research and design.com

Orchard Park Store:    4109 North Buffalo St.   |    716.662.0800

Lewiston Store:   721 Center St.    |    716.405.7090

Fill out the form below and someone from our team will reach out to confirm your order! Visit our Facebook or Instagram for frequent updates on available products. 

Shipping or Curbside Pickup Available!

* Please note each product is subject to availability. 

Ordering via this form does not guarantee purchase.

Store Hours

Monday - Wednesday, 10:30am - 6pm

Thursday, 10:30am - 8pm

Friday - Saturday, 10:30am - 6pm

Sunday, 11am - 4pm

Now Hiring!

Click here to apply online today:

Orchard Park Store:

4109 North Buffalo Street

Orchard Park, NY 14127

716.662.0800

Lewiston Store:

721 Center Street

Lewiston, New York

716.405.7090

Privacy Policy

Research and design privacy policy.

This privacy policy has been compiled to better serve those who are concerned with how their 'Personally identifiable information' (PII) is being used online. PII, as used in US privacy law and information security, is information that can be used on its own or with other information to identify, contact, or locate a single person, or to identify an individual in context. Please read our privacy policy carefully to get a clear understanding of how we collect, use, protect or otherwise handle your Personally Identifiable Information in accordance with our website.

WHAT PERSONAL INFORMATION DO WE COLLECT FROM THE PEOPLE THAT VISIT OUR WEBSITE?

As appropriate, you may be asked to enter your name, email address, phone number or other details to help you with your experience.

WHEN DO WE COLLECT INFORMATION?

We collect information from you when you fill out a form, subscribe to our email list, submit a resume, or enter other information on our site.

HOW DO WE USE YOUR INFORMATION?

We may use the information we collect from you when you register, make a purchase, sign up for our newsletter, respond to a survey or marketing communication, surf the website, or use certain other site features in the following ways:

• To improve our website in order to better serve you.
• To allow us to better service you in responding to your customer service requests.
• To administer a contest, promotion, survey or other site feature.
• To send periodic emails regarding news and specials.

HOW DO WE PROTECT VISITOR INFORMATION?

Our website is scanned on a regular basis for malware, security holes, and known vulnerabilities in order to make your visit to our site as safe as possible.

THIRD PARTY DISCLOSURE

We do not sell, trade, or otherwise transfer to outside parties your personally identifiable information.

THIRD PARTY LINKS

We do not include or offer third party products or services on our website.

We along with third-party vendors, such as Google use first-party cookies (such as the Google Analytics cookies) and third-party cookies (such as the Facebook cookie) or other third-party identifiers together to compile data regarding user interactions with ad impressions, and other ad service functions as they relate to our website.

Opting out:

Users can set preferences for how Google advertises to you using the Google Ad Settings page. Alternatively, you can opt out by visiting the Network Advertising initiative opt out page or permanently using the Google Analytics Opt Out Browser add on.

If you don’t want Facebook to determine ads to show you based on your use of websites and apps off of Facebook, you can opt out through your settings . Learn more about online interest-based advertising from Facebook .

CALIFORNIA ONLINE PRIVACY PROTECTION ACT

CalOPPA is the first state law in the nation to require commercial websites and online services to post a privacy policy. The law's reach stretches well beyond California to require a person or company in the United States (and conceivably the world) that operates websites collecting personally identifiable information from California consumers to post a conspicuous privacy policy on its website stating exactly the information being collected and those individuals with whom it is being shared, and to comply with this policy. - See more at: http://consumercal.org/california-online-privacy-protection-act-caloppa/#st…

According to CalOPPA we agree to the following: 

Users can visit our site anonymously.

Once this privacy policy is created, we will add a link to it on our home page, or as a minimum on the first significant page after entering our website. 

Our Privacy Policy link includes the word 'Privacy', and can be easily be found on the page specified above. 

Users will be notified of any privacy policy changes on our Privacy Policy page

Users are able to change their personal information By emailing or calling us.

HOW DOES OUR SITE HANDLE DO NOT TRACK SIGNALS?

We honor do not track signals and do not track, plant cookies, or use advertising when a Do Not Track (DNT) browser mechanism is in place.

