design research report

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Design Research Report Examples

Design research reports take on many different formats. Notice how each of these research reports (often referred to as “Case Studies”) tells a story. They aren’t just data, charts, and conclusions—they bring the human element into the story.

Research Report Examples

These examples specifically report research.

• Oxford Commuters and the Efficacy of Political Advertising : xdMFA Fall 2016 Students, Mateus & Chen
• Exploring Adult Satisfaction Within Healthcare Facilities : Comm Design Fall 2018 Students, Goheen, Cortez, Eshett
• The Use of Plastic at Miami University Off-Campus Parties : Comm Design Fall 2019 Students, Troyer & Nguyen

Research Reports with Design Prototypes

These reports share research outcomes and take the next step—design prototyping. These students in the xdMFA program conducted research and created outcomes based on their research.

• Conversation Groups & The Intersections of Language & Culture : xdMFA Fall 2018 Students Dean, Huffman, Mauk
• The Anthropomorphizing of Technology : xdMFA 2016 Student Jerry Belich
• Pokémon-Go AR Game: Designing To Match The Pokémon Fans Needs : xdMFA 2015 Students Cela, Chen, Mateus Forero, Mandke
• Teaching Anatomy to Yoga Teacher Trainees : xdMFA Fall 2016 Student Ali Place

Case Studies

The rest of these reports show a wide range of types of reporting. Most are geared toward stakeholders/supporters of each project and their communities.

• City of Owasso Character Initiative : Cheatham, Hicks, Keefer, Yang
• The Design Difference : Japan Society, the Designers Accord, Common Ground, and GOOD
• Valuing Design: Mapping Design Impact and Value in Six Public and 3rd Sector Projects : Yee, White, Lennon

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Miami University

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Methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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Design Research

What is design research.

Design research is the practice of gaining insights by observing users and understanding industry and market shifts. For example, in service design it involves designers’ using ethnography—an area of anthropology—to access study participants, to gain the best insights and so be able to start to design popular services.

“We think we listen, but very rarely do we listen with real understanding, true empathy. Yet listening, of this very special kind, is one of the most potent forces for change that I know.” — Carl Rogers, Psychologist and founding father of the humanistic approach & psychotherapy research

Service design expert and Senior Director of User Research at Twitch Kendra Shimmell explains what goes into good design research in this video.

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Get Powerful Insights with Proper Design Research

When you do user research well, you can fuel your design process with rich insights into how your target users interact—or might interact—in contexts to do the things they must do to achieve their goals using whatever they need on the way. That’s why it’s essential to choose the right research methods and execute them properly. Then, you’ll be able to reach those participants who agree to be test users/customers, so they’ll be comfortable enough to give you accurate, truthful insights about their needs, desires, pain points and much more. As service design can involve highly intricate user journeys , things can be far more complex than in “regular” user experience (UX) design . That’s where design research comes in, with its two main ingredients:

Qualitative research – to understand core human behaviors, habits and tasks/goals

Industry and Market research – to understand shifts in technology and in business models and design-relevant signs

An ideal situation—where you have enough resources and input from experts—is to combine the above to obtain the clearest view of the target customers of your proposed—or improved—service and get the most accurate barometer reading of what your market wants and why. In any case, ethnography is essential. It’s your key to decoding this very human economy of habits, motivations, pain points, values and other hard-to-spot factors that influence what people think, feel, say and do on their user journeys. It’s your pathway to creating personas —fictitious distillations that prove you empathize with your target users as customers—and to gain the best insights means you carefully consider how to access these people on their level. When you do ethnographic field studies, you strive for accurate observations of your users/customers in the context of using a service .

© Interaction Design Foundation, CC BY-SA 4.0

How to Leverage Ethnography to Do Proper Design Research

Whatever your method or combination of methods (e.g., semi-structured interviews and video ethnography), the “golden rules” are:

Build rapport – Your “test users” will only open up in trusting, relaxed, informal, natural settings. Simple courtesies such as thanking them and not pressuring them to answer will go a long way. Remember, human users want a human touch, and as customers they will have the final say on a design’s success.

Hide/Forget your own bias – This is a skill that will show in how you ask questions, which can subtly tell users what you might want to hear. Instead of asking (e.g.) “The last time you used a pay app on your phone, what was your worst security concern?”, try “Can you tell me about the last time you used an app on your phone to pay for something?”. Questions that betray how you might view things can make people distort their answers.

Embrace the not-knowing mindset and a blank-slate approach – to help you find users’ deep motivations and why they’ve created workarounds. Trying to forget—temporarily—everything you’ve learned about one or more things can be challenging. However, it can pay big dividends if you can ignore the assumptions that naturally creep into our understanding of our world.

Accept ambiguity – Try to avoid imposing a rigid binary (black-and-white/“yes”-or-“no”) scientific framework over your users’ human world.

Don’t jump to conclusions – Try to stay objective. The patterns we tend to establish to help us make sense of our world more easily can work against you as an observer if you let them. It’s perfectly human to rely on these patterns so we can think on our feet. But your users/customers already will be doing this with what they encounter. If you add your own subjectivity, you’ll distort things.

Keep an open mind to absorb the users’ world as present it – hence why it’s vital to get some proper grounding in user research. It takes a skilled eye, ear and mouth to zero in on everything there is to observe, without losing sight of anything by catering to your own agendas, etc.

Gentle encouragement helps; Silence is golden – a big part of keeping a naturalistic setting means letting your users stay comfortable at their own pace (within reason). Your “Mm-mmhs” of encouragement and appropriate silent stretches can keep your research safe from users’ suddenly putting politeness ahead of honesty if they feel (or feel that you’re) uncomfortable.

Overall, remember that two people can see the same thing very differently, and it takes an open-minded, inquisitive, informal approach to find truly valuable insights to understand users’ real problems.

Learn More about Design Research

Take our Service Design course, featuring many helpful templates: Service Design: How to Design Integrated Service Experiences

This Smashing Magazine piece nicely explores the human dimensions of design research: How To Get To Know Your Users

Let Invision expand your understanding of design research’s value, here: 4 types of research methods all designers should know .

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What is the main goal of design research?

• To increase the speed of the design process
• To learn what informs design decisions
• To lower the cost of production

Why are ethnographic studies important in design research?

• They focus on quantitative data collection.
• They help understand user behavior in natural contexts.
• They prioritize technological advancements.

What are the two main types of research methods used in design research?

• Qualitative and market research
• Qualitative and quantitative research
• Quantitative and user experience design

What is a key aspect of empathy in design research?

• Focus on aesthetic design
• Prioritize designers' ideas and needs
• Understand users' perspectives and needs

Why is it important to avoid bias in design research?

• To decrease the overall research costs
• To make sure insights are objective and accurate
• To speed up the research process

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Literature on Design Research

Here’s the entire UX literature on Design Research by the Interaction Design Foundation, collated in one place:

Learn more about Design Research

Take a deep dive into Design Research with our course Service Design: How to Design Integrated Service Experiences .

Services are everywhere! When you get a new passport, order a pizza or make a reservation on AirBnB, you're engaging with services. How those services are designed is crucial to whether they provide a pleasant experience or an exasperating one. The experience of a service is essential to its success or failure no matter if your goal is to gain and retain customers for your app or to design an efficient waiting system for a doctor’s office.

In a service design process, you use an in-depth understanding of the business and its customers to ensure that all the touchpoints of your service are perfect and, just as importantly, that your organization can deliver a great service experience every time . It’s not just about designing the customer interactions; you also need to design the entire ecosystem surrounding those interactions.

In this course, you’ll learn how to go through a robust service design process and which methods to use at each step along the way. You’ll also learn how to create a service design culture in your organization and set up a service design team . We’ll provide you with lots of case studies to learn from as well as interviews with top designers in the field. For each practical method, you’ll get downloadable templates that guide you on how to use the methods in your own work.

This course contains a series of practical exercises that build on one another to create a complete service design project . The exercises are optional, but you’ll get invaluable hands-on experience with the methods you encounter in this course if you complete them, because they will teach you to take your first steps as a service designer. What’s equally important is that you can use your work as a case study for your portfolio to showcase your abilities to future employers! A portfolio is essential if you want to step into or move ahead in a career in service design.

Your primary instructor in the course is Frank Spillers . Frank is CXO of award-winning design agency Experience Dynamics and a service design expert who has consulted with companies all over the world. Much of the written learning material also comes from John Zimmerman and Jodi Forlizzi , both Professors in Human-Computer Interaction at Carnegie Mellon University and highly influential in establishing design research as we know it today.

You’ll earn a verifiable and industry-trusted Course Certificate once you complete the course. You can highlight it on your resume, CV, LinkedIn profile or on your website.

