [ 2 , 6 ]. The perceived distance between theoretical knowledge and what is expected in a healthcare placement, as opposed to what occurs on clinical placement, has been well documented as the theory-practice gap or an experience of cognitive dissonance [ 2 , 3 ].

Given the pivotal role of the first clinical placement in nursing students’ trajectories to nursing practice, it is important to understand students’ experiences and to explore how the placement experience shapes initial perceptions. Existing research focusses almost entirely either on describing nursing students’ projected emotions and perceptions prior to undertaking a first clinical placement [ 3 ] or examines student perceptions of reflecting on a completed first placement [ 15 ]. We wished to examine consistency and change in student perception of their first clinical placement by tracking their experiences longitudinally. We focused on a first clinical placement undertaken in a Master of Nursing Science. This two-year postgraduate qualification provides entry-to-practice nursing training for students who have completed any undergraduate qualification. The first clinical placement component of the course aimed to orient students to the clinical environment, support students to acquire skills and develop their clinical reasoning through experiential learning with experienced nursing mentors.

This paper makes a significant contribution to understanding how nursing students’ perceptions might develop over time because of their clinical placement experiences. Our research addresses a further gap in the existing literature, by focusing on students completing an accelerated postgraduate two-year entry-to-practice degree open to students with any prior undergraduate degree. Thus, the current research aimed to understand nursing students’ emotional responses and expectations and their perceptions of preparedness before attending their first clinical placement and to contrast these initial perceptions with their end-of-placement perspectives.

Study design

A descriptive qualitative study was undertaken, utilising a pre- and post-design for data collection. Focus groups with first-year postgraduate entry-to-practice nursing students were conducted before the first clinical placement, with individual semi-structured interviews undertaken during the first clinical placement.

Setting and participants

All first-year students enrolled in the two-year Master of Nursing Science program ( n  = 190) at a tertiary institution in Melbourne, Australia, were eligible to participate. There were no exclusion criteria. At the time of this study, students were enrolled in a semester-long subject focused on nursing assessment and care. They studied the theoretical underpinnings of nursing and science, theoretical and practical nursing clinical skills and Indigenous health over the first six weeks of the course. Students completed a preclinical assessment as a hurdle before commencing a three-week clinical placement in a hospital setting, a subacute or acute environment. Overall, the clinical placement aimed to provide opportunities for experiential learning, skill acquisition, development of clinical reasoning skills and professional socialisation [ 16 , 17 ].

In total, sixteen students participated voluntarily in a focus group of between 60 and 90 min duration; ten of these students also participated in individual interviews of between 30 and 60 min duration, a number sufficient to reach data saturation. Table  1 shows the questions used in the focus groups conducted before clinical placement commenced and the questions for the semi-structured interview questions conducted during clinical placement. Study participants’ undergraduate qualifications included bachelor’s degrees in science, arts and business. A small number of participants had previous healthcare experience (e.g. as healthcare assistants). The participants attended clinical placement in the Melbourne metropolitan, Victorian regional and rural hospital locations.

Data collection

The study comprised two phases. The first phase comprised six focus groups prior to the first clinical placement, and the second phase comprised ten individual semi-structured interviews towards the end of the first clinical placement. Focus groups (with a maximum of four participants) and individual interviews were conducted by the lead author online via Zoom and were audio-recorded. Capping group size to a relatively small number considered diversity of perceptions and opportunities for participants to share their insights and to confirm or contradict their peers, particularly in the online environment [ 18 , 19 ].

Focus groups and interview questions were developed with reference to relevant literature, piloted with volunteer final-year nursing students, and then verified with the coauthors. All focus groups and interviewees received the same structured questions (Table  1 ) to ensure consistency and to facilitate comparison across the placement experience in the development of themes. Selective probing of interviewees’ responses for clarification to gain in-depth responses was undertaken. Nonverbal cues, impressions, or observations were noted.

The lead author was a registered nurse who had a clinical teaching role within the nursing department and was responsible for coordinating clinical placement experiences. To ensure rigour during the data collection process, the lead author maintained a reflective account, exploring her experiences of the discussions, reflecting on her interactions with participants as a researcher and as a clinical educator, and identifying areas for improvement (for instance allowing participants to tell their stories with fewer prompts). These reflections in conjunction with regular discussion with the other authors throughout the data collection period, aided in identifying any researcher biases, feelings and thoughts that possibly influenced the research [ 20 ].

To maintain rigour during the data analysis phase, we adhered to a systematic process involving input from all authors to code the data and to identify, refine and describe the themes and subthemes reported in this work. This comprehensive analytic process, reported in detail in the following section, was designed to ensure that the findings arising from this research were derived from a rigorous approach to analysing the data.

Data analysis

Focus groups and interviews were transcribed using the online transcription service Otter ( https://otter.ai/ ) and then checked and anonymised by the first author. Preliminary data analysis was carried out simultaneously by the first author using thematic content analysis proposed by Braun and Clarke [ 21 ] using NVivo 12 software [ 22 ]. All three authors undertook a detailed reading of the first three transcripts from both the focus groups and interviews and independently identified major themes. This preliminary coding was used as the basis of a discussion session to identify common themes between authors, to clarify sources of disagreement and to establish guidelines for further coding. Subsequent coding of the complete data set by the lead author identified a total of 533 descriptive codes; no descriptive code was duplicated across the themes. Initially, the descriptive codes were grouped into major themes identified from the literature, but with further analysis, themes emerged that were unique to the current study.

The research team met frequently during data analysis to discuss the initial descriptive codes, to confirm the major themes and subthemes, to revise themes on which there was disagreement and to identify any additional themes. Samples of quotes were reviewed by the second and third authors to decide whether these quotes were representative of the identified themes. The process occurred iteratively to refine the thematic categories, to discuss the definitions of each theme and to identify exemplar quotes.

Ethical considerations

The lead author was a clinical teacher and the clinical placement coordinator in the nursing department at the time of the study. Potential risks of perceived coercion and power imbalances were identified because of the lead author’s dual roles as an academic and as a researcher. To manage these potential risks, an academic staff member who was not part of the research study informed students about the study during a face-to-face lecture and ensured that all participants received a plain language statement identifying the lead author’s role and how perceived conflicts of interest would be managed. These included the lead author not undertaking any teaching or assessment role for the duration of the study and ensuring that placement allocations were completed prior to undertaking recruitment for the study. All students who participated in the study provided informed written consent. No financial or other incentives were offered. Approval to conduct the study was granted by the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1).

Three main themes emerged describing students’ feelings and perceptions of their first clinical placement. In presenting the findings, before or during has been assigned to participants’ quotes to clarify the timing of students’ perspectives related to the clinical placement.

Major theme 1: Adjusting and managing a raft of feelings

The first theme encompassed the many positive and negative feelings about work-integrated learning expressed by participants before and during their clinical placement. Positive feelings before clinical placement were expressed by participants who were comfortable with the unknown and cautiously optimistic.

I am ready to just go with the flow, roll with the punches (Participant [P]1 before).

Overwhelmingly, however, the majority of feelings and thoughts anticipating the first clinical placement were negatively oriented. Students who expressed feelings of fear, anxiety, lack of knowledge, lack of preparedness, uncertainty about nursing as a career, or strong concerns about being a burden were all classified as conveying negative feelings. These negative feelings were categorised into four subthemes.