DOES OUR SITE ALLOW THIRD PARTY BEHAVIORAL TRACKING?

It's also important to note that we do not allow third party behavioral tracking.

COPPA (CHILDREN ONLINE PRIVACY PROTECTION ACT)

When it comes to the collection of personal information from children under 13, the Children's Online Privacy Protection Act (COPPA) puts parents in control. The Federal Trade Commission, the nation's consumer protection agency, enforces the COPPA Rule, which spells out what operators of websites and online services must do to protect children's privacy and safety online. We do not specifically market to children under 13.

FAIR INFORMATION PRACTICES

The Fair Information Practices Principles form the backbone of privacy law in the United States and the concepts they include have played a significant role in the development of data protection laws around the globe. Understanding the Fair Information Practice Principles and how they should be implemented is critical to comply with the various privacy laws that protect personal information.

In order to be in line with Fair Information Practices we will take the following responsive action, should a data breach occur:

• We will notify the users via email within 7 business days.
• We will notify the users via in site notification within 7 business days.

We also agree to the individual redress principle, which requires that individuals have a right to pursue legally enforceable rights against data collectors and processors who fail to adhere to the law. This principle requires not only that individuals have enforceable rights against data users, but also that individuals have recourse to courts or a government agency to investigate and/or prosecute non-compliance by data processors.

CAN SPAM ACT

The CAN-SPAM Act is a law that sets the rules for commercial email, establishes requirements for commercial messages, gives recipients the right to have emails stopped from being sent to them, and spells out tough penalties for violations.

We collect your email address in order to:

• Send information, respond to inquiries, and/or other requests or questions.
• Market to our mailing list or continue to send emails to our clients after the original transaction has occurred.

To be in accordance with CANSPAM we agree to the following:

• NOT use false, or misleading subjects or email addresses.
• Identify the message as an advertisement in some reasonable way.
• Include the physical address of our business or site headquarters.
• Monitor third party email marketing services for compliance, if one is used.
• Honor opt-out/unsubscribe requests quickly.
• Allow users to unsubscribe by using the link at the bottom of each email.

If at any time you would like to unsubscribe from receiving future emails, follow the instructions at the bottom of each email. and we will promptly remove you from ALL correspondence.

CONTACTING US

If there are any questions regarding this privacy policy you may contact us using the information below

http://www.researchanddesign.com/ 

Orchard Park, New York 14127

United States 

[email protected]

(716) 648-3200

Last Edited On: 06-24-16

Research and Design

Review Highlights

“ Their tees, denim and more offer a rainbow of colors and tend to stick with classic solids and stripes. ” in 4 reviews

“ Stopped in when I was in the Southtowns because I have heard great things. ” in 2 reviews

“ Research and Design is a small, family-owned boutique in the village of Orchard Park. ” in 2 reviews

Location & Hours

Suggest an edit

4109 N Buffalo St

Orchard Park, NY 14127

Amenities and More

Ask the community.

Ask a question

Yelp users haven’t asked any questions yet about Research and Design .

People also searched for

Leather Goods

Shopping Malls

Clothing Stores

Boutique Shopping

Discount Clothing Stores

Recommended Reviews

Research and Design

An experimental boutique located in Orchard Park, Research and Design is one of the most exciting things to happen to the city’s retail scene in recent years. Every item offers a cutting edge sense of design, with a killer selection of women’s clothing, jewelry, and accessories. Not to mention their highly curated gallery upstairs that is a nice addition to the retail haven.

Visit Buffalo Niagara

Visitor Information: 800.283.3256

Main Office: 716.852.0511

Main Fax: 716.852.0131

403 Main Street, Suite 630 Buffalo, New York 14203-1496

• Eat & Drink
• Travel Trade
• Sports Commission
• Buffalo FAQ

© 2024 Visit Buffalo Niagara. All Rights Reserved

Member Directory

• Add to Address Book.

Research and Design

retail womens fashion jewelry clothing gifts candles sorel the north face

10% off your purchase excludes Sorel.

4109 N. Buffalo Rd., Ste. 2 Orchard Park NY 14127

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.

