how to plan a research design

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 26 August 2024, from https://www.scribbr.co.uk/research-methods/research-design/

Is this article helpful?

Shona McCombes

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

Last updated

30 January 2024

Reviewed by

Short on time? Get an AI generated summary of this article instead

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

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

• What is a research plan?

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

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

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

• Why do you need a research plan?

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

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

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

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

Project organization and structure

Well-informed participants

All stakeholders and teams align in support of the project

Clearly defined project definitions and purposes

Distractions are eliminated, prioritizing task focus

Timely management of individual task schedules and roles

Costly reworks are avoided

• What should a research plan include?

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

Specific project goals and individual objectives

Ideal strategies or methods for reaching those goals

Required resources

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

Key performance indicators (KPIs)

Project background

Research and testing support

Preliminary studies and progress reporting mechanisms

Cost estimates and change order processes

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

• How to write a research plan for your project

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

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

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

Define your project’s purpose

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

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

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

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

What you’re doing

Why you’re doing it

What you expect from it

Identify individual objectives

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

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

Select research methods

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

Research methods might include any of the following:

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

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

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

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

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

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

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

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

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

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

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

What do you plan to do with the research findings?

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

Recruit participants and allocate tasks

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

Prepare a thorough project summary

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

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

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

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

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

Create a realistic timeline

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

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

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

Determine how to present your results

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

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

Presentations and slides

A project report booklet

A project findings pamphlet

Documents with key takeaways and statistics

Graphic visuals to support your findings

• Format your research plan

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

Find format inspiration among the following layouts:

Written outlines

Narrative storytelling

Visual mapping

Graphic timelines

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

• Research plan example

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

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

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

Project title

Project members involved in the research plan

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

Objective 1 (provide a short description for each objective)

Objective 2

Objective 3

Proposed timeline

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

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

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

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

Customizing a research plan template

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

Introductions to participants and stakeholders

Background problems and needs statement

Significance, ethics, and purpose

Research methods, questions, and designs

Preliminary beliefs and expectations

Implications and intended outcomes

Realistic timelines for each phase

Conclusion and presentations

How many pages should a research plan be?

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

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

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

What are the seven steps to developing a research plan?

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

Defining the problem

Identifying goals

Choosing research methods

Recruiting participants

Preparing the brief or summary

Establishing task timelines

Defining how you will present the findings

Should you be using a customer insights hub?

Do you want to discover previous research faster?

Do you share your research findings with others?

Do you analyze research data?

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

Editor’s picks

Last updated: 18 April 2023

Last updated: 27 February 2023

Last updated: 22 August 2024

Last updated: 5 February 2023

Last updated: 16 August 2024

Last updated: 9 March 2023

Last updated: 30 April 2024

Last updated: 12 December 2023

Last updated: 11 March 2024

Last updated: 4 July 2024

Last updated: 6 March 2024

Last updated: 5 March 2024

Last updated: 13 May 2024

Latest articles

Related topics, .css-je19u9{-webkit-align-items:flex-end;-webkit-box-align:flex-end;-ms-flex-align:flex-end;align-items:flex-end;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-flex-direction:row;-ms-flex-direction:row;flex-direction:row;-webkit-box-flex-wrap:wrap;-webkit-flex-wrap:wrap;-ms-flex-wrap:wrap;flex-wrap:wrap;-webkit-box-pack:center;-ms-flex-pack:center;-webkit-justify-content:center;justify-content:center;row-gap:0;text-align:center;max-width:671px;}@media (max-width: 1079px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}}@media (max-width: 799px){.css-je19u9{max-width:400px;}.css-je19u9>span{white-space:pre;}} decide what to .css-1kiodld{max-height:56px;display:-webkit-box;display:-webkit-flex;display:-ms-flexbox;display:flex;-webkit-align-items:center;-webkit-box-align:center;-ms-flex-align:center;align-items:center;}@media (max-width: 1079px){.css-1kiodld{display:none;}} build next, decide what to build next, log in or sign up.

Get started for free

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 ...

12 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

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

• How it works

How to Write a Research Design – Guide with Examples

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

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

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

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

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

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

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

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

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

Need help with a thesis chapter?

• Hire an expert from ResearchProspect today!
• Statistical analysis, research methodology, discussion of the results or conclusion – our experts can help you no matter how complex the requirements are.

Step 3: Data Collection Techniques

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

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

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

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

Research Methods

Step 4: Procedure of Data Analysis

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

Quantitative Data Analysis

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

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

Qualitative Data Analysis

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

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

Step 5: Write your Research Proposal

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

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

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

How to Write a Research Design – Conclusion

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

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

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

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

Frequently Asked Questions

What is research design.

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

How to write a research design?

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

How to write the design section of a research paper?

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

How to write a research design in methodology?

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

You May Also Like

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

Find how to write research questions with the mentioned steps required for a perfect research question. Choose an interesting topic and begin your research.

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

USEFUL LINKS

LEARNING RESOURCES

COMPANY DETAILS

• How It Works

• 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

Figures in Research Paper – Examples and Guide

Research Methodology – Types, Examples and...

Research Process – Steps, Examples and Tips

Research Problem – Examples, Types and Guide

Research Paper Outline – Types, Example, Template

Problem Statement – Writing Guide, Examples and...

Leave a comment x.

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

• University Libraries
• Research Guides
• Topic Guides
• Research Methods Guide
• Research Design & Method

Research Methods Guide: Research Design & Method

• Introduction
• Survey Research
• Interview Research
• Data Analysis
• Resources & Consultation

Tutorial Videos: Research Design & Method

Research Methods (sociology-focused)

Qualitative vs. Quantitative Methods (intro)

Qualitative vs. Quantitative Methods (advanced)

FAQ: Research Design & Method

What is the difference between Research Design and Research Method?

Research design is a plan to answer your research question.  A research method is a strategy used to implement that plan.  Research design and methods are different but closely related, because good research design ensures that the data you obtain will help you answer your research question more effectively.

Which research method should I choose ?

It depends on your research goal.  It depends on what subjects (and who) you want to study.  Let's say you are interested in studying what makes people happy, or why some students are more conscious about recycling on campus.  To answer these questions, you need to make a decision about how to collect your data.  Most frequently used methods include:

• Observation / Participant Observation
• Focus Groups
• Experiments
• Secondary Data Analysis / Archival Study
• Mixed Methods (combination of some of the above)

One particular method could be better suited to your research goal than others, because the data you collect from different methods will be different in quality and quantity.   For instance, surveys are usually designed to produce relatively short answers, rather than the extensive responses expected in qualitative interviews.

What other factors should I consider when choosing one method over another?

Time for data collection and analysis is something you want to consider.  An observation or interview method, so-called qualitative approach, helps you collect richer information, but it takes time.  Using a survey helps you collect more data quickly, yet it may lack details.  So, you will need to consider the time you have for research and the balance between strengths and weaknesses associated with each method (e.g., qualitative vs. quantitative).

• << Previous: Introduction
• Next: Survey Research >>
• Last Updated: Aug 21, 2023 10:42 AM

• Artificial Intelligence
• Product Management
• UX Research

Research Design 101: A Guide To Planning Experiment Design

Brigitta Puskás

Every day, we conduct research. Every research study has its own purpose it lines up with. But how do our researchers plan their research ? What methods for designing research reflect the goals and delivers results? In this article, we go back to the very basics of research and its types. Then, we walk you through our process of assumption validation and experiment design in an everyday setting.

What we will cover in this article:

• The basic types of research
• The different types of research methods
• Study design in research
• The types of qualitative research and a research design in qualitative research
• The types of quantitative research and a research design in quantitative research

The research problem defines research design

According to American sociologist Earl Robert Babbie, “Research is a systematic inquiry to describe, explain, predict and control the observed phenomenon.”

The design of your research , on the other hand, provides your customized toolkit for a specific research problem. You need to make sure that the tools fit the problem. Research design represents the set of methods and procedures you utilize during the process of data collection and analysis specified in the research problem.

We create a research design as a framework to deliver answers to research questions. Based on the research problem, the design of a study defines defines:

• The right choice of study type (descriptive or experimental)
• Sub-type (e.g., descriptive-longitudinal case study)
• The hypotheses
• The independent and dependent variables
• The scope of experimental design
• Data collection methods and a statistical analysis plan, if applicable.

Types of research: Inductive and deductive research

You will find this familiar if you have ever written a thesis. Basically, you can start researching a subject from two ends.

We use inductive research methods to analyze a phenomenon, while deductive research methods verify it.

To put it into practice: We either want to analyze why more people spend more time texting on weekends than on weekdays (inductive research), or assume that it results from them having more time on those days — and then we test this assumption (deductive research).

We associate inductive research approaches generally with qualitative methods and techniques, while deductive methods connect more to quantitative research.

Researching business and technology

The above holds true for any type of research, from physics to neurology, ornithology to user research. “Average people” don’t usually deal with all these fancy research-related expressions (other than that one time with your thesis paper back in college).

But businesses and tech companies do research all the time as well. In a business setting, researchers mainly ask:

• What do organizations or businesses really want to find out?
• What processes and mechanisms need analyzing to chase the idea?
• What arguments need building up around a concept?
• What evidence will people require to believe in the idea or concept?

Research purposes

Research serves three purposes, depending on prior knowledge and the context. We might not even know what will come out in the end (exploratory research). We might want to structure already existing information in a newer / better way (descriptive research) or to find explanations for a given phenomenon.

Let’s dive more into detail!

1. Exploratory research

If we want to explore the phenomenon and research questions but don’t know for sure whether to offer a final conclusion, choose explanatory research. Conduct this type of research to take a look at new problem areas which no one has explored yet.

For example, we want to know what people use their phones for during the week and on weekends. We dive into what apps exist, how we can group them, how people choose, how their prioritize apps, etc.

Exploratory research proves essential for laying the foundation for more conclusive research and data collection.

2. Descriptive Research:

Descriptive research focuses on shedding light on specific issues through the process of data collection. Lead these studies to describe a behavior or phenomenon.

Descriptive research has three main goals: describing, explaining and validating research findings.

For example, we look at when people use apps and what for.

3. Explanatory Research:

Conduct explanatory research or causal research to understand the impact of certain changes in existing standard procedures. Conducting experiments represents the most popular form of casual research, such as research conducted to understand the effect of rebranding on customer loyalty.

For example, we look at why people seem to use their phones longer on average on weekends than on weekdays.

The research process

We broadly classify research methods as qualitative research and quantitative research.

Both methods have distinctive properties and data collection methods. In this segment, we will learn more about both.

Whichever research method you decide to go with, first evaluate the problem from an analytical point of view.

Qualitative research design: Types of qualitative research

As a research method, qualitative research collects data using conversational methods in which participants involved in the research answer open-ended questions. We collect the essentially non-numerical responses.

This method not only helps a researcher understand what participants think but also why they think in a particular way.

These qualitative research methods see wide usage:

• One-to-one Interviews
• Focus Groups
• Ethnographic Research
• Text Analysis
• Case Study Research

Quantitative research design: Types of quantitative research

Quantitative research methods deal with numbers and anything that can deal with a measurable form in a systematic way of investigating the phenomenon. We use it to answer questions in terms of justifying relationships with measurable variables to explain, predict or control a phenomenon.

Researchers often use three methods to conduct this type of research

• Survey Research
• Descriptive Research
• Correlational Research

What makes up research design? Identifying the ideal research methodologies

To choose the appropriate research methods, you must clearly identify the research objectives. Take into consideration this example of research objectives you may have for your business:

• First, find out your clients’ needs.
• Know their preferences and understand what they find important.
• Find an appropriate way to make them aware of your products and services.
• Find ways to improve your products or services to suit your customers’ needs.

After identifying what you need to know, ask which research methods will offer you that information.

Organize your questions within the framework of the 7 Ps of marketing, which influences your company – product, price, promotion, place, people, processes and physical tests.

Research methods in psychology

Psychologists use many different methods for conducting research. Each has advantages and disadvantages that make it suitable for certain situations and unsuitable for others.

Case studies, surveys, naturalistic observation and laboratory observation exemplify descriptive or correlational research methods. Using them, researchers can describe different events, experiences or behaviors, and look for links between them. However, they do not enable researchers to determine causes of behavior.

Remember: Correlation Is Not Causation! Two factors may have a connection without one causing the other to occur. Often, a third factor explains the correlation.

Why does it matter to know the basics of psychological research? Because in any situation when we deal with people, psychological occurrences might come into play.

Differences between research methods and research design

Research methods.

Generalized and established, research methods address research questions (e.g., qualitative vs. quantitative methods). Not all methods apply for all research questions, so the area of research that you want to explore limits the choice of method.

Research Design

Research design involves determining how to apply your chosen method to answer your research question. Think of your study’s design as a blueprint detailing what to do and how to accomplish it.

Key aspects of research design include research methodology, participant/sample collection and assignment and data collection procedures and instruments.

Relationship

Think of the choice of research methods, then design a reciprocal process extending well into your study. For example, a flaw in the design may arise over the course of your study.

Changing the design of the study may lead to the choice of a different method. In turn, this may lead to subsequent changes in the design to accommodate the new method(s).

UX research design

UX research design makes up the plan. It provides the logical structure of any scientific work. It helps you stay on track and systematize the research so to deliver valid data and confidence in decision making based on the results.

Research design functions to ensure the effectiveness and objectivity of your work by providing a blueprint of sorts for the collection, measurement and analysis of the data.

Assumptions and validation in practice: Experiment design

How it uses the assumptions and experiments below:

• Figure out which kind of assumption you have.
• Conduct an experiment like the one listed to see if you assumed correctly.
• If your team did, move forward to the next assumption..
• If they didn’t, evaluate other options.

Assumption 1: We think we have found a problem. Experiment 1 — Online research: Let’s research whether people discuss this problem online. Google, Twitter, and Quora can help. Also check if a solution already exists. Assumption 2: Based on our research, we still think Group X finds this a problem. This group consists of a lot of people, and they all experience the problem. Experiment 2 — Census data and interviews: How many people actually comprise this group? Lead demographic research based on stats and numbers. If this group seems large, talk to some of them in person. See if they all mention the problem. If so, you seem to have proven your point. Assumption 3: We think we have found a solution to this great problem. Experiment 3 — Field research: Now sketch it and talk to some potential users. Then, get out of the building and show it to the target group because we want to make sure they think that your solution will help. If they do, we can move on to the next step.

Assumption 4: We now assume Group X will indeed pay for our solution to their problem. Experiment 4 — Price before Product, Period: Ask potential customers how much they’d pay for this solution, if anything. If they do, figure out if we can actually make it happen.

Assumption 5: We find the solution feasible. Experiment 5 — Feasibility testing: Chat with your engineers/devs. What do they think about building it? Establish if they find it not super hard to do. They will likely appreciate getting involved early on. Assumption 6: We think adding Extra Feature Z will add a lot of value to our solution. Experiment 6 — A/B testing with a mockup: Go and interview users to find out whether the feature makes its inclusion critical. Perhaps create a landing page with and without the feature listed and look at conversion. Don’t ask users if they’ll miss it; show them the product without it and check if they complain. (Useful tools: Invision, UserTesting.com, or AlphaHQ) Assumption 7: We think people use what we designed to solve the problem. Experiment 7  — Usability testing with prototypes: Create a paper or clickable mock and ask users to complete the task. Better yet, just see what happens without any prompt. Invision, UserTesting.com, AlphaHQ, Validately can help you out. Assumption 8 : We think we can build this in Time Period Y. Experiment 8 — Project length estimation: At this point, get more people on board. First, get the engineers into a room, breaking down the product into high-level flows and features. Have them provide high-level point estimates (difficulty: 1-5 points) or T-shirt sizes (difficulty: S, M, L, XL) to get a better overview of how complex your product idea winds up, and how long it would take to build. Assumption 9: Based on what we know, we think the product is running on the right track. Experiment 9 — User testing: The time has come to involve some real users in the process. Talk to some customers about whether they value it enough to actually pay for. Assumption 10: We think we might have reached the stage to kick it all off and launch. Experiment 10 — Prepare the battlefield: Test the product within the organization. Ask the marketing and sales departments whether they all have what they need. A launch roadmap might also help. Here, we’re checking for internal feasibility and how it will all fit the given timeframe. Assumption 11: We assume people will use the product we’re launching. Experiment 11 — Setting up analytics: Setting up Google Analytics, Hotjar, Heap.io and/or other tracking tools. Set these up before launch. Assumption 12: We assume people use our product to solve the problem. Experiment 12 — Ask your customers: Go back to your target users and see how they use the tool you’ve built. Talk to random other users about what they use it for. You may learn of an additional market. Assumption 13: We might miss another feature that we think might work. Experiment 13 : Return to Assumption 6 .

Having trouble assembling your research experiment series?

To get started with your own UX strategy, we are here to help.

Reach out to us via email at [email protected] , or using our Contact Form.

More into in-person learning. Reach out to if you are thinking about organizing an in-house UX training for your team. We sure have some ideas! 😉

Let's talk

Have a language expert improve your writing

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

• Knowledge Base
• Starting the research process

A Beginner's Guide to Starting the Research Process

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

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

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

Table of contents

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

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

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

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

>>Read more about narrowing down a research topic

Receive feedback on language, structure, and formatting

Professional editors proofread and edit your paper by focusing on:

• Academic style
• Vague sentences
• Style consistency

See an example

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

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

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

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

>>Read more about defining a research problem

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

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

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

>>See research question examples

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

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

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

>>Read more about creating a research design

Here's why students love Scribbr's proofreading services

Discover proofreading & editing

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

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

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

>>Read more about writing a research proposal

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

Methodology

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

 Statistics

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

Research bias

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

Is this article helpful?

Other students also liked.

