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How to Conduct a Research Survey: A Step-by-Step Guide

Mastering the Art of Research Surveys: A Step-by-Step Guide to Conduct Research Survey

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Mastering the art of research surveys: a step-by-step guide.

Research surveys are valuable tools for gathering data and insights from a targeted audience. Whether you’re a student conducting academic research, a professional seeking feedback from customers, or an organization looking to understand public opinion, conducting a well-designed survey can provide you with valuable information. In this article, iLovePhD will guide you through the process of conducting a research survey effectively.

Learn how to conduct research surveys like a pro with our comprehensive step-by-step guide. From defining your objectives to analyzing data, this article provides valuable insights and practical tips for designing, distributing, and interpreting research surveys effectively. Start gathering accurate and actionable information today!

• Define your research objectives: Before diving into the survey creation process, clearly define your research objectives . What specific information do you aim to gather? Clearly identifying your goals will help shape the questions and structure of your survey.
• Identify your target audience: Determine the specific group of people you want to survey. Understanding your target audience is crucial for tailoring your questions and ensuring the data you collect is relevant and representative.
• Choose the appropriate survey method: Select a survey method that suits your research objectives and target audience. Common methods include online surveys, phone interviews, in-person interviews, or mail-in questionnaires. Online surveys are popular due to their ease of use, wide reach, and cost-effectiveness.
• Design your survey questions: Craft clear and concise questions that are easy for respondents to understand. Start with demographic questions (age, gender, location) to gather basic information. Use a mix of closed-ended questions (multiple choice, rating scales) and open-ended questions (allowing respondents to provide detailed answers) to capture a range of data.
• Structure your survey: Organize your survey in a logical and coherent manner. Begin with an introduction that explains the purpose and confidentiality of the survey, followed by instructions on how to complete it. Group related questions together and consider the flow of the survey to keep respondents engaged.
• Test your survey : Before launching your survey, test it with a small sample of respondents to identify any potential issues or areas for improvement. This step ensures that the survey is error-free, user-friendly, and effectively captures the desired information.
• Choose a survey distribution method: If conducting an online survey, you can use various platforms like Google Forms, SurveyMonkey, or Qualtrics to distribute your survey link. If using other methods, determine the best way to reach your target audience, such as through phone calls, in-person interactions, or mail.
• Monitor survey responses: Regularly monitor the responses you receive to ensure data collection is progressing smoothly. Consider setting reminders or notifications to keep track of new submissions. This step is particularly crucial for time-sensitive surveys.
• Analyze and interpret the data: Once you’ve collected a sufficient number of responses, analyze the data using appropriate statistical techniques. Look for trends, patterns, and key insights that address your research objectives. Visualize the data through charts or graphs to enhance understanding and communication.
• Report and share your findings: Compile your research findings into a comprehensive report, including an introduction, methodology, results, analysis, and conclusions. Ensure that your report is well-structured and provides clear and actionable insights. Share the report with relevant stakeholders or audiences who can benefit from the information.

Conducting a research survey can be a valuable means of gathering data and insights. By following the step-by-step guide outlined in this article, you’ll be well-equipped to design, distribute, and analyze your survey effectively.

Remember to focus on your research objectives, tailor your questions to your target audience, and ensure the survey process is user-friendly. With a well-executed research survey, you can unlock valuable insights that drive informed decision-making.

• Data Analysis
• data collection
• research objectives
• research surveys
• survey analysis
• survey best practices
• survey data interpretation
• survey design
• survey distribution
• survey implementation
• survey insights
• survey methodology
• survey questions
• survey reporting
• survey research
• survey response rates
• survey techniques
• survey tools
• target audience

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7 Best Survey Tools for Research [2023]

As a Ph.D. researcher, one of the essential components of your academic journey is conducting surveys. Surveys serve as a valuable tool for collecting data, opinions, and insights from a targeted group of respondents. In today’s digital era, selecting the right survey tool is critical to ensure that your research process is efficient and yields accurate results. In this comprehensive guide, we will explore the best survey tools for research, discussing their popularity, pros, cons, and addressing frequently asked questions.

Why Choose the Best Survey Tools for Research

Before diving into the specifics of survey tools, it’s crucial to understand why selecting the right tool matters. As a researcher, your primary goal is to gather high-quality data that supports your research objectives and contributes to the academic community’s knowledge. Here are some key reasons why choosing the right survey tool is essential:

• Data Quality: The survey tool you choose can impact the quality of the data you collect. A well-designed tool helps ensure that respondents understand and answer questions accurately.
• Efficiency: The right tool can streamline the survey creation process, saving you time and effort. This efficiency allows you to focus on other aspects of your research.
• Data Analysis: Many survey tools offer built-in data analysis and reporting features, simplifying the process of extracting meaningful insights from your data.
• Scalability: Depending on your research scope, you may need a tool that can handle a large number of respondents and responses without compromising performance.
• Customization: Your research may require specific question types, logic branching, and branding. Choosing a tool that allows for customization is crucial.

Now that we understand the importance of selecting the right survey tools, let’s explore some of the best options available for Ph.D. research.

1. Google Forms

Google Forms is a free and user-friendly online survey and form-building tool developed by Google. It is part of the Google Workspace suite of productivity tools and is widely used for creating surveys, quizzes, and feedback forms for various purposes, including academic research.

Key Features:

• User-Friendly: Google Forms offers a straightforward and intuitive interface, making it easy for users, including those familiar with Google Workspace, to create surveys.
• Basic Customization: Users can customize the appearance of forms with themes and add logos, providing basic branding options.
• Integration with Google Sheets: Survey responses are collected directly in Google Sheets, allowing for easy data storage and basic analysis.
• Question Types: Google Forms supports various question types, including multiple choice, short text, long text, and scale questions.
• Simple Surveys: It is suitable for creating simple surveys and feedback forms, making it accessible to users with varying levels of technical expertise.
• User-friendly and easy to set up, especially for those familiar with Google Workspace.
• Allows for basic survey customization and integration with Google Sheets.
• Suitable for simple surveys and research with fewer complexities.
• Responses can be collected directly in Google Sheets for basic data analysis.
• May not provide advanced features required for complex research projects.
• Limited customization and analysis options compared to more robust survey tools.
• Integration options with external tools and applications are limited.

Google Forms is an excellent choice for researchers who need a straightforward and accessible tool for basic surveys and feedback forms.

It is particularly well-suited for academics who already use Google Workspace, as it seamlessly integrates with other Google productivity tools.

However, for more complex research projects requiring advanced features, researchers may need to explore other survey tools with enhanced capabilities.

2. Qualtrics

Qualtrics is a widely recognized and highly popular survey and research platform used by a variety of organizations, including academic institutions, for conducting surveys and collecting research data. It offers a comprehensive suite of features designed to meet the needs of researchers and businesses alike.

• Advanced Survey Customization: Qualtrics provides extensive customization options, allowing researchers to create surveys tailored to their specific needs. This includes a wide range of question types, logic branching, and survey design customization.
• Survey Logic and Branching: Researchers can implement complex survey logic and branching to ensure that respondents are presented with relevant questions based on their previous responses.
• Data Analysis and Reporting: Qualtrics offers robust data analysis and reporting tools, making it easier to derive meaningful insights from survey data. Researchers can create charts, graphs, and export data for further analysis.
• Survey Distribution: Surveys can be distributed via email, social media, website embedding, or QR codes, offering flexibility in reaching respondents.
• Integration: Qualtrics integrates with various third-party applications and services, facilitating data transfer and analysis.
• Highly customizable and suitable for complex research projects.
• Advanced survey logic and branching capabilities.
• Robust data analysis and reporting features.
• Supports various question types and distribution methods.
• Trusted by academic researchers and institutions.
• May have a steeper learning curve, especially for beginners.
• The free version has limitations, including a cap on the number of responses.
• Access to advanced features and enhanced support typically requires a paid subscription.

Qualtrics is a comprehensive and powerful survey tool that is particularly well-suited for researchers working on complex research projects that demand advanced customization and analysis capabilities.

While it may require some time to become proficient with the platform, its robust features make it a top choice for many academic researchers and institutions.

Also Read: How to Design Questionnaire for Research

3. Zoho Survey

Zoho Survey is an online survey tool provided by Zoho Corporation, known for its suite of business software applications. This survey tool allows users to create and distribute surveys for various purposes, including academic research.

• Survey Creation: Zoho Survey offers an intuitive interface for creating surveys with various question types, logic branching, and design customization.
• Data Collection: Surveys can be distributed via email, social media, or embedded in websites to reach respondents.
• Data Analysis: Zoho Survey provides basic data analysis and reporting features, allowing researchers to analyze survey responses.
• Integration: As part of the Zoho ecosystem, it integrates with other Zoho applications for streamlined data management.
• User-friendly interface, making it accessible for both beginners and experienced users.
• Customizable survey design with options for branding.
• Integration with other Zoho apps for efficient data handling.
• Supports various question types and survey distribution methods.
• The free plan has limitations, including a cap on the number of responses.
• Advanced features may require a paid subscription.
• While it offers basic reporting, it may not provide as extensive data analysis tools as some other survey tools.