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• How it works

How to Write a Research Design – Guide with Examples

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

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

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

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

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

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

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

Step 1: Establish Priorities for Research Design

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

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

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

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

For example:

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

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

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

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

Step 2: Data Type you Need for Research

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

Primary Data Vs. Secondary Data

Qualitative Vs. Quantitative Data

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

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Step 3: Data Collection Techniques

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

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

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

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

Research Methods

Step 4: Procedure of Data Analysis

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

Quantitative Data Analysis

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

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

Qualitative Data Analysis

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

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

Step 5: Write your Research Proposal

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

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

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

How to Write a Research Design – Conclusion

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

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

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

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

Frequently Asked Questions

What is research design.

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

How to write a research design?

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

How to write the design section of a research paper?

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

How to write a research design in methodology?

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

You May Also Like

To help students organise their dissertation proposal paper correctly, we have put together detailed guidelines on how to structure a dissertation proposal.

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.

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Research Design | Step-by-Step Guide with Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

Prevent plagiarism, run a free check.

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

Types of quantitative research designs

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalisation

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

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

Operationalisation means turning abstract conceptual ideas into measurable observations.

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

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

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

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

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Inside Design

A quick guide to design research

Emily esposito,   •   may 8, 2018.

Y ou create a beautiful design, get stakeholder buy-in, and launch ahead of schedule, but it falls flat with your customers. Why? Well, did you stop and take the time to see what they thought about it?

Design research is a critical, but sometimes ignored, step in creating the optimal user experience. It allows you to understand complex human behavior and turn that into actionable insights to improve your design.

In this post, we’ll walk you through what design research is, how to use it, and its top benefits.

“There’s always something new to learn about your users.”

What is design research?

Design research is a customer-focused approach that helps you answer questions like:

• Who are our users?
• What problems are they facing?
• How are they going to use this product?

Through qualitative and quantitative research, the goal is to find inspiration for design based on how your customers would actually use your product or service.

Design research vs. market research

While design and market research have things in common, they are very different in terms of scope, data, and end results. At a high level, market research focuses on the purchase and sales of the product or service, while design research looks at how customers will use and experience it.

Related: Get better qualitative data on your user experience with microfeedback

Market research is more quantitative in nature. You’re typically analyzing large data sets to identify business insights, segments, trends, and demographics that speak to the market segment. Design research skews more toward qualitative data, where you’re trying to answer the “why” behind customer behavior and interaction.

Participatory design research (or what looks like it) at SFO. pic.twitter.com/vcZB6ZIttU — Sarah Fathallah (@SFath) May 5, 2018

3 kinds of research methods designers should know

There’s an endless number of ways to collect data about your customers. Whichever methods you choose, they usually fall into these buckets: attitudinal, behavioral, qualitative, quantitative, and context of use.

“Design research results in happier customers.”

Here are some of the most common research methods:

• Primary : Perhaps the most important method in design research, this involves you or your team going directly to the source (your customers) to ask questions and gather data. Examples of primary research are focus groups, usability sessions, surveys , and interviews. And in primary research, you are usually gathering two types of information: exploratory (general, open-ended research), and specific (research used to solve a problem identified during the exploratory phase).
• Secondary : Secondary research is when you use existing data like books, articles, or the internet to validate or support existing research. You may use secondary research to create a stronger case for your design choices and provide additional insights into what you learned during primary research.
• Evaluative: Evaluative research looks at a specific problem to evaluate usability and interaction. One of the most popular ways to conduct evaluative research is to have people use your product or service and have them think out loud as they interact with it. There are two types of evaluative studies: summative and formative . Summative emphasizes the outcome more than the process (looking at whether the desired effect is achieved) and formative is used to strengthen idea being tested (monitoring the success of a process).

How do you decide which research method to use? It all depends on what you’re creating and what you’re trying to learn. Often, you’ll start with primary research and find that more, new questions arise after gathering preliminary data (that’s a good thing!). These new questions will likely guide you on what you need to learn next.

Benefits of design research

Design research takes time, resources, and preparation, but the results are worth it. Here are four of the top benefits of design research:

• Allows you to design based on facts and not assumptions. It’s easy to fall into the trap of thinking, “I already know who my customers are, so I don’t need to do any more work.” It’s true that we all have a working knowledge of who our users are, but understanding their pain points, what they’re looking for in a product, and how they would use your product are not things you can learn from a one-off email interaction.
• Helps with focus and prioritization. When you’re juggling feature requests, stakeholder feedback, and a short project schedule, customer data can help you focus on what is most important. After all, if something came up during the research phase that wasn’t addressed before launch, you can bet that issue won’t go away on its own.
• Fosters more empathy for your customers. Getting facetime with your customers reminds everyone that they are actual people with thoughts and feelings, not just a number on a growth projection. Building deeper connections with your customers helps you in your day-to-day work and decisions.
• Results in happier customers. By observing your design in the wild, with actual customers, you can create a user experience that will delight and not frustrate. You can fix simple things like a confusing navigation or unclear path to purchase that would otherwise have resulted in support calls or frustrated customer emails.

While we can’t sit next to our ideal customer each and every day as we design, iterate, and test (that would be too easy!), conducting design research is a wonderful alternative.

And yes, no matter how much data you’ve gathered in the past or how well you think you know your customers, there is always something new to learn.

by Emily Esposito

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

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Research Report

A research report is a deliverable that summarizes insights from a user research study. The report can take various forms, such as a written report or a slide deck. This report should reference the research plan that was created before the study was conducted.

A research report should tell a compelling and accurate story about the users. The findings may be shared across an organization, so they should be digestible and actionable. An effective report helps stakeholders make important decisions about the next steps to take in the project or design.

A research report typically contains the following:

• Executive Summary: The executive summary is an overview included at the beginning of the research report that summarizes the key insights and the purpose of the study. The executive summary should be concise and include enough context to stand on its own. This section introduces the report and helps engage the audience.
• Background: The background includes the what and the why of the project. This section should describe the context, discuss what prompted this research, and explain the problem that the team is trying to solve.
• Research Method: This section describes which user research methods were used and why. The number of participants in the study and the demographic or target audience should be included. Additionally, this section covers how participants were recruited, how the study was facilitated, and how the results were analyzed.
• Research Questions: Research questions provide context about the study’s purpose and indicate the main topics that the report will explore. The research questions should come directly from the research plan .
• Findings: Findings summarize what was learned from the research study. This could be accompanied by a quote from a user interview or a graph from a user survey. What are the essential insights that the audience should take away? Findings can be referenced in a research report more than once. For example, they can appear in the executive summary or be paired with recommendations.
• Recommendations: Recommendations are like a call to action. How might the team solve problems that the research uncovered? What could make a service, product, or process better for users? If research shows that users struggled to complete a task, the recommendations could detail how the platform might be improved to make this task easier.
• What’s Next: The “What’s Next?” section explains what the team plans to do after the research is complete. For example, they may create research-backed personas to define characteristics of the target audience or conduct an additional study to gather more data.

Example Research Report

To see a sample report, check out the Example Research Report slide deck, which captures insights from a user interview study.

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What is design research methodology and why is it important?

What is design research.

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

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

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

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Design research vs. market research

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

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

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

Design research in action: A mini mock case study

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

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

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

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

When to use design research

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

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

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

The benefits of design research

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

Deeper customer knowledge

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

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

Efficiency and cost savings

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

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

New opportunities

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

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

Enhanced competitive edge

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

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

Design research methods

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

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

Literature review

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

Field observations

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

Stakeholder interviews

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

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

Design review

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

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

Persona building

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

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

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

Usability testing

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

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

Benchmark testing

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

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

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

Feedback surveys

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

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

Focus groups

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

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Writing up a Research Report

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Field, A. (2016). An adventure in statistics. The reality enigma . SAGE.

Field, A. (2020). Discovering statistics using IBM SPSS statistics (5th ed.). SAGE.

Früh, M., Keimer, I., & Blankenagel, M. (2019). The impact of Balanced Scorecard excellence on shareholder returns. IFZ Working Paper No. 0003/2019. https://zenodo.org/record/2571603#.YMDUafkzZaQ . Accessed: 9 June 2021.

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Home » Research Design – Types, Methods and Examples

Research Design – Types, Methods and Examples

Table of Contents

Research Design

Definition:

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

Types of Research Design

Types of Research Design are as follows:

Descriptive Research Design

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

Correlational Research Design

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

Experimental Research Design

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

Quasi-experimental Research Design

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

Case Study Research Design

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

Longitudinal Research Design

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

Structure of Research Design

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

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

Example of Research Design

An Example of Research Design could be:

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

Research design:

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

How to Write Research Design

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

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

When to Write Research Design

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

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

Purpose of Research Design

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

Some of the key purposes of research design include:

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

Applications of Research Design

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

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

Advantages of Research Design

Here are some advantages of research design:

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

Research Design Vs Research Methodology

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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.