Editage All Access is a subscription-based platform that unifies the best AI tools and services designed to speed up, simplify, and streamline every step of a researcher’s journey. The Editage All Access Pack is a one-of-a-kind subscription that unlocks full access to an AI writing assistant, literature recommender, journal finder, scientific illustration tool, and exclusive discounts on professional publication services from Editage.  

Based on 22+ years of experience in academia, Editage All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $14 a month !    

Related Posts

Back to School – Lock-in All Access Pack for a Year at the Best Price

Journal Turnaround Time: Researcher.Life and Scholarly Intelligence Join Hands to Empower Researchers with Publication Time Insights 

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, generate accurate citations for free.

• Knowledge Base

Methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

• Your overall research objectives and approach
• Whether you’ll rely on primary research or secondary research
• Your sampling methods or criteria for selecting subjects
• Your data collection methods
• The procedures you’ll follow to collect data
• Your data analysis methods

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism. Run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

• Experimental and   quasi-experimental designs allow you to test cause-and-effect relationships
• Descriptive and correlational designs allow you to measure variables and describe relationships between them.

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

Observational studies allow you to collect data unobtrusively, observing characteristics, behaviors or social interactions without relying on self-reporting.

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

McCombes, S. (2023, November 20). What Is a Research Design | Types, Guide & Examples. Scribbr. Retrieved September 7, 2024, from https://www.scribbr.com/methodology/research-design/

Is this article helpful?

Shona McCombes

Other students also liked, guide to experimental design | overview, steps, & examples, how to write a research proposal | examples & templates, ethical considerations in research | types & examples, "i thought ai proofreading was useless but..".

I've been using Scribbr for years now and I know it's a service that won't disappoint. It does a good job spotting mistakes”

Have a language expert improve your writing

Run a free plagiarism check in 10 minutes, automatically generate references for free.

• Knowledge Base
• Methodology

Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

McCombes, S. (2023, March 20). Research Design | Step-by-Step Guide with Examples. Scribbr. Retrieved 3 September 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

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…

Need a helping hand?

Experimental Research Design

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

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

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

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

Quasi-Experimental Research Design

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

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

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

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

Research Design: Qualitative Studies

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

Phenomenological Research Design

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

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

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

Grounded Theory Research Design

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

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

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

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

Ethnographic Research Design

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

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

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

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

Case Study Design

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

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

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

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

How To Choose A Research Design

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

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

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

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

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

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

Recap: Key Takeaways

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

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

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

Psst... there’s more!

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

14 Comments

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

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

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

This was really helpful. thanks

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

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

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

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

This post is really helpful.

how to cite this page

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

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

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

This is very helpful and very useful!

Wow! This post has an awful explanation. Appreciated.

Submit a Comment Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Print Friendly

• Privacy Policy

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

You may also like

Scope of the Research – Writing Guide and...

Research Paper Conclusion – Writing Guide and...

Research Paper – Structure, Examples and Writing...

Institutional Review Board – Application Sample...

Research Methods – Types, Examples and Guide

Significance of the Study – Examples and Writing...

Leave a comment x.

Save my name, email, and website in this browser for the next time I comment.

University Libraries

• Contact a Librarian
• Databases A-Z
• Guides by Subject
• Resources by Type
• Find Books & Articles
• Government Information
• Iowa Digital Library
• Iowa Research Online
• Special Collections & University Archives
• Iowa Women's Archives
• Course Reserves
• Office Delivery
• Borrowing From Another Library & Document Delivery
• Undergraduate Research Services (The SEAM)
• Research Consultations
• Instructional Services
• Research Data Services
• Open Educational Resources
• Distance Education
• Scholarly Publishing & Copyright
• More services...
• Check My Account
• Renew My Books
• My Interlibrary Loan
• Recommend Library Purchase
• EndNote Basic
• Departments
• Collection Management
• Diversity, Equity, and Inclusion
• Publications, Plans & Reports
• Make a Gift
• History of the Library
• For the Media
• Research Guides & Tutorials
• Directions & Maps
• Assistance for Users with Disabilities
• All Campus Libraries
• Learning Commons
• Main Library Gallery
• Art Library
• Business Library
• Engineering Library
• Health Sciences Library
• Law Library
• Music Library
• Sciences Library
• Contact a Librarian or the UI Libraries
• Staff directory by name
• Staff directory by organizational unit
• Campus Libraries