Zoho Survey is a versatile tool suitable for researchers who prefer an integrated approach to data collection and analysis within the Zoho ecosystem.

It offers user-friendly survey creation and distribution, making it accessible to a wide range of users, including academics conducting research.

However, researchers with more complex survey needs may want to explore the paid plans for advanced features.

4. SurveyMonkey

SurveyMonkey is a widely recognized and user-friendly online survey tool that caters to various users, including academic researchers, businesses, and organizations.

It provides a range of features for creating and distributing surveys, collecting responses, and analyzing data.

• Survey Design: SurveyMonkey offers a user-friendly interface for designing surveys. Users can create surveys with various question types, including multiple choice, open-ended, and Likert scale questions.
• Customization: Researchers can customize the appearance of surveys with themes, logos, and branding to maintain a consistent look and feel.
• Advanced Features: SurveyMonkey provides advanced survey design features such as skip logic, randomization, and question branching, allowing researchers to create dynamic surveys.
• Distribution: Surveys can be distributed via email, social media, web links, or embedded in websites, providing flexibility in reaching respondents.
• Data Analysis: The platform offers basic data analysis tools, including summary statistics and data export options. Advanced reporting features are available in paid plans.
• Integration: SurveyMonkey integrates with various third-party applications, streamlining data transfer and analysis.
• User-friendly and suitable for both beginners and experienced users.
• Advanced survey design options for creating dynamic surveys.
• Customization options for branding and consistent design.
• Integration with other applications for data management.
• Trusted by academic researchers, businesses, and organizations.
• Access to advanced features, such as skip logic and custom themes, requires upgrading to paid plans.
• Advanced reporting and analysis features are available in paid plans.

SurveyMonkey is an accessible and versatile survey tool that is widely used by academic researchers for its user-friendly interface and range of features.

It’s well-suited for researchers conducting surveys with various complexities and customization needs, making it a popular choice in both academic and business settings.

5. Typeform

Typeform is an innovative and visually appealing online survey tool known for its interactive and engaging survey design.

It aims to provide a conversational experience for respondents and is used by a variety of users, including academic researchers, businesses, and creative professionals.

• Interactive Surveys: Typeform is recognized for creating visually engaging and interactive surveys that resemble conversations rather than traditional forms.
• Advanced Features: Researchers can implement logic branching, piping, and other advanced features to create dynamic and personalized surveys.
• Customization: Typeform allows for extensive customization and branding, enabling users to create surveys that align with their organization’s identity.
• Question Types: The platform supports various question types, including multiple choice, short text, long text, and rating scales.
• Integration: Typeform integrates with various third-party applications and services, facilitating data transfer and analysis.
• Data Analysis: Basic data analysis tools are available, and responses can be exported for further analysis.
• Engaging and interactive survey design, making it particularly appealing for creative and design-focused research projects.
• Advanced features like logic branching and piping for personalized surveys.
• Extensive customization options for branding and design.
• Integration with other applications for streamlined data management.
• Suitable for various research needs, including academic research.
• Access to advanced features and in-depth reporting requires a paid subscription.

Typeform is an excellent choice for researchers who prioritize creating visually engaging and interactive surveys.

Its modern and user-friendly interface makes it suitable for academic researchers looking to collect data in a more engaging and dynamic way, particularly for creative and design-related research projects.

6. LimeSurvey

LimeSurvey is an open-source survey software that provides users with extensive control over survey design, data collection, and analysis.

It is favored by open-source enthusiasts and researchers who seek complete customization and flexibility in their survey projects.

• Open Source: LimeSurvey is open-source software, allowing users to host it on their servers and have full control over the installation and configuration.
• Customization: Researchers can customize every aspect of their surveys, including design, question types, and survey flow, to meet specific research requirements.
• Question Variety: LimeSurvey supports a wide range of question types, including multiple choice, open-ended, ranking, and matrix questions.
• No Response Limitations: LimeSurvey does not impose limitations on the number of surveys or responses, making it suitable for large-scale research projects.
• Data Export: Survey data can be exported in various formats, enabling further analysis with external tools.
• Survey Logic: Logic branching and conditional survey flow can be implemented to ensure that respondents see relevant questions based on their previous answers.
• Complete control over survey design and hosting, making it ideal for tech-savvy researchers.
• No limitations on the number of surveys or responses, allowing for scalability.
• Extensive customization options, suitable for complex and specialized research projects.
• Supports various question types and survey logic for tailored surveys.
• No subscription fees or costs associated with hosting on your server.
• Hosting and server setup can be technically challenging for users without experience in server management.
• The user interface may not be as intuitive as some other survey tools.
• Limited user support compared to commercial survey tools.

LimeSurvey is an excellent choice for researchers who prioritize complete control and customization over their survey projects.

It is particularly well-suited for researchers with technical expertise who are comfortable hosting and configuring the software on their servers.

 LimeSurvey’s flexibility and open-source nature make it an attractive option for academic researchers and organizations looking for tailored survey solutions.

7. SoGoSurvey (Now Sogolytics)

Sogolytics is an online survey software platform designed to assist users in creating, distributing, and analyzing surveys for various purposes, including academic research, employee feedback, and customer satisfaction.

• Advanced Features: Sogolytics offers advanced features such as skip logic, branching, and customizable survey templates, allowing researchers to create dynamic and tailored surveys.
• Customization: Researchers can customize survey templates and branding to match their research goals and maintain a consistent look and feel.
• Question Types: The platform supports multiple question types, including multiple choice, open-ended, and ranking questions.
• Unlimited Surveys: The free core version of Sogolytics allows an unlimited number of surveys, offering scalability.
• Data Export: Survey data can be exported in various formats for further analysis with external tools.
• Data Analysis: Basic reporting and analysis features are available, while more advanced reporting options are available in paid plans.
• Advanced features like skip logic and branching for personalized surveys.
• Customizable survey templates and branding options.
• Supports multiple question types to cater to various research needs.
• Offers an unlimited number of surveys in the free core version.
• Trusted by academic researchers and organizations for feedback and research projects.
• The free version has limitations, including response limitations and access to advanced reporting features.
• Access to advanced reporting and analysis features requires a subscription to paid plans.

Sogolytics is gaining recognition among researchers for its advanced features and customizable survey templates.

It is a viable choice for academics conducting both simple and complex research projects, particularly those looking for scalable survey solutions.

Researchers should consider their specific needs and budget when choosing between the free and paid versions of Sogolytics.

Selecting the right survey tool is a crucial decision for Ph.D. researchers. Your choice will significantly impact the efficiency of your research, the quality of your data, and your ability to derive meaningful insights.

Whether you opt for the advanced capabilities of tools like Qualtrics, Zoho Survey and SurveyMonkey, the visually appealing design of Typeform, the open-source flexibility of LimeSurvey, or the user-friendliness of Google Forms, each tool has its strengths and is suited to different research needs.

Ultimately, the best survey tool for your Ph.D. research depends on your research objectives, technical expertise, and budget.

Take the time to assess your specific requirements and choose a tool that empowers you to collect and analyze data effectively, contributing valuable insights to your field of study.

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FAQs (Frequently Asked Questions)

Now that we’ve explored the best survey tools for Ph.D. research, let’s address some frequently asked questions that can help you make an informed decision:

Q1: Are these survey tools free?

Some of the mentioned survey tools offer free versions, while others have free trials or limited free plans.

The availability of free features and limitations can vary, so it’s essential to review each tool’s pricing and features.

Q2: Which tool is best for beginners?

Google Forms is often recommended for beginners due to its user-friendly interface and simplicity. If you’re new to survey research, starting with Google Forms can be a great way to get acquainted with the process.

Q3: What if I need advanced features?

If your research project requires advanced features like skip logic, branching, and in-depth reporting, consider tools like Qualtrics, SurveyMonkey, or Typeform, which offer robust options in their paid plans.

Q4: How can I choose the right tool for my research?

When choosing a survey tool, consider factors such as the complexity of your research design, the expected number of respondents, your familiarity with the platform, and your budget.

Additionally, consult with your advisor or peers for recommendations tailored to your specific research goals.

Q5: Can I change my survey tool later in my research project?

Yes, you can switch survey tools during your research project. However, this may involve transferring data and adjusting your survey design to fit the new tool’s capabilities.

It’s essential to plan such transitions carefully to minimize disruptions to your research.

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PhD Assistance

Expert guide to designing an online / a web-based survey questionnaire for your phd research i 2024 edition, introduction.

Questionnaire surveys are a popular data collection method for academic research in a variety of disciplines. Given the growing global access to the internet facility and the price of technology devices and software, online questionnaire surveys have become popular. In fact, qualitative data collection through online focus groups has also been acceptable, suggesting that research participants in the digital age can now interact with each other and the interviewer/facilitator in an online- multimedia setting. However, developing a questionnaire is not just an easy task, especially when we think of designing an online survey /web-based survey.  Since the questionnaire is a medium of remote conversation between researcher and respondent, it should be designed in such a way that it fulfills the objectives of the survey.  The present blog will shed light on designing a questionnaire.