• Writing Strong Research Questions | Criteria & Examples

What Is a Research Design | Types, Guide & Examples

• How to Write a Research Proposal | Examples & Templates

More interesting articles

• 10 Research Question Examples to Guide Your Research Project
• How to Choose a Dissertation Topic | 8 Steps to Follow
• How to Define a Research Problem | Ideas & Examples
• How to Write a Problem Statement | Guide & Examples
• Relevance of Your Dissertation Topic | Criteria & Tips
• Research Objectives | Definition & Examples
• What Is a Fishbone Diagram? | Templates & Examples
• What Is Root Cause Analysis? | Definition & Examples

Get unlimited documents corrected

✔ Free APA citation check included ✔ Unlimited document corrections ✔ Specialized in correcting academic texts

FLEET LIBRARY | Research Guides

Rhode island school of design, create a research plan: research plan.

• Research Plan
• Literature Review
• Ulrich's Global Serials Directory
• Related Guides

A research plan is a framework that shows how you intend to approach your topic. The plan can take many forms: a written outline, a narrative, a visual/concept map or timeline. It's a document that will change and develop as you conduct your research. Components of a research plan

1. Research conceptualization - introduces your research question

2. Research methodology - describes your approach to the research question

3. Literature review, critical evaluation and synthesis - systematic approach to locating,

    reviewing and evaluating the work (text, exhibitions, critiques, etc) relating to your topic

4. Communication - geared toward an intended audience, shows evidence of your inquiry

Research conceptualization refers to the ability to identify specific research questions, problems or opportunities that are worthy of inquiry. Research conceptualization also includes the skills and discipline that go beyond the initial moment of conception, and which enable the researcher to formulate and develop an idea into something researchable ( Newbury 373).

Research methodology refers to the knowledge and skills required to select and apply appropriate methods to carry through the research project ( Newbury 374) .

Method describes a single mode of proceeding; methodology describes the overall process.

Method - a way of doing anything especially according to a defined and regular plan; a mode of procedure in any activity

Methodology - the study of the direction and implications of empirical research, or the sustainability of techniques employed in it; a method or body of methods used in a particular field of study or activity *Browse a list of research methodology books  or this guide on Art & Design Research

Literature Review, critical evaluation & synthesis

A literature review is a systematic approach to locating, reviewing, and evaluating the published work and work in progress of scholars, researchers, and practitioners on a given topic.

Critical evaluation and synthesis is the ability to handle (or process) existing sources. It includes knowledge of the sources of literature and contextual research field within which the person is working ( Newbury 373).

Literature reviews are done for many reasons and situations. Here's a short list:

Sources to consult while conducting a literature review:

Online catalogs of local, regional, national, and special libraries

meta-catalogs such as worldcat , Art Discovery Group , europeana , world digital library or RIBA

subject-specific online article databases (such as the Avery Index, JSTOR, Project Muse)

digital institutional repositories such as Digital Commons @RISD ; see Registry of Open Access Repositories

Open Access Resources recommended by RISD Research LIbrarians

works cited in scholarly books and articles

print bibliographies

the internet-locate major nonprofit, research institutes, museum, university, and government websites

search google scholar to locate grey literature & referenced citations

trade and scholarly publishers

fellow scholars and peers

Communication                              

Communication refers to the ability to

• structure a coherent line of inquiry
• communicate your findings to your intended audience
• make skilled use of visual material to express ideas for presentations, writing, and the creation of exhibitions ( Newbury 374)

Research plan framework: Newbury, Darren. "Research Training in the Creative Arts and Design." The Routledge Companion to Research in the Arts . Ed. Michael Biggs and Henrik Karlsson. New York: Routledge, 2010. 368-87. Print.

About the author

Except where otherwise noted, this guide is subject to a Creative Commons Attribution license

source document

  Routledge Companion to Research in the Arts

• Next: Literature Review >>
• Last Updated: Aug 15, 2024 1:29 AM
• URL: https://risd.libguides.com/researchplan

How to... Design a research study

The design of a piece of research refers to the practical way in which the research was conducted according to a systematic attempt to generate evidence to answer the research question. The term "research methodology" is often used to mean something similar, however different writers use both terms in slightly different ways: some writers, for example, use the term "methodology" to describe the tools used for data collection, which others (more properly) refer to as methods.

On this page

What is research design, sampling techniques, quantitative approaches to research design, qualitative approaches to research design, planning your research design.

The following are some definitions of research design by researchers:

Design is the deliberately planned 'arrangement of conditions for analysis and collection of data in a manner that aims to combine relevance to the research purpose with economy of procedure'.

Selltiz C.S., Wrightsman L.S. and Cook S.W. 1981  Research Methods in Social Relations, Holt, Rinehart & Winston, London, quoted in Jankowicz, A.D.,  Business Research Methods , Thomson Learning, p.190.)

The idea behind a design is that different kinds of issues logically demand different kinds of data-gathering arrangement so that the data will be:

• relevant to your thesis or the argument you wish to present;
• an adequate test of your thesis (i.e. unbiased and reliable);
• accurate in establishing causality, in situations where you wish to go beyond description to provide explanations for whatever is happening around you;
• capable of providing findings that can be generalised to situations other than those of your immediate organisation.

(Jankowicz, A.D.,  Business Research Methods  , Thomson Learning, p. 190)

The design of the research involves consideration of the best method of collecting data to provide a relevant and accurate test of your thesis, one that can establish causality if required (see  What type of study are you undertaking? ), and one that will enable you to generalise your findings.

Design of the research should take account of the following factors, which are briefly discussed below with links to subsequent pages or other parts of the site where there is fuller information.

What is your theoretical and epistemological perspective?

Although management research is much concerned with observation of humans and their behaviour, to a certain extent the epistemological framework derives from that of science. Positivism assumes the independent existence of measurable facts in the social world, and researchers who assume this perspective will want to have a fairly exact system of measurement. On the other hand, interpretivism assumes that humans interpret events and researchers employing this method will adopt a more subjective approach.

What type of study are you undertaking?

Are you conducting an exploratory study, obtaining an initial grasp of a phenomenon, a descriptive study, providing a profile of a topic or institution:

Karin Klenke provides an exploratory study of issues of gender in management decisions in  Gender influences in decision-making processes in top management teams  ( Management Decision , Volume 41 Number 10)

Damien McLoughlin provides a descriptive study of action learning as a case study in  There can be no learning without action and no action without learning  in ( European Journal of Marketing , Volume 38 Number 3/4)

Or it can be explanatory, examining the causal relationship between variables: this can include the testing of hypotheses or examination of causes:

Martin  et al.  examined ad zipping and repetition in  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  ( Marketing Intelligence & Planning , Volume 20 Number 1) with a number of hypotheses e.g. that people are more likely to remember an ad that they have seen repeatedly.

What is your research question?

The most important issue here is that the design you use should be appropriate to your initial question. Implicit within your question will be issues of size, breadth, relationship between variables, how easy is it to measure variables etc.

The two different questions below call for very different types of design:

The example  Dimensions of library anxiety and social interdependence: implications for library services  (Jiao and Onwuegbuzie,  Library Review , Volume 51 Number 2) looks at attitudes and the relationship between variables, and uses very precise measurement instruments in the form of two questionnaires, with 43 and 22 items respectively.

In the example  Equity in Corporate Co-branding  (Judy Motion  et al. ,  European Journal of Marketing , Volume 37 Number 7),  the RQs posit a need to describe rather than to link variables, and the methodology used is one of discourse theory, which involves looking at material within the context of its use by the company.

What sample size will you base your data on?

The sample is the source of your data, and it is important to decide how you are going to select it.

See  Sampling techniques .

What research methods will you use and why?

We referred above to the distinction between methods and methodology. There are two main approaches to methodology – qualitative and quantitative.

For more detail on each of the approaches,  Quantitative approaches to design  and  Qualitative approaches to design  later in this feature.

Note, you do not have to stick to one methodology (although some writers recommend that you do). Combining methodologies is a matter of seeing which part of the design of your research is better suited to which methodology.

How will you triangulate your research?

Triangulation refers to the process of ensuring that any defects in a particular methodology are compensated by use of another at appropriate points in the design. For example, if you carry out a quantitative survey and need more in depth information about particular aspects of the survey you may decide to use in-depth interviews, a qualitative method.

Here are a couple of useful articles to read which cover the issue of triangulation:

• Combining quantitative and qualitative methodologies in logistics research  by John Mangan, Chandra Lalwani and Bernard Gardner ( International Journal of Physical Distribution & Logistics Management , Volume 34 Number 7) looks at ways of combining methodologies in a particular area of research, but much of what they say is generally applicable.
• Quantitative and qualitative research in the built environment: application of "mixed" research approach  by Dilanthi Amaratunga, David Baldry, Marjan Sarshar and Rita Newton ( Work Study , Volume 51 Number 1) looks at the relative merits of the two research approaches, and despite reference to the built environment in the title acts as a very good introduction to quantitative and qualitative methodology and their relative research literatures. The section on triangulation comes under the heading 'The mixed (or balanced) approach'. 

What steps will you take to ensure that your research is ethical?

Ethics in research is a very important issue. You should design the research in such a way that you take account of such ethical issues as:

• informed consent (have the participants had the nature of the research explained to them)?
• checking whether you have permission to transcribe conversations with a tape recorder
• always treating people with respect, consideration and concern.

How will you ensure the reliability of your research?

Reliability

This is about the replicability of your research and the accuracy of the procedures and research techniques. Will the same results be repeated if the research is repeated? Are the measurements of the research methods accurate and consistent? Could they be used in other similar contexts with equivalent results? Would the same results be achieved by another researcher using the same instruments? Is the research free from error or bias on the part of the researcher, or the participants? (E.g. do the participants say what they believe the management, or the researcher, wants? For example, in a survey done on some course material, that on a mathematical module received glowing reports – which led the researcher to wonder whether this was anything to do with the author being the Head of Department!)

How successfully has the research actually achieved what it set out to achieve? Can the results of the study be transferred to other situations? Does x really cause y, in other words is the researcher correct in maintaining a causal link between these two variables? Is the research design sufficiently rigorous, have alternative explanations been considered? Have the findings really be accurately interpreted? Have other events intervened which might impact on the study, e.g. a large scale redundancy programme? (For example, in an evaluation of the use of CDs for self study with a world-wide group of students, it was established that some groups had not had sufficient explanation from the tutors as to how to use the CD. This could have affected their rather negative views.)

Generalisability

Are the findings applicable in other research settings? Can a theory be developed that can apply to other populations? For example, can a particular study about dissatisfaction amongst lecturers in a particular university be applied generally? This is particularly applicable to research which has a relatively wide sample, as in a questionnaire, or which adopts a scientific technique, as with the experiment.

Transferability

Can the research be applied to other situations? Particularly relevant when applied to case studies.

In addition, each of the sections in this feature on quantitative and qualitative approaches to research design contain notes on how to ensure that the research is reliable.

Some basic definitions

In order to answer a particular research question, the researcher needs to investigate a particular area or group, to which the conclusions from the research will apply. The former may comprise a geographical location such as a city, an industry (for example the clothing industry), an organisation/group of organisations such as a particular firm/type of firm, a particular group of people defined by occupation (e.g. student, manager etc.), consumption of a particular product or service (e.g. users of a shopping mall, new library system etc.), gender etc. This group is termed the  research population .

The  unit of analysis  is the level at which the data is aggregated: for example, it could be a study of individuals as in a study of women managers, of dyads, as in a study of mentor/mentee relationships, of groups (as in studies of departments in an organisation), of organisations, or of industries.

Unless the research population is very small, we need to study a subset of it, which needs to be general enough to be applicable to the whole. This is known as a  sample , and the selection of components of the sample that will give a representative view of the whole is known as  sampling technique  . It is from this sample that you will collect your data.

In order to draw up a sample, you need first to identify the total number of people in the research population. This information may be available in a telephone directory, a list of company members, or a list of companies in the area. It is known as a  sampling frame .

In  Networking for female managers' career development  (Margaret Linehan,  Journal of Management Development , Volume 20 Number 10), he sampling technique is described as follows:

"A total of 50 senior female managers were selected for inclusion in this study. Two sources were used for targeting interviewees, the first was a listing of Fortune 500 top companies in England, Belgium, France and Germany, and, second, The Marketing Guide to Ireland. The 50 managers who participated in the study were representative of a broad range of industries and service sectors including: mining, software engineering, pharmaceutical manufacturing, financial services, car manufacturing, tourism, oil refining, medical and state-owned enterprises."

Sampling may be done either a  probability  or a  non-probability  basis. This is an important research design decision, and one which will depend on such factors as whether the theory behind the research is positivist or idealist, whether qualitative or quantitative methods are used etc. Note that the two methods are not mutually exclusive, and may be used for different purposes at different points in the research, say purposive sampling to find out key attitudes, followed by a more general, random approach.

Note that there is a very good section from an online textbook on sampling: see William Trochim's  Research Methods Knowledge Base .

Probability sampling

In  probability  sampling, each member of a given research population has an equal chance of being selected. It involves, literally, the selection of respondents at random from the sampling frame, having decided on the sample size. This type of sampling is more likely if the theoretical orientation of the research is  positivist , and the methodology used is likely to be  quantitative .

Probability sampling can be:

• random  – the selection is completely arbitrary, and a given number of the total population is selected completely at random.
• systematic  – every  nth element  of the population is selected. This can cause a problem if the interval of selection means that the elements share a characteristic: for example, if every fourth seat of a coach is selected it is likely that all the seats will be beside a window.
• stratified   random  – the population is divided into segments, for example, in a University, you could divide the population into academic, administrators, and academic related (related professional staff). A random number of each group is then selected. It has the advantage of allowing you to categorise your population according to particular features. A.D. Jankowicz provides useful advice (Business Research Methods,Thomson Learning, 2000, p.197).

The concept of fit in services flexibility and research: an empirical approach  (Antonio J Verdú-Jover  et al. ,  International Journal of Service Industry Management , Volume 15 Number 5) uses stratified sampling: the study concentrates on three sectors within the EU, chemicals, electronics and vehicles, with the sample being stratified within this sector.

• cluster  – a particular subgroup is chosen at random. The subgroup may be based on a particular geographical area, say you may decide to sample particular areas of the country.

Non probability sampling

Here, the population does not have an equal chance of being selected; instead, selection happens according to some factor such as:

• convenience/accidental  – being present at a particular time e.g. at lunch in the canteen. This is an easy way of getting a sample, but may not be strictly accurate, because the factor you have chosen is based on your convenience rather than on a true understanding of the characteristics of the sample.

In  "Saying is one thing; doing is another": the role of observation in marketing research  ( Qualitative Market Research: An International Journal , Volume 2 Number 1), Matthews and Boote use a two-stage sampling process, with convenience sampling followed by time sampling: see their methodology.

• "key informant technique" – i.e. people with specialist knowledge
• using people at selected points in the organisational hierarchy 
• snowball, with one person being approached and then suggesting others.

In "The benefits of the implementation of the ISO 9000 standard: empirical research in 288 Spanish companies", a sample was selected based on all certified companies in a particular area, because this was where the highest number of certified companies could be found.

• quota  – the assumption is made that there are subgroups in the population, and a quota of respondents is chosen to reflect this diversity. This subgroup should be reasonably representative of the whole, but care should be taken in drawing conclusions for the whole population. For example, a quota sample taken in New York State would not be representative of the whole of the United States.

Monitoring consumer confidence in food safety: an exploratory study , de Jonge  et al . use quota sampling using age, gender, household size and region as selection variables in a food safety survey. Read about the methodology under Materials and methods.

Non probability sampling methods are more likely to be used in qualitative research, with the greater degree of collaboration with the respondents affording the opportunity of greater detail of data gathering. The researcher is more likely to be involved in the process and be adopting an  interpretivist theoretical  stance.

Calculating the sample size

In purposive sampling, this will be determined by judgement; in other more random types of sample it is calculated as a  proportion  of the sampling frame, the key criterion being to ensure that it is representative of the whole. (E.g. 10 per cent is fine for a large population, say over 1000, but for a small population you would want a larger proportion.)

If you are using stratified sampling you may need to adjust your strata and collapse into smaller strata if you find that some of your sample sizes are too small.

The response rate

It is important to keep track of the response rate against your sample frame. If you are depending on postal questionnaires, you will need to plan into your design time to follow up the questionnaires. What is considered to be a good response rate varies according to the type of survey: if you are, say, surveying managers, then a good response would be 50 per cent; for consumer surveys, the response rate is likely to be lower, say 10 to 20 per cent.

The thing that characterises quantitative research is that it is objective. The assumption is that facts exist totally independently and the researcher is a totally  objective  observer of situations, and has no power to influence them. At such, it probably starts from a positivist or empiricist position.

The research design is based on one iteration in collection of the data: the categories are isolated prior to the study, and the design is planned out and generally not changed during the study (as it may be in qualitative research).

What is my research question? What variables am I interested in exploring?

It is usual to start your research by carrying out a  literature review , which should help you formulate a research question.

Part of the task of the above is to help you determine what  variables  you are considering. What are the key variables for your research and what is the relationship between them – are you looking to  explore  issues, to  compare  two variables or to look at  cause and effect ?

The Dutch heart health community intervention "Hartslag Limburg": evaluation design and baseline data  (Gaby Ronda  et al. ,  Health Education , Volume 103 Number 6) describes a trial of a cardiovascular prevention programme which indicated the importance of its further implementation. The key variables are the types of health related behaviours which affect a person's chance of heart disease.

The following studies compare variables:

Service failures away from home: benefits in intercultural service encounters  (Clyde A Warden  et al. ,  International Journal of Service Industry Management , Volume 14 Number 4) compares service encounters (the independent variable) inside and outside Taiwan (the dependent variable) in order to look at certain aspects of 'critical incidents' in intercultural service encounters.

