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

11 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

This post is really helpful.

how to cite this page

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

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

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Shona McCombes

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

Updated: July 23, 2024

Published: January 18, 2023

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

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

Table of Contents

What is research design?

The four types of research design, research design examples.

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

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

Marketers use different types of research design when conducting research .

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

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

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

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Experimental

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

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

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

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

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

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

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

Correlational

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

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

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

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

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

Descriptive

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

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

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

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

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

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

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

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

When you have to write a thesis or dissertation , it can be hard to know where to begin, but there are some clear steps you can follow.

The research process often begins with a very broad idea for a topic you’d like to know more about. You do some preliminary research to identify a  problem . After refining your research questions , you can lay out the foundations of your research design , leading to a proposal that outlines your ideas and plans.

This article takes you through the first steps of the research process, helping you narrow down your ideas and build up a strong foundation for your research project.

Table of contents

Step 1: choose your topic, step 2: identify a problem, step 3: formulate research questions, step 4: create a research design, step 5: write a research proposal, other interesting articles.

First you have to come up with some ideas. Your thesis or dissertation topic can start out very broad. Think about the general area or field you’re interested in—maybe you already have specific research interests based on classes you’ve taken, or maybe you had to consider your topic when applying to graduate school and writing a statement of purpose .

Even if you already have a good sense of your topic, you’ll need to read widely to build background knowledge and begin narrowing down your ideas. Conduct an initial literature review to begin gathering relevant sources. As you read, take notes and try to identify problems, questions, debates, contradictions and gaps. Your aim is to narrow down from a broad area of interest to a specific niche.

Make sure to consider the practicalities: the requirements of your programme, the amount of time you have to complete the research, and how difficult it will be to access sources and data on the topic. Before moving onto the next stage, it’s a good idea to discuss the topic with your thesis supervisor.

>>Read more about narrowing down a research topic

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So you’ve settled on a topic and found a niche—but what exactly will your research investigate, and why does it matter? To give your project focus and purpose, you have to define a research problem .

The problem might be a practical issue—for example, a process or practice that isn’t working well, an area of concern in an organization’s performance, or a difficulty faced by a specific group of people in society.

Alternatively, you might choose to investigate a theoretical problem—for example, an underexplored phenomenon or relationship, a contradiction between different models or theories, or an unresolved debate among scholars.

To put the problem in context and set your objectives, you can write a problem statement . This describes who the problem affects, why research is needed, and how your research project will contribute to solving it.

>>Read more about defining a research problem

Next, based on the problem statement, you need to write one or more research questions . These target exactly what you want to find out. They might focus on describing, comparing, evaluating, or explaining the research problem.

A strong research question should be specific enough that you can answer it thoroughly using appropriate qualitative or quantitative research methods. It should also be complex enough to require in-depth investigation, analysis, and argument. Questions that can be answered with “yes/no” or with easily available facts are not complex enough for a thesis or dissertation.

In some types of research, at this stage you might also have to develop a conceptual framework and testable hypotheses .

>>See research question examples

The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you’ll use to collect and analyze it, and the location and timescale of your research.

There are often many possible paths you can take to answering your questions. The decisions you make will partly be based on your priorities. For example, do you want to determine causes and effects, draw generalizable conclusions, or understand the details of a specific context?

You need to decide whether you will use primary or secondary data and qualitative or quantitative methods . You also need to determine the specific tools, procedures, and materials you’ll use to collect and analyze your data, as well as your criteria for selecting participants or sources.

>>Read more about creating a research design

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Finally, after completing these steps, you are ready to complete a research proposal . The proposal outlines the context, relevance, purpose, and plan of your research.

As well as outlining the background, problem statement, and research questions, the proposal should also include a literature review that shows how your project will fit into existing work on the topic. The research design section describes your approach and explains exactly what you will do.

You might have to get the proposal approved by your supervisor before you get started, and it will guide the process of writing your thesis or dissertation.

>>Read more about writing a research proposal

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

Methodology

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

 Statistics

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

Research bias

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

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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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Researcher, Academic Writer, Web developer

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Designing research projects

How to design better research projects, and how to develop your skill as someone who generates research projects.

Eleanor C Sayre

Designing good research studies is an important part of becoming a researcher, no matter what your field is. The exercises on this page are aimed at junior researchers who are designing their first studies in education research. If you’ve already done one or two projects, these exercises will help you get better at seeking funding and developing more projects. If you’ve never done research before, these exercises will help your first project be more successful.

If you are looking to design an education research project, the exercises on this page will help you. If you’re looking for advice on how to plan research projects is a good choice. You might also look at research process models to help you think about how research projects progress, or Iterative Design to think about to structure them for maximum likelihood of success.

If you’re doing video-based observational research, here’s a good companion piece to consider. If you’re thinking about Design-based research, check out this article .

More broadly, check out all articles tagged with “ doing research ”.

What does my project need?

Every project in education research needs to address four areas. While the details of these areas can be (should be) emergent, well-formed and successful research projects identify as much as possible ahead of time.

Every project needs:

Additionally, when you present your work for publication or funding, you will need to consider two more areas:

We’re going to leave these two aside for now because they reference a broader sense of where the research community is, what societal needs are, and how your project fits into a much larger narrative. Those considerations are outside the scope of this guide, though you might consider reading ahead to other guides on writing. Let’s work on the four primary areas.

In a good research project, the four areas are all tightly related and supportive of each other. You should develop them in concert with each other. The exercises on this page will help you design a research study, and they will also help you develop your design skills in general.

Details of the four areas

Research questions.

Your research question(s) tie together your theoretical frameworks, methods, and access. They give purpose to your data collection and analysis. Answering them generates new knowledge about human behavior. In the ordinary process of doing research or thinking about the world, you will ask lots of questions. As you pursue some of them, you’ll develop follow-up questions and related lines of inquiry.

Research Question templates

If you’re in the very beginning stages of thinking about your project, you might need help brainstorming some possible research questions. Here are some templates to get you started. It’s not an exhaustive list.

• Theory X says A, but theory Y says B. How can they be made commensurate?
• This paper used population A, but I have population B. How can I apply their findings to my population?
• Surveys shows that students can do X. What is the actual process of learning to do X?
• What are the moderating factors which control success at task X?
• Our previous work shows X happens sometimes. Why does X occur?
• What’s better at teaching X, curriculum A or curriculum B?
• How do teachers make sense of X in light of Y?

Making your research question better

When you have an idea about what you’d like to investigate, you need to refine your ideas into a research question that suggests how you will answer it and how you will know when it is answered.

A good research question has the following properties:

• It is phrased a question, not a statement of problem
• Specific enough to be answerable
• Open to complicated, robust answers
• Interesting to investigate

You will want to have two versions of your research question: one that uses regular language, and a longer one (possibly with subquestions!) that uses specific, technical language.

This exercise helps you refine your ideas into a research question.

Write your question in the form of a question. Use regular language.

Make it specific. Your research question needs to be answerable in principle, and your research design needs to have a high likelihood of answering it.

• If your research question uses comparison language, what are you comparing? For example, if your research question is about whether a new curriculum is “better” or if students are learning “more”, what will you be comparing it to? Do you need to collect baseline data? Will you be able to run a treatment group and a control group at the same time?
• If your research question uses development language (e.g. “learning”), over what time are your subjects changing? An hour? Four years? Their lives? How will you know if change is durable? how will you know if it occurs at all?

Open it up to complicated, robust answers.

• If your research question has a binary answer (“does X happen?”), revise it to permit a more subtle answer (“to what extent does X happen?”; “how much does Y mediate X happening?”; “under what conditions can X be optimized to happen?”)
• If your research question is too specific (“what is the correlation coefficient of X with Y?”), you are too specific. Revise your question to have more robust answers (“how do X and Y relate?”; “what factors affect X and by how much?”)

Check: does answering this question sound like fun to you?

• If you refined your question so much that finding the answer sounds boring, trivial, or insurmountably hard, try new ways to refine it so that it really captures your interest in this topic.

In the process of refining your research question, you might realize that there are a bunch of interesting sub-questions to pursue. Go ahead and list them out, and follow this same process to refine them. Your refinements probably also include technical language and reserved words that mean something specific to the research project. Define each reserved word and link it to specific theoretical frameworks, methods, or data streams.

A good research question is a living question. As you interact with theories and data , it will necessarily change. The more specific you can make it in the beginning, the better you will be able to see it change and adjust your future work in an intentional way. You may find it useful to read Engle et al’s “ Progressive Refinement of Hypotheses in Video-Supported Research ” to understand how research questions can change and in response to repeated engagement with data, and Iterative Design to think about how to design for this feature.

The Access area is about practical constraints on your project: what populations do you have access to, and in which modalities? how much time do you have, and which analysis resources can you marshal? Of all the areas, Access is the one which is usually fixed earliest in the project, because the kinds and amounts of data you have access to are usually determined before you can collect any data at all, and the scope of your project is usually outside your control.

Questions that detail your access to data:

• What kinds of people will you measure? Some examples: introductory students, pre-service teachers, graduate teaching assistants, third graders in a specific elementary school.
• In what modalities can you collect data from them? Some examples: I can talk to them individually in interviews once per person, I can video them in class every day, I can put a problem on their final exam, I have three years of archival data but cannot collect new data, etc.
• How many people / how much data? One or two significant figures are ok here: about 10 students, about 300 students, about 20 hours of video, about 100 matched pre-post tests, etc

You probably can’t answer all of these questions alone. Get specific guidance from your collaborators, advisor, and people who control your access to research subjects (their instructors, their principals, the registrar, the data librarian, etc) – the members of your Advisory Board . At early design stages, you don’t need to seek IRB approval yet, and you don’t need written permission from every stakeholder. When your study is more fully designed , you will talk to these people again to firm up the details of your access and adjust your research questions and methods.

Questions that detail your access to resources:

• How long can you spend collecting data? How long analyzing it?
• How many researchers will be involved in data collection and analysis? What are their skill levels?
• How much data (and what kinds) can you reasonably expect to collect / analyze in the amount of time and effort that are available to you?