1. Designing an Online questionnaire

• Introduction/Welcome Message : Begin with an engaging introduction or welcome message on the first page. This should motivate respondents to complete the survey, similar to a cover letter in a paper-based survey, persuading participation.
• Registration/Login : If the survey is intended for specific respondents only, include a registration or login section to ensure restricted access.
• Introduction Message : This part should clearly state the study’s purpose, along with assurances regarding privacy, data confidentiality, and storage. Include the researcher’s contact information for transparency.
• Exclusion/Security Questions : Implement questions to determine if certain respondents should be excluded from the survey, especially to maintain the confidentiality of the survey content.
• Screening Questions : Use these to verify respondent eligibility, ensuring that participants are representative of the research population.
• Main Questionnaire : This is the main section where questions are presented with defined response formats. Options for additional instructions can be included to assist respondents in answering the questions accurately.
• Appreciation Message : To acknowledge respondents’ time and effort, conclude with a thank-you message. Provide an option for respondents to email comments or feedback about the survey to the administrator.
• Each step is crafted to ensure a smooth and effective survey experience, both for the respondents and the researchers conducting the study.

1.1 How the response format should be

Drop-down Boxes:

• Usage: Ideal for long lists like states or countries.
• Interaction: Requires three mouse actions. Beware of bias towards top entries.
• Design Tips: Avoid default selections. Use a placeholder like “-select here-” as the first entry. Ensure the first option isn’t visible by default to avoid leading respondents. Incorporate clear visual clues for usage.
• Example: A question asking for the respondent’s country of residence. The respondent clicks on a drop-down menu and selects their country from a long list. The first option in the menu is “-select here-” , prompting the respondent to scroll through to find their country.
• Explore our services for a deep dive into the realm of PhD research. Elevate your academic journey with our expertise and support.

Radio Buttons:

• Appearan ce: Small circles next to response options of a closed-ended question.
• Functionality: Useful for selecting one among mutually exclusive items.
• Example : A survey question asking, “Do you own a pet?” Next to the question are two small circles, one labeled “Yes” and the other labeled “No”. The respondent can select only one of these options by clicking on the corresponding circle.

Check Boxes:

• Design: Small squares that display a tick when selected.
• Difference from Radio Buttons: Allows for multiple selections, unlike radio buttons.
• Example : A form asking about dietary preferences with options like “Vegetarian”, “Vegan”, “Gluten-Free”, “Dairy-Free”. Each option has a small square box next to it. The respondent can check multiple boxes that apply to their diet.

Text Input:

• Considerations: Mind the size of the box. Avoid the need for scrolling.
• Application: Best suited for open-ended questions where detailed responses are required.
• Example : An open-ended question on a feedback form asking, “Please describe your experience with our service.” Below the question is a rectangular text box where the respondent can type in their detailed response.

✔ Each of these response formats has specific utilities and design considerations. Their effective use enhances user experience and the quality of data collected in surveys or forms.

1.2 How does the Questionnaire type

1.3. Selecting Survey Participants:

• Access for All: Ensure easy access for all participants in online surveys.
• Target Demographics: Online questionnaires may be more suitable for certain age groups.
• Diverse Promotion: To capture a wide audience, promote surveys through various channels, including social media, telephone, and paper surveys.
• Participant Characteristics: Online survey participants are often more internet-savvy, possibly younger, and from higher-income households.
• Global Internet Usage: With increasing internet access globally, the gap in usage is narrowing.

1.4. Avoiding Multiple Responses:

• Participant Registration: Use email registration to assign unique participant numbers, reducing multiple enrollments.
• Personalized Links: Send surveys via personalized email links to ensure one response per participant.
• IP Address Restrictions: Be cautious as this can limit those sharing the same IP (e.g., family members).
• Survey Continuation: Allow participants to complete the survey in multiple sessions using the same device.

1.5. Data Management:

• Reliable Platforms: Online platforms offer secure data management and facilitate easy data transfer for analysis.
• Error Reduction: Digital responses reduce transcription errors and prevent survey modification by participants.
• Ease of Use: Well-designed online questionnaires improve the reliability and validity of data collection.

1.6. Ethical Issues:

• Informed Consent: Provide detailed study information on the survey’s first page, as verbal consent is challenging online.
• Privacy and Confidentiality: Set up surveys with privacy settings and be cautious with tools like Survey Monkey.
• Right to Withdrawal: Ensure participants can opt-out or skip questions, maintaining ethical research conduct.

1.7. Piloting:

• Testing: Conduct pilot studies to test the adequacy of questions, survey flow, technology feasibility, and data compatibility.
• Cognitive Interviews: Use cognitive interviews in pilot studies to refine the survey, alongside electronic version testing for aspects like download time.

✔ Explore our comprehensive study guide for an in-depth insight into the world of PhD research.

In conclusion, the design of a questionnaire is a fundamental aspect of survey research that directly influences the outcomes. This paper has emphasized the importance of meticulous planning in questionnaire development, tailored to the specific objectives of the study. The formation of hypotheses or research questions critically shapes the design process, casting a significant impact on the overall results. Additionally, we have outlined key considerations in the crafting, structuring, and ordering of survey questions. The paper also briefly touches upon crucial factors such as response rates and ethical considerations in survey research. Ultimately, this guide serves as a comprehensive resource for creating effective and reliable questionnaires, ensuring the integrity and success of your research study.

• Kish, M. A. (2001). Guide to development of practice guidelines.  Clinical Infectious Diseases ,  32 (6), 851-854.
• Anusuriya, D. (n.d.). The development of practical guidelines .
• Regmi, P. R., Waithaka, E., Paudyal, A., Simkhada, P., & Van Teijlingen, E. (2016). Guide to the design and application of online questionnaire surveys.  Nepal journal of epidemiology ,  6 (4), 640.
• Taherdoost, H. (2022). Designing a Questionnaire for a Research Paper: A Comprehensive Guide to Design and Develop an Effective Questionnaire.  Asian Journal of Managerial Science ,  11 , 8-16.
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An Introduction to Survey Research: Benefits and Pitfalls from Design to Implementation

Short description

This short course will introduce participants to the art of survey design. This includes understanding the logic of survey design, learn how to develop a multidimensional survey, ethical implications of survey research and tips and tricks on how to implement it. The target group for this course is PhD researchers at the start of their doctoral research, however, students who are further ahead may wish to participate if they would like to brush up their skills, learn a new method, or seek feedback on their already developed surveys. No prior experience in survey methodology is required to take this course.

How this course works

The workshop that will be a blend of both theory and practical applications on survey development, implementation, and practical applications of this research method. The course will be concluded by a feedback session for participants who wish to create their own survey. A short assignment is expected prior to the first session. A list of recommended readings will also be provided prior to the course.

Target group

Researchers in the first year of their PhD. 

By the end of the workshop, participants will have:

•  Develop a comprehensive understanding of the underlying logic of survey research, including different types of surveys and the lifecycle of survey research.
•  Acquire the necessary skills to construct scientifically rigorous surveys with reliable and valid data.
• Gain the ability to identify and navigate common pitfalls encountered in survey research, equipping them with strategies to address these challenges effectively.
• Build practical knowledge through engaging group activities, valuable peer feedback, and a hands-on survey design project.

Dr. Jessica Breaugh  is Senior Lecturer in the Department of Management at ESCP Business School, and a fellow at the Centre for Digital Governance at the Hertie School. She is Alumna of the Hertie School’s PhD program (2020). Her current research focuses on various aspects of the world of work, including, motivation, leadership and change management in the context of digitalisation. Her survey-based research can be found in journals such as Public Management Review and Public Policy and Administration.

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National Center for Science and Engineering Statistics

• All previous cycle years

The SED is an annual census of research doctorate recipients from U.S. academic institutions that collects information on educational history, demographic characteristics, graduate funding source and educational debts, and postgraduation plans.

Survey Info

• tag for use when URL is provided --> Methodology
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The SED is an annual census conducted since 1957 of all individuals receiving a research doctorate from an accredited U.S. institution in a given academic year. The SED is sponsored by the National Center for Science and Engineering Statistics within the National Science Foundation and by three other federal agencies: the National Institutes of Health, Department of Education, and National Endowment for the Humanities. The SED collects information on the doctoral recipient's educational history, demographic characteristics, graduate funding source and educational debts, and postgraduation plans. Results are used to assess characteristics of the doctoral population and trends in doctoral education and degrees.

Areas of Interest

• STEM Education
• Innovation and Global Competitiveness

Survey Administration

The 2021 survey was conducted by RTI International under contract to the National Center for Science and Engineering Statistics.