Design Research: Home

Design research.

image via OpenClipArt.org

Materials Resources

• MaterialDistrict MaterialDistrict is the world’s leading match-making platform in the field of innovative materials. MaterialDistrict’s value as a high-end materials inspiration source is clear: R&D and design professionals of all industries are using our platform to discover new material solutions.
• Material Order Search The online shared source for design material collections.
• Material Bank Browse or create an account (free) to save and create design boards.
• Materials Lab Database A resource dedicated to in-depth material investigation in design. Online counterpart to physical lab/library.

Human Factors

• HFES - Human Factors and Ergonomics Society Website for professional organization.
• Human factors : the journal of the Human Factors and Ergonomics Society Full text of the journal from 1958 to the present.
• Human Engineering Books at UI that cover human factors and ergonomics.
• OSHA Ergonomics Resources from the US Department of Labor
• Anthropometric Data from Design Lab Open Design Lab at Penn State

Industry and Market Research

Professional Organizations

Design Journals

3D World: The International Magazine for 3D Artstis Print

Access by Design: Journal of the Centre for Accessible Environments Online

American Craft Print and Online

Architectural Design: A.D.  Print and  Online

Design Management Journal Online

Design Management Review Online

Design Principles and Practices Online

Design Quarterly Online

Design: Retail Online

Design Studies Online

Domus Print

Ergonomics in Design Online

Garden Design Online

Hand Papermaking Print

Interior Design Online

International Journal of Art and Design Education Online

International Journal of Design Online

Journal of Design History Online

Journal of Health Design Online

Journal of Mechanical Design Online

Materials and Design Online

Product Design and Development Online

Research in Engineering Design Online

Surface Design Online

• Last Updated: Sep 6, 2024 11:46 AM
• URL: https://guides.lib.uiowa.edu/designresearch

UX Design Intern – Brunswick

• Student Employment & Internships

About Brunswick

With over 19,000 employees and operations in over 27 countries, Brunswick is the world leader in recreational marine vessels and propulsion. Recently named to the Forbes list of Best Employers for Diversity, we take equality and work/life balance seriously. With our 15 boat brands, various P&A brands, and world-leading propulsion brands, we aim to make the boating experience frictionless for our valued customers.

Machine learning has been around for decades but has become a household name in the last several years. Brunswick has been investigating and leveraging machine learning for years, and we are excited for the vast untapped opportunities in the recreational marine space for this technology. It has the potential to transform everything we do as a company and as an industry.  

About the Role

The Boating Intelligence (BI) Design Lab is seeking a creative and innovative UX Design intern. In this role, you will be supporting UX activities within a lab setting, leading user research, and ensuring a cohesive user experience for the final products.

Responsibilities

•  Lead in conducting user research and gathering insights through various methodologies such as interviews, surveys, and usability testing.
• Present findings in concise, professional share-outs to team.
• Create wireframes, concept sketches, and user flows to effectively communicate design ideas.
• Collaborate with cross-functional teams, including product managers, developers, and other designers, to ensure the end product is delivering a cohesive experience that incorporates user research insights and usability guidelines.
• Lead brainstorming sessions and contribute to the development of innovative design solutions.

Required Qualifications

• To be considered for the internship, candidates must have permanent US Work Authorization.
• Current enrollment in a BS or master’s degree in a field related to Design at the University of Illinois Urbana Champaign
• Portfolio highlighting a variety of projects.
• Competent and comfortable with hand sketching, wireframing, and creating user flows.
• Intermediate capability in Photoshop, Figma, Miro, or similar.
• Understanding of UX design principles, design thinking, user research basics.
• Collaborates well with others, can communicate design intent though visuals and verbal, and able to apply feedback and user research insights into work.

Preferred Experience/Skills:

• Storyboarding or illustrative capability.
• Experience and knowledge in qualitative and quantitative user research practices, such as ethnographic observational studies, semi-structured user interviews, a/b testing, and Surveys.
• Additional capability in other creative software such as but not limited to Illustrator, Whimsical, Sketch, etc.