The concept of fit in services flexibility and research: an empirical approach  (Antonio J Verdú-Jover  et al. ,  International Journal of Service Industry Management , Volume 15 Number 5) looks at managerial flexibility in relation to different types of business, service and manufacturing.

They can also look at cause and effect:

In  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  (Brett A.S. Martin  et al. ,  Marketing Intelligence & Planning , Volume 20 Number 1), the authors look at two variables associated with advertising, notably zipping and fast forwarding, and in their effect on a third variable, consumer behaviour - i.e. ability to remember ads. Furthermore, it looks at the interaction between the first two variables - i.e. whether they interact on one another to help increase recall.

What is the hypothesis?

It is usual with quantitative research to proceed from a particular hypothesis. The object of research would then be to test the hypothesis.

In the example quoted above,  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers , the researchers decided to explore a neglected area of the literature: the interaction between ad zipping and repetition, and came up with three hypotheses:

The influence of zipping H1 . Individuals viewing advertisements played at normal speed will exhibit higher ad recall and recognition than those who view zipped advertisements.

Ad repetition effects H2 . Individuals viewing a repeated advertisement will exhibit higher ad recall and recognition than those who see an advertisement once.

Zipping and ad repetition H3 . Individuals viewing zipped, repeated advertisements will exhibit higher ad recall and recognition than those who see a normal speed advertisement that is played once.

What are the appropriate measures to use

It is very important, when designing your research, to understand  what  you are measuring. This will call for a close examination of the issues involved: is your measure suitable to the hypothesis and research question under consideration? The type of scale you will use will dictate the statistical procedure which you can use to analyse your data, and it is important to have an understanding of the latter at the outset in order to obtain the correct level of analysis, and one that will throw the best light on your research question, and help test your hypothesis.

It is also important to understand what type of data you are trying to collect. Are you wanting to collect data that relates simply to different types of categories, for example, men and women (as in, say, differences in decision-making between men and women managers), or do you want to rank the data in some way? Choices as far as the nature of data are concerned again dictate the type of statistical analysis.

Data can be categorised as follows:

• Nominal – Representing particular categories, e.g. men or women.
• Ordinal – Ranked in some way such as order of passing a particular point in a shopping centre.
• Interval – Ranked according to the interval between the data, which remains the same. Most typical of this type of data is temperature.
• Ratio – Where it is possible to measure the difference between different types of data - for example applying a measurement.
• Scalar – This type of data has intervals between it, which are not quantifiable.

Note that some of the above categories, especially 'interval' and 'ratio' are drawn from a scientific model which assumes exact measurement of data (temperature, length etc.). In management research, you are unlikely to want to or be able to apply such a high degree of exactitude, and are more likely to be measuring less exact criteria which do not have an exact interval between them.

Here are some examples of use of data in management research. This one illustrates the use of different categories:

The concept of fit in services flexibility and research: an empirical approach  (see above) uses an approach which itemises the different aspects which the researchers wished to measure flexibility mix, performance and the form's general data. 

This one looks at categories and also at ranked data (ordinal):

In  Remote control marketing: how ad fast-forwarding and ad repetition affect consumers  (also see above), the measure involved 2 (speed of ad presentation: normal, fast-forwarded) ×\ 2 (repetition: none, one repetition) between-subjects factorial design.

The following examples look at measures on a scale, which may relate to tangible factors such as frequency, or more intangible ones which relate to attitude or opinion:

How many holidays do you take in a year?

One __  Between 2 and 5 __  Between 5 and 10 __  More than 10 __

Tick the option which most agrees with your views.

Navigating my way around the CD was:

Very easy __  Easy __  Neither easy nor hard __  Hard __  Very hard __

The later type of data are very common in management research, and are known as scalar data. A very common measure for such data is known as the Likert scale:

Strongly agree __________ Agree __________ Neither agree nor disagree __________ Disagree __________ Strongly disagree __________

How will I analyse the data?

Quantitative data are invariably analysed by some sort of statistical means, such as a t-test, a chi test, cluster analysis etc. It is very important to decide at the planning stage what your method of analysis will be: this will in turn affect your choice of measure. Both your analysis and measure should be suitable to test your hypothesis.

You need also to consider what type of package will you need to analyse your data. It may be sufficient to enter it into an Excel spreadsheet, or you may wish to use a statistical package such as SPSS or Mintab.

What are the instruments used in quantitative research?

Or, put more simply, what methods will you use to collect your data?

In scientific research, it is possible to be reasonably precise by generating experiments in laboratory conditions. Whilst the  field experiment  has a place in management research, as does  observation , the most usual instrument for producing quantitative data is the  survey , most often carried out by means of a  questionnaire .

You will find numerous examples of questionnaires and surveys in research published by Emerald, as you will in any database of management research. Questionnaires will be discussed at a later stage but here are some key issues:

• It is important to know exactly what questions you want answers to. A common failing is to realise, once you have got the questionnaire back, that you really need answers to a question which you never asked. Thus the questionnaire should be rigorously researched and the questions phrased as precisely as possible.
• You are more likely to get a response if you give people a reason to respond - commercial companies sometimes offer a prize, which may not be possible or appropriate if you are a researcher in a university, but it is usual in that case to give the reason behind your research, which gives your respondent a context. Even more motivational is the ease with which the questionnaire can be filled in.
• How many responses will I need? This concerns the eventual size of your dataset and depends upon the degree of complexity of your planned analysis, how you are treating your variables (for example, if you are wanting to show the effect of a variable, you will need a larger response size, likewise if you are showing changes in variables).

Other instruments that are used in quantitative research to generate data are experiments, historical records and documents, and observation.

Note that some authors claim that for a design to be a  true experiment , items must be randomly assigned to groups; if there is some sort of control group or multiple measures, then it may be  quasi experimental . If your survey fits neither of these descriptions, it may according to these authors be sufficient for descriptive purposes, but not if you seek to establish a causal relationship.

For more information on types of design, see William Trochim's Research Methods Knowledge Base section on  types of design .

What are the advantages and drawbacks of quantitative research?

The main advantage of quantitative research is that it is easy to determine its rigour: because of the objectivity of quantitative studies, it is easy to replicate them in another situation. For example, a well-constructed questionnaire can be used to analyse job satisfaction in two different companies; likewise, an observation studying consumer behaviour in a shopping centre can take place in two different such centres.

Quantitative methods are also good at obtaining a good deal of reliable data from a large number of sources. Their drawback is that they are heavily dependent on the reliability of the instrument: that is, in the case of the questionnaire, it is vital to ask the right questions in the right way. This in turn is dependent upon having sufficient information about a situation, which is not always possible. In addition, quantitative studies may generate a large amount of data, but the data may lack depth and fail to explain complex human processes such as attitudes to organisational change, or how how learning takes place.

For example, a quantitative study on a piece of educational software may show that on the whole people felt that they had learnt something, but may not necessarily show how they learnt, which an observation could.

For this reason, quantitative methods are often used in conjunction with qualitative methods: for example, qualitative methods of interviewing may be used as a way of finding out more about a situation in order to draw up an informed quantitative instrument; or to explore certain issues which have appeared in the quantitative study in greater depth.

Qualitative research operates from a different epistemological perspective than quantitative, which is essentially objective. It is a perspective that acknowledges the essential difference between the social world and the scientific one, recognising that people do not always observe the laws of nature, but rather comprise a whole range of feelings, observations, attitudes which are essentially subjective in nature. The theoretical framework is thus likely to be interpretivist or realist. Indeed, the researcher and the research instrument are often combined, with the former being the interviewer, or observer – as opposed to quantitative studies where the research instrument may be a survey and the subjects may never see the researcher.

In an  interview for Emerald ,  Professor Slawomir Magala , Editor of the  Journal of Organizational Change Management , has this to say about qualitative methods:

"We follow the view that the social construction of reality is personal, experienced by individuals and between individuals – in fact, the interactions which connect us are the building blocks of reality, and there is much meaning in the space between individuals."

As opposed to the statistical reliance of quantitative research, data from qualitative research is based on observation and words, and analysis is based on interpretation and pattern recognition rather than statistical analysis.

Miles and Huberman list the following as typical criteria of qualitative research:

• Intense and prolonged contact in the field.
• Designed to achieve a holistic or systemic picture.
• Perception is gained from the inside based on actors' understanding.
• Little standardised instrumentation is used.
• Most analysis is done with words.
• There are multiple interpretations available in the data.

Miles, M. and Huberman, A.M. (1994) Qualitative Data Analysis: An Expanded Sourcebook , Sage, London

To what types of research questions is qualitative research relevant?

Qualitative research is best suited to the types of questions which require exploration of data  in depth  over a not particularly large sample. For example, it would be too time consuming to ask questions such as "Please describe in detail your reaction to colour x" to a large number of people, it would be more appropriate to simply ask "Do you like colour x" and give people a "yes/no" option. By asking the former question to a smaller number of people, you would get a more detailed result.

Qualitative research is also best suited to  exploratory  and  comparative  studies; to a more limited extent, it can also be used for  "cause-effect"  type questions, providing these are fairly limited in scope.

One of the strengths of qualitative research is that it allows the researcher to gain an in-depth perspective, and to grapple with complexity and ambiguity. This is what makes it suitable to analysis of  particular  groups or situations, or unusual events.

What is the relationship of qualitative research to hypotheses?

Qualitative research is usually inductive: that is, researchers gather data, and then formulate a hypothesis which can be applied to other situations.

In fact, one of the strengths of qualitative research is that it can proceed from a relatively small understanding of a particular situation, and generate new questions during the course of data collection as opposed to needing to have all the questions set out beforehand. Indeed, it is good practice in quantitative research to go into a situation as free from preconceptions as possible.

How will you analyse the data?

There is not the same need with qualitative research to determine the measure and the method of analysis at an early stage of the research process, mainly because there are no standard ways of analysing data as there are for quantitative research: it is usual to go with whatever is appropriate for the research question. However, because qualitative data usually involves a large amount of transcription (e.g. of taped interviews, videos of focus groups etc.) it is a good idea to have a plan of how this should be done, and to allow time for the transcription process.

There are a couple of attested methods of qualitative data analysis:  content analysis , which involves looking at emerging patterns, and  grounded analysis , which involves going through a number of guided stages and which is closely linked to  grounded theory .

What are the main instruments of qualitative research?

Or put another way, what are the main methods used to collect data? These can be organised according to their methodology (note, the following is not an exhaustive list, for which you should consult a good book on qualitative research):

Ethnographic methods

As the name suggests, this methodology derives from anthropology and involves observing people as a participant within their social and cultural system. Most common methods of data collection are:

• Interviewing, which means discussions with people either on the phone, by email or in person when the purpose is to collect data which is by its nature unquantifiable and more difficult to analyse by statistical means, but which provides in-depth information. The interview can be either:  Structured , which means that the interviewer has a set number of questions.  Semi-structured , which means that the interviewer has a number of questions or a purpose, but the interview can still go off in unanticipated directions.
• Focus groups, which is where a group of people are assembled at one time to give their reaction to a product, or to discuss an issue. There is usually some sort of facilitation which involves either guided discussion or some sort of product demonstration.
• Participant observation – the researcher observes behaviour of people in the organisation, their language, actions, behaviour etc.

For some examples of participant observation, see Methods of empirical research ,  and for examples of interview technique, see  Techniques of data collection and analysis .

Historical analysis

This is literally, the analysis of historical documents of a particular company, industry etc. It is important to understand exactly what your focus is, and also which historical school or theoretical perspective you are drawing on.

Grounded theory

This is an essentially inductive approach, and is applied when the understanding of a particular phenomenen is sought. A feature is that the design of the research has several iterations: there is initial exploration followed by a theory which is then tested.

In  Grounded theory methodology and practitioner reflexivity in TQM research  ( International Journal of Quality & Reliability Management  , Volume 18 Number 2), Leonard and McAdam use grounded theory to explore TQM, on the grounds that quantitative methods "fail to give deep insights and rich data into TQM in practice within organizations", and that it is much more appropriate to listen to the individual experiences of participants. 

Action research

This is a highly participative form of research where the research is carried out in collaboration with those involved in a particular process, which is often concerned with some sort of change.

Narrative methods

This is when the researcher listens to the stories of people in the organisation and triangulates them against official documents.

Discourse theory

This methodology draws on a theory which allows language to have a meaning that is not set but is negotiated through social context.

Helen Francis in  The power of "talk" in HRM-based change  ( Personnel Review , Volume 31 Number 4) describes her use of discourse theory as follows:

"The approach to discourse analysis drew upon Fairclough's seminal work in which discourse is treated as a form of social practice and meaning is something that is essentially fluid and negotiated rather than being authored individually (Fairclough, 1992, 1995).

"For Fairclough (1992, 1995) the analysis of discursive events is three dimensional and includes simultaneously a piece of text, an instance of discursive practice, and an instance of social practice. Text refers to written and spoken language in use, while "discursive practices" allude to the processes by which texts are produced and interpreted. The social practice dimension refers to the institutional and organisational factors surrounding the discursive event and how they might shape the nature of the discursive practice.

"For the purposes of this research, the method of analysis included a description of the language text and how it was produced or interpreted amongst managers and their subordinates. Particular emphasis was placed on investigating the import of metaphors that are characteristic of HRM, and the introduction of HRM-based techniques adopted by change leaders in their attempt to privilege certain themes and issues over others."

Fairclough, N., 1992,  Discourse and Social Change , Polity Press, Cambridge.

Fairclough, N., 1995,  Critical Discourse Analysis: Papers in the Critical Study of Language , Longman, London.

Discourse theory can be applied to the written as well as the spoken word and can be used to analyse marketing literature as in the following example:

Equity in corporate co-branding: the case of Adidas and the all-blacks  by Judy Motion  et al.  ( European Journal of Marketing , Volume 37 Number 7), where discourse theory is used to analyse branding messages.

How rigorous is qualitative research?

It is often considered harder to demonstrate the rigour of qualitative research, simply because it may be harder to replicate the conditions of the study, and apply the data in other similar circumstances. The rigour may partly lie in the ability to generate a theory which can be applied in other situations, and which takes our understanding of a particular area further.

Rigour in qualitative research is greatly aided by:

• confirmability - which does not necessarily mean that someone else would adopt the same conclusion, but rather there is a clear audit trail between your data and your interpretation; and that interpretations are based on a wide range of data (for example, from several interviews rather than just one). (This is related to  triangulation , see below.)
• authenticity - are you drawing on a sufficiently wide range of rich data, do the interpretations ring true, have you considered rival interpretations, do your informants agree with your interpretation?

In  Cultural assumptions in career management: practice implications from Germany;  (Hansen and Willcox,  Career Development International , Volume 2 Number 4), the main method used is ethnographic interviews, and findings are verified by comparing data from the two samples.

Reliability is also enhanced if you can triangulate your data from a number of different sources or methods of data collection, at different times and from different participants.

Dennis Cahill, in  When to use qualitative methods: a new approach  ( Marketing Intelligence & Planning , Volume 14 Number 6), has this to say about the reliability of qualitative research:

"While there are times when qualitative techniques are inappropriate to the research goal, or appropriate only in certain portions of a research project, quantitative techniques do not have universal applicability, either. Although these techniques may be used to measure "reality" rather precisely, they often suffer from a lack of good descriptive material of the type which brings the information to life. This lack is particularly felt in corporate applications where implementation of the results is sought. Therefore, whether one has any interest in the specific research described above, if one is involved in implementation of research results – something we all should be involved in – the use of qualitative research at midpoint is a technique with which we should become familiar.

"It is at this point that some qualitative follow up – interviews or focus groups for example – can serve to flesh out the results, making it possible for people at the firm to understand and internalize those results."

Can qualitative research be used in with quantitative research?

Whereas some researchers only use either qualitative or quantitative methodologies, the two are frequently combined, as when for example qualitative methods are used exploratatively in order to obtain further information prior to developing a quantitative research instrument. In other cases, qualitative methods are used to complement quantitative methods and obtain a greater degree of descriptive richness:

In  When to use qualitative methods: a new approach , Dennis Cahill describes how qualitative methods were used after an extensive questionnaire used to carry out research for a new publication dedicated to the needs of the real estate market. The analysis for the questionnaire produced a five-segment typology (winners, authentics, heartlanders, wannabes and maintainers), which was tested by means of an EYE-TRAC test, when a selected sample was videotaped looking at a magazine of houses for sale.

Once you have established the key features of your design, you need to create an outline project plan which will include a budget and a timetable. In order to do this you need to think first about the activities of your data collection: how much data are you collecting, where etc. (See the section on  Sampling techniques .) You also need to consider your time period for data collection.

Over what time period will you collect your data?

This refers to two types of issues:

Type of study

Should the research be a 'snapshot', examining a particular phenomenon at a particular time, or should it be  longitutinal , examining an issue over a time period? If the latter, the object will be to explore changes over the period.

A longitudinal study of corporate social reporting in Singapore  (Eric W K Tsang,  Accounting, Auditing & Accountability Journal , Volume 11 Number 5) examines social reporting in that country from 1986 to 1995.

Methodology

Sometimes, you may have 'one shot' at the collection of your data - in other words, you plan your sample, your method of data collection, and then analyse the result. This is more likely to be the case if your research approach is more quantitative.

However, other types of research approach involve stages in the collection of data. For example, in  grounded theory  research, data is collected and analysed and then the process is repeated as more is discovered about the subject. Likewise in  action research , there is a cyclical process of data collection, reflection and more collection and analysis.