Survey Details

• Survey Description (PDF 135 KB)

Featured Survey Analysis

Doctorate Recipients from U.S. Universities: 2021

SED Overview

Data highlights, the number of research doctoral degrees awarded by u.s. institutions in 2021 decreased to 52,250, down from 55,224 in 2020..

Black or African American doctorate recipients were the largest minority group in health sciences and in non-science and engineering fields, particularly in education.

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Methodology, survey description, survey overview (2021 survey cycle).

The SED collects data on the number and characteristics of individuals receiving research doctoral degrees from U.S. academic institutions.

Data collection authority

The information collected by the SED is solicited under the authority of the National Science Foundation Act of 1950, as amended, and the America COMPETES Reauthorization Act of 2010. The Office of Management and Budget control number is 3145-0019, expiration date 30 April 2022.

Survey contractor

RTI International.

Major changes to recent survey cycle

Changed the SED field of study taxonomy to the modified 2020 Curriculum of Instructional Program (CIP).

Key Survey Information

Initial survey year.

Academic year 1957–58.

Reference period

Academic year 2020–21 (1 July 2020 to 30 June 2021).

Response unit

Individuals.

Sample or census

Population size, sample size.

Not applicable.

Key variables

Key variables of interest are listed below.

• Academic institution of doctorate
• Baccalaureate-origin institution (U.S. and foreign)
• Citizenship status at graduation
• Country of birth and citizenship
• Disability status
• Educational attainment of parents
• Educational history in college
• Field of each degree earned
• Graduate and undergraduate educational debt
• Marital status, as well as the number and age of dependents
• Postgraduation plans (e.g., work, postdoc, other study or training)
• Primary and secondary work activities
• Source and type of financial support for postdoctoral study or research
• Type and location of employer
• Basic annual salary
• Race and ethnicity
• Sources of financial support during graduate school
• Type of academic institution (e.g., historically Black colleges and universities, Carnegie codes, public or private) awarding the doctorate

Survey Design

Target population.

The population for the 2021 SED consists of all individuals receiving a research doctorate from a U.S. academic institution in the 12-month period beginning 1 July 2020 and ending 30 June 2021. A research doctorate is a doctoral degree that (1) requires completion of an original intellectual contribution in the form of a dissertation or an equivalent culminating project (e.g., musical composition) and (2) is not primarily intended as a degree for the practice of a profession. The most common research doctoral degree is the PhD. Recipients of professional doctoral degrees, such as MD, DDS, DVM, JD, DPharm, DMin, and PsyD, are not included in the SED.

Sampling frame

The sampling frame of doctorate recipients is created by first identifying all institutions that confer research doctorates and then identifying all individuals receiving a research doctorate from those institutions in the 12-month period ending 30 June 2021. The list of institutions is based principally on the institutions in the prior survey cycle augmented by any new institutions on the Integrated Postsecondary Education Data System (IPEDS) and other higher education resource databases. The list of doctorate recipients is created from administrative sources such as commencement lists from the institutions and commercial databases of doctorate dissertations. The total universe in 2021 included 52,250 persons from 448 institutions that conferred research doctorates.

Sample design

The SED is a census.

Data Collection and Processing

Data collection.

Two modes of data collection are used in the SED: self-administered Web survey and computer-assisted telephone interviewing (CATI). The self-administered Web survey is the primary mode of SED completion. The proportion of SED completions using the Web has increased each year since it was introduced in 2001, and it reached 97.6% in 2021.

Nonrespondents are contacted by e-mail and mail to complete the Web survey. If the series of follow-up e-mails and mailings is unsuccessful, the survey contractor attempts to reach nonrespondents to complete an abbreviated survey by CATI. Approximately 2.4% of SED completions were from CATI in 2021. At the end of data collection phase, institutional coordinators are contacted to obtain information on a small number of critical SED data items for nonrespondents from their institution.

Data processing

The data collected in the SED are subject to automated editing and coding procedures. Imputation is not performed for missing survey data items except for the month value used in calculation of the age at doctorate and time to degree variables.

Estimation techniques

The survey is a census, which does not require any sampling; weighting is not used to adjust for nonresponse.

Survey Quality Measures

Sampling error.

Not applicable because the SED is a census.

Coverage error

Due to the availability of comprehensive lists of doctorate-granting institutions and the institutions’ high levels of participation in the survey, coverage error of institutions is minimal. Because the graduate schools collect the survey data from degree recipients at the time of doctorate completion, coverage error for the universe of doctorate recipients is also minimal. Comparisons of the institutions and the number of research doctorate recipients covered by the SED with the total number of doctorate recipients (including non-research doctoral degree recipients) reported by institutions to the National Center for Education Statistics confirm that there is minimal coverage error of doctorate recipients. In 2021, 12 doctorate-granting institutions declined to fully enumerate their doctorate recipients for academic year 2021. Information on the graduates for all of these institutions were found from other sources, such as ProQuest.

Nonresponse error

Unit nonresponse.

Of the 52,250 individuals granted a research doctorate in 2021, 91.5% completed the survey. A limited number of SED critical data items (doctoral institution, year of doctorate, field of doctorate, type of doctorate, and, if available, baccalaureate institution, master’s degree institution, and sex) are constructed for nonrespondents from commencement programs, graduation lists, and other similar public records. Nonresponse was concentrated in a small number of institutions, with 22 institutions accounting for 50% of the total unit nonresponse.

Item nonresponse

Among the 52,250 individuals who received a research doctorate in 2021, item nonresponse rates for the five key SED demographic variables—sex, citizenship, country of citizenship, race and ethnicity, and location after graduation—range from 0.0% for sex to 7.9% for location after graduation.

Measurement error

Measurement error in the SED is attributable to several sources, including errors in respondent reporting and errors that occur during data processing. Data reported by respondents about their educational history, including degree institutions and field of study, that are not self-coded within the survey instrument are reviewed and coded by trained coders. Generally, the percentage of responses that require manual coding is low. In 2021, 1.6% of doctorate fields of study were manually coded, as well as 1.8% of associate’s degree fields, 0.7% of bachelor’s degree fields, and 1.3% of master’s degree fields.

Data Comparability

Data availability.

Each year’s survey data are compiled into the Doctorate Records File (DRF) and trend data are available back to 1958; more limited information (sex, institution, field, and year of doctorate) is contained on the DRF for PhDs who graduated in 1920–56.

Data comparability

Because of procedural changes implemented during the 1990 survey cycle to improve the completeness of race, ethnicity, and citizenship data, the data from 1990 and later years are not directly comparable to data before 1990.

In 2021, the education history section was changed to a table format in which respondents can select all the postsecondary degrees received, up to nine degrees. Based on the degrees reported, follow-up questions asked about each type of degree, including any professional doctorate being earned.

Beginning in 2021, field of study and doctorate dissertation field data are collected using a modified version of the 2020 Classification of Instructional Programs (CIP) codes. Therefore, the field of study data in 2021 may not be comparable to prior survey cycle data; see the 2021 survey cycle “Technical Notes” at https://ncses.nsf.gov/sed for more information.

Data Products

Publications.

The data from this survey are published annually in the series Doctorate Recipients from U.S. Universities . Other survey data products are also available at https://www.nsf.gov/statistics/srvydoctorates/#tabs-3 .

Information from the survey is also included in the series Science and Engineering Degrees by Race/Ethnicity of Recipients , in Science and Engineering Indicators , and in Women, Minorities, and Persons with Disabilities in Science and Engineering .

Electronic access

Access to tabular data on selected variables from 1958 onward is available in an interactive data tool from the National Center for Science and Engineering Statistics (NCSES). Users can create custom tables of the number of doctorate recipients by academic discipline and institutional characteristics of either the doctorate-granting institution or the baccalaureate-granting institution of doctorate recipients. A limited set of demographic characteristics are available to create custom tables by academic discipline. A more comprehensive set of analysis variables are available in the SED Restricted Data Analysis System (RDAS) for the data user to generate custom data tables in a secured platform.

Restricted access

Access to restricted data for researchers interested in analyzing microdata can be arranged through a licensing agreement. For more information, see the NCSES Licensing Page .

Technical Notes

Technical tables, questionnaires, view archived questionnaires, featured analysis.

Survey of Earned Doctorates Field of Study Taxonomy Changes in 2021 and Impact on Trend Data

Related content, related collections, survey contact.

For additional information about this survey or the methodology, contact

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PhD in Survey and Data Science

• Program Overview
• Courses Overview
• Policies & Requirements

Courses for the onsite programs may not be taken online. Onsite courses include those for JPSM MS and PhD programs. 

• The onsite programs holds classes in a traditional classroom setting on the University of Maryland College Park campus and offers  PhD and Master's degree programs in survey and data science.

The Ph.D. in Survey in Data Science is designed for students seeking a career in research or university teaching requiring knowledge of theories and applications in one or more key disciplines. It will equip its graduates with the knowledge and skills needed to create methodologies for the collection and analysis of quantitative data suitable for statistical inference to large populations. The core Ph.D. seminars provide students with exposure to cutting edge research issues in the field. The courses of study designed by the student and the advisory committee will create intellectual bridges between survey and data science and more traditional disciplines with a focus on seeking knowledge to improve surveys. This is a designated STEM program.

