task research methods

The Complete Guide to UX Research Methods

UX research provides invaluable insight into product users and what they need and value. Not only will research reduce the risk of a miscalculated guess, it will uncover new opportunities for innovation.

By Miklos Philips

Miklos is a UX designer, product design strategist, author, and speaker with more than 18 years of experience in the design field.

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“Empathy is at the heart of design. Without the understanding of what others see, feel, and experience, design is a pointless task.” —Tim Brown, CEO of the innovation and design firm IDEO

User experience (UX) design is the process of designing products that are useful, easy to use, and a pleasure to engage. It’s about enhancing the entire experience people have while interacting with a product and making sure they find value, satisfaction, and delight. If a mountain peak represents that goal, employing various types of UX research is the path UX designers use to get to the top of the mountain.

User experience research is one of the most misunderstood yet critical steps in UX design. Sometimes treated as an afterthought or an unaffordable luxury, UX research, and user testing should inform every design decision.

Every product, service, or user interface designers create in the safety and comfort of their workplaces has to survive and prosper in the real world. Countless people will engage our creations in an unpredictable environment over which designers have no control. UX research is the key to grounding ideas in reality and improving the odds of success, but research can be a scary word. It may sound like money we don’t have, time we can’t spare, and expertise we have to seek.

In order to do UX research effectively—to get a clear picture of what users think and why they do what they do—e.g., to “walk a mile in the user’s shoes” as a favorite UX maxim goes, it is essential that user experience designers and product teams conduct user research often and regularly. Contingent upon time, resources, and budget, the deeper they can dive the better.

What Is UX Research?

There is a long, comprehensive list of UX design research methods employed by user researchers , but at its center is the user and how they think and behave —their needs and motivations. Typically, UX research does this through observation techniques, task analysis, and other feedback methodologies.

There are two main types of user research: quantitative (statistics: can be calculated and computed; focuses on numbers and mathematical calculations) and qualitative (insights: concerned with descriptions, which can be observed but cannot be computed).

Quantitative research is primarily exploratory research and is used to quantify the problem by way of generating numerical data or data that can be transformed into usable statistics. Some common data collection methods include various forms of surveys – online surveys , paper surveys , mobile surveys and kiosk surveys , longitudinal studies, website interceptors, online polls, and systematic observations.

This user research method may also include analytics, such as Google Analytics .

Google Analytics is part of a suite of interconnected tools that help interpret data on your site’s visitors including Data Studio , a powerful data-visualization tool, and Google Optimize, for running and analyzing dynamic A/B testing.

Quantitative data from analytics platforms should ideally be balanced with qualitative insights gathered from other UX testing methods , such as focus groups or usability testing. The analytical data will show patterns that may be useful for deciding what assumptions to test further.

Qualitative user research is a direct assessment of behavior based on observation. It’s about understanding people’s beliefs and practices on their terms. It can involve several different methods including contextual observation, ethnographic studies, interviews, field studies, and moderated usability tests.

Jakob Nielsen of the Nielsen Norman Group feels that in the case of UX research, it is better to emphasize insights (qualitative research) and that although quant has some advantages, qualitative research breaks down complicated information so it’s easy to understand, and overall delivers better results more cost effectively—in other words, it is much cheaper to find and fix problems during the design phase before you start to build. Often the most important information is not quantifiable, and he goes on to suggest that “quantitative studies are often too narrow to be useful and are sometimes directly misleading.”

Not everything that can be counted counts, and not everything that counts can be counted. William Bruce Cameron

Design research is not typical of traditional science with ethnography being its closest equivalent—effective usability is contextual and depends on a broad understanding of human behavior if it is going to work.

Nevertheless, the types of user research you can or should perform will depend on the type of site, system or app you are developing, your timeline, and your environment.

Top UX Research Methods and When to Use Them

Here are some examples of the types of user research performed at each phase of a project.

Card Sorting : Allows users to group and sort a site’s information into a logical structure that will typically drive navigation and the site’s information architecture. This helps ensure that the site structure matches the way users think.

Contextual Interviews : Enables the observation of users in their natural environment, giving you a better understanding of the way users work.

First Click Testing : A testing method focused on navigation, which can be performed on a functioning website, a prototype, or a wireframe.

Focus Groups : Moderated discussion with a group of users, allowing insight into user attitudes, ideas, and desires.

Heuristic Evaluation/Expert Review : A group of usability experts evaluating a website against a list of established guidelines .

Interviews : One-on-one discussions with users show how a particular user works. They enable you to get detailed information about a user’s attitudes, desires, and experiences.

Parallel Design : A design methodology that involves several designers pursuing the same effort simultaneously but independently, with the intention to combine the best aspects of each for the ultimate solution.

Personas : The creation of a representative user based on available data and user interviews. Though the personal details of the persona may be fictional, the information used to create the user type is not.

Prototyping : Allows the design team to explore ideas before implementing them by creating a mock-up of the site. A prototype can range from a paper mock-up to interactive HTML pages.

Surveys : A series of questions asked to multiple users of your website that help you learn about the people who visit your site.

System Usability Scale (SUS) : SUS is a technology-independent ten-item scale for subjective evaluation of the usability.

Task Analysis : Involves learning about user goals, including what users want to do on your website, and helps you understand the tasks that users will perform on your site.

Usability Testing : Identifies user frustrations and problems with a site through one-on-one sessions where a “real-life” user performs tasks on the site being studied.

Use Cases : Provide a description of how users use a particular feature of your website. They provide a detailed look at how users interact with the site, including the steps users take to accomplish each task.

You can do user research at all stages or whatever stage you are in currently. However, the Nielsen Norman Group advises that most of it be done during the earlier phases when it will have the biggest impact. They also suggest it’s a good idea to save some of your budget for additional research that may become necessary (or helpful) later in the project.

Here is a diagram listing recommended options that can be done as a project moves through the design stages. The process will vary, and may only include a few things on the list during each phase. The most frequently used methods are shown in bold.

Reasons for Doing UX Research

Here are three great reasons for doing user research :

To create a product that is truly relevant to users

• If you don’t have a clear understanding of your users and their mental models, you have no way of knowing whether your design will be relevant. A design that is not relevant to its target audience will never be a success.

To create a product that is easy and pleasurable to use

• A favorite quote from Steve Jobs: “ If the user is having a problem, it’s our problem .” If your user experience is not optimal, chances are that people will move on to another product.

To have the return on investment (ROI) of user experience design validated and be able to show:

• An improvement in performance and credibility
• Increased exposure and sales—growth in customer base
• A reduced burden on resources—more efficient work processes

Aside from the reasons mentioned above, doing user research gives insight into which features to prioritize, and in general, helps develop clarity around a project.

What Results Can I Expect from UX Research?

In the words of Mike Kuniaysky, user research is “ the process of understanding the impact of design on an audience. ”

User research has been essential to the success of behemoths like USAA and Amazon ; Joe Gebbia, CEO of Airbnb is an enthusiastic proponent, testifying that its implementation helped turn things around for the company when it was floundering as an early startup.

Some of the results generated through UX research confirm that improving the usability of a site or app will:

• Increase conversion rates
• Increase sign-ups
• Increase NPS (net promoter score)
• Increase customer satisfaction
• Increase purchase rates
• Boost loyalty to the brand
• Reduce customer service calls

Additionally, and aside from benefiting the overall user experience, the integration of UX research into the development process can:

• Minimize development time
• Reduce production costs
• Uncover valuable insights about your audience
• Give an in-depth view into users’ mental models, pain points, and goals

User research is at the core of every exceptional user experience. As the name suggests, UX is subjective—the experience that a person goes through while using a product. Therefore, it is necessary to understand the needs and goals of potential users, the context, and their tasks which are unique for each product. By selecting appropriate UX research methods and applying them rigorously, designers can shape a product’s design and can come up with products that serve both customers and businesses more effectively.

Further Reading on the Toptal Blog:

• How to Conduct Effective UX Research: A Guide
• The Value of User Research
• UX Research Methods and the Path to User Empathy
• Design Talks: Research in Action with UX Researcher Caitria O'Neill
• Swipe Right: 3 Ways to Boost Safety in Dating App Design
• How to Avoid 5 Types of Cognitive Bias in User Research

Understanding the basics

How do you do user research in ux.

UX research includes two main types: quantitative (statistical data) and qualitative (insights that can be observed but not computed), done through observation techniques, task analysis, and other feedback methodologies. The UX research methods used depend on the type of site, system, or app being developed.

What are UX methods?

There is a long list of methods employed by user research, but at its center is the user and how they think, behave—their needs and motivations. Typically, UX research does this through observation techniques, task analysis, and other UX methodologies.

What is the best research methodology for user experience design?

The type of UX methodology depends on the type of site, system or app being developed, its timeline, and environment. There are 2 main types: quantitative (statistics) and qualitative (insights).

What does a UX researcher do?

A user researcher removes the need for false assumptions and guesswork by using observation techniques, task analysis, and other feedback methodologies to understand a user’s motivation, behavior, and needs.

Why is UX research important?

UX research will help create a product that is relevant to users and is easy and pleasurable to use while boosting a product’s ROI. Aside from these reasons, user research gives insight into which features to prioritize, and in general, helps develop clarity around a project.

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Miklos Philips

London, United Kingdom

Member since May 20, 2016

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What is UX Research: The Ultimate Guide for UX Researchers

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The UX researcher’s toolkit: 11 UX research methods and when to use them

After defining your objectives and planning your research framework, it’s time to choose the research technique that will best serve your project's goals and yield the right insights. While user research is often treated as an afterthought, it should inform every design decision. In this chapter, we walk you through the most common research methods and help you choose the right one for you.

What are UX research methods?

A UX research method is a way of generating insights about your users, their behavior, motivations, and needs.

These methods help:

• Learn about user behavior and attitudes
• Identify key pain points and challenges in the user interface
• Develop user personas to identify user needs and drive solutions
• Test user interface designs to see what works and what doesn’t

You can use research methodologies like user interviews, surveys, focus groups, card sorting, usability testing to identify user challenges and turn them into opportunities to improve the user experience.

More of a visual learner? Check out this video for a speedy rundown. If you’re ready to get stuck in, jump straight to our full breakdown .

The most common types of user research

First, let’s talk about the types of UX research. Every individual research method falls under these types, which reflect different goals and objectives for conducting research.

Here’s a quick overview:

Qualitative vs. quantitative

All research methods are either quantitative or qualitative . Qualitative research focuses on capturing subjective insights into users' experiences. It aims to understand the underlying reasons, motivations, and behaviors of individuals.

Quantitative research, on the other hand, involves collecting and analyzing numerical data to identify patterns, trends, and significance. It aims to quantify user behaviors, preferences, and attitudes, allowing for generalizations and statistical insights.

Qualitative research also typically involves a smaller sample size than quantitative research. Nielsen Norman Group recommends 40 participants—see our full rundown of how many user testers you need for different research methods .

Attitudinal vs. behavioral

Attitudinal research is about understanding users' attitudes, perceptions, and beliefs. It delves into the 'why' behind user decisions and actions. It often involves surveys or interviews where users are asked about their feelings, preferences, or perceptions towards a product or service. It's subjective in nature, aiming to capture people's emotions and opinions.

Behavioral research is about what users do rather than what they say they do or would do. This kind of research is often based on observation methods like usability testing, eye-tracking, or heat maps to understand user behavior.

Generative vs. evaluative

Generative research is all about generating new ideas, concepts, and insights to fuel the design process. You might run brainstorming sessions with groups of users, card sorting, and co-design sessions to inspire creativity and guide the development of user-centered solutions.

On the other hand, evaluative research focuses on assessing the usability, effectiveness, and overall quality of existing designs or prototypes. Once you’ve developed a prototype of your product, it's time to evaluate its strengths and weaknesses. You can compare different versions of a product design or feature through A/B testing—ensuring your UX design meets user needs and expectations.

Remove the guesswork from product decisions

Collect both quantitative and qualitative insights from your customers and build truly user-centric products with Maze.

11 Best UX research methods and when to use them

There are various UX research techniques—each method serves a specific purpose and can provide unique insights into user behaviors and preferences. In this section, we’ll highlight the most common research techniques you need to know.

Read on for an at-a-glance table, and full breakdown of each method.

1. User interviews

Tl;dr: user interviews.

Directly ask users about their experiences with a product to understand their thoughts, feelings, and problems

✅ Provides detailed insights that survey may miss ❌ May not represent the wider user base; depends on user’s memory and honesty

User interviews are a qualitative research method that involves having open-ended and guided discussions with users to gather in-depth insights about their experiences, needs, motivations, and behaviors.

Typically, you would ask a few questions on a specific topic during a user interview and analyze participants' answers. The results you get will depend on how well you form and ask questions, as well as follow up on participants’ answers.

“As a researcher, it's our responsibility to drive the user to their actual problems,” says Yuliya Martinavichene , User Experience Researcher at Zinio. She adds, “The narration of incidents can help you analyze a lot of hidden details with regard to user behavior.”

That’s why you should:

• Start with a wide context : Make sure that your questions don’t start with your product
• Ask questions: Always ask questions that focus on the tasks that users are trying to complete
• Invest in analysis : Get transcripts done and share the findings with your team

Tanya Nativ , Design Researcher at Sketch recommends defining the goals and assumptions internally. “Our beliefs about our users’ behavior really help to structure good questions and get to the root of the problem and its solution,” she explains.

It's easy to be misunderstood if you don't have experience writing interview questions. You can get someone to review them for you or use our Question Bank of 350+ research questions .

When to conduct user interviews

This method is typically used at the start and end of your project. At the start of a project, you can establish a strong understanding of your target users, their perspectives, and the context in which they’ll interact with your product. By the end of your project, new user interviews—often with a different set of individuals—offer a litmus test for your product's usability and appeal, providing firsthand accounts of experiences, perceived strengths, and potential areas for refinement.

2. Field studies

Tl;dr: field studies.

Observe users in their natural environment to inform design decisions with real-world context

✅ Provides contextual insights into user behavior in real-world situations ✅ Helps identify external factors and conditions that influence user experience ❌ Can be time-consuming and resource-intensive to conduct ❌ Participants may behave differently when they know they are being observed (Hawthorne effect)

Field studies—also known as ethnographic research—are research activities that take place in the user’s environment rather than in your lab or office. They’re a great method for uncovering context, unknown motivations, or constraints that affect the user experience.

An advantage of field studies is observing people in their natural environment, giving you a glimpse at the context in which your product is used. It’s useful to understand the context in which users complete tasks, learn about their needs, and collect in-depth user stories.

When to conduct field studies

This method can be used at all stages of your project—two key times you may want to conduct field studies are:

• As part of the discovery and exploration stage to define direction and understand the context around when and how users interact with the product
• During usability testing, once you have a prototype, to evaluate the effectiveness of the solution or validate design assumptions in real-world contexts

3. Focus groups

Tl;dr: focus groups.

Gather qualitative data from a group of users discussing their experiences and opinions about a product

✅ Allows for diverse perspectives to be shared and discussed ❌ Group dynamics may influence individual opinions

A focus group is a qualitative research method that includes the study of a group of people, their beliefs, and opinions. It’s typically used for market research or gathering feedback on products and messaging.

Focus groups can help you better grasp:

• How users perceive your product
• What users believe are a product’s most important features
• What problems do users experience with the product

As with any qualitative research method, the quality of the data collected through focus groups is only as robust as the preparation. So, it’s important to prepare a UX research plan you can refer to during the discussion.

Here’s some things to consider:

• Write a script to guide the conversation
• Ask clear, open-ended questions focused on the topics you’re trying to learn about
• Include around five to ten participants to keep the sessions focused and organized

When to conduct focus groups

It’s easier to use this research technique when you're still formulating your concept, product, or service—to explore user preferences, gather initial reactions, and generate ideas. This is because, in the early stages, you have flexibility and can make significant changes without incurring high costs.

Another way some researchers employ focus groups is post-launch to gather feedback and identify potential improvements. However, you can also use other methods here which may be more effective for identifying usability issues. For example, a platform like Maze can provide detailed, actionable data about how users interact with your product. These quantitative results are a great accompaniment to the qualitative data gathered from your focus group.

4. Diary studies

Tl;dr: diary studies.

Get deep insights into user thoughts and feelings by having them keep a product-related diary over a set period of time, typically a couple of weeks

✅ Gives you a peak into how users interact with your product in their day-to-day ❌ Depends on how motivated and dedicated the users are

Diary studies involve asking users to track their usage and thoughts on your product by keeping logs or diaries, taking photos, explaining their activities, and highlighting things that stood out to them.

“Diary studies are one of the few ways you can get a peek into how users interact with our product in a real-world scenario,” says Tanya.

A diary study helps you tell the story of how products and services fit into people’s daily lives, and the touch-points and channels they choose to complete their tasks.

There’s several key questions to consider before conducting diary research, from what kind of diary you want—freeform or structured, and digital or paper—to how often you want participants to log their thoughts.

• Open, ‘freeform’ diary: Users have more freedom to record what and when they like, but can also lead to missed opportunities to capture data users might overlook
• Closed, ‘structured; diary: Users follow a stricter entry-logging process and answer pre-set questions

Remember to determine the trigger: a signal that lets the participants know when they should log their feedback. Tanya breaks these triggers down into the following:

• Interval-contingent trigger : Participants fill out the diary at specific intervals such as one entry per day, or one entry per week
• Signal-contingent trigger : You tell the participant when to make an entry and how you would prefer them to communicate it to you as well as your preferred type of communication
• Event-contingent trigger : The participant makes an entry whenever a defined event occurs

When to conduct diary studies

Diary studies are often valuable when you need to deeply understand users' behaviors, routines, and pain points in real-life contexts. This could be when you're:

• Conceptualizing a new product or feature: Gain insights into user habits, needs, and frustrations to inspire your design
• Trying to enhance an existing product: Identify areas where users are having difficulties or where there are opportunities for better user engagement

TL;DR: Surveys

Collect quantitative data from a large sample of users about their experiences, preferences, and satisfaction with a product

✅ Provides a broad overview of user opinions and trends ❌ May lack in-depth insights and context behind user responses

Although surveys are primarily used for quantitative research, they can also provided qualitative data, depending on whether you use closed or open-ended questions:

• Closed-ended questions come with a predefined set of answers to choose from using formats like rating scales, rankings, or multiple choice. This results in quantitative data.
• Open-ended question s are typically open-text questions where test participants give their responses in a free-form style. This results in qualitative data.

Matthieu Dixte , Product Researcher at Maze, explains the benefit of surveys: “With open-ended questions, researchers get insight into respondents' opinions, experiences, and explanations in their own words. This helps explore nuances that quantitative data alone may not capture.”

So, how do you make sure you’re asking the right survey questions? Gregg Bernstein , UX Researcher at Signal, says that when planning online surveys, it’s best to avoid questions that begin with “How likely are you to…?” Instead, Gregg says asking questions that start with “Have you ever… ?” will prompt users to give more specific and measurable answers.

Make sure your questions:

• Are easy to understand
• Don't guide participants towards a particular answer
• Include both closed-ended and open-ended questions
• Respect users and their privacy
• Are consistent in terms of format

To learn more about survey design, check out this guide .

When to conduct surveys

While surveys can be used at all stages of project development, and are ideal for continuous product discovery , the specific timing and purpose may vary depending on the research goals. For example, you can run surveys at:

• Conceptualization phase to gather preliminary data, and identify patterns, trends, or potential user segments
• Post-launch or during iterative design cycles to gather feedback on user satisfaction, feature usage, or suggestions for improvements

6. Card sorting

Tl;dr: card sorting.

Understand how users categorize and prioritize information within a product or service to structure your information in line with user expectations

✅ Helps create intuitive information architecture and navigation ❌ May not accurately reflect real-world user behavior and decision-making

Card sorting is an important step in creating an intuitive information architecture (IA) and user experience. It’s also a great technique to generate ideas, naming conventions, or simply see how users understand topics.

In this UX research method, participants are presented with cards featuring different topics or information, and tasked with grouping the cards into categories that make sense to them.

There are three types of card sorting:

• Open card sorting: Participants organize topics into categories that make sense to them and name those categories, thus generating new ideas and names
• Hybrid card sorting: Participants can sort cards into predefined categories, but also have the option to create their own categories
• Closed card sorting: Participants are given predefined categories and asked to sort the items into the available groups

Card sorting type comparison table

You can run a card sorting session using physical index cards or digitally with a UX research tool like Maze to simulate the drag-and-drop activity of dividing cards into groups. Running digital card sorting is ideal for any type of card sort, and moderated or unmoderated sessions .

Read more about card sorting and learn how to run a card sorting session here .

When to conduct card sorting

Card sorting isn’t limited to a single stage of design or development—it can be employed anytime you need to explore how users categorize or perceive information. For example, you may want to use card sorting if you need to:

• Understand how users perceive ideas
• Evaluate and prioritize potential solutions
• Generate name ideas and understand naming conventions
• Learn how users expect navigation to work
• Decide how to group content on a new or existing site
• Restructure information architecture

7. Tree testing

Tl;dr: tree testing.

Evaluate the findability of existing information within a product's hierarchical structure or navigation

✅ Identifies potential issues in the information architecture ❌ Focuses on navigation structure, not visual design or content

During tree testing a text-only version of the site is given to your participants, who are asked to complete a series of tasks requiring them to locate items on the app or website.

The data collected from a tree test helps you understand where users intuitively navigate first, and is an effective way to assess the findability, labeling, and information architecture of a product.

We recommend keeping these sessions short, ranging from 15 to 20 minutes, and asking participants to complete no more than ten tasks. This helps ensure participants remain focused and engaged, leading to more reliable and accurate data, and avoiding fatigue.

If you’re using a platform like Maze to run remote testing, you can easily recruit participants based on various demographic filters, including industry and country. This way, you can uncover a broader range of user preferences, ensuring a more comprehensive understanding of your target audience.

To learn more about tree testing, check out this chapter .

When to conduct tree testing

Tree testing is often done at an early stage in the design or redesign process. That’s because it’s more cost-effective to address errors at the start of a project—rather than making changes later in the development process or after launch.

However, it can be helpful to employ tree testing as a method when adding new features, particularly alongside card sorting.

While tree testing and card sorting can both help you with categorizing the content on a website, it’s important to note that they each approach this from a different angle and are used at different stages during the research process. Ideally, you should use the two in tandem: card sorting is recommended when defining and testing a new website architecture, while tree testing is meant to help you test how the navigation performs with users.

8. Usability testing

Tl;dr: usability testing.

Observe users completing specific tasks with a product to identify usability issues and potential improvements

✅ Provides direct insights into user behavior and reveals pain points ❌ Conducted in a controlled environment, may not fully represent real-world usage

Usability testing evaluates your product with people by getting them to complete tasks while you observe and note their interactions (either during or after the test). The goal of conducting usability testing is to understand if your design is intuitive and easy to use. A sign of success is if users can easily accomplish their goals and complete tasks with your product.

There are various usability testing methods that you can use, such as moderated vs. unmoderated or qualitative vs. quantitative —and selecting the right one depends on your research goals, resources, and timeline.

Usability testing is usually performed with functional mid or hi-fi prototypes . If you have a Figma, InVision, Sketch, or prototype ready, you can import it into a platform like Maze and start testing your design with users immediately.

The tasks you create for usability tests should be:

• Realistic, and describe a scenario
• Actionable, and use action verbs (create, sign up, buy, etc)

Be mindful of using leading words such as ‘click here’ or ‘go to that page’ in your tasks. These instructions bias the results by helping users complete their tasks—something that doesn’t happen in real life.

✨ Product tip

With Maze, you can test your prototype and live website with real users to filter out cognitive biases, and gather actionable insights that fuel product decisions.