If you adopt an approach where you  combine quantitative and qualitative methods , then this methodology will dictate that you do a series of studies, whether qualitative followed by quantitative, or vice versa, or qualitative/quantitative/qualitative.

Grounded theory methodology and practitioner reflexivity in TQM research  (Leonard and McAdam,  International Journal of Quality & Reliability Management , Volume 18 Number 2) adopts a three-stage approach to the collection of data.

Doing the plan

The following are some of the costs which need to be considered:

• Travel to interview people.
• Postal surveys, including follow-up.
• The design and printing of the questionnaire, especially if there is use of Optical Mark Reader (OMR) and Optical Character Recognition (OCR) technology.
• Programming to "read" the above.
• Programming the data into meaningful results.
• Transcription of any tape recorded interviews.
• Cost of design of any internet survey.
• Employment of a research assistant.

Timetabling

Make a list of the key stages of your research. Does it have several phases, for example, a questionnaire, then interviews?

How long will each phase take? Take account of factors such as:

• Sourcing your sampling frame
• Determining the sample
• Approaching interview subjects
• Preparations for interviews
• Writing questionnaires
• Response time for questionnaires (include a follow-up stage)
• Analysing the responses
• Writing the report

When doing a schedule, it's tempting to make it as short as possible in the belief that you actually can achieve more in the time than you think. However, it's very important to be as accurate as possible in your scheduling.

Planning is particularly important if you are working to a specific budget and timetable as for example if you are doing a PhD, or if you are working on a funded research project, which has a specific amount of money available and probably also specific deadlines.

Illustration by James Round

How to plan a research project

Whether for a paper or a thesis, define your question, review the work of others – and leave yourself open to discovery.

by Brooke Harrington   + BIO

is professor of sociology at Dartmouth College in New Hampshire. Her research has won international awards both for scholarly quality and impact on public life. She has published dozens of articles and three books, most recently the bestseller Capital without Borders (2016), now translated into five languages.

Edited by Sam Haselby

Need to know

‘When curiosity turns to serious matters, it’s called research.’ – From Aphorisms (1880-1905) by Marie von Ebner-Eschenbach

Planning research projects is a time-honoured intellectual exercise: one that requires both creativity and sharp analytical skills. The purpose of this Guide is to make the process systematic and easy to understand. While there is a great deal of freedom and discovery involved – from the topics you choose, to the data and methods you apply – there are also some norms and constraints that obtain, no matter what your academic level or field of study. For those in high school through to doctoral students, and from art history to archaeology, research planning involves broadly similar steps, including: formulating a question, developing an argument or predictions based on previous research, then selecting the information needed to answer your question.

Some of this might sound self-evident but, as you’ll find, research requires a different way of approaching and using information than most of us are accustomed to in everyday life. That is why I include orienting yourself to knowledge-creation as an initial step in the process. This is a crucial and underappreciated phase in education, akin to making the transition from salaried employment to entrepreneurship: suddenly, you’re on your own, and that requires a new way of thinking about your work.

What follows is a distillation of what I’ve learned about this process over 27 years as a professional social scientist. It reflects the skills that my own professors imparted in the sociology doctoral programme at Harvard, as well as what I learned later on as a research supervisor for Ivy League PhD and MA students, and then as the author of award-winning scholarly books and articles. It can be adapted to the demands of both short projects (such as course term papers) and long ones, such as a thesis.

At its simplest, research planning involves the four distinct steps outlined below: orienting yourself to knowledge-creation; defining your research question; reviewing previous research on your question; and then choosing relevant data to formulate your own answers. Because the focus of this Guide is on planning a research project, as opposed to conducting a research project, this section won’t delve into the details of data-collection or analysis; those steps happen after you plan the project. In addition, the topic is vast: year-long doctoral courses are devoted to data and analysis. Instead, the fourth part of this section will outline some basic strategies you could use in planning a data-selection and analysis process appropriate to your research question.

Step 1: Orient yourself

Planning and conducting research requires you to make a transition, from thinking like a consumer of information to thinking like a producer of information. That sounds simple, but it’s actually a complex task. As a practical matter, this means putting aside the mindset of a student, which treats knowledge as something created by other people. As students, we are often passive receivers of knowledge: asked to do a specified set of readings, then graded on how well we reproduce what we’ve read.

Researchers, however, must take on an active role as knowledge producers . Doing research requires more of you than reading and absorbing what other people have written: you have to engage in a dialogue with it. That includes arguing with previous knowledge and perhaps trying to show that ideas we have accepted as given are actually wrong or incomplete. For example, rather than simply taking in the claims of an author you read, you’ll need to draw out the implications of those claims: if what the author is saying is true, what else does that suggest must be true? What predictions could you make based on the author’s claims?

In other words, rather than treating a reading as a source of truth – even if it comes from a revered source, such as Plato or Marie Curie – this orientation step asks you to treat the claims you read as provisional and subject to interrogation. That is one of the great pieces of wisdom that science and philosophy can teach us: that the biggest advances in human understanding have been made not by being correct about trivial things, but by being wrong in an interesting way . For example, Albert Einstein was wrong about quantum mechanics, but his arguments about it with his fellow physicist Niels Bohr have led to some of the biggest breakthroughs in science, even a century later.

Step 2: Define your research question

Students often give this step cursory attention, but experienced researchers know that formulating a good question is sometimes the most difficult part of the research planning process. That is because the precise language of the question frames the rest of the project. It’s therefore important to pose the question carefully, in a way that’s both possible to answer and likely to yield interesting results. Of course, you must choose a question that interests you, but that’s only the beginning of what’s likely to be an iterative process: most researchers come back to this step repeatedly, modifying their questions in light of previous research, resource limitations and other considerations.

Researchers face limits in terms of time and money. They, like everyone else, have to pose research questions that they can plausibly answer given the constraints they face. For example, it would be inadvisable to frame a project around the question ‘What are the roots of the Arab-Israeli conflict?’ if you have only a week to develop an answer and no background on that topic. That’s not to limit your imagination: you can come up with any question you’d like. But it typically does require some creativity to frame a question that you can answer well – that is, by investigating thoroughly and providing new insights – within the limits you face.

In addition to being interesting to you, and feasible within your resource constraints, the third and most important characteristic of a ‘good’ research topic is whether it allows you to create new knowledge. It might turn out that your question has already been asked and answered to your satisfaction: if so, you’ll find out in the next step of this process. On the other hand, you might come up with a research question that hasn’t been addressed previously. Before you get too excited about breaking uncharted ground, consider this: a lot of potentially researchable questions haven’t been studied for good reason ; they might have answers that are trivial or of very limited interest. This could include questions such as ‘Why does the area of a circle equal π r²?’ or ‘Did winter conditions affect Napoleon’s plans to invade Russia?’ Of course, you might be able to make the argument that a seemingly trivial question is actually vitally important, but you must be prepared to back that up with convincing evidence. The exercise in the ‘Learn More’ section below will help you think through some of these issues.

Finally, scholarly research questions must in some way lead to new and distinctive insights. For example, lots of people have studied gender roles in sports teams; what can you ask that hasn’t been asked before? Reinventing the wheel is the number-one no-no in this endeavour. That’s why the next step is so important: reviewing previous research on your topic. Depending on what you find in that step, you might need to revise your research question; iterating between your question and the existing literature is a normal process. But don’t worry: it doesn’t go on forever. In fact, the iterations taper off – and your research question stabilises – as you develop a firm grasp of the current state of knowledge on your topic.

Step 3: Review previous research

In academic research, from articles to books, it’s common to find a section called a ‘literature review’. The purpose of that section is to describe the state of the art in knowledge on the research question that a project has posed. It demonstrates that researchers have thoroughly and systematically reviewed the relevant findings of previous studies on their topic, and that they have something novel to contribute.

Your own research project should include something like this, even if it’s a high-school term paper. In the research planning process, you’ll want to list at least half a dozen bullet points stating the major findings on your topic by other people. In relation to those findings, you should be able to specify where your project could provide new and necessary insights. There are two basic rhetorical positions one can take in framing the novelty-plus-importance argument required of academic research:

• Position 1 requires you to build on or extend a set of existing ideas; that means saying something like: ‘Person A has argued that X is true about gender; this implies Y, which has not yet been tested. My project will test Y, and if I find evidence to support it, that will change the way we understand gender.’
• Position 2 is to argue that there is a gap in existing knowledge, either because previous research has reached conflicting conclusions or has failed to consider something important. For example, one could say that research on middle schoolers and gender has been limited by being conducted primarily in coeducational environments, and that findings might differ dramatically if research were conducted in more schools where the student body was all-male or all-female.

Your overall goal in this step of the process is to show that your research will be part of a larger conversation: that is, how your project flows from what’s already known, and how it advances, extends or challenges that existing body of knowledge. That will be the contribution of your project, and it constitutes the motivation for your research.

Two things are worth mentioning about your search for sources of relevant previous research. First, you needn’t look only at studies on your precise topic. For example, if you want to study gender-identity formation in schools, you shouldn’t restrict yourself to studies of schools; the empirical setting (schools) is secondary to the larger social process that interests you (how people form gender identity). That process occurs in many different settings, so cast a wide net. Second, be sure to use legitimate sources – meaning publications that have been through some sort of vetting process, whether that involves peer review (as with academic journal articles you might find via Google Scholar) or editorial review (as you’d find in well-known mass media publications, such as The Economist or The Washington Post ). What you’ll want to avoid is using unvetted sources such as personal blogs or Wikipedia. Why? Because anybody can write anything in those forums, and there is no way to know – unless you’re already an expert – if the claims you find there are accurate. Often, they’re not.

Step 4: Choose your data and methods

Whatever your research question is, eventually you’ll need to consider which data source and analytical strategy are most likely to provide the answers you’re seeking. One starting point is to consider whether your question would be best addressed by qualitative data (such as interviews, observations or historical records), quantitative data (such as surveys or census records) or some combination of both. Your ideas about data sources will, in turn, suggest options for analytical methods.

You might need to collect your own data, or you might find everything you need readily available in an existing dataset someone else has created. A great place to start is with a research librarian: university libraries always have them and, at public universities, those librarians can work with the public, including people who aren’t affiliated with the university. If you don’t happen to have a public university and its library close at hand, an ordinary public library can still be a good place to start: the librarians are often well versed in accessing data sources that might be relevant to your study, such as the census, or historical archives, or the Survey of Consumer Finances.

Because your task at this point is to plan research, rather than conduct it, the purpose of this step is not to commit you irrevocably to a course of action. Instead, your goal here is to think through a feasible approach to answering your research question. You’ll need to find out, for example, whether the data you want exist; if not, do you have a realistic chance of gathering the data yourself, or would it be better to modify your research question? In terms of analysis, would your strategy require you to apply statistical methods? If so, do you have those skills? If not, do you have time to learn them, or money to hire a research assistant to run the analysis for you?

Please be aware that qualitative methods in particular are not the casual undertaking they might appear to be. Many people make the mistake of thinking that only quantitative data and methods are scientific and systematic, while qualitative methods are just a fancy way of saying: ‘I talked to some people, read some old newspapers, and drew my own conclusions.’ Nothing could be further from the truth. In the final section of this guide, you’ll find some links to resources that will provide more insight on standards and procedures governing qualitative research, but suffice it to say: there are rules about what constitutes legitimate evidence and valid analytical procedure for qualitative data, just as there are for quantitative data.

Circle back and consider revising your initial plans

As you work through these four steps in planning your project, it’s perfectly normal to circle back and revise. Research planning is rarely a linear process. It’s also common for new and unexpected avenues to suggest themselves. As the sociologist Thorstein Veblen wrote in 1908 : ‘The outcome of any serious research can only be to make two questions grow where only one grew before.’ That’s as true of research planning as it is of a completed project. Try to enjoy the horizons that open up for you in this process, rather than becoming overwhelmed; the four steps, along with the two exercises that follow, will help you focus your plan and make it manageable.

Key points – How to plan a research project

• Planning a research project is essential no matter your academic level or field of study. There is no one ‘best’ way to design research, but there are certain guidelines that can be helpfully applied across disciplines.
• Orient yourself to knowledge-creation. Make the shift from being a consumer of information to being a producer of information.
• Define your research question. Your question frames the rest of your project, sets the scope, and determines the kinds of answers you can find.
• Review previous research on your question. Survey the existing body of relevant knowledge to ensure that your research will be part of a larger conversation.
• Choose your data and methods. For instance, will you be collecting qualitative data, via interviews, or numerical data, via surveys?
• Circle back and consider revising your initial plans. Expect your research question in particular to undergo multiple rounds of refinement as you learn more about your topic.

Good research questions tend to beget more questions. This can be frustrating for those who want to get down to business right away. Try to make room for the unexpected: this is usually how knowledge advances. Many of the most significant discoveries in human history have been made by people who were looking for something else entirely. There are ways to structure your research planning process without over-constraining yourself; the two exercises below are a start, and you can find further methods in the Links and Books section.

The following exercise provides a structured process for advancing your research project planning. After completing it, you’ll be able to do the following:

• describe clearly and concisely the question you’ve chosen to study
• summarise the state of the art in knowledge about the question, and where your project could contribute new insight
• identify the best strategy for gathering and analysing relevant data

In other words, the following provides a systematic means to establish the building blocks of your research project.

Exercise 1: Definition of research question and sources

This exercise prompts you to select and clarify your general interest area, develop a research question, and investigate sources of information. The annotated bibliography will also help you refine your research question so that you can begin the second assignment, a description of the phenomenon you wish to study.

Jot down a few bullet points in response to these two questions, with the understanding that you’ll probably go back and modify your answers as you begin reading other studies relevant to your topic:

• What will be the general topic of your paper?
• What will be the specific topic of your paper?

b) Research question(s)

Use the following guidelines to frame a research question – or questions – that will drive your analysis. As with Part 1 above, you’ll probably find it necessary to change or refine your research question(s) as you complete future assignments.

• Your question should be phrased so that it can’t be answered with a simple ‘yes’ or ‘no’.
• Your question should have more than one plausible answer.
• Your question should draw relationships between two or more concepts; framing the question in terms of How? or What? often works better than asking Why ?

c) Annotated bibliography

Most or all of your background information should come from two sources: scholarly books and journals, or reputable mass media sources. You might be able to access journal articles electronically through your library, using search engines such as JSTOR and Google Scholar. This can save you a great deal of time compared with going to the library in person to search periodicals. General news sources, such as those accessible through LexisNexis, are acceptable, but should be cited sparingly, since they don’t carry the same level of credibility as scholarly sources. As discussed above, unvetted sources such as blogs and Wikipedia should be avoided, because the quality of the information they provide is unreliable and often misleading.

To create an annotated bibliography, provide the following information for at least 10 sources relevant to your specific topic, using the format suggested below.

Name of author(s):

Publication date:

Title of book, chapter, or article:

If a chapter or article, title of journal or book where they appear:

Brief description of this work, including main findings and methods ( c 75 words):

Summary of how this work contributes to your project ( c 75 words):

Brief description of the implications of this work ( c 25 words):

Identify any gap or controversy in knowledge this work points up, and how your project could address those problems ( c 50 words):

Exercise 2: Towards an analysis

Develop a short statement ( c 250 words) about the kind of data that would be useful to address your research question, and how you’d analyse it. Some questions to consider in writing this statement include:

• What are the central concepts or variables in your project? Offer a brief definition of each.
• Do any data sources exist on those concepts or variables, or would you need to collect data?
• Of the analytical strategies you could apply to that data, which would be the most appropriate to answer your question? Which would be the most feasible for you? Consider at least two methods, noting their advantages or disadvantages for your project.

Links & books

One of the best texts ever written about planning and executing research comes from a source that might be unexpected: a 60-year-old work on urban planning by a self-trained scholar. The classic book The Death and Life of Great American Cities (1961) by Jane Jacobs (available complete and free of charge via this link ) is worth reading in its entirety just for the pleasure of it. But the final 20 pages – a concluding chapter titled ‘The Kind of Problem a City Is’ – are really about the process of thinking through and investigating a problem. Highly recommended as a window into the craft of research.

Jacobs’s text references an essay on advancing human knowledge by the mathematician Warren Weaver. At the time, Weaver was director of the Rockefeller Foundation, in charge of funding basic research in the natural and medical sciences. Although the essay is titled ‘A Quarter Century in the Natural Sciences’ (1960) and appears at first blush to be merely a summation of one man’s career, it turns out to be something much bigger and more interesting: a meditation on the history of human beings seeking answers to big questions about the world. Weaver goes back to the 17th century to trace the origins of systematic research thinking, with enthusiasm and vivid anecdotes that make the process come alive. The essay is worth reading in its entirety, and is available free of charge via this link .

For those seeking a more in-depth, professional-level discussion of the logic of research design, the political scientist Harvey Starr provides insight in a compact format in the article ‘Cumulation from Proper Specification: Theory, Logic, Research Design, and “Nice” Laws’ (2005). Starr reviews the ‘research triad’, consisting of the interlinked considerations of formulating a question, selecting relevant theories and applying appropriate methods. The full text of the article, published in the scholarly journal Conflict Management and Peace Science , is available, free of charge, via this link .

Finally, the book Getting What You Came For (1992) by Robert Peters is not only an outstanding guide for anyone contemplating graduate school – from the application process onward – but it also includes several excellent chapters on planning and executing research, applicable across a wide variety of subject areas. It was an invaluable resource for me 25 years ago, and it remains in print with good reason; I recommend it to all my students, particularly Chapter 16 (‘The Thesis Topic: Finding It’), Chapter 17 (‘The Thesis Proposal’) and Chapter 18 (‘The Thesis: Writing It’).