It is entirely possible that you have access to more data and analysis resources than you will need or use in your project. That’s great! You don’t need to collect (or use) everything. Alternately, you might not have enough access (or the right kind of access) to do the study you really want to do. That’s disappointing. You will need to adjust your research questions and methods in light of how much (and what kinds) of data you can collect or analyze with your resources.

On rare occasions, you can use your research questions to argue for access to more resources or different modalities. For example, suppose your research question is about student epistemology and persistence, and you already have access to students’ attitude survey scores. You might be able to ask the registrar for students’ demographics and final grades to enhance your analysis.

Theoretical frameworks

The role of your theoretical frameworks is to tell you why your observations are meaningful and in what ways your analyses generate new knowledge. Without a theoretical framework, your observations are meaningless and your work is unpublishable.

The primer on theory covers what you need theory for, an organizing framework for deciding which theory or theories to use, some theory options in education research, and some other common considerations.

The best theoretical frameworks are a) explicit; b) well-matched to your research question and methods; and c) intentionally chosen. There isn’t a “best framework” for everyone, or even every research question, and there are a lot of options available.

I’m using “Theoretical framework” in a loose sense to include things like knowledge-in-pieces , communities of practice, speech genres, models of institutional change, error-based learning, etc. (I’ve used all of these, and there are a lot more out there.) Some people use the phrase “theoretical-methodological framework” to acknowledge that good frameworks must tie theory, methods, and data together.

In this article, I’m not going to explore those subtleties.

Methodology

The role of your methodology is to tell you how to generate observations to answer your research question, how to convert those observations into data , and how to analyze that data. While theoretical frameworks are mostly concerned with why those observations and analyses are meaningful or interesting, methodologies are mostly concerned with the practicality of converting observations into analyses and the reasons for those analyses.

It is becoming a lot more common in discipline-based education research to be explicit about the methods that you choose and why. While it used to be sufficient in papers to outline what you did, now you also need to discuss why you did it and how it fits into a broader research tradition.

Many projects – especially large projects – coordinate multiple kinds of data and multiple kinds of analyses in order to make robust conclusions. This is (broadly) called “mixed-methods” or “multi-methods” design. There are lots of ways to mix methods well (and some ways to do it badly). If your research questions demand multi- or mixed-methods, you will need to write sub-research questions and choose theoretical frameworks for each method, and you will need to think about how the analyses from each method will interact to generate new knowledge. Before you jump into a mixed-methods design, ask yourself carefully if your research questions really warrant it, and if your access really allows it.

Sources for theoretical frameworks and methodologies:

There are books and papers written on this subject. Some of them are textbook-style for students; others are monograph style for researchers. To find them, you will have to step outside your particular discipline and look at the broader educational research literature, the learning sciences, or psychology (depending on your research questions).

• The Journal of the Learning Sciences has an excellent series on methodology and many beautiful papers on theory.
• Reviews in PER has a few papers with brief overviews of some kinds of methods and theories.
• Probably the most highly-cited book on methods is Creswell’s book on research design. It is not comprehensive, but it is extensive.
• There’s a quick overview of coding qualitative data (aimed at UX researchers) on Delve
• Shayan Doroudi wrote an excellent primer on learning theories.
• When you read papers , make note of their frameworks and methods (and their citations!).

You can also talk to other humans!

• Talk to your advisor or collaborators about what they would use (or require you to use).
• Write a one-page prospectus that outlines what you want to do and why you think it’s interesting or important, and send it to someone who does similar work. Ask them (nicely) for suggestions.

Develop the four areas in concert for a specific research project

In this exercise, you’re going to iteratively refine each of the four areas so that they are tightly integrated with each other.

On a whiteboard, write down a preliminary research question. If you don’t have a preliminary research question, start with one of the research question templates or do the exercise on making better research questions.

Write down what kind of access you have. Be specific about what populations, what kind of resources you have to undertake this research and how long it will take, and what kind of data modalities are available to you.

If you’re structurally constrained (by your funder, or your advisor, or your equipment) to use particular methods or theories, write them down as well.

Return to your research question, and update it so that it is constrained to the populations you have access to (as well as other structural constraints). It will get longer and more detailed. That’s great.

Which theories support your research question? Write them down. Amend your research question to explicitly reference at least one theoretical framework. If your question is about how individuals develop, you might look at the Resource Framework . If it’s about how communities form, try Communities of Practice. If you don’t know any theories, what have you read that makes you think this would be an interesting research question? You might need to use two or three frameworks in concert with each other to fully answer your research question.

What kind of data will you collect? Here’s a quick overview of the common kinds of data .

• Make sure that your access permits this kind of data, that your theoretical framework will be able to use the data from it, and that it will be able to answer your research question.
• Amend your research question and theoretical framework(s) to reflect the kind of observations you will collect. You might triangulate across several different data streams: preliminary surveys will identify participants for in depth interviews , and you ask them for their homework, for example.

How much data will you need to collect or analyze to show the effects you are looking for? Part of the answer to this question is about where you plan to publish your results at the end of your study: if you want to exhaustively prove your solution, you need a lot of evidence, but if you are only looking to prove its existence, you don’t need as much. Even a thoroughly theory-driven, theory-generating project needs something data-like (reinterpretation of old data, for example).

• If your project is based on finding patterns of human behavior, there are formalized methods for estimating effect sizes (generally known as a “power analysis” or “power estimate”). A quick-n-dirty estimate is that your error bars will go like 1/sqrt(N). If you can estimate differences in your treatments based on the literature, you can guess about how many subjects you will need. If your estimates suggest you will need many more subjects than you have access to, you need to revise your research question.
• If your project is based on finding cases of human behavior, you will need to think carefully about episode selection. How many episodes will you need to prove your point substantially? A good estimate is 3-5, most of which should be similar and one of which should be contrasting. More or fewer are possible.

Adjust your research question and methods in light of how much data you will be able to generate.

Write down a preliminary data collection and analysis plan.

• You may find that drawing a logic model or conjecture map is helpful. You may find that a narrative of what you’re planning to do and how is helpful.
• Compare your plan with your chosen theories and research question. Does your plan make use of your theories? Is it likely to answer your research question? Is it possible with the time and resources you have allotted?

Imagine that everything in data collection goes swimmingly and all of your data are fantastic. What does the answer to your research question look like? To what extent can you answer it with your methods and access? If course, you won’t know exactly what the answer will be – if you already knew, it wouldn’t be research – but you should be able to guess at an approximate shape to the answer.

• If you think you’ll need additional kinds of data to better triangulate an answer your question, amend your access and methods.
• If you think you’ll need a lot more data than you can get, amend your research question.

Another process which can help with intentional research design is conjecture mapping ; you might also consider the emergent processes outlined in “ Progressive Refinement… ”. If your research project is larger than you can complete in one semester, you are strongly encouraged to think about an iterative design using the principles in Planning Research Projects . Alternately, if your research project has a substantial curriculum development aspect, you should consider Design-Based Research (DBR). Lastly, you might consider whether your project is research at all: maybe you’re doing evaluation, not research .

Develop your skill in designing research projects

These exercises will develop your skill in designing research projects. If you do them a lot, then designing research studies will become a habit for you.

When you read papers , imagine using their theory and methods with a different population, or using their access with different theory and methods, or their research question with different access and methods. Make notes about your choices, so that later you can cite these papers in your own work. This exercise also makes you a better reader of papers.

Read through the abstracts of NSF’s recent awards for either IUSE or EDU:CORE . For every project, imagine that you have been given a supplement to do some research related to that project. What would be interesting? What would be possible, but not personally interesting? What would be exciting, but you don’t know very much about? You should be able to find something personally interesting or exciting in almost all of the projects. Design a study for each. This exercise also makes you a better citizen of the broader education research community, because you will know a lot more about the shape of current work in the community.

Read through the NSF’s upcoming deadlines for programs sponsored by Directorate for STEM Education (EDU), particularly the DUE and DRL divisions. For each one, sketch out a research study: what would you investigate? who might you partner with? This exercise also makes you a better researcher, because you will become more knowledgeable about how to frame your work to get funding.

Generative writing is the biggest tool in your researcher toolbox. Go back to your old notes about research designs, and enrich them with your new thoughts as you learn more.

Check out all articles in this Handbook tagged with “ doing research ”.

Read this delightful piece by the former editor of Sociology of Education.

Read this paper on quality in qualitative research design: Tracy, S.J., 2010. Qualitative quality: Eight “big-tent” criteria for excellent qualitative research. Qualitative inquiry, 16(10), pp.837-851.

Read this paper on elements of research project designs.

Read this overview on designing projects for the scholarship of teaching and learning.

Additional topics to consider

Planning research projects.

How to develop a timeline for an education research project that makes space for emergence.

Evaluation and Research

What is the difference between evaluation and research?

Data and Access

What are the common kinds of data in education research, and what are their affordances and constraints?

This article was first written on January 1, 2015, and last modified on May 30, 2024.

Educational resources and simple solutions for your research journey

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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• How it works

How to Write a Research Design – Guide with Examples

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

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

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

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

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

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

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

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

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

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Step 3: Data Collection Techniques

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

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

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

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

Research Methods

Step 4: Procedure of Data Analysis

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

Quantitative Data Analysis

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

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

Qualitative Data Analysis

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

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

Step 5: Write your Research Proposal

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

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

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

How to Write a Research Design – Conclusion

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

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

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

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

Frequently Asked Questions

What is research design.

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

How to write a research design?

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

How to write the design section of a research paper?

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

How to write a research design in methodology?

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

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Struggling to find relevant and up-to-date topics for your dissertation? Here is all you need to know if unsure about how to choose dissertation topic.

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• How It Works

Project Planning for the Beginner: Research Design

• Defining a Topic
• Reviewing the Literature
• Developing a Researchable Question

Research Design

• Planning, Data, Writing and Dissemination

What Is a Research Plan?