Two specialty areas are available to choose from:

• Statistical Science
• Social Science

The statistical science area is designed for students who wish to specialize in topics such as sample design, estimation associated with data from complex samples, variance estimation, and statistical adjustments for missing data.

The key components of the Ph.D. program are:

• An advisory committee that will assist the student in tailoring a program of study permitting the student to demonstrate both theoretical and applied knowledge and skills in the conduct of research in an area of survey and data science.
• A set of doctoral courses in key areas of survey and data science and statistics as well as in a basic discipline from which survey and data science draws.
• A qualifying examination in theory and methods of survey and data science.
• A comprehensive examination to assess whether a student has sufficient knowledge and creativity to complete a dissertation.
• Completion of a dissertation to demonstrate research skills.

Note: online courses may be used as electives in the JPSM MS and PhD degree programs.  Required courses for those degrees may not be taken online

Qualifier Exam Policy

Candidacy Requirements

How to Apply

The Ph.D. student's advisor is free to shape a schedule of courses tailored to the educational needs and research goals of the student. Courses can be classified in several categories.

All Ph.D. students will be required to take four semesters of the Ph.D. seminar. The Ph.D. seminar will taught by at least two faculty members, one from the statistical science and one from the social science group of the JPSM faculty.

First Year Seminar

This is a two term, six credit introduction to the integration of social science and statistical science approaches to the design, collection, and analysis of surveys. The seminar will focus on six to eight areas of the statistical and methodological literature that have benefited from alternative approaches. Students will demonstrate mastery of those literatures through critical review papers, ideas for extensions of the literature, and empirical projects related to the research reviewed.

Second Year Seminar

This is a two term, six credit seminar designed to develop and hone skills involved in the identification of research problems, specification of hypothesis/theorems to extend current understanding of the field, and planning for original research. A common set of readings in advanced research activities of JPSM faculty will be studied, with the faculty engaged in the research discussing areas of potential innovation. There will be four to six such topics, with students completing technical proposals for future research in each. Students will present the proposals in both written and oral form, and critique proposals.

Advanced Research Seminars

JPSM will also offer doctoral seminars focusing on topics such as longitudinal design, collection, and, analysis, advanced topics in survey statistics, and advanced topics in the social and cognitive foundations of survey measurement.

Courses in Other Departments

Students will also take courses in another department (or in some cases, other departments), as specified by their advisory committee. The departments (e.g., Economics, Psychology, Sociology, and Mathematics) will represent disciplines from which survey statistics and methodology draw. For some students two full years of courses will be required; for others, somewhat less.

Course List

Graduation requirements, candidacy requirements.

Advancement to candidacy requires that a student earn a passing grade on the Qualifying Exam, generally within twelve months of entering the program; complete four terms in the Doctoral Seminar, culminating in a final paper consisting of a proposal for research that would contribute to the field of survey and data science; and earn a passing grade on the final paper and final presentation at the end of the last term of the Doctoral Seminar.

Dissertation

After advancing to candidacy, with the ongoing guidance of the dissertation committee, the student will propose and conduct dissertation research, leading to an original scholarly contribution. At least twelve credit hours of dissertation research are required. The dissertation will involve the creation of new knowledge in the field of survey and data science, demonstrating that the student has mastered the research skills necessary for a career in advanced research.

Residency Requirement

All PhD students at JPSM must fulfill a residency requirement. This consists of spending at least two days per week at JPSM for a period of three semesters after the student has passed the qualifying exam.

Additional Documents

Qualifying Exam Policy

PhD Requirements

Graduate School Forms for Current PhD Students

Maryland Rules for Oral Defense of Dissertation

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Survey vs. questionnaire: what’s the difference, published by branford mcallister on march 11, 2022 march 11, 2022.

Last Updated on: 29th August 2022, 08:09 am

Surveys are very common and effective (mostly) in scholarly research. They are an excellent way to collect data related to human behavior and opinions. And, a survey can support both qualitative and quantitative research and analysis.

In this article I explain the differences between surveys and questionnaires, discuss sampling, and talk about considerations for each of these concepts.

Survey vs. Questionnaire 

Yes, there is a difference! A  questionnaire  is an instrument (like an interview protocol, an observation plan, or an experiment)—a written set of questions. 

Survey  is a broader term that encompasses both the instrument (questionnaire) and the process of employing the instrument—collecting and analyzing the responses from those questions. 

So, you might say, the questionnaire is one component of the survey. Planning a survey is a different task from constructing a questionnaire. The potential errors and bias, and their impacts on reliability are different.

• In surveys, we’re concerned with coverage error (ensuring all prospective groups or characteristics have an opportunity to participate to avoid selection bias ), and nonresponse error (low response rate). 
• For questionnaires, we’re concerned with clarity, length, and construct validity , which relates to measurement error (accurately measuring the constructs the questionnaire purports to measure).

Why Is This Difference Important?

Terms matter, and using them properly contributes to your credibility. And, from a practical perspective, understanding the tasks ensures that your research is rigorous, unbiased, and valid. The reliability of your research depends on how you handle both the questionnaire and the survey, and the reliability issues are different for each.

So, let’s delve into each in a little more detail.

Survey Research

The survey is the overall process of using a questionnaire to collect data. 

There are some very important considerations when choosing the survey method. Any choice about your research methodology should fit the purpose and the research objectives. These questions should take you in the right direction:

• What is your research problem? 
• What’s the gap in the current research?
• What must you learn to address that research question? 
• What kind of data do you need?
• What’s the population about which you wish to infer some attribute? 
• What method best generates those data? Quantitative or qualitative research? Survey, interviews, observation, experiment?

Once you choose the survey method, then the next set of questions help scope your effort. Needless to say, especially for busy, starving, stressed students, there are real-world constraints in terms of costs, time, and effort (which is why we sample). 

• How much time do you have?
• How much money do you have to invest in it?
• How feasible is your plan? Can you reach members of the population? Are there any insurmountable hurdles to reaching your population?

You have options: 

• Self-administered survey using mail or hand-delivery?
• Internet-based?
• Access your population through a professional or social group, association, or online social media platform (LinkedIn, Facebook)?
• Use a survey service (e.g., SurveyMonkey)? 

There are advantages and disadvantages to each option. While self-administered surveys provide control and flexibility, they suffer from low response rates and potentially high costs. A service may guarantee a specified sample size of respondents who meet your criteria, and may help construct the questionnaire. But, they also incur some costs to the researcher. Using an association leads to convenience sampling (discussed a bit later).

Once you decide on the survey mode, many of your considerations relate to choosing or designing an instrument, or with sampling. Let’s tackle sampling now.

Sampling is a very important aspect of survey research (and, for that matter, most scholarly research). Some simple definitions:

• Sampling : It would be great to obtain data for the entire population. But, due to constraints on resources, you may need to sample and infer characteristics of the entire population.
• Population : The entire set of all objects (or participants) sharing characteristics or qualifications (e.g., all undergraduate students in the U.S.); and to which the researcher intends to infer something of interest.
• Target population : A subset of the population, delimited by some additional characteristics (e.g., undergraduate students in public universities); or, feasibility or access issue.
• Sample frame : The subset of the target population from which an actual sample will be drawn, to which the researcher has access (e.g., undergraduate students in California state universities). The sample frame may be the same as the target population.
• Sample : A subset of the sample frame—those who meet the participant criteria and are contacted to complete the questionnaire; selection is based on a sampling technique (e.g., random sample of candidates within the sample frame).
• Participant criteria : The people who comprise your sample must meet the criteria (characteristics and qualifications) you establish.

Sampling Techniques 

• The purest form of sampling is a completely random sample from the sample frame. This requires the researcher to develop some mechanism for randomly selecting participants (or rely on a service to do it). 
• Convenience sampling is using a mechanism to contact qualified candidates in the sample frame, such as LinkedIn; or people attending a conference. 
• Stratified sampling divides the target population and sample frame into distinct cells: combinations of attributes (e.g., race, gender) proportionate to the population; then samples randomly within the cells.

A final consideration, no matter what sampling technique you use: How you will find and contact participants?

Sample Size 

In quantitative studies for which you will test hypotheses and make inferences about population attributes, there are online tools to compute minimum sample size, including G*Power . Here’s an example:

Say you’re comparing GRE scores by race and gender, using ANOVA, medium effect size, α = .05, power = .90. Using G*Power, you compute a minimum sample size of n min = 270.

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Overcoming Response Error

With a minimum sample size ( n min ) calculated, you must ensure that either the survey service obtains the minimum sample size, or you must send out a sufficient number of questionnaires to account for the response rate . AND, you should consider the likelihood of incomplete, invalid, or corrupted questionnaires.