Spirituality and religion

How to find new spiritual practices

Even if religion isn’t for you, there’s a world of rituals and tools to lift yourself up and connect to something greater

by Morgan Shipley

Emerging therapies

How to look after your emotional health

Find out which of your emotional needs you’ve been neglecting and use tips from human givens therapy to address them

by Denise Winn

How to get the most out of caregiving

Giving care is hard, but it’s one of the most meaningful things you can do. Here’s how to change up your perspective

by Elissa Strauss

Project Planning for the Beginner: Research Design

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

Research Design

• Planning, Data, Writing and Dissemination

What Is a Research Plan?

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

• A specification of your research questions

• An outline of your proposed research methods

• A timetable for doing the work

What Is Research Design?

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

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

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

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

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

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

• Meta-analytic (e.g. meta-analysis )

Research Design Choices

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

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

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

• You may be working with multiple methods

• Methods can answer different sorts of questions

• Questions can be answered in different ways.

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

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

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

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

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

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

• You can use qualitative material derived from:

• Documentary research

• Qualitative interviews

• Focus groups

• Visual research

• Ethnographic methods

• Any combination of the above may be deployed.

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

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

• You can do this through building simulations .

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

• You can combine both approaches.

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

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

• You can conduct qualitative interviews .

• You can hold focus groups .

• You can do this using ethnographic observation .

• You can combine any or all of above approaches.

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

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

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

Checklist: Question to Ask When Deciding On a Method

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

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

• statistical software

• qualitative data analysis software

• an adequate computer

• access to secondary data sets

• audio-visual equipment

• language training

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

• If you are using multiple methods, do you know how you are going to combine them to carry out the research?
• If you are using multiple methods, do you know how you are going to combine the  products of using them when writing up your research? 
• << Previous: Developing a Researchable Question
• Next: Planning, Data, Writing and Dissemination >>
• Last Updated: May 11, 2022 2:56 PM
• URL: https://libguides.sph.uth.tmc.edu/c.php?g=949457

Research Methodologies

Research design, external validity, internal validity, threats to validity.

• What are research methodologies?
• What are research methods?
• Additional Sources

According to Jenkins-Smith, et al. (2017), a research design is the set of steps you take to collect and analyze your research data.  In other words, it is the general plan to answer your research topic or question.  You can also think of it as a combination of your research methodology and your research method.  Your research design should include the following: 

• A clear research question
• Theoretical frameworks you will use to analyze your data
• Key concepts
• Your hypothesis/hypotheses
• Independent and dependent variables (if applicable)
• Strengths and weaknesses of your chosen design

There are two types of research designs:

• Experimental design: This design is like a standard science lab experiment because the researcher controls as many variables as they can and assigns research subjects to groups.  The researcher manipulates the experimental treatment and gives it to one group.  The other group receives the unmanipulated treatment (or not treatment) and the researcher examines affect of the treatment in each group (dependent variable).  This design can have more than two groups depending on your study requirements.
• Observational design: This is when the researcher has no control over the independent variable and which research participants get exposed to it.  Depending on your research topic, this is the only design you can use.  This is a more natural approach to a study because you are not controlling the experimental treatment.  You are allowing the variable to occur on its own without your interference.  Weather experiments are a great example of observational design because the researcher has no control over the weather and how it changes.

When considering your research design, you will also need to consider your study's validity and any potential threats to its validity.  There are two types of validity: external and internal validity.  Each type demonstrates a degree of accuracy and thoughtfulness in a study and they contribute to a study's reliability.  Information about external and internal validity is included below.

External validity is the degree to which you can generalize the findings of your research study.  It is determining whether or not the findings are applicable to other settings (Jenkins-Smith, 2017).  In many cases, the external validity of a study is strongly linked to the sample population.  For example, if you studied a group of twenty-five year old male Americans, you could potentially generalize your findings to all twenty-five year old American males.  External validity is also the ability for someone else to replicate your study and achieve the same results (Jenkins-Smith, 2017).  If someone replicates your exact study and gets different results, then your study may have weak external validity.

Questions to ask when assessing external validity:

• Do my conclusions apply to other studies?
• If someone were to replicate my study, would they get the same results?
• Are my findings generalizable to a certain population?

Internal validity is when a researcher can conclude a causal relationship between their independent variable and their dependent variable.  It is a way to verify the study's findings because it draws a relationship between the variables (Jenkins-Smith, 2017).  In other words, it is the actual factors that result in the study's outcome (Singh, 2007).  According to Singh (2007), internal validity can be placed into 4 subcategories:

• Face validity: This confirms the fact that the measure accurately reflects the research question.
• Content validity: This assesses the measurement technique's compatibility with other literature on the topic.  It determines how well the tool used to gather data measures the item or concept that the researcher is interested in.
• Criterion validity: This demonstrates the accuracy of a study by comparing it to a similar study.
• Construct validity: This measures the appropriateness of the conclusions drawn from a study.

According to Jenkins-Smith (2017), there are several threats that may impact the internal and external validity of a study:

Threats to External Validity

• Interaction with testing: Any testing done before the actual experiment may decrease participants' sensitivity to the actual treatment.
• Sample misrepresentation: A population sample that is unrepresentative of the entire population.
• Selection bias: Researchers may have bias towards selecting certain subjects to participate in the study who may be more or less sensitive to the experimental treatment.
• Environment: If the study was conducted in a lab setting, the findings may not be able to transfer to a more natural setting.

Threats to Internal Validity

• Unplanned events that occur during the experiment that effect the results.
• Changes to the participants during the experiment, such as fatigue, aging, etc.
• Selection bias: When research subjects are not selected randomly.
• If participants drop out of the study without completing it.
• Changing the way the data is collected or measured during the study.
• << Previous: Welcome
• Next: What are research methodologies? >>
• Last Updated: Aug 2, 2022 2:36 PM
• URL: https://library.tiffin.edu/researchmethodologies

• STRATEGIC PLAN /
• DROSDICK HALL /
• DROSDICK HALL FAST FACTS

Drosdick Hall: Home of Villanova University's College of Engineering

Name: Drosdick Hall Location: Villanova University, main campus Expansion Size: 150,000-square-feet Building Size (total): 245,742-square-feet Expansion Cost: $125 million Building Exterior: Precast concrete Architects: Robert A.M. Stern Architects, LLP; BLTa–A Perkins Eastman Studio Construction Company: Wohlsen Construction Company Project Lead: Marilou Smith, Assistant Vice President for Engineering and Construction, Villanova University Construction Start Date: February 2022 Construction End Date: April 2024

Building Specifications

• More than 20 additional research laboratory spaces—a 63% increase in engineering lab space.
• The two-story Drosdick Innovation Lab, which includes laser and 3-D printers, workbenches and workspaces
• The Jones Family Student Learning Commons, a community space at the heart of the building for all Villanovans to gather, study and learn
• State-of-the-art instruction spaces that can adapt to small discussion and larger lecture formats
• Dedicated and centrally located spaces for the College of Engineering’s master’s and doctoral students
• Green roofs—instrumented with smart systems to collect and monitor climate and soil moisture data—which will serve as resources for cutting-edge teaching and research

Architectural Features

• Designed in a Collegiate Gothic Revival style inspired by noted Philadelphia architect Charles Klauder, who was active in the 1920s through 1940s

Construction Data

• 25,000 cubic yards of earth excavated
• 367 windows installed (104 in the existing building and 263 in the expansion)
• 971.2 tons of steel utilized
• 488 pieces of precast with stone veneer

Environmentally Friendly Design

• Designed for and seeking LEED Silver certification
• An enhanced HVAC system that allows for the carbon reduction equivalent of five acres of mixed hardwood forest saved yearly
• Low flow faucets and toilets—including a rainwater collection system—resulting in a 70% reduction of indoor water consumption for daily operations
• Native and adaptive plant species planted in exterior landscapes, requiring little to no watering
• Natural site hydrology processes for enhanced water conservation, including two rain gardens and a cistern
• Use of high-performance glazing to reduce the effect of outdoor conditions on the indoor environment
• Efficient LED lighting and advanced lighting controls such as daylighting and room occupancy sensors
• Exterior parking and charging spaces dedicated for electric vehicles

Design Team Profiles

Robert a.m. stern architects, llp.

Robert A.M. Stern Architects, LLP, is a 200-person firm of architects, interior designers, and supporting staff. Over its fifty-year history, the firm has established an international reputation as a leading design firm with wide experience in residential, commercial, and institutional work. As the firm’s practice has diversified, its geographical scope has widened to include projects in Europe, Asia, South America, and throughout the United States. The firm maintains an attention to detail and commitment to design quality which has earned international recognition, numerous awards and citations for design excellence, including National Honor Awards of the American Institute of Architects.

BLTa–A Perkins Eastman Studio

Perkins Eastman is a global planning and design firm that has grown to include nearly 1,100 employees working out of a combined 24 interdisciplinary offices around the world. Our talented team of dreamers, thinkers, and doers promote a diverse design dialogue resulting in more thoughtful solutions to meet the challenges of today’s world.  As leaders in higher education, we have brought a deep understanding of how institutions are responding to advances in pedagogy, technology, behavioral science, and market forces to our projects with more than 200 colleges and universities around the globe. As a firm committed to the 2030 Challenge and the Paris Agreement through We Are Still In, we understand that we carry a tremendous responsibility in addressing climate change.

Wohlsen Construction Company

With over 130 years of building expertise, Wohlsen Construction Company is a trusted leader in construction management and general contracting in the Mid-Atlantic and Northeast Regions. We offer clients comprehensive preconstruction and construction services for projects in diverse markets such as education, senior living, healthcare, industrial/manufacturing, and community-focused. Our 300+ team members are industry experts who apply their knowledge and skills to every project, from initial planning to completion. As a fully 100% employee-owned company, our team members are invested in the success of every project.

The Straits Times

• International
• Print Edition
• news with benefits
• SPH Rewards
• STClassifieds
• Berita Harian
• Hardwarezone
• Shin Min Daily News
• Tamil Murasu
• The Business Times
• The New Paper
• Lianhe Zaobao
• Advertise with us

NUS and CapitaLand Development to collaborate on urban planning, design research for 3 years

SINGAPORE – A National University of Singapore (NUS) interdisciplinary research centre, NUS Cities, and real estate developer CapitaLand Development (CLD) are set to work together on research and programmes relating to urban planning and design.

Under the partnership, announced on Aug 26, NUS Cities and CLD will collaborate on three fronts – awareness and engagement programmes for the public, academic research, as well as sponsorship and capacity-building opportunities.

NUS said the two entities will work on solutions for complex urban challenges, such as improving urban liveability, decarbonisation, health and wellness.

It added that the research could involve case studies from CLD’s portfolio of developments, which include CapitaSpring, which has been recognised for promoting health and well-being through features such as an open-air garden that mimics a tropical rainforest.

NUS Cities director Khoo Teng Chye said the collaboration will support the centre in its mission to create an open and inclusive platform for education, research and advisory services.

CLD chief executive Jonathan Yap said the two parties will work towards the environmental and social well-being of communities, by leveraging synergies of CLD’s real estate expertise and NUS’ research prowess.

The collaboration was announced at an NUS Cities event that featured a panel discussion on green buildings and public health.

Moderated by Dr Lam Khee Poh, the provost’s chair professor of architecture and the built environment at NUS, the panel discussion featured Minister for Sustainability and the Environment Grace Fu, Singapore Green Building Council president Lee Ang Seng, Housing Board deputy chief executive Johnny Wong and CLD’s head of sustainability Giovanni Cossu.

The panellists said there has been increasing interest in green buildings in recent years, with Mr Lee noting that there has been an “unprecedented focus on green buildings” in the wake of the Covid-19 pandemic.

He attributed this to increased awareness of how air quality in indoor environments may impact building occupants’ health – something that green buildings that are naturally ventilated can address.

Mr Cossu said the market for “wellness real estate” is growing, with developers and building users increasingly interested in how buildings can be designed to promote physical, psychological and emotional health.

Giving the example of the increasing adoption of wellness certification for buildings across the world, he believes the demand for healthy design will grow in the coming years.

Ms Fu, who cited the clean-up of the Singapore River in the 1970s and 1980s as an example of Singapore’s commitment to public health, said new challenges that threaten public health will emerge.

Citing Singapore’s plan to achieve net-zero carbon emissions by 2050, amid climate change and global warming, she said universities have a key role in leading education and thinking about the “solutions for tomorrow”.

“If the country has to decarbonise by 2050, we need solutions now. And the universities have this important, critical role of showing us the way to go, the pathway to take,” she said.

Join ST's WhatsApp Channel and get the latest news and must-reads.

• Urbanisation and urban planning
• Technology and research

Read 3 articles and stand to win rewards

Spin the wheel now

2024 Top 40 Under 40: Grant Wheeler

Grant Wheeler

As part of his graduate program at Texas A&M University, Grant Wheeler started out certifying sound levels for bathroom fans and range hoods, working closely with manufacturers to optimize sound, airflow, and the power of exhaust fans.

“For my thesis, an ASHRAE-funded project, I focused on evaluating a technique to evaluate bigger and more efficient residential systems in psychrometric chambers with limited space,” Wheeler said. “Both projects illustrated how much engineering is required to design and develop HVAC equipment. I grew an appreciation for how critical HVAC is in everyday life and saw I could make an immediate impact by focusing on the HVAC industry.”

Since Wheeler began his focus on the HVAC industry, he’s led the specification development for the U.S. Department of Energy (DOE) Commercial Building Heat Pump Technology Challenge, and led the planning, design, and construction of the Commercial Buildings Research Infrastructure (CBRI). By starting with bringing life cycle cost and emissions-effective heat

pump RTUs into the market by 2027, the goal

of the Commercial Heat Pump Challenge is to make significant step improvements to heat pump RTU performance.

The CBRI, known as a “flight simulator for commercial buildings,” is “a unique, world-class capability that can research the latest questions in the HVAC industry around grid connections, load flexibility and resilience.” Wheeler is the primary researcher responsible for the operation of the CBRI and bringing in new projects.

Moving forward, Wheeler hopes to advance HVAC technology by bridging research and development with industry and market and further expand NREL’s impact by developing relationships and making connections within the HVACR industry and outside of it.

Additionally, Wheeler hopes to “lead development of capabilities at NREL for cross-cutting research that accounts for changes in HVAC industry such as new refrigerants, smarter controls, and more connections with other industries.”

Share This Story

Report Abusive Comment

Related articles.

2024 Top 40 Under 40: Jonatan Portillo Reyes

2024 Top 40 Under 40: Rawand Rasheed

2024 Top 40 Under 40: Kyla Robinson

Related products.

Top 100 HVAC/R Tips

HVACR Troubleshooting Fundamentals Technician Training Package

Acg: cxa workshop & exam, 4 ways to make your hvac business more efficient, sign up. stay informed., the #1 trusted source for the hvacr industry since 1926.

Copyright ©2024. All Rights Reserved BNP Media.

Design, CMS, Hosting & Web Development :: ePublishing

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 27 August 2024

Charting sustainable urban development through a systematic review of SDG11 research

• Abdulaziz I. Almulhim   ORCID: orcid.org/0000-0002-5384-7219 1 ,
• Ayyoob Sharifi   ORCID: orcid.org/0000-0002-8983-8613 2 ,
• Yusuf A. Aina   ORCID: orcid.org/0000-0002-0763-9865 3 ,
• Shakil Ahmad 4 ,
• Luca Mora 5 , 6 ,
• Walter Leal Filho 7 , 8 &
• Ismaila Rimi Abubakar   ORCID: orcid.org/0000-0002-7994-2302 9  

Nature Cities ( 2024 ) Cite this article

Metrics details

• Environmental studies
• Social policy

The Sustainable Development Goal (SDG) 11 underscores the imperative of creating inclusive, safe, resilient and sustainable cities and communities by 2030. Here we employ bibliometric techniques to assess the evolving landscape of SDG11 research. Using a comprehensive dataset of over 21,000 scholarly publications, we investigate publication trends, thematic focus areas, authorship patterns, keyword co-occurrences and citation networks related to SDG11 research. The results reveal a consistent increase in research output, reflecting the growing global interest in urban sustainability studies. We identify influential authors, organizations and countries shaping the research landscape, highlighting existing global collaborative networks and emerging research hubs. Core thematic areas emphasize critical topics and interdisciplinary connections. Citation networks underscore the impacts of disseminating research outputs, including seminal works. This study offers insights for policymakers, academics and practitioners to align their collective efforts toward sustainable, inclusive and climate-resilient urban development. Moreover, it advances SDG11 by noting opportunities for further research, knowledge dissemination and international collaboration.

The growing interest in sustainable urban development is driven by challenges posed by urbanization, socioeconomic activities and environmental issues 1 . Urban areas contribute 80% of the world’s gross domestic product 2 , but also account for around 75% of global resource consumption, 65% of energy use and over 70% of carbon emissions 3 . The ecological footprint of urban environments, which measures the resources required to sustain socioeconomic activities, has been increasing 4 , 5 , and the global urban extent is projected to double by 2030 6 . Similarly, the global urban population is projected to reach 68% by 2050 7 , which could surpass the capacity of most urban areas 8 . Africa and Asia will host most of the future urban populations despite housing and infrastructure inadequacies 7 . Rapid urbanization, poverty and climate change (CC) further intensify the vulnerability of urban dwellers 9 .

Sustainable urban development aims to balance economic production, environmental protection and social inclusiveness. It emerged as a response to the critique of modernist views that prioritized physical appearance and order in cities over context, equity and inclusion 6 . Due to the limited progress in achieving the Millennium Development Goals, the Sustainable Development Goals (SDGs) were established in 2015 to ensure that no country is left behind in achieving sustainable development by 2030 10 . Many of the SDGs are closely related to urban settings, where sustainability challenges are complex and interwoven 11 . SDG11 specifically focuses on urban challenges and aims to make ‘cities and human settlements inclusive, safe resilient and sustainable’ by reducing the negative effects of urban development while improving socioeconomic development 10 .