Subtheme 1.1 I don’t have enough knowledge

All participants expressed some concerns and anxiety before their first clinical placement. These encompassed concerns about knowledge inadequacy and were linked to a perception of under preparedness. Participants’ fears related to harming patients, responsibility for managing ‘real’ people, medication administration, and incomplete understanding of the language and communication skills within a healthcare setting. Anxiety for many participants merged with the logistics and management of their life during the clinical placement.

I’m scared that they will assume that I have more knowledge than I do (P3 before). I feel quite similar with P10, especially when she said fear of unknown and fear that she might do something wrong (P9 before).

Subtheme 1.2 Worry about judgment, being seen through that lens

Participants voiced concerns that they would be judged negatively by patients or healthcare staff because they perceived that the student nurse belonged to specific social groups related to their cultural background, ethnicity or gender. Affiliation with these groups contributed to students’ sense of self or identity, with students often describing such groups as a community. Before the clinical placement, participants worried that such judgements would impact the support they received on placement and their ability to deliver patient care.

Some older patients might prefer to have nurses from their own background, their own ethnicity, how they would react to me, or if racism is involved (P10 before). I just don’t want to reinforce like, whatever negative perceptions people might have of that community (P16 before).

Participants’ concerns prior to the first clinical placement about judgement or poor treatment because of patients’ preconceived ideas about specific ethnic groups did not eventuate.

I mean, it didn’t really feel like very much of a thing once I was actually there. It is one of those things you stress about, and it does not really amount to anything (P16 during).

Some students’ placement experiences revealed the positive benefits of their cultural background to enhancing patient care. One student affirmed that the placement experience reinforced their commitment to nursing and that this was related to their ability to communicate with patients whose first language was not English.

Yeah, definitely. Like, I can speak a few dialects. You know, I can actually see a difference with a lot of the non-English speaking background people. As soon as you, as soon as they’re aware that you’re trying and you’re trying to speak your language, they, they just open up. Yeah, yes. And it improves the care (P10 during).

However, a perceived lack of judgement was sometimes attributed to wearing the full personal protective equipment required during the COVID-19 pandemic, which meant that their personal features were largely obscured. For this reason, it was more difficult for patients to make assumptions or attributions about students’ ethnic or gender identity based on their appearance.

People tend to assume and call us all girls, which was irritating. It was mostly just because all of us were so covered up, no one could see anyone’s faces (P16 during).

Subtheme 1.3 Is nursing really for me?

Prior to their first clinical placement experience, many participants expressed ambivalence about a nursing career and anticipated that undertaking clinical placement could determine their suitability for the profession. Once exposed to clinical placement, the majority of students were completely committed to their chosen profession, with a minority remaining ambivalent or, in rare cases, choosing to leave the course. Not yet achieving full commitment to a nursing career was related to not wishing to work in the ward they had for their clinical placement, while remaining open to trying different specialities.

I didn’t have an actual idea of what I wanted to do after arts, this wasn’t something that I was aiming towards specifically (P14 before). I think I’m still not 100%, but enough to go on, that I’m happy to continue the course as best as I can (P11 during).

Subtheme 1.4 Being a burden

Before clinical placement, participants had concerns about being burdensome and how this would affect their clinical placement experiences.

If we end up being a burden to them, an extra responsibility for them on top of their day, then we might not be treated as well (P10 before).

A sense of burden remained a theme during the clinical placement for participants for the first five to seven days, after which most participants acknowledged that their role became more active. As students contributed more productively to their placement, their feelings of being a burden reduced.

Major theme 2: Sinking or swimming

The second major theme, sinking or swimming, described participants’ expectations about a successful placement experience and identified themes related to students’ successes (‘swimming’) or difficulties (‘sinking’) during their placement experience. Prior to clinical placement, without a realistic preview of what the experience might entail, participants were uncertain of their role, hoped for ‘nice’ supervising nurses and anticipated an observational role that would keep them afloat.

I will focus on what I want to learn and see if that coincides with what is expected, I guess (P15 before).

During the clinical placement, the reality was very different, with a sense of sinking. Participants discovered, some with shock, that they were expected to participate actively in the healthcare team.

I got the sense that they were not going to muck around, and, you know, they’re ‘gonna’ use the free labour that came with me (P1 during).

Adding to the confusion about the expected placement experience, participants believed that healthcare staff were unclear about students’ scope of practice for a postgraduate entry-to-practice degree, creating misalignment between students’ and supervising nurses’ expectations.

It seems to me like the educators don’t really seem to have a clear picture of what the scope is, and what is actually required or expected of us (P10 during).

In exploring perceived expectations of the clinical placement and the modifying effect of placement on initial expectations, three subthemes were identified: translation to practice is overwhelming, trying to find the rhythm or jigsaw pieces, and individual agency.

Subtheme 2.1 Translation to practice is overwhelming

Before clinical placement, participants described concerns about insufficient knowledge to enable them to engage effectively with the placement experience.

If I am doing an assessment understanding what are those indications and why I would be doing it or not doing it at a certain time (P1 before).

Integrating and applying theoretical content while navigating an unfamiliar clinical environment created a significant gap between theory and practice during clinical placement. As the clinical placement experience proceeded and initial fears dissipated, students became more aware of applying their theoretical knowledge in the clinical context.

We’re learning all this theory and clinical stuff, but then we don’t really have a realistic idea of what it’s like until we’re kind of thrown into it for three weeks (P10 during).

Subtheme 2.2 Trying to find the rhythm or the jigsaw pieces

Before clinical placement, participants described learning theory and clinical skills with contextual unfamiliarity. They had the jigsaw pieces but did not know how to assemble it; they had the music but did not know the final song. When discussing their expectations about clinical placement, the small number of participants with a healthcare background (e.g. as healthcare assistants) proposed realistic answers, whereas others struggled to answer or cited stories from friends or television. With a lack of context, feelings of unpreparedness were exacerbated. Once in the clinical environment, participants further emphasised that they could not identify what they needed to know to successfully prepare for clinical placement.

It was never really pieced together. We’ve learned bits and pieces, and then we’re putting it together ourselves (P8 during). On this course I feel it was this is how you do it, but I did not know how it was supposed to be played, I did not know the rhythm (P4 during).

Subtheme 2.3 Individual agency

Participants’ individual agency, their attitude, self-efficacy, and self-motivation affected their clinical placement experiences. Participant perceptions in advance of the clinical placement experience remained consistent with their perspectives following clinical placement. Before clinical placement, participants who were highly motivated to learn exhibited a growth mindset [ 23 ] and planned to be proactive in delivering patient care. During their clinical placement, initially positive students remained positive and optimistic about their future. Participants who believed that their first clinical placement role would be largely observational and were less proactive about applying their knowledge and skills identified boredom and a lack of learning opportunities on clinical placement.

A shadowing position, we don’t have enough skills and authority to do any work, not do any worthwhile skills (P3 before). I thought it would be a lot busier, because we’re limited with our scope, so there’s not much we can do, it’s just a bit slower than I thought (P12 during).

Individual agency appears to influence a successful first clinical placement; other factors may also be implicated but were not the focus of this study. Further research exploring the relationships between students’ age, life experience, resilience, individual agency, and the use of coping strategies during a first clinical placement would be useful.

Major theme 3: The reality of navigating placement relationships

The third main theme emphasised the reality of navigating clinical placement relationships and explored students’ relationships with healthcare staff, patients, and peers. Before clinical placement, many participants, especially those with healthcare backgrounds, expressed fears about relationships with supervising nurses. They perceived that the dynamics of the team and the healthcare workplace might influence the support they received. Several participants were nervous about attending placement on their own without peers for support, especially if the experience was challenging. Participants identified expectations of being mistreated, believing that it was unavoidable, and prepared themselves to not take it personally.