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Published May 11 th 2020

10 Design Tips For Creating Research Reports People Want to Read

Visme's Chloe West shares her design tips for creating killer research reports that give the insights the best chance of being actioned

People work hard on generating data that can help their business, but all too often that data isn’t shared in a way that means others can act on the insights.

To help your report really stand out and ensure your information is as comprehensive, digestible, and actionable as possible, you want to put a lot of emphasis on your research report design.

Here are some tips on creating engaging research reports that easily convey your point and help your colleagues take action.

1. Play with research report formats

While everyone is used to receiving PDFs, that doesn’t have to be the only way you share your data and other information.

Instead, play with other research report formats, like a presentation, infographic , or even a live data report with real-time displays .

The best way to decide which format to use is to think about the amount of content you have to put into your report, and the best way to present those different pieces of information in the context of your business. You should also gather feedback from those you’re creating the report for on their preferred methods of receiving information. If they want a quick update, a simple one-page report might be your best bet.

2. Get readers on board from the first page

From the very first page, slide, content block, or whatever starts off your report, you want to reel your audience in.

The first thing they’re likely going to see is a report cover page , so you want to make a great first impression. Your colors, fonts, and images matter.

Titles and subheadings help set expectations, so make sure that they’re 100% accurate and give the reader an indication of the value they will get from the report. Create a clear and concise title in a large font and a descriptive subtitle directly below it to convey your report’s contents.

You’ll also want to include visuals that grab your reader’s eye and get them intrigued to read more. This can be stock photos, graphic illustrations or even animated icons. Just make sure they’re relevant and, especially if your report is around something sensitive, well thought out.

Take a look at this report’s cover page as an example.

The large font stands out as the title, with the subtitle underneath explaining what the reader can expect. The color scheme is warm and welcoming, and the background photo illustrates the report’s topic.

Note: See ‘9. Brand your report’ below.

3. Stay away from walls of text

No one wants to read a document or presentation that’s full of intimidating, dull-looking walls of text. When creating a research report with design in mind, it’s important to break up text into paragraphs, callouts, visuals, and other design elements to help make your content easy to read.

Take a look at this report page as an example.

By using various fonts and font sizes, as well as background colors to create accentuated callouts, this report page content is incredibly easy to digest.

Make sure to keep your report content concise and use visuals whenever you can to convey your point instead of another paragraph of text, especially when writing about something complex.

4. Call out important bits of information

We mentioned callouts in the last point, but we want to call them out a little bit more. Callouts are blurbs that help to accentuate important points within your report.

You want to use your report design to put emphasis on the most important bits of information so your reader has the option to easily skim and grasp the must-know facts.

The bottom elements of this report page below are great examples of this.

Although users could look to the bar graphs and determine which products are highest and lowest performing, the callout at the bottom immediately lets the reader know at a glance.

5. Visualize your data

If you have any important numbers to share within your research report, use data visualization to do so. Charts, graphs, pictograms, and other tools are great ways to showcase numbers at a glance.

It’s important to remember, though, that you want your charts to be clear and concise rather than flashy. Data visualization is supposed to highlight your report’s data, not to distract from it.

When referring to something like social media performance, data visualization is the perfect way to demonstrate success.

Rather than trying to explain performance in a paragraph filled with numbers, this example is using charts to demonstrate how well or poorly the ads did for a quick, at-a-glance insight.

Make sure you use the best chart or graph for your data so that your information is clearly conveyed, and ensure that you label data correctly so all the important information is easily available for the reader.

6. Pay attention to your margins

Give yourself a good bit of space around the edges of your report design. Taking up the full amount of page space can make your report look cluttered, so it’s important to allow yourself some negative space.

This report page is a great example of both margins around your page edges and white space between your elements so your report feels clean and minimalistic.

Ensuring there are margins and space between your design elements also gives your reader the perfect opportunity to take notes if the report is printed off or accompanying a presentation.

7. Add imagery

You can convey your information with more than just text and charts. Incorporating images, icons, and illustrations into your research report is another great way to help your reader navigate the pages.

If you create a digital report, you can even embed video clips or upload GIFs or other animated elements to your document.

Take a look at this report page to see how visuals can help break up content.

The stock photos interlaced with the text areas help to provide a visual balance in the report, making the content more visually appealing and easy to read. The trick is not to over-do it or use confusing or distracting images. You want readers to focus on the information.

8. Make your report interactive

Whether you’re creating an online presentation or a digital document, interactivity can make it even more engaging.

Find a way to make your report interactive by adding audio, embedding videos or past live streams, or enabling the reader to click on charts or prompt animations.

At the same time, be sure to keep in mind that time-short readers just want the insight directly from your report, so don’t add things that aren’t necessary. Your report shouldn’t be flashy, just engaging and easy to read.

A great example of interactivity that can add to your report is having your chart legends appear as your readers hover over each different bar or line in your graph.

You can easily share your interactive, digital reports via an online link or by embedding them on a landing page on your website.

There are some instances where you might want to encourage the reader to seek out their own insights through interactivity. The Pudding creates amazing data visualizations that encourage user interactivity. But, generally speaking, if you’re delivering business insights or corporate training information, you should keep things as to-the-point as possible.

9. Brand your report

Incorporate your brand colors and fonts into your report so it’s obvious that it belongs to your company.

Your company likely already has branding guidelines that help you understand how your colors and fonts should be used in documents or presentations. You might also have ready-made templates..

Even if it’s just an internal report, using your brand colors, fonts, and logo can give your report a more professional feel which can help add weight to the enclosed insights.

10. Create a report template

Once you finalize your design, save it as a template to reuse over and over again. Next time you need to create a similar report, you can simply copy and paste your data and update the information.

This’ll help make your job easier each time you have to create a report. Even with research reports that are directed at a different audience or needed for a different person, having a template as a starting point will be a game changer. Working with your design team to create these templates can ensure the branding is consistent.

Insights aren’t valuable unless they’re acted upon

Ensuring your business insights are seen throughout the organization is important – if no one reads them, they won’t be acted upon.

By improving the design of your report to allow for maximum engagement and readability, you’re a step closer to creating a bigger impact with your research and analysis.

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

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

Table of Contents

What is research design?  

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

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

Research design elements  

Research design elements include the following:  

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

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

Characteristics of research design  

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

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

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

Different types of research design  

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

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

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

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

Qualitative research vs. Quantitative research  

Qualitative research design types and qualitative research design examples  

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

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

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

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

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

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

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

Quantitative research design types and quantitative research design examples  

Note the following research design categories in quantitative research:  

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

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

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

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

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

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

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

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

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

Example : Comparing school dropout levels and possible bullying events.  

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

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

Benefits of research design  

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

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

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

Frequently Asked Questions (FAQ) on Research Design

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

Broadly speaking there are two basic types of research design –

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

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

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

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

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

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

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

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Home Market Research

Research Reports: Definition and How to Write Them

Reports are usually spread across a vast horizon of topics but are focused on communicating information about a particular topic and a niche target market. The primary motive of research reports is to convey integral details about a study for marketers to consider while designing new strategies.

Certain events, facts, and other information based on incidents need to be relayed to the people in charge, and creating research reports is the most effective communication tool. Ideal research reports are extremely accurate in the offered information with a clear objective and conclusion. These reports should have a clean and structured format to relay information effectively.

What are Research Reports?

Research reports are recorded data prepared by researchers or statisticians after analyzing the information gathered by conducting organized research, typically in the form of surveys or qualitative methods .

A research report is a reliable source to recount details about a conducted research. It is most often considered to be a true testimony of all the work done to garner specificities of research.

The various sections of a research report are:

• Background/Introduction
• Implemented Methods
• Results based on Analysis
• Deliberation

Learn more: Quantitative Research

Components of Research Reports

Research is imperative for launching a new product/service or a new feature. The markets today are extremely volatile and competitive due to new entrants every day who may or may not provide effective products. An organization needs to make the right decisions at the right time to be relevant in such a market with updated products that suffice customer demands.

The details of a research report may change with the purpose of research but the main components of a report will remain constant. The research approach of the market researcher also influences the style of writing reports. Here are seven main components of a productive research report:

• Research Report Summary: The entire objective along with the overview of research are to be included in a summary which is a couple of paragraphs in length. All the multiple components of the research are explained in brief under the report summary.  It should be interesting enough to capture all the key elements of the report.
• Research Introduction: There always is a primary goal that the researcher is trying to achieve through a report. In the introduction section, he/she can cover answers related to this goal and establish a thesis which will be included to strive and answer it in detail.  This section should answer an integral question: “What is the current situation of the goal?”.  After the research design was conducted, did the organization conclude the goal successfully or they are still a work in progress –  provide such details in the introduction part of the research report.
• Research Methodology: This is the most important section of the report where all the important information lies. The readers can gain data for the topic along with analyzing the quality of provided content and the research can also be approved by other market researchers . Thus, this section needs to be highly informative with each aspect of research discussed in detail.  Information needs to be expressed in chronological order according to its priority and importance. Researchers should include references in case they gained information from existing techniques.
• Research Results: A short description of the results along with calculations conducted to achieve the goal will form this section of results. Usually, the exposition after data analysis is carried out in the discussion part of the report.