The importance of SDG11 stems from the principles of inclusive, safe and resilient city. An inclusive city is characterized by the idea that all individuals, irrespective of their economic status, gender, race, ethnicity or religion, have the ability and empowerment to actively engage in the social, economic and political opportunities available within urban environments 6 . It seeks to address environmental racism and promote inclusive and fair urban development through social justice and equitable distribution of environmental benefits and burdens. In such a city, everyone is afforded equal access and participation in the diverse aspects that cities provide. On the other hand, a safe city refers to a city that possesses the capacity to provide protection and security against potential dangers, harm or risks, while a resilient city denotes a city’s ability to recover and restore its fundamental functions and structures following natural disasters and crises caused by human activities 6 , 8 . SDG11 is significant because it aims to ensure that cities develop sustainably.

However, SDG11 has been criticized for its limited emphasis on urban inequalities, decentralization and funding for local authorities 6 . Other challenges include localizing the universal indicators 12 , governance issues 13 , data accessibility and comparability 14 and smart city development 12 , 15 . Nevertheless, SDG11 serves as a platform for directing and monitoring urban development, fostering socioeconomic development and ensuring equity, inclusion and environmental protection 16 . Therefore, it is crucial to assess the literature on progress toward SDG11 targets 10 , especially at the halfway point to the target year, to inform interventions necessary for their achievement 17 .

While SDG11 has attracted significant global research attention 18 , comprehensive reviews of SDG11 literature are limited. Existing studies have primarily focused on assessing all the SDGs 19 , 20 , which obscures specific challenges and makes it difficult to track progress or design targeted interventions for individual goals. Recent work has highlighted the insufficient achievement of the SDGs and the need for transformative governance and participatory approaches 21 . Other studies have underscored the gap between research and policies, the underutilization of cities as pivotal arenas for achieving SDGs 22 and the lack of indicators to measure progress toward implementing SDGs 15 . Some studies have assessed SDGs’ implementation in specific region 17 , their impacts on addressing risks 23 and crises 1 , and their implications for health and well-being 24 , environmental research 25 and private sector involvement 26 . Most of the SDG research emanates from developed countries, showing a gap in the coverage of developing countries 27 . The few SDG11 studies in the Global South have narrow focus. While one paper investigated the impact of SDG11 on forest-based livelihoods 28 , another study researched the challenges of SDG11 implementation using a single-country experience 6 . Therefore, an in-depth and broad review of SDG11 literature is necessary to bridge this knowledge gap and identify key challenges and opportunities as well as potential pathways for achieving the targets set in SDG11.

Therefore, this research aims to assess the SDG11 research trends and themes using a bibliometric technique. It is the first global and comprehensive scientometric study on the SDG11 domain. By focusing on research conducted since the formulation of the SDGs, the study addresses the following research questions: (1) what are the global trends in SDG11 research? (2) How has the thematic focus of SDG11 research evolved over time? (3) What are the challenges and priority areas for SDG11 research? The contributions of the study to theory and practice are to:

Identify significant thematic areas and trends in SDG11 research since the promulgation of the SDGs, which can inform researchers, policymakers and practitioners about the current state of knowledge within the field and highlight priority areas for SDG11 research.

Map research clusters, knowledge sharing and collaboration patterns, thereby providing insights into the dynamics of research networks and facilitating the formulation of strategies to foster research excellence, interdisciplinary and international collaborations and the effective allocating of research resources.

Underscore the knowledge gaps, emerging topics and challenges within SDG11 research, offering evidence-based insights to align urban development initiatives with SDG11 research frontiers, enhance the efficacy of interventions and contribute to the development of inclusive, safe, resilient and sustainable cities.

SDG11 research trends

Research on SDG11 has significantly grown in terms of annual publications and citations since 2016, indicating a rising interest in this field (Fig. 1 ). The number of publications has increased by 1.3-fold, and this upward trajectory is expected to continue. Notable emerging research areas include the institutionalization of SDGs within local and global settings 18 and the impact of smart cities on advancing the SDGs 12 , 15 . Previously, studies on the epistemology and challenges of urban population growth were prevalent 29 . However, SDG11 research has now evolved into multidisciplinary fields, driven by heightened attention to urban challenges such as CC, urbanization and population growth.

A total of 21,153 articles were published, receiving 229,182 citations. The number of publications rose from 9,238 in period 1 (2016–2019) to 11,915 in period 2 (2020–2022).

Source data

The increasing trend in SDG11 publications can be attributed to several factors, including the desire to improve institutional rankings, a supportive research environment, investments and endowments, faculty promotion requirements and advancements in information and communication technology. There are also socioeconomic factors, such as increasing urbanization rates and gross domestic product, urban expansion and transformation, a deeper understanding of urban dynamics and challenges. Additionally, the policy environments in different countries can influence academic interests and research in urban studies, shaping research priorities and collaborations. Other contributing factors include research challenges faced by low-income countries and research support by governments, the private sector, international development agencies and scholars, all focusing on sustainable urban development.

SDG11 research is further propelled by recent international summits and collaborations that highlight the urgency of protecting the ecosystem and ensuring human safety 1 . Since 2015, CC issues have received greater attention due to key factors. The adoption of the Paris Agreement raised awareness and urgency for action on CC, resulting in a greater focus on related issues in various sectors, including urban planning and policy 13 . Scientific consensus on CC impacts and the role of human activities has also strengthened over the years, with Intergovernmental Panel on Climate Change assessments emphasizing the significance of cities in addressing CC 23 . As a result, CC considerations are increasingly integrated into research, policy and planning processes.

Urban planning and development strategies have prioritized climate mitigation and adaptation measures, such as reducing greenhouse gas emissions, promoting renewable energy, enhancing resilience to extreme weather events and incorporating green infrastructure. The focus on CC has accelerated the transition toward low-carbon and resilient cities, with efforts directed toward sustainable transportation, energy-efficient buildings, green spaces and climate-responsive infrastructure 6 . Collaboration and international cooperation are essential in addressing climate change, with cities and countries sharing best practices, knowledge and resources to develop and implement climate action plans 24 . Initiatives such as the C40 Cities Climate Leadership Group facilitate knowledge exchange and collective action among cities 30 . The increased attention to CC signifies a shift toward more sustainable and resilient urban development, emphasizing the need for proactive measures to mitigate greenhouse gas emissions, adapt to climate risks and promote equitable and sustainable urban environments.

Thematic focus of SDG11 research

There is an imbalance in the attention given to research themes within SDG11 as revealed by co-occurrence map (Supplementary Fig. 1 ). The dominant themes are affordable housing (SDG11.1), urban transport (SDG11.2), policy and governance (SDG11.3) and access to public spaces (SDG11.7). Housing affordability issues have consistently remained a focal point in SDG11 research, with urban studies, policy development and community-driven efforts for finding solutions to these complex challenges 30 , 31 . These issues were highlighted in Habitat I (Vancouver, 1976), which emphasized the importance of shifting governance and planning paradigms to develop policies and strategies to address rapid urbanization challenges, including shelter shortages and urban inequalities, and promote affordable housing options 30 , 32 . Habitat I has laid the foundation for subsequent global efforts and policy frameworks, such as Habitat II (Istanbul, 1996) and the New Urban Agenda, which continue to prioritize housing as a pivotal component of sustainable urban development. The persistent focus on affordable housing shows that cities still face many challenges in providing adequate housing for all 30 .

Urban policy and governance are other significant terms, indicating scholarly focus on strategies for promoting inclusive and sustainable urban development, enhancing participatory, integrated and sustainable urban planning and management. However, many cities lack the capacity to address urban inequalities, provide adequate housing 31 , public spaces and other urban services, which disproportionately affect women and racial minorities 30 . Moreover, urban redevelopment practices that lead to gentrification exacerbate existing inequalities 32 . Governance-based approaches seek to improve collaboration between public agencies and civil society to prioritize the implementation of urban planning strategies that enhance livability standards while addressing challenges such as CC and sustainability 30 .

Urban transport, which is related to SDG11.2 aiming to ensure safe, affordable, accessible and sustainable transport systems for all, has emerged as a key research theme. Important issues related to mobility, transportation and urban form include increased automobile dependence amid growing urbanization and suburbanization, challenges faced by public transit systems, growing awareness of environmental concerns, shift toward sustainable and multimodal transportation, transit-oriented development, integration of technology in transportation systems and the relationship between transportation and urban densification, compact development, CC adaptation and resilience, equity and social inclusion, and shifts in policy and governance approaches 1 , 6 , 11 . This theme also emphasizes the importance of walkability, public transit infrastructure and their role in enhancing transportation accessibility and influencing mode choice 33 . The transportation cluster also suggests that improving accessibility through urban form and built environment interventions can impact the travel behavior of urban residents and offer cobenefits for human health and environmental sustainability 24 . Incorporating such cobenefits in SDG11.2 could provide more incentives for access to safe efficient, equitable and sustainable transport infrastructure and systems in cities.

The implications of urbanization and land-use changes for sustainability, resilience and CC adaptation and mitigation in cities are also major themes. SDG11.6 aims to reduce the environmental impacts of cities, particularly in relation to air pollution and waste. The literature suggests that regulating urban growth 6 , controlling land-use changes, conserving biodiversity 27 and promoting green infrastructure are essential for achieving this target 34 . These actions, when implemented within integrated planning frameworks, can also reduce vulnerability, enhance resilience and contribute to progress in CC adaptation and mitigation, as emphasized in SDG11.5 (ref. 6 ). Such integrated frameworks should recognize the interconnections between various urban systems, including water, food, energy, waste and transportation, to promote sustainable and resilient urban development 35 . Cities are adopting strategies to reduce their carbon footprint, enhance energy efficiency and prepare for climate risks.

Smart cities and innovation enabled by information and communication technologies have increasingly been utilized to tackle urban development challenges and facilitate innovative and transformative urban governance mechanisms that contribute to the SDGs 15 . The rapid development and integration of digital technologies, such as the Internet of Things, artificial intelligence, big data analytics and sensor networks, have opened new possibilities for improving urban services, infrastructure and quality of life 33 . Smart cities leverage these technologies to enhance efficiency, connectivity and sustainability. The interest in smart cities stems from the recognition that technology can play a transformative role in addressing urban challenges, improving quality of life, promoting sustainability and fostering economic growth 12 , 36 . However, it is important to ensure that smart city initiatives are inclusive, equitable and responsive to the needs and aspirations of all residents.

Comparing the co-occurrence maps of period 1 and period 2 reveals limited changes in key thematic areas, despite the emergence of the coronavirus disease 2019 (COVID-19) pandemic during period 2 (Fig. 2 ). The key thematic areas in period 2, including urban governance and policy, transportation, urban sustainability and resilience, and urbanization and urban growth, remain consistent with period 1, indicating the continued relevance of these topics in research, albeit with potential expansions. However, a closer analysis of the clusters reveals that COVID-19 has emerged as a new area of SDG11 research in period 2, as attention has shifted toward adapting to the pandemic’s detrimental effects on cities. The pandemic has triggered paradigm shifts in various SDG11 domains, including public health, remote work, digitalization, vulnerabilities, inequalities, resilience, sustainability, urban spaces, proximity-based planning approaches such as the 15-minute city and global cooperation 9 . These shifts have influenced work, health, social equity, environmental stewardship 2 and urban planning, shaping innovative approaches and priorities in the postpandemic world. Urban inequality terms, such as slums and informality, inadequate housing and poverty, are brought to the forefront by the pandemic. Controlling the pandemic and addressing the citizen demand in slums and informal settlements has received significant attention 37 , 38 , 39 , 40 . Mobility restrictions and lockdowns to curb the virus’s transmission have presented challenges for service accessibility, particularly in disadvantaged neighborhoods where vulnerable groups reside. Lastly, the connection between sustainability and resilience has strengthened in the postpandemic period. The pandemic has offered new insights into the susceptibility of cities to various stressors and highlighted the inseparable connections between urban resilience and SDG11 (ref. 28 ).

a , b , The key thematic areas in period 1 (2016–2019) ( a ) are urban governance and policy (red), transportation (blue), urban sustainability and resilience (green), and urbanization and urban growth (yellow), while period 2 (2020–2022) ( b ) primarily focuses on urban governance and policies (red), urban studies (red), transportation (blue) and urbanization (green), particularly after the pandemic.

However, three SDG11 targets are not well-represented in both periods. One such target is SDG11.4, which aims to enhance efforts in preserving and conserving natural heritage, vital for improving urban sustainability 41 . Another target, SDG11.a, which focuses on strengthening urban–rural linkages, is also not adequately reflected in Fig. 2 . The intrinsic connection between cities and their surrounding rural areas necessitates the incorporation and strengthening of ties between urban and rural regions to achieve SDG11 (ref. 6 ). Gaps related to rural–urban linkages include limited understanding of interdependencies, inadequate infrastructure and services in rural areas, weak governance and coordination mechanisms, and social and cultural disconnect 13 . These gaps hinder the development of integrated strategies, contribute to economic disparities, limit access to services, impact agricultural productivity and food security, and create environmental and social challenges. Lastly, there is a lack of research on SDG11.c, which aims to support least-developed nations in developing safe and resilient urban areas, which is not surprising as these countries are often underrepresented in urban studies research 30 .

Major contributors to SDG11 research

Various countries, institutions, journals and authors have contributed to SDG11 research between 2016 and 2022. China leads in terms of the number of publications and citations generated, followed by the United States and the United Kingdom (Supplementary Fig. 2 and Supplementary Table 1 ). Among the top 20 productive countries, 14 are from the Global North countries, with South Africa and Brazil as the sole representative of Africa and Latin America and the Caribbean, respectively (Supplementary Fig. 3 and Supplementary Table 2 ). Increasing research collaboration among the top countries (Fig. 3 ), research infrastructure and facilities, manpower and financial support significantly contribute to their high SDG11 research output.

China followed by the United States and the United Kingdom dominates SDG11 research collaborations. There are significant connections among European, North American and Asian institutions, while Africa is less connected with Asia and Latin America and the Caribbean. Freq, frequently.

A co-citation analysis (Supplementary Table 3 ) reveals that Chinese institutions, such as the Chinese Academy of Sciences, have the highest number of articles and citation counts, followed by University College London and the University of Melbourne. The leading affiliations have changed over time, highlighting the strengthening of research institutes and the correlation between research collaboration and societal impacts (Supplementary Table 4 ). In terms of influential journals for SDG11 research, ‘land’ followed by ‘cities and land use’ policy tops the list (Supplementary Tables 5 and 6 ), with a growing interest in fields related to smart and sustainable cities, transport policies, regional planning and environmentally conscious building practices (Supplementary Fig. 4 ). These journals also address multiple issues related to environmental concerns, technological advancements, economic benefits, quality of life, justice and public awareness, driving the development of smart and sustainable cities.

The 15 most published authors in both periods focused on urbanization and urban growth, and the implementation, challenges and achievements of SDG11 (Supplementary Fig 5 ). This indicates an increased recognition of the SDG11 targets and their implementation over time, with the contributions of these authors significantly increasing from 2002 to 2016. Supplementary Table 7 shows that Chinese authors dominate the SDG11 publications, which correlates with China’s lead in institutions, affiliations and collaborations related to SDG11 research. The most cited SDG11 articles are revealed in Supplementary Table 8 , while the prominent authors that influenced SDG11 research are reported in Supplementary Table 9 . The top cited papers by SDG11 research are presented in Supplementary Tables 10 and 11 .

Key facts from the bibliometric analysis

The research on SDG11 has gained significant prominence across various fields, including urban studies, environmental sciences, geography, transportation and urban governance (Supplementary Table 12 ). The increasing environmental concerns, urbanization and global economic growth have spurred academic interest in SDG11 research from disciplines such as human geography, transportation, forestry, CC and sustainability science (Supplementary Table 13 ). Key thematic areas within SDG11 research encompass urban governance, affordable housing, transportation, urban sustainability and resilience, smart cities, urbanization and urban growth, which align closely with SDG11 targets 18 , 20 , 42 , 43 . However, research focus on SDG11 has remained relatively stable, with limited attention given to urban inequalities, safeguarding cultural and natural heritage 41 and specific impacts of the COVID-19 pandemic on urban sustainability.

This study reveals a notable increase in the total SDG11 research output from 2016 to 2022, reflecting the growing emphasis on SDG11 research in recent years compared with earlier periods. China emerges as the leaders in terms of research outputs, citations, authors, institutions and collaborations, closely followed by the United States and the United Kingdom. These three countries contribute 47.71% of SDG11 research productivity within this period, which is higher than 31% reported in a previous similar study 28 .

The dominance of Global North countries in the top 20 countries with the highest number of publications and citations related to SDG11 research is expected given their strong institutional capacity, research funding, highly ranked universities and collaborations. China’s surge in publications on SDG11 can be attributed to rapid urbanization, economic growth, government support and active international collaborations 2 , 11 . Generally, the landscape of research on SDG11 demonstrates an Anglo–American hegemony, which may reinforce power asymmetries and have significant implications for sustainability and resilience 30 . It is concerning that while projections indicate that 90% of future urban population growth will occur in cities of the Global South, particularly Africa and Asia, there is limited research on urban development challenges in these regions 7 .

The debate about the politics of knowledge production in SDG11 research often revolves around the controls of knowledge production processes. Large, well-funded institutions in developed countries tend to dominate research agendas, focusing on themes and solutions relevant to their own contexts, overlooking the unique needs and challenges of the Global South, which perpetuate existing inequalities and privileging certain types of knowledge. Also, knowledge production involves recognizing and integrating diverse ways of knowing. While Western scientific paradigms have traditionally dominated SDG11 research, there is an increasing recognition of the importance of indigenous and non-Western knowledge systems. Integrating these diverse epistemologies enriches understanding and leads to more effective and culturally relevant solutions.