For me it’s where we’re going to land, are we going to be in a supportive, kind of nurturing environment, or is it just kind of sink or swim? (P5 before). If you don’t really trust them, you’re nervous the entire time and you’ll be like what if I get it wrong (P16 before).

Despite these concerns, students strongly emphasised the value of relationships during their first clinical placement, with these perceptions unchanged by their clinical placement experience. Where relationships were positive, participants felt empowered to be autonomous, and their self-confidence increased.

You get that that instant reaction from the patients. And that makes you feel more confident. So that really got me through the first week (P14 during). I felt like I was intruding, then as I started to build a bit of rapport with the people, and they saw that I was around, I don’t feel that as much now (P1 during).

Such development hinged on the receptiveness and support of supervising nurses, the team on the ward, and patients and could be hindered by poor relationships.

He was the old-style buddy nurse in his fifties, every time I questioned him, he would go ssshh, just listen, no questions, it was very stressful (P10 during). It depends whether the buddy sees us as an extra pair of hands, or we’re learners (P11 during).

Where students experienced poor behaviour from supervising nurses, they described a range of emotional responses to these interactions and also coping strategies including avoiding unfriendly staff and actively seeking out those who were more inclusive.

If they weren’t very nice, it wouldn’t be very enjoyable and if they didn’t trust you, then it would be a bit frustrating, that like I can do this, but you won’t let me (P12 during). If another nurse was not nice to me, and I was their buddy, I would literally just not buddy with them and go and follow whoever was nice to me (P4 during).

Relationships with peers were equally important; students on clinical placement with peers valued the shared experience. In contrast, students who attended clinical placement alone at a regional or rural hospital felt disconnected from the opportunities that learning with peers afforded.

Our research explored the emotional responses and perceptions of preparedness of postgraduate entry-to-practice nursing students prior to and during their first clinical placement. In this study, we described how the perceptions of nursing students remained consistent or were modified by their clinical placement experiences. Our analysis of students’ experiences identified three major themes: adjusting and managing a raft of feelings; sinking or swimming; and the reality of navigating placement relationships. We captured similar themes identified in the literature; however, our study also identified novel aspects of nursing students’ experiences of their first clinical placement.

The key theme, adjusting and managing a raft of feelings, which encapsulates anxiety before clinical placement, is consistent with previous research. This theme included concerns in communicating with healthcare staff and managing registered nurses’ negative attitudes and expectations, in addition to an academic workload [ 11 , 24 ]. Concerns not previously identified in the literature included a fear of judgement or discrimination by healthcare staff or patients that might impact the reputation of marginalised communities. Fortunately, these initial fears largely dissipated during clinical placement. Some students discovered that a diverse cultural background was an asset during their clinical placement. Although these initial fears were ameliorated by clinical placement experiences, evidence of such fears before clinical placement is concerning. Further research to identify appropriate support for nursing students from culturally diverse or marginalised communities is warranted. For example, a Finnish study highlighted the importance of mentoring culturally diverse students, creating a pedagogical atmosphere during clinical placement and integrating cultural diversity into nursing education [ 25 ].

Preclinical expectations of being mistreated can be viewed as an unavoidable phenomenon for nursing students [ 26 ]. The existing literature highlights power imbalances and hierarchical differences within the healthcare system, where student nurses may be marginalised, disrespected, and ignored [ 9 , 27 , 28 ]. During their clinical placement, students in our study reported unintentional incivility by supervising nurses: feeling not wanted, ignored, or asked to remain quiet by supervising nurses who were unfriendly or highly critical. These findings were similar to those of Thomas et al.’s [ 29 ] UK study and were particularly heightened at the beginning of clinical placement. Several students acknowledged that nursing staff fatigue from a high turnover of students on their ward and the COVID-19 pandemic could be contributing factors. In response to such incivility, students reported decreased self-confidence and described becoming quiet and withdrawing from active participation with their patients. Students oriented their behaviour towards repetitive low-level tasks, aiming to please and help their supervising nurse, to the detriment of learning opportunities. Fortunately, these incidents did not appear to impact nursing students’ overall experience of clinical placement. Indeed, students found positive experiences with different supervising nurses and their own self-reflection assisted with coping. Other active strategies to combat incivility identified in the current study that were also identified by Thomas et al. [ 29 ] included avoiding nurses who were uncivil, asking to work with nurses who were ‘nice’ to them, and seeking out support from other staff as a coping strategy. The nursing students in our study were undertaking a postgraduate entry-to-practice qualification and already had an undergraduate degree. The likely greater levels of experience and maturity of this cohort may influence their resilience when working with unsupportive supervising nurses and identifying strategies to manage challenging situations.

The theory-practice gap emerged in the theme of sinking or swimming. A theory-practice gap describes the perceived dissonance between theoretical knowledge and expectations for the first clinical placement, as opposed to the reality of the experience, and has been reported in previous studies (see, for instance, 24 , 30 , 31 , 32 ). Existing research has shown that when the first clinical placement does not meet inexperienced student nurses’ expectations, a disconnect between theory and practice occurs, creating feelings of being lost and insecure within the new environment, potentially impacting students’ motivation and risk of attrition [ 19 , 33 ]. The current study identified further areas exacerbating the theory-practice gap. Before the clinical placement, students without a healthcare background lacked context for their learning. They lacked understanding of nurses’ shift work and were apprehensive about applying clinical skills learned in the classroom. Hence, some students were uncertain if they were prepared for their first clinical placement or even how to prepare, which increased their anxiety. Prior research has demonstrated that applying theoretical knowledge more seamlessly during clinical placement was supported when students knew what to expect [ 6 ]. For instance, a Canadian study exposed students as observers to the healthcare setting before starting clinical placement, enabling early theory to practice connections that minimised misconceptions and false assumptions during clinical placement [ 34 ].

In the current study, the theory-practice gap was further exacerbated during clinical placement, where healthcare staff were confused about students’ scope of practice and the course learning objectives and expectations in a postgraduate entry-to-practice nursing qualification. The central booking system for clinical placements classifies first-year nursing students who participated in this study as equivalent to second-year undergraduate nursing students. Such a classification could create a misalignment between clinical educators’ expectations and their delivery of education versus students’ actual learning needs and capacity [ 3 , 31 ]. Additional communication to healthcare partners is warranted to enhance understanding of the scope of practice and expectations of a first-year postgraduate entry-to-practice nursing student. Educating and empowering students to communicate their learning needs within their scope of practice is also required.

Our research identified a link between students’ personality traits or individual agency and their first clinical placement experience. The importance of a positive orientation towards learning and the nursing profession in preparedness for clinical placement has been highlighted in previous studies [ 31 ]. Students’ experience of their first clinical placement in our study appeared to be strongly influenced by their mindset [ 23 ]. Some students demonstrated motivation to learn, were happy to ‘roll with the punches’, yet remain active in their learning requirements, whereas others perceived their role as observational and expected supervising nurses to provide learning opportunities. Students who anticipated a passive learning approach prior to their first clinical placement reported boredom, limited activity, and lack of opportunities during their first clinical placement. These students could have a lowered sense of self-efficacy, which may lead to a greater risk of doubt, stress, and reduced commitment to the profession [ 35 ]. Self-efficacy theory explores self-perceived confidence and competence around people’s beliefs in their ability to influence events, which is associated with motivation and is key to nursing students progressing in their career path confidently [ 35 , 36 ]. In the current study, students who actively engaged in their learning process used strategies such as self-reflection and sought support from clinical educators, peers and family. Such active approaches to learning appeared to increase their resilience and motivation to learn as they progressed in their first clinical placement.