Continuing with the example:

Survey response rate (from a similar study documented in a journal article) is 30%. Rate of corrupted, incomplete, invalid questionnaires is 10%.

n min = 270 (min required sample size of valid questionnaires)

270 = .90 × n 2 ⇒ n 2 = 300 ( n 2 is the number of questionnaire returned)

300 = .30 × n 3 ⇒ n 3 = 1000 ( n 3 is the number of questionnaires sent out)

So, to ensure you have the minimum number of valid, complete questionnaires (270), you would need to send out 1000 questionnaires to prospective participants.

Minimum Sample Size

Some survey services may guarantee your minimum sample size. But, ( huge point! ), be sure to consider the rate of questionnaire validity, and call that the minimum sample size for the survey service .

There’s little you can do after data collection to obtain more data if later you find that some of it is corrupt. And, it’s tragic to get to data analysis only to find out your sample is too small.

Let’s turn now to the instrument used in the survey method.

Questionnaires

The style of a questionnaire should fit the purpose and the research objectives. That means, using the kind of vocabulary that your target population is comfortable with. And, choosing a format that serves your data collection needs.

Here are some considerations:

• In either case, you must provide evidence of instrument validity, and the details of development and purpose and prior use. If previously used, this information should be available in a citable source. 
• One measurement of internal construct validity is Cronbach’s alpha. For off-the-shelf questionnaires, did the author report Cronbach’s alpha? If self-developed, you as the researcher must report on Cronbach’s alpha.
• For self-developed questionnaires, perform a pilot study of your instrument to ensure it is understandable, with no confusing questions or potential bias; that the length is appropriate; and to compute Cronbach’s alpha.
• Provide an introduction to your study, which is clear and professional, and addresses your purpose.
• Consider return envelopes and postage, anonymity of the participants, deadlines, and incentives. ( Another important point! Consider the Institutional Review Board [IRB] policies for any agency with a stake in the research, such as a university or an organization targeted for a survey.)
• Will it be cross-sectional (one point in time across sample frame) or longitudinal (data collected over time)?
• If a structured questionnaire, what kind of responses are needed, which determines what kind of scale?
• Will the responses be categorical/nominal? Dichotomous (e.g., yes or no)? Ordinal (such as a Likert scale)? Or numerical (a measured or counted quantity such as age or test scores)? 
• When writing the questions, consider objectivity and avoid language or questions that might be perceived to be biased.
• Consider complexity and length of time to complete.
• Use consistent ordinal responses (positive is high and negative is low); or reverse scoring will be needed.

Survey vs. Questionnaire: What’s It All Mean?

Surveys and questionnaires are different animals. A questionnaire is a component of a survey. And, there are different considerations for each, different sources of error and bias, impacting reliability and validity. 

But, why is this all important?

The survey method, with a properly designed questionnaire and rigorous sampling, is very useful in scholarly research.

Performing good survey research boils down to these three principles:

• The purpose of the research, which drives research methodology, the survey process, and the questionnaire as a data collection instrument.
• Rigorous planning for each component of that research, to meticulously address each potential source of bias and error.
• Disciplined execution to obtain, through sampling, a valid data set and to perform analysis to make inferences about the population.

Happy surveying!

Branford McAllister

Branford McAllister received his PhD from Walden University in 2005. He has been an instructor and PhD mentor for the University of Phoenix, Baker College, and Walden University; and a professor and lecturer on military strategy and operations at the National Defense University. He has technical and management experience in the military and private sector, has research interests related to leadership, and is an expert in advanced quantitative analysis techniques. He is passionately committed to mentoring students in post-secondary educational programs. Book a Free Consultation with Branford McAllister

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How short or long should be a questionnaire for any research? Researchers dilemma in deciding the appropriate questionnaire length

Hunny sharma.

Department of Community and Family Medicine, All India Institute of Medical Sciences, Raipur, Chhattisgarh, India

A questionnaire plays a pivotal role in various surveys. Within the realm of biomedical research, questionnaires serve a role in epidemiological surveys and mental health surveys and to obtain information about knowledge, attitude, and practice (KAP) on various topics of interest. Questionnaire in border perspective can be of different types like self-administered or professionally administered and according to the mode of delivery paper-based or electronic media–based. Various studies have been conducted to assess the appropriateness of a questionnaire in a particular field and methods to translate and validate them. But very little is known regarding the appropriate length and number of questions in a questionnaire and what role it has in data quality, reliability, and response rates. Hence, this narrative review is to explore the critical issue of appropriate length and number of questions in a questionnaire while questionnaire designing.

Introduction

A questionnaire is an essential tool in epidemiological surveys and mental health surveys and to assess knowledge, attitude, and practice (KAP) on a particular topic of interest. In general, it is a set of predefined questions based on the aim of the research.[ 1 ]

Designing a questionnaire is an art which unfortunately is neglected by most researchers.[ 2 ] A well-designed questionnaire not only saves time for a researcher but helps to obtain relevant information most efficiently, but designing such a questionnaire is complex and time-consuming.[ 3 , 4 ]

The quality of the data obtained by a specific questionnaire depends on the length and number of questions in the questionnaire, the language, and the ease of comprehension of the questions, relevance of the population to which it is administered, and the mode of administration, i.e., the self-administered or paper method or the electronic method [ Figure 1 ].[ 5 , 6 ]

Qualities of a well-designed questionnaire

Response rate is defined as the number of people who responded to a question asked divided by the number of total potential respondents. Response rate which is a crucial factor in determining the quality and generalizability of the outcome of the survey depends indirectly on the length and number of questions in a questionnaire.[ 7 , 8 ]

Several studies have been conducted to assess the appropriateness of the questionnaire in a particular field and methods to translate and validate them. But very little is known regarding the appropriate length and number of questions in a questionnaire and what role it has in data quality and reliability. Hence, this narrative review is to explore the critical issue of appropriate length and number of questions in a questionnaire while questionnaire designing.

What is a questionnaire

Merriam Webster defines the questionnaire as “a set of questions for obtaining statistically useful or personal information from individuals,” whereas Collins defines a questionnaire as “a questionnaire is a written list of questions which are answered by a lot of people to provide information for a report or a survey.” The oxford learners’ dictionaries also give a somewhat similar definition which states that a questionnaire is “a written list of questions that are answered by several people so that information can be collected from the answers.”[ 9 , 10 , 11 ]

Thus, this provides a simpler meaning that a questionnaire in simpler terms is a collection of questions that can be used to collect information from various individuals relevant to the research aims.

Where are questionnaires generally applied?

A questionnaire, in general, can be applied to a wide variety of research which can either be quantitative or qualitative research which completely depends on how and in which a number of open-ended questions are asked.[ 12 ]

Questionnaires are generally applied when a large population has to be assessed or surveyed with relative ease where they play a crucial role in gathering information on the perspectives of individuals in the population.

There is a variety of applications of questionnaire in opinion polls, marketing surveys, and in politics, wherein the context of biomedical research questionnaires are generally used in epidemiological surveys, mental health surveys, surveys on attitudes to a health service or health service utilization, to conduction knowledge, attitude, and practice (KAP) studies on a particular issue or topic of interest.[ 13 , 14 ]

What are the types of questionnaire?

Questionnaires in general are of two types those which are in paper format and those which are in electronic format. The questionnaire can further be of two types i.e., self-administered or professionally administered via interview. The paper format can be administered easily both in self-administered mode or professional administered mode via direct administration when the population is relatively small as it is cumbersome to manage and store the physical questionnaire, paper format can also be administered to a larger population via postal surveys. Electronic questionnaires can be easily administered to a larger population in self-administered mode via Internet-based services like google forms, e-mails, SurveyMonkey, or Survey Junkie, etc. When administering professional-administered questionnaires professional telephonic services must be utilized to interview a larger population in a shorter duration of time.[ 15 , 16 , 17 ]

What it is required to answer individual questions in the questionnaire or the burden imparted on respondents

As mentioned by Bowling, in general, there are at least four intricated steps required in answering a particular question in a questionnaire, these steps are comprehension, recall of information asked by the question from the memory, judgment on the link between the asked question and the recall of information, and at last communication of the information to the questionnaire or evaluator [ Figure 2 ].[ 18 ]

Steps involved for answering a particular question in the questionnaire

In the case of a self-administered questionnaire, there is also a need for critical reading skills which is not required in one-to-one or face-to-face interview which only requires listening and verbal skills to respond to questions in the same language in which they are being asked or interviewed.[ 18 ]

There are many other crucial factors which play an important role in deciding the utility of questionnaire in various research, one such factor is the literacy of the participants which is a major limiting factor in self-administered questionnaires. Whereas, the other factors include the respondent's age, maturity, and level of understanding and cognition, which are some of the other ways related to the comprehension of the questions.[ 19 ]

Do the length of the questionnaire matters?