Learn more: Quantitative Data

• Research Discussion: The results are discussed in extreme detail in this section along with a comparative analysis of reports that could probably exist in the same domain. Any abnormality uncovered during research will be deliberated in the discussion section.  While writing research reports, the researcher will have to connect the dots on how the results will be applicable in the real world.
• Research References and Conclusion: Conclude all the research findings along with mentioning each and every author, article or any content piece from where references were taken.

Learn more: Qualitative Observation

15 Tips for Writing Research Reports

Writing research reports in the manner can lead to all the efforts going down the drain. Here are 15 tips for writing impactful research reports:

• Prepare the context before starting to write and start from the basics:  This was always taught to us in school – be well-prepared before taking a plunge into new topics. The order of survey questions might not be the ideal or most effective order for writing research reports. The idea is to start with a broader topic and work towards a more specific one and focus on a conclusion or support, which a research should support with the facts.  The most difficult thing to do in reporting, without a doubt is to start. Start with the title, the introduction, then document the first discoveries and continue from that. Once the marketers have the information well documented, they can write a general conclusion.
• Keep the target audience in mind while selecting a format that is clear, logical and obvious to them:  Will the research reports be presented to decision makers or other researchers? What are the general perceptions around that topic? This requires more care and diligence. A researcher will need a significant amount of information to start writing the research report. Be consistent with the wording, the numbering of the annexes and so on. Follow the approved format of the company for the delivery of research reports and demonstrate the integrity of the project with the objectives of the company.
• Have a clear research objective: A researcher should read the entire proposal again, and make sure that the data they provide contributes to the objectives that were raised from the beginning. Remember that speculations are for conversations, not for research reports, if a researcher speculates, they directly question their own research.
• Establish a working model:  Each study must have an internal logic, which will have to be established in the report and in the evidence. The researchers’ worst nightmare is to be required to write research reports and realize that key questions were not included.

Learn more: Quantitative Observation

• Gather all the information about the research topic. Who are the competitors of our customers? Talk to other researchers who have studied the subject of research, know the language of the industry. Misuse of the terms can discourage the readers of research reports from reading further.
• Read aloud while writing. While reading the report, if the researcher hears something inappropriate, for example, if they stumble over the words when reading them, surely the reader will too. If the researcher can’t put an idea in a single sentence, then it is very long and they must change it so that the idea is clear to everyone.
• Check grammar and spelling. Without a doubt, good practices help to understand the report. Use verbs in the present tense. Consider using the present tense, which makes the results sound more immediate. Find new words and other ways of saying things. Have fun with the language whenever possible.
• Discuss only the discoveries that are significant. If some data are not really significant, do not mention them. Remember that not everything is truly important or essential within research reports.

Learn more: Qualitative Data

• Try and stick to the survey questions. For example, do not say that the people surveyed “were worried” about an research issue , when there are different degrees of concern.
• The graphs must be clear enough so that they understand themselves. Do not let graphs lead the reader to make mistakes: give them a title, include the indications, the size of the sample, and the correct wording of the question.
• Be clear with messages. A researcher should always write every section of the report with an accuracy of details and language.
• Be creative with titles – Particularly in segmentation studies choose names “that give life to research”. Such names can survive for a long time after the initial investigation.
• Create an effective conclusion: The conclusion in the research reports is the most difficult to write, but it is an incredible opportunity to excel. Make a precise summary. Sometimes it helps to start the conclusion with something specific, then it describes the most important part of the study, and finally, it provides the implications of the conclusions.
• Get a couple more pair of eyes to read the report. Writers have trouble detecting their own mistakes. But they are responsible for what is presented. Ensure it has been approved by colleagues or friends before sending the find draft out.

Learn more: Market Research and Analysis

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Design Research: Home

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Research Article

In-shoe plantar shear stress sensor design, calibration and evaluation for the diabetic foot

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Validation, Visualization, Writing – original draft, Writing – review & editing

Affiliation Department of Mechanical, Aerospace and Civil Engineering (MACE), University of Manchester, Manchester, United Kingdom

Roles Investigation, Methodology, Writing – review & editing

Roles Conceptualization, Data curation, Funding acquisition, Investigation, Resources, Supervision, Validation, Writing – review & editing

Affiliation Medical School, NIHR Exeter BRC, University of Exeter, Exeter, United Kingdom

Roles Investigation, Methodology, Software, Writing – review & editing

Roles Investigation, Software, Writing – review & editing

Roles Resources, Supervision, Writing – review & editing

Affiliation Musculoskeletal Biomechanics and Research in Science and Engineering faculty of Manchester Metropolitan University, Manchester, United Kingdom

Affiliation Manchester University NHS Foundation Trust within the Departments of Diabetes and Vascular Surgery, Manchester, United Kingdom

Roles Conceptualization, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Athia H. Haron, 
• Lutong Li, 
• Jiawei Shuang, 
• Chaofan Lin, 
• Helen Dawes, 
• Maedeh Mansoubi, 
• Damian Crosby, 
• Garry Massey, 
• Neil Reeves, 

• Published: September 4, 2024
• https://doi.org/10.1371/journal.pone.0309514
• Peer Review
• Reader Comments

Plantar shear stress may have an important role in the formation of a Diabetic Foot Ulcer, but its measurement is regarded as challenging and has limited research. This paper highlights the importance of anatomical specific shear sensor calibration and presents a feasibility study of a novel shear sensing system which has measured in-shoe shear stress from gait activity on both healthy and diabetic subjects. The sensing insole was based on a strain gauge array embedded in a silicone insole backed with a commercial normal pressure sensor. Sensor calibration factors were investigated using a custom mechanical test rig with indenter to exert both normal and shear forces. Indenter size and location were varied to investigate the importance of both loading area and position on measurement accuracy. The sensing insole, coupled with the calibration procedure, was tested one participant with diabetes and one healthy participant during two sessions of 15 minutes of treadmill walking. Calibration with different indenter areas (from 78.5 mm 2 to 707 mm 2 ) and different positions (up to 40 mm from sensor centre) showed variation in measurements of up to 80% and 90% respectively. Shear sensing results demonstrated high repeatability (>97%) and good accuracy (mean absolute error < ±18 kPa) in bench top mechanical tests and less than 21% variability within walking of 15-minutes duration. The results indicate the importance of mechanical coupling between embedded shear sensors and insole materials. It also highlights the importance of using an appropriate calibration method to ensure accurate shear stress measurement. The novel shear stress measurement system presented in this paper, demonstrates a viable method to measure accurate and repeatable in-shoe shear stress using the calibration procedure described. The validation and calibration methods outlined in this paper could be utilised as a standardised approach for the research community to develop and validate similar measurement technologies.

Citation: Haron AH, Li L, Shuang J, Lin C, Dawes H, Mansoubi M, et al. (2024) In-shoe plantar shear stress sensor design, calibration and evaluation for the diabetic foot. PLoS ONE 19(9): e0309514. https://doi.org/10.1371/journal.pone.0309514

Editor: Andrea Tigrini, Polytechnic University of Marche: Universita Politecnica delle Marche, ITALY

Received: January 16, 2024; Accepted: August 14, 2024; Published: September 4, 2024

Copyright: © 2024 Haron et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are available from the Mendeley Data database (DOI 10.17632/pcggh2rzm3.1 ). Only raw anonymised data can be shared due to GDPR restrictions from HRC ethics committee.

Funding: This work was partially funded by Engineering and Physical Sciences Research Council (EPSRC) grant number EP/W00366X/1.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Diabetic foot ulceration (DFU) affects 15–25% of people with diabetes at some point in their lifetime [ 1 ] and has a high social and economic cost with countries like the UK spending approximately £1 billion annually [ 2 ]. Worldwide the prevalence of diabetes is rising, and it is predicted that 552 million people will have the condition by 2030 [ 3 ]. Measurement of plantar normal stress and plantar shear stress has shown the potential to predict DFU risk [ 4 , 5 ]. However, whilst commercial systems are available to measure normal plantar stress in-shoe there are no commercially available in-shoe plantar shear stress measurement systems. Shear stress has been directly measured during barefoot gait using mechanical sensor arrays coupled with resistive or capacitive sensors [ 6 – 8 ], utilising piezoelectric materials and their charge outputs [ 9 ] and through a variety of optical methods including polycarbonate arrays [ 6 ], optical bend loss [ 7 ] and laser interferometry of bi-refringent films [ 8 ]. Perry et al. [ 10 ] used an array-based device [ 11 ] to study bunching and stretching of adjacent plantar tissue and they found that tissue stretching from shear stress was the predominant mechanism. They report that peak shear stress and peak plantar pressure occur in the same place in 50% of cases, but actually occur at different times, which is contradictory to results reported by other researchers [ 12 ]. Contradictory results are typical from these studies using custom-built shear stress measurement devices due to the relatively low numbers of participants with diabetes tested in the trials, with typical sample sizes of ten. All these measurement methods are bespoke devices and only a handful of foot-to-floor shear stress measurement devices exist worldwide. Larger scale studies with matched control groups are required to provide firm conclusions on plantar surface shear stresses experienced by people with diabetes.