This refers to the overall plan for your research, and will be used by you and your supervisor to indicate your intentions for your research and the method(s) you’ll use to carry it out. It includes:

• A specification of your research questions

• An outline of your proposed research methods

• A timetable for doing the work

What Is Research Design?

The term “ research design “ is usually used in reference to experimental research, and refers to the design of your experiment. However, you will also see the term “research design” used in other types of research. Below is a list of possible research designs you might encounter or adopt for your research:

• Descriptive or exploratory (e.g., case study , naturalistic observation )

• Correlational (e.g., case-control study, observational study )

• Quasi-experimental (e.g., field experiment , quasi-experiment )

• Experimental (experiment with random allocation and a control and test group )

• Review (e.g. literature review , systematic review )

• Meta-analytic (e.g. meta-analysis )

Research Design Choices

How do i match my research method to my research question.

The method(s) you use must be capable of answering the research questions you have set. Here are some things you may have to consider:

• Often questions can be answered in different ways using different methods

• You may be working with multiple methods

• Methods can answer different sorts of questions

• Questions can be answered in different ways.

The matching of method(s) to questions always matters . Some methods work better for particular sorts of questions.

If your question is a hypothesis which must be falsifiable, you can answer it using the following possible methods:

• An experimental method using statistical methods to test your hypothesis.

• Survey data (either generated by you or secondary data) using statistical methods to test your hypothesis.

If your question requires you to describe a social context and/or process, then you can answer it using the following possible methods:

• You can use data from your own surveys and/or secondary data to carry out descriptive statistics and numerical taxonomy methods for classification .

• You can use qualitative material derived from:

• Documentary research

• Qualitative interviews

• Focus groups

• Visual research

• Ethnographic methods

• Any combination of the above may be deployed.

If your question(s) require you to make causal statements about how certain things have come to be as they are, then you might consider using the following:

• You can build quantitative causal models using techniques which derive from statistical regression analysis and seeing if the models “fit” your quantitative data set.

• You can do this through building simulations .

• You can do this by using figurational methods, particularly qualitative comparative analysis , which start either with the construction of quantitative descriptions of cases from qualitative accounts of those cases, or with an existing data set which contains quantitative descriptions of cases. 

• You can combine both approaches.

If your question(s) require you to produce interpretive accounts of human social actions with a focus on the meanings actors have attached to those actions, then you might consider using the following:

• You can use documentary resources which include accounts of action(s) and the meanings actors have attached to those actions. This is a key approach in historical research.

• You can conduct qualitative interviews .

• You can hold focus groups .

• You can do this using ethnographic observation .

• You can combine any or all of above approaches.

If your question(s) are evaluative, this could mean that you have to find out if some intervention has worked, how it has worked if it has, and why it didn’t work if it didn’t. You might then consider using the following:

• Any combination of quantitative and qualitative methods which fit the data you have.

• You should always use process tracing to generate a careful historical account of the intervention and its context(s). 

Checklist: Question to Ask When Deciding On a Method

Here are seven questions you should be able to answer about the methods you have chosen for your research. 

• Does your method/do your methods fit the research question(s)?
• Do you understand how the methods relate to your methodological position?
• Do you know how to use the method(s)  ?  If not, can you learn how to use the method(s)?
• Do you have the resources you need to use the methods? For example:

• statistical software

• qualitative data analysis software

• an adequate computer

• access to secondary data sets

• audio-visual equipment

• language training

• transport You need to work through this list and add anything else that you need.

• If you are using multiple methods, do you know how you are going to combine them to carry out the research?
• If you are using multiple methods, do you know how you are going to combine the  products of using them when writing up your research? 
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• Last Updated: May 11, 2022 2:56 PM
• URL: https://libguides.sph.uth.tmc.edu/c.php?g=949457

How to Do Research: A Practical Guide to Designing and Managing Research Projects (3rd revised edition)

Records Management Journal

ISSN : 0956-5698

Article publication date: 19 June 2007

• Project management

Williams, C. (2007), "How to Do Research: A Practical Guide to Designing and Managing Research Projects (3rd revised edition)", Records Management Journal , Vol. 17 No. 2. https://doi.org/10.1108/rmj.2007.28117bae.008

Emerald Group Publishing Limited

Copyright © 2007, Emerald Group Publishing Limited

Nick MooreFacet Publishing2006176 pp.ISBN 978-1-85604-594-0 Keywords: Research, Project management, Projects Review DOI: 10.1108/09565690710757986

This book should be read by anyone involved in academic research, students, early career researchers and lecturers alike. Students and researchers will be introduced in a structured way to the research process while lecturers will gain a useful teaching resource. Its success is evident: first published in 1983 this is the third revised edition. It does exactly what it says on the packet – it “focuses on the day-to-day requirements of project managing a piece of research right through from the formulation of the initial idea, to the development of a research proposal and then to the writing up and dissemination of results”. In order to achieve this the book is divided into two sections: the research process and methods.

The section on the research process leads the new researcher through the business of defining a research question – the issue, aims and objectives of the research – adopting an appropriate methodology, managing the research itself and writing it up and disseminating results. It also includes guidance on writing research proposals and on obtaining funding to support research.

The second section provides more detailed guidance on the technicalities of research methods, offering a brief description of available research techniques, and taking the reader through desk research, the collection and analysis of quantitative and qualitative data generated mainly through questionnaires and interviews and offering guidance on sampling and statistical analysis. Four pages of “further reading” are supplied. Finally a useful case study is provided. This is an example of an actual proposal submitted by the author when a member of the Policy Studies Institute, the aim of which was to identify the range of skills that would be required by future information professionals.

This book is most useful to those engaged in social science research: indeed it is clearly directed toward those researching “people and institutions and the relationships between them”. It identifies a range of funding bodies, with the ESRC (Economic & Social Research Council) as the most appropriate research council (the Arts & Humanities Research Council is not mentioned). While it recognises the needs of those undertaking research as part of undergraduate, master’s and doctoral degrees – and indeed much of the methodology outlined in section 2 will be extremely valuable to them – it is perhaps those aiming to secure funding for applied research that are the main audience. As such it concentrates on the requirements of applied and policy, rather than theoretical research arguing that while “theory” is useful for explaining things, and more likely to be undertaken as part of a qualification, the need to provide practical solutions “means that strategic research tends to place more reliance on empirical data and evidence than it does on theories and concepts”.

Given that the focus is on social science research it follows that the description of methodologies concentrates on the collection of data about people and their behaviour. Since this is best extracted via questionnaires and interviews, the main sources of raw data for social research, discussion of these methods predominate. Examples within these are centred on the selection and quantitative and qualitative analysis of data generated through samples of human populations. There is less direct guidance for those engaged on research into other humanities-related issues using inanimate sources. Records managers or archivists might want to investigate standards, systems, processes, records, finding aids or historical archives for example. While the overall content of the book will be tremendously helpful to them they should not expect to find examples or recommendations relating to research in these kinds of areas or resources.

This volume maintains discussion at the basic introductory level throughout. This serves to present the whole business of research as less daunting than it might otherwise appear, and this is ideal both for the novice researcher and as a check for the more experienced. It would have been good to have had more specific pointers to further sources, so that once confidence has been gained or when exploration in more depth (sampling, for example) is required there were more ready access to these. While the book list in Chapter 16 is helpful, a fuller bibliography (even the occasional footnote) would have been helpful.

This book is highly recommended. It is – like the research methods it advocates – well structured, with clear aims and objectives that are undoubtedly achieved, well written and accessible to its readers.

Caroline Williams University of Liverpool, Liverpool, UK

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Organizing Your Social Sciences Research Paper: Types of Research Designs

• Purpose of Guide
• Writing a Research Proposal
• Design Flaws to Avoid
• Independent and Dependent Variables
• Narrowing a Topic Idea
• Broadening a Topic Idea
• The Research Problem/Question
• Academic Writing Style
• Choosing a Title
• Making an Outline
• Paragraph Development
• The C.A.R.S. Model
• Background Information
• Theoretical Framework
• Citation Tracking
• Evaluating Sources
• Reading Research Effectively
• Primary Sources
• Secondary Sources
• What Is Scholarly vs. Popular?
• Is it Peer-Reviewed?
• Qualitative Methods
• Quantitative Methods
• Common Grammar Mistakes
• Writing Concisely
• Avoiding Plagiarism [linked guide]
• Annotated Bibliography
• Grading Someone Else's Paper

Introduction

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

The research design refers to the overall strategy that you choose to integrate the different components of the study in a coherent and logical way, thereby, ensuring you will effectively address the research problem; it constitutes the blueprint for the collection, measurement, and analysis of data. Note that your research problem determines the type of design you should use, 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 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 far too early, 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 research designs in your paper can vary considerably, but any well-developed design 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 data which will be necessary for an adequate testing of the hypotheses and explain how such 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 and varies in length depending on the type of design you are using. However, you can get a sense of what to do by reviewing the literature of studies that have utilized the same research design. This can provide 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.

Videos in Business and Management , Criminology and Criminal Justice , Education , and Media, Communication and Cultural Studies specifically created for use in higher education.

A literature review tool that highlights the most influential works in Business & Management, Education, Politics & International Relations, Psychology and Sociology. Does not contain full text of the cited works. Dates vary.

Encyclopedias, handbooks, ebooks, and videos published by Sage and CQ Press. 2000 to present

Causal Design

Definition and Purpose

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.

What do these studies tell you ?

• 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.

What these studies don't tell you ?

• Not all relationships are casual! 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, r ather 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. Explorable.com website.

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.

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.

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.

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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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4 types of research methods all designers should know

Emily esposito,   •   oct 22, 2018.

R emember that fifth grade science project where you learned about primary research for the first time? Like most things we learned in elementary school, you probably didn’t expect it to creep back into your day-to-day adult life. However, in reality, designers have to conduct research and analyze data all the time.

Design research is a critical step in creating the best user experience. It helps you understand your customers’ behavior and turn it into actionable insights to improve your design.