When to conduct usability testing

To inform your design decisions, you should do usability testing early and often in the process . Here are some guidelines to help you decide when to do usability testing:

• Before you start designing
• Once you have a wireframe or prototype
• Prior to the launch of the product
• At regular intervals after launch

To learn more about usability testing, check out our complete guide to usability testing .

9. Five-second testing

Tl;dr: five-second testing.

Gauge users' first impressions and understanding of a design or layout

✅ Provides insights into the instant clarity and effectiveness of visual communication ❌ Limited to first impressions, does not assess full user experience or interaction

In five-second testing , participants are (unsurprisingly) given five seconds to view an image like a design or web page, and then they’re asked questions about the design to gauge their first impressions.

Why five seconds? According to data , 55% of visitors spend less than 15 seconds on a website, so it;s essential to grab someone’s attention in the first few seconds of their visit. With a five-second test, you can quickly determine what information users perceive and their impressions during the first five seconds of viewing a design.

Product tip 💡

And if you’re using Maze, you can simply upload an image of the screen you want to test, or browse your prototype and select a screen. Plus, you can star individual comments and automatically add them to your report to share with stakeholders.

When to conduct five-second testing

Five-second testing is typically conducted in the early stages of the design process, specifically during initial concept testing or prototype development. This way, you can evaluate your design's initial impact and make early refinements or adjustments to ensure its effectiveness, before putting design to development.

To learn more, check out our chapter on five-second testing .

10. A/B testing

Tl;dr: a/b testing.

Compare two versions of a design or feature to determine which performs better based on user engagement

✅ Provides data-driven insights to guide design decisions and optimize user experience ❌ Requires a large sample size and may not account for long-term effects or complex interactions

A/B testing , also known as split testing, compares two or more versions of a webpage, interface, or feature to determine which performs better regarding engagement, conversions, or other predefined metrics.

It involves randomly dividing users into different groups and giving each group a different version of the design element being tested. For example, let's say the primary call-to-action on the page is a button that says ‘buy now’.

You're considering making changes to its design to see if it can lead to higher conversions, so you create two versions:

• Version A : The original design with the ‘buy now’ button positioned below the product description—shown to group A
• Version B : A variation with the ‘buy now’ button now prominently displayed above the product description—shown to group B

Over a planned period, you measure metrics like click-through rates, add-to-cart rates, and actual purchases to assess the performance of each variation. You find that Group B had significantly higher click-through and conversion rates than Group A. This indicates that showing the button above the product description drove higher user engagement and conversions.

Check out our A/B testing guide for more in-depth examples and guidance on how to run these tests.

When to conduct A/B testing

A/B testing can be used at all stages of the design and development process—whenever you want to collect direct, quantitative data and confirm a suspicion, or settle a design debate. This iterative testing approach allows you to continually improve your website's performance and user experience based on data-driven insights.

11. Concept testing

Tl;dr: concept testing.

Evaluate users' reception and understanding of a new product, feature, or design idea before moving on to development

✅ Helps validate and refine concepts based on user feedback ❌ Relies on users' perception and imagination, may not reflect actual use

Concept testing is a type of research that evaluates the feasibility, appeal, and potential success of a new product before you build it. It centers the user in the ideation process, using UX research methods like A/B testing, surveys, and customer interviews.

There’s no one way to run a concept test—you can opt for concept testing surveys, interviews, focus groups, or any other method that gets qualitative data on your concept.

*Dive into our complete guide to concept testing for more tips and tricks on getting started. *

When to conduct concept testing

Concept testing helps gauge your audience’s interest, understanding, and likelihood-to-purchase, before committing time and resources to a concept. However, it can also be useful further down the product development line—such as when defining marketing messaging or just before launching.

Which is the best UX research type?

The best research type varies depending on your project; what your objectives are, and what stage you’re in. Ultimately, the ideal type of research is one which provides the insights required, using the available resources.

For example, if you're at the early ideation or product discovery stage, generative research methods can help you generate new ideas, understand user needs, and explore possibilities. As you move to the design and development phase, evaluative research methods and quantitative data become crucial.

Discover the UX research trends shaping the future of the industry and why the best results come from a combination of different research methods.

How to choose the right user experience research method

In an ideal world, a combination of all the insights you gain from multiple types of user research methods would guide every design decision. In practice, this can be hard to execute due to resources.

Sometimes the right methodology is the one you can get buy-in, budget, and time for.

Gregg Bernstein , UX Researcher at Signal

UX research tools can help streamline the research process, making regular testing and application of diverse methods more accessible—so you always keep the user at the center of your design process. Some other key tips to remember when choosing your method are:

Define the goals and problems

A good way to inform your choice of user experience research method is to start by considering your goals. You might want to browse UX research templates or read about examples of research.

Michael Margolis , UX Research Partner at Google Ventures, recommends answering questions like:

• “What do your users need?”
• “What are your users struggling with?”
• “How can you help your users?”

Understand the design process stage

If your team is very early in product development, generative research —like field studies—make sense. If you need to test design mockups or a prototype, evaluative research methods—such as usability testing—will work best.

This is something they’re big on at Sketch, as we heard from Design Researcher, Tanya Nativ. She says, “In the discovery phase, we focus on user interviews and contextual inquiries. The testing phase is more about dogfooding, concept testing, and usability testing. Once a feature has been launched, it’s about ongoing listening.”

Consider the type of insights required

If you're looking for rich, qualitative data that delves into user behaviors, motivations, and emotions, then methods like user interviews or field studies are ideal. They’ll help you uncover the ‘why’ behind user actions.

On the other hand, if you need to gather quantitative data to measure user satisfaction or compare different design variations, methods like surveys or A/B testing are more suitable. These methods will help you get hard numbers and concrete data on preferences and behavior.

*Discover the UX research trends shaping the future of the industry and why the best results come from a combination of different research methods. *

Build a deeper understanding of your users with UX research

Think of UX research methods as building blocks that work together to create a well-rounded understanding of your users. Each method brings its own unique strengths, whether it's human empathy from user interviews or the vast data from surveys.

But it's not just about choosing the right UX research methods; the research platform you use is equally important. You need a platform that empowers your team to collect data, analyze, and collaborate seamlessly.

Simplifying product research is simple with Maze. From tree testing to card sorting, prototype testing to user interview analysis—Maze makes getting actionable insights easy, whatever method you opt for.

Meanwhile, if you want to know more about testing methods, head on to the next chapter all about tree testing .

Get valuable insights from real users

Conduct impactful UX research with Maze and improve your product experience and customer satisfaction.

Frequently asked questions

How do you choose the right UX research method?

Choosing the right research method depends on your goals. Some key things to consider are:

• The feature/product you’re testing
• The type of data you’re looking for
• The design stage
• The time and resources you have available

What is the best UX research method?

The best research method is the one you have the time, resources, and budget for that meets your specific needs and goals. Most research tools, like Maze, will accommodate a variety of UX research and testing techniques.

When to use which user experience research method?

Selecting which user research method to use—if budget and resources aren’t a factor—depends on your goals. UX research methods provide different types of data:

• Qualitative vs quantitative
• Attitudinal vs behavioral
• Generative vs evaluative

Identify your goals, then choose a research method that gathers the user data you need.

What results can I expect from UX research?

Here are some of the key results you can expect from actioning the insights uncovered during UX research:

• Improved user satisfaction
• Increased usability
• Better product fit
• Informed design decisions
• Reduced development costs
• Higher conversion rates
• Increased customer loyalty and retention

Tree Testing: Your Guide to Improve Navigation and UX

How To Choose Your Research Methodology

Qualitative vs quantitative vs mixed methods.

By: Derek Jansen (MBA). Expert Reviewed By: Dr Eunice Rautenbach | June 2021

Without a doubt, one of the most common questions we receive at Grad Coach is “ How do I choose the right methodology for my research? ”. It’s easy to see why – with so many options on the research design table, it’s easy to get intimidated, especially with all the complex lingo!

In this post, we’ll explain the three overarching types of research – qualitative, quantitative and mixed methods – and how you can go about choosing the best methodological approach for your research.

Overview: Choosing Your Methodology

Understanding the options – Qualitative research – Quantitative research – Mixed methods-based research

Choosing a research methodology – Nature of the research – Research area norms – Practicalities

1. Understanding the options

Before we jump into the question of how to choose a research methodology, it’s useful to take a step back to understand the three overarching types of research – qualitative , quantitative and mixed methods -based research. Each of these options takes a different methodological approach.

Qualitative research utilises data that is not numbers-based. In other words, qualitative research focuses on words , descriptions , concepts or ideas – while quantitative research makes use of numbers and statistics. Qualitative research investigates the “softer side” of things to explore and describe, while quantitative research focuses on the “hard numbers”, to measure differences between variables and the relationships between them.

Importantly, qualitative research methods are typically used to explore and gain a deeper understanding of the complexity of a situation – to draw a rich picture . In contrast to this, quantitative methods are usually used to confirm or test hypotheses . In other words, they have distinctly different purposes. The table below highlights a few of the key differences between qualitative and quantitative research – you can learn more about the differences here.

• Uses an inductive approach
• Is used to build theories
• Takes a subjective approach
• Adopts an open and flexible approach
• The researcher is close to the respondents
• Interviews and focus groups are oftentimes used to collect word-based data.
• Generally, draws on small sample sizes
• Uses qualitative data analysis techniques (e.g. content analysis , thematic analysis , etc)
• Uses a deductive approach
• Is used to test theories
• Takes an objective approach
• Adopts a closed, highly planned approach
• The research is disconnected from respondents
• Surveys or laboratory equipment are often used to collect number-based data.
• Generally, requires large sample sizes
• Uses statistical analysis techniques to make sense of the data

Mixed methods -based research, as you’d expect, attempts to bring these two types of research together, drawing on both qualitative and quantitative data. Quite often, mixed methods-based studies will use qualitative research to explore a situation and develop a potential model of understanding (this is called a conceptual framework), and then go on to use quantitative methods to test that model empirically.

In other words, while qualitative and quantitative methods (and the philosophies that underpin them) are completely different, they are not at odds with each other. It’s not a competition of qualitative vs quantitative. On the contrary, they can be used together to develop a high-quality piece of research. Of course, this is easier said than done, so we usually recommend that first-time researchers stick to a single approach , unless the nature of their study truly warrants a mixed-methods approach.

The key takeaway here, and the reason we started by looking at the three options, is that it’s important to understand that each methodological approach has a different purpose – for example, to explore and understand situations (qualitative), to test and measure (quantitative) or to do both. They’re not simply alternative tools for the same job. 

Right – now that we’ve got that out of the way, let’s look at how you can go about choosing the right methodology for your research.

2. How to choose a research methodology

To choose the right research methodology for your dissertation or thesis, you need to consider three important factors . Based on these three factors, you can decide on your overarching approach – qualitative, quantitative or mixed methods. Once you’ve made that decision, you can flesh out the finer details of your methodology, such as the sampling , data collection methods and analysis techniques (we discuss these separately in other posts ).

The three factors you need to consider are:

• The nature of your research aims, objectives and research questions
• The methodological approaches taken in the existing literature
• Practicalities and constraints

Let’s take a look at each of these.

Factor #1: The nature of your research

As I mentioned earlier, each type of research (and therefore, research methodology), whether qualitative, quantitative or mixed, has a different purpose and helps solve a different type of question. So, it’s logical that the key deciding factor in terms of which research methodology you adopt is the nature of your research aims, objectives and research questions .

But, what types of research exist?

Broadly speaking, research can fall into one of three categories:

• Exploratory – getting a better understanding of an issue and potentially developing a theory regarding it
• Confirmatory – confirming a potential theory or hypothesis by testing it empirically
• A mix of both – building a potential theory or hypothesis and then testing it

As a rule of thumb, exploratory research tends to adopt a qualitative approach , whereas confirmatory research tends to use quantitative methods . This isn’t set in stone, but it’s a very useful heuristic. Naturally then, research that combines a mix of both, or is seeking to develop a theory from the ground up and then test that theory, would utilize a mixed-methods approach.

Let’s look at an example in action.

If your research aims were to understand the perspectives of war veterans regarding certain political matters, you’d likely adopt a qualitative methodology, making use of interviews to collect data and one or more qualitative data analysis methods to make sense of the data.

If, on the other hand, your research aims involved testing a set of hypotheses regarding the link between political leaning and income levels, you’d likely adopt a quantitative methodology, using numbers-based data from a survey to measure the links between variables and/or constructs .

So, the first (and most important thing) thing you need to consider when deciding which methodological approach to use for your research project is the nature of your research aims , objectives and research questions. Specifically, you need to assess whether your research leans in an exploratory or confirmatory direction or involves a mix of both.

The importance of achieving solid alignment between these three factors and your methodology can’t be overstated. If they’re misaligned, you’re going to be forcing a square peg into a round hole. In other words, you’ll be using the wrong tool for the job, and your research will become a disjointed mess.

If your research is a mix of both exploratory and confirmatory, but you have a tight word count limit, you may need to consider trimming down the scope a little and focusing on one or the other. One methodology executed well has a far better chance of earning marks than a poorly executed mixed methods approach. So, don’t try to be a hero, unless there is a very strong underpinning logic.

Need a helping hand?

Factor #2: The disciplinary norms

Choosing the right methodology for your research also involves looking at the approaches used by other researchers in the field, and studies with similar research aims and objectives to yours. Oftentimes, within a discipline, there is a common methodological approach (or set of approaches) used in studies. While this doesn’t mean you should follow the herd “just because”, you should at least consider these approaches and evaluate their merit within your context.

A major benefit of reviewing the research methodologies used by similar studies in your field is that you can often piggyback on the data collection techniques that other (more experienced) researchers have developed. For example, if you’re undertaking a quantitative study, you can often find tried and tested survey scales with high Cronbach’s alphas. These are usually included in the appendices of journal articles, so you don’t even have to contact the original authors. By using these, you’ll save a lot of time and ensure that your study stands on the proverbial “shoulders of giants” by using high-quality measurement instruments .

Of course, when reviewing existing literature, keep point #1 front of mind. In other words, your methodology needs to align with your research aims, objectives and questions. Don’t fall into the trap of adopting the methodological “norm” of other studies just because it’s popular. Only adopt that which is relevant to your research.

Factor #3: Practicalities

When choosing a research methodology, there will always be a tension between doing what’s theoretically best (i.e., the most scientifically rigorous research design ) and doing what’s practical , given your constraints . This is the nature of doing research and there are always trade-offs, as with anything else.

But what constraints, you ask?

When you’re evaluating your methodological options, you need to consider the following constraints:

• Data access
• Equipment and software
• Your knowledge and skills

Let’s look at each of these.

Constraint #1: Data access

The first practical constraint you need to consider is your access to data . If you’re going to be undertaking primary research , you need to think critically about the sample of respondents you realistically have access to. For example, if you plan to use in-person interviews , you need to ask yourself how many people you’ll need to interview, whether they’ll be agreeable to being interviewed, where they’re located, and so on.

If you’re wanting to undertake a quantitative approach using surveys to collect data, you’ll need to consider how many responses you’ll require to achieve statistically significant results. For many statistical tests, a sample of a few hundred respondents is typically needed to develop convincing conclusions.

So, think carefully about what data you’ll need access to, how much data you’ll need and how you’ll collect it. The last thing you want is to spend a huge amount of time on your research only to find that you can’t get access to the required data.

Constraint #2: Time

The next constraint is time. If you’re undertaking research as part of a PhD, you may have a fairly open-ended time limit, but this is unlikely to be the case for undergrad and Masters-level projects. So, pay attention to your timeline, as the data collection and analysis components of different methodologies have a major impact on time requirements . Also, keep in mind that these stages of the research often take a lot longer than originally anticipated.

Another practical implication of time limits is that it will directly impact which time horizon you can use – i.e. longitudinal vs cross-sectional . For example, if you’ve got a 6-month limit for your entire research project, it’s quite unlikely that you’ll be able to adopt a longitudinal time horizon. 

Constraint #3: Money

As with so many things, money is another important constraint you’ll need to consider when deciding on your research methodology. While some research designs will cost near zero to execute, others may require a substantial budget .

Some of the costs that may arise include:

• Software costs – e.g. survey hosting services, analysis software, etc.
• Promotion costs – e.g. advertising a survey to attract respondents
• Incentive costs – e.g. providing a prize or cash payment incentive to attract respondents
• Equipment rental costs – e.g. recording equipment, lab equipment, etc.
• Travel costs
• Food & beverages

These are just a handful of costs that can creep into your research budget. Like most projects, the actual costs tend to be higher than the estimates, so be sure to err on the conservative side and expect the unexpected. It’s critically important that you’re honest with yourself about these costs, or you could end up getting stuck midway through your project because you’ve run out of money.

Constraint #4: Equipment & software

Another practical consideration is the hardware and/or software you’ll need in order to undertake your research. Of course, this variable will depend on the type of data you’re collecting and analysing. For example, you may need lab equipment to analyse substances, or you may need specific analysis software to analyse statistical data. So, be sure to think about what hardware and/or software you’ll need for each potential methodological approach, and whether you have access to these.

Constraint #5: Your knowledge and skillset

The final practical constraint is a big one. Naturally, the research process involves a lot of learning and development along the way, so you will accrue knowledge and skills as you progress. However, when considering your methodological options, you should still consider your current position on the ladder.

Some of the questions you should ask yourself are:

• Am I more of a “numbers person” or a “words person”?
• How much do I know about the analysis methods I’ll potentially use (e.g. statistical analysis)?
• How much do I know about the software and/or hardware that I’ll potentially use?
• How excited am I to learn new research skills and gain new knowledge?
• How much time do I have to learn the things I need to learn?

Answering these questions honestly will provide you with another set of criteria against which you can evaluate the research methodology options you’ve shortlisted.

So, as you can see, there is a wide range of practicalities and constraints that you need to take into account when you’re deciding on a research methodology. These practicalities create a tension between the “ideal” methodology and the methodology that you can realistically pull off. This is perfectly normal, and it’s your job to find the option that presents the best set of trade-offs.

Recap: Choosing a methodology

In this post, we’ve discussed how to go about choosing a research methodology. The three major deciding factors we looked at were:

• Exploratory
• Confirmatory
• Combination
• Research area norms
• Hardware and software
• Your knowledge and skillset

If you have any questions, feel free to leave a comment below. If you’d like a helping hand with your research methodology, check out our 1-on-1 research coaching service , or book a free consultation with a friendly Grad Coach.

Psst... there’s more!

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

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Very useful and informative especially for beginners

Nice article! I’m a beginner in the field of cybersecurity research. I am a Telecom and Network Engineer and Also aiming for PhD scholarship.

I find the article very informative especially for my decitation it has been helpful and an eye opener.

Hi I am Anna ,

I am a PHD candidate in the area of cyber security, maybe we can link up

The Examples shows by you, for sure they are really direct me and others to knows and practices the Research Design and prepration.

I found the post very informative and practical.

I struggle so much with designs of the research for sure!

I’m the process of constructing my research design and I want to know if the data analysis I plan to present in my thesis defense proposal possibly change especially after I gathered the data already.

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• USC Libraries
• Research Guides

Organizing Your Social Sciences Research Paper

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

The methods section describes actions taken to investigate a research problem and the rationale for the application of specific procedures or techniques used to identify, select, process, and analyze information applied to understanding the problem, thereby, allowing the reader to critically evaluate a study’s overall validity and reliability. The methodology section of a research paper answers two main questions: How was the data collected or generated? And, how was it analyzed? The writing should be direct and precise and always written in the past tense.

Kallet, Richard H. "How to Write the Methods Section of a Research Paper." Respiratory Care 49 (October 2004): 1229-1232.

Importance of a Good Methodology Section

You must explain how you obtained and analyzed your results for the following reasons:

• Readers need to know how the data was obtained because the method you chose affects the results and, by extension, how you interpreted their significance in the discussion section of your paper.
• Methodology is crucial for any branch of scholarship because an unreliable method produces unreliable results and, as a consequence, undermines the value of your analysis of the findings.
• In most cases, there are a variety of different methods you can choose to investigate a research problem. The methodology section of your paper should clearly articulate the reasons why you have chosen a particular procedure or technique.
• The reader wants to know that the data was collected or generated in a way that is consistent with accepted practice in the field of study. For example, if you are using a multiple choice questionnaire, readers need to know that it offered your respondents a reasonable range of answers to choose from.
• The method must be appropriate to fulfilling the overall aims of the study. For example, you need to ensure that you have a large enough sample size to be able to generalize and make recommendations based upon the findings.
• The methodology should discuss the problems that were anticipated and the steps you took to prevent them from occurring. For any problems that do arise, you must describe the ways in which they were minimized or why these problems do not impact in any meaningful way your interpretation of the findings.
• In the social and behavioral sciences, it is important to always provide sufficient information to allow other researchers to adopt or replicate your methodology. This information is particularly important when a new method has been developed or an innovative use of an existing method is utilized.

Bem, Daryl J. Writing the Empirical Journal Article. Psychology Writing Center. University of Washington; Denscombe, Martyn. The Good Research Guide: For Small-Scale Social Research Projects . 5th edition. Buckingham, UK: Open University Press, 2014; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008.

Structure and Writing Style

I.  Groups of Research Methods

There are two main groups of research methods in the social sciences:

• The e mpirical-analytical group approaches the study of social sciences in a similar manner that researchers study the natural sciences . This type of research focuses on objective knowledge, research questions that can be answered yes or no, and operational definitions of variables to be measured. The empirical-analytical group employs deductive reasoning that uses existing theory as a foundation for formulating hypotheses that need to be tested. This approach is focused on explanation.
• The i nterpretative group of methods is focused on understanding phenomenon in a comprehensive, holistic way . Interpretive methods focus on analytically disclosing the meaning-making practices of human subjects [the why, how, or by what means people do what they do], while showing how those practices arrange so that it can be used to generate observable outcomes. Interpretive methods allow you to recognize your connection to the phenomena under investigation. However, the interpretative group requires careful examination of variables because it focuses more on subjective knowledge.

II.  Content

The introduction to your methodology section should begin by restating the research problem and underlying assumptions underpinning your study. This is followed by situating the methods you used to gather, analyze, and process information within the overall “tradition” of your field of study and within the particular research design you have chosen to study the problem. If the method you choose lies outside of the tradition of your field [i.e., your review of the literature demonstrates that the method is not commonly used], provide a justification for how your choice of methods specifically addresses the research problem in ways that have not been utilized in prior studies.

The remainder of your methodology section should describe the following:

• Decisions made in selecting the data you have analyzed or, in the case of qualitative research, the subjects and research setting you have examined,
• Tools and methods used to identify and collect information, and how you identified relevant variables,
• The ways in which you processed the data and the procedures you used to analyze that data, and
• The specific research tools or strategies that you utilized to study the underlying hypothesis and research questions.