Important relationships with supervising nurses, peers, or patients were highlighted in the theme of the reality of navigating placement relationships. This theme links with previous research findings about belongingness. Belongingness is a fundamental human need and impacts students’ behaviour, emotions, cognitive processes, overall well-being, and socialisation into the profession [ 37 , 38 ]. Nursing students who experience belongingness feel part of a team and are more likely to report positive experiences. Several students in the current study described how feeling part of a team improved self-confidence and empowered work-integrated learning. Nonetheless, compared with previous literature (see for instance, 2), working as a team and belongingness were infrequent themes. Such infrequency could be related to the short duration of the clinical placement. In shorter clinical placements, nursing students learn a range of technical skills but have less time to develop teamwork skills and experience socialisation to the profession [ 29 , 39 ].

Positive relationships with supervising nurses appeared fundamental to students’ experiences. Previous research has shown that in wards with safe psycho-social climates, where the culture tolerates mistakes, regarding them as learning opportunities, a pedagogical atmosphere prevails [ 25 , 39 ]. Whereas, if nursing students experience insolent behaviours or incivility, this not only impacts learning it can also affect career progression [ 26 ]. Participants who felt safe asking questions were given responsibility, had autonomy to conduct skills within their scope of practice and thrived in their learning. This finding aligns with previous research affirming that a welcoming and supportive clinical placement environment, where staff are caring, approachable and helpful, enables student nurses to flourish [ 36 , 40 , 41 , 42 ]. Related research highlights that students’ perception of a good clinical placement is linked to participation within the community and instructor behaviour over the quality of the clinical environment and opportunities [ 27 , 28 ]. Over a decade ago, a large European study found that the single most important element for students’ clinical learning was the supervisory relationship [ 39 ]. In our study, students identified how supervising nurses impacted their emotions and this was critical to their experience of clinical placement, rather than how effective they were in their teaching, delivery of feedback, or their knowledge base.

Students’ relationships with patients were similarly important for a successful clinical placement. Before the clinical placement, students expressed anxiety and fears in communicating and interacting with patients, particularly if they were dying or acutely unwell, which is reflective of the literature [ 2 , 10 , 11 ]. However, during clinical placement, relationships with patients positively impacted nursing students’ experiences, especially at the beginning when they felt particularly vulnerable in a new environment. Towards the end of clinical placement, feelings of incompetence, nervousness and uncertainty had subsided. Students were more active in patient care, which increased self-confidence, empowerment, and independence, in turn further improving relationships with patients and creating a positive feedback loop [ 36 , 42 , 43 ].

Limitations

This study involved participants from one university and a single course, thus limiting the generalisability of the results. Thus, verification of the major themes identified in this research in future studies is needed. Nonetheless, the purpose of this study was to explore in detail the way in which the experiences of clinical placement for student nurses modified initial emotional responses towards undertaking placement and their perceptions of preparedness. Participants in this study undertook their clinical placement in a variety of different hospital wards in different specialties, which contributed to the rigour of the study in identifying similar themes in nursing students’ experiences across diverse placement contexts.

This study explored the narratives of first-year nursing students undertaking a postgraduate entry-to-practice qualification on their preparedness for clinical placement. Exploring students’ changing perspectives before and during the clinical placement adds to extant knowledge about nursing students’ emotional responses and perceptions of preparedness. Our research highlighted the role that preplacement emotions and expectations may have in shaping nursing students’ clinical placement experiences. Emerging themes from this study highlighted the importance students placed on relationships with peers, patients, and supervising nurses. Significant anxiety and other negative emotions experienced by nursing students prior to the first clinical placement suggests that further research is needed to explore the impact of contextual learning to scaffold students’ transition to the clinical environment. The findings of this research also have significant implications for educational practice. Additional educational support for nursing students prior to entering the clinical environment for the first time might include developing students’ understanding of the clinical environment, such as through increasing students’ understanding of the different roles of nurses in the clinical context through pre-recorded interviews with nurses. Modified approaches to simulated teaching prior to the first clinical placement would also be useful to increase the emphasis on students applying their learning in a team-based, student-led context, rather than emphasising discrete clinical skill competencies. Finally, increasing contact between students and university-based educators throughout the placement would provide further opportunities for students to debrief, to receive support and to manage some of the negative emotions identified in this study. Further supporting the transition to the first clinical placement could be fundamental to reducing the theory-practice gap and allaying anxiety. Such support is crucial during their first clinical placement to reduce attrition and boost the nursing workforce.

Data availability

The datasets generated and/or analysed during the current study are not publicly available due to the conditions of our ethics approval but may be available from the corresponding author on reasonable request and subject to permission from the Human Research Ethics Committee.

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Acknowledgements

The authors wish to thank the first-year nursing students who participated in this study and generously shared their experiences of undertaking their first clinical placement.

No funding was received for this study.

Author information

Jennifer M. Weller-Newton

Present address: School of Nursing and Midwifery, University of Canberra, Kirinari Drive, Bruce, Canberra, ACT, 2617, Australia

Authors and Affiliations

Department of Nursing, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Philippa H. M. Marriott

Department of Rural Health, The University of Melbourne, Grattan St, Shepparton, VIC, 3630, Australia

Present address: Department of Medical Education, Melbourne Medical School, The University of Melbourne, Grattan St, Parkville, VIC, 3010, Australia

Katharine J. Reid

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All authors made a substantial contribution to conducting the research and preparing the manuscript for publication. P.M., J.W-N. and K.R. conceptualised the research and designed the study. P.M. undertook the data collection, and all authors were involved in thematic analysis and interpretation. P.M. wrote the first draft of the manuscript, K.R. undertook a further revision and all authors contributed to subsequent versions. All authors approved the final version for submission. Each author is prepared to take public responsibility for the research.

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Correspondence to Katharine J. Reid .

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The research was undertaken in accordance with the National Health and Medical Research Council of Australia’s National Statement on Ethical Conduct in Human Research and the Australian Code for the Responsible Conduct of Research. Ethical approval to conduct the study was obtained from the University of Melbourne Human Research Ethics Committee (Ethics ID 1955997.1). All participants received a plain language statement that described the requirements of the study. All participants provided informed written consent to participate, which was affirmed verbally at the beginning of focus groups and interviews.

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Marriott, P.H.M., Weller-Newton, J.M. & Reid, K.J. Preparedness for a first clinical placement in nursing: a descriptive qualitative study. BMC Nurs 23 , 345 (2024). https://doi.org/10.1186/s12912-024-01916-x

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Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada

• Meaghen Quinlan-Davidson 1 , 2 ,
• Mahalia Dixon 1 ,
• Gina Chinnery 3 ,
• Lisa D. Hawke 1 , 4 ,
• Srividya Iyer 5 , 6 ,
• Katherine Moxness 7 ,
• Matthew Prebeg 1 , 8 ,
• Lehana Thabane 9 , 10 , 11 &
• J. L. Henderson 1 , 4  

BMC Public Health volume  24 , Article number:  1402 ( 2024 ) Cite this article

Metrics details

Prior research has showed the importance of providing integrated support services to prevent and reduce youth not in education, employment, or training (NEET) related challenges. There is limited evidence on NEET youth’s perspectives and preferences for employment, education, and training services. The objective of this study was to identify employment, education and training service preferences of NEET youth. We acknowledge the deficit-based lens associated with the term NEET and use ‘upcoming youth’ to refer to this population group.