Top Stories

Primary research.

Perhaps the most important method in design research, this involves you or your team going directly to the source (your customers) to ask questions and gather data. Most often, the goal is to better understand who you are designing for or to validate your ideas with the actual end user.

Some examples of primary research include:

One-on-one interviews are a great place to start when collecting primary research. There are three main types of interviews: directed, non-directed, and ethnographic. Direct interviews are the most common and follow the standard question and answer format. Non-direct interviews are used when participants may not feel comfortable with direct questions. Instead, this interview is set up as a conversation (with some rough guidelines). Ethnographic interviews involve observing people in their day-to-day environment (very similar to the contextual inquiry method covered below).

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User groups.

Also known as focus groups, these are structured interviews involving three to six participants. A moderator guides the discussion, providing verbal and written feedback through the exercises. This research method is best when you need to get a lot of user insight in a short period of time.

Contextual inquiry

You first ask users a set of standard questions, then observe them in their natural environment as they complete their everyday tasks. It’s not just an interview or an observation—you want to watch people perform tasks as they explain what they are doing and why. This type of research is especially important in the beginning of the design process to learn what is important to users and how they interact with similar tools or services.

Asking users to document their own experience will help you see your product through their eyes.

“Design research helps you understand your customers’ behavior and turn it into actionable insights to improve your design.”

Diary study

Occurring over an extended period of time (from a week to a month, or even longer), participants are asked to keep a diary and log specific information about their activities. In-situ logging is the simplest way to collect data from diaries—users report all details about the activities as they complete them.

Usability testing

Once you’re deeper into the design process and have a prototype to share, usability testing helps you put that design into the wild to gather feedback. Here, you would ask potential or current users to complete a set of tasks using your prototype.

Secondary research

Secondary research is when you use existing data like books, articles, or the internet to validate or support existing research. You may use secondary research to create a stronger case for your design choices and provide additional insight into what you learned during primary research.

Work with existing content, like presentations or articles, to present a strong case for your design choices.

This type of research method is quick and cheap—all you need is internet access or a library card to start. However, some common challenges with secondary research include not being able to find the specific information you need, or battling outdated, low-quality data.Here are some places where you could gather secondary research:

• Internal data, like your company database, sales reports, or historical information
• Government statistics or information from government agencies
• University research centers
• Respected magazines and newspapers

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Generative or exploratory research.

Generative research, also known as exploratory research, focuses on a deeper understanding of user needs and desires. It is usually conducted at the beginning of the design project when you need to answer basic questions like, “What problem are we solving for our customers?” This discovery phase helps you to identify a design hypothesis and validate it with your customers. You won’t always know what the outcome or answers will be, but they will create a strong foundation to make good design decisions going forward.

You’ll see a lot of overlap between generative research and primary research since the whole point of generative research is to get out and talk to your users. Examples of generative research include interviews, user groups, surveys, and contextual inquiries.

Before you start your research, make sure you know what you intend to learn from the results.

Evaluative research.

After gathering your generative research, you’re prepared to design a solution for your customers. Evaluative research allows you to test that solution, giving users the opportunity to “evaluate” your prototype. Your goal is to collect feedback to help refine and improve the design experience. One of the most popular ways to conduct evaluative research is to have people use your product or service as they think out loud (again, a subset of primary research). A perfect example of this research method is usability studies.And, for whichever type of evaluative research you choose, there are two types: summative and formative. Summative emphasizes the outcome more than the process (looking at whether the desired effect is achieved) and formative is used to strengthen idea being tested (monitoring the success of a process).

Keep asking questions

How do you decide which research method to use? It depends on what you’re trying to learn. You may start with primary research and find that more questions arise after getting to know your customers better (and that’s a good thing!). These new questions will help you decide what you need to learn next. When in doubt, always follow the questions.

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by Emily Esposito

Emily has written for some of the top tech companies, covering everything from creative copywriting to UX design. When she's not writing, she's traveling the world (next stop: Japan!), brewing kombucha, and biking through the Pacific Northwest.

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Design Research Methods

A Repository of Research Methods for Design

This page represents a growing list of application toolkits and other great resources for conducting design research, organized into General , Specific , and Thematic Tools.

General Tools

General tools provide guidance on an over all approach to user research, or an organized framework of many methods to inform your choice.

Austin Center for Design AC4D Design Library By AC4D. “Practical resources to support the process of design.”

Beginner’s Guide To Design Research By UX Booth. “In this Complete Beginner’s Guide, we’ll look at the many elements of design research, from interviews and observations, to usability testing and A/B testing. Readers will get a head start on how to use these design research techniques in their work, and improve experiences for all users.”

theDesignExchange A joint project led by UC Berkeley and M.I.T. working with the international community of design academics and practitioners. “TheDesignExchange provides a central repository of early design stage methods, engaging all stakeholders in the design community of practice, and integrating online learning with real case studies to demonstrate the methods.”

Design Kit IDEO.org. “Think of these Methods as a step-by-step guide to unleashing your creativity, putting the people you serve at the center of your design process to come up with new answers to difficult problems.”

Design Research Techniques “A simple visual guide to a range of techniques which you may want to further research, when they may be used and a little bit about them. They also have some great case studies with specific techniques for Discovery available here.”

Designing With People By the i-design project. “20 research methods that help designers engage with people during the design process. Some methods are widely used; others represent emerging practice. To help you find the right methods for your project, each method is explored and assessed here from a number of different angles.”

d.school Mixtapes Three “mixtapes” of methods to jumpstart your work: Understand – Experiment – Ideate.

Gamestorming “A toolkit of co-creation tools for innovators, rule-breakers and changemakers.”

IBM Design Research Resources Toolkit “New methods and models created by IBMers, for IBMers.”

IDEO Method Cards “ IDEO Method Cards  are a tool to showcase methods we use to inspire great design and keep people at the center of our design process. Each of the 51 cards describes one method and includes a brief story about how and when to use it.” (Also available as a smartphone app)

LifeHack “Top 10 Design Research Resources”

MakeTools Method Cards By Liz Sanders, 29 Method Cards for Generative Design

Research Toolbox Chart  |   Booklet By Daedalus + Thoughtform. “Twenty-three research methods to discover what your users really want.” Presented in a convenient chart with short definitions of methods, or a compelling illustrated booklet.

Usability.gov “Usability.gov is the leading resource for user experience (UX) best practices and guidelines, serving practitioners and students in the government and private sectors.”

Usability Body of Knowledge – Methods “This section of the Usability BoK presents descriptions of methods, including procedures, resources needed, outcomes, appropriate uses, benefits, and costs. These descriptions form the core of a knowledge base that defines our field. They also help communicate usability methods to clients, project managers, and team members. Usability practitioners will also benefit from cross-referencing of related methods and pointers to outside resources for more details.”

UX Research & Strategy By DesignLab.”Want to make products people love? Start with a deep understanding of your customers. Learn the who, what, why, when and where of customer research to help you create amazing user experiences.”

UX Research Cheat Sheet Nielsen Norman Group. “User research can be done at any point in the design cycle. This list of methods and activities can help you decide which to use when.”

Specific Methods Tools

Specific tools correspond to methods for particular applications, for example, diary studies, cultural probes, card sorting or user testing.

Dscout For mobile diary studies. “dscout’s remote research platform uses a mobile app and +100K eager participants to efficiently capture in-the-moment video and make insights easy to synthesize and share.”

EthOS Ethnographic Observation System “Today we are the go-to platform for anyone wishing to carry out remote qualitative, quantitative and ethnographic research projects anywhere in the world.”

Lego Serious Play Although marketed toward business performance, Lego can be successfully used as a participatory co-design make-tool for inspired creativity.

Optimal Workshop A user research platform with a suite of online usability tools, including “Optimal Sort” card-sorting software.

Probetools Interaction Research Studio. “Our goal is to build on contemporary making and hacking trends to update Probes and make them widely accessible to researchers of any background.”

PremoTool By SusaGroup. “A unique, scientifically validated tool to instantly get insight in consumer emotions! People can report their emotions with the use of expressive cartoon animations instead of relying on the use of words.”

UsabilityHub A set of five online usability tools, geared toward fast testing. “Remote user testing to help you make confident design decisions”

MakeTools: Papers on Participatory Design & Generative Research

By Liz Sanders et al.

Thematic Tools

Thematic tools are designed for research in broad topic areas, such as emotion, behavior change, healthcare, or service design.

Design and Emotion “Since 2005, the Design & Emotion Society has been collecting tools and methods that support the application of design for emotion.”

Design with Intent By Dan Lockton, PhD. “Aims to give practitioners a more nuanced approach to design and behaviour, working with people, people’s understanding, and the complexities of everyday human experience. It’s a collection of design patterns—and a design and research approach—for exploring the interactions between design and people’s behaviour, across products, services and environments, both digital and physical.”

MethodKit for Public Health “A healthy population is the backbone of a sustainable society. We have created a tool that can help you organize, plan and shape the future landscape of patient care, health and wellbeing. This kit is aimed at both professionals and enthusiasts who want to understand more and create new systems for the future of public health.”

Service Design Tools “An open collection of communication tools used in design processes that deal with complex systems.”

How to Write a Research Proposal: (with Examples & Templates)

Table of Contents

Before conducting a study, a research proposal should be created that outlines researchers’ plans and methodology and is submitted to the concerned evaluating organization or person. Creating a research proposal is an important step to ensure that researchers are on track and are moving forward as intended. A research proposal can be defined as a detailed plan or blueprint for the proposed research that you intend to undertake. It provides readers with a snapshot of your project by describing what you will investigate, why it is needed, and how you will conduct the research.  

Your research proposal should aim to explain to the readers why your research is relevant and original, that you understand the context and current scenario in the field, have the appropriate resources to conduct the research, and that the research is feasible given the usual constraints.  

This article will describe in detail the purpose and typical structure of a research proposal , along with examples and templates to help you ace this step in your research journey.  