Shear stress measurement is further complicated as all diabetic patients are strongly advised to walk using footwear (and never barefoot), therefore, to understand the shear stresses induced on the plantar surface, in-shoe shear stress measurement must be taken. Although direct shear stress measurement is important in DFU risk management, future use of artificial intelligence methods [ 13 , 14 ] may enable risk management with current measurement technologies.

In-shoe plantar shear stress is difficult to measure and reported measurements vary widely, for example, measurements of shear stress on the 1st metatarsal head varied from 16 kPa [ 15 ] to 140 kPa [ 5 ] in healthy participants. Therefore, either there is widespread inter-participant variability and/or there are mechanisms which cause errors for in-shoe shear stress measurement. Measurement error has been widely reported for in-shoe normal stress systems with causation linked to sensor wear and calibration [ 16 , 17 ]. Specifically, calibrating with similar load ranges to those desired to be measured improved accuracy by up to 20 times [ 16 ] and accuracy was reduced when smaller areas of loading were applied [ 17 ]. It is likely that similar calibration issues will affect in-shoe shear stress sensor measurement accuracy. Researchers have made excellent progress in developing novel in-shoe plantar shear stress measurement systems; however, they have not yet fully considered the implications of calibration methods on measurement accuracy [ 4 , 5 ]. The choice of indenter area of loading, shape and location is also an important consideration for accurate and reliable sensor calibration; despite this, to the authors’ knowledge this has not been investigated and reported in the literature. A key principle in calibration is that the applied loading should be a good representation of the real-world scenario. In the context of plantar foot mechanics, and for example the metatarsal heads, there is variation in the magnitude of loading, area of loading, shape and potentially the location of the bone in relation to the sensor. This paper presents the design and evaluation of an in-shoe shear stress sensor and considers the impact of calibration on measurement accuracy.

This paper describes a sensor system design and conducts a performance investigation. Three investigations were conducted: calibration investigation, loading profile comparison and sensor validation. These investigations and how they relate to one another are shown in Fig 1 .

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https://doi.org/10.1371/journal.pone.0309514.g001

Sensor system design

Sensing principle..

Coulomb’s law of friction describes frictional force being proportional to reaction load. In the case of shear sensing insoles this means that there can be no shear stress (friction) without normal stress (reaction load) and that the magnitude of associated shear stress will always be less than that of normal stress. Like most other shear sensors in the literature [ 5 , 15 , 18 – 22 ] the shear sensor is embedded in a hyperelastic or viscoelastic, isotropic incompressible elastomer, as opposed to a discrete sensor placed on the insole or isolated from the main body of the insole material.

Fig 2A shows a cylindrical section of elastomer insole with cross-sectional area, A, containing a strain gauge orientated in the shear plane and a normal stress sensor with sensor readings in mV, S, and N, respectively. The material properties (stress-strain relationship) for the silicone are non-linear but can be approximated as three linear regions (low: ≤ ε 1 = 0.04 strain ; medium: ≤ ε 2 = 0.115 strain ; high: > ε 2 strain ); see Fig 2B . The strains for the three linear regions were determined from the stress-strain curve of the silicone under compressive loading at the target stresses of 14 kPa (low), 70 kPa (medium) and 140 kPa (high). Stress-strain relationships for normal compressive loading are given by Eq 1 , where C medium , and C high are negative intercepts in units of pascal ( C low = 0) and E is the gradient in Pascal.

[A] Cylindrical section of elastomer containing strain gauge and normal force sensor [B] Stress-strain curve of the elastomer under compression stress. Linear approximations for deformation were made for three regions of the curve (low, medium, and high stress magnitudes), sectioned by the compressive strains ε 1 and ε 2 , with corresponding gradient E used for calibration. [C] Cylindrical section deformed by normal force only. [D] Cylindrical section deformed by both normal and shear forces.

https://doi.org/10.1371/journal.pone.0309514.g002

Fig 2C shows the section being loaded with a normal force which creates a reduction in thickness but an increase in diameter described by Eq 2 (assuming constant volume) which gives sensor readings S N and N N , which are signal voltage measurements (mV) for the shear stress and normal stress respectively, described by Eqs 3 and 4 where k N and k S are constants (sensor gains) determined by experiment with units Pa/mV and strain/mV respectively (other equation parameters defined in Fig 2 with SI units).

Fig 2D shows applied loading from both normal and shear force giving a sensor reading S N + S and N N for the shear stress and normal stress respectively. The applied shear stress, σ S , can be determined from Eq 5 and Eq 1 (assuming an isotropic material) which requires measurements from the normal stress sensor, N N , to decouple the effect on the strain gauge from normal force (where i = low , medium or high ).

Shear stress sensor design.

The shear stress sensing system primarily consists of the strain gauge rosette, a normal stress sensor, and the flexion stiffener and load concentrator; here on in referred to as the ‘shear stress system sensor’ or ‘SSS sensor’. A 3-element strain measuring rosette (1-RY81-3/120, Hottinger Bruel & Kjaer UK Ltd, Royston, England) was chosen for the shear stress sensor ( Fig 3A ) arranged in rectangular 0°-45°-90° directions to allow for calculation of resultant shear in both the anterior-posterior (AP) and medial-lateral (ML) directions. The sensor was then embedded in silicone (Sil A50 Smooth- Sil Addition Cure silicone, Smooth-On Inc. Macungie, USA). To assemble the sensor, the first 2mm silicone base layer was poured into a custom 3D printed square mould with dimensions of 20 x 20 x 4 mm (width x length x height). After curing the surface was cleaned and the strain gauges were soldered to 2-core 2.8 mm 2 external diameter shielded wires (JY-1060, Pro-Power by Newark, Chicago, USA). The strain gauges were then placed on the surface of the silicone using a custom 3D printed jig with tabs and bolts to align the strain gauges in the correct angular position. A thin second layer of silicone (approximately 0.5 mm thick) was then poured and allowed to fully cure, the jig was then removed and a final layer of silicone was poured on top to give a total thickness of 4 mm. A 15 mm diameter, 0.8 mm thick phenolic sheet material flexion stiffener and load concentrator was placed at the center of the sensor assembly and the top layer of silicone was then allowed to cure. The full assembly of the sensor is shown in Fig 3B and 3C .

[A] Configuration of the strain rosette in the sensor with three strain gauges arranged at 0°– 45° - 90°, and the relationship between the local strain axes and the global applied shear direction axes (Medial-Lateral, ML, and Anterior-Posterior, AP). [B] Section view of the SSS sensor. [C] Top view of the SSS sensor and its dimensions. [D] Locations of SSS sensors in the sensing insole.

https://doi.org/10.1371/journal.pone.0309514.g003

As mentioned in the sensing principle, the shear stress is obtained from Eq 5 , however, for the SSS sensor to measure both AP and ML shear stress, orientation of the strain gauges needs to be considered. From the configuration shown in Fig 3A for stress measurements calculated from strain gauges A, B and C the shear stress is given by Eqs 6 and 7 .

Where θ AB and θ BC are the angles between the individual strain gauges in the rosette, which were at 45°.

Shear stress sensor number, placement, and integration.

Key DFU risk areas, accounting for at more than one-third of DFU cases are the calcaneus, first metatarsal head and hallux areas of the foot [ 23 – 26 ], so placement of the SSS sensors in the insole was in these three locations. To maximize accuracy of the measured sensing data, all sensors were anatomically matched to the participant. This was achieved through a ‘palpation and marking paper’ approach in which a healthcare professional identified the bony landmarks of the foot, marked these areas on the foot surface with skin-safe marker, and the participant stands on the paper to transfer the markings. These markings were then used to ensure SSS sensors were correctly located on the silicone insole, with the sensor x-axis aligned with the anterior posterior direction. The signal wires were laid out from the SSS sensor in the ML direction to reduce fatigue loading from flexion during gait. A 1–2 mm depth of silicone was then poured and cured before a further layer of silicone was poured and cured to make a total insole thickness of 5 mm to complete the insole, as shown in Fig 3D . Three normal stress sensors (A301 FlexiForce 0-44N, Tekscan Inc., Norwood, Massachusetts, USA) were then secured to the bottom of the insole with silicone glue (Permatex 80050 Clear RTV Silicone Adhesive Sealant, Permatex, Illinois Tool Works Inc., Solon, Ohio, USA) with their center coincident with the SSS sensors.