Canadian youth (14–29 years) who reported Upcoming status or at-risk of Upcoming status were recruited to the study. We used a discrete choice experiment (DCE) survey, which included ten attributes with three levels each indicating service characteristics. Sawtooth software was used to design and administer the DCE. Participants also provided demographic information and completed the Global Appraisal of Individual Needs–Short Screener. We analyzed the data using hierarchical Bayesian methods to determine service attribute importance and latent class analyses to identify groups of participants with similar service preferences.

A total of n =503 youth participated in the study. 51% of participants were 24–29 years of age; 18.7% identified as having Upcoming status; 41.1% were from rural areas; and 36.0% of youth stated that they met basic needs with a little left. Participants strongly preferred services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Three latent classes were identified and included: (i) job and educational services (38.9%), or services that include career counseling and securing a work or educational placement; (ii) mental health and wellness services (34.9%), or services that offer support for mental health and wellness in the workplace and free mental health and substance use services; and (iii) holistic skills building services (26.1%), or services that endorsed skills for school and job success, and life skills.

Conclusions

This study identified employment, education, and training service preferences among Upcoming youth. The findings indicate a need to create a service model that supports holistic skills building, mental health and wellness, and long-term school and job opportunities.

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Youth not in education, employment, or training (NEET) struggle to navigate school to work transitions and experience difficulties accessing jobs [ 1 ]. These youth are disconnected from school, have limited work experience [ 2 ], and experience a loss of economic, social, and human capital [ 3 ]. NEET status is associated with lower education, parental unemployment, low socioeconomic status, low self-confidence, more precarious housing, and young parenthood [ 4 , 5 , 6 , 7 , 8 ]. In Canada, the percentage of NEET youth (15–29 years) was estimated at 11% in 2022 [ 9 ]. Importantly, NEET status is not homogenous across the country, ranging from 36% in Nunavut, 20% in Northwest Territories, and 17% in Newfoundland and Labrador to 10% in Quebec, Prince Edward Island and British Columbia) [ 10 ]. Supporting and protecting these marginalized youth remains a challenge, particularly in light of the Coronavirus disease 2019 (COVID-19) pandemic, which adversely impacted the school to workforce transition for youth across the country [ 11 ]. Although the term NEET has been used to describe this population, it is considered stigmatizing and associated with a deficit-based lens[ 12 ]. As such, and in consultation with one of our youth team members, we refer to this population as ‘Upcoming youth’[ 13 ].

Upcoming status has gained attention across Canada in recent decades [ 14 ]. As an illustration of this focus, federal, provincial/territorial, and local programs exist to support Upcoming youth across the country [ 15 ]. Despite these efforts, evidence indicates program fragmentation, limited coordination across sectors and regions, and a lack of evaluation of these programs [ 16 ]. Further, these programs may be available to youth on a short-term basis and specific to youth who meet education, income, and age criteria [ 17 ]. There is a lack of knowledge of how to (re)engage Upcoming youth in general education and employment support services. Often the same limited outcomes are measured and reported (e.g., job attainment) with services focusing on these outcomes. At the same time, youth have not been asked what outcomes they prefer and accordingly what services they would like. Indeed, selective outcome reporting and lack of engagement of youth impairs the quality of evidence and contributes to research waste [ 18 ]. Given the heterogeneity of Upcoming status, this lack of evidence is particularly important for subgroups of youth (e.g., geographic location; socioeconomic status; mental health status) who face challenges in the school-to-work transition.

Prior global research has emphasized the importance of integrated, coordinated interventions that offer a range of support services (e.g., on-the-job, classroom-based, and social skills training) to prevent and reduce Upcoming status [ 19 , 20 , 21 , 22 , 23 ] [ 24 ]. Integrated youth service (IYS) models, which integrate education, employment, mental and physical health, substance use, peer support, and navigation in one, youth-friendly location have been established in Canada [ 25 ]. IYS deliver services that meet the needs, goals, and preferences of youth, and hold promise in serving vulnerable Upcoming youth through the provision of holistic services in a youth-friendly environment. Indeed, IYS models are investigating how to optimize employment, education, and training services as a critical component of supporting youth wellbeing and their successful transition to adulthood. This point is particularly important as Upcoming youth experience greater mental health and substance use (MHSU) concerns compared to youth who do not identify as Upcoming [ 26 , 27 ].

An essential component to designing and enhancing health and social services for Upcoming youth is understanding their perspectives [ 28 ]. Yet, there is a lack of evidence on Upcoming youth’s perspectives and preferences for employment, education, and training services within the Canadian context. For interventions to be relevant to the needs and experiences of youth—which will increase their chances of using the services and benefiting from them—it is important to understand what youth aim to achieve when participating in an intervention. Engaging youth in identifying service components and interventions will ensure that programs and services are relevant, feasible, and appropriate to this population group [ 29 ].

An approach that can be used to identify the demands and preferences of youth is the discrete choice experiment (DCE) [ 30 , 31 ]. The DCE is a quantitative method that requires participants to state their choice over sets of alternatives described in terms of several characteristics called attributes and the value placed on each attribute [ 30 , 31 ]. In this way, the DCE is able to identify the importance of attributes along which a variety of service options vary, as well as service preferences among subgroups. DCEs are one of the most popular methods for eliciting stated- preferences in health care [ 32 , 33 ]. They force participants to make trade-offs, identifying the importance of different service attributes [ 32 , 33 ]. Previous findings generated from DCE studies have been useful in informing service design and delivery, resource allocation, and policies, including the preferred design of IYS services [ 34 , 35 , 36 , 37 ].

Understanding service preferences from the perspective of Upcoming youth is critical for the development of interventions and policies that will help youth navigate the school-to-work transition. As such, the objective of the current study was to identify employment, education and training service preferences of Upcoming youth. As our approach to COVID-19-related impacts shifts, the need for this research is more urgent than ever, as a way to support vulnerable youth, reduce Upcoming status, prevent further exclusion, and help them on their path towards adulthood.

Discrete Choice Experiment (DCE)

A discrete choice experiment (DCE) methodology was used in this study, as described in the study protocol [ 13 ]. We followed the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) guidelines on good research practices for conjoint analysis [ 38 ]. Attributes and levels were developed using the following methods. First, we reviewed the literature on relevant and preferred services for youth with Upcoming and at-risk Upcoming status [ 26 ]. An initial set of six attributes with three to four levels was developed from the literature review, highlighting components such as mental health, goals, and skills training. Second, focus groups were conducted among youth (16–29 years) with Upcoming and at-risk Upcoming status across Canada to obtain youth feedback on proposed service outcomes [ 39 ]. Thematic analysis [ 40 ] of the focus group data identified prominent attributes and levels, including skills training, mentorship, and networking. The project team included youth team members with lived/living experience of MHSU concerns and researchers; meetings were held with the team to refine the attributes and levels.

The list of attributes and levels were piloted among n  = 9 youth (16–29 years) across Canada. Pilot participants completed the DCE with a member of the project team. The aim of the pilot was to obtain youth feedback on the proposed list of attributes and levels, as well as the design and functionality of the DCE. Based on pilot feedback, a final list of attributes and levels was developed. The final DCE list included ten attributes, each with three levels. The attributes included mentorship; skills for school and job success; technical skills; life skills; basic income; networking opportunities; securing a work or educational placement; career counselling; access to free mental health and substance use services; and support for mental health and wellness in the workplace. Using a 3 × 3 partial-profile design, we used Sawtooth software (version 9.14.2) [ 41 ] to administer the 14 randomized choice tasks. This design was chosen to optimize orthogonality, minimize participant burden, and ensure data robustness [ 42 ]. Table 1 shows a sample choice task; Additional File 1 contains the full list of attributes and levels in the study.