In addition, an effectively written methodology section should:

• Introduce the overall methodological approach for investigating your research problem . Is your study qualitative or quantitative or a combination of both (mixed method)? Are you going to take a special approach, such as action research, or a more neutral stance?
• Indicate how the approach fits the overall research design . Your methods for gathering data should have a clear connection to your research problem. In other words, make sure that your methods will actually address the problem. One of the most common deficiencies found in research papers is that the proposed methodology is not suitable to achieving the stated objective of your paper.
• Describe the specific methods of data collection you are going to use , such as, surveys, interviews, questionnaires, observation, archival research. If you are analyzing existing data, such as a data set or archival documents, describe how it was originally created or gathered and by whom. Also be sure to explain how older data is still relevant to investigating the current research problem.
• Explain how you intend to analyze your results . Will you use statistical analysis? Will you use specific theoretical perspectives to help you analyze a text or explain observed behaviors? Describe how you plan to obtain an accurate assessment of relationships, patterns, trends, distributions, and possible contradictions found in the data.
• Provide background and a rationale for methodologies that are unfamiliar for your readers . Very often in the social sciences, research problems and the methods for investigating them require more explanation/rationale than widely accepted rules governing the natural and physical sciences. Be clear and concise in your explanation.
• Provide a justification for subject selection and sampling procedure . For instance, if you propose to conduct interviews, how do you intend to select the sample population? If you are analyzing texts, which texts have you chosen, and why? If you are using statistics, why is this set of data being used? If other data sources exist, explain why the data you chose is most appropriate to addressing the research problem.
• Provide a justification for case study selection . A common method of analyzing research problems in the social sciences is to analyze specific cases. These can be a person, place, event, phenomenon, or other type of subject of analysis that are either examined as a singular topic of in-depth investigation or multiple topics of investigation studied for the purpose of comparing or contrasting findings. In either method, you should explain why a case or cases were chosen and how they specifically relate to the research problem.
• Describe potential limitations . Are there any practical limitations that could affect your data collection? How will you attempt to control for potential confounding variables and errors? If your methodology may lead to problems you can anticipate, state this openly and show why pursuing this methodology outweighs the risk of these problems cropping up.

NOTE:   Once you have written all of the elements of the methods section, subsequent revisions should focus on how to present those elements as clearly and as logically as possibly. The description of how you prepared to study the research problem, how you gathered the data, and the protocol for analyzing the data should be organized chronologically. For clarity, when a large amount of detail must be presented, information should be presented in sub-sections according to topic. If necessary, consider using appendices for raw data.

ANOTHER NOTE: If you are conducting a qualitative analysis of a research problem , the methodology section generally requires a more elaborate description of the methods used as well as an explanation of the processes applied to gathering and analyzing of data than is generally required for studies using quantitative methods. Because you are the primary instrument for generating the data [e.g., through interviews or observations], the process for collecting that data has a significantly greater impact on producing the findings. Therefore, qualitative research requires a more detailed description of the methods used.

YET ANOTHER NOTE:   If your study involves interviews, observations, or other qualitative techniques involving human subjects , you may be required to obtain approval from the university's Office for the Protection of Research Subjects before beginning your research. This is not a common procedure for most undergraduate level student research assignments. However, i f your professor states you need approval, you must include a statement in your methods section that you received official endorsement and adequate informed consent from the office and that there was a clear assessment and minimization of risks to participants and to the university. This statement informs the reader that your study was conducted in an ethical and responsible manner. In some cases, the approval notice is included as an appendix to your paper.

III.  Problems to Avoid

Irrelevant Detail The methodology section of your paper should be thorough but concise. Do not provide any background information that does not directly help the reader understand why a particular method was chosen, how the data was gathered or obtained, and how the data was analyzed in relation to the research problem [note: analyzed, not interpreted! Save how you interpreted the findings for the discussion section]. With this in mind, the page length of your methods section will generally be less than any other section of your paper except the conclusion.

Unnecessary Explanation of Basic Procedures Remember that you are not writing a how-to guide about a particular method. You should make the assumption that readers possess a basic understanding of how to investigate the research problem on their own and, therefore, you do not have to go into great detail about specific methodological procedures. The focus should be on how you applied a method , not on the mechanics of doing a method. An exception to this rule is if you select an unconventional methodological approach; if this is the case, be sure to explain why this approach was chosen and how it enhances the overall process of discovery.

Problem Blindness It is almost a given that you will encounter problems when collecting or generating your data, or, gaps will exist in existing data or archival materials. Do not ignore these problems or pretend they did not occur. Often, documenting how you overcame obstacles can form an interesting part of the methodology. It demonstrates to the reader that you can provide a cogent rationale for the decisions you made to minimize the impact of any problems that arose.

Literature Review Just as the literature review section of your paper provides an overview of sources you have examined while researching a particular topic, the methodology section should cite any sources that informed your choice and application of a particular method [i.e., the choice of a survey should include any citations to the works you used to help construct the survey].

It’s More than Sources of Information! A description of a research study's method should not be confused with a description of the sources of information. Such a list of sources is useful in and of itself, especially if it is accompanied by an explanation about the selection and use of the sources. The description of the project's methodology complements a list of sources in that it sets forth the organization and interpretation of information emanating from those sources.

Azevedo, L.F. et al. "How to Write a Scientific Paper: Writing the Methods Section." Revista Portuguesa de Pneumologia 17 (2011): 232-238; Blair Lorrie. “Choosing a Methodology.” In Writing a Graduate Thesis or Dissertation , Teaching Writing Series. (Rotterdam: Sense Publishers 2016), pp. 49-72; Butin, Dan W. The Education Dissertation A Guide for Practitioner Scholars . Thousand Oaks, CA: Corwin, 2010; Carter, Susan. Structuring Your Research Thesis . New York: Palgrave Macmillan, 2012; Kallet, Richard H. “How to Write the Methods Section of a Research Paper.” Respiratory Care 49 (October 2004):1229-1232; Lunenburg, Frederick C. Writing a Successful Thesis or Dissertation: Tips and Strategies for Students in the Social and Behavioral Sciences . Thousand Oaks, CA: Corwin Press, 2008. Methods Section. The Writer’s Handbook. Writing Center. University of Wisconsin, Madison; Rudestam, Kjell Erik and Rae R. Newton. “The Method Chapter: Describing Your Research Plan.” In Surviving Your Dissertation: A Comprehensive Guide to Content and Process . (Thousand Oaks, Sage Publications, 2015), pp. 87-115; What is Interpretive Research. Institute of Public and International Affairs, University of Utah; Writing the Experimental Report: Methods, Results, and Discussion. The Writing Lab and The OWL. Purdue University; Methods and Materials. The Structure, Format, Content, and Style of a Journal-Style Scientific Paper. Department of Biology. Bates College.

Writing Tip

Statistical Designs and Tests? Do Not Fear Them!

Don't avoid using a quantitative approach to analyzing your research problem just because you fear the idea of applying statistical designs and tests. A qualitative approach, such as conducting interviews or content analysis of archival texts, can yield exciting new insights about a research problem, but it should not be undertaken simply because you have a disdain for running a simple regression. A well designed quantitative research study can often be accomplished in very clear and direct ways, whereas, a similar study of a qualitative nature usually requires considerable time to analyze large volumes of data and a tremendous burden to create new paths for analysis where previously no path associated with your research problem had existed.

To locate data and statistics, GO HERE .

Another Writing Tip

Knowing the Relationship Between Theories and Methods

There can be multiple meaning associated with the term "theories" and the term "methods" in social sciences research. A helpful way to delineate between them is to understand "theories" as representing different ways of characterizing the social world when you research it and "methods" as representing different ways of generating and analyzing data about that social world. Framed in this way, all empirical social sciences research involves theories and methods, whether they are stated explicitly or not. However, while theories and methods are often related, it is important that, as a researcher, you deliberately separate them in order to avoid your theories playing a disproportionate role in shaping what outcomes your chosen methods produce.

Introspectively engage in an ongoing dialectic between the application of theories and methods to help enable you to use the outcomes from your methods to interrogate and develop new theories, or ways of framing conceptually the research problem. This is how scholarship grows and branches out into new intellectual territory.

Reynolds, R. Larry. Ways of Knowing. Alternative Microeconomics . Part 1, Chapter 3. Boise State University; The Theory-Method Relationship. S-Cool Revision. United Kingdom.

Yet Another Writing Tip

Methods and the Methodology

Do not confuse the terms "methods" and "methodology." As Schneider notes, a method refers to the technical steps taken to do research . Descriptions of methods usually include defining and stating why you have chosen specific techniques to investigate a research problem, followed by an outline of the procedures you used to systematically select, gather, and process the data [remember to always save the interpretation of data for the discussion section of your paper].

The methodology refers to a discussion of the underlying reasoning why particular methods were used . This discussion includes describing the theoretical concepts that inform the choice of methods to be applied, placing the choice of methods within the more general nature of academic work, and reviewing its relevance to examining the research problem. The methodology section also includes a thorough review of the methods other scholars have used to study the topic.

Bryman, Alan. "Of Methods and Methodology." Qualitative Research in Organizations and Management: An International Journal 3 (2008): 159-168; Schneider, Florian. “What's in a Methodology: The Difference between Method, Methodology, and Theory…and How to Get the Balance Right?” PoliticsEastAsia.com. Chinese Department, University of Leiden, Netherlands.

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Task Analysis

What is task analysis.

Task analysis begins by defining any of the user’s problems as scenarios and concludes with creating a task flow that outlines the journey from problem to solution.

For example, when interviewing users who are interested in gardening and the designer realizes the majority of them have the problem of forgetting to water their plants every morning, the designer may include an alarm-feature in the final design to address this problem.

The designer’s goal is to keep the tasks as simple as possible and eliminate any unnecessary steps, keeping the process simple and straightforward.

Literature on Task Analysis

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

Learn more about Task Analysis

Take a deep dive into Task Analysis with our course User Research – Methods and Best Practices .

How do you plan to design a product or service that your users will love , if you don't know what they want in the first place? As a user experience designer, you shouldn't leave it to chance to design something outstanding; you should make the effort to understand your users and build on that knowledge from the outset. User research is the way to do this, and it can therefore be thought of as the largest part of user experience design .

In fact, user research is often the first step of a UX design process—after all, you cannot begin to design a product or service without first understanding what your users want! As you gain the skills required, and learn about the best practices in user research, you’ll get first-hand knowledge of your users and be able to design the optimal product—one that’s truly relevant for your users and, subsequently, outperforms your competitors’ .

This course will give you insights into the most essential qualitative research methods around and will teach you how to put them into practice in your design work. You’ll also have the opportunity to embark on three practical projects where you can apply what you’ve learned to carry out user research in the real world . You’ll learn details about how to plan user research projects and fit them into your own work processes in a way that maximizes the impact your research can have on your designs. On top of that, you’ll gain practice with different methods that will help you analyze the results of your research and communicate your findings to your clients and stakeholders—workshops, user journeys and personas, just to name a few!

By the end of the course, you’ll have not only a Course Certificate but also three case studies to add to your portfolio. And remember, a portfolio with engaging case studies is invaluable if you are looking to break into a career in UX design or user research!

We believe you should learn from the best, so we’ve gathered a team of experts to help teach this course alongside our own course instructors. That means you’ll meet a new instructor in each of the lessons on research methods who is an expert in their field—we hope you enjoy what they have in store for you!

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World Leaders in Research-Based User Experience

Ux research methods: glossary.

September 10, 2023 2023-09-10

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UX research requires knowledge and understanding of many jargon terms. Use this glossary as a reference as you delve into research.

Jump to a definition in the table or review the complete glossary.

Attitudinal Methods

A class of research methods that collects self-reported data about users’ perceptions and attitudes. Attitudinal data is based on “what users say.” Surveys, user interviews, and focus groups are attitudinal methods. Attitudinal methods are often contrasted with behavioral methods, which collect data about user actions and behaviors.

• See also: behavioral methods

A/B test (A/B testing)

An analytics method that involves randomly deploying two different versions of a product to two different user groups in order to identify which works best. The winning version is usually selected based on metrics such as conversion rate or click-through rate. To conduct an A/B test, you will need to install specialized analytics software.

• Related article: Putting A/B Testing in Its Place
• See also: analytics , multivariate testing

A class of research methods that involve collecting real-time usage data for a product. Examples of collected metrics include the number of user visits, the number of clicks on a particular element, percentage of users who took a particular action (e.g., checkout, scroll) on a web page. Analytics research methods are not controlled: the data collected reflects users’ behaviors in their natural environment. Using such methods requires that the product is instrumented with analytics software such as Adobe Analytics or Google Analytics.

• Related article: Three Uses for Analytics in User-Experience Practice
• See also: A/B testing , multivariate testing , clickstream analytics

Behavioral Methods

A class of research methods that collect data reflecting users’ actions and behaviors. Unlike attitudinal methods, which are based on “what users say,” behavioral methods are based on “what users do.” Examples of behavioral methods include usability testing and analytics methods.

• See also: attitudinal methods

Card Sorting

A research method in which study participants group individual labels according to criteria that make sense to them. This method helps designers to group items into categories and create an information architecture of a site or application. Card sorting can be “open” (if the categories are not defined in advance of the study and participants group similar items into clusters) or “closed” (if a predefined set of categories is given to participants and they are asked to assign items to these categories).

• Related article: Card Sorting: Uncover Users' Mental Models for Better Information Architecture
• See also: tree testing

Clickstream Analytics 

An analytics method that involves analyzing the sequence of pages that users visit as they use a site or application. It can provide insights about potential issues, typical navigation routes, and the content that users interact with right before completing key actions on a site or in an application.

• Related article: Understanding User Pathways in Analytics
• See also: analytics

Concept Testing

An attitudinal research method that involves collecting users’ thoughts and attitudes about a product idea (“concept”) in its incipient stages, usually through a qualitative survey. It is used very early in the discovery phase of the design process to understand whether a specific product idea meets users’ needs and expectations.

• See also: survey

Context Methods

A class of research methods that involves capturing the context in which users naturally engage in a specific behavior. The context can be captured directly, by observing users in their natural environment (field studies), or indirectly, by asking them to log context details whenever that behavior occurs (diary studies).

• Related article: Context Methods: Study Guide
• See also: diary study , field study

Contextual Inquiry

A type of field study in which the researcher watches the user as they naturally perform their task in their normal environment (e.g., home, office) and asks for information to understand how and why the user did what they did. Contextual inquiry is a combination of in-depth observation and interview and aims to gain a robust understanding of work practices and behaviors. Contextual inquiry is typically conducted in the early discovery stages of a project.

• Related article: Contextual Inquiry
• See also: field study , context methods

Desirability Study

A research method that involves exposing participants to a product and then asking them to select a few words that describe their reaction to that product out of a list of possible options. This type of method can be done qualitatively or quantitatively, to assess the aesthetic properties of an image and whether they are consistent with a desired brand image.

• Related article: Using the Microsoft Desirability Toolkit to Test Visual Appeal
• See also: five-second test

Diary Study

A research method used to collect self-reported data about user behaviors, activities, and experiences over an extended period that can range from a few days to months. During that period, study participants are asked to keep a diary and log specific information about the activities of interest.

• Related article: Diary Studies
• See also: context methods

Ethnographic Study

A class of qualitative research methods that involves observing users in their natural habitat. In UX, the term is used as a synonym for “field study.” However, in social sciences, ethnographic studies involve immersion in a particular culture or community, to observe the behaviors and rules of that community.

• Related article: Field Studies

Eyetracking

A behavioral research method that involves tracking users’ eye movements as they interact with a product or perform a specific activity, to determine where they focus their attention. Eyetracking studies require special equipment to capture participants’ eye movements. Eyetracking data can be used to understand which design elements attract users’ attention and which are ignored.

• Related article: Text Scanning Patterns: Eyetracking Evidence
• See also: retrospective think aloud

Field Study

A research method that involves observing the users in their natural context. Field studies are a type of context method. Field studies vary in the amount of interaction between the researcher and the participant. Some field studies are purely observational, while others involve active participation from the researcher in the form of interviews or tasks given to the user.

• See also: context methods , diary study , ethnographic study

First-Click Test

A behavioral, task-based research method in which participants are given a specific task before they are exposed to a design and then stopped immediately after their first click on the corresponding screen. This test works best when users have a specific goal in mind the first time they encounter the site. The first-click test is similar to the five-second test in that they are both very quick; however, in the first-click test, participants are likely to ignore most page elements that are not directly related to their goal.

• Related article: How to Test Visual Design

Five-Second Test

An attitudinal research method in which a study participant is shown a design for five seconds and then asked to describe what they saw. A five-second test is meant to gather users’ first reactions to the aesthetic qualities of a design.

• Related video: 5-Second Usability Test
• See also: desirability test

Focus Group

A qualitative, attitudinal research method in which a facilitator conducts a meeting with a group of 6–9 people to discuss their experiences with a product or service. The term “focus” relates to the role of the facilitator, who maintains the group’s focus on certain topics during discussions. Focus groups are used in the early discovery stages of product development to gauge users’ mental models and expectations.

• Related article: Focus Groups
• See also: attitudinal methods , qualitative methods

Formative Method

Any method that focuses on determining which aspects of the design work well or not, and why. Formative evaluations occur throughout a design or redesign and provide information to incrementally improve the interface. Formative methods are often contrasted with summative methods, which are used after a design has been finalized to provide an overall assessment of its usability.

• Related article: Formative vs. Summative Evaluations
• See also: summative method

Moderated Usability Testing

The traditional type of usability testing which involves a facilitator (moderator) and a participant interacting synchronously. The facilitator gives participants tasks to do one at a time and may probe with further questions and clarifications; they can also ask the participant to stop when they consider it necessary.

• Related article: Moderated (Usability) Testing
• See also: usability testing , unmoderated usability testing

Multivariate Test

A design-optimization method in which multiple variants of specified design elements are tested in a user interface, with the goal of maximizing an analytics metric such as conversion. This method determines which combination of the variants results in the highest-performing design. For example, a multivariate test could be used to determine whether a button should be labeled Place Order or Submit and whether it should be blue or red.

• Related article: Multivariate vs. A/B Testing: Incremental vs. Radical Changes
• See also: A/B testing , analytics

Participatory Design

A research method in which one or several users are invited to offer their own solution to a particular design problem. Participants may be provided with some basic building blocks that they could use to create their designs. The resulting designs are not usually implemented, but rather used to get an understanding of users’ needs and expectations.

Paper Prototyping

A type of usability testing that is related to the Wizard of Oz method and in which an early-stage design is shown to the participant on paper. The piece of paper schematically represents the prototype of a specific page. The participant indicates to the facilitator which action(s) they would take on that page in order to complete the assigned task. The facilitator (or another person present in the room who plays the role of the computer) then changes the page to reflect the new state of the system. Paper prototyping is a type of prototype testing.

• Related article: Paper Prototyping: A Cutout Kit
• See also: usability testing, prototype testing , Wizard of Oz

Prototype Testing

A type of usability testing in which the interface being tested is a design prototype rather than a live product. The prototype can be presented to the participant on paper (paper prototyping) or using interactive prototyping software. Prototype testing is used before a design is implemented to identify potential usability issues and fix them, or to explore how alternative design solutions fare with users.

• See also: paper prototyping , usability testing

Qualitative Method

A type of research method that aims to collect observational data about users’ behaviors and interactions. Such data may identify whether particular aspects of the interface are easy or hard to use. Focus groups and user interviews are examples of qualitative methods. Usability testing can also be qualitative when used to uncover issues in a design.

• Related article: Quantitative vs. Qualitative Usability Tests
• See also: qualitative method

Qualitative Usability Testing

A type of usability testing that aims to collect observational data about user behaviors and interactions and that is often used to identify problems in an interface. Qualitative usability testing can be moderated or unmoderated.

• See also: usability testing , qualitative method , quantitative usability testing

Quantitative Method

A type of research method that collects metrics such as success, satisfaction, conversion, task time, or number of user visits. Quantitative methods focus on numbers. Examples of quantitative methods include analytics-based methods and quantitative usability testing. In quantitative usability testing, metrics such as task time and success are gathered in order to assess whether particular tasks are easy to perform.

• See also: quantitative method

Quantitative Usability Testing

A type of usability testing that collects metrics such as task success, user satisfaction, and time on task to understand whether particular tasks were easy to perform. Quantitative usability testing is usually used to assess the overall experience of a product and make inferences about user behavior in the target user population.

• See also: usability testing , quantitative method , qualitative usability testing

Remote Research Method

Any type of research method that involves a facilitator and one or more participants and in which the participant is in a different location than the facilitator. Usability testing, focus groups, user interviews, and contextual inquiries can be done remotely, with various degrees of success.

• See also: usability testing , moderated usability testing , remote moderated usability testing

Remote Moderated Usability Testing

A type of user testing in which the facilitator and the participant are not in the same room and interact through video-conferencing software.

• Related article: Remote Moderated Usability Tests: How to Do Them
• See also: usability testing , moderated usability testing , remote research method

Retrospective Think-Aloud Protocol

A variation of the think-aloud protocol in which participants are asked to explain their behavior immediately after they interact with a product. In a variation of the protocol, participants may be shown a replay of their interaction and asked to explain out loud what they were thinking when they were doing those actions. Retrospective think-aloud is used in situations where thinking out loud would be too distracting for participants (e.g., during complex activities) or would contaminate data (e.g., in eyetracking or quantitative usability testing).

• See also: think aloud , usability testing , eyetracking

Summative Method

A research method that focuses on evaluating the user experience of a product, usually by comparing it with that of a competitor or of one of its own prior versions. Quantitative usability testing is usually used as a summative method.

• See also: formative method

A research method in which a participant responds to multiple-choice or open-ended questions that are presented to them online, on paper, or by phone. Surveys are an attitudinal research method that collects participants’ self-reported perceptions and attitudes. Surveys can be used to collect qualitative or quantitative data.

• Related article: 10 Survey Challenges and How to Avoid Them

Task Analysis

A research method that studies how users perform a specific task: their goals, the different steps they take, the order in which they do them, when and where they do it, and what information they need during the task. Task analysis often involves a mix of interviews and context methods such as contextual inquiry, field studies, or diary studies. It is used to inform the design of complex workflows for a product.

• Related article: Task Analysis: Support Users in Achieving Their Goals
• See also: contextual inquiry , user interview ,  field study , ethnographic study , context methods

Think-Aloud Protocol

The practice of asking a usability-testing participant to “think out loud” as they are interacting with an interface – in other words, to verbalize their actions and thoughts. It is widely used in qualitative usability testing to supplement the behavioral data provided by the participant’s actions.

• Related article: Thinking Aloud: The #1 Usability Tool
• See also: retrospective think-aloud protocol , usability testing

Tree Testing

A task-based research method that evaluates a hierarchical category structure (or tree) by having users find the locations in the tree where specific resources or features can be found. It is an information-architecture method used to assess how well the navigational hierarchy of a site matches users’ expectations.

• Related article: Tree Testing
• See also: card sorting

Unmoderated Usability Testing

A type of usability testing in which there is no facilitator (moderator) and in which the tasks are presented to the participant by special software. Unmoderated testing is usually remote, with the participant testing the software at their location of choice, through an Internet connection. However, it is possible to run unmoderated usability tests in person, by having the participant interact by themselves with the interface of interest.

• Related article: Unmoderated User Tests: How and Why to Do Them
• See also: usability testing , remote moderated usability testing , remote research method

Usability (UX) Benchmarking

A type of summative research method that involves evaluating a product’s user experience by comparing its performance against a competitor or a previous version of the same product. Many UX benchmarking studies are quantitative usability-testing studies in which participants perform a set of top tasks; metrics such as task success, task time, and satisfaction are collected and compared across product versions or competitors.

• Related article: Benchmarking UX: Tracking Metrics
• See also: quantitative usability testing ,  quantitative method , summative method

User Interview

A one-on-one attitudinal research method in which an interviewer asks a participant questions about a topic, listens to their responses, and follows up with further questions to learn more details. User interviews can be used by themselves in discoveries to inform the early stages of product design or can be combined with other methods such as contextual inquiry and usability testing.

• Related article: User Interviews: How, When, and Why to Conduct Them

User Testing

See usability testing

Usability (User) Testing

A research method in which a researcher (called a “facilitator” or a “moderator”) asks a participant to perform tasks, usually using one or more specific user interfaces. While the participant completes each task, the researcher observes the participant’s behavior and listens for feedback. Usability testing can be qualitative or quantitative. Qualitative usability testing is used to identify problems in an interface, whereas quantitative usability testing focuses on collecting metrics that help assess the overall user experience of the product.