Data acquisition system (DAQ) and signal processing.

A Teensy 4.1 32-bit microcontroller (PJRC, Portland, Oregon, USA), ARM Cortex-M7 processor, with clock speed of 600 MHz and integrated SD storage card, was used to collect and store the voltage readings from the SSS sensors ( Fig 4B ). Flexiforce normal stress sensors were connected via a 10 kOhm circuit divider to analog inputs, whilst shear sensing strain gauges were amplified using a 24-bit high-precision analog-to-digital amplifier (HX711 ADC, HALJIA, Zhongai, China) then routed to digital inputs of the microcontroller. All signals were collected at a sampling rate of 80 Hz. Data was logged to the 16GB SD card and streamed via an ESP8266 UART WiFi adapter (Espressif Systems, Shanghai, China) to allow for continuous monitoring. Power was supplied to all components via 3V and 5V power rails from the microcontroller, sourced from an external 3.7V 3500mAh Lithium Polymer battery (LP104567, EEMB, Moscow, Russian Federation) that was regulated through a linear regulator (LDO, B08HQQ32M2, DollaTek, Hong Kong, China). For both left and right foot measurements, two identical systems were used to collect the measurements, and placed on a custom, adjustable neoprene fitness belt (Frienda, China), during walking trials ( Fig 4 ).

[A] Participant walking on a treadmill with the sensor insole system. The data acquisition system (DAQ) was attached to a belt, and each insole (left and right foot) has a separate but identical DAQ input. [B] Block diagram of the DAQ system, collecting data at 80 Hz.

https://doi.org/10.1371/journal.pone.0309514.g004

A custom MATLAB (The Mathworks Inc., Natick Massachusetts, USA) script was used to parse and analyse the data collected. The data was minimally pre-processed before finalized into calibrated stress measurements. This pre-processing stage included removing only obvious outliers (which accounted for up to 0.05% of the measurement data if present). This was made using the filloutlier function with the ‘quartile’ outlier detection option: ‘quartiles’ outliers which were elements more than 1.5 interquartile ranges above the upper quartile (75 percent) or below the lower quartile (25 percent)) and correcting DC offsets. Data from each foot were analyzed separately.

Calibration investigation: Bench top mechanical testing

Experimental setup and test method..

To investigate the effect of calibration on the sensor’s performance, both shear and normal force were applied to the SSS sensor insole (summarised in Fig 1A ). A uniaxial mechanical testing machine (Instron 5982K2680 100kN 350°C, 500N load cell, Instron ® Norwood, Massachusetts, USA) applied and measured shear force using a bespoke shear stress rig through an indenter of area, A, shown in Fig 5A . A normal reaction force was applied through a screw thread to the indenter to facilitate frictional shear stress application. Measurement of normal reaction force was through a load cell and ADC (‎ADN1903027, 196.2 N Weight Sensor Load Cell, Haljia, China) capturing data at 80Hz using an Arduino (Arduino Mega 2560 Rev3, Arduino, Somerville, MA, USA). For pure normal stress loading calibration, the insole was placed flat on a plate in the uniaxial testing machine fitted with a large compression platen on the bottom and an indenter with a specific area, A, applying compression force from the top, shown in Fig 5B .

[A] Custom shear stress rig made of rigid 10 mm acrylic sheet plates which applied the force of the mechanical testing machine as a shear force onto the insole. The shear stress was calculated using the applied force and area of the custom indenter. The indenter’s compressive stiffness was 30.1 MPa, ~12 times stiffer than the silicone sensor of 2.5 MPa. [B] Custom normal stress calibration setup where the insole was placed on a compression platen.

https://doi.org/10.1371/journal.pone.0309514.g005

Sensor loading area investigation.

To evaluate the effect of indenter area, A, five flat ended cylindrical indenters with diameters of, 10, 15, 20, 25, 30 mm were used to load the SSS sensor at its center. While studies have shown that there is a difference between various indenter shape loading profiles and the corresponding mechanical responses of the material [ 27 , 28 ], we determined that the normal stress distribution that was measured at the surface of the SSS sensor was similar for both flat and rounded indenter profiles. The only notable difference was the size of the normal stress distribution, as a flat indenter covered a larger area than the rounded indenter of the same diameter. Thus, choosing a flat indenter of a smaller size gave the same loading results as a larger rounded indenter.

The tests applied a cyclic shear force with a 1 Hz triangular waveform pattern ranging from 0 to 50 N in combination with a constant normal stress of 140 kPa through all the indenters. SSS Sensor output signal, S N + S , in mV was measured for each of the loading areas.

Sensor loading location investigation.

Ideally a sensor would be co-located with the anatomical part applying the load, however, this may not always be practically possible so an understanding of the relationship between the location of the SSS sensor, the location of the applied loading and the accuracy of measurement is required. To investigate the effect of loading location, twelve loading locations were chosen, six in the anterior direction and six in the lateral direction both measuring 0, 10, 15, 20, 30, 40 mm from the center of the shear stress sensor. Loads were applied in both the medial or posterior direction respectively. Cyclic loading was applied to the SSS sensor insole of the same characteristic as the area of loading investigation (see ‘Sensing loading area investigation’ section). SSS Sensor output signal, S N + S , in mV was measured for each of the loading locations.

Loading profile comparison: Human plantar loading specific sensor calibration

Comparison of normal stress profiles..

Shear loading application area and location affect strain measurements, so it is important to consider plantar stress loading from the human foot. During walking plantar stress is dependent on many factors including foot size and anatomy, weight, morbidity and walking patterns, all of which are different between participants. From the sensor calibration investigations in the results section, we can see that (i) loading location and (ii) loading area may affect the output of the SSS sensor so these must be considered during calibration.

• Loading location variation can be removed by placing the SSS sensors at personalised anatomical locations in the insole, which is the approach we have taken.
• Loading area variation can be controlled through calibration. This was determined through a comparison and matching of normal stress loading profiles of the specific participant’s foot anatomy with bench top mechanical test experiments involving various loading area sizes (flat cylindrical indenters).

To capture the plantar normal stress loading profiles of our participants, in the SSS sensor locations of the calcaneus, first metatarsal head and the hallux, we conducted measurements in-shoe during a two-minute treadmill walk using an F-scan insole (Tekscan Inc., Boston, USA) coupled with a non-instrumented insole of the same material properties and thickness as our designed insole. Then the test rig ( Fig 4B ) was used with 15, 20, 30 and 40 mm diameter indenter sizes to load the silicone insole from 0 to 250 N (to simulate a normal stress range up to 1400 kPa, which is comparable to the 1000–1900 kPa normal plantar stresses during gait reported in the literature [ 29 , 30 ].

Measurements of plantar normal stress distribution were captured with the same F-scan and insole used with the participants. To simulate the different foot structures, we adjusted the diameter of cylindrical indenters (15, 20, 30 and 40 mm), which were based on the ranges of average anatomical dimensions of the hallux, metatarsal head, and calcaneus bones [ 31 – 36 ], see results and discussion ‘Human plantar loading consideration for sensor calibration’ section. An illustrated summary of this investigation can also be found in Fig 1B .

Statistical analysis as a method for calibration indenter choice.

Comparisons were made between the participant’s mean normal stress profiles with the bench top test rig results (gait data averaged over 20 gait cycles from three different sensing locations hallux, first metatarsal head, and calcaneus, bench top test rig results for 15, 20, 30 and 40 mm indenter diameters). Magnitudes of both results were scaled to have a maximum unity magnitude to enable comparison. The normal stress profiles (normal stress vs displacement across anatomical location) were collected along a 2D cross section of 40 mm in length across the foot-width of loaded area (see results and discussion ‘Human plantar loading consideration for sensor calibration’ section). Calibration indenter diameters for the hallux, first metatarsal head and calcaneus locations were chosen based on either the highest R 2 value from a multiple linear regression between the gait measures and the test rig measures or the maximum measurement sensitivity area of the SSS sensor (see results and discussion ‘Sensor calibration’ section).

Sensor validation: Bench top mechanical testing

The following section describes the sensor validation, as summarised in Fig 1C . A 30 mm diameter indenter was used to calibrate the SSS sensor, as this was determined to be the maximum sensing area of the sensor (see results and discussion ‘Sensor calibration’ section). This was achieved through a series of mechanical tests detailed in Table 1 , with shear stresses applied in both ML and AP directions and conducted at 1Hz, to simulate average walking speed frequency.

https://doi.org/10.1371/journal.pone.0309514.t001

The shear stress magnitudes chosen for low, medium, and high levels were 10%, 50% and 100% of the 140 kPa maximum in-shoe plantar shear stress reported in the literature respectively [ 37 ]. This enabled calculation of the calibration parameters coefficients E low , E high , C medium and C high , according to Eq 1 .