Participants and procedure

The study was approved by the Centre for Addiction and Mental Health’s (CAMH) Research Ethics Board in Toronto, Canada. This study consisted of n  = 503 youth (14–29 years), recruited over a three-month period in late 2022 and early 2023. The sample size was based on a priori power calculations and exceeds the sample size of most DCE studies [ 13 ]. Study flyers with survey links were distributed through internal CAMH and external professional networks, as well as through social media (Facebook and Instagram).

Participants were eligible to complete the DCE if they were between the ages of 14 and 29 years; lived in Canada at the time of survey completion; and identified as Upcoming status or having ever been concerned of being at-risk of Upcoming status (self-identified). They were screened through an online survey sent via email, hosted on REDCap electronic software [ 43 ]. Participants gave informed consent and filled out anti-spam and eligibility questions. Those who were eligible were sent a link to complete the DCE through Sawtooth Software [ 41 ]. The survey was in English only. They also filled out self-report questionnaires on demographics, and mental health and substance use. Reminder emails to complete the survey were sent to participants once per week, with a maximum of three reminders sent. A total of n  = 515 participants initiated the survey and n  = 503 completed the survey, yielding a response rate of 97.7%. The median time to complete the DCE was 20.63 min. Participants received a $30 gift card as honorarium for survey completion.

Mental health and substance use measures

Participants completed the Global Appraisal of Individual Needs–Short Screener (GAIN-SS) (version 3) [ 44 ]. Internalizing disorders (depression, anxiety, somatic complaints, trauma etc.); externalizing disorders (hyperactivity, conduct problems, attention deficits, impulsivity etc.); and substance use disorders are domain subscales that are screened in the GAIN-SS [ 44 ]. The GAIN-SS also includes a crime/violence domain, however, low level of endorsement in this study precluded the inclusion of this subscale. Participants rated each administered symptom “never” to “within the past month”, indicating how recently they experienced symptom difficulties. Within each domain subscale, endorsed past month symptoms were counted and summed. Scores could range between 0–6, 0–7, and 0–5 for the Internalizing, Externalizing, and Substance Use Problems domain, respectively. Following previous literature, three or more items endorsed within the past month indicate a high likelihood of needing services and/or meeting threshold criteria for psychiatric diagnoses [ 44 , 45 ].

Demographic characteristics were collected. We included age (categorical measure), gender identity (man/boy [cis, trans]; woman/girl [cis, trans]; Gender diverse); ethnicity (White; Indigenous, Black, Asian, Mixed); region in Canada (Prairies, Western/Northern, Atlantic, Central); self-rated physical and mental health (good/very good/excellent; fair/poor) [ 46 ]; socioeconomic status (live comfortably; income meets needs with a little left; just meet basic expenses; don’t meet basic expenses); living arrangement (alone; with partner; with family; other); and area of residence (large city and suburbs of large city; small city, town, village or rural area).

Youth engagement

Following the McCain Model of Youth Engagement [ 47 ], and working with the Youth Engagement Initiative at the Margaret and Wallace McCain Centre for Child, Youth & Family Mental Health, we engaged youth throughout the study. To enhance study design, promote youth buy-in, and relevance of the study, youth were involved from project inception and implementation of study activities to interpretation of findings and manuscript development.

Statistical analysis

Statistical analyses were performed using Sawtooth Software version 9.14.2 [ 41 ] and Stata version 16.1 [ 48 ]. Descriptive statistics were calculated for all study variables overall and by latent-class grouping. Using hierarchical Bayesian methods within Sawtooth Software [ 41 ], utility estimates were calculated for each participant. Standardized zero-centered utilities were used and the average utility range of attribute levels was set to 100 (49) to calculate the estimates. Attributes with higher utility estimates indicated higher relative value compared with other attributes (Table 2 ).

To identify groups of participants with similar service preferences, we conducted latent class analyses [ 41 ]. To belong to a latent class, probabilities were assigned to each participant. Using different starting seeds, five replications for each latent class group was calculated, with log-likelihood decreases of 0.01 or less indicating convergence. Based on the analysis, we retained a three-class model. This model was determined by analyzing the Bayesian Information Criteria (BIC), Akaike Information Criteria (AIC), Consistent Akaike Information Criterion (CAIC), and Akaike’s Bayesian Information Criterion (ABIC); latent class sizes; and the interpretation of latent class groupings (Table 3. ). Team discussions with youth team members were held to review the importance scores and rankings and establish the names of the latent glass groupings (Table 4 ). Stata 16.1 [ 48 ] was used to compare latent classes on demographic characteristics and GAIN-SS scores using chi-square tests.

Table 5 presents participant demographic characteristics. The majority of participants were between 24 and 29 years of age; lived in urban areas; were engaged in employment and training only; and identified as White and girl/woman (Trans, cis). Almost two thirds of participants (65.79%) met threshold criteria for an internalizing disorder, followed by 36.62% with an externalizing disorder, and 8.65% with a substance use disorder.

Overall service preferences

The overall service preferences and importance scores of participants are presented in Table  2 . Participants positively endorsed services that promoted life skills, mentorship, basic income, and securing a work or educational placement. Participants were least likely to endorse technical skills. Within life skills services, youth positively endorsed services that included managing finances, taxes, and skills associated with self-care and cooking. The provision of a mentor who worked within the participant’s field of interest was preferred by all youth. Participants positively endorsed basic income until having secured employment that matched the basic income level. All youth preferred services that provided support to secure long-term job positions or school placements that aligned with their career interests or long-term goals.

Table 3. illustrates the fit indices of the latent class analysis. A three-class model was retained based on fit, size of latent class grouping, and interpretation of findings. Attribute importance scores and rankings are presented in Table  4 by latent class. There were some commonalities identified across the latent classes. All latent classes positively endorsed services that offered mentorship (mentors in their field of interest, with similar backgrounds, or peer mentors), basic income, and networking. Youth preferred the provision of a mentor with work experience in their field of interest. Participants also positively endorsed receiving basic income until 25 years of age (regardless of school or job status), or until they had found a job that matched the basic income level. In addition, all participants endorsed skills to network and opportunities to network in their area of interest.

Over 60% of participants from all of the latent classes reported fair/poor mental health. In addition, over 60% of participants in each latent class grouping met threshold criteria for an internalizing disorder, compared to the other GAIN-SS disorders. Furthermore, more participants identified as having lived in large cities/suburbs compared to small cities/towns in each latent class.

Latent Class 1: Job and educational services

The first latent class endorsed services that focused on education and long-term job services, focusing on a career trajectory ( n  = 204, 38.9%). Attributes that drove these decisions included career counselling and securing a work or educational placement. Youth positively endorsed career counselling that helped to figure out career goals, create a resume, and complete job applications. Further, youth positively endorsed receiving long-term job positions or school placements that align with their career interests and long-term goals, as opposed to temporary or any job position. Participants from this latent class (Table 6 ) were more likely to be 24–29 years of age compared to other ages. Approximately 22.06% of youth in this latent class identified as Upcoming.