• Related article: Usability Testing
• See also: qualitative usability testing , quantitative usability testing , think-aloud protocol , unmoderated usability testing , moderated usability testing

Wizard of Oz

A moderated research method in which a participant interacts with a prototype manned by a human who controls the system responses. This method is used to test early stages of the design, before investing resources in developing it. It is also used to understand users’ expectations, mental models, and vocabularies.

• Related article: The Wizard of Oz Method in UX
• See also: paper prototyping

Is there a method that you think is missing? Please send suggestions at [email protected] and we will consider them for further versions of this glossary.

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Get science-backed answers as you write with Paperpal's Research feature

What is Research Methodology? Definition, Types, and Examples

Research methodology 1,2 is a structured and scientific approach used to collect, analyze, and interpret quantitative or qualitative data to answer research questions or test hypotheses. A research methodology is like a plan for carrying out research and helps keep researchers on track by limiting the scope of the research. Several aspects must be considered before selecting an appropriate research methodology, such as research limitations and ethical concerns that may affect your research.

The research methodology section in a scientific paper describes the different methodological choices made, such as the data collection and analysis methods, and why these choices were selected. The reasons should explain why the methods chosen are the most appropriate to answer the research question. A good research methodology also helps ensure the reliability and validity of the research findings. There are three types of research methodology—quantitative, qualitative, and mixed-method, which can be chosen based on the research objectives.

What is research methodology ?

A research methodology describes the techniques and procedures used to identify and analyze information regarding a specific research topic. It is a process by which researchers design their study so that they can achieve their objectives using the selected research instruments. It includes all the important aspects of research, including research design, data collection methods, data analysis methods, and the overall framework within which the research is conducted. While these points can help you understand what is research methodology, you also need to know why it is important to pick the right methodology.

Why is research methodology important?

Having a good research methodology in place has the following advantages: 3

• Helps other researchers who may want to replicate your research; the explanations will be of benefit to them.
• You can easily answer any questions about your research if they arise at a later stage.
• A research methodology provides a framework and guidelines for researchers to clearly define research questions, hypotheses, and objectives.
• It helps researchers identify the most appropriate research design, sampling technique, and data collection and analysis methods.
• A sound research methodology helps researchers ensure that their findings are valid and reliable and free from biases and errors.
• It also helps ensure that ethical guidelines are followed while conducting research.
• A good research methodology helps researchers in planning their research efficiently, by ensuring optimum usage of their time and resources.

Writing the methods section of a research paper? Let Paperpal help you achieve perfection

Types of research methodology.

There are three types of research methodology based on the type of research and the data required. 1

• Quantitative research methodology focuses on measuring and testing numerical data. This approach is good for reaching a large number of people in a short amount of time. This type of research helps in testing the causal relationships between variables, making predictions, and generalizing results to wider populations.
• Qualitative research methodology examines the opinions, behaviors, and experiences of people. It collects and analyzes words and textual data. This research methodology requires fewer participants but is still more time consuming because the time spent per participant is quite large. This method is used in exploratory research where the research problem being investigated is not clearly defined.
• Mixed-method research methodology uses the characteristics of both quantitative and qualitative research methodologies in the same study. This method allows researchers to validate their findings, verify if the results observed using both methods are complementary, and explain any unexpected results obtained from one method by using the other method.

What are the types of sampling designs in research methodology?

Sampling 4 is an important part of a research methodology and involves selecting a representative sample of the population to conduct the study, making statistical inferences about them, and estimating the characteristics of the whole population based on these inferences. There are two types of sampling designs in research methodology—probability and nonprobability.

• Probability sampling

In this type of sampling design, a sample is chosen from a larger population using some form of random selection, that is, every member of the population has an equal chance of being selected. The different types of probability sampling are:

• Systematic —sample members are chosen at regular intervals. It requires selecting a starting point for the sample and sample size determination that can be repeated at regular intervals. This type of sampling method has a predefined range; hence, it is the least time consuming.
• Stratified —researchers divide the population into smaller groups that don’t overlap but represent the entire population. While sampling, these groups can be organized, and then a sample can be drawn from each group separately.
• Cluster —the population is divided into clusters based on demographic parameters like age, sex, location, etc.
• Convenience —selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.
• Purposive —participants are selected at the researcher’s discretion. Researchers consider the purpose of the study and the understanding of the target audience.
• Snowball —already selected participants use their social networks to refer the researcher to other potential participants.
• Quota —while designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.

What are data collection methods?

During research, data are collected using various methods depending on the research methodology being followed and the research methods being undertaken. Both qualitative and quantitative research have different data collection methods, as listed below.

Qualitative research 5

• One-on-one interviews: Helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event
• Document study/literature review/record keeping: Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.
• Focus groups: Constructive discussions that usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic.
• Qualitative observation : Researchers collect data using their five senses (sight, smell, touch, taste, and hearing).

Quantitative research 6

• Sampling: The most common type is probability sampling.
• Interviews: Commonly telephonic or done in-person.
• Observations: Structured observations are most commonly used in quantitative research. In this method, researchers make observations about specific behaviors of individuals in a structured setting.
• Document review: Reviewing existing research or documents to collect evidence for supporting the research.
• Surveys and questionnaires. Surveys can be administered both online and offline depending on the requirement and sample size.

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What are data analysis methods.

The data collected using the various methods for qualitative and quantitative research need to be analyzed to generate meaningful conclusions. These data analysis methods 7 also differ between quantitative and qualitative research.

Quantitative research involves a deductive method for data analysis where hypotheses are developed at the beginning of the research and precise measurement is required. The methods include statistical analysis applications to analyze numerical data and are grouped into two categories—descriptive and inferential.

Descriptive analysis is used to describe the basic features of different types of data to present it in a way that ensures the patterns become meaningful. The different types of descriptive analysis methods are:

• Measures of frequency (count, percent, frequency)
• Measures of central tendency (mean, median, mode)
• Measures of dispersion or variation (range, variance, standard deviation)
• Measure of position (percentile ranks, quartile ranks)

Inferential analysis is used to make predictions about a larger population based on the analysis of the data collected from a smaller population. This analysis is used to study the relationships between different variables. Some commonly used inferential data analysis methods are:

• Correlation: To understand the relationship between two or more variables.
• Cross-tabulation: Analyze the relationship between multiple variables.
• Regression analysis: Study the impact of independent variables on the dependent variable.
• Frequency tables: To understand the frequency of data.
• Analysis of variance: To test the degree to which two or more variables differ in an experiment.

Qualitative research involves an inductive method for data analysis where hypotheses are developed after data collection. The methods include:

• Content analysis: For analyzing documented information from text and images by determining the presence of certain words or concepts in texts.
• Narrative analysis: For analyzing content obtained from sources such as interviews, field observations, and surveys. The stories and opinions shared by people are used to answer research questions.
• Discourse analysis: For analyzing interactions with people considering the social context, that is, the lifestyle and environment, under which the interaction occurs.
• Grounded theory: Involves hypothesis creation by data collection and analysis to explain why a phenomenon occurred.
• Thematic analysis: To identify important themes or patterns in data and use these to address an issue.

How to choose a research methodology?

Here are some important factors to consider when choosing a research methodology: 8

• Research objectives, aims, and questions —these would help structure the research design.
• Review existing literature to identify any gaps in knowledge.
• Check the statistical requirements —if data-driven or statistical results are needed then quantitative research is the best. If the research questions can be answered based on people’s opinions and perceptions, then qualitative research is most suitable.
• Sample size —sample size can often determine the feasibility of a research methodology. For a large sample, less effort- and time-intensive methods are appropriate.
• Constraints —constraints of time, geography, and resources can help define the appropriate methodology.

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How to write a research methodology .

A research methodology should include the following components: 3,9

• Research design —should be selected based on the research question and the data required. Common research designs include experimental, quasi-experimental, correlational, descriptive, and exploratory.
• Research method —this can be quantitative, qualitative, or mixed-method.
• Reason for selecting a specific methodology —explain why this methodology is the most suitable to answer your research problem.
• Research instruments —explain the research instruments you plan to use, mainly referring to the data collection methods such as interviews, surveys, etc. Here as well, a reason should be mentioned for selecting the particular instrument.
• Sampling —this involves selecting a representative subset of the population being studied.
• Data collection —involves gathering data using several data collection methods, such as surveys, interviews, etc.
• Data analysis —describe the data analysis methods you will use once you’ve collected the data.
• Research limitations —mention any limitations you foresee while conducting your research.
• Validity and reliability —validity helps identify the accuracy and truthfulness of the findings; reliability refers to the consistency and stability of the results over time and across different conditions.
• Ethical considerations —research should be conducted ethically. The considerations include obtaining consent from participants, maintaining confidentiality, and addressing conflicts of interest.

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Frequently Asked Questions

Q1. What are the key components of research methodology?

A1. A good research methodology has the following key components:

• Research design
• Data collection procedures
• Data analysis methods
• Ethical considerations

Q2. Why is ethical consideration important in research methodology?

A2. Ethical consideration is important in research methodology to ensure the readers of the reliability and validity of the study. Researchers must clearly mention the ethical norms and standards followed during the conduct of the research and also mention if the research has been cleared by any institutional board. The following 10 points are the important principles related to ethical considerations: 10

• Participants should not be subjected to harm.
• Respect for the dignity of participants should be prioritized.
• Full consent should be obtained from participants before the study.
• Participants’ privacy should be ensured.
• Confidentiality of the research data should be ensured.
• Anonymity of individuals and organizations participating in the research should be maintained.
• The aims and objectives of the research should not be exaggerated.
• Affiliations, sources of funding, and any possible conflicts of interest should be declared.
• Communication in relation to the research should be honest and transparent.
• Misleading information and biased representation of primary data findings should be avoided.

Q3. What is the difference between methodology and method?

A3. Research methodology is different from a research method, although both terms are often confused. Research methods are the tools used to gather data, while the research methodology provides a framework for how research is planned, conducted, and analyzed. The latter guides researchers in making decisions about the most appropriate methods for their research. Research methods refer to the specific techniques, procedures, and tools used by researchers to collect, analyze, and interpret data, for instance surveys, questionnaires, interviews, etc.

Research methodology is, thus, an integral part of a research study. It helps ensure that you stay on track to meet your research objectives and answer your research questions using the most appropriate data collection and analysis tools based on your research design.

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• Research methodologies. Pfeiffer Library website. Accessed August 15, 2023. https://library.tiffin.edu/researchmethodologies/whatareresearchmethodologies
• Types of research methodology. Eduvoice website. Accessed August 16, 2023. https://eduvoice.in/types-research-methodology/
• The basics of research methodology: A key to quality research. Voxco. Accessed August 16, 2023. https://www.voxco.com/blog/what-is-research-methodology/
• Sampling methods: Types with examples. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/types-of-sampling-for-social-research/
• What is qualitative research? Methods, types, approaches, examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-qualitative-research-methods-types-examples/
• What is quantitative research? Definition, methods, types, and examples. Researcher.Life blog. Accessed August 15, 2023. https://researcher.life/blog/article/what-is-quantitative-research-types-and-examples/
• Data analysis in research: Types & methods. QuestionPro website. Accessed August 16, 2023. https://www.questionpro.com/blog/data-analysis-in-research/#Data_analysis_in_qualitative_research
• Factors to consider while choosing the right research methodology. PhD Monster website. Accessed August 17, 2023. https://www.phdmonster.com/factors-to-consider-while-choosing-the-right-research-methodology/
• What is research methodology? Research and writing guides. Accessed August 14, 2023. https://paperpile.com/g/what-is-research-methodology/
• Ethical considerations. Business research methodology website. Accessed August 17, 2023. https://research-methodology.net/research-methodology/ethical-considerations/

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Home » Research Methodology – Types, Examples and writing Guide

Research Methodology – Types, Examples and writing Guide

Table of Contents

Research Methodology

Definition:

Research Methodology refers to the systematic and scientific approach used to conduct research, investigate problems, and gather data and information for a specific purpose. It involves the techniques and procedures used to identify, collect , analyze , and interpret data to answer research questions or solve research problems . Moreover, They are philosophical and theoretical frameworks that guide the research process.

Structure of Research Methodology

Research methodology formats can vary depending on the specific requirements of the research project, but the following is a basic example of a structure for a research methodology section:

I. Introduction

• Provide an overview of the research problem and the need for a research methodology section
• Outline the main research questions and objectives

II. Research Design

• Explain the research design chosen and why it is appropriate for the research question(s) and objectives
• Discuss any alternative research designs considered and why they were not chosen
• Describe the research setting and participants (if applicable)

III. Data Collection Methods

• Describe the methods used to collect data (e.g., surveys, interviews, observations)
• Explain how the data collection methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or instruments used for data collection

IV. Data Analysis Methods

• Describe the methods used to analyze the data (e.g., statistical analysis, content analysis )
• Explain how the data analysis methods were chosen and why they are appropriate for the research question(s) and objectives
• Detail any procedures or software used for data analysis

V. Ethical Considerations

• Discuss any ethical issues that may arise from the research and how they were addressed
• Explain how informed consent was obtained (if applicable)
• Detail any measures taken to ensure confidentiality and anonymity

VI. Limitations

• Identify any potential limitations of the research methodology and how they may impact the results and conclusions

VII. Conclusion

• Summarize the key aspects of the research methodology section
• Explain how the research methodology addresses the research question(s) and objectives

Research Methodology Types

Types of Research Methodology are as follows:

Quantitative Research Methodology

This is a research methodology that involves the collection and analysis of numerical data using statistical methods. This type of research is often used to study cause-and-effect relationships and to make predictions.

Qualitative Research Methodology

This is a research methodology that involves the collection and analysis of non-numerical data such as words, images, and observations. This type of research is often used to explore complex phenomena, to gain an in-depth understanding of a particular topic, and to generate hypotheses.

Mixed-Methods Research Methodology

This is a research methodology that combines elements of both quantitative and qualitative research. This approach can be particularly useful for studies that aim to explore complex phenomena and to provide a more comprehensive understanding of a particular topic.

Case Study Research Methodology

This is a research methodology that involves in-depth examination of a single case or a small number of cases. Case studies are often used in psychology, sociology, and anthropology to gain a detailed understanding of a particular individual or group.

Action Research Methodology

This is a research methodology that involves a collaborative process between researchers and practitioners to identify and solve real-world problems. Action research is often used in education, healthcare, and social work.

Experimental Research Methodology

This is a research methodology that involves the manipulation of one or more independent variables to observe their effects on a dependent variable. Experimental research is often used to study cause-and-effect relationships and to make predictions.

Survey Research Methodology

This is a research methodology that involves the collection of data from a sample of individuals using questionnaires or interviews. Survey research is often used to study attitudes, opinions, and behaviors.

Grounded Theory Research Methodology

This is a research methodology that involves the development of theories based on the data collected during the research process. Grounded theory is often used in sociology and anthropology to generate theories about social phenomena.

Research Methodology Example

An Example of Research Methodology could be the following:

Research Methodology for Investigating the Effectiveness of Cognitive Behavioral Therapy in Reducing Symptoms of Depression in Adults

Introduction:

The aim of this research is to investigate the effectiveness of cognitive-behavioral therapy (CBT) in reducing symptoms of depression in adults. To achieve this objective, a randomized controlled trial (RCT) will be conducted using a mixed-methods approach.

Research Design:

The study will follow a pre-test and post-test design with two groups: an experimental group receiving CBT and a control group receiving no intervention. The study will also include a qualitative component, in which semi-structured interviews will be conducted with a subset of participants to explore their experiences of receiving CBT.

Participants:

Participants will be recruited from community mental health clinics in the local area. The sample will consist of 100 adults aged 18-65 years old who meet the diagnostic criteria for major depressive disorder. Participants will be randomly assigned to either the experimental group or the control group.

Intervention :

The experimental group will receive 12 weekly sessions of CBT, each lasting 60 minutes. The intervention will be delivered by licensed mental health professionals who have been trained in CBT. The control group will receive no intervention during the study period.

Data Collection:

Quantitative data will be collected through the use of standardized measures such as the Beck Depression Inventory-II (BDI-II) and the Generalized Anxiety Disorder-7 (GAD-7). Data will be collected at baseline, immediately after the intervention, and at a 3-month follow-up. Qualitative data will be collected through semi-structured interviews with a subset of participants from the experimental group. The interviews will be conducted at the end of the intervention period, and will explore participants’ experiences of receiving CBT.

Data Analysis:

Quantitative data will be analyzed using descriptive statistics, t-tests, and mixed-model analyses of variance (ANOVA) to assess the effectiveness of the intervention. Qualitative data will be analyzed using thematic analysis to identify common themes and patterns in participants’ experiences of receiving CBT.

Ethical Considerations:

This study will comply with ethical guidelines for research involving human subjects. Participants will provide informed consent before participating in the study, and their privacy and confidentiality will be protected throughout the study. Any adverse events or reactions will be reported and managed appropriately.

Data Management:

All data collected will be kept confidential and stored securely using password-protected databases. Identifying information will be removed from qualitative data transcripts to ensure participants’ anonymity.

Limitations:

One potential limitation of this study is that it only focuses on one type of psychotherapy, CBT, and may not generalize to other types of therapy or interventions. Another limitation is that the study will only include participants from community mental health clinics, which may not be representative of the general population.

Conclusion:

This research aims to investigate the effectiveness of CBT in reducing symptoms of depression in adults. By using a randomized controlled trial and a mixed-methods approach, the study will provide valuable insights into the mechanisms underlying the relationship between CBT and depression. The results of this study will have important implications for the development of effective treatments for depression in clinical settings.

How to Write Research Methodology

Writing a research methodology involves explaining the methods and techniques you used to conduct research, collect data, and analyze results. It’s an essential section of any research paper or thesis, as it helps readers understand the validity and reliability of your findings. Here are the steps to write a research methodology:

• Start by explaining your research question: Begin the methodology section by restating your research question and explaining why it’s important. This helps readers understand the purpose of your research and the rationale behind your methods.
• Describe your research design: Explain the overall approach you used to conduct research. This could be a qualitative or quantitative research design, experimental or non-experimental, case study or survey, etc. Discuss the advantages and limitations of the chosen design.
• Discuss your sample: Describe the participants or subjects you included in your study. Include details such as their demographics, sampling method, sample size, and any exclusion criteria used.
• Describe your data collection methods : Explain how you collected data from your participants. This could include surveys, interviews, observations, questionnaires, or experiments. Include details on how you obtained informed consent, how you administered the tools, and how you minimized the risk of bias.
• Explain your data analysis techniques: Describe the methods you used to analyze the data you collected. This could include statistical analysis, content analysis, thematic analysis, or discourse analysis. Explain how you dealt with missing data, outliers, and any other issues that arose during the analysis.
• Discuss the validity and reliability of your research : Explain how you ensured the validity and reliability of your study. This could include measures such as triangulation, member checking, peer review, or inter-coder reliability.
• Acknowledge any limitations of your research: Discuss any limitations of your study, including any potential threats to validity or generalizability. This helps readers understand the scope of your findings and how they might apply to other contexts.
• Provide a summary: End the methodology section by summarizing the methods and techniques you used to conduct your research. This provides a clear overview of your research methodology and helps readers understand the process you followed to arrive at your findings.

When to Write Research Methodology

Research methodology is typically written after the research proposal has been approved and before the actual research is conducted. It should be written prior to data collection and analysis, as it provides a clear roadmap for the research project.

The research methodology is an important section of any research paper or thesis, as it describes the methods and procedures that will be used to conduct the research. It should include details about the research design, data collection methods, data analysis techniques, and any ethical considerations.

The methodology should be written in a clear and concise manner, and it should be based on established research practices and standards. It is important to provide enough detail so that the reader can understand how the research was conducted and evaluate the validity of the results.

Applications of Research Methodology

Here are some of the applications of research methodology:

• To identify the research problem: Research methodology is used to identify the research problem, which is the first step in conducting any research.
• To design the research: Research methodology helps in designing the research by selecting the appropriate research method, research design, and sampling technique.
• To collect data: Research methodology provides a systematic approach to collect data from primary and secondary sources.
• To analyze data: Research methodology helps in analyzing the collected data using various statistical and non-statistical techniques.
• To test hypotheses: Research methodology provides a framework for testing hypotheses and drawing conclusions based on the analysis of data.
• To generalize findings: Research methodology helps in generalizing the findings of the research to the target population.
• To develop theories : Research methodology is used to develop new theories and modify existing theories based on the findings of the research.
• To evaluate programs and policies : Research methodology is used to evaluate the effectiveness of programs and policies by collecting data and analyzing it.
• To improve decision-making: Research methodology helps in making informed decisions by providing reliable and valid data.

Purpose of Research Methodology

Research methodology serves several important purposes, including:

• To guide the research process: Research methodology provides a systematic framework for conducting research. It helps researchers to plan their research, define their research questions, and select appropriate methods and techniques for collecting and analyzing data.
• To ensure research quality: Research methodology helps researchers to ensure that their research is rigorous, reliable, and valid. It provides guidelines for minimizing bias and error in data collection and analysis, and for ensuring that research findings are accurate and trustworthy.
• To replicate research: Research methodology provides a clear and detailed account of the research process, making it possible for other researchers to replicate the study and verify its findings.
• To advance knowledge: Research methodology enables researchers to generate new knowledge and to contribute to the body of knowledge in their field. It provides a means for testing hypotheses, exploring new ideas, and discovering new insights.
• To inform decision-making: Research methodology provides evidence-based information that can inform policy and decision-making in a variety of fields, including medicine, public health, education, and business.

Advantages of Research Methodology

Research methodology has several advantages that make it a valuable tool for conducting research in various fields. Here are some of the key advantages of research methodology:

• Systematic and structured approach : Research methodology provides a systematic and structured approach to conducting research, which ensures that the research is conducted in a rigorous and comprehensive manner.
• Objectivity : Research methodology aims to ensure objectivity in the research process, which means that the research findings are based on evidence and not influenced by personal bias or subjective opinions.
• Replicability : Research methodology ensures that research can be replicated by other researchers, which is essential for validating research findings and ensuring their accuracy.
• Reliability : Research methodology aims to ensure that the research findings are reliable, which means that they are consistent and can be depended upon.
• Validity : Research methodology ensures that the research findings are valid, which means that they accurately reflect the research question or hypothesis being tested.
• Efficiency : Research methodology provides a structured and efficient way of conducting research, which helps to save time and resources.
• Flexibility : Research methodology allows researchers to choose the most appropriate research methods and techniques based on the research question, data availability, and other relevant factors.
• Scope for innovation: Research methodology provides scope for innovation and creativity in designing research studies and developing new research techniques.

Research Methodology Vs Research Methods

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Research Methods In Psychology

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Research methods in psychology are systematic procedures used to observe, describe, predict, and explain behavior and mental processes. They include experiments, surveys, case studies, and naturalistic observations, ensuring data collection is objective and reliable to understand and explain psychological phenomena.

Hypotheses are statements about the prediction of the results, that can be verified or disproved by some investigation.

There are four types of hypotheses :

• Null Hypotheses (H0 ) – these predict that no difference will be found in the results between the conditions. Typically these are written ‘There will be no difference…’
• Alternative Hypotheses (Ha or H1) – these predict that there will be a significant difference in the results between the two conditions. This is also known as the experimental hypothesis.
• One-tailed (directional) hypotheses – these state the specific direction the researcher expects the results to move in, e.g. higher, lower, more, less. In a correlation study, the predicted direction of the correlation can be either positive or negative.
• Two-tailed (non-directional) hypotheses – these state that a difference will be found between the conditions of the independent variable but does not state the direction of a difference or relationship. Typically these are always written ‘There will be a difference ….’