To validate the calibrated SSS sensor, a shear stress of 70 kPa with a normal stress of 125 kPa was applied in both the ML and AP direction at 0.8 Hz. Additionally, a shear stress was also applied in the 45° direction (14 kPa shear stress, 28 kPa normal stress at 1Hz).

Two measurements of error were made. The first was an overall mean absolute error (MAE), which is the mean of the difference between the measurement from the test rig and the calibrated SSS sensor measurement (in kPa). The second was peak error, measured as the percentage error at peak loads between the applied measurement from the test rig and the calibrated SSS sensor measurement. Peak values of measured shear stress were taken from 10 cycles and a standard deviation was calculated. Repeatability was calculated from the SSS sensor measurements as the standard deviation of the peak plantar stresses divided by the mean of the peak plantar stress, presented as percentage (e.g. a mean peak measurement of 100 kPa and a standard deviation of those peak measurements at ± 10 kPa, would result in (10/100) x 100% = 10% deviation from the peak value, and thus 90% repeatability).

Sensor validation: Gait lab treadmill walking

To further validate the sensors, a gait laboratory treadmill walking test was performed on a single anthropometrically matched healthy participant and a single participant with diabetes (both male and 45 years old, weighing 88 kg and 75 kg, height of 1.75 and 1.66 m, EU shoe size 44 and 42, weight per insole area 32 kPa and 35 kPa, walking speed 0.92 ms -1 and 0.95 ms -1 for the healthy participant and participant with diabetes respectively). The study received approval from the NHS Health Research Authority and Health and Care Research Wales (HCRW) Ethics Committee (REC reference: 22/NW/0216), and all participants provided written consent. Trial Registration number: NCT05865353. Participants were recruited between 1 st November 2022 till 30 th May 2023. Data collection was conducted in two parts (1) baseline visit and (2) main data collection, Table 2 .

https://doi.org/10.1371/journal.pone.0309514.t002

Baseline visit.

Anthropometric data was collected, and anatomical landmarks determined using the ‘palpation and marking paper’ method described in the ‘Shear stress sensor number, placement and integration’ section. The participants conducted a 2 minute treadmill walk while wearing a pair of silicone insoles, made from the same materials and dimensions as the sensor insole but without active sensors, and a pair of F-Scan pressure sensing insoles, in a prophylactic shoe (Sponarind 97308, Finn Comfort Inc. Hassfurt, Bavaria, Germany), designed with shock-absorbing properties and a larger volume, ideal for people with diabetes. Normal stress data was collected using the F-scan insoles, at a self-selected gait speed to determine normal plantar stress profiles (results of which were used for the comparison of normal stress profiles, in ‘Human plantar loading specific sensor calibration’ section). Table 2 shows the participant data collected during the baseline visit.

Main data collection.

The participants returned for the main data collection where they were asked to wear the sensing insole in the specialist diabetic shoe. They then walked twice on a split belt treadmill with integrated force plates (M-Gait, Motek Medical BV, Amsterdam, Netherlands) for 15 minutes at their self-selected speed (see Table 2 ).

Data analysis: Shear stress gait measures and repeatability.

Mean and standard deviation of peak shear stress and peak normal stress measurements were extracted from 20 gait cycles measured by the sensing insole. Measurement repeatability was determined and comparisons, between the two walking periods within each individual walking session (start, middle, and end). We collected statistical data for both plantar shear stress and normal stress measurements to perform inter-participant comparisons. These included statistics for Plantar Stresses (Normal, AP Shear, and ML Shear) across all three sensor areas, encompassing mean values, standard deviations, peak stresses, and variability (or percentage difference) of measurements within the 15-minute treadmill walk (intra-walk) and between two treadmill walks (inter-walk).

Results and discussion

Sensor calibration.

Shear stress measurement accuracy is affected by the calibration method. Specifically, the shear stress sensor measured output signal decreases exponentially with both increasing loading application area, and increasing loading distance away from sensor center, see Fig 6 . The results in Fig 6A show that the measured output decreases by ~80% from 1.5 mV to 0.3 mV, for a calibration loading application area of 10 mm diameter to 30 mm diameter respectively. This means that if the sensor was calibrated for the smaller 10 mm area and a larger 30 mm diameter load was applied, the measurements would be underestimated by 80%. Likewise, calibrating for a larger area, and applying load for a small area will greatly overestimate the measurements. Increasing the loading application area increases the area over which the force is distributed over the sensor, thus more of the loading is applied away from the center of the shear stress sensor. From the results shown in Fig 6B and 6C the location of loading application also reduces sensor sensitivity. All this means that the shear stress sensor will only be able to measure accurately if the calibration loading area matches the desired measurement loading application area (or are reasonable similar areas).

Mean peak signal of shear stress (SSS sensor) total output (mV) from 10 cyclic triangular loading. [A]—Effect of area of loading on SSS sensor measured outputs. [B, C]- Effect of location of loading on SSS sensor output for medial and posterior respectively.

https://doi.org/10.1371/journal.pone.0309514.g006

Fig 6B and 6C show the influence of loading location on SSS sensor measurements for the same applied loading area (25 mm diameter indenter). As expected, the SSS sensor measurement for both Anterior-Posterior (AP) and Medial-Lateral (ML) shear loading decreased as the loading distance moved away from the sensor center. This is due to a decrease in deformation of the shear stress sensor as the loading is applied further away from the sensor center. However, it is important to note that there was still a measurable signal at these distances as they are not yet relatively far away from the sensor. This means that measured shear stress from an embedded sensor will not just be from the coincident anatomical location but also have a contribution from adjacent and other relatively close anatomies (e.g. first metatarsal head located sensor may be measuring shear stress contribution from the second metatarsal head). This is due to material coupling, which is that stress applied in one area of the material, in this case the silicone insole, will stress surrounding areas of the material. The implication is that the shear stress sensor will provide more accurate measurements if the loading application location is coincident with the centre of the sensor. This emphasizes the importance of the placement of these discrete sensors, which is why a participant specific sensing insole was manufactured, placing sensors at the exact anatomical location of the boney landmarks, where peak loading is expected.

Although this paper presents the shear stress sensor sensitivities to calibration loading area and calibration loading locations for this sensor it is likely that these observations are true for other embedded in-shoe shear stress sensors. Other researchers measured in-shoe peak shear stresses from gait varied from 9 kPa to 140 kPa and calibration loading area varied from 20 mm diameter area (314 mm 2 ) –10,000 mm 2 (up to half the insole, approximated from the experimental Fig 3 in the paper as there was insufficient detail to give conclusive information on the loading area used) [ 5 , 15 ]. It is likely that these variations in measurements are not due to inherent sensor inaccuracy or participant gait differences but likely to stem from calibration method differences. To the authors’ knowledge, calibration loading area has not been investigated in other published studies, but it is suggested that calibration should be considered for all future in-shoe shear stress measurements.

Human plantar loading consideration for sensor calibration

Fig 7 shows that calibration loading indenter diameters should be 20 mm and 40 mm for the hallux and both the first metatarsal head and the calcaneus respectively. However, due to limitations on sensor sensitivity beyond 30 mm from the center of the sensor a 30 mm indenter diameter was chosen for the first metatarsal head and calcaneus. These choices of calibration indenter diameters were determined from the comparison of the bench top testing normal stress profiles of different indenter diameters, with the participants’ measured normal stress profile during walking. The bench top test showed that all the indenters resulted in normally distributed normal stress profile curves ( Fig 7A ), increasing in curve width with increasing indenter diameters, reflecting a larger contact area of the applied force. An increasing curve width is also expected for the normal stress profiles of anatomical bones with increasing diameters (first metatarsal head ~15 mm, hallux ~20 mm, and calcaneus ~ 40 mm [ 31 – 36 , 38 ]). The participants’ measured normal stress for the hallux and the calcaneus regions of the foot had normal pressure distribution profiles that reflected their anatomical sizes, however, the presence of the second close metatarsal bone influenced the normal stress profile in the first metatarsal head area and widened the normal stress profile, more than what is expected from its anatomical diameter of ~15 mm ( Fig 7B ). The R 2 results of the multiple linear regression reflected this ( Fig 7C ), as the first metatarsal head correlates to the indenter size of 40 mm diameter. The R 2 value of the metatarsal head, however, is small at 0.41, indicating that there may be variability in the pressure distributions in that area, likely from gait variability within a participant’s walk or between participants. The hallux and calcaneus regions of the foot have a normal pressure distribution profile that reflects the loading of the anatomical bones clearly (R 2 ≥ 0.95) and can be matched with an indenter of a similar size to give a representative loading for calibration of 20 mm and 40 mm respectively. However, loading area results from Fig 6A show that sensor sensitivity converges for indenter areas greater than 25–30 mm diameter. Therefore, calibration indenter diameters were reduced to 30 mm for the first metatarsal head and calcaneus.