Latent Class 2: Mental health and wellness services

The second latent class endorsed mental health and wellness services ( n  = 171, 34.9%). This latent class preferred that services offer support for mental health and wellness in the workplace and free mental health and substance use services. Specifically, participants positively endorsed the provision of on-site in-person, individual mental health and substance use services, as opposed to the provision of virtual or in-person group services. Further, youth positively endorsed ongoing access to a support worker to help in securing accommodations in the workplace, as opposed to learning how to advocate for oneself in workplace or support during job onboarding. Participants that endorsed this latent class (Table  5 ) tended to identify as Indigenous, Black, Asian, and Mixed; girl/woman (cis, trans); both student and employed; income met needs with a little left; and rated their physical health as good/excellent. Approximately 16.96% of youth in this latent class identified as Upcoming.

Latent Class 3: Holistic skills building services

Skills building was the focus of the third latent class ( n  = 128, 26.1%). Participants positively endorsed skills for school and job success, as well as life skills. Specifically, youth were interested in learning about how to organize time; prioritize tasks; identify problems and solutions; as well as professionalism, communication and relationship building. Youth were also interested in life skills that focused on learning about how to manage finances and taxes, as well as self-care and cooking. Participants in this latent class (Table  5 ) tended to identify as White; employed only; living in an urban area; and income that just met basic expenses. Approximately 15.63% of youth in this latent class identified as Upcoming.

To our knowledge, this study was the first to identify employment, education, and training service preferences among Upcoming youth and those at-risk of Upcoming status using discrete choice experiment methods. The findings indicate that overall, youth value services that enhance their ability to deal effectively with life demands; receive advice and guidance by a mentor; and obtain financial support through basic income. In examining youth participants by latent class, the findings indicate a need to create a service model that supports long-term school and job opportunities, holistic skills building, and mental health and wellness. Job and educational services prioritized long-term job and school placements, with career counselling. Mental health and wellness services endorsed free, easily accessible and in-person support services. Meanwhile, holistic skills building focused on problem solving, communication, relationship building, and organization of time, as well as building skills to help youth manage daily life.

Participants highly endorsed services that promote life skills, mentorship, and basic income. For life skills, participants valued skills that included managing finances, taxes, and skills like self-care or cooking. Participants may have valued this service attribute because life skills empower youth. These skills are positive behaviours that give youth the knowledge, values, attitudes, and abilities necessary to effectively meet and deal with everyday challenges [ 50 , 51 ]. Prior research has shown how these skills strengthen psychosocial competencies, promote health and social relationships, and protect against risk-taking behaviours [ 51 ].

For mentorship, participants valued having a mentor who has work experience in the field they are interested in. Prior research has shown the negative associations between unemployment, exclusion, and economic hardship among Upcoming youth [ 52 , 53 ]. Participants may have chosen this service attribute as mentoring is a key component of career development. Career mentoring provides opportunities for career exploration and strengthening decision-making within this domain [ 54 , 55 , 56 , 57 ]. Research has shown the benefits of mentorship. Mentors are a positive resource, providing support and guiding youth as they navigate and succeed in their careers [ 54 , 58 , 59 ].

Youth also prioritized receiving basic income until they secured employment that matches the basic income level. Empirical evidence has shown associations between income and youth mental health outcomes [ 60 , 61 , 62 ]. Indeed, Johnson et al. [ 63 ] [ 63 ] posit that a universal basic income can positively affect health through behaviour, resources, and stress. Defined as income support to populations with minimal or no conditions [ 64 ], prior research has shown the benefits of a basic income plan in terms of poverty reduction, improvements in physical and mental health, economic growth, and human capital gains [ 65 , 66 , 67 , 68 , 69 ]. In a qualitative study in England [ 70 ], youth (14–24 years) reported that a universal basic income plan would improve their mental health through financial security, agency, greater equality, and improvements in relationships.

Differences in service preferences were observed among youth subgroups based on the identified latent classes. Youth who identify as Indigenous, Black, Asian, and Mixed prioritized mental health and wellness services compared to youth who identify as White. Previous literature has showed that Indigenous , Black, and racialized youth have experience longer wait times and poorer quality of mental health care compared to their White counterparts [ 71 , 72 , 73 ]. Prior literature has described how MHSU systems often do not consider or address the discrimination, systemic racism, economic marginalization, and intergenerational traumas that Indigenous, Black, and racialized populations experience within and outside of the service system [ 74 , 75 ]. These negative experiences adversely affect their access to and quality of MHSU care, leading to inequities in MHSU outcomes. To ensure that mental health and substance use services are culturally responsive, safe, effective, and available to Indigenous, Black, and racialized youth, services should incorporate their perspectives into service design and delivery. The finding that youth 24–29 years of age endorsed job and educational services focused on long term career planning could be attributed to being more advanced in thinking about their careers and a desire to find a career as opposed to a job [ 57 , 76 , 77 ]. It could also be due to older youth experiencing poorer labour market conditions [ 19 , 78 , 79 ]. To improve long-term job opportunities for youth, Canada’s labour standards need to be updated, ensuring protection and benefits to informal and non-standard youth workers (17).

A critical component for education, employment, and training services is raising awareness about these services and their benefits among youth. A 2019 survey among NEET youth (16–29 years) in Canada showed that 54% reported a hard time finding information on the labour market services, while 42% said the information available on these services was not easy to understand [ 80 ]. One way to address this issue is by delivering services to Upcoming youth at the local, community level. In fact, as IYS strengthen education, employment and training services, these community-based services can support youth by connecting them with local job opportunities. IYS can also work with other public, private and community organizations to change local, fragmented school and work policies [ 19 ]. Another way to address this issue would be to provide access to this information at an earlier age, as shown in a parliamentary enquiry in Victoria, Australia in which career management was recommended for incorporation into primary school curriculum[ 81 ].

All three latent classes preferred services that provided mentorship, basic income, and networking opportunities. Youth value mentorship opportunities from individuals with experience in their field of interest. Similarly, youth prioritized networking opportunities in their field of interest. Federal, provincial/territorial and local programs could harness this preference by creating mentorship and networking structures across public, private, and community organizations for youth [ 17 ]. Further, the provision of basic income would help support youth as they re-engage with school and the labour market [ 17 ]. Interestingly, technical skills were not endorsed by youth in this study. Although technical skills are endorsed as part of technical and vocational education and training programs [ 82 ], it may be that youth were not as concerned about enhancing technical skills as they were other services. Future research should investigate youth experiences of technical skill programs.

It is important to note that participants in all latent classes endorsed poor mental health, while a higher proportion of youth screened positive for internalizing disorders compared to other disorders. These findings are in line with prior literature, particularly in light of the COVID-19 pandemic [ 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. It could also reflect the positionality of the researchers. The survey was administered by CAMH, a mental health teaching hospital, and could have been seen more among youth connected with mental health services compared to those not connected to CAMH. Prior research has showed that life skills training can promote positive development, mental wellbeing, and prevent risky behaviours [ 92 , 93 ]. The prioritization of long-term school and job placements among youth with mental health concerns indicates a need to strengthen these services for this cohort.

In fact, in 2020 the Individual Placement and Support (IPS) model [ 94 , 95 ], which provides mental health service users with personalized vocational support alongside mental health support to obtain employment, education, and training opportunities was launched in Alberta, British Columbia, Nova Scotia, Ontario and Quebec to strengthen existing IYS, including ACCESS Open Minds, Foundry, and Youth Wellness Hubs Ontario [ 96 ]. The program was implemented in 12 hubs across the country and is currently being evaluated. Despite the challenges that have arisen over the course of the pandemic, COVID-19 has highlighted an opportunity to improve the education, employment and training support systems that serve these youth. Some of the core principles of implementation of the IPS model align with findings from the current study and include integration of mental health treatment teams, employment specialists to support young people as they navigate the labour market, rapid job search approaches, and tailored job supports, among other principles [ 94 ].