All research has an alternative hypothesis (either a one-tailed or two-tailed) and a corresponding null hypothesis.

Once the research is conducted and results are found, psychologists must accept one hypothesis and reject the other. 

So, if a difference is found, the Psychologist would accept the alternative hypothesis and reject the null.  The opposite applies if no difference is found.

Sampling techniques

Sampling is the process of selecting a representative group from the population under study.

A sample is the participants you select from a target population (the group you are interested in) to make generalizations about.

Representative means the extent to which a sample mirrors a researcher’s target population and reflects its characteristics.

Generalisability means the extent to which their findings can be applied to the larger population of which their sample was a part.

• Volunteer sample : where participants pick themselves through newspaper adverts, noticeboards or online.
• Opportunity sampling : also known as convenience sampling , uses people who are available at the time the study is carried out and willing to take part. It is based on convenience.
• Random sampling : when every person in the target population has an equal chance of being selected. An example of random sampling would be picking names out of a hat.
• Systematic sampling : when a system is used to select participants. Picking every Nth person from all possible participants. N = the number of people in the research population / the number of people needed for the sample.
• Stratified sampling : when you identify the subgroups and select participants in proportion to their occurrences.
• Snowball sampling : when researchers find a few participants, and then ask them to find participants themselves and so on.
• Quota sampling : when researchers will be told to ensure the sample fits certain quotas, for example they might be told to find 90 participants, with 30 of them being unemployed.

Experiments always have an independent and dependent variable .

• The independent variable is the one the experimenter manipulates (the thing that changes between the conditions the participants are placed into). It is assumed to have a direct effect on the dependent variable.
• The dependent variable is the thing being measured, or the results of the experiment.

Operationalization of variables means making them measurable/quantifiable. We must use operationalization to ensure that variables are in a form that can be easily tested.

For instance, we can’t really measure ‘happiness’, but we can measure how many times a person smiles within a two-hour period. 

By operationalizing variables, we make it easy for someone else to replicate our research. Remember, this is important because we can check if our findings are reliable.

Extraneous variables are all variables which are not independent variable but could affect the results of the experiment.

It can be a natural characteristic of the participant, such as intelligence levels, gender, or age for example, or it could be a situational feature of the environment such as lighting or noise.

Demand characteristics are a type of extraneous variable that occurs if the participants work out the aims of the research study, they may begin to behave in a certain way.

For example, in Milgram’s research , critics argued that participants worked out that the shocks were not real and they administered them as they thought this was what was required of them. 

Extraneous variables must be controlled so that they do not affect (confound) the results.

Randomly allocating participants to their conditions or using a matched pairs experimental design can help to reduce participant variables. 

Situational variables are controlled by using standardized procedures, ensuring every participant in a given condition is treated in the same way

Experimental Design

Experimental design refers to how participants are allocated to each condition of the independent variable, such as a control or experimental group.

• Independent design ( between-groups design ): each participant is selected for only one group. With the independent design, the most common way of deciding which participants go into which group is by means of randomization. 
• Matched participants design : each participant is selected for only one group, but the participants in the two groups are matched for some relevant factor or factors (e.g. ability; sex; age).
• Repeated measures design ( within groups) : each participant appears in both groups, so that there are exactly the same participants in each group.
• The main problem with the repeated measures design is that there may well be order effects. Their experiences during the experiment may change the participants in various ways.
• They may perform better when they appear in the second group because they have gained useful information about the experiment or about the task. On the other hand, they may perform less well on the second occasion because of tiredness or boredom.
• Counterbalancing is the best way of preventing order effects from disrupting the findings of an experiment, and involves ensuring that each condition is equally likely to be used first and second by the participants.

If we wish to compare two groups with respect to a given independent variable, it is essential to make sure that the two groups do not differ in any other important way. 

Experimental Methods

All experimental methods involve an iv (independent variable) and dv (dependent variable)..

• Field experiments are conducted in the everyday (natural) environment of the participants. The experimenter still manipulates the IV, but in a real-life setting. It may be possible to control extraneous variables, though such control is more difficult than in a lab experiment.
• Natural experiments are when a naturally occurring IV is investigated that isn’t deliberately manipulated, it exists anyway. Participants are not randomly allocated, and the natural event may only occur rarely.

Case studies are in-depth investigations of a person, group, event, or community. It uses information from a range of sources, such as from the person concerned and also from their family and friends.

Many techniques may be used such as interviews, psychological tests, observations and experiments. Case studies are generally longitudinal: in other words, they follow the individual or group over an extended period of time. 

Case studies are widely used in psychology and among the best-known ones carried out were by Sigmund Freud . He conducted very detailed investigations into the private lives of his patients in an attempt to both understand and help them overcome their illnesses.

Case studies provide rich qualitative data and have high levels of ecological validity. However, it is difficult to generalize from individual cases as each one has unique characteristics.

Correlational Studies

Correlation means association; it is a measure of the extent to which two variables are related. One of the variables can be regarded as the predictor variable with the other one as the outcome variable.

Correlational studies typically involve obtaining two different measures from a group of participants, and then assessing the degree of association between the measures. 

The predictor variable can be seen as occurring before the outcome variable in some sense. It is called the predictor variable, because it forms the basis for predicting the value of the outcome variable.

Relationships between variables can be displayed on a graph or as a numerical score called a correlation coefficient.

• If an increase in one variable tends to be associated with an increase in the other, then this is known as a positive correlation .
• If an increase in one variable tends to be associated with a decrease in the other, then this is known as a negative correlation .
• A zero correlation occurs when there is no relationship between variables.

After looking at the scattergraph, if we want to be sure that a significant relationship does exist between the two variables, a statistical test of correlation can be conducted, such as Spearman’s rho.

The test will give us a score, called a correlation coefficient . This is a value between 0 and 1, and the closer to 1 the score is, the stronger the relationship between the variables. This value can be both positive e.g. 0.63, or negative -0.63.

A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable. A correlation only shows if there is a relationship between variables.

Correlation does not always prove causation, as a third variable may be involved. 

Interview Methods

Interviews are commonly divided into two types: structured and unstructured.

A fixed, predetermined set of questions is put to every participant in the same order and in the same way. 

Responses are recorded on a questionnaire, and the researcher presets the order and wording of questions, and sometimes the range of alternative answers.

The interviewer stays within their role and maintains social distance from the interviewee.

There are no set questions, and the participant can raise whatever topics he/she feels are relevant and ask them in their own way. Questions are posed about participants’ answers to the subject

Unstructured interviews are most useful in qualitative research to analyze attitudes and values.

Though they rarely provide a valid basis for generalization, their main advantage is that they enable the researcher to probe social actors’ subjective point of view. 

Questionnaire Method

Questionnaires can be thought of as a kind of written interview. They can be carried out face to face, by telephone, or post.

The choice of questions is important because of the need to avoid bias or ambiguity in the questions, ‘leading’ the respondent or causing offense.

• Open questions are designed to encourage a full, meaningful answer using the subject’s own knowledge and feelings. They provide insights into feelings, opinions, and understanding. Example: “How do you feel about that situation?”
• Closed questions can be answered with a simple “yes” or “no” or specific information, limiting the depth of response. They are useful for gathering specific facts or confirming details. Example: “Do you feel anxious in crowds?”

Its other practical advantages are that it is cheaper than face-to-face interviews and can be used to contact many respondents scattered over a wide area relatively quickly.

Observations

There are different types of observation methods :

• Covert observation is where the researcher doesn’t tell the participants they are being observed until after the study is complete. There could be ethical problems or deception and consent with this particular observation method.
• Overt observation is where a researcher tells the participants they are being observed and what they are being observed for.
• Controlled : behavior is observed under controlled laboratory conditions (e.g., Bandura’s Bobo doll study).
• Natural : Here, spontaneous behavior is recorded in a natural setting.
• Participant : Here, the observer has direct contact with the group of people they are observing. The researcher becomes a member of the group they are researching.  
• Non-participant (aka “fly on the wall): The researcher does not have direct contact with the people being observed. The observation of participants’ behavior is from a distance

Pilot Study

A pilot  study is a small scale preliminary study conducted in order to evaluate the feasibility of the key s teps in a future, full-scale project.

A pilot study is an initial run-through of the procedures to be used in an investigation; it involves selecting a few people and trying out the study on them. It is possible to save time, and in some cases, money, by identifying any flaws in the procedures designed by the researcher.

A pilot study can help the researcher spot any ambiguities (i.e. unusual things) or confusion in the information given to participants or problems with the task devised.

Sometimes the task is too hard, and the researcher may get a floor effect, because none of the participants can score at all or can complete the task – all performances are low.

The opposite effect is a ceiling effect, when the task is so easy that all achieve virtually full marks or top performances and are “hitting the ceiling”.

Research Design

In cross-sectional research , a researcher compares multiple segments of the population at the same time

Sometimes, we want to see how people change over time, as in studies of human development and lifespan. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time.

In cohort studies , the participants must share a common factor or characteristic such as age, demographic, or occupation. A cohort study is a type of longitudinal study in which researchers monitor and observe a chosen population over an extended period.

Triangulation means using more than one research method to improve the study’s validity.

Reliability

Reliability is a measure of consistency, if a particular measurement is repeated and the same result is obtained then it is described as being reliable.

• Test-retest reliability :  assessing the same person on two different occasions which shows the extent to which the test produces the same answers.
• Inter-observer reliability : the extent to which there is an agreement between two or more observers.

Meta-Analysis

A meta-analysis is a systematic review that involves identifying an aim and then searching for research studies that have addressed similar aims/hypotheses.

This is done by looking through various databases, and then decisions are made about what studies are to be included/excluded.

Strengths: Increases the conclusions’ validity as they’re based on a wider range.

Weaknesses: Research designs in studies can vary, so they are not truly comparable.

Peer Review

A researcher submits an article to a journal. The choice of the journal may be determined by the journal’s audience or prestige.

The journal selects two or more appropriate experts (psychologists working in a similar field) to peer review the article without payment. The peer reviewers assess: the methods and designs used, originality of the findings, the validity of the original research findings and its content, structure and language.

Feedback from the reviewer determines whether the article is accepted. The article may be: Accepted as it is, accepted with revisions, sent back to the author to revise and re-submit or rejected without the possibility of submission.

The editor makes the final decision whether to accept or reject the research report based on the reviewers comments/ recommendations.

Peer review is important because it prevent faulty data from entering the public domain, it provides a way of checking the validity of findings and the quality of the methodology and is used to assess the research rating of university departments.

Peer reviews may be an ideal, whereas in practice there are lots of problems. For example, it slows publication down and may prevent unusual, new work being published. Some reviewers might use it as an opportunity to prevent competing researchers from publishing work.

Some people doubt whether peer review can really prevent the publication of fraudulent research.

The advent of the internet means that a lot of research and academic comment is being published without official peer reviews than before, though systems are evolving on the internet where everyone really has a chance to offer their opinions and police the quality of research.

Types of Data

• Quantitative data is numerical data e.g. reaction time or number of mistakes. It represents how much or how long, how many there are of something. A tally of behavioral categories and closed questions in a questionnaire collect quantitative data.
• Qualitative data is virtually any type of information that can be observed and recorded that is not numerical in nature and can be in the form of written or verbal communication. Open questions in questionnaires and accounts from observational studies collect qualitative data.
• Primary data is first-hand data collected for the purpose of the investigation.
• Secondary data is information that has been collected by someone other than the person who is conducting the research e.g. taken from journals, books or articles.

Validity means how well a piece of research actually measures what it sets out to, or how well it reflects the reality it claims to represent.

Validity is whether the observed effect is genuine and represents what is actually out there in the world.

• Concurrent validity is the extent to which a psychological measure relates to an existing similar measure and obtains close results. For example, a new intelligence test compared to an established test.
• Face validity : does the test measure what it’s supposed to measure ‘on the face of it’. This is done by ‘eyeballing’ the measuring or by passing it to an expert to check.
• Ecological validit y is the extent to which findings from a research study can be generalized to other settings / real life.
• Temporal validity is the extent to which findings from a research study can be generalized to other historical times.

Features of Science

• Paradigm – A set of shared assumptions and agreed methods within a scientific discipline.
• Paradigm shift – The result of the scientific revolution: a significant change in the dominant unifying theory within a scientific discipline.
• Objectivity – When all sources of personal bias are minimised so not to distort or influence the research process.
• Empirical method – Scientific approaches that are based on the gathering of evidence through direct observation and experience.
• Replicability – The extent to which scientific procedures and findings can be repeated by other researchers.
• Falsifiability – The principle that a theory cannot be considered scientific unless it admits the possibility of being proved untrue.

Statistical Testing

A significant result is one where there is a low probability that chance factors were responsible for any observed difference, correlation, or association in the variables tested.

If our test is significant, we can reject our null hypothesis and accept our alternative hypothesis.

If our test is not significant, we can accept our null hypothesis and reject our alternative hypothesis. A null hypothesis is a statement of no effect.

In Psychology, we use p < 0.05 (as it strikes a balance between making a type I and II error) but p < 0.01 is used in tests that could cause harm like introducing a new drug.

A type I error is when the null hypothesis is rejected when it should have been accepted (happens when a lenient significance level is used, an error of optimism).

A type II error is when the null hypothesis is accepted when it should have been rejected (happens when a stringent significance level is used, an error of pessimism).

Ethical Issues

• Informed consent is when participants are able to make an informed judgment about whether to take part. It causes them to guess the aims of the study and change their behavior.
• To deal with it, we can gain presumptive consent or ask them to formally indicate their agreement to participate but it may invalidate the purpose of the study and it is not guaranteed that the participants would understand.
• Deception should only be used when it is approved by an ethics committee, as it involves deliberately misleading or withholding information. Participants should be fully debriefed after the study but debriefing can’t turn the clock back.
• All participants should be informed at the beginning that they have the right to withdraw if they ever feel distressed or uncomfortable.
• It causes bias as the ones that stayed are obedient and some may not withdraw as they may have been given incentives or feel like they’re spoiling the study. Researchers can offer the right to withdraw data after participation.
• Participants should all have protection from harm . The researcher should avoid risks greater than those experienced in everyday life and they should stop the study if any harm is suspected. However, the harm may not be apparent at the time of the study.
• Confidentiality concerns the communication of personal information. The researchers should not record any names but use numbers or false names though it may not be possible as it is sometimes possible to work out who the researchers were.

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What Is Internal Validity In Research?

Research Methodology , Statistics

What Is Face Validity In Research? Importance & How To Measure

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• Interaction, Feedback and Task Research in Second Language Learning

Interaction, Feedback and Task Research in Second Language Learning

Methods and design.

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The role of interaction and corrective feedback is central to research in second language learning and teaching, and this volume is the first of its kind to explain and apply design methodologies and materials in an approachable way. Using examples from interaction, feedback and task studies, it presents clear and practical advice on how to carry out research in these areas, providing step-by step guides to design and methodological principles, suggestions for reading, short activities, memory aids and an A-Z glossary for easy reference. Its informative approach to study design, and in-depth discussions of implementing research methodology, make it accessible to novice and experienced researchers alike. Commonly used tools in these paradigms are explained, including stimulated recalls, surveys, eye-tracking, metanalysis and research synthesis. Open research areas and gaps in the literature are also discussed, providing a point-of-departure for researchers making their first foray into interaction, feedback and task-based teaching research.

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Interaction, Feedback and Task Research in Second Language Learning pp i-ii

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Interaction, Feedback and Task Research in Second Language Learning - Title page pp iii-iii

• Methods and Design

Copyright page pp iv-iv

Dedication pp v-vi, contents pp vii-xi, figures pp xii-xii, tables pp xiii-xiv, preface pp xv-xviii, chapter one - theory and approaches in research into interaction, corrective feedback, and tasks in l2 learning pp 1-26, chapter two - designing studies of the roles of interaction, feedback, and tasks in second language learning pp 27-53, chapter three - investigating individual differences in interaction, feedback, and task studies: aptitude, working memory, cognitive creativity, and new findings in l2 learning pp 54-70, chapter four - collecting introspective data in interaction, feedback, and task research pp 71-82, chapter five - creating and using surveys, interviews, and mixed methods for research into interaction, corrective feedback, tasks, and l2 learning pp 83-105, chapter six - doing meta-analytic and synthetic research on interaction, feedback, tasks, and l2 learning pp 106-131, chapter seven - investigating interaction, feedback, tasks, and l2 learning in instructional settings pp 132-149, chapter eight - choosing and using eye-tracking, imaging, and prompted production measures to investigate interaction, feedback, and tasks in l2 learning pp 150-169, chapter nine - working with data in interaction, feedback, and task research pp 170-194, chapter ten - common problems, pitfalls, and how to address them in research on interaction, corrective feedback, and tasks in l2 learning pp 195-205, glossary pp 206-213, references pp 214-239, index pp 240-250, altmetric attention score, full text views.

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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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The Pomodoro Technique

Beat procrastination and improve your focus one pomodoro at a time

• Introduction

What is the Pomodoro Technique?

What is the history of the pomodoro technique, three pomodoro technique rules for maximum productivity, does the pomodoro technique work, quick tips for pomodoro-ing, how to pomodoro with todoist.

Todoist makes the world’s #1 task manager to organize your work and life. Tell me more

The secret to effective time management is...thinking in tomatoes rather than hours. It may seem silly initially, but millions of people swear by the life-changing power of the Pomodoro Technique. (Pomodoro is Italian for tomato. 🍅)

This popular time management method asks you to alternate pomodoros — focused work sessions — with frequent short breaks to promote sustained concentration and stave off mental fatigue.

Here at Todoist, we know the daily grind can be exhausting. That’s why we’re all about offering ways to help you live your most productive, stress-free life. The Pomodoro Technique is one of the most effective ways to maximize productivity and minimize overwhelm.

The Pomodoro Technique may be for you if you…

Find little distractions often derail the whole workday

Consistently work past the point of optimal productivity

Have lots of open-ended work that could take unlimited amounts of time (e.g., studying for an exam, researching a blog post, etc.)

Are overly optimistic when it comes to how much you can get done in a day (aren't we all 🙃)

Enjoy gamified goal-setting

Really like tomatoes

But because everyone learns differently — and we know some of you prefer to watch instead of read — we've made a companion video for the Pomodoro Technique . Check out that video below, or continue reading for a deeper dive.

The Pomodoro Technique is a time management method in which you do focused work during 25-minute intervals — known as pomodoros — and take a five-minute break. We love this method because it:

Improves focus

Minimizes distractions

Prevents burnout

Promotes accountability

Boosts motivation

Which is why perfectionists and procrastinators will find it useful. It’s easier to commit to 25 minutes of work at a time than a whole afternoon of non-stop work.

Now that you understand what the Pomodoro method is and more importantly what a Pomodoro is, let’s hear the history of it — and find out what’s up with all the tomato references.

The Pomodoro Technique was invented in the late 1980s by Francesco Cirillo, a university student. Like many students overwhelmed with assignments and intense study schedules, Cirillo struggled to complete tasks without feeling burnout.

Believing that any progress is good progress, he challenged himself to just ten minutes of focus. To commit to this challenge, he used a tomato-shaped kitchen timer, and the Pomodoro Technique was born.

Even though Cirillo went on to write a 160-page book on the Pomodoro Technique for time management, what prompts people to try this method is its simplicity.

But how does Pomodoro work?

Here’s the basic step-by-step to start applying the Pomodoro Technique today:

Get your to-do list and a timer.

Set your timer for 25 minutes, and focus on a single task until the timer rings.

When your session ends, mark off one Pomodoro and record what you completed.

Then enjoy a five-minute break.

After four pomodoros, take a longer, more restorative 15-30 minute break.

Better yet, the Pomodoro method is adaptable. You don't have to stick to 25-minute intervals. You can customize your pomodoros to fit your individual needs — whether that's shorter bursts for challenging tasks or longer focus periods for deep work .

The 25-minute work sprints are the core of the method, but a Pomodoro practice also includes three rules for getting the most out of each interval:

Break down complex projects. If a task requires more than four pomodoros, it needs to be divided into smaller, actionable steps. Sticking to this rule will help ensure you make clear progress on your projects.

Small tasks go together. Any tasks that will take less than one Pomodoro should be combined with other simple tasks. For example, "write rent check," "set vet appointment," and "read Pomodoro article" could go together in one session.

Once a Pomodoro is set, it must ring. The Pomodoro is an indivisible unit of time and can not be broken, especially not to check incoming emails, team chats, or text messages. Any ideas, tasks, or requests that come up should be noted to return to later. A digital task manager like Todoist is a great place for these, but pen and paper will do, too.

In the event of an unavoidable disruption, take your five-minute break and start again. Cirillo recommends that you track interruptions (internal or external) as they occur and reflect on how to avoid them in your next session.

The rule applies even if you finish your task before the timer goes off. Use the rest of your time for overlearning , or improving skills or scope of knowledge. For example, you could spend the extra time reading up on professional journals or researching networking opportunities.

Todoist Tip

Keep an "Overlearning" project in Todoist with a list of tasks you can quickly choose from the next time you find yourself with pomodoro time to spare.

If the system seems simple, that’s because it is. The Pomodoro technique is all about getting your mind in the zone to finish your tasks.

Yes, the arbitrary silliness of using a tomato as a stand-in for units of time really helps people get things done . What makes the Pomodoro Technique so effective is that it builds consistency. It helps you establish routines and consistent work habits rather than waiting for inspiration to hit.

When you get used to the Pomodoro Technique, you avoid cognitive biases for time management . You stop worrying about the endless list of tasks and start focusing on what you can achieve now. Routines and healthy habits teach you to be kinder to yourself and have a work/rest balance that keeps your brain engaged.

Here are some other benefits of the Pomodoro Technique that make it uniquely suited to boost your productivity.

Makes it easy to just get started

Tim Pychyl, a professor in Carleton University’s Psychology Department and author of Procrastination, Health, and Well-Being , argues that our ability to start procrastinating is directly related to our ability to deal with negative emotions.

It’s uncomfortable to stare down a big task or project — one you may not know how to start. It feels overwhelming, and suddenly, everything else looks more appealing. Checking emails, scrolling through social media, and even cleaning your desk. You start procrastinating without realizing it because you’re faced with a problem you don’t want to deal with.

Luckily, there’s an effective way to break out this avoidance cycle: 👉 Shrink whatever you're putting off down to a tiny, unintimidating first step.

For example, instead of sitting down to write an entire novel, sit down to write a chapter. Still feeling that knot in your stomach? Try writing for just ten minutes. Doing something small for a short period is easier to face than taking on a big project all at once.

This is precisely what the Pomodoro strategy asks you to do: break down your projects or goals into manageable tasks that only take 25 minutes each. This approach keeps you motivated and focused on the next thing you need to do rather than being overwhelmed by the enormity of the task at hand.

Don't worry about the outcome — just take it one Pomodoro at a time.

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Combats distractions

The constant stream of information pouring in via emails, team chats, and social media notifications demands more and more of our attention. And if you’ve ever been interrupted while in a flow state, you know how difficult regaining focus can be.

While it would be nice to blame technology for everything, Gloria Mark , a Professor of Informatics at the University of California — with a PhD in psychology from Columbia University — suggests half of all workday distractions are self-inflicted. Meaning we pull ourselves out of focus.

We often find ourselves justifying these distractions with thoughts like:

“Should I work on this other task instead?”