Length and number of items in the questionnaire play a crucial role in questionnaire-based studies or surveys, it has a direct effect on the time taken by the respondent to complete the questionnaire, cost of the survey or study, response rate, and quality of data obtained.[ 20 ]

As evident from the study conducted by Iglesias and Torgerson in 2000, on the response rate of a mailed questionnaire, an increase in the length of the questionnaire from five pages to seven pages reduces the response rate from women aged 70 years and over but on contrary does not seems to affect the quality of response to questions.[ 21 ]

Another study conducted by Similar Koitsalu et al .[ 22 ] in 2018 reported that they were able to increase overall participation and information gathered through a long questionnaire with the help of prenotification and the use of a reminder without risking a lower response rate.

Whereas Sahlqvist, et al .[ 23 ] in 2011 reported that participants were more likely to respond to the short version of the questionnaire as compared to a long questionnaire.

Testing of ultrashort, short, and long surveys of 13, 25, and 75 questions, respectively by Kost et al .[ 24 ] in 2018, revealed that a shorter survey utilizing a short questionnaire was reliable and produce high response and completion rates than a long survey.

Bolt, on the other hand, in 2014, found a surprising find that reducing the length of a long questionnaire in a physician survey does not mean that it will necessarily improve response rate hence to improve the response rate in nonresponders’ researchers may think to utilize a drastically shortened version of the questionnaire to obtain some relevant information rather than no information.[ 25 ]

But the most interesting find comes from the web-based survey giant “Survey Monkey,” which states that there is a nonlinear relationship between the number of questions in a survey and the time spent answering each question. Which in other words can be explained as more there are questions in a survey lesser time respondent spend answering each question which is known as “speeding up” or “satisficing” through the questions. It is also observed that as the length of and the number of questions asked increased there is an increase in a nonresponse rate. This in term affects the quantity and reliability of the data gathered.[ 26 ]

What happens when respondents lose interest?

When there is a loss of interest, in the case of a long length questionnaire or extensive interviews, the bored respondents provide unconsidered and unreliable answers, or in other scenarios, it may lead to high nonresponse to questions. Where on one side a high nonresponse rate may lead to difficulty in data analysis or an unacceptable reduction in sample size, whereas on the other side, unconsidered or unreliable answers may defeat the whole purpose of the research [ Figure 3 ].[ 19 ]

Consequences of Loss of interest in research participant

Considerations while using a long questionnaire

While using a long questionnaire, a high nonresponse rate should always be expected hence appropriate measures to address the missing data should be considered such as data trimming or data imputation depending on the amount of data missing.[ 27 , 28 ]

While the loss of interest can be administering counteracted by dividing the questionnaire into sections and administering each section separating to avoid respondents’ fatigue or boredom.[ 19 ]

It is always advised that the administration of telephonic interview–based questionnaire should be kept short in general about 30 min to prevent fatigue or inattention which may adversely affect the quality of data. In the case of a very long telephonic interview, questions can be divided into sections, and each section can be administered on separate days or shifts lasting 30 min each. A long questionnaire should preferably be administered through face-to-face interviews.

Designing a questionnaire is an art and requires time and dedication, which in turn leads to the easiest way to measure the relevant information on a desired topic of interest. But many a times, this crucial step in biomedical research is ignored by researchers. With this narrative review, we were able to provide a glimpse of the importance of a good questionnaire. A good questionnaire can be of 25 to 30 questions and should be able to be administered within 30 min to keep the interest and attention of the participants intact. It is observed that as the number of questions increases there is a tendency of the participants speeding up or satisficing through the questions, which severely affect the quality, reliability, and response rates. In case a long questionnaire is essential, it should be divided into sections of 25 to 30 questions each to be delivered at a different time or day. In the case of a long questionnaire i.e., more than 30 questions, a larger amount of missing data or nonresponse rates must be anticipated and provisions should be made to address them. At last, it is always advised that shortening a relatively lengthy questionnaire significantly increases the response.

Financial support and sponsorship

Conflicts of interest.

There are no conflicts of interest.

National Center for Science and Engineering Statistics

• All previous cycle years

The SED is an annual census of research doctorate recipients from U.S. academic institutions that collects information on educational history, demographic characteristics, graduate funding source and educational debts, and postgraduation plans.

Survey Info

• tag for use when URL is provided --> Methodology
• tag for use when URL is provided --> Data
• tag for use when URL is provided --> Analysis

The Survey of Earned Doctorates is an annual census conducted since 1957 of all individuals receiving a research doctorate from an accredited U.S. institution in a given academic year. The SED is sponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF) and by three other federal agencies: the National Institutes of Health, Department of Education, and National Endowment for the Humanities. The SED collects information on the doctoral recipient’s educational history, demographic characteristics, and postgraduation plans. Results are used to assess characteristics of the doctoral population and trends in doctoral education and degrees.

Areas of Interest

• STEM Education
• Innovation and Global Competitiveness

Survey Administration

The 2022 survey was conducted by RTI International under contract to NCSES.

Survey Details

Featured survey analysis.

Doctorate Recipients from U.S. Universities: 2022

SED Overview

Data highlights, the number of research doctorates conferred by u.s. institutions, which began a sharp 15-month decline in spring 2020 due to the covid-19 pandemic, rebounded in 2022 with the highest number of research doctorates awarded in any academic year to date.

Over the past 20 years, most of the growth in the number of doctorates earned by both men and women has been in science and engineering (S&E) fields 

Methodology

Survey description, technical notes, technical tables, questionnaires, view archived questionnaires, featured analysis.

Research Doctorate Conferrals Rebound, Leading to Record Number of U.S. Doctorate Recipients in 2022

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Survey and Data Science (SURV)

Graduate Degree Program College: Behavioral and Social Sciences

The Survey and Data Science PhD Program blends together faculty with diverse disciplinary backgrounds, all devoted to teaching state-of-the-art practices in the statistical and methodological aspects of surveys. The program's faculty come primarily from the University of Maryland, University of Michigan, and Westat, supplemented by instructors from a number of federal statistical agencies.

Both the Master of Science ( program code: SUDS ) and PhD in Survey and Data Science degree programs have three areas of concentration: Statistical Science , Social  and Psychological Science and Data Science.

The statistical science area of concentration is designed for students who wish to specialize in areas such as sample design, estimation in complex samples, variance estimation, statistical measurement error models, and statistical adjustments for missing data.

The social science area of concentration is designed for students who wish to specialize in areas such as questionnaire design, design of interviewing systems, computer assistance in data collection, effects of mode of data collection, cognitive psychological insights into survey measurement, and efforts to reduce various nonsampling errors in data collection.

The data science area of concentration is designed for students who wish to specialize in the more computational aspects of survey methodology and research involving "big data," including data visualization, management and analysis of large and messy data sets, human-computer interaction in survey research, and machine learning algorithms.

The three areas of specialization share a set of courses in the core curriculum , including a two term survey practicum, a course in data collection methods, two terms of statistical methods, a two term course in total survey quality, and a survey design seminar.

Financial Assistance

Financial assistance is available in the form of competitive fellowships and graduate assistantships for teaching or research. 

Fellowships in Support of Diversity and Inclusion : Financial support option for Ph.D. students only. If you're interested in applying for the Fellowships in Support of Diversity and Inclusion in the Behavioral and Social Sciences (2017-18), please be sure to upload your materials under "4. Supplementary Application Two" when submitting your online admissions application.

Rupa Jethwa Eapen Director of Administrative Services Joint Program in Survey Methodology 1218 LeFrak Hall 7251 Preinkert Drive College Park, MD 20742 Telephone: 301.314.7911 Fax: 301.314.7912 Email:   jpsm.umd.edu/webform/contact-us

Website:    http://www.jpsm.umd.edu

Courses:  SURV

Keywords Social Science Concentration: Questionnaire Design, Social and Cognitive Foundations of Survey Measurement, Analysis of Complex Data, Survey Management. Statistical Science Concentration: Statistical Methods, Topics in Sampling, Inference from Complex Surveys, Probability Theory, Mathematical Statistics, Data Science Concentration: Big Data, Data Visualization, Human- computer interaction in survey research, machine learning algorithms, analysis of large and messy data sets.

Relationships:   Intermediate Survey Methodology (Z011)      Survey and Data Science (online) (MPDS)     Survey Statistics (Z010)

General Requirements

• Statement of Purpose
• Transcript(s)
• TOEFL/IELTS/PTE ( international graduate students )

Program-Specific Requirements

• Letters of Recommendation (3)
• Description of Research/Work Experience (optional)

Applicants to the M.S. program are expected to hold a baccalaureate degree from a regionally accredited institution with a minimum of a 'B' average. Post-baccalaureate coursework and relevant work experience will also be used in the application evaluation. Entry to the statistical science concentration requires three undergraduate courses in calculus, one in linear algebra, and one in statistics. Entry to the social science concentration requires two undergraduate quantitative courses, at least one of which is in statistics, and at least two undergraduate courses in the social sciences. Entry to the data science concentration requires some background in computer science, with a minimum of two semesters of programming coursework, and also at least one semester of statistics.

Applicants to the Ph.D. program are expected to have a graduate degree in some field (such as statistics or psychology) that is related to survey methodology. Applicants must also demonstrate an appropriate quantitative background.