What is a Research Proposal ?  

A research proposal¹ ,²  can be defined as a formal report that describes your proposed research, its objectives, methodology, implications, and other important details. Research proposals are the framework of your research and are used to obtain approvals or grants to conduct the study from various committees or organizations. Consequently, research proposals should convince readers of your study’s credibility, accuracy, achievability, practicality, and reproducibility.   

With research proposals , researchers usually aim to persuade the readers, funding agencies, educational institutions, and supervisors to approve the proposal. To achieve this, the report should be well structured with the objectives written in clear, understandable language devoid of jargon. A well-organized research proposal conveys to the readers or evaluators that the writer has thought out the research plan meticulously and has the resources to ensure timely completion.  

Purpose of Research Proposals  

A research proposal is a sales pitch and therefore should be detailed enough to convince your readers, who could be supervisors, ethics committees, universities, etc., that what you’re proposing has merit and is feasible . Research proposals can help students discuss their dissertation with their faculty or fulfill course requirements and also help researchers obtain funding. A well-structured proposal instills confidence among readers about your ability to conduct and complete the study as proposed.  

Research proposals can be written for several reasons:³  

• To describe the importance of research in the specific topic  
• Address any potential challenges you may encounter  
• Showcase knowledge in the field and your ability to conduct a study  
• Apply for a role at a research institute  
• Convince a research supervisor or university that your research can satisfy the requirements of a degree program  
• Highlight the importance of your research to organizations that may sponsor your project  
• Identify implications of your project and how it can benefit the audience  

What Goes in a Research Proposal?    

Research proposals should aim to answer the three basic questions—what, why, and how.  

The What question should be answered by describing the specific subject being researched. It should typically include the objectives, the cohort details, and the location or setting.  

The Why question should be answered by describing the existing scenario of the subject, listing unanswered questions, identifying gaps in the existing research, and describing how your study can address these gaps, along with the implications and significance.  

The How question should be answered by describing the proposed research methodology, data analysis tools expected to be used, and other details to describe your proposed methodology.   

Research Proposal Example  

Here is a research proposal sample template (with examples) from the University of Rochester Medical Center. 4 The sections in all research proposals are essentially the same although different terminology and other specific sections may be used depending on the subject.  

Structure of a Research Proposal  

If you want to know how to make a research proposal impactful, include the following components:¹  

1. Introduction  

This section provides a background of the study, including the research topic, what is already known about it and the gaps, and the significance of the proposed research.  

2. Literature review  

This section contains descriptions of all the previous relevant studies pertaining to the research topic. Every study cited should be described in a few sentences, starting with the general studies to the more specific ones. This section builds on the understanding gained by readers in the Introduction section and supports it by citing relevant prior literature, indicating to readers that you have thoroughly researched your subject.  

3. Objectives  

Once the background and gaps in the research topic have been established, authors must now state the aims of the research clearly. Hypotheses should be mentioned here. This section further helps readers understand what your study’s specific goals are.  

4. Research design and methodology  

Here, authors should clearly describe the methods they intend to use to achieve their proposed objectives. Important components of this section include the population and sample size, data collection and analysis methods and duration, statistical analysis software, measures to avoid bias (randomization, blinding), etc.  

5. Ethical considerations  

This refers to the protection of participants’ rights, such as the right to privacy, right to confidentiality, etc. Researchers need to obtain informed consent and institutional review approval by the required authorities and mention this clearly for transparency.  

6. Budget/funding  

Researchers should prepare their budget and include all expected expenditures. An additional allowance for contingencies such as delays should also be factored in.  

7. Appendices  

This section typically includes information that supports the research proposal and may include informed consent forms, questionnaires, participant information, measurement tools, etc.  

8. Citations  

Important Tips for Writing a Research Proposal  

Writing a research proposal begins much before the actual task of writing. Planning the research proposal structure and content is an important stage, which if done efficiently, can help you seamlessly transition into the writing stage. 3,5  

The Planning Stage  

• Manage your time efficiently. Plan to have the draft version ready at least two weeks before your deadline and the final version at least two to three days before the deadline.
• What is the primary objective of your research?  
• Will your research address any existing gap?  
• What is the impact of your proposed research?  
• Do people outside your field find your research applicable in other areas?  
• If your research is unsuccessful, would there still be other useful research outcomes?  

  The Writing Stage  

• Create an outline with main section headings that are typically used.  
• Focus only on writing and getting your points across without worrying about the format of the research proposal , grammar, punctuation, etc. These can be fixed during the subsequent passes. Add details to each section heading you created in the beginning.   
• Ensure your sentences are concise and use plain language. A research proposal usually contains about 2,000 to 4,000 words or four to seven pages.  
• Don’t use too many technical terms and abbreviations assuming that the readers would know them. Define the abbreviations and technical terms.  
• Ensure that the entire content is readable. Avoid using long paragraphs because they affect the continuity in reading. Break them into shorter paragraphs and introduce some white space for readability.  
• Focus on only the major research issues and cite sources accordingly. Don’t include generic information or their sources in the literature review.  
• Proofread your final document to ensure there are no grammatical errors so readers can enjoy a seamless, uninterrupted read.  
• Use academic, scholarly language because it brings formality into a document.  
• Ensure that your title is created using the keywords in the document and is neither too long and specific nor too short and general.  
• Cite all sources appropriately to avoid plagiarism.  
• Make sure that you follow guidelines, if provided. This includes rules as simple as using a specific font or a hyphen or en dash between numerical ranges.  
• Ensure that you’ve answered all questions requested by the evaluating authority.  

Key Takeaways   

Here’s a summary of the main points about research proposals discussed in the previous sections:  

• A research proposal is a document that outlines the details of a proposed study and is created by researchers to submit to evaluators who could be research institutions, universities, faculty, etc.  
• Research proposals are usually about 2,000-4,000 words long, but this depends on the evaluating authority’s guidelines.  
• A good research proposal ensures that you’ve done your background research and assessed the feasibility of the research.  
• Research proposals have the following main sections—introduction, literature review, objectives, methodology, ethical considerations, and budget.  

Frequently Asked Questions  

Q1. How is a research proposal evaluated?  

A1. In general, most evaluators, including universities, broadly use the following criteria to evaluate research proposals . 6  

• Significance —Does the research address any important subject or issue, which may or may not be specific to the evaluator or university?  
• Content and design —Is the proposed methodology appropriate to answer the research question? Are the objectives clear and well aligned with the proposed methodology?  
• Sample size and selection —Is the target population or cohort size clearly mentioned? Is the sampling process used to select participants randomized, appropriate, and free of bias?  
• Timing —Are the proposed data collection dates mentioned clearly? Is the project feasible given the specified resources and timeline?  
• Data management and dissemination —Who will have access to the data? What is the plan for data analysis?  

Q2. What is the difference between the Introduction and Literature Review sections in a research proposal ?  

A2. The Introduction or Background section in a research proposal sets the context of the study by describing the current scenario of the subject and identifying the gaps and need for the research. A Literature Review, on the other hand, provides references to all prior relevant literature to help corroborate the gaps identified and the research need.  

Q3. How long should a research proposal be?  

A3. Research proposal lengths vary with the evaluating authority like universities or committees and also the subject. Here’s a table that lists the typical research proposal lengths for a few universities.  

Q4. What are the common mistakes to avoid in a research proposal ?  

A4. Here are a few common mistakes that you must avoid while writing a research proposal . 7  

• No clear objectives: Objectives should be clear, specific, and measurable for the easy understanding among readers.  
• Incomplete or unconvincing background research: Background research usually includes a review of the current scenario of the particular industry and also a review of the previous literature on the subject. This helps readers understand your reasons for undertaking this research because you identified gaps in the existing research.  
• Overlooking project feasibility: The project scope and estimates should be realistic considering the resources and time available.   
• Neglecting the impact and significance of the study: In a research proposal , readers and evaluators look for the implications or significance of your research and how it contributes to the existing research. This information should always be included.  
• Unstructured format of a research proposal : A well-structured document gives confidence to evaluators that you have read the guidelines carefully and are well organized in your approach, consequently affirming that you will be able to undertake the research as mentioned in your proposal.  
• Ineffective writing style: The language used should be formal and grammatically correct. If required, editors could be consulted, including AI-based tools such as Paperpal , to refine the research proposal structure and language.  

Thus, a research proposal is an essential document that can help you promote your research and secure funds and grants for conducting your research. Consequently, it should be well written in clear language and include all essential details to convince the evaluators of your ability to conduct the research as proposed.  

This article has described all the important components of a research proposal and has also provided tips to improve your writing style. We hope all these tips will help you write a well-structured research proposal to ensure receipt of grants or any other purpose.  

References  

• Sudheesh K, Duggappa DR, Nethra SS. How to write a research proposal? Indian J Anaesth. 2016;60(9):631-634. Accessed July 15, 2024. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5037942/  
• Writing research proposals. Harvard College Office of Undergraduate Research and Fellowships. Harvard University. Accessed July 14, 2024. https://uraf.harvard.edu/apply-opportunities/app-components/essays/research-proposals  
• What is a research proposal? Plus how to write one. Indeed website. Accessed July 17, 2024. https://www.indeed.com/career-advice/career-development/research-proposal  
• Research proposal template. University of Rochester Medical Center. Accessed July 16, 2024. https://www.urmc.rochester.edu/MediaLibraries/URMCMedia/pediatrics/research/documents/Research-proposal-Template.pdf  
• Tips for successful proposal writing. Johns Hopkins University. Accessed July 17, 2024. https://research.jhu.edu/wp-content/uploads/2018/09/Tips-for-Successful-Proposal-Writing.pdf  
• Formal review of research proposals. Cornell University. Accessed July 18, 2024. https://irp.dpb.cornell.edu/surveys/survey-assessment-review-group/research-proposals  
• 7 Mistakes you must avoid in your research proposal. Aveksana (via LinkedIn). Accessed July 17, 2024. https://www.linkedin.com/pulse/7-mistakes-you-must-avoid-your-research-proposal-aveksana-cmtwf/  

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Get accurate academic translations, rewriting support, grammar checks, vocabulary suggestions, and generative AI assistance that delivers human precision at machine speed. Try for free or upgrade to Paperpal Prime starting at US$19 a month to access premium features, including consistency, plagiarism, and 30+ submission readiness checks to help you succeed.  