“Taking a break now seems good”

“I’m going to check this email real quick”

“I have to reply to that one friend I left on read…”

“I’m craving some boba tea right now, I should go get it”

Those small interruptions add up! It isn’t just the time you lose on distractions… it also takes energy to refocus your attention. After switching gears, our minds can linger over the previous task for over 20 minutes until we regain our full concentration. Indulging the impulse to check TikTok "just for a minute" can turn into 20 minutes of trying to get back on task.

The Pomodoro Technique helps you resist all of those self-interruptions and re-train your brain to focus. Each Pomodoro is dedicated to one task, and each break is a chance to reset and bring your attention back to what you should be working on

Helps you track time

When planning out our future projects, most of us fall victim to the planning fallacy . We tend to underestimate the time needed to complete future tasks, even when we know similar tasks have taken longer in the past. Your present bias pictures your future self operating under entirely different circumstances and time restraints.

The Pomodoro Technique is a valuable weapon against the planning fallacy. When you start working in short, timed sessions, time is no longer an abstract concept but a concrete event. It becomes a Pomodoro — a unit of both time and effort. Distinct from the idea of 25 minutes of general "work," the Pomodoro is an event that measures focus on a single, important task (or several simple, important tasks).

The concept of time changes from a negative — something that has been lost — to a positive representation of events accomplished. This Pomodoro effect is what Cirillo calls "inverting time" because it changes the perception of time passing from an abstract source of anxiety to an exact measure of productivity.

Writer Dean Kissick describes how his perception of time changed while using the Pomodoro method for time management:

"Now that my breaks are short and fleeting, I think more carefully about what I’d like to do with them, and I’ve found it’s quite different from the unimaginative temptations I would otherwise default to (flopping on the sofa, scrolling on my phone, becoming annoyed). Instead, I’ll make a sandwich, do a quick French lesson, reply to a few texts, have a shower, go to the laundromat; and such humdrum activities, now that they’re restricted, have become sources of great pleasure.”

When you use the Pomodoro technique, you have a clear measurement of your finite time and your efforts, allowing you to reflect and plan your days more accurately and efficiently. With practice, you can accurately assess how many pomodoros a task will take and build more consistent work habits.

Gamifies your productivity

Every Pomodoro provides an opportunity to improve upon the last. Cirillo argues that “concentration and consciousness lead to speed, one Pomodoro at a time.”

The Pomodoro technique is approachable because it’s more about consistency than perfection. Each session is a fresh start. You make the system work for you by:

Reevaluating your goals

Challenging yourself to focus

Limiting distractions

Tracking your progress

Achieving goals

Getting rewards (the little breaks!)

Another way to gamify your productivity is to set a goal to add an extra Pomodoro each day. This helps you motivate yourself to build on your success.

You can also challenge yourself to finish a big task within a specific number of pomodoros. Try setting a goal number of pomodoros for each day without breaking the chain . Thinking in tomatoes rather than hours is just more fun.

While the 25/5 minute work/break intervals are the heart of the Pomodoro Technique, there are a few things you can do to make your pomodoros more effective:

Plan out your pomodoros in advance

Take 15 minutes at the beginning of your workday (or at the end if you're planning for the next day) to plan out your pomodoros. Take your to-do list for the day and note how many pomodoros each task will take.

Remember, tasks that will take more than 5 pomodoros should be broken down into smaller, more manageable tasks. Smaller tasks, like responding to emails, can be batched together in a single Pomodoro.

If you work an 8-hour workday, make sure your pomodoros for the day don't go over sixteen. If they do, postpone the least urgent/least important tasks for later in the week.

Build overflow pomodoros into your day

While an 8-hour workday technically leaves room for sixteen pomodoros, it's best to build in a buffer of 2-4 "overflow" pomodoros. Use your overflow pomodoros for tasks that take longer than planned or for unexpected tasks that come up during the day.

If you don't end up needing them, use the extra pomodoros for learning or lower-priority tasks that always get pushed to the end of your to-do list. It's much less stressful to end the day with pomodoros to spare than to overschedule yourself and get behind.

How many pomodoros are in a day?

Over time, you'll get a better sense of how many high-quality pomodoros you're actually capable of completing in a day. It's ok if it's not a full sixteen. The vast majority of people aren't productive for the full 8 hours of a workday, and those who think they are probably haven't been paying close enough attention. When it comes to pomodoros, challenge yourself, but keep the focus on quality over quantity.

Experiment with the length of your pomodoros

For some types of work that require extended periods in a creative "flow" state — think coding, writing, composing, etc. — 25 minutes may be too short. Try extended work sessions with longer breaks.

A DeskTime study from 2014 found that a 52-minute focus and 17-minute break is the perfect balance. However, in 2021 they ran the study again to see what’s changed. It was found that the more productive individuals work 112 minutes and take a 26-minute break. There’s no strict rule here, you decide what length works for you.

For tasks that you've been putting off for one reason or another, 25 minutes might be too long. If you're feeling a lot of mental resistance, or you just can't get yourself to stay focused for 25 minutes, try a 15-, 10-, or even 5-minute Pomodoro.

For most people, the sweet spot will be in the 25-50 minute range for peak concentration with a 5-15 minute break. Try mixing your intervals based on your available energy, the type of work, and how much a task makes you want to bury your head in cute puppy videos on YouTube instead.

Get away from screens during breaks

Not all breaks are created equal.

If your Pomodoro work sessions happen on your computer, don't just switch over to X or Instagram when the timer goes off. Give your eyes and brain a break from screens — that means your phone, too! Stand up, move around, stretch, go outside, do a mini meditation, grab a snack, or watch birds out the window. If you work from home, fold some clothes or clear off the kitchen table.

Whatever you do, your break will be much more mentally refreshing if you escape the glowing hypnosis of your computer or phone.

Use an app to enforce your pomodoros

Humans are fallible. No matter how motivated you are at the start of the day, it's really hard to stick to your pomodoros. Hold yourself accountable with a break reminder app.

The best ones let you customize how long your work sessions are, how obtrusive you want your reminders to be, and how strictly you want your breaks enforced. Some will lock you out of your computer for the duration of your breaks.

We recommend BreakTimer (for both Windows and Mac.)

So you're convinced the Pomodoro Technique is the greatest thing since sliced bread. Now, it's time to put the method into action. Here's how to plan your pomodoros with Todoist:

Plan and schedule your tasks

At the start of each day (or the night before), review all your active projects and one-off tasks and schedule everything you want to accomplish for "Today."

Estimate how many pomodoros each task will take. Add tomato emojis to the end of the task name to indicate your Pomodoro estimate.

Hold down the Alt/Option key while clicking on a task to quickly edit the task name without opening the full task view.

Break anything bigger than four pomodoros down into smaller sub-tasks . For example, a project titled "redesign website" might need a more Pomodoro-sized sub-task like "find 5 example websites as inspiration."

Now, when you open your Today view, you'll see your scheduled tasks and how many pomodoros each will take. Drag and drop your tasks to reflect the order in which you'll work on them.

If you have more than 12-14 pomodoros (remember that buffer!), postpone some of your tasks to the next day or later in the week. If you have 10 tasks you want to do in a day, you may find it helpful to schedule only half of the list and to assign an "@on_deck" label to indicate the tasks you'll get to if you have time.

You may want to add tasks you do every day — or even multiple times a day — as recurring tasks. For example, you might have a task called "Get to inbox zero" scheduled for "every weekday".

To add recurring due dates in Todoist simply turn on your Smart date recognition by clicking on your profile picture, selecting Settings > General, and flipping the switch. Now every time you use keywords like “every day,” “every week,” or “every month,” — when naming your task — Todoist will automatically set these recurring dates for you.

Work on your projects

After scheduling your tasks, you'll start your day with a clear plan of what you'll work on during each Pomodoro. You can use the timer on your phone, a physical Pomodoro timer, or any of the many digital Pomodoro applications that integrate with Todoist, such as:

Toggl track

Once that’s done, you can choose your Pomodoro timer for each task.

When the timer is up, it will automatically start timing your break, but not without an alert. You should stop working at this point.

Stay focused by adding any ideas or requests that come in as new tasks in your Todoist Inbox. When your timer runs out, you can review the list, schedule urgent tasks for a later Pomodoro, and file away less urgent things for another day.

Build your concentration muscle by making your Pomodoro planning a daily routine. Add a task in Todoist for the same time each morning to remind yourself to plan out your pomodoros. Challenge yourself to hit a certain number of pomodoros each day, and take time at the end to reflect on what went well and how you could improve your focus in the future.

Using the Pomodoro Technique is like having a superpower to finally tackle your to-do list without the guilt and anxiety. Instead of “pushing through” and overworking yourself to exhaustion, take little breaks to keep your mind alert.

And if you think this tomato method is too simple and doesn’t make a difference, run a little experiment and try it for a week! It may be one of the simplest productivity methods , but that doesn’t make it easy. Remember, humans are fallible.

The good news is, that if you stick to the Pomodoro Technique long enough, you’ll train your self-discipline and will feel that smug satisfaction of a day not only well planned but well executed.

Laura Scroggs

Laura is a freelance writer, PhD candidate, and pug mom living in Minneapolis, MN.

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Research: Why We Choose the Hard Way to Do Tedious Tasks

• Alicea Lieberman

And how to stop getting stuck in ruts.

In life and at work, we often get stuck persisting in unpleasant activities even when more enjoyable and equally effective alternatives exist. Research shows this happens due to “entrenchment,” where repeating an activity blocks consideration of better options and makes you more likely to keep doing it. The author’s research focuses on enhancing well-being by limiting over-persistence in these tasks and suggests solutions that include reducing repetition, dividing attention, and alternating tasks to break the cycle of entrenchment. By adopting strategies to prevent entrenchment, individuals and organizations can increase employee satisfaction and efficiency.

We often find ourselves mired in unpleasant tasks. And the longer we do an unpleasant task, the more stuck we become — persisting even if there are opportunities to switch to more enjoyable alternatives that would achieve the same goal. For instance, think about the last time you struggled to type a long email on your phone rather than switch to your nearby computer, where you could complete the task more comfortably. Or consider the last time you painstakingly formatted a document by hand rather than using a readily available software that could automate the process. Or even reflect on the last time you had an hour to pass and spent it watching an unenjoyable TV show rather than going outside for a pleasant stroll.

• AL Alicea Lieberman , PhD, MPH is an Assistant Professor in Marketing and Behavioral Decision Making at The UCLA Anderson School of Management. Her primary research focuses on judgment and decision making with an emphasis on self-control, motivation, and behavior change. She uses lab and field experiments and is committed to harnessing social and behavioral research to impact policy and improve societal health and well-being.

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• Knowledge Base

Methodology

• Survey Research | Definition, Examples & Methods

Survey Research | Definition, Examples & Methods

Published on August 20, 2019 by Shona McCombes . Revised on June 22, 2023.

Survey research means collecting information about a group of people by asking them questions and analyzing the results. To conduct an effective survey, follow these six steps:

• Determine who will participate in the survey
• Decide the type of survey (mail, online, or in-person)
• Design the survey questions and layout
• Distribute the survey
• Analyze the responses
• Write up the results

Surveys are a flexible method of data collection that can be used in many different types of research .

Table of contents

What are surveys used for, step 1: define the population and sample, step 2: decide on the type of survey, step 3: design the survey questions, step 4: distribute the survey and collect responses, step 5: analyze the survey results, step 6: write up the survey results, other interesting articles, frequently asked questions about surveys.

Surveys are used as a method of gathering data in many different fields. They are a good choice when you want to find out about the characteristics, preferences, opinions, or beliefs of a group of people.

Common uses of survey research include:

• Social research : investigating the experiences and characteristics of different social groups
• Market research : finding out what customers think about products, services, and companies
• Health research : collecting data from patients about symptoms and treatments
• Politics : measuring public opinion about parties and policies
• Psychology : researching personality traits, preferences and behaviours

Surveys can be used in both cross-sectional studies , where you collect data just once, and in longitudinal studies , where you survey the same sample several times over an extended period.

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Before you start conducting survey research, you should already have a clear research question that defines what you want to find out. Based on this question, you need to determine exactly who you will target to participate in the survey.

Populations

The target population is the specific group of people that you want to find out about. This group can be very broad or relatively narrow. For example:

• The population of Brazil
• US college students
• Second-generation immigrants in the Netherlands
• Customers of a specific company aged 18-24
• British transgender women over the age of 50

Your survey should aim to produce results that can be generalized to the whole population. That means you need to carefully define exactly who you want to draw conclusions about.

Several common research biases can arise if your survey is not generalizable, particularly sampling bias and selection bias . The presence of these biases have serious repercussions for the validity of your results.

It’s rarely possible to survey the entire population of your research – it would be very difficult to get a response from every person in Brazil or every college student in the US. Instead, you will usually survey a sample from the population.

The sample size depends on how big the population is. You can use an online sample calculator to work out how many responses you need.

There are many sampling methods that allow you to generalize to broad populations. In general, though, the sample should aim to be representative of the population as a whole. The larger and more representative your sample, the more valid your conclusions. Again, beware of various types of sampling bias as you design your sample, particularly self-selection bias , nonresponse bias , undercoverage bias , and survivorship bias .

There are two main types of survey:

• A questionnaire , where a list of questions is distributed by mail, online or in person, and respondents fill it out themselves.
• An interview , where the researcher asks a set of questions by phone or in person and records the responses.

Which type you choose depends on the sample size and location, as well as the focus of the research.

Questionnaires

Sending out a paper survey by mail is a common method of gathering demographic information (for example, in a government census of the population).

• You can easily access a large sample.
• You have some control over who is included in the sample (e.g. residents of a specific region).
• The response rate is often low, and at risk for biases like self-selection bias .

Online surveys are a popular choice for students doing dissertation research , due to the low cost and flexibility of this method. There are many online tools available for constructing surveys, such as SurveyMonkey and Google Forms .

• You can quickly access a large sample without constraints on time or location.
• The data is easy to process and analyze.
• The anonymity and accessibility of online surveys mean you have less control over who responds, which can lead to biases like self-selection bias .

If your research focuses on a specific location, you can distribute a written questionnaire to be completed by respondents on the spot. For example, you could approach the customers of a shopping mall or ask all students to complete a questionnaire at the end of a class.

• You can screen respondents to make sure only people in the target population are included in the sample.
• You can collect time- and location-specific data (e.g. the opinions of a store’s weekday customers).
• The sample size will be smaller, so this method is less suitable for collecting data on broad populations and is at risk for sampling bias .

Oral interviews are a useful method for smaller sample sizes. They allow you to gather more in-depth information on people’s opinions and preferences. You can conduct interviews by phone or in person.

• You have personal contact with respondents, so you know exactly who will be included in the sample in advance.
• You can clarify questions and ask for follow-up information when necessary.
• The lack of anonymity may cause respondents to answer less honestly, and there is more risk of researcher bias.

Like questionnaires, interviews can be used to collect quantitative data: the researcher records each response as a category or rating and statistically analyzes the results. But they are more commonly used to collect qualitative data : the interviewees’ full responses are transcribed and analyzed individually to gain a richer understanding of their opinions and feelings.

Next, you need to decide which questions you will ask and how you will ask them. It’s important to consider:

• The type of questions
• The content of the questions
• The phrasing of the questions
• The ordering and layout of the survey

Open-ended vs closed-ended questions

There are two main forms of survey questions: open-ended and closed-ended. Many surveys use a combination of both.

Closed-ended questions give the respondent a predetermined set of answers to choose from. A closed-ended question can include:

• A binary answer (e.g. yes/no or agree/disagree )
• A scale (e.g. a Likert scale with five points ranging from strongly agree to strongly disagree )
• A list of options with a single answer possible (e.g. age categories)
• A list of options with multiple answers possible (e.g. leisure interests)

Closed-ended questions are best for quantitative research . They provide you with numerical data that can be statistically analyzed to find patterns, trends, and correlations .

Open-ended questions are best for qualitative research. This type of question has no predetermined answers to choose from. Instead, the respondent answers in their own words.

Open questions are most common in interviews, but you can also use them in questionnaires. They are often useful as follow-up questions to ask for more detailed explanations of responses to the closed questions.

The content of the survey questions

To ensure the validity and reliability of your results, you need to carefully consider each question in the survey. All questions should be narrowly focused with enough context for the respondent to answer accurately. Avoid questions that are not directly relevant to the survey’s purpose.

When constructing closed-ended questions, ensure that the options cover all possibilities. If you include a list of options that isn’t exhaustive, you can add an “other” field.

Phrasing the survey questions

In terms of language, the survey questions should be as clear and precise as possible. Tailor the questions to your target population, keeping in mind their level of knowledge of the topic. Avoid jargon or industry-specific terminology.

Survey questions are at risk for biases like social desirability bias , the Hawthorne effect , or demand characteristics . It’s critical to use language that respondents will easily understand, and avoid words with vague or ambiguous meanings. Make sure your questions are phrased neutrally, with no indication that you’d prefer a particular answer or emotion.

Ordering the survey questions

The questions should be arranged in a logical order. Start with easy, non-sensitive, closed-ended questions that will encourage the respondent to continue.

If the survey covers several different topics or themes, group together related questions. You can divide a questionnaire into sections to help respondents understand what is being asked in each part.

If a question refers back to or depends on the answer to a previous question, they should be placed directly next to one another.

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Before you start, create a clear plan for where, when, how, and with whom you will conduct the survey. Determine in advance how many responses you require and how you will gain access to the sample.

When you are satisfied that you have created a strong research design suitable for answering your research questions, you can conduct the survey through your method of choice – by mail, online, or in person.

There are many methods of analyzing the results of your survey. First you have to process the data, usually with the help of a computer program to sort all the responses. You should also clean the data by removing incomplete or incorrectly completed responses.

If you asked open-ended questions, you will have to code the responses by assigning labels to each response and organizing them into categories or themes. You can also use more qualitative methods, such as thematic analysis , which is especially suitable for analyzing interviews.

Statistical analysis is usually conducted using programs like SPSS or Stata. The same set of survey data can be subject to many analyses.

Finally, when you have collected and analyzed all the necessary data, you will write it up as part of your thesis, dissertation , or research paper .

In the methodology section, you describe exactly how you conducted the survey. You should explain the types of questions you used, the sampling method, when and where the survey took place, and the response rate. You can include the full questionnaire as an appendix and refer to it in the text if relevant.

Then introduce the analysis by describing how you prepared the data and the statistical methods you used to analyze it. In the results section, you summarize the key results from your analysis.

In the discussion and conclusion , you give your explanations and interpretations of these results, answer your research question, and reflect on the implications and limitations of the research.

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

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

A questionnaire is a data collection tool or instrument, while a survey is an overarching research method that involves collecting and analyzing data from people using questionnaires.

A Likert scale is a rating scale that quantitatively assesses opinions, attitudes, or behaviors. It is made up of 4 or more questions that measure a single attitude or trait when response scores are combined.

To use a Likert scale in a survey , you present participants with Likert-type questions or statements, and a continuum of items, usually with 5 or 7 possible responses, to capture their degree of agreement.

Individual Likert-type questions are generally considered ordinal data , because the items have clear rank order, but don’t have an even distribution.

Overall Likert scale scores are sometimes treated as interval data. These scores are considered to have directionality and even spacing between them.

The type of data determines what statistical tests you should use to analyze your data.

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 sampling methods or criteria for selecting subjects
• 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 )

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• Article Information

Green shading indicates recommendations to which updated US Preventive Services Task Force methods for sex and gender have been applied. BRCA indicates breast cancer susceptibility gene.

a Recommendation abbreviated, some letter grades excluded, or both, to highlight use of sex and gender language.

b Text from body of recommendation statement, not topline recommendation.

Green shading indicates recommendations to which updated USPSTF methods for sex and gender have been applied.

• Incorrect Recommendation Wording in Figure JAMA Correction December 21, 2021
• JAMA and the US Preventive Services Task Force JAMA Editorial December 28, 2021 Phil B. Fontanarosa, MD, MBA; Robert M. Golub, MD

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Caughey AB , Krist AH , Wolff TA, et al. USPSTF Approach to Addressing Sex and Gender When Making Recommendations for Clinical Preventive Services. JAMA. 2021;326(19):1953–1961. doi:10.1001/jama.2021.15731

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USPSTF Approach to Addressing Sex and Gender When Making Recommendations for Clinical Preventive Services

• 1 Department of Obstetrics and Gynecology, Oregon Health & Science University, Portland
• 2 Department of Family Medicine and Population Health, Virginia Commonwealth University, Richmond
• 3 Center for Evidence and Practice Improvement (CEPI), Agency for Healthcare Research and Quality, Rockville, Maryland
• 4 Informed Medical Decisions Program, Division of General Internal Medicine, Massachusetts General Hospital, Boston
• 5 Kaiser Permanente, Northwest, Center for Health Research, Kaiser Permanente Evidence-based Practice Center, Portland, Oregon
• 6 Stanford Health Policy, Department of Medicine and Freeman Spogli Institute for International Studies, Stanford University, Stanford, California
• 7 Feinstein Institutes for Medical Research at Northwell Health, New York, New York
• 8 Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois
• 9 Departments of Medicine and Health Policy and Management, University of California at Los Angeles
• Editorial JAMA and the US Preventive Services Task Force Phil B. Fontanarosa, MD, MBA; Robert M. Golub, MD JAMA
• Correction Incorrect Recommendation Wording in Figure JAMA

Clinical preventive service recommendations from the US Preventive Services Task Force (USPSTF) are based on transparent, systematic, and rigorous methods that consider the certainty of the evidence and magnitude of net benefit. These guidelines aim to address the needs of diverse populations. Biological sex and gender identity are sources of diversity that are not often considered in studies of clinical preventive services that inform the recommendations, resulting in challenges when evaluating the evidence and communicating recommendations for persons in specific gender identification categories (man/woman/gender nonbinary/gender nonconforming/transgender). To advance its methods, the USPSTF reviewed its past recommendations that included the use of sex and gender terms, reviewed the approaches of other guideline-making bodies, and pilot tested strategies to address sex and gender diversity. Based on the findings, the USPSTF intends to use an inclusive approach to identify issues related to sex and gender at the start of the guideline development process; assess the applicability, variability, and quality of evidence as a function of sex and gender; ensure clarity in the use of language regarding sex and gender; and identify evidence gaps related to sex and gender. Evidence reviews will identify the limitations of applying findings to diverse groups from underlying studies that used unclear terminology regarding sex and gender. The USPSTF will use gender-neutral language when appropriate to communicate that recommendations are inclusive of people of any gender and will clearly state when recommendations apply to individuals with specific anatomy associated with biological sex (male/female) or to specific categories of gender identity. The USPSTF recognizes limited evidence to inform the preventive care of populations based on gender identity.