[A] Experimental normal pressure profiles: (i) Indenter experimental setup, (ii) Normal pressure profile curves width increases with increasing indenter diameter, (iii) F-scan pressure result that shows the cross section used to obtain these values used in ii. [B] Participant pressure profiles: (i) In-shoe gait lab experimental setup (ii) An example of participant’s pressure profile over 20 gait cycles, showing the three normal pressure profiles of the foot at the calcaneus, first met head and hallux, (iii) F-scan pressure result that shows the cross section used to obtain the values. The image also shows the peaks for these three regions (Calcaneus peak CP, Hallux Peak HP and the metatarsal peaks MHP1 and MHP2). [C] Graphical representation of the regression analysis’ coefficient of determination (or R-squared) results. Larger circles indicate a higher R-squared value, and red circles indicate the maximum R-squared in the sensor group. R-squared values are shown above the circles, and maximum is indicated as red font.

https://doi.org/10.1371/journal.pone.0309514.g007

The implications of this for the SSS sensor are that calibration indenter sizes should be between 10–30 mm dependent on expected shear stress application areas. This finding is likely to be true for other embedded in-shoe shear stress sensors in the literature. The limitation from this finding is that to obtain accurate shear stress measurements the user must know something about the shear stress loading profile which may be unknown. A possible way to mitigate for this may be to calibrate the sensor for a range of loading areas and to use a normal stress sensor to determine which indenter calibration area to use in post-processing.

Shear sensor calibration and bench top mechanical test validation

The SSS sensor was highly accurate and repeatable when compared against the bench top mechanical test as seen in Fig 8 . Results from Table 3 show that calibration error was insignificant with the mean absolute error (MAE) over the entire cycle in calibration < 0.00007 kPa for all magnitudes of loading, and errors at peak loading were < 5.8%.

[A] Sensor calibration for both anterior-posterior (AP) and medial-lateral (ML) directions at a ‘medium’ level of posterior and medial shear loading of 1 Hz cyclic loading of up to 70 kPa shear stress, at a constant normal stress of 140 kPa. [B] Sensor validation test result at medium level of shear cyclic loading (up to 70 kPa), at a different loading frequency (~0.85 Hz) and different constant normal stress (125kPa). All results for the different configurations of loading are shown in Table 3 .

https://doi.org/10.1371/journal.pone.0309514.g008

https://doi.org/10.1371/journal.pone.0309514.t003

The errors in the validation of the sensors at loading conditions different from the calibration were higher, but still showed a high accuracy for the sensors. The sensor was most accurate for low–medium shear stress magnitudes with up to <1.8 kPa for MAE, and < 8.7% for error at peak loading (see example of medium magnitude measurements in Fig 8 ). Followed by the measurements at a resultant loading angle of 45° clockwise from the anterior direction (MAE <1.4 kPa; <11.5% peak error). These small errors could be attributed to errors in the validation setup, as an error of ± 5° would correspond to a peak shear stress error of up to 4.6%. The SSS sensor also showed good repeatability for all loading conditions (>97% repeatability in calibration and >96% repeatability in validation).

The highest errors in validation were at high shear stress magnitudes, over the expected plantar shear stress from gait, these were MAE <17.3 kPa and peak error <22.4%. This was likely due to the mechanical coupling of the high normal stress, pushing the total material deformation higher up the hyperelastic stress-strain curve of the sensor material ( Fig 2D ). At this region of the stress-strain curve, very small strains relate to high changes in stress making the SSS sensor more prone to measurement errors. However, the maximum errors translate to an error of ± 31.3 kPa, which is within the standard deviation of most plantar stress measurements from the literature of ± 50 kPa for shear stress [ 1 – 5 , 15 ].

Treadmill walking validation

For treadmill walking the SSS sensors measured the magnitude of shear stresses between 66.5 kPa—152.6 kPa in the AP direction, and 28.4 kPa– 128 kPa in the ML direction, full results are shown in Table 4 . As expected, the ML shear range was lower than the AP shear range, as loading was expected to be predominantly in the AP direction. Loads were cyclic going from zero to peak value with the same frequency as gait which were at speeds of 0.92 and 0.95ms -1 for the healthy participant and participant with diabetes respectively. The only notable differences were in the direction of some of the peak plantar shear stresses.

https://doi.org/10.1371/journal.pone.0309514.t004

No significant differences between both participants peak plantar stress values were observed (t-test of mean peak plantar stresses PPS, p>0.36, p>0.58 and p>0.57). This was expected, as both participants had a similar walking speed (0.92–0.95 ms -1 , and weight per insole area 32.4–35 kPa). However, this study aimed to demonstrate the feasibility, accuracy, and repeatability of the SSS system so no conclusions should be drawn on plantar stress for general people with diabetes and healthy populations for this study.

The shear measurements of the SSS sensor was highly repeatable when comparing data recorded for both within the 15-minute treadmill walk (intra-walk), and between the two 15-minute walks (inter-walk). The mean and standard deviation of the percentage difference of peak plantar stresses were ≤ 8% ± 6% for both investigations. Intra-walk differences were lower than inter-walk–with the highest percentage difference of 21% measured by the SSS sensor for the ML Shear (Hallux, Left foot, participant with diabetes). Other measurements from the shear stress sensors were < 15% difference. For inter-walk, the highest PPS percentage difference was measured by the commercial Flexiforce sensor of 47% difference in normal stress (Hallux, left foot, participant with diabetes), followed by 37% for the AP shear of the SSS sensor (Hallux, right, healthy) and 33% for the ML shear of the SSS sensor (Calcaneus, right, participant with diabetes).

Calibration and material coupling for shear stress sensors

To the author’s knowledge, this study is the first to address in-shoe shear sensing material coupling and unexplored complexities in calibration for shear sensing. The results illustrate that due to sensor and material coupling with adjacent structures the area which contributes to the measured shear can be larger than the area of the sensor. This has important implications for shear sensor calibration, firstly in terms of the location of the sensor and the anatomical region that is to be measured, and secondly in terms of the indenter area used for calibration. These results have significance for all researchers developing systems to measure in-shoe plantar shear stress as these factors will affect the magnitude of shear sensed. Furthermore, these results may partially explain the variation in magnitudes of shear measured at the same anatomical locations by different researchers. A suggested approach for shear sensor calibration is shown below (for detail see methods ‘Human plantar loading specific sensor calibration’ section):

• Determine the sensing area : Material coupling between the shear sensor and adjacent regions can result in the area sensed being greater than then actual area of the sensor.
• Determine the distribution of plantar loading : Normal stress distribution will be indicative of shear stress distribution, whilst foot anatomy, for example the hallux, will determine the loading area.
• Decision for calibration indenter area : Informed by both the sensing area and the distribution and magnitude of plantar loading.

Developed shear stress system sensor

Sensor performance..

A novel Shear Stress System (SSS) sensor composed of a strain gauge rosette, normal pressure sensor and stiffener to concentrate loading at the desired sensor location and mitigate against material coupling was developed and evaluated. Sensor locations were anatomically matched and measured the plantar loading profiles to inform calibration of each sensor at a specific location. This study conducted a thorough experimental validation of the shear sensor through mechanical bench top testing and with human participant treadmill walking. Shear sensing results demonstrated high repeatability (>97%) and high accuracy in the expected measurement range for plantar shear stress (mean absolute errors < ±2 kPa) with error increasing for very high shear stresses (mean absolute errors < ±17 kPa) compared to bench top mechanical tests and repeatability for treadmill walking of 15-minutes duration with less than 21% variability within walking, and less than 37% variability between walks (which was lower than the commercial normal pressure sensors of 47% used in this study).

Limitations.

A rosette strain gauge was chosen for determining unknown principal directions, however it restricted complete strain separation in the AP and ML directions. For exclusive separation, a 0°–90° strain gauge in the ML and AP axes could be adopted. The manual assembly of the sensors and alignment of the sensor in relation to the AP and ML directions affect shear measurement. This has been controlled through careful manufacture, but some small errors will remain. The chosen alignment of the strain gauge rosette in the ML direction was to reduce the fatigue on the soldered joints, this resulted in a decreased sensitivity in the AP direction due to the 45° off-alignment of the gauges with this axis.

Relative stiffness of the silicone and the strain gauge rosette will affect strain transfer between the two materials. Material properties of the silicone is highly important for measurement accuracy, sensitivity, and range, and warrants further investigation.

Future work.

A three-part linear fitting procedure was adopted to calibrate the SSS sensor accommodating the hyperelastic material properties, in the future consideration of alternative fits to capture viscoelastic effects could be made. Despite observing minimal shear sensor temperature response, variability between 20–30°C, literature indicates foot temperatures may be as high as 35° in people with diabetes [ 39 , 40 ], this should be considered in the future. In this proof-of-concept study, the size of calibration area was based on average pressure profiles, a suitable assumption with little participant variation. However, future larger studies may require participant-specific calibration to address varying loading profiles, particularly due to gait variability.

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