Working Conditions

• Learning Opportunities: Our internships are designed to provide hands-on experience in a professional setting. You will work alongside experienced professionals and get a chance to apply your academic knowledge to real-world tasks.
• Project Work: The work environment is supportive, collaborative, and conducive to learning. Interns typically work on specific projects or tasks that contribute to the organization’s goals. This may range from supporting larger projects to handling smaller, individual assignments.
• Hours: Interns must work at least 8 hours in person at the lab each week during the fall and spring terms. You may work up to 20 hours maximum. During summer, interns are expected to work 40 hours in person at the lab each week.
• Feedback and Performance Reviews: As an intern, you will receive feedback and performance reviews throughout your assignment. We value the importance for interns to understand their progress and areas for improvement.
• Responsibility: Interns at BI Design Lab are expected to manage their own schedules, set goals, and seek feedback for their professional development.

**This position will be based out of the Brunswick BI Design Lab in Research Park on the University of Illinois Urbana-Champaign campus. There may be opportunities to travel by accompanying others on UX research trips or going out to survey users in the environment in local boating areas. Although travel is encouraged if your schedule allows, travel is never mandatory, and performance evaluations are not based on the ability to travel.

To apply for this job please visit www.linkedin.com .

• Request info
• Majors & Degrees
• Prospective Students
• Current Undergraduate Students
• Current Graduate Students
• Online Students
• Alumni and Friends
• Faculty and Staff

Quality Enhancement Programs

QEP Research and Design

Page content.

During the 2023-2024 academic year, the QEP Topic Selection Task Force reviewed input from the university community through the strategic planning process and institutional data to identify the topic for our next QEP. The next phase of the QEP is developing the topic into an action plan based on a review of research-based best practices and continued dialogue with the university community. The QEP Research and Design (R&D) Team, representing a wide range of stakeholders across the university, including faculty, staff, administrators, and students, will lead this phase. The QEP research and design process will take place primarily during the 2024-2025 academic year leading up to the on-site visit in March 2026.

The Reseach and Design Team, appointed by University President Joseph S. Paul, includes X members representing a borad cross-section of the University community. Dr. Paul appointed Kristi Motter, Ph.D., Vice President, Student Affairs and Enrollment Management, and Russ Willis, J.D., Title to serve as co-chairs of the QEP Research and Design Team

• Ashley Burnside, Ed.D., Director, Center for Student Success
• Kenneth Christenson, Ed.D., Lecturer, Interdisciplinary Studies, College of Arts and Sciences, Undergraduate Council - General Education Committee Chair
• Bridgette L. Davis, Ph.D., Assistant Teaching Professor, University College, Gulf Park
• Alison Fridley, Ph.D., Assistant Professor, School of Marketing, Sport Management, College of Business and Economic Developoment
• Adina Green, Ph.D., Director, TRIO Student Support Services Program
• Julie Howdeshell, Ph.D., Director, Quality Enhancement, SACS Quality Enhancement Programs
• Michael King, Ph.D., Assistant Vice President of Student Learning and Strategic Initiatives, Division of Student Affairs
• Amy LeBert, Ph.D., Assistant Teaching Professor, Speech and Hearing Sciences, College of Nursing and Health Professions
• Ryan Luethje, Ed.D., Instructor and Student Support Specialist, Child and Family Sciences, College of Education and Human Sciences
• Doug Masterson, Ph.D., Senior Associate Provost, Institutional Success, SACSCOC Liaison, and Professor of Chemistry and Biochemistry
• Elizabeth McCardle, M.Ed., Faculty Senate Chair-Elect, 2024-25, Mathematics Lecturer and Center Director, School of Mathematics and Natural Sciences, College of Arts and Sciences
• Megan McCay, Ph.D., Director, Institutional Research
• Kristi Motter, Ph.D., Vice President, Student Affairs and Enrollment Management
• Bonnie Nicholson, Ph.D., Associate Director and Professor, Psychology, College of Education and Human Sciences
• Braxton Ruddock, Student Government Association President
• Heather Stur, Ph.D., Professor, School of Humanities, History, College of Arts and Sciences
• Elizabeth Tinnon, Ph.D., Associate Professor, Professional Nursing Practice, College of Nursing and Health Professions
• Russ Willis, J.D., Associate Provost for Student Success, and Assistant Teaching Professor, Management, College of Business and Economic Development 