The US Preventive Services Task Force (USPSTF) makes evidence-based recommendations on clinical preventive services, including screening, behavioral counseling, and preventive medications. Frequently, USPSTF recommendations are sex-specific, most commonly based on the biological basis of the preventive service. For example, cervical cancer screening recommendations apply to females (individuals born with a cervix), and prostate cancer screening recommendations apply to males (individuals born with a prostate). However, many people have gender identities that differ from their sex assigned at birth. Transgender people, who were assigned male sex at birth but identify as women, or who were assigned female sex at birth but identify as men, are examples of such individuals, but there are also people with gender nonconforming or nonbinary identities who do not identify as either a man or a woman. A synthesis of data from 12 surveys involving 1 232 667 participants suggested that in the US, about 1 in 250 individuals, or about 1 million people, self-identify as transgender or gender nonconforming. 1 Moreover, in the anonymous Behavioral Risk Factor Surveillance System survey conducted in 2014 among 151 456 US adults, 0.53% of respondents self-identified as transgender. Among these individuals, about 53% self-identified as male-to-female transgender, 31% as female-to-male transgender, and 17% self-identified as gender nonconforming. 2

Although data are limited, 3 transgender, gender nonconforming, and gender nonbinary people report barriers to health care, including negative experiences in health care settings, and report avoiding health care because of concern about being mistreated. 4 Moreover, disparities in preventive care, such as cancer screening, have been demonstrated for transgender and gender-nonconforming people. 5

Therefore, the USPSTF recognizes that the language used in sex-specific recommendations (specifically related to the sex assigned to a person at birth) needs to be clear and consistent so clinicians and patients can effectively and respectfully apply these recommendations in practice. The purpose of this report is to describe the methods that the USPSTF used to identify recommendations that have sex or gender components and clarify the populations for which the recommendations should apply and also to present a proposed approach to making recommendations that are respectful of gender diversity and that identify when biological sex assigned at birth has limitations as a factor for whom should receive recommended services. This report builds on previous USPSTF methods for developing evidence-based recommendations for diverse populations. 6

It is critical to be able to use terminology with clear definitions. The word sex describes particular biological attributes commonly associated with specific chromosomes, the effect of particular endogenous hormones, and reproductive anatomy. Although individuals can change their hormonal levels and anatomy through medical or surgical approaches, sex is meant to identify those individuals who would likely have been assigned a sex at birth of either female, male, or intersex. Sex is commonly dichotomized into female or male, but this dichotomy is not inclusive of individuals who are intersex —ie, those who have reproductive anatomy that is inconsistent with usual definitions of female or male. For example, an individual with external female genitalia but internal male reproductive organs would be intersex but may by identified by the individual or clinician as male, female, or intersex.

The USPSTF uses gender terms to refer to identities that reflect how individuals generally perceive themselves with regard to social or cultural norms as men, women, gender nonbinary, or gender nonconforming. Gender identity is not specifically confined to a binary categorization and can exist as a continuum; additionally, it can change over time. How individuals identify themselves can differ from how others perceive them based on traditional stereotypes of gender presentation. Assumptions about sex assigned at birth or gender may not be aligned with a person's biology or personal identity. The use of cis- and trans- prefixes are sometimes used to indicate whether an individual has a gender identity that aligns with their biological sex assigned at birth (cisgender) or does not (transgender).

To consider incorporating meaningful sex and gender terminology into its recommendations, the USPSTF created an internal workgroup of USPSTF members, evidence-based practice center (EPC) researchers, and Agency for Healthcare Research and Quality staff supporting the USPSTF program. The workgroup reviewed all USPSTF recommendations published by September 2021 (from September 7, 2015, to September 16, 2021) and identified all USPSTF recommendations with sex- and gender-specific language, performed an environmental scan of how other guideline-making bodies approach the use of the terms describing sex and gender, interviewed experts and leaders in the care of transgender persons, and pilot tested an approach on multiple recommendations to develop a policy to better address sex and gender. Findings from reviews and the cataloging were presented for discussion to the workgroup and the topic experts. The workgroup convened between September 2018 and March 2021 and may continue to meet periodically to further advance its methods around sex and gender.

The USPSTF makes recommendations for specific populations as determined by the evidence, and it pays particular attention to groups that may be underrepresented, underserved, or the target of racism or discrimination. When developing recommendations for specific populations, the USPSTF has 3 general goals. First, USPSTF recommendations must be consistent with the evidence. When the evidence is insufficient to determine the balance of benefits and harms, the USPSTF does not use professional opinion. Instead, the USPSTF either issues an “I” or insufficient evidence statement or does not issue a formal statement, but in both cases makes a call for more research. 7

Second, the USPSTF strives to be inclusive with recommendations. Clinicians need to know how the evidence about clinical preventive services applies to different groups of people. However, no USPSTF recommendation can address all distinct populations because there is inadequate evidence on the benefits and harms of preventive interventions explicitly addressing every population. When evidence is limited, the USPSTF decides whether evidence from studies in an unselected, general population is applicable to or can be extrapolated to specific populations. 8 Applying the evidence in this way creates some level of uncertainty. The issue of limited evidence is amplified in transgender, gender nonbinary, gender-nonconforming, and intersex populations, who are not routinely identified or well-represented in studies. 9

Third, the USPSTF must ensure recommendations are clear so that they are understood and implemented appropriately. 10 - 12 The current language in many USPSTF recommendations lacks clarity and uses sex and gender terms inconsistently and interchangeably. For example, the USPSTF recommendation statement for breast cancer screening states that it “applies to…women aged 40 years or older.” 13 However, it is unclear how or whether it applies to transgender, gender nonbinary, or gender nonconforming persons assigned female sex at birth or intersex individuals with breasts.

To catalog USPSTF recommendations, during March of 2021 workgroup members (n = 24) reviewed the current USPSTF recommendations to identify those that included a sex or gender term. The terms used and their intentions were cataloged. Of the 84 topics for which the USPSTF had made recommendations at the time of cataloging, 32 made sex- or gender-specific reference in the recommendation ( Figure 1 and Figure 2 ). The USPSTF most often used gender terms, referring to men or women, instead of the sex terminology of male and female. However, most recommendations were based on sex differences and not gender identity or were for pregnant persons. No recommendation found sufficient evidence to make a specific recommendation for transgender, gender nonbinary, or gender nonconforming populations. One recommendation identified individuals who were not cisgender as a risk factor (preexposure prophylaxis for HIV), 28 2 recommendations called for more evidence in transgender and gender nonbinary persons (preexposure prophylaxis for HIV and behavioral counseling to prevent sexually transmitted infections), 28 , 39 and 1 recommendation stated that there were no screening data for transgender and gender nonbinary persons. 20

Workgroup members reviewed publicly available guidance and methods documents from other national and international guideline bodies to identify (1) approaches to making guidelines based on sex and gender and (2) specific guidelines for transsexual, transgender, and gender nonconforming people. This effort was supplemented with a nonsystematic literature search (August 13, 2013, to August 12, 2018) and interviews with 23 members of the USPSTF partner organizations in an effort to ensure that the workgroup did not miss any approaches or guidelines. 44

No publicly available documents or statements were identified at that time describing how other guideline bodies handle sex and gender or transgender populations when making recommendations. However, the National Institutes of Health has a website dedicated to the Methods and Measurement in Sexual & Gender Minority Health Research. 45 Many professional groups identify LGBTQ (lesbian, gay, bisexual, transgender, queer) persons as a specific population and have policies or guidelines reflecting support, the role of the practitioner, and education on the various terms and definitions. 46 - 56 Several guidelines outlined general medical and prevention approaches to the clinical care of transgender, gender nonbinary, and gender nonconforming persons. 57 - 59 Many medical professional societies also have policies regarding the use of inclusive language on health care forms, demographic questionnaires, handouts, and paperwork.

Over a series of 6 meetings, the workgroup considered revisions to the USPSTF processes and methods and defined guiding principles to strengthen sex and gender inclusivity when communicating recommendation statements. The EPC and USPSTF members were asked to apply preliminary changes throughout the process of updating 2 recommendations—screening for abdominal aortic aneurysm (AAA) and risk assessment and genetic counseling for BRCA -related cancers. 14 , 17 Additionally, the EPCs and USPSTF members have prospectively applied the revised approach to subsequent topics to guide further insight and adaptation.

To advance its methods, the USPSTF pilot tested (1) prospectively and explicitly determining whether the preventive service was expected to have a biological (sex) or identity (gender) basis, (2) conducting the evidence review based on the biological or identity basis, (3) identifying whether specific populations based on sex or gender were disproportionately affected by the condition, and (4) developing language to make a clear recommendation for sex and gender. The 2 topics reviewed (AAA screening and BRCA -related cancers) had multiple recommendations that were thought to be based on sex, not gender. However, the primary studies supporting these topics used gender-based language (eg, man, woman) when referring to study populations, without further clarification of these terms. 60 , 61 For these 2 topics, no studies that addressed transgender, gender nonbinary, gender nonconforming, or intersex populations met inclusion criteria. Thus, when assessing the available evidence, the USPSTF could not determine whether the findings could be applied to these specific populations and could only make recommendations based on sex assigned at birth for both AAA screening and BRCA counseling.

Further, there was no specific evidence for the effect of treatment with gender-conforming therapies (eg, hormonal, surgical) for transgender, gender nonbinary, gender nonconforming, or intersex individuals and its potential effects on the biological basis for these recommendations. For example, for a person assigned female sex at birth who had transitioned during or shortly after puberty to a transgender man and who received maintenance exogenous testosterone, it is unclear whether their risk for AAA or breast cancer would be that of someone born female or male, or if their risk was somewhere on a continuum. Thus, more research is needed to make specific evidence-based recommendations. Still, until such evidence is available, it will be essential to take an inclusive, respectful approach to making preventive service recommendations to avoid further marginalization of these populations.

Very early in the pilot process, the concept emerged of using neutral language in recommendations whenever possible. The first example adopted was using the term “pregnant persons” instead of referring to pregnant women. The USPSTF used this language in several recommendations not included in the planned pilot 15 , 16 and received no comments, criticisms, or concerns with the transition in the language in their public comment periods or thereafter.

When not possible to use neutral language, the USPSTF decided it was more appropriate to use gender terms throughout the recommendation statement, even when the service was based on biology. However, the USPSTF developed explicit language in the Patient Population Under Consideration section to make the USPSTF intent clear to readers. This addition was first piloted in a 2019 AAA screening recommendation statement in which the recommendation stated, “the recommendations are stratified by “men” and “women,” although the net benefit estimates are driven by biologic sex (ie, male/female) rather than gender identity. Persons should consider their sex at birth to determine which recommendation best applies to them . ” 14 Similar language was used for the recommendation statement on BRCA -related cancer. 17 The USPSTF received no comments or feedback, positive or negative, about using this language during the public posting periods for the draft recommendation statement or when the final recommendation statements were published.

The USPSTF is committed to promoting health equity for diverse populations, including based on sex and gender, and ensuring both the specificity and inclusivity of its recommendations. Therefore, the USPSTF will advance its methods and language in every step of its recommendation development process, as outlined in the Box .

USPSTF Methods for Considering Sex and Gender in Recommendations

Developing the research plan.

Prior to developing the research plan for a new or updated recommendation, the USPSTF will consider potential sex and gender issues, including:

How biology (sex) and identity (gender) inform the risks, outcomes, and provision of the preventive service;

Whether certain populations based on sex, gender, or both may be disproportionately affected by a condition or susceptible to variation in the effectiveness of the preventive service; and

Whether there is potentially adequate evidence to consider a specific review and recommendation for transgender, intersex, gender nonbinary, and gender nonconforming populations.

Based on the above considerations, the USPSTF will develop a research plan to guide the systematic evidence review.

Unless explicitly stated, the research plan will be inclusive to identify available evidence applying to diverse populations based on sex and gender.

As appropriate, specific sex and gender, including transgender, intersex, and gender nonconforming populations, will be identified explicitly in the inclusion and exclusion criteria.

Prior to finalizing any research plan, the USPSTF seeks additional input from outside review, as appropriate to the topic, and public comment. All public comments are considered.

As appropriate, the USPSTF will seek input from topic experts with knowledge related to sex and gender if applicable to the recommendation.

Conducting the Systematic Evidence Review

The EPC will conduct the evidence review based on the defined research plan.

As appropriate, the EPC will seek out sex- and gender-specific evidence, including:

Incidence and prevalence of target condition;

Outcomes of the preventive service;

Benefits from the preventive service; and

Harms from the preventive service.

Prior to finalizing the evidence review, the EPC will solicit external input, including topic input related to sex and gender as appropriate.

Assessing the Evidence to Make a Recommendation

When considering the evidence, the USPSTF will assess

How biological sex, gender identity, or both, pertain to the evidence;

Applicability of evidence to transgender, intersex, and gender nonconforming populations;

Variability in the quality of evidence based on sex, gender, or both; and

Whether the net benefit varies based on sex, gender, or both.

Per the USPSTF methodology, all recommendations, including sex- and gender-based recommendations, will be based on (1) the certainty of the evidence and (2) the magnitude of net benefit.

The USPSTF will consider recommendations to be inclusive unless evidence is not applicable to specific populations.

The USPSTF will make specific sex and/or gender recommendations when there is at least moderate certainty that there is differential magnitude of net benefit for a preventive service.

Communicating to Users of USPSTF Recommendations

To promote clarity and inclusion in its recommendations, the USPSTF will make a clear statement about who the recommendation applies to with respect to sex and gender in the Patient Population Under Consideration section, using

Sex- and gender-neutral terms when appropriate (eg, persons);

Clarify whether persons and clinicians should consider their sex at birth, gender, and/or anatomy when determining to whom the recommendation applies;

Clear and respectful terms, including transgender, intersex, gender nonbinary, or gender nonconforming when specific to those populations.

When there is at least moderate certainty that there is a different magnitude in net benefit for a preventive service based on sex, gender, or both, the USPSTF will assign a different letter grade and highlight the difference in the Recommendation Summary.

Information on populations with variation in condition incidence and prevalence, preventive service outcomes, benefits from the preventive service, or harms from the preventive service based on sex, gender, or both will be highlighted in the Practice Considerations section, even if the evidence is too limited to make a specific sex- and/or gender-specific recommendation.

As appropriate, the USPSTF will summarize whether evidence is available for transgender, intersex, gender nonbinary, and gender nonconforming populations in the Clinical Considerations section and call for more evidence for these populations as needed in the Evidence Gaps section.

Prior to finalizing a recommendation statement, the USPSTF will seek additional input from outside review and public comment. All public comments will be considered and, as appropriate, the USPSTF will seek input from topic experts with knowledge related to sex and gender.

Abbreviations: EPC, evidence-based practice center; USPSTF, US Preventive Services Task Force.

When making a new recommendation or updating an existing recommendation, the USPSTF collaboratively creates a research plan with an EPC. The research plan defines the key questions that need to be answered with evidence for the USPSTF to make a recommendation and defines the types of evidence and populations included in the review. At the outset of this process, the USPSTF will consider whether the preventive service is expected to be applied according to biological or physiologic sex characteristics, gender identity, or potentially both. This consideration will be guided by whether specific populations have a higher prevalence or experience worse outcomes from a condition or if there are unique considerations for risk assessment or service delivery based on sex or gender identity. 6 If specific sex or gender populations are not called out in the research plan for inclusion or exclusion, the research plan will be considered inclusive of all populations. Once a draft research plan is created, the USPSTF will seek review by transgender, gender nonbinary, gender nonconforming, and intersex individuals and groups with specific expertise in representing these populations, as appropriate. Input will be incorporated into the final research plan.

Based on the research plan, EPC investigators will conduct the systematic review. Each systematic review provides background on the epidemiology across all relevant populations (eg, incidence, prevalence, and mortality); systematically searches the literature for evidence to address each key question in the research plan; conducts data abstraction, critical appraisal, data analysis, and synthesis; and summarizes the evidence in a report. The full systematic review is available and published with each USPSTF recommendation. Throughout this process, EPC investigators will aim to describe the gender of participants from the included studies accurately. They will also note when the terminology used in the underlying evidence is unclear or based on assumptions about gender or the biology of participants. The language will be included in the EPC report to acknowledge the absence or incompleteness of information on gender and biological sex and its implications for the interpretation and applicability of the evidence. EPC investigators also will seek review by transgender, gender nonbinary, and gender nonconforming individuals and groups with specific expertise in representing these populations for services with unique considerations for these communities.

When assessing the evidence to make a recommendation, the USPSTF will consider the sex or gender identity basis of the evidence; applicability of evidence to transgender, gender nonbinary, and gender nonconforming populations and to intersex persons; variability in the quality of evidence based on sex or gender; and whether the net benefit varies based on sex or gender. Consistent with USPSTF methods, letter grade assignments will be based on the certainty and net magnitude of benefits (eg, benefits minus harms) based on the evidence. In many cases, the USPSTF may not have adequate certainty to make a specific letter grade recommendation but may decide that the available evidence applies to transgender, gender nonbinary, gender nonconforming, and intersex persons. In this case, the USPSTF may specifically mention these populations in the Patient Population Under Consideration or Clinical Considerations sections.

Whenever appropriate, the USPSTF will use sex- and gender-neutral terms. The use of gender-neutral language does not seek to deny or diminish the importance of gender in framing personal and social life, nor does it preclude a synthesis of the evidence that recognizes the unique social, economic, and political factors that influence the health risks that accrue to people based on sex and gender. In cases in which the recommendation is to be applied to specific populations based on sex or gender, the USPSTF will make a clear statement to whom the recommendation applies in the Patient Population Under Consideration section. In the Practice Considerations section, the USPSTF will review the evidence supporting the basis for a recommendation focused on a specific population defined by biological sex or gender attributes. When the USPSTF finds important evidence gaps for transgender, gender nonbinary, and gender nonconforming persons, the USPSTF will call for more evidence in the Research Needs and Gaps section of the recommendation.

The USPSTF intends that these new approaches for developing recommendations attuned to sex and gender diversity will improve the clarity of its statements and help clinicians and their patients make informed decisions about preventive care. The USPSTF plans to continue its engagement with individuals and groups with specific expertise in representing these populations to learn how best to formulate recommendations that are gender inclusive, more clearly communicate with regard to sex and gender diversity, and improve understanding of the research gaps. This policy statement should be viewed as a first step in advancing the task force’s methods on these issues. The USPSTF updates its recommendations for each preventive services topic, with a goal of approximately every 5 years. As topics are updated, the approaches outlined above will be applied during the updates. The process is underway or complete for several topics and will continue over the next several years for all others.

It is common for the USPSTF to identify evidence gaps for specific populations of patients—even when there is evidence that these populations are more likely to be diagnosed or experience specific preventable conditions. 62 The evidence gaps for preventive services are substantial for transgender, gender nonbinary, gender nonconforming, and intersex persons, limiting the ability of the USPSTF to make a specific recommendation. As science and understanding evolve, the USPSTF will remain committed to advancing its processes and methods to further promote equity for all persons regardless of sex or gender. However, until primary studies that inform USPSTF recommendations adopt more nuanced approaches to assessment and reporting on the sex and gender of study participants, there will continue to be gaps in the evidence and challenges to formulating and communicating inclusive clinical recommendations.

Corresponding Author: Carol M. Mangione, MD, MSPH, Department of Medicine, University of California at Los Angeles, 1100 Glendon Ave, Ste 850, Los Angeles, CA 90024 ( [email protected] ).

Accepted for Publication: August 31, 2021.

Published Online: October 25, 2021. doi:10.1001/jama.2021.15731

Correction: This article was corrected on December 21, 2021, to fix an error in Figure 1.

Author Contributions: Dr Caughey had full access to all of the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of Interest Disclosures: None reported.

Funding/Support: The USPSTF is an independent, voluntary body. The US Congress mandates that the Agency for Healthcare Research and Quality (AHRQ) support the operations of the USPSTF.

Role of the Funder/Sponsor: AHRQ staff assisted in the writing and preparation of this report and its submission for publication. AHRQ staff had no role in the approval of the final report or the decision to submit for publication.

Disclaimer: The findings and conclusions in this document are those of the authors, who are responsible for its content, and do not necessarily represent the views of the Agency for Healthcare Research and Quality (AHRQ). No statement in this report should be construed as an official position of AHRQ or the US Department of Health and Human Services.

Additional Information: We would like to acknowledge Madeline Deutsch, MD, MPH (University of California San Francisco), Jennifer Potter, MD (Harvard Medical School), and Howard Libman, MD (Harvard Medical School), who provided valuable feedback on early drafts of the USPSTF approach. Martha Duffy, MD, MPH, provided support in cataloging USPSTF recommendations and reviewing guidelines from other organizations and feedback on draft language for recommendations. Drs Deutsch, Potter, Libman, and Duffy did not receive compensation for their contributions to this paper. Amy Cantor, MD, MPH, and Elizabeth O’Connor, PhD, are members of the review teams that support the USPSTF and were involved in pilot testing the approach during systematic reviews. Drs Cantor and O’Connor received compensation through their work as part of the Evidence-based Practice Centers’ (EPC) (Pacific Northwest Evidence-based Practice Center and Kaiser Permanente Evidence-based Practice Center, respectively) support of the USPSTF.

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• Published: 10 June 2024

Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm

• Xiaolian LIU 1 ,
• Shaopeng Gong 1 ,
• Xiangxu Hua 1 ,
• Taotao Chen 1 &
• Chunjiang Zhao 1 , 2  

Scientific Reports volume  14 , Article number:  13292 ( 2024 ) Cite this article

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• Computer science
• Electrical and electronic engineering

In the process of feeding the distilling bucket after vapor detection, the existing methods can only realize the lag detection after the steam overflow, and can not accurately detect the location of the steam, etc. At the same time, in order to effectively reduce the occupancy of the computational resources and improve the deployment performance, this study established infrared image dataset of fermented grains surface, and fused the YOLO v5n and the knowledge distillation and the model pruning algorithms, and an lightweight method YOLO v5ns-DP was proposed as as a model for detecting temperature changes in the surface layer of fermented grains during the process of feeding the distilling. The experimental results indicated that the improvement makes YOLOv5n improve its performance in all aspects. The number of parameters, GLOPs and model size of YOLO v5ns-DP have been reduced by 28.6%, 16.5%, and 26.4%, respectively, and the mAP has been improved by 0.6. Therefore, the algorithm is able to predict in advance and accurately detect the location of the liquor vapor, which effectively improves the precision and speed of the detection of the temperature of the surface fermented grains , and well completes the real-time detecting task.

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Introduction.

Chinese Baijiu is one of the six major distilled liquors in the world, which has a history of more than two thousand years 1 . It has a unique fermentation and distillation method under solid-state conditions, which is characterized by a long fermentation cycle and rich microorganisms 2 . From grain to Baijiu the technological process includes crushing raw grain, dosing, entering the distillation bucket, distillation, picking liquor, drying, adding Daqu, fermenting, and aging 3 . Liquor refers specifically to Chinese Baijiu in the following text.

Distilling bucket feeding operation is one of the key steps in the distillation process, and it must meet the requirements of "feeding the distilling bucket after steam detection, spread the fermented grains evenly", in order to improve the distillation rate of alcohol in the fermented grains and ensure the production and quality of liquor. "Feeding the distilling bucket after vapor detection" is a method used to detect the overflow of alcohol steam in the distilling bucket. Its main purpose is to heat the surface layer of fermented grains as much as possible while avoiding the "running of vapor" and to obtain a larger temperature difference after spreading the cold material so that the alcohol steam condenses into liquid when cooled, thereby increasing the extraction of alcohol from the fermented grains. In the traditional brewing process, the process of "Feeding the distilling bucket after vapor detection" relies entirely on the brewer's experience. Premature spreading of fermented grains can cause the phenomenon of "liquor steam not reaching the surface of the fermenting grains", while delayed spreading of fermented grains can lead to the phenomenon of "a lot of liquor vapor escaping", both of which can reduce the production of liquor. Therefore, it is of great significance to study the real-time and accurate detection method of fermented grains surface temperature during the feeding process of the distilling bucket for the distillation process of liquor brewing.