Additionally, SDG11 research is inherently interdisciplinary, involving fields such as urban planning, sociology, environmental science and public policy. However, interdisciplinary collaboration can be challenging due to differing terminologies, methodologies and research priorities. Navigating these differences becomes crucial in the politics of knowledge production to create cohesive and comprehensive research outputs. Finally, bridging the gap between knowledge production and its implementation faces political, economic and social barriers. Researchers and practitioners are increasingly considering how knowledge on urban sustainability can effectively influence policymaking and practice in diverse urban contexts. Mobilizing knowledge to address these barriers becomes a key consideration in the politics of knowledge production.

Challenges to achieving SDG11

There are several challenges to achieving SDG11 targets, including inadequate provision of affordable housing 31 , essential services 24 , green spaces 2 , 34 , efficient transportation 33 and conservation of cultural and natural assets 25 . Rapid urbanization 1 , 7 , CC impacts 44 , insufficient investment in public infrastructure 30 , poor governance 13 and widening livelihood, land and resources inequalities 43 further exacerbate these challenges. For example, rapid urbanization puts immense pressure on housing, infrastructure, services and resources, making it challenging to effectively manage urban growth and ensure sustainable urban development 11 . Inadequate urban planning and land-use policies lead to inefficient land utilization, urban sprawl and inadequate provision of basic services 7 , 21 . The existence of slums and informal settlements where a large portion of the urban dwellers live in substandard housing conditions without tenure security 14 and limited access to electricity, water, sanitation, education, healthcare and employment opportunities 23 , 37 , and marginalized and vulnerable populations facing social exclusion, add to the complexity.

Moreover, competing priorities and trade-offs, lack of integration among various urban sectors and agencies 35 , inadequate human, technical and material resources at local government levels 45 , and insufficient local indicators and methods for implementation and monitoring 46 often hamper the implementation of SDG11 targets. Additionally, limited awareness of SDG-related challenges for policy formulation and implementation hinders context-depended decision-making and targeted interventions 21 , 27 . Addressing social inequalities, ensuring inclusivity in urban development and synergy among multiple fields, including social, technical, environmental, policy and management are crucial for achieving SDG11 (refs. 14 , 26 , 46 ). A valuable lesson can be learned from the success of the framework for assessing the implementation of SDG11 targets at the local level in Japan 42 .

Conclusions

This study aims to enhance our understanding of urban sustainability and provide insights for future research, policies and actions needed to achieve SDG11 targets. By conducting a comprehensive bibliometric assessment of over 21,000 publications from 2016 to 2022, it significantly contributes to the existing body of knowledge, highlighting trends, thematic areas and knowledge gaps related to SDG11 research across countries, institutions, authors and journals. SDG11 research has evolved into a multidisciplinary field, encompassing diverse themes, such as transportation, housing, urban sustainability, smart cities, urbanization and urban governance and policy. However, there is a need to address the gaps in research on urban safety and inclusion, which are critical dimensions often overlooked in favor of environmental and economic aspects of sustainability. This imbalance in research thematic areas risks perpetuation of already existing disparities within SDG11 research and its goals.

China, the United States and the United Kingdom emerge as the top contributors to SDG11 research and collaboration. To foster more SDG11 research in low-income economies, it is essential to provide increased funding support, capacity building and training for scholars, promote collaboration and knowledge exchange, and improve research infrastructure and data collection. Despite global challenges such as armed conflicts, CC and the COVID-19 pandemic, progress toward achieving the SDGs will become apparent by 2030. However, there are still opportunities for further research, knowledge dissemination and international collaboration toward developing safe, sustainable and inclusive urban development. The following are priority areas for SDG11 research:

Urban policy and governance: reforms should focus on providing equitable access to basic services such as water, sanitation, electricity, healthcare and education; upgrading and formalizing informal settlements; and improving living conditions of over one billion people residing in slums 37 . Participatory governance, community engagement and empowerment can enhance social inclusion by considering the voices and needs of marginalized groups 13 , 23 . Urban policy should also prioritize preserving historic and natural resources, protecting vulnerable areas and implementing sustainable urban design principles 47 . Future studies can help understand the dynamics, challenges and opportunities and monitor progress toward SDG11 targets 15 .

Localizing SDG11 targets: spatial planning and land-use strategies should consider the needs of diverse urban populations, promote inclusive zoning and engage local communities and stakeholders in decision-making processes, crucial for fostering ownership, empowerment and social cohesion, leading to more sustainable and inclusive urban development 3 . However, enhancing the capacity for localizing SDG11 targets requires building the knowledge and skills of local governments, policymakers and practitioners. Capacity-building initiatives, such as training programs, workshops and knowledge exchange, can promote interdisciplinary understanding and sharing of best practices.

Concerted and collaborative efforts: the international community, academics, policymakers and stakeholders can work together to create inclusive, safe, resilient and sustainable communities. Collaborative efforts can facilitate a comprehensive understanding of urban challenges and potential solutions by integrating diverse perspectives, data and methodologies. Disseminating research findings contributes to evidence-based policy development and informed decision-making, enabling the learning of lessons and replication of successful interventions.

Breaking down silos: integrated and cross-sectoral approaches help narrow the gaps between sectors, local governments, policymakers and stakeholders, leveraging local resources and capacities while fostering communication, knowledge sharing and collaboration 31 . Cross-sectoral working groups, joint planning processes and integrated policy frameworks promote holistic and coordinated decision-making among various sectors, including urban planning, housing, transportation, health, education, environment and social welfare 47 .

Digitalization and smart city development: maximizing the benefits of digitalization and smart city solutions requires addressing challenges such as bridging digital divides and ensuring data access, privacy and security. Prioritizing citizen-centric approaches and public accessibility to technology 36 are essential for leveraging expertise and resources 15 . Interoperability, scalability, data-driven decision-making and inclusivity contribute to evidence-based planning and equitable access to smart city technologies 12 , 48 , 49 , 50 , 51 .

This study comprehensively assessed SDG11 research, emphasizing significant thematic areas, trends, challenges and suggestions for prioritizing SDG11, including effective urban policy and governance, localizing SDG11 targets, concerted and collaborative efforts, and digitalization and smart city development. To broaden the scope of SDG11 research, future bibliometric reviews should encompass non-Web of Science databases and gray literature, including publications from government and nongovernmental agencies. Despite its limitations, this study’s findings provide valuable references for further research on SDG11.

The present study utilized a bibliometric technique to analyze academic publication on SDG11, tracing the research trend, the evolving key themes and identifying contributing authors, institutions and countries. Bibliometrics is a quantitative technique that allows for the analysis of trends in scholarly publications, such as research articles, conference papers and books, and visualizes scholarly publication patterns 52 . This technique is instrumental in analyzing extensive literature sets by relying on statistical observations and text-mining capabilities, which qualitative review methods such as systematic reviews cannot accomplish 53 . Additionally, it presents a scientific landscape of authors, countries, organizations and collaborations that contribute to worldwide scientific literature.

Bibliometric analysis requires interpretation, introducing an element of subjectivity 54 . Therefore, a sensemaking approach was adopted to transition from describing the bibliometric results to interpreting them. Sensemaking helps derive insightful information from bibliometric analysis and can be integrated into systematic literature reviews 55 , 56 . It applies to various international indexing, abstracting and citation databases, such as Scopus, Web of Science, Dimensions, PubMed and Education Resources Information Center, which cover journals, books, reviews and conference proceedings from around the world and different regions. For this study, Web of Science was chosen as the database to obtain bibliographic data due to its wide range of topics in various fields of study such as natural sciences, health sciences, engineering, social science, computer science and materials sciences. It is one of the world’s largest peer-reviewed scientific literature databases, with 87 million indexed items.

Specialized bibliometrics software were employed, including VOSviewer (version 1.6.19) 52 , Biblioshiny (version 4.1.3) 55 and BibExcel (version 2017) 57 . VOSviewer, known for its user-friendly interface, was used to understand the thematic focus and evolution of research on SDG11. It generates networks of nodes and links, with node size representing the frequency of the studied item, and link width indicating the strength of connections between items. Clusters of intricately linked nodes are shown in distinct colors. The thematic focus was examined for two periods: period 1 (2016–2019) and period 2 (2020–2022), considering the time since the SDGs were introduced to the time of data collection in this study. Another reason for this categorization is that evidence shows that the pandemic has significantly affected progress toward achieving SDGs 58 . VOSviewer allows for various types of analysis, including term co-occurrence, co-citation, citation and bibliographic coupling 53 . A term co-occurrence analysis was used in this study to highlight key thematic areas. To ensure accuracy and avoid separate counting of synonyms, a thesaurus file was developed and added to the software before the analysis. A summary of the data, including the number of authors and journals, used in the analysis is presented in Table 1 and will be further explained below.

A comprehensive search query was formulated to retrieve relevant data on SDG11, and it was executed in the title, abstract and keywords fields (TS) in Web of Science on 5 July 2023. The initial query shown the following box resulted in a total of 334,224 documents. Co-citation analysis was employed to identify the most influential journals contributing to SDG11 research. Two works are considered co-cited when they are both mentioned in the works cited section of a subsequent publication 59 (Zhao, 2006).

TS = ((‘city’ OR ‘cities’ OR ‘human settlement*’ OR ‘urban’ OR ‘metropoli*’ OR ‘town*’ OR ‘municipal*’ OR ‘peri-urban*’ OR ‘urban-rural’ OR ‘rural-urban’) AND (‘gentrification’ OR ‘congestion’ OR ‘transport*’ OR ‘housing’ OR ‘slum*’ OR ‘informal settlement*’ OR ‘sendai framework’ OR ‘Disaster Risk Reduction’ OR ‘disaster’ OR ‘DRR’ OR ‘smart cit*’ OR ‘resilient building*’ OR ‘sustainable building*’ OR ‘building design’ OR ‘buildings design’ OR ‘urbani?ation’ OR ‘zero energy’ OR ‘zero-energy’ OR ‘basic service*’ OR ‘governance’ OR ‘citizen participation’ OR ‘collaborative planning’ OR ‘participatory planning’ OR ‘inclusiveness’ OR ‘cultural heritage’ OR ‘natural heritage’ OR ‘UNESCO’ OR ‘ecological footprint’ OR ‘environmental footprint’ OR ‘waste’ OR ‘pollution’ OR ‘pollutant*’ OR ‘waste water’ OR wastewater* OR waste-water* OR ‘recycling’ OR ‘circular economy’ OR ‘air quality’ OR ‘green space’ OR ‘green spaces’ OR ‘nature inclusive’ OR ‘nature inclusive building’ OR ‘nature inclusive buildings’ OR ‘resilient’ OR ‘resilience’ OR ‘healthy cit*’ OR ‘sustainable’ OR ‘sustainability’ OR ‘green’ OR ‘nature*’ OR ‘Green infrastructure*’ OR ‘nature-based solution*’ OR ‘nature based solution*’ OR ‘child*’ OR ‘wom?n’ OR ‘elderl*’ OR ‘disabl*’ OR ‘disabilit*’ OR ‘disabled’)) AND PY = (2016–2022) NOT PY = (2023)

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework was used to report document search and filtration process. The PRISMA framework is designed to help scholars transparently report why their review study is conducted, what activities are performed and what discoveries are made, ideal for both systematic reviews and bibliometric studies 60 . PRISMA presents the four stages of the above query’s overall searching and filtration process (Fig. 4 ). The identification stage yielded 334,224 records, which were then screened to select only article-type documents ( n  = 277,165). Subsequently, documents were further screened based on language, selecting only English documents ( n  = 257,374). In the final stage, documents were screened based on specific categories closely related to cities and SDG11, resulting in a selection of six major categories: urban studies, environmental studies, geography, urban and regional planning, architecture, transportation and physical geography ( n  = 21,168). Finally, 15 duplicated documents were removed, resulting in a final dataset of 21,153 documents.

A four-phase flow diagram of the data extraction and filtration process of SDG11 literature, adapted from Priyadarshini 57 . WoS, Web of Science.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The data that support the findings of this study are available as supplementary information. The steps for curating the data from the Web of Science have been provided in the text. If there is a further need, data are available on figshare at https://doi.org/10.6084/m9.figshare.26360125 . Source data are provided with this paper.

Almulhim, A. I. & Cobbinah, P. B. Can rapid urbanization be sustainable? The case of Saudi Arabian cities. Habitat Int. 139 , 102884 (2023).

Article   Google Scholar  

Huang, W. et al. Evaluating green city development in China using an integrated analytical toolbox. J. Clean. Prod. 400 , 136703 (2023).

Feng, T. & Zhou, B. Impact of urban spatial structure elements on carbon emissions efficiency in growing megacities: the case of Chengdu. Sci. Rep. 13 , 9939 (2023).

Javeed, S., Siddique, H. M. A. & Javed, F. Ecological footprint, globalization, and economic growth: evidence from Asia. Environ. Sci. Pollut. Res. 30 , 77006–77021 (2023).

Rees, W. E. Cities, energy, and the uncertain future of urban civilization. Oxf. Dev. Stud. 51 , 11–17 (2023).

Abubakar, I. R. & Aina, Y. A. The prospects and challenges of developing more inclusive, safe, resilient, and sustainable cities in Nigeria. Land Use Policy 87 , 104105 (2019).

UN-Habitat World Cities Report 2022: Envisaging the Future of Cities. (UN-Habitat, 2022).

Almulhim, A. I. & Cobbinah, P. B. Framing resilience in Saudi Arabian cities: on climate change and urban policy. Sustain. Cities Soc. 101 , 105172 (2024).

Barbier, E. B. & Burgess, J. C. Sustainability and development after COVID-19. World Dev. 135 , 105082 (2020).

The Sustainable Development Goals Report 2018 (United Nations, 2018).

Chen, M., Chen, L., Cheng, J. & Yu, J. Identifying interlinkages between urbanization and Sustainable Development Goals. Geogr. Sustain. 3 , 339–346 (2022).

Clement, J., Ruysschaert, B. & Crutzen, N. Smart city strategies—a driver for localizing sustainable development goals? Ecol. Econ. 213 , 107941 (2023).

Hansson, S., Arfvidsson, H. & Simon, D. Governance for sustainable urban development: the double function of SDG indicators. Area Dev. Policy 4 , 217–235 (2019).

Leal Filho, W. et al. Using the sustainable development goals towards a better understanding of sustainability challenges. Int. J. Sustain. Dev. World Ecol. 26 , 179–190 (2019).

Parra-Dominguez, J., Gil-Egido, A. & Rodríguez-González, S. SDGs as one of the drivers of smart city development: the indicator selection process. Smart Cities 5 , 1025–1038 (2022).

Grainger-Brown, J., Malekpour, S., Raven, R. & Taylor, E. Exploring urban transformation to inform the implementation of the Sustainable Development Goals. Cities 131 , 103928 (2022).

Xu, Z. et al. Assessing progress towards sustainable development over space and time. Nature 577 , 74–78 (2020).

Salvia, A. L., Leal Filho, W., Brandli, L. L. & Griebeler, J. S. Assessing research trends related to Sustainable Development Goals: local and global issues. J. Clean. Prod. 208 , 841–849 (2019).

Indana, F. & Pahlevi, R. W. A bibliometric approach to Sustainable Development Goals (SDGs) systematic analysis. Cogent Bus. Manag. 10 , 2224174 (2023).

Yamaguchi, N. U. et al. Sustainable development goals: a bibliometric analysis of literature reviews. Environ. Sci. Pollut. Res. 30 , 5502–5515 (2023).

Pattberg, P. & Bäckstrand, K. Enhancing the achievement of the SDGs: lessons learned at the half-way point of the 2030 Agenda. Int. Environ. Agree. 23 , 107–114 (2023).

Keith, M. et al. A new urban narrative for sustainable development. Nat. Sustain. 6 , 115–117 (2023).

Pedersen, A. B. et al. SDGs at the halfway point: how the 17 global goals address risks and wicked problems. Ambio 52 , 679–682 (2023).

Sweileh, W. M. Bibliometric analysis of scientific publications on ‘sustainable development goals’ with emphasis on ‘good health and well-being’ goal (2015–2019). Global. Health 16 , 68 (2020).

Mihelcic, J. R. et al. Environmental research addressing Sustainable Development Goals. Environ. Sci. Technol. 57 , 3457–3460 (2023).

Palau-Pinyana, E., Llach, J. & Bagur-Femenías, L. Mapping enablers for SDG implementation in the private sector: a systematic literature review and research agenda. Manag. Rev. Q. 26 , 1–30 (2023).

Google Scholar  

Mishra, M. et al. A bibliometric analysis of sustainable development goals (SDGs): a review of progress, challenges, and opportunities. Environ. Dev. Sustain. 26 , 11101–11143 (2023).

Devisscher, T. et al. in Sustainable Development Goals: Their Impacts on Forests and People (eds. Katila, P. et al.) (Cambridge Univ. Press, 2019).

Brenner, N. & Theodore, N. Cities and the geographies of ‘actually existing neoliberalism’. Antipode 34 , 349–379 (2002).

Sharifi, A., Khavarian-Garmsir, A. R., Allam, Z. & Asadzadeh, A. Progress and prospects in planning: a bibliometric review of literature in urban studies and regional and urban planning, 1956–2022. Prog. Plann. 173 , 100740 (2023).

Olanrewaju, A., Tan, S. Y. & Abdul-Aziz, A.-R. Housing providers’ insights on the benefits of sustainable, affordable housing. Sustain. Dev. 26 , 847–858 (2018).