Indeed, in building on the services endorsed in this study, it would be important to incorporate an evaluation framework such as the Consolidated Framework for Implementation Research [ 97 ] to evaluate the effectiveness and impact of these services. Determining potential outcomes that could be measured would also be important. Following the IPS model, for job and educational placements, services could implement the Youth Employment and Education Survey [ 94 , 95 ]. Potential outcomes could include status of school or employment, job permanency, educational placement duration, and satisfaction with the program, among others. For mental health and wellness support services, outcomes could focus on the number of in-person visits, satisfaction with the services, and self-reported mental health, among others. For holistic services, potential outcomes could focus on reporting and monitoring self-reported goals for problem-solving and communication, among others. It would be important to continuously assess and match services to Upcoming youth preferences.

We would like to acknowledge some limitations. This study includes a non-randomized sample of youth across Canada. Our study included less than 20% of youth who identified as Upcoming, which limits our ability to generalize the findings to this population group. Further research is needed among youth who identify as Upcoming to determine if these education, employment, and training services represent their preferences. Further, youth without stable and consistent internet access would also have been missed. We were unable to recruit large populations of youth from specific Indigenous and racialized backgrounds, although these did account for nearly half the sample. These groups may have different needs and preferences. Future research should investigate their perspectives on employment, education, and training services. In addition to the structure of the DCE survey, along with following a rigorous process in the development of the attributes and levels, there could have been some youth service priorities not assessed. Furthermore, as some of the attributes and levels built on each other, these commonalities could have influenced preference elicitation for specific service attributes. We tried to ensure that the survey was youth-friendly for all youth, however due to the cognitive capacity required to complete the survey, some youth with greater mental health and learning challenges may have been missed.

This study identified employment, education, and training service preferences among Upcoming youth and those at-risk of Upcoming status in Canada. The findings indicate a need at the federal, provincial/territorial, and local level to create a service model that supports school and job opportunities long-term; mental health and wellness; and building holistic skills. The model also requires community-based and youth-centred approaches in the design and delivery of these services. Our findings further support the need for widespread policy support for broader-spectrum IYS for Upcoming youth and those at-risk of Upcoming status.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Akaike’s Bayesian Information Criterion

Akaike Information Criterion

Bayesian Information Criteria

Consistent Akaike Information Criterion

Centre for Addiction and Mental Health

Coronavirus disease 2019

Discrete Choice Experiment

Global Appraisal of Individual Needs Short Screener

Importance Scores

Individual Placement and Support model

International Society for Pharmacoeconomics and Outcomes Research

Integrated Youth Services

Mental health and substance use

Not in education, employment, or training

Standard Errors

Youth Wellness Hubs Ontario

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Acknowledgements

We would like to thank the participants for their participation in this study. We would like to thank members of the Centre for Addiction and Mental Health’s Youth Engagement Initiative for their support of this study.

This research was funded by the Social Sciences and Humanities Research Council (SSHRC) (435–2019-0393).

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Gina Chinnery

Department of Psychiatry, University of Toronto, Toronto, ON, Canada

Lisa D. Hawke & J. L. Henderson

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Srividya Iyer

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MQD contributed to designing the research question and conducted the analysis, interpretation of the data, and drafted the manuscript. All authors read and approved the final manuscript. JLH contributed to designing the research, oversaw the conduct of the study, interpreted the data, reviewed the manuscript and provided study leadership; JLH is the overall guarantor of the work.

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Quinlan-Davidson, M., Dixon, M., Chinnery, G. et al. Youth not engaged in education, employment, or training: a discrete choice experiment of service preferences in Canada. BMC Public Health 24 , 1402 (2024). https://doi.org/10.1186/s12889-024-18877-0

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DOI : https://doi.org/10.1186/s12889-024-18877-0

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Entropy values for each token are used to enhance the model’s memory capability by selectively preserving the key-value states of only the key tokens, leading to the proposal of SirLLM. The framework overview of SirLLM involves maintaining both a key-value (KV) cache and a token entropy cache. When the number of tokens stored in the KV cache exceeds the pre-training length L, SirLLM calculates the entropy of each token and selects tokens with higher entropy, thus conserving space in the KV cache. This is achieved by selecting the top k tokens with the highest token entropy. Higher token entropy implies a lower probability of word generation, indicating key tokens with more information. SirLLM also adjusts token positions within the cache for relative distances, focusing on cache positions rather than 

original text positions. However, preserving tokens solely based on entropy can lead to a rigid memory within the model, hindering adaptability. To overcome this, a decay ratio ηdecay less than 1 is proposed, allowing the model to forget older key information after each round of dialogue, thereby enhancing flexibility and user experience.

Analysis of the Rock-Paper-Scissors dataset demonstrates SirLLM’s consistent outperformance compared to the baseline StreamLLM across players with diverse throwing preferences. SirLLM exhibits a steady improvement in win rates against players of various preferences, maintaining this elevated performance consistently across all evaluated models. The integrated decay mechanism in SirLLM contributes significantly to sustaining balanced performance over multiple rounds, as evidenced by uniformly elevated win rates. This characteristic is particularly advantageous in scenarios involving prolonged interactions like extended Rock-Paper-Scissors games, highlighting SirLLM’s capacity to adapt and recall previous moves, essential for success.

Introducing SirLLM, this study addresses the critical challenges of managing infinite input lengths and memory capability. SirLLM achieves long dialogue retention without requiring model fine-tuning by selectively reinforcing the focus on pivotal information. Across three tailored tasks: DailyDialog, Grocery Shopping, and Rock-Paper-Scissors, SirLLM consistently demonstrates stable improvement over existing models, regardless of dialogue complexity or length. Experimental outcomes validate SirLLM’s robustness and versatility, positioning it as a valuable asset for future explorations and applications in natural language processing.

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Asjad is an intern consultant at Marktechpost. He is persuing B.Tech in mechanical engineering at the Indian Institute of Technology, Kharagpur. Asjad is a Machine learning and deep learning enthusiast who is always researching the applications of machine learning in healthcare.

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High birefringence low loss nearly zero flat dispersion similar to slotted core photonic crystal fibers

Studying high-performance photonic crystal fibers (PCF) is of significant scientific importance for terahertz (THz) waveguide systems. This study introduces a novel PCF design with a core composed of the smallest sub-wavelength units resembling a slotted structure, aiming to achieve high birefringence and low loss. The optical properties of the proposed PCF are analyzed through simulations, yielding impressive results. The PCF exhibits an ultra-high birefringence of 0.07848, a minimum limiting loss of 10 −17  dB/cm, and an effective material loss as low as 0.04251 cm −1 . Moreover, it demonstrates near-zero flat dispersion of −0.012 ± 0.074 ps/THz/cm over a broad frequency range of 1.2–2.2 THz. This fiber stands out by not only providing high birefringence but also by striking an optimal balance among birefringence, transmission loss, and dispersion for THz waveguides. The implications of this work are profound for the development of THz communication systems, THz polarization-maintaining transmission, and sensing applications. Furthermore, it established an important benchmark for the design of THz-PCFs that prioritize high birefringence, low loss, and near-zero flat dispersion, offering an essential reference for future research and development in this field.

Funding source: Science and Technology Research Project of Hebei Provincial Department of Education

Award Identifier / Grant number: No.ZD2021332

Research ethics: Not applicable.

Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.

Competing interests: The authors state no conflict of interest.

Research funding: Science and Technology Research Project of Hebei Provincial Department of Education (No. ZD2021332).

Data availability: Not applicable.

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