Currently, in order to promote the intelligent brewing of liquor, many scholars have conducted in-depth research on the automatic detection of steam during feeding process of the distilling bucket. Li et al. 4 proposed to use the steam characteristics of the process in the feeding the distilling bucket as the identification target, and utilized image processing to obtain the location of the vaporized area to guide the mechanical structure paving the fermented grains. Yang et al. 5 used image processing to separate the foreground and background of the fermented grains surface image, and determined the laying area by detecting the liquor vapor overflow feature area in the background. However, both of the above methods are characterized by liquor vapor, which can only be detected after the vapor has risen and cannot be predicted in advance. Tian et al. 6 and Wang et al. 7 used an infrared thermal camera to obtain images of fermented grains surface temperature and extracted the image temperature features by combining image pre-processing techniques. Then they proposed methods based on Support Vector Machine (SVM) and BP neural network to detect the vapor. Although this method can predict the location of the vapor in advance through the infrared thermal camera, the SVM and BP methods used were only used for image classification, and could not monitor the surface temperature of the fermented grains and steam changes in real time, which could not satisfy the process requirements of "feeding the distilling bucket after steam detection" in advance.

With the rapid development of deep learning technology, more and more algorithms are being used for target recognition and detection tasks in unstructured environments. The YOLO (You Only Look Once) network is a one-stage detection network that achieves both fast and accurate object detection. This algorithm has been widely used in real-time target detection. However, the algorithm is more difficult to be deployed on low computing power platforms due to cost control and application environment limitations, and the research on lightweighting of the model has gradually attracted attention. Wang, and He 8 compressed the apple fruitlet detection model before fruit thinning based on YOLO v5s by a channel pruning algorithm, resulting in an average detection time of 8 ms per image and a model size of only 1.4 MB. Li et al. 9 proposed a real-time tea shoot detection method using YOLO v3 SPP deep learning algorithm with channel and layer pruned, which reduced the number of parameters, inference time, and the model size by 96.82%, 59.62%, and 96.81%, respectively, while the mean average precision was reduced by only 0.40%. Li, Li, Zhao, Su, and Wu 10 used a lightweight network GhostNet instead of a backbone network and designed a depthwise separable convolution instead of a standard convolution based on the improved YOLO v4 in tea bud detection. Relative to the original YOLOv4, the mean average precision was improved by 1.08%, whereas, the number of parameters and the computational complexity of the proposed model was reduced by 82.36% and 89.11%. A lightweight tea bud detection algorithm based on YOLOv5 was proposed by Gui et al. 11 , incorporating optimizations such as Ghost_conv, BAM, MS-WFF, and CIoU. This method achieved a 9.66% increase in average precision, a 52.402 G and 22.71 M reduction in the floating point operations and the number of parameters, respectively.

In the process of feeding the distilling bucket after vapor detection, the existing methods can only realize the lag detection after the steam overflow, and can not accurately detect the location of the steam, etc. At the same time, in order to effectively reduce the occupancy of the computational resources and improve the deployment performance, an lightweight method YOLO v5ns-DP based on YOLO v5n was proposed. This method can realize the real-time detection of the surface temperature of the fermented grains, and can proactively prejudge the location of the liquor steam in advance to avoid the phenomenon of steam running, so as to improve the production of liquor. The study consists of two main parts as follows: (1) an infrared image dataset of the surface layer of fermented grains was established and YOLO v5n network model was used for training; (2) the model was compressed on the basis of knowledge distillation and channel pruning.

Materials and methods

Overview of the method.

The overall technical route of this research algorithm is shown in Fig.  1 . First, images of the surface layer of fermented grains during the distillation process in the feeding the distilling bucket were acquired using an infrared thermal camera and the target area of the white-hot zone was labeled, so as to establish the temperature detection dataset. Second, YOLO v5n network was used to detect the surface temperature of the fermented grains. Then, using YOLO v5s as the teacher network and YOLO v5n as the student network, a knowledge distillation algorithm was used to migrate the knowledge from the teacher model to the student model to improve the model accuracy. Finally, sparse training and channel pruning were performed to filter and prune non-important channels, and then the pruned network is trained with fine-tuning. Model compression was realized through knowledge distillation and channel pruning while maintaining model accuracy.

Overall technical route of the proposed the surface temperature detection algorithm.

Image acquisition and dataset construction

Image acquisition.

Images were acquired at a winery workshop in Shanxi Province, China. Figure  2 shows a scene of image acquisition of the surface of fermented grains during the process of traditional manual feeding distilling bucket. The acquisition equipment consists of a K12E2 in-line infrared thermal camera and the supplementary software IRdemo_4.9. The height of the distilling bucket is 0.9 m and the diameter of the upper rim is 2.2 m. The camera was installed on a bracket 0.6 m from the upper edge of distilling bucket, with the camera optical axis at an angle of 45°-60° to the horizontal plane and aligned with the center of the circle on the upper edge of the distilling bucket, so that the camera can capture the entire fermented grains surface. In this paper, we focus on solving the problem of target detection in pixel space, with special attention to the labeling of upper vapor points. Therefore, a fixed camera mounting position was used in the data acquisition process, ignoring the effect of camera mounting position on the quality of data acquisition.

Image acquisition scenarios. ( a ) camera shooting scenario, ( b ) IRdemo_4.9 software application scenario.

In addition, image acquisition was performed in July (temperature of 30 ℃) and November (temperature of 15 ℃), respectively. After comparing and analyzing the images, it was found that the infrared camera captured the relative temperature, which could only reflect the temperature difference between the temperature of the upper vapor point and the surrounding temperature, and could not accurately reflect the absolute temperature of the upper vapor point. Since the temperature of the upper vapor point and the surrounding temperature are equally affected by the external ambient temperature, the external ambient temperature has little effect on the quality of the data and the accuracy of the model training.

Finally, Fig.  3 shows the collected infrared image with a resolution of 256 pixels × 192 pixels. In the image, the protruding parts such as the distilling bucket body can be clearly seen. Figure  3 a shows that the infrared image has a dark blue color with no white-hot area, which indicates that the lower layer of high-temperature liquor vapor has not yet reached the surface of fermented grains. Figure  3 b shows an infrared image with white-hot area in some parts of the image, but the majority of the image is in dark blue color, which indicates that the lower layer of high-temperature liquor vapor partially reaches the surface of the fermented grains. Figure  3 c shows a large white-hot area in the infrared image, indicating that the liquor vapor reaches the surface of the fermented grains over a large area. After manual selection to remove images with insufficient pixel area and redundancy, 929 infrared images of fermented grains surface were finally selected.

Infrared images of the surface of fermented grains ( a ) Infrared image of the area without white-hot ( b ) Infrared image of localized white-hot areas ( c ) Infrared image of large white-hot areas.

Dataset construction

In this study, the white-hot area was used as the target detection region. In order to adapt to the priority principle of feeding trajectory planning at the end of the robotic arm, the samples were divided into four categories, L, M, HS, and HB, according to the brightness and size of the white-hot area of the vapor on the surface layer of the fermented grains during the feeding process. As shown in Fig.  4 a, there is a faint brightness in the white-hot region, which is noted as label L. As shown in Fig.  4 b, the white-hot area is moderately bright. It indicates that the fermented grains surface has not yet begun to leak and is noted as label M. Figure  4 c shows that there is discrete, highlighted, small area of white-hot. This indicates a small area of vapor leakage on fermented grains surface, noted as label HS, which is noted as label HS. As shown in Fig.  4 d, there is continuous, highlighted, large white-hot area, which is noted as label HB.

Sample labeling criteria. ( a ) label L, ( b ) label M, ( c ) label HS, ( d ) label HB.

The target areas of the selected image were labeled using the LabelImg, and the labeled box is the smallest outer rectangle of the white-hot area. What’s more, considering that the picture of the waiting for feeding condition does not show a white-hot area and does not require distilling pot feeding operation, the picture of this condition was not labeled. Finally, there were 18 unlabeled images out of 929 infrared images of the fermented grains surface, and the remaining images were labeled with a total of 5360 white-hot areas containing 2633 L labels, 1671 M labels, 1234 HS labels, and 330 HB labels.

In order to improve the richness of the dataset and restore as much as possible the characteristics of the temperature changes on the surface of the fermented grains during the distilling pot feeding process, this study uses several ways to augment the infrared images. These include mixup data enhancement 12 and random combinations of adding noise, changing brightness, and rotating images to improve the robustness and generalization ability of the model training results. As shown in Fig. 5 a is the original image and Fig. 5 b is the resultant image by mixup data enhancement method.

Data-enhanced images based on image processing ( a ) original images ( b ) the resultant image of mixup data enhancement.

All labeled original images were divided into training, validation and test sets in the ratio of 70%:15%:15%. The training set samples were extended by image enhancement and labeled using LabelImg. Finally, the final augmented dataset is listed in Table 1 .

Fermented grains surface temperature detection based on YOLO v5n

In recent years, deep-learning-based image detection networks have been divided into two-stage and one-stage detection networks 13 . Faster R-CNN 14 is a classical two-stage detection network with high detection accuracy, but the inference speed is relatively slow and cannot meet the real-time requirements. YOLO, SSD, and RetinaNet are representative single-stage detection networks, which are usually faster than Faster R-CNN in terms of speed and suitable for application scenarios requiring real-time performance. Under resource constraints, SSD 15 and RetinaNet 16 may have a slight disadvantage in processing speed compared to YOLO. YOLO models are characterized by simplicity and are easier to train and deploy. In addition, YOLO has achieved a better balance between accuracy and speed, which is particularly suitable for application scenarios that require rapid deployment and real-time target detection.

YOLO v5 series models have better versatility, ease of use, and compatibility, and balances recognition accuracy with detection speed 17 , 18 , 19 , 20 . Among them, the YOLO v5n version is the less structured and more accurate target detection model in the series. Compared with other models in the YOLO v5 series, YOLO v5n greatly reduces the number of model parameters and computation while ensuring a certain recognition accuracy 21 . Therefore, it is suitable for target detection tasks with fewer categories and simple features. In this study, the YOLO v5n model was used for training as the original model. The hyperparameters for model training are listed in Table 2 .

Fermented grains surface temperature detection based on knowledge distillation

In this study, YOLO v5s was selected as the teacher network and YOLO v5n as the student network to improve the accuracy of the student network in the task of detecting the surface temperature of the fermented grains through knowledge distillation for later pruning.

Knowledge distillation training is different from traditional model training. Softmax is commonly used as the output layer in traditional models to generate probabilities for different categories. When the probability distribution entropy generated by Softmax output is relatively low, the values for negative labels tend to approach 0. As a result, the contribution of negative labels to the loss function becomes negligible. However, negative labels also contain a great deal of information. Some negative labels may correspond to higher probabilities than others, and even contain more information than positive labels. In order to obtain more information from negative labels, knowledge distillation introduces a temperature variable, denoted by T . By adjusting the value of T , the entropy of the Softmax output probability distribution can be increased, thus amplifying the information carried by negative labels.

where q i is the "softened" probability vector, obtained by exponential operation and normalization; Z i indicates the logit value for the current category; j denotes the number of output nodes (number of categories); Z j represents the logit value for each category output by the full connectivity layer; T is the temperature parameter, and when T  = 1, the function is the original Softmax function. The knowledge distillation process was shown in the knowledge distillation model in Fig.  1 22 , 23 . First, the teacher network model is trained and the logits output of the teacher network is divided by the T after doing Softmax calculation to get the soft label value. Then, the same training as for the teacher network is performed to get the logits output. Next, a two-step calculation is performed. The first step is to perform a Softmax calculation by dividing the logits output of the student network by the same T as the teacher model to obtain the soft prediction. Soft predictions were compared to soft labels, and the difference between the two probability distributions was measured using the distillation loss function. The second step is to perform Softmax computation on the logits output of the student network to get the hard predicted values. The hard predicted values were compared to the actual labels and the difference between them was measured using the student loss function. The two parts of the loss function are added together to get the total loss function, which is calculated as

where V loss is the value of total loss function; V loss-SL is the value of the student loss function; V loss-KD is the value of distillation loss function; α is the scaling factor, which is used to adjust the weights of the two loss functions. When α is equal to zero, this corresponds to the network not being distilled and trained using only the student loss function.

The same training strategy as the original YOLO v5n model was used for the training process in this study. The temperature parameter was taken as T  = 20 and the scaling factor was taken as α  = 0.5.

Fermented grains surface temperature detection based on knowledge distillation channel pruning

The YOLO v5n model in this study can accurately detect surface temperature of fermented grains, but the structure and the number of parameters of the model have not yet reached the optimal effect, which occupies more computational resources. In order to improve the usefulness of the model and reduce the computational effort, the model is compressed using channel pruning 24 , 25 .

The channel pruning process is shown in Fig.  6 . First, Sparse training using L 1 norm on the scaling factor of the BN layer in the model to find the channel where the scaling factor tends to zero 26 . The closer scaling factor is to 0, the less important the corresponding output is to the final result 27 . After sparse training, the unimportant channels were pruned to obtain a smaller pruned model. Finally, fine tuning the pruned model to overcome the average accuracy decrease caused by the channel pruning algorithm.

Principles of channel pruning.

The formulas for the BN layer scaling factor evaluation method are shown in (3) and (4):

where x i and y i are the inputs and outputs of the BN layer; μ B and σ B 2 are the mean and variance of the batch data; ε denote the tiny positive numbers used to avoid divisors of 0; γ and β are the BN layer scaling factor and bias, respectively, with smaller scaling factors usually corresponding to less important channels.

An L1-norm is done on the BN layer scaling factor (as shown in Eq. ( 3 )) to force learning to sparse γ . A global ranking is done by the magnitude of the absolute value of γ to evaluate the importance of the channels. Then, the pruning ratio is set to prune unimportant channels.

where the first summation term is the normal training loss function, ( x, y ) denotes the input and target of training, W denotes the trainable weights, and l ( ) denotes the normal training loss; The second summation term denotes sparse training by L1-norm on the scaling factor, g( γ ) denotes the penalty function for the scaling factor, Γdenotes the scaling layer parameter, λ denotes the balance factor between normal and sparse training, i.e., the sparsity rate.

The sparse process requires a balance between accuracy and sparsity, which is achieved by adjusting the sparsity rate. Larger coefficients cause the γ tend to zero more quickly, but the average recognition accuracy decreases; smaller coefficients cause a slower tendency to zero, but a more stable accuracy. After sparse training, the model is compressed by removing unimportant channels in the convolution layer based on the mean ordering of the scaling factors of the BN layers. The accuracy and memory usage are considered together so as to determine the number of deleted channels to realize pruning. Once the model has been pruned, there is a notable reduction in the number of parameters and model size. However, this reduction often leads to a significant decrease in model accuracy. In order to overcome the problem of excessive loss of model accuracy after pruning, it is necessary to fine-tune the model after pruning. In this study, fine-tuning training was performed by loading the pruning weights file and setting the number of training iterations to 1000 epochs.

Experiments and result analysis

Training environment and evaluation indicators, training environment.

All training in this study was performed under the Win10 operating system with Intel(R) Core (TM) i7-7700 [email protected] GHz processor and NVIDIA GeForce GTX 950 graphics board. We used PyTorch1.10, PyCharm and Python3.8.5. Meanwhile, all comparison algorithms were run in the same environment to ensure the comparability of the experiments.

Evaluation criteria

In order to validate the model performance, model recognition performance metrics, computational performance metrics, and model memory footprint were selected to evaluate the model in this study. The mean average precision (mAP) was used to measure recognition performance, which was calculated using IOU threshold of 0.5. Parameters and floating-point operations (FLOPs) were used to measure computational performance. In addition, model size and FPS were considered. These metrics provide insight into accuracy, efficiency, and resource requirements, providing guidance for future model improvements.

Results based on knowledge distillation

To compensate for the inevitable decrease in accuracy of the model during subsequent pruning, this study uses a knowledge distillation algorithm to migrate the knowledge from the YOLO v5s teacher model to the YOLO v5n student model. The distilled model was named YOLO v5ns-D. As clearly shown in Table 3 , the YOLO v5ns-D model improves the mAP value by 2.9 compared with the YOLO v5n model, while the model size, number of parameters, FLOPs and FPS are basically unchanged. The result showed that the knowledge distillation was successful in improving the model performance and laid the foundation for the subsequent pruning process.

Results based on channel pruning

Results of sparse training.

In order to maintain a good recognition performance while sparse training, the choice of the sparsity rate is very important, which determines the proportion of non-zero parameters to be retained in the model. Experimentation and validation are required to determine the most appropriate sparsity rate coefficient.

When different sparsity rates are to be set, the model's BN layer weights and mean average precision change accordingly. The BN layer γ coefficient distribution of the original detection model is shown in Fig.  7 a, which was nearly normally distributed overall. Figure  7 b–e show the BN layer γ coefficient distribution for sparsity rates of 0.0005,0.00075,0.001, 0.0025, respectively, and the number of sparse training epochs is 100. It can be seen that the distribution centers of γ coefficients of all BN layers gradually moved closer to 0 with training, and the larger the sparsity rate is, the faster the distribution centers of γ coefficients moved closer to 0 and the more concentrated the distribution is. After comparison, six datasets were chosen to be taken at 0.00005 intervals between sparsity rates from 0.00075 to 0.001 to analyze the model performance. The results of mean average precision at different sparsity rates is shown in Table 4 . It can be seen that the mAP is highest when the sparsity rate is 0.0009, so the sparsity rate is selected as 0.0009 in this study.

BN layer γ coefficient under different sparsity rates (λ). ( a ) λ = 0, ( b ) λ = 0.0005, ( c ) λ = 0.00075, ( d ) λ = 0.001, (d) λ = 0.0025.

Results of pruning and fine-tuning

After determining the sparsity rate, in this study, YOLO v5ns-D model and the original model YOLO v5n are pruned with different proportions and fine-tuned, and the fine-tuned models were named YOLO v5ns-DP and YOLO v5n-P. The optimal pruning rate is selected by comparative analysis of model performance. When the pruning rate is greater than 0.681, one of the channels in the convolution layer will be pruned as a whole, affecting the model structure and causing a significant reduction in model accuracy. To determine the optimal channel pruning coefficient, different pruning coefficients were tested in the experiments, using a step size of 0.1. The results of the experiments can be seen in Fig.  8 .

Model performance under different pruning rates. ( a ) mAP, ( b ) Detection speed, ( c )Params, ( d ) GFLOPs, ( e ) Model size.

As clearly shown in Fig.  6 , the number of parameters, GFLOPs and model size of YOLO v5ns-DP and YOLO v5n-P models decreased to different degrees with the increasing of pruning rate. When the pruning rate is 0.2, the mAP of the YOLO v5ns-DP model is 81.7, which is 0.6 higher than the mAP of the original YOLO v5n model, whereas the number of parameters, the computation and the model size are decreased by 28.6%, 16.5% and 26.4%, respectively. Meanwhile, the distillation and then pruning model improved the mAP values for the same pruning rate compared with the direct pruning of the original model, while the number of model parameters, GFLOPs and the size of the model were basically unchanged. With a pruning rate of 0.2, for example, the mAP value of YOLO v5ns-DP is 1.5 higher than that of YOLO v5n-P. It is demonstrated that knowledge distillation and then pruning can effectively improve the model performance and avoid information loss and performance loss caused by relying only on pruning fine-tuning training.

The change in the number of the channel in each convolution layer of the model after pruning at 0.2 pruning rate is shown in Fig.  9 . It can be seen that the number of channels in most convolution layers is effectively reduced, with an average of about 13 channels per layer being pruned, indicating that the pruning algorithm is effective.

Channel changes before and after pruning.

Comparative experiments and analysis of results

To evaluate the effectiveness of the proposed method, this study compares our proposed YOLO v5ns-DP model with YOLO v5n, Faster R-CNN and SSD. An enhanced dataset of infrared images of fermented grains surface layers was used to train the detection models for the four algorithms, and then a test set was used to evaluate the performance of the different detection algorithms. The detailed results of the experiment are shown in Table 5 .

The above experimental results show the significant advantages of the YOLO v5ns-DP in target detection tasks. Compared with the classical two-stage detection network Faster R-CNN, although the mAP was reduced by 0.7, the model size and the number of parameters were reduced by 96.1% and 93.8%, respectively, while the detection speed was improved by a factor of three. Also as a single-stage detection network, the model size and the number of parameters of YOLO v5ns-DP were reduced by 89.8% and 91.1%, respectively, and the detection speed was improved by 72.9% compared to SSD, although there was no significant difference in mAP. In addition, compared with YOLO v5n, although the number of parameters and model size were reduced by 28.6% and 26.4%, respectively, the mAP was improved by 0.6 and the detection speed reached 89.891 FPS. These results show that YOLO v5ns-DP has a smaller model size and is easier to deploy on low-computing-power devices while balancing detection speed and accuracy, which reduces cost and improves efficiency.

Figure  10 shows the effectiveness of the YOLO v5ns-DP in detecting temperature changes in the surface layer of the fermented grains. In Fig.  10 a, five L-targets, two M-targets and two HB-targets were successfully identified and could be automatically labeled. In Fig.  10 b, one M-target and one HS-target were successfully recognized, but there was a white-hot region leakage. In Fig.  10 c, one L-target and two M-targets were successfully recognized, but at the same time there were cases where white-hot targets were detected outside the distilling pot region.

Detection effect images of YOLO v5ns-DP model.

In the overall analysis, YOLO v5ns-DP showed good performance in detecting the temperature change of the surface layer of fermented grains, with high detection accuracy and target recognition ability. However, the model still has some room for improvement, such as the leakage problem and the false detection problem which need to be further optimized. In the future work, we will further study how to improve the accuracy of the algorithm in the practice of target detection of temperature changes in the surface layer of fermented grains.

In this study, a lightweight method of fermented grains surface temperature detection was proposed on the basis of the YOLO v5n network. The knowledge from the YOLO v5s teacher model was migrated to the YOLO v5n student model through the knowledge distillation algorithm. Then, the knowledge distilled detection model was compressed by sparse training, channel pruning, and fine-tuning. Finally, the compressed model was named YOLO v5ns-DP. After compression, the lightweight model YOLO v5ns-DP improved the mAP value by 0.6 and achieved a detection speed of 89.891 FPS compared with the original model YOLO v5n, while the number of parameters, GFLOPs and model size reduced by 28.6%, 16.5% and 26.4%, respectively. All the results demonstrate that it is practical to use this method to achieve both rapid and accurate detection of fermented grains surface temperature in the process of feeding the distilling bucket. At the same time, the lightweight model not only provided a theoretical basis for the model to be deployed in the edge computing device, but also provided a technical basis for the intelligence of “feeding the distillation bucket after vapor detection”.

Data availability

Data or code presented in this study is available. The computer code that support the findings of this study have been deposited on Zenodo with the primary accession link: https://doi.org/10.5281/zenodo.11165115 .

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Acknowledgements

This work was supported in part by the Key Research and Development Project for Collaborative Innovation between Schools and Enterprises in Lvliang City (2023XDHZ01), Shanxi Coal Based Low Carbon Joint Fund (No. U1610118), and the Major Scientific and Technological Projects in Shanxi Province (20201102003).

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School of Mechanical Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

Xiaolian LIU, Shaopeng Gong, Xiangxu Hua, Taotao Chen & Chunjiang Zhao

College of Intelligent Manufacturing Industry, Shanxi Electronic Science and Technology Institute, Linfen, 041000, China

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X.L. and S.G. developed the experimental plan and methodology; X.L. and X.H. conducted the experiments; X.H. and T.C. performed the experimental validation; X.L., S.G. and C.Z. conducted the literature survey and collected the data; All authors reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.

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Correspondence to Chunjiang Zhao .

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LIU, X., Gong, S., Hua, X. et al. Research on temperature detection method of liquor distilling pot feeding operation based on a compressed algorithm. Sci Rep 14 , 13292 (2024). https://doi.org/10.1038/s41598-024-64289-w

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Received : 21 January 2024

Accepted : 06 June 2024

Published : 10 June 2024

DOI : https://doi.org/10.1038/s41598-024-64289-w

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