Rice, J. L., Cohen, D. A., Long, J. & Jurjevich, J. R. Contradictions of the climate-friendly city: new perspectives on eco-gentrification and housing justice. Int. J. Urban Reg. Res. 44 , 145–165 (2020).

Park, S., Choi, K. & Lee, J. S. To walk or not to walk: testing the effect of path walkability on transit users’ access mode choices to the station. Int. J. Sustain. Transp. 9 , 529–541 (2015).

Jayasooriya, V. M., Ng, A. W. M., Muthukumaran, S. & Perera, B. J. C. Green infrastructure practices for the improvement of urban air quality. Urban For. Urban Green. 21 , 34–47 (2017).

Hachaichi, M. & Egieya, J. Water–food–energy nexus in global cities: addressing complex urban interdependencies. Water Resour. Manag. 37 , 1811–1825 (2023).

Mora, L., Deakin, M. & Reid, A. Combining co-citation clustering and text-based analysis to reveal the main development paths of smart cities. Technol. Forecast. Soc. Change 142 , 56–69 (2019).

McFarlane, C. Rethinking informality: politics, crisis, and the city. Plan. Theory Pract. 13 , 89–108 (2012).

Harvey, D. From managerialism to entrepreneurialism: the transformation in urban governance in late capitalism. Geogr. Ann. B 71 , 3–17 (1989).

Peck, J. Political economies of scale: fast policy, interscalar relations, and neoliberal workfare. Econ. Geogr. 78 , 331–360 (2002).

Cervero, R. & Landis, J. Twenty years of the Bay Area rapid transit system: land use and development impacts. Transport. Res. A 31 , 309–333 (1997).

Guzman, P. C., Roders, A. R. P. & Colenbrander, B. J. F. Measuring links between cultural heritage management and sustainable urban development: an overview of global monitoring tools. Cities 60 , 192–201 (2017).

Yamasaki, K. & Yamada, T. A framework to assess the local implementation of Sustainable Development Goal 11. Sustain. Cities Soc. 84 , 104002 (2022).

van Zanten, J. A. & van Tulder, R. Towards nexus-based governance: defining interactions between economic activities and Sustainable Development Goals (SDGs). Int. J. Sustain. Dev. World Ecol. 28 , 210–226 (2021).

Londono-Pineda, A. A. & Cano, J. A. Assessments under the United Nations sustainable development goals: a bibliometric analysis. Environ. Clim. Technol. 26 , 166–181 (2022).

Biggeri, M. A. ‘Decade for Action’ on SDG localization. J. Hum. Dev. Capabil. 22 , 706–712 (2021).

Benedek, J., Ivan, K., Török, I., Temerdek, A. & Holobâcă, I. H. Indicator-based assessment of local and regional progress toward the Sustainable Development Goals (SDGs): an integrated approach from Romania. Sustain. Dev. 29 , 860–875 (2021).

Abubakar, I. R. & Alshammari, M. S. Urban planning schemes for developing low-carbon cities in the Gulf Cooperation Council region. Habitat Int. 138 , 102881 (2023).

Batty, M. Smart cities, big data. Environ. Plann. B 39 , 191–193 (2012).

Smith, N. Toward a theory of gentrification, a back to the city movement by capital, not people. J. Am. Plann. Assoc. 45 , 538–548 (1979).

Jacobs, J. The Death and Life of Great American Cities (Random House, 1961).

Smith, N. The New Urban Frontier: Gentrification and the Revanchist City (Routledge, 1996).

Donthu, N., Kumar, S., Mukherjee, D., Pandey, N. & Lim, W. M. How to conduct a bibliometric analysis: an overview and guidelines. J. Bus. Res. 133 , 285–296 (2021).

van Eck, N. J. & Waltman, L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics 84 , 523–538 (2010).

Hajek, P., Youssef, A. & Hajkova, V. Recent developments in smart city assessment: a bibliometric and content analysis-based literature review. Cities 126 , 103709 (2022).

Lim, W. M. & Kumar, S. Guidelines for interpreting the results of bibliometrics analysis: a sensemaking approach. Glob. Bus. Organ. Excell. 43 , 17–26 (2023).

Aria, M. & Cuccurullo, C. bibliometrix: an R-tool for comprehensive science mapping analysis. J. Informetr. 11 , 959–975 (2017).

Åström, F., Danell, R., Larsen, B. & Schneider, J. (eds) Celebrating Scholarly Communication Studies: A Festschrift for Olle Persson at His 60th Birthday Vol. 05-S (International Society for Scientometrics and Informetrics, 2009).

Priyadarshini, P. The COVID-19 pandemic has derailed the progress of Sustainable Development Goals. Anthr. Sci. 1 , 410–412 (2022).

Zhao, D. Towards all-author co-citation analysis. Inf. Process. Manag. 42 , 1578–1591 (2006).

Shamseer, L. et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015: elaboration and explanation. BMJ 349 , g7647 (2015).

Download references

Acknowledgements

A.I.A. acknowledges Imam Abdulrahman Bin Faisal University in Dammam, Saudi Arabia, for their support in conducting this study. A.S. acknowledges the support of the Japan Society for the Promotion of Science KAKENHI grant number 22K04493. We appreciate Hiroshima University for supporting the open-access publication of this article.

Author information

Authors and affiliations.

Department of Urban and Regional Planning, College of Architecture and Planning, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Abdulaziz I. Almulhim

The IDEC Institute and Network for Education and Research on Peace and Sustainability, Hiroshima University, Hiroshima, Japan

Ayyoob Sharifi

Department of Geomatics Engineering Technology, Yanbu Industrial College, Yanbu, Saudi Arabia

Yusuf A. Aina

Directorate of Library Affairs, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Shakil Ahmad

The Business School, Edinburgh Napier University, Edinburgh, UK

Academy of Architecture and Urban Studies, Tallinn University of Technology, Tallinn, Estonia

Department of Natural Sciences, Manchester Metropolitan University, Manchester, UK

Walter Leal Filho

Research and Transfer Centre ‘Sustainable Development and Climate Change Management’, Hamburg University of Applied Sciences, Hamburg, Germany

College of Architecture and Planning, Imam Abdulrahman Bin Faisal University, Dammam, Saudi Arabia

Ismaila Rimi Abubakar

You can also search for this author in PubMed   Google Scholar

Contributions

A.I.A.: conceptualization, methodology, formal analysis, data curation, writing—original draft, writing—review and editing, investigation and project administration. A.S.: methodology, software, formal analysis, visualization and writing—original draft. Y.A.A.: conceptualization, writing—original draft, investigation and validation. S.A.: methodology, software, formal analysis, visualization and data curation. L.M.: writing—review and editing, and investigation. W.L.F.: writing—review and editing, and investigation. I.R.A.: writing—review and editing, investigation, supervision, validation and resources.

Corresponding authors

Correspondence to Abdulaziz I. Almulhim or Ayyoob Sharifi .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Peer review

Peer review information.

Nature Cities thanks Aliyu Salisu Barau, Oluwatobi Owojori, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information.

Supplementary Figs. 1–5 and Tables 1–13.

Reporting Summary

Source data fig. 1.

Statistical source data in Excel format.

Source Data Fig. 2

Source data fig. 3, source data fig. 4, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Almulhim, A.I., Sharifi, A., Aina, Y.A. et al. Charting sustainable urban development through a systematic review of SDG11 research. Nat Cities (2024). https://doi.org/10.1038/s44284-024-00117-6

Download citation

Received : 22 September 2023

Accepted : 30 July 2024

Published : 27 August 2024

DOI : https://doi.org/10.1038/s44284-024-00117-6

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Virginia Tech’s Undergraduate Science Laboratory Building advances scientific education

• Alex Garner

25 Aug 2024

• Share on Facebook
• Share on Twitter
• Copy address link to clipboard

The Undergraduate Science Laboratory Building on Virginia Tech’s Blacksburg campus is now open, marking a significant milestone in the university's commitment to advancing scientific education and research. This new facility, spanning approximately 102,000 gross-square-feet, is designed to support modern laboratory practices and foster interdisciplinary collaboration among students and faculty from various colleges.

Consistent with the Division of Facilities ’ capital construction closeout process, remaining cosmetic finishes will continue for several weeks into the fall semester. In close partnership with the building’s contractor, minor construction activities will occur inside and outside the facility during periods that accommodate teaching and learning schedules. While these efforts are ongoing in bringing the now-open building to its fullest potential – both functionally and aesthetically – students and faculty are requested to only use the building during scheduled classes, labs, and associated activities. When fully completed later this fall, the building will be available and ready for individual study, collaboration sessions, group projects, and other activities that are outside scheduled class/lab timeframes.

The Undergraduate Science Laboratory Building features 26 flexible and adaptable laboratories, including wet, dry, and specialty labs. These modern spaces are equipped to meet the evolving instructional needs of the College of Science, the College of Engineering, the College of Natural Resources and Environment, and the College of Agriculture and Life Sciences. These laboratories will enable students to work on projects that bridge academic instruction with experiential learning and real-world applications.

In addition to advanced laboratories, the facility also includes general-use classrooms, collaboration spaces, offices, informal study areas, and workspaces for graduate teaching assistants. The new building’s completion represents a major milestone in Virginia Tech’s campus master plan , which outlines land use strategies to support the university’s strategic vision.

Careful design and construction

Sustainability is a cornerstone of the Undergraduate Science Laboratory Building's design. In support of the Virginia Tech Climate Action Commitment , the building aims to achieve at least Leadership in Energy and Environmental Design Silver certification, reflecting Virginia Tech's commitment to environmental stewardship and energy efficiency. 

The Undergraduate Science Laboratory Building's development involved collaboration with the Division of Facilities and ZGF , who provided architectural and engineering services, and Skanska USA , who served as the construction manager. Their combined expertise ensured that the building met the highest standards of design and functionality.

Looking ahead

As Virginia Tech and its students continue to innovate, the Undergraduate Science Laboratory Building stands as a testament to the university's dedication to providing top-tier educational facilities. By fostering a collaborative and cutting-edge learning environment, the Undergraduate Science Laboratory Building is already playing a crucial role in shaping the future of scientific education and research at Virginia Tech.

For those in need of assistance locating the new Undergraduate Science Laboratory Building, positioned near the corner of Prices Fork Road and West Campus Drive, or any other campus locations, please visit the Interactive Campus Map .

Meghan Marsh

(540) 231-8870

Stephanie Overton

(540) 231-7892

• Campus Experience
• Campus Master Plan
• Campus sustainability
• College of Agriculture and Life Sciences
• College of Engineering
• College of Natural Resources and Environment
• College of Science
• Division of Facilities
• Executive Vice President and Provost
• Sustainability
• Undergraduate Education
• Undergraduate Research

Related Content

More From Forbes

5 ways you might pay for your ai humanoid robot in retirement.

• Share to Facebook
• Share to Twitter
• Share to Linkedin

AI humanoid robots may become part of retirement and older age, but how will you acquire and pay for ... [+] them?

Technology is rapidly transforming the landscape of life in retirement and older age. A myriad of devices—big and small—are becoming part of our everyday lives as we age. The technology is coming. The less clear question about life in older age is how will we acquire and pay for these technologies.

A New High-Tech Retirement Lifestyle

With the advent of advanced computing power, AI algorithms, and nearly ubiquitous sensing, a new wave of startups and multinational corporations are developing solutions tailored to the needs of an aging population. This technology is becoming ever-present—integrated into our homes, wearable on our bodies, and even embedded into our clothing and footwear.

These emerging technologies enable a high-tech retirement lifestyle that goes well beyond basic communications and entertainment. Devices and smart systems are already available to monitor physical health, manage medications, ensure proper nutrition, and encourage healthy behaviors like staying hydrated and maintaining social connections. According to AARP , Americans ages 50 and older spent $77 billion on all types of technology in 2022. By 2030, this figure is expected to soar to $120 billion. Keren Etkin, creator of The Gerontechnologist , notes the global aging and technology market could reach $2 trillion.

In my previous article on Forbes , I discussed the arrival of AI humanoid robots that are quickly capturing our imaginations and may soon be part of our retirement lives. Developers of these robots claim their AI systems will perform various tasks, from household chores and meal preparation to supporting our health and even acting as our social companions.

Beyond The Hype: The Real Cost Of AI In Aging

While there is no denying the excitement around the potential of home robotics, developers and their supporters largely focus on the possible functions and promise to improve the lives of older adults. This perspective is exciting but also incomplete. True innovation occurs when technology moves out of the lab and into our living rooms. That transition requires more than awesome design functionality; it involves understanding how users will acquire these devices and who will pay the bill.

Trump Signals He May Skip ABC News Debate After Bashing Network

Real madrid coach ancelotti fires warning to vinicius jr., fc barcelona announces third transfer in four days, usership: home robot-as-a-service.

Robot developers assume that consumers will want these devices once they know what a humanoid AI robot can do. If so, that new want may evolve into a need, adding a new expense in retirement with an initial price tag projected to be comparable to the cost of a new car.

Younger Baby Boomers and early Gen-Xers will be the first to have a wide array of tech-driven services available as they age. A very small percentage of these future retirees may have these tech-related costs accounted for in their retirement financial plan, but most will not.

But what if you didn’t have to buy your robot? Instead of ownership, think of usership. Imagine subscribing to a service instead. Rather than owning a robot, you could subscribe to a future robot service provider that could be a manufacturer such as Tesla or Figure , or even through a traditional smart home systems integrator like ADT or perhaps a retailer like Best Buy Health . For a monthly fee, these companies could offer robot-as-a-service (also known as RaaS) options that might include user training, maintenance, insurance, and upgrades all at a predictable monthly cost.

The concept of RaaS isn’t as far-fetched as it might seem. It is already used in industrial robotics . The automobile industry provides one approach for the consumer-facing market. Volvo, for example, introduced a subscription plan alternative to traditional car ownership or leasing. These plans often include insurance and maintenance, flexibility to change vehicles, and convenience that could translate to home robotics.

Who Will Pay For Your Robot?

There are likely to be multiple paths to paying for your new robot. Here are five.

1. Personal Purchase: AI Automation As The New Luxury

Purchasing a personal robot is likely to become the next luxury buy, similar to buying a new car. Overtime, as prices decrease, it may become the new standard home appliance. Initial cost projections offered by Tesla begin at around $30,000 while others suggest that a home robot will surpass $100,000, depending on functions and accessories. Future robot owners will also have to account for the initial purchase and ongoing expenses like maintenance, software updates, and insurance.

2. Robotics: High-Tech Hands Of Adult Children

Adult children might buy a robot for their aging parents. For the sandwich generation—those caught between raising children and caring for aging parents—robotic assistants could offer a much-needed helping hand. By investing in AI at home family caregivers could help ensure their parents' well-being while managing their own demanding schedules, as they balance children, careers, and care.

3. Caring Companies: Employer-Sponsored Robotic Care

As companies struggle with the impact of employee caregiving on productivity, innovative benefits packages are emerging. Many firms already provide subsidies for childcare and pet care, why not robot benefits? Forward-thinking firms might someday offer subsidies or subscription plans for home robots in the homes of employees' parents, recognizing the potential to improve work-life balance and reduce absenteeism among employees with caregiving responsibilities.

4. Insurance-Provided AI Helpers

Life, health, and disability insurers may see home robots as a way to reduce risk and claims. If data eventually show that home robot assistance do lower home accident rates, such as falls, and improves health outcomes for older adults, insurance companies might subsidize or directly provide these high-tech helpers to their policyholders. Moreover, an insurance-branded robot in the home would be a daily reminder of your insurance company’s brand and role in your daily life, not just when you pay premiums or make a claim.

5. A Government-Issued Robot

As the population ages, governments might view robotic assistants as a cost-effective solution to responding to growing healthcare and social service demands. Public programs could be created to finance or provide robots to achieve broader social benefits.

Planning And Paying For A Tech-Enabled Retirement

For most people, the idea of AI humanoid robots in the home is little more than the stuff of science fiction. Over time science fiction often has a way of becoming fact. Technology and global aging are converging, greatly increasing the likelihood that robots will play a significant role in our lives — particularly in older age. Much like planning and paying for where we live, our transportation, and access to healthcare, retirement planning will evolve to include the financial impacts of integrating novel technologies, including robots, into our lives. Whether through direct purchase, subscription, insurance benefit, or corporate and government initiatives, the question of who will pay for these innovations is critical to moving these ideas from the laboratory into our lives.

• Editorial Standards
• Reprints & Permissions

Join The Conversation

One Community. Many Voices. Create a free account to share your thoughts. 

Forbes Community Guidelines

Our community is about connecting people through open and thoughtful conversations. We want our readers to share their views and exchange ideas and facts in a safe space.

In order to do so, please follow the posting rules in our site's  Terms of Service.   We've summarized some of those key rules below. Simply put, keep it civil.

Your post will be rejected if we notice that it seems to contain:

• False or intentionally out-of-context or misleading information
• Insults, profanity, incoherent, obscene or inflammatory language or threats of any kind
• Attacks on the identity of other commenters or the article's author
• Content that otherwise violates our site's  terms.

User accounts will be blocked if we notice or believe that users are engaged in:

• Continuous attempts to re-post comments that have been previously moderated/rejected
• Racist, sexist, homophobic or other discriminatory comments
• Attempts or tactics that put the site security at risk
• Actions that otherwise violate our site's  terms.

So, how can you be a power user?

• Stay on topic and share your insights
• Feel free to be clear and thoughtful to get your point across
• ‘Like’ or ‘Dislike’ to show your point of view.
• Protect your community.
• Use the report tool to alert us when someone breaks the rules.

Thanks for reading our community guidelines. Please read the full list of posting rules found in our site's  Terms of Service.