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• How to Avoid Plagiarism When Using Generative AI Tools
• What is Hedging in Academic Writing?  

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Formulating a Research Topic

by Evan Kramer

Motivation and scope

As master’s and PhD students, we all aspire to conduct quality research. The question many of us are faced with is: how do we formulate a research topic that is well poised for performing quality research? Research topics are meant to encompass the majority or entirety of our work during our graduate career and, when well-defined, can result in opportunities to publish several high-impact academic papers. The effort required to formulate a well-defined research topic is significant, but necessary to avoid running into unforeseen challenges during your PhD. This blog post discusses the concepts that should be considered for anyone looking to define their research topic. While students have varying degrees of autonomy in shaping their research due to funding constraints and advisor expectations, the concepts discussed in this blog post account for these facets and can serve as a framework for any situation.

Overview diagram of a framework for formulating a well-defined research topic.

What is quality research?

Quality research is independent , important , and unique .

This definition identifies a set of requirements that a research topic must meet. These requirements will be discussed in more detail to orient the research topic formulation process.

Independent – Independent research can be conducted entirely by you without assistance from outside sources. While you should actively seek collaborations with others to boost the reach of your work, will you be able to complete your research objectives without relying on resources provided by others? Framing your research topic and objectives in this manner gives you protection to flakey collaborators and will keep you on track to graduate on time. For example, something you may want to avoid is crafting a research topic around the usage of one particular data set maintained by a private company. While initial collaboration talks may go smoothly, you don’t want your ability to pursue your research project in the hands of someone else!

Important – Important research makes a contribution towards answering a specific question, or a gap in knowledge, among a research community that has been posed by several scholars. You may ask yourself: if you carry out your research to completion, will your contributions answer outstanding questions posed by multiple scholars in your research community? Note that the question your work addresses may not be explicitly posed in the literature, but identifying common limitations can help formulate a gap in knowledge that you can work towards filling. Aligning your research objectives with specific and commonly posed questions can increase the chance of your work being cited by other scholars and integrated into practices in industry. 

Unique – Unique research makes a first-of-its-kind contribution. There are several ways in which your research can be unique. For example, uniqueness may be assumed if you contribute the first work to a completely unanswered question in your field. Alternatively, you may make a unique contribution to a question that has already been addressed by approaching it in a new way. Knowledge of your chosen field’s state of the art and previous foundations is useful when checking the uniqueness of your work, which can only be verified by thorough literature review. Regardless of the way your research is unique, it is important to identify the uniqueness of your work within the context of existing work in related areas.

With these three research topic characteristics in mind, the following presents a high level path to formulating your well-defined research topic.

A framework for formulating a well-defined research topic

1. look inwards.

Based on previous experiences in coursework, internships, and extracurricular activities, create a two-column list. The first column lists research fields you found interesting. The second column lists ideas that align with your personal motivations for pursuing a career in STEM research. An example of this list may look like the following:

2. Read Widely

Given the two-column lists you created, start familiarizing yourself with the current state of the art. Starting with articles in popular science media outlets can be effective for initial cursory surveys. Any articles that pique your interest should be followed by deeper dives into related literature in Google Scholar. It is likely that several of the topics in the left column of your list get crossed off quickly when you realize they no longer interest you. Continue this process until a subset of around three areas remains. Your two-column list may then look like this:

Note that the right hand column remains unchanged. You very likely will not be able to address all of your personal motivations for pursuing STEM research in your eventual research topic, but now is when you can start connecting topics you find interesting to research applications that personally motivate you. 

3. Consider funding and lab focus areas

While the research topic definition process should be approached predominantly with your own interests in mind, at this stage, it is important to consider where your funding is coming from. Typically, there will be specific fields your research must overlap with based on your funding source. Schedule a discussion with your advisor to share your topic definition process so far and ask if there are topics you should add to your list based on research group and funding requirements. Based on this discussion, add a third column to the list you’ve created that describes the necessary areas of overlap for your research.

4. Continue reading and form a research statement

At this point you are trying to iterate on combinations identified in your three-column list. You can begin to formulate an overarching research statement from these combinations. Research statements generally have the form “To…by…while…”. This sentence structure explicitly identifies what you are trying to accomplish, how you will accomplish it, and which constraints you will account for. A possible research statement could be defined with one entry from each column, or you may be able to create a topic with multiple entries from each column. In this blog’s example list, a research statement could be the following:

To enable efficient natural disaster response for remote communities by developing an AI-powered rapid response scheduling algorithm for a remote sensing satellite while accounting for limitations to satellite operations imposed by the space environment .

5. Iterate and keep track of your work

You may create a few iterations of overarching research statements like this. As you continue to read focused areas in the literature, formulate a focus area Venn Diagram. By allocating articles in your literature search to portions of the diagram, you can stay organized and keep track of the work you’re doing. For the example statement above, your Venn Diagram could look like this:

Venn diagram of research topic focus areas. The most relevant literature review items can be added to each region of the diagram to track and organize your efforts.

At this point, you are well on your way to formalizing your research topic. The formalization step involves writing research questions, drafting objective statements, and identifying your research contributions. AeroAstro Communications Lab fellows can help you with these next steps through one-on-one appointments !

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The Evaluation Exchange

We are a partnership between UCL and the voluntary and community sector that aims to improve evaluation practice.

9 November 2023

What is the Evaluation Exchange? 

The Evaluation Exchange brings together voluntary and community sector groups wanting to improve their capacity to evaluate their work, with postgraduate students and researchers who want to put their research and evaluation skills into practice in a real-life setting.

We work in different ways with different people depending on their needs. At the heart of everything we do is supporting organisations, students and researchers to work together, to break down barriers to accessing different evidence and build evaluation and research skills that work in the real world.    

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To find out why we run the programme, watch our animation . To learn about how organisations, students and researchers have recently benefited, watch our film . 

News from the Evaluation Exchange

People-centred evaluation: Putting it into practice

As UCL explores potential partnership opportunities with international development organisation VSO, the Evaluation Exchange invites Dr Alok Rath, VSO’s Global Head of Knowledge, Evidence and Learning to share his experience of putting people-centric and action-oriented evaluation approaches in to practice in Africa and Asia.

An opportunity for you and those you work with

The Evaluation Exchange is an opportunity for you to enhance your research and evaluation skills outside of a university setting - a real world application of your knowledge that has the potential to make a difference to the workings of voluntary and community sector organisations. You will help embed the use of successful evaluation approaches leading to a lasting change in practice and making a valued contribution to an organisation. 

You will also be meeting and connecting with individuals outside of the university, establishing a new network. You will enhance your research and evaluation skills, tap into the workings of community and voluntary sector organisations, and have the opportunity to enhance your experience outside of the typical university setting.  

Read about the key impacts and lessons learnt from the  Evaluation Exchange 2017/18 ,  Evaluation Exchange 2021/22  and watch our film .

The Evaluation Exchange aims to help voluntary and community organisations, in Camden and Newham, build evaluation and learning into their work. By matching you to a team of UCL researchers, you will work collaboratively with them to tackle your evaluation challenge. Working together you can develop appropriate learning systems that help you understand what is going well and not so well and demonstrate the value of your organisation’s services. The team of researchers matched to your organisation will have a variety of backgrounds and interests and will be keen to learn from you too. 

Watch our film featuring organsations who have already been involved.

We can help you think through whether your evaluation challenge is appropriate for the Evaluation Exchange. If you would like to discuss your idea please get in touch . 

Find out more

Building skills and creating impact: Evaluation Training for Community Consultancy

Recognising & celebrating the energy that is created: Street Storage & The UCL Volunteering Awards

A celebration: The Evaluation Exchange 2021/22 

What is the long-lasting impact of participating in the Evaluation Exchange? Previous UCL students share their experience.

Making time for evaluation and learning: top tips when resources are tight

Blogs from the Evaluation Exchange pilot

Evaluation Exchange taster sessions for voluntary and community sector organisations

Why cross disciplinary work is crucial for building researchers' skills and expertise

The Evaluation Exchange launches in Newham

Working with an organisation undergoing transition

Ready, set, go! The Evaluation Exchange has launched in Camden

Evaluation Exchange: Reflecting and Adapting to Constant Change in Social Enterprises

Evaluation Exchange: There's a place you can go

Evaluation Exchange: Deaf Community Empowerment and Campaigning

Ideas to actions: the Evaluation Exchange

Evaluation Exchange: Kentish Town City Farm

Evaluation Exchange: Supporting Vulnerable Residents in Diverse Communities

Evaluation Exchange: Women + Health

Evaluation Exchange: designing surveys to capture the support 'Skills Enterprise' offers in Newham

Evaluation Exchange: Lifeafterhummus

Evaluation Exchange: Wac Arts empowering young people to change their world through the arts

Evaluation Exchange: Who are Street Storage?

Evaluation Exchange: Calthorpe Community Garden - Relax, Play, Eat

Evaluation Exchange: UCL students collaborate with community health inequalities project: Evaluation Exchange  

Bringing compassion into public project evaluation: the UCL Evaluation Exchange

The UCL Evaluation Exchange & the Co-production Collective announce collaboration

New film features Evaluation Exchange and the impact of digital skills in change making

Applications open for funding and support for evaluation and co-production

Getting creative: the Evaluation Exchange x Trellis collaboration

Evaluating differently: Supporting projects to co-produce evaluations

• What is The Evaluation Exchange? (Animation)
• Evaluation Exchange 2021-22 (Film)
• Victoria, from Caritas Anchor House in Newham, shares what she and her organisation gained from the Evaluation Exchange
• Anne and Aradhna, UCL students, talk about their experience of the Evaluation Exchange
• Money A+E: The team share their experience

If you want to find out more or have any questions, please get in touch .