Quality Enhancement Programs 517 International Center (IC) 118 College Drive #5026 Hattiesburg, MS 39406

Email QEPFREEMississippi

Phone 601.266.4525

• DOI: 10.1109/CVIDL62147.2024.10603498
• Corpus ID: 271462684

Research on Multimodal fusion Object Detection Algorithm of Image Design and Computational Vision

• Lanchuan Wang , Chunxiao Li
• Published in 5th International Conference… 19 April 2024
• Computer Science, Engineering
• 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL)

Figures and Tables from this paper

11 References

Centerfusion: center-based radar and camera fusion for 3d object detection, recent advances in deep learning for object detection, 3d-cvf: generating joint camera and lidar features using cross-view spatial feature fusion for 3d object detection, deep multi-modal object detection and semantic segmentation for autonomous driving: datasets, methods, and challenges.

• Highly Influential

DOTIE - Detecting Objects through Temporal Isolation of Events using a Spiking Architecture

Object detection algorithm based on improved yolov3, recurrent vision transformers for object detection with event cameras, sa-det3d: self-attention based context-aware 3d object detection, imbalance problems in object detection: a review, object detection with spiking neural networks on automotive event data, related papers.

Showing 1 through 3 of 0 Related Papers

Maintenance work is planned from 22:00 BST on Monday 16th September 2024 to 22:00 BST on Tuesday 17th September 2024.

During this time the performance of our website may be affected - searches may run slowly, some pages may be temporarily unavailable, and you may be unable to access content. If this happens, please try refreshing your web browser or try waiting two to three minutes before trying again.

We apologise for any inconvenience this might cause and thank you for your patience.

Green Chemistry

Toward safer and more sustainable by design biocatalytic amide-bond coupling.

Amide bond synthesis is ranked as the second most important challenge in key green chemistry research areas identified by the ACS Green Chemistry Institute. While developing more sustainable amide bond forming reactions has been in focus, significantly less attention has been given to human toxicity and environmental aspects of the underlying amine and acid substrates and their corresponding coupled products; a potentially important contribution to the overall sustainability of the amide-bond-forming reactions. Here, we explore biocatalytic amide bond formation from a safer-and-more-sustainable-by-design perspective in which commercially available amines and acids as well as their corresponding amide products were evaluated in silico based on potential human toxicity and environmental fate and exposure. This in silico filtering resulted in a panel of 188 amine and 54 acid building blocks that could be classified as safe; referred to herein as “safechems”. To enable couplings of the safechems, we generated a panel of robust and promiscuous ancestral ATP-dependent amide bond synthetases (ABS) using McbA from Marinactinospora thermotolerans SCSIO 00652 as a template. Ancestral ABS enzymes exhibited complementary specificities in the coupling of a representative safechem subset of 17 amines and 16 acids while showing an increased thermostability of up to 20 °C compared to the extant biocatalyst. Finally, the pool of safechems and their corresponding amides were evaluated by USEtox (the UNEP-SETAC toxicity model), analysing not only the intrinsic properties of the compounds but evaluating their complete impact pathway including fate, exposure and effects. The amides were in general predicted as more toxic compared to the starting acids and amines through non-additive effects, emphasising that focusing on the toxicity of the building blocks alone is not sufficient to strive towards low human and ecotoxicity impact. Pursuing a safer and more sustainable by design perspective in the implementation of safechems did not prevent us from generating an array of novel products with potentially potent applications as exemplified here by enzymatic synthesis of substructures that are part of drug candidates for e.g. cancer treatment.

Supplementary files

• Supplementary information PDF (10301K)

Article information

Download Citation

Permissions.

E. Söderberg, K. von Borries, U. Norinder, M. Petchey, G. Ranjani, S. Chavan, H. Holmquist, M. J. Johansson, I. Cotgreave, M. Hayes, P. Fantke and P. Syrén, Green Chem. , 2024, Accepted Manuscript , DOI: 10.1039/D4GC03665D

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence . You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

Read more about how to correctly acknowledge RSC content .

Social activity

Search articles by author.

This article has not yet been cited.

Advertisements

Country/region

• USD $ | Afghanistan
• USD $ | Åland Islands
• USD $ | Albania
• USD $ | Algeria
• USD $ | Andorra
• USD $ | Angola
• USD $ | Anguilla
• USD $ | Antigua & Barbuda
• USD $ | Argentina
• USD $ | Armenia
• USD $ | Aruba
• USD $ | Ascension Island
• USD $ | Australia
• USD $ | Austria
• USD $ | Azerbaijan
• USD $ | Bahamas
• USD $ | Bahrain
• USD $ | Bangladesh
• USD $ | Barbados
• USD $ | Belarus
• USD $ | Belgium
• USD $ | Belize
• USD $ | Benin
• USD $ | Bermuda
• USD $ | Bhutan
• USD $ | Bolivia
• USD $ | Bosnia & Herzegovina
• USD $ | Botswana
• USD $ | Brazil
• USD $ | British Indian Ocean Territory
• USD $ | British Virgin Islands
• USD $ | Brunei
• USD $ | Bulgaria
• USD $ | Burkina Faso
• USD $ | Burundi
• USD $ | Cambodia
• USD $ | Cameroon
• USD $ | Canada
• USD $ | Cape Verde
• USD $ | Caribbean Netherlands
• USD $ | Cayman Islands
• USD $ | Central African Republic
• USD $ | Chad
• USD $ | Chile
• USD $ | China
• USD $ | Christmas Island
• USD $ | Cocos (Keeling) Islands
• USD $ | Colombia
• USD $ | Comoros
• USD $ | Congo - Brazzaville
• USD $ | Congo - Kinshasa
• USD $ | Cook Islands
• USD $ | Costa Rica
• USD $ | Côte d’Ivoire
• USD $ | Croatia
• USD $ | Curaçao
• USD $ | Cyprus
• USD $ | Czechia
• USD $ | Denmark
• USD $ | Djibouti
• USD $ | Dominica
• USD $ | Dominican Republic
• USD $ | Ecuador
• USD $ | Egypt
• USD $ | El Salvador
• USD $ | Equatorial Guinea
• USD $ | Eritrea
• USD $ | Estonia
• USD $ | Eswatini
• USD $ | Ethiopia
• USD $ | Falkland Islands
• USD $ | Faroe Islands
• USD $ | Fiji
• USD $ | Finland
• USD $ | France
• USD $ | French Guiana
• USD $ | French Polynesia
• USD $ | French Southern Territories
• USD $ | Gabon
• USD $ | Gambia
• USD $ | Georgia
• USD $ | Germany
• USD $ | Ghana
• USD $ | Gibraltar
• USD $ | Greece
• USD $ | Greenland
• USD $ | Grenada
• USD $ | Guadeloupe
• USD $ | Guatemala
• USD $ | Guernsey
• USD $ | Guinea
• USD $ | Guinea-Bissau
• USD $ | Guyana
• USD $ | Haiti
• USD $ | Honduras
• USD $ | Hong Kong SAR
• USD $ | Hungary
• USD $ | Iceland
• USD $ | India
• USD $ | Indonesia
• USD $ | Iraq
• USD $ | Ireland
• USD $ | Isle of Man
• USD $ | Israel
• USD $ | Italy
• USD $ | Jamaica
• USD $ | Japan
• USD $ | Jersey
• USD $ | Jordan
• USD $ | Kazakhstan
• USD $ | Kenya
• USD $ | Kiribati
• USD $ | Kosovo
• USD $ | Kuwait
• USD $ | Kyrgyzstan
• USD $ | Laos
• USD $ | Latvia
• USD $ | Lebanon
• USD $ | Lesotho
• USD $ | Liberia
• USD $ | Libya
• USD $ | Liechtenstein
• USD $ | Lithuania
• USD $ | Luxembourg
• USD $ | Macao SAR
• USD $ | Madagascar
• USD $ | Malawi
• USD $ | Malaysia
• USD $ | Maldives
• USD $ | Mali
• USD $ | Malta
• USD $ | Martinique
• USD $ | Mauritania
• USD $ | Mauritius