Follow us on X/Twitter @EvaluationExch .

Follow us on LinkedIn .

Who we work with

The Evaluation Exchange is delivered with  Compost London and  Voluntary Action Camden .

Project leads: Dr Gemma Moore and Ruth Unstead-Joss .

How can I create new assignments that encourage student use of AI?

Integrating AI into assignments can enrich the learning experience and prepare students for future careers where AI is increasingly prevalent. Here are strategies to create assignments that encourage or require the use of AI in ways that promote deep and critical thinking:

Critical analysis and reflection

By incorporating critical analysis and reflection into assignments that require the use of AI, instructors can help students develop a deeper understanding of AI technologies and their implications. These assignments not only foster critical thinking skills but also encourage students to consider the ethical implications of AI in various contexts.

• Assign tasks where students critically analyze AI-generated articles, reports, or media for accuracy and bias.
• Engage students in discussions and writing assignments about the ethical implications of AI in various fields.

Example prompts

• Analyze an AI-generated news article for accuracy and bias. Compare it to a human-written article on the same topic and discuss the differences in content and perspective.
• Write a reflective essay on the ethical implications of using AI 

Research and data analysis

By integrating AI into research and data analysis assignments, students can gain valuable experience in handling large datasets and extracting meaningful insights using cutting-edge technology. These assignments not only enhance students’ research skills but also prepare them for data-driven decision-making in their future careers.

• Assign students to use AI tools like Google Scholar, ResearchGate, or specific AI literature review tools to identify relevant research articles, summarize findings, and generate research questions.
• Have students use AI-powered data analysis software, such as IBM Watson, SAS, or even simpler tools like Excel’s AI features, to analyze datasets, identify trends, and draw conclusions.
• Use AI to conduct a literature review on a specific topic in your field. Summarize key findings, identify gaps in the research, and propose a research question based on your analysis.
• Analyze a dataset related to your course using AI-powered software. Present your findings in a report, including visualizations created by the AI tool, and discuss the implications of your results.

Creative projects

Incorporating AI into creative projects empowers students to explore new possibilities and push the boundaries of traditional artistic and design processes. By leveraging AI, students can unleash their creativity, experiment with innovative techniques, and produce unique works of art that blend human ingenuity with machine intelligence.

• Assign projects where students use AI tools like Adobe Sensei, DeepArt, or Canva’s AI features to create visual art or graphic designs .
• Have students use AI writing assistants like GPT-4 or Jasper to draft content, then refine and critique the AI’s output.
• Encourage the use of AI in video editing or music production, using tools like Adobe Premiere Pro’s AI features or AIVA for composing music.
• Create a series of digital artworks using an AI art generator. Write a reflective essay on the process, discussing how the AI influenced your creative decisions and the final outcome.
• Use an AI writing assistant to draft a short story or essay. Review and revise the AI-generated content, and provide a critique of the AI’s contributions versus your own.

AI in real-world applications

Assignments that explore real-world applications of AI enable students to bridge the gap between theory and practice, gaining insights into how AI technologies are shaping various industries and sectors. By analyzing case studies and researching AI implementations in specific fields, students can understand the potential impact of AI on society, economy, and individual professions. These assignments equip students with the knowledge and skills needed to navigate the evolving landscape of AI-driven innovation and contribute meaningfully to their chosen fields.

• Assign projects that involve using AI tools commonly used in specific industries , such as AI for medical diagnostics, financial forecasting, or marketing analytics.
• Have students analyze real-world case studies where AI has been implemented, discussing the successes, challenges, and lessons learned.
• Research a real-world application of AI in your field of study. Prepare a presentation that explains the technology, its impact, and the challenges encountered during implementation.
• Analyze a case study of an AI-driven project in a specific industry. Write a report on the project’s goals, outcomes, and the role AI played in achieving those outcomes.

Simulation and modeling

Assignments involving AI-driven simulations and modeling provide students with practical experience in solving complex problems and predicting outcomes in various domains. By using AI for simulation and modeling tasks, students can develop valuable analytical and predictive skills that are essential for addressing real-world challenges and making informed decisions.

• Have students use AI-driven simulation software to model scientific phenomena , such as climate change impacts or chemical reactions.
• Assign tasks that involve creating models of economic systems, social behaviors, or market trends using AI tools like MATLAB or R with AI libraries.
• Use AI simulation software to model the impact of different variables on climate change. Present your findings in a report, including a discussion of the potential real-world implications.
• Develop an economic model using AI to predict market trends based on historical data. Write a paper discussing the model’s accuracy and the factors that most influence the predictions.

Collaborative projects

Encouraging the use of AI in collaborative projects fosters teamwork, communication, and efficiency among students. By leveraging AI-powered collaboration tools and brainstorming techniques, students can streamline their workflow, generate innovative ideas, and collaborate effectively to achieve shared goals. These assignments prepare students for collaborative work environments and empower them to harness the power of AI for collective problem-solving.

• Use AI-powered project management and collaboration tools like Trello with AI features, Microsoft Teams, or Slack to manage group work and streamline communication.
• Assign tasks that involve using AI for brainstorming and idea generation , such as mind-mapping software or AI-based ideation tools.
• Work in a group to develop a project proposal using AI-powered collaboration tools. Document how AI facilitated your teamwork and decision-making process.
• Use an AI brainstorming tool to generate ideas for a class project. Present your top ideas and explain how the AI contributed to the ideation process.

By creating assignments that encourage or require the use of AI, instructors can help students develop important skills and a deeper understanding of AI technologies. These assignments not only promote critical thinking and creativity but also prepare students to navigate and succeed in a world where AI plays a significant role in various professional fields.

This content was developed with the assistance of Open AI’s ChatGPT.

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Sustainable Mobility Matters—Summer 2024

This quarterly newsletter highlights recent projects, partnerships, and publications related to NREL’s sustainable mobility research.

Subscribe to receive this newsletter via email.

The Sustainable Transportation Transformation Is Gaining Momentum

As a physicist (and an automotive engineer), I've always had great respect for momentum.

Thanks to Sir Isaac Newton, we know that a moving object will keep going in the same direction and at the same speed forever—unless a force acts to change it. This isn't just true of trucks and trains. It's true of the transformation of our transportation sector, too.

We've seen powerful momentum building behind the nation's efforts to decarbonize our transportation systems. Strong federal strategies, like the  U.S. National Blueprint for Transportation Decarbonization , have pushed the nation in the right direction . Federal, state, and private investments in clean energy research, development, and deployment have increased the speed of the transition.

This momentum has grown even stronger with the removal of barriers . One by one, I'm seeing the world-class researchers of the National Renewable Energy Laboratory (NREL) addressing clean energy challenges and friction points.

Our researchers are tackling pain points at electric vehicle (EV) charging stations and making high-power EV charging stations safer and more reliable. Through technical assistance programs, we’re helping forward-thinking communities meet their goals of electrifying vehicle fleets and planning for robust charging infrastructure . On a national scale, our open-source modeling and simulation tools are helping to draw the contours of optimized EV charging networks , future electric grid upgrades , and domestic lithium-ion battery supply chains . That’s some serious momentum.

Because here's the thing: The strongest momentum is not created by a single event. It builds over time. Every time we remove a barrier to sustainable transportation systems, reinforce their direction, and accelerate their speed, we help that momentum grow—bringing us ever closer to the clean, affordable, accessible, and convenient transportation of the future.

Chris Gearhart Director, NREL's Center for Integrated Mobility Sciences

EVI-X Modeling Suite Accelerates Optimized Electric Vehicle Charging Infrastructure Deployments

NREL's EVI-X modeling suite of EV charging infrastructure analysis tools answers the most complex questions addressing every aspect of EV charging infrastructure—from network planning and site design to financial analysis—for every vehicle weight class and across geographic scales. It enables actionable insights at a never-before-seen level of detail and has helped inform billions of dollars in planned EV charging investments to ensure convenient, reliable, affordable, and equitable charging.

NREL Battery Supply Chain Database Paints a Picture of the North American Manufacturing Base

A strong domestic lithium-ion battery supply chain with increased availability of critical materials is important to support greater domestic manufacturing. NREL and NAATBatt International recently updated the Lithium-Ion Battery Supply Chain Database , an effort to scope the battery landscape. Updates include information about companies in the supply chain, an online version of the North American map of company locations, and filters to allow companies to identify others working in different parts of the supply chain.

Next-Generation Profiles Project Makes Electric Vehicle High-Power Charging Safer and More Reliable

High-power charging is expected to help EV drivers spend less time charging, get back on the road faster, and travel longer distances. The U.S. Department of Energy’s Electric Vehicles at Scale (EVs@Scale) Consortium Next-Generation Profiles project conducts cutting-edge research to advance the state of EVs and EV supply equipment with high-power charging and benefit project partners. All high-level findings thus far are published in a new report series.

Report Targets Electric Vehicle Charging Payment Challenges and Offers Recommendations

A new report from NREL—alongside consortium partner Idaho National Laboratory—summarizes EV charging payment challenges and proposed solutions . EV charging should be accessible, convenient, and reliable. Payment issues affect both accessibility and reliability and can affect EV drivers' charging experiences. Addressing payment issues presents an opportunity for improvements that may speed along EV adoption and improve drivers' time with their EVs, keeping them on the road and not waiting at a charging station.

NREL-Led Clean Energy to Communities Expert Match Program Helps Communities Plan for Electric Vehicles

NREL helped communities in Michigan and Connecticut integrate EVs into county fleets and craft ordinances to permit EV charging stations. As part of the Clean Energy to Communities (C2C) Expert Match technical assistance offering, NREL and Argonne National Laboratory used NREL analysis tools and integrated on-the-ground knowledge from Clean Cities and Communities coalitions, building the communities' capacities to meet energy goals.

NREL's Open-Source Vehicle and Mobility Tools Aim for Reduced Transportation Energy Use and Emissions

For decades, NREL has developed open-source tools to benefit the transportation sector and accelerate the broader clean energy transition. Some tools—like the Advanced Locomotive Technology and Rail Infrastructure Optimization System (ALTRIOS), Future Automotive Systems Technology Simulator (FASTSim™), and Highly Integrated Vehicle Ecosystem Simulation Framework (HIVE™)—target decarbonization and energy efficiency. Others—like NREL's Open Platform for Agile Trip Heuristics (OpenPATH™) and the Route Energy Prediction Model (RouteE)—help predict or track energy consumption while traveling. Because this type of software is so customizable, it allows users to gear tools to meet their unique needs and to answer their specific questions, while also providing a common framework for automakers, regulators, and other research entities to share and validate each other's work.

Short-Circuiting on Purpose: How NREL-Licensed Tech Continues To Make a Difference in Space

As part of exhaustive equipment testing, NASA and NREL created a device that causes an internal short circuit inside the layers of a battery cell, intentionally corrupting the cell by melting a thin layer of wax and forcing test cells to fail at a precise time and location within the cell. This R&D 100 Award-winning device from 2016 is still crucial today, allowing NASA to meet its stringent battery safety requirements, and can be used to test EV or grid batteries as well. This is essential for testing batteries in the International Space Station, the Artemis missions to the moon, and future missions to Mars.

NREL Biofuels Research Guides the Pursuit of Sustainable Vehicle Fuels

NREL fuels and combustion researchers, in partnership with ExxonMobil, published two papers detailing research into the properties of a new type of biofuel. NREL's partners wanted to know whether the new fuel had advantages relative to conventional biodiesel. Through experiments and chemical kinetic modeling, the researchers discovered that the new fuel met quality and reliability standards , with many superior properties. The analysis helps inform decisions targeting fuel design and production.

Arizona City Gets Electric Vehicle Charging Guidance Through Clean Energy to Communities Peer-Learning Cohort

The city of Sedona, Arizona, and 14 other entities recently received tailored expert advice on strategically expanding their EV charger installation efforts through a U.S. Department of Energy C2C program peer-learning cohort. NREL provided tools and resources to help Sedona's municipal leaders identify funding opportunities and explore best practices for EV-friendly city codes. NREL also paired Sedona with their nearby Clean Cities and Communities coalition, a partnership of the U.S. Department of Energy's Vehicle Technologies Office, to build regional and national connections.

NREL Partners With Industry Leaders To Prove Out Grid Integration Solutions

Two utility companies will be using NREL’s premier capability for grid control research, the Advanced Distribution Management System Test Bed, to validate forward-looking solutions for charging vehicles and improving service for the community. Colorado Springs Utilities and Dominion Energy were both selected to demonstrate ways distribution utilities can adapt as more of their customers drive and charge EVs, adding load to the grid.

Get To Know Our Team: Andrew Meintz

A conversation with Andrew Meintz , NREL's chief engineer for electric vehicle charging and grid integration research.

What does your work focus on?

I provide strategic oversight for the lab's electric vehicle charging and grid integration research, with a focus on developing and evaluating integrated systems connecting EVs, charging infrastructure, power grids, buildings, storage, and renewable energy sources.

Some of my activities include examining opportunities and impacts associated with a full range of charging technologies—from home-based Level 1 charging to megawatt-scale fast charging for on- and off-road heavy vehicles. This body of work aims to enable a more adaptive, renewable, and resilient electric grid to accelerate the adoption of electric vehicles at scale. Toward this goal, I also chair the U.S. Department of Energy's EVs@Scale Consortium.

What single mobility challenge would you say we need to prioritize in the next 5 years?

Significant infrastructure research and deployment are needed to establish a flexible grid that enables low-cost, low-impact charging for electric vehicles to access wherever they are, whenever they need it.

Can you share a defining moment in your research career?

When I was a Ph.D. student, I took on a project to support a collegiate competition for charging and learned how to build a plug-in hybrid fuel cell vehicle. It was my first foray into EV work as an electrical engineer.

What piece of research are you most proud of conducting?

The work we did for the development of a megawatt charging connector (now the Megawatt Charging System [MCS]) was very noteworthy because it involved multiple stakeholders across the industry. I'm hopeful that it will play a major role in accelerating the development of megawatt charging to remove source emissions from heavy vehicles.

New Simulation Tool Helps Stakeholders Plan for Electrified Aircraft Emerging aircraft, like electric vertical takeoff and landing vehicles, promise flexibility, improved mobility, and lower emissions, but questions remain about their energy consequences. Check out the latest fact sheet on NREL's Aviation Energy Research and Operation Simulator , a flexible, user-friendly, and accurate tool to rapidly screen and validate technology designs across a range of scenarios.

EVs@Scale Reflects on Another Year of Innovative Research and Development In its second year, the EVs@Scale Laboratory Consortium continued to bring together national laboratories and key stakeholders to conduct infrastructure R&D to address challenges and barriers for high-power EV charging infrastructure, enabling greater safety, grid operation reliability, and consumer confidence. Read about the consortium's accomplishments across vehicle-grid integration, smart charge management, cyber-physical security, and more in the U.S. Department of Energy EVs@Scale Fiscal Year 2023 Year in Review .

New Tool Simplifies, Significantly Speeds Up the Electric Vehicle Infrastructure Planning Process for Federal Fleets After visiting nearly 100 federal sites to assess requirements for EV charging stations, NREL developed an interactive web tool— EVI-LOCATE: Electric Vehicle Infrastructure-Locally Optimized Charging Assessment Tool and Estimator —to generate site layouts and cost estimates remotely. Read the Federal Energy Management Program's feature article to learn how the tool is being used to support federal EV charging station deployment.

Did You Know?

The American Council for an Energy-Efficient Economy (ACEEE) used data from the NREL-developed Mobility Energy Productivity (MEP) tool to inform the ACEEE 2024 City Clean Energy Scorecard , which analyzes efforts by the most populous U.S. cities to improve energy efficiency and reduce greenhouse gas emissions. The MEP tool measures how well residents can reach destinations in a convenient, cost-effective, and energy-efficient way. Many existing metrics quantify access to opportunities with travel time as the primary weighting factor but ignore other externalities, such as the energy consumption and cost of using a particular mode, which the MEP tool takes into account.

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In the News

Is Electric Vehicle Charging Infrastructure Ready To Keep Up With Demand? Consumer Affairs interviews Brennan Borlaug EV charging infrastructure is rapidly being deployed around the country, with some states having more charging stations available than others. The current pace of EV charger build-out may need to increase to meet future demand.

The Greening of Planes, Trains and Automobiles Knowable Magazine interviews Keith Wipke The transportation fuel landscape is expanding and diversifying as replacements for gasoline and diesel are being developed to power the energy transition. Researchers and fuel producers are exploring new fuels to meet the unique operational needs of road passenger vehicles, freight trucks, trains, ships, and airplanes.

Why Aren't We All Flying in Electric Planes? [video] Deutsche Welle interviews Scott Cary Scaling up electric aircraft is restricted by battery weight, current charging speeds being too slow for busy airport settings, and long regulatory processes. But electric aircraft could still solve part of aviation decarbonization by serving some short cargo routes and remote communities that already use small aircraft for transport.

Read the NREL LinkedIn Blog Feature

Doug Arent, NREL's executive director of strategic public-private partnerships, wrote a blog post on insights from the 2024 Sustainable Aviation Energy Conference, co-convened and hosted by Dallas-Fort Worth International Airport and NREL. Read Doug's NREL LinkedIn blog for takeaways from the conference—and why NREL stands ready to be the trusted partner that provides research solutions needed for a sustainable aviation future.

Sustainable Mobility Matters Delivered Quarterly to Your Inbox

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Circular Energy Generation: Building Tomorrow's Recyclable Turbine Blades

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Hong Kong should approach South Lantau project with purpose

• From Bali’s resorts to Beijing’s countryside attractions, other ecotourism sites provide useful templates for developing South Lantau

One key concern is that the proposed holiday space would only attract “check-box” travellers – visitors focused on taking pictures of attractions rather than experiencing the local culture and environment. An influx of such tourists who might lack awareness about environmental responsibility could damage South Lantau’s natural habitats.

As an ethical and sustainable answer to overdevelopment, eco-resorts can be a solution to address these concerns for South Lantau. In places like Bali, eco-resorts aim to put sustainability at the centre of every traveller’s stay. They set out and gradually meet objectives anchored in sustainability.

In Desa Hay, all of the artwork is created by local artisans with a small placard crediting the artist, while the resort exclusively hires Indonesian staff and gives back to the community through initiatives including sponsoring school tuition for local orphans.

For example, the Eden Project in Cornwall, England adopted a bottom-up approach. The local council provided £25,000 (US$31,910) of seed funding. Funds were also secured from the UK Millennium Commission, which weighed in with £37.5 million.

There is a potential conundrum that could arise as development proceeds. As more crowds visit South Lantau, there will be more demand for transport services, which causes greater environmental impact while also enabling more visitors.

However, development does not necessarily entail massive construction projects. The current plan outlines a myriad of facilities, including the Lower Cheung Sha Visitor Centre and the water sports and recreation centre at Upper Cheung Sha Beach, as well as educational areas in Shui Hau and Pui O. These facilities are geographically dispersed, but at the same time also lack a compelling overarching storyline.

To make this a uniquely Hong Kong project, a planning and design competition can be held to solicit ideas from professionals in the architectural, surveying and landscaping sectors.

The library was made from local materials, mostly wooden sticks that villagers use all year round to fuel their stoves. The building volume was designed, such that the library blends perfectly into the landscape with minimal visual impact. The project was initiated through a bottom-up approach, funded by a charitable trust and designed by a local architect.

Ryan Ip is vice-president and co-head of research at Our Hong Kong Foundation

Jason Leung is head of land and housing research at Our Hong Kong Foundation

Calvin Au is a researcher at Our Hong Kong Foundation