• USD $ | Mayotte
• USD $ | Mexico
• USD $ | Moldova
• USD $ | Monaco
• USD $ | Mongolia
• USD $ | Montenegro
• USD $ | Montserrat
• USD $ | Morocco
• USD $ | Mozambique
• USD $ | Myanmar (Burma)
• USD $ | Namibia
• USD $ | Nauru
• USD $ | Nepal
• USD $ | Netherlands
• USD $ | New Caledonia
• USD $ | New Zealand
• USD $ | Nicaragua
• USD $ | Niger
• USD $ | Nigeria
• USD $ | Niue
• USD $ | Norfolk Island
• USD $ | North Macedonia
• USD $ | Norway
• USD $ | Oman
• USD $ | Pakistan
• USD $ | Palestinian Territories
• USD $ | Panama
• USD $ | Papua New Guinea
• USD $ | Paraguay
• USD $ | Peru
• USD $ | Philippines
• USD $ | Pitcairn Islands
• USD $ | Poland
• USD $ | Portugal
• USD $ | Qatar
• USD $ | Réunion
• USD $ | Romania
• USD $ | Russia
• USD $ | Rwanda
• USD $ | Samoa
• USD $ | San Marino
• USD $ | São Tomé & Príncipe
• USD $ | Saudi Arabia
• USD $ | Senegal
• USD $ | Serbia
• USD $ | Seychelles
• USD $ | Sierra Leone
• USD $ | Singapore
• USD $ | Sint Maarten
• USD $ | Slovakia
• USD $ | Slovenia
• USD $ | Solomon Islands
• USD $ | Somalia
• USD $ | South Africa
• USD $ | South Georgia & South Sandwich Islands
• USD $ | South Korea
• USD $ | South Sudan
• USD $ | Spain
• USD $ | Sri Lanka
• USD $ | St. Barthélemy
• USD $ | St. Helena
• USD $ | St. Kitts & Nevis
• USD $ | St. Lucia
• USD $ | St. Martin
• USD $ | St. Pierre & Miquelon
• USD $ | St. Vincent & Grenadines
• USD $ | Sudan
• USD $ | Suriname
• USD $ | Svalbard & Jan Mayen
• USD $ | Sweden
• USD $ | Switzerland
• USD $ | Taiwan
• USD $ | Tajikistan
• USD $ | Tanzania
• USD $ | Thailand
• USD $ | Timor-Leste
• USD $ | Togo
• USD $ | Tokelau
• USD $ | Tonga
• USD $ | Trinidad & Tobago
• USD $ | Tristan da Cunha
• USD $ | Tunisia
• USD $ | Türkiye
• USD $ | Turkmenistan
• USD $ | Turks & Caicos Islands
• USD $ | Tuvalu
• USD $ | U.S. Outlying Islands
• USD $ | Uganda
• USD $ | Ukraine
• USD $ | United Arab Emirates
• USD $ | United Kingdom
• USD $ | United States
• USD $ | Uruguay
• USD $ | Uzbekistan
• USD $ | Vanuatu
• USD $ | Vatican City
• USD $ | Venezuela
• USD $ | Vietnam
• USD $ | Wallis & Futuna
• USD $ | Western Sahara
• USD $ | Yemen
• USD $ | Zambia
• USD $ | Zimbabwe

Item added to your cart

Collection: bags, vegan carry all quilted bag in pink, vegan carry all quilted bag in beige, vegan carry all quilted bag in black, carry all quilted bag in aqua, lona jute bag in turquoise, lona jute bag in coral, cali basket bag in green, cali basket bag in pink, cali basket bag in yellow, fi checkered bag, quilted sling bag in red, quilted sling bag in blue, lexi crossbody in vanilla, greyson tote in noir, lexi crossbody in solo, quilt wallet in mustard, quilt wallet in sage, quilt wallet in tan, quilt wallet in beige.

• Choosing a selection results in a full page refresh.
• Opens in a new window.