For more admissions information or to apply to the program, please visit our  Graduate School website .

Application Deadlines

Other Deadlines: Please visit the program website at http://www.jpsm.umd.edu

• Survey and Data Science, Doctor of Philosophy (Ph.D.)

SURV has the goal of offering training to all qualified students, regardless of the employment sector of interest to them. Several features of the program are designed with the working student in mind. Many class times are tailored to be compatible with the work day; a 12-month curriculum offers core courses throughout the year; and research experience requirements are integrated with work activities.

Courses have been offered at a Federal agency facility located in Washington, D.C. and interactive 2-way audio/video transmission equipment is used to transmit some courses between the College Park campus and the Ann Arbor campus of the University of Michigan,also - between College Park and the Census headquarters in Suitland, MD.

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Methodology

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

What Is a Research Design | Types, Guide & Examples

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

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

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

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

Table of contents

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

• Introduction

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

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

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

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

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

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

Practical and ethical considerations when designing research

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

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

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

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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

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

Operationalization

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

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

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

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

Reliability and validity

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

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

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

Sampling procedures

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

That means making decisions about things like:

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

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

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

Data management

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

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

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

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

Quantitative data analysis

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

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

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

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

Using inferential statistics , you can:

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

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

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

Qualitative data analysis

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

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

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

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

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

 Statistics

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

Research bias

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

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

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

Quantitative research designs can be divided into two main categories:

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

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

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

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

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

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

Operationalization means turning abstract conceptual ideas into measurable observations.

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

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

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

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METHODS article

This article is part of the research topic.

Tools, Frameworks, and Approaches for Enhancing Research Methods Teaching

Simulating Surveys for Graduate Researcher Development Provisionally Accepted

• 1 University of Colorado Colorado Springs, United States

The final, formatted version of the article will be published soon.

Recognizing the value of experiential education in social/behavioral science research training, we designed and offered a simulation of a survey research project for doctoral students in education. Through three phases of the project, from instrument design through scale investigation and quantitative analyses, students are developed as researchers in a realistic and authentic way. In this paper, we highlight the advantages, challenges, and outcomes from applying simulation methods within graduate research methods courses, with a specific focus on survey methodology and quantitative skill development.

Keywords: survey design, Graduate student research, statistics education, Survey methods, item development

Received: 22 Dec 2023; Accepted: 02 Apr 2024.

Copyright: © 2024 Morris and Thompson. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Mx. Phillip A. Morris, University of Colorado Colorado Springs, Colorado Springs, United States

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SurveyCircle ist wirklich top. Es ist eine super Möglichkeit viele Studienteilnehmer für seine Umfrage zu gewinnen. Mit ein bisschen Aufwand kommt man im Ranking schnell nach oben und wird dann durch viele Teilnahmen belohnt. Auch der Support der Seite war einwandfrei. Innerhalb kurzer Zeit habe ich bereits Feedback bekommen, sofern ich mal eine Nachfrage hatte.

Cool concept and nice staff

SurveyCircle has been very useful for me for my master thesis. I was able to get a few hundred participants in a few short weeks, which would have otherwise cost me a lot of effort. Thanks for making... read more 

SurveyCircle has been very useful for me for my master thesis. I was able to get a few hundred participants in a few short weeks, which would have otherwise cost me a lot of effort. Thanks for making this service for us all!

Wirklich eine tolle Plattform, um schnell Teilnehmer für eine Umfrage zu gewinnen. Super fair und sehr freundlicher Kontakt mit dem Support :) Kann ich nur empfehlen!

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This website is very helpful. I highly recommend it if you are in need of participants, a research enthusiast, starting a business, or for educational purposes. It is a fair way to gain participants... read more 

This website is very helpful. I highly recommend it if you are in need of participants, a research enthusiast, starting a business, or for educational purposes. It is a fair way to gain participants and contribute to other individual's surveys. It saved me during my dissertation!

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This is the best platform to use if you need quality data for your research in a short period of time and you also get to participate in various interesting studies yourself. This made a huge... read more 

This is the best platform to use if you need quality data for your research in a short period of time and you also get to participate in various interesting studies yourself. This made a huge difference to my research!

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I can highly recommend Survey Circle as a platform! We managed to get participants for our survey quickly and without any problems. I liked how you can select a region to better target the... read more 

I can highly recommend Survey Circle as a platform! We managed to get participants for our survey quickly and without any problems. I liked how you can select a region to better target the participants needed for your survey. The customer service was also very friendly and quick in replying.

Useful and, most importantly, very helpful website to reach a reliable stream of participants for your survey.

View 500+ reviews on Google  and  Facebook

• How to Choose a PhD Research Topic
• Finding a PhD

Introduction

Whilst there are plenty of resources available to help prospective PhD students find doctoral programmes, deciding on a research topic is a process students often find more difficult.

Some advertised PhD programmes have predefined titles, so the exact topic is decided already. Generally, these programmes exist mainly in STEM, though other fields also have them. Funded projects are more likely to have defined titles, and structured aims and objectives.

Self funded projects, and those in fields such as arts and humanities, are less likely to have defined titles. The flexibility of topic selection means more scope exists for applicants to propose research ideas and suit the topic of research to their interests.

A middle ground also exists where Universities advertise funded PhD programmes in subjects without a defined scope, for example: “PhD Studentship in Biomechanics”. The applicant can then liaise with the project supervisor to choose a particular title such as “A study of fatigue and impact resistance of biodegradable knee implants”.

If a predefined programme is not right for you, then you need to propose your own research topic. There are several factors to consider when choosing a good research topic, which will be outlined in this article.

How to Choose a Research Topic

Our first piece of advice is to PhD candidates is to stop thinking about ‘finding’ a research topic, as it is unlikely that you will. Instead, think about developing a research topic (from research and conversations with advisors).

Consider several ideas and critically appraise them:

• You must be able to explain to others why your chosen topic is worth studying.
• You must be genuinely interested in the subject area.
• You must be competent and equipped to answer the research question.
• You must set achievable and measurable aims and objectives.
• You need to be able to achieve your objectives within a given timeframe.
• Your research question must be original and contribute to the field of study.

We have outlined the key considerations you should use when developing possible topics. We explore these below:

Focus on your interests and career aspirations

It is important to choose a topic of research that you are genuinely interested in. The decision you make will shape the rest of your career. Remember, a full-time programme lasts 3-4 years, and there will be unforeseen challenges during this time. If you are not passionate about the study, you will struggle to find motivation during these difficult periods.

You should also look to your academic and professional background. If there are any modules you undertook as part of your Undergraduate/Master degree that you particularly enjoyed or excelled in? These could form part of your PhD research topic. Similarly, if you have professional work experience, this could lead to you asking questions which can only be answered through research.

When deciding on a PhD research topic you should always consider your long-term career aspirations. For example, as a physicist, if you wish to become an astrophysicist, a research project studying black holes would be more relevant to you than a research project studying nuclear fission.

Read dissertations and published journals

Reading dissertations and published journals is a great way to identify potential PhD topics. When reviewing existing research ask yourself:

• What has been done and what do existing results show?
• What did previous projects involve (e.g. lab-work or fieldwork)?
• How often are papers published in the field?
• Are your research ideas original?
• Is there value in your research question?
• Could I expand on or put my own spin on this research?

Reading dissertations will also give you an insight into the practical aspects of doctoral study, such as what methodology the author used, how much data analysis was required and how was information presented.

You can also think of this process as a miniature literature review . You are searching for gaps in knowledge and developing a PhD project to address them. Focus on recent publications (e.g. in the last five years). In particular, the literature review of recent publications will give an excellent summary of the state of existing knowledge, and what research questions remain unanswered.

If you have the opportunity to attend an academic conference, go for it! This is often an excellent way to find out current theories in the industry and the research direction. This knowledge could reveal a possible research idea or topic for further study.

Finding a PhD has never been this easy – search for a PhD by keyword, location or academic area of interest.

Discuss research topic ideas with a PhD supervisor

Discuss your research topic ideas with a supervisor. This could be your current undergraduate/masters supervisor, or potential supervisors of advertised PhD programmes at different institutions. Come to these meetings prepared with initial PhD topic ideas, and your findings from reading published journals. PhD supervisors will be more receptive to your ideas if you can demonstrate you have thought about them and are committed to your research.

You should discuss your research interests, what you have found through reading publications, and what you are proposing to research. Supervisors who have expertise in your chosen field will have insight into the gaps in knowledge that exist, what is being done to address them, and if there is any overlap between your proposed research ideas and ongoing research projects.

Talking to an expert in the field can shape your research topic to something more tangible, which has clear aims and objectives. It can also find potential shortfalls of your PhD ideas.

It is important to remember, however, that although it is good to develop your research topic based on feedback, you should not let the supervisor decide a topic for you. An interesting topic for a supervisor may not be interesting to you, and a supervisor is more likely to advise on a topic title which lends itself to a career in academia.

Another tip is to talk to a PhD student or researcher who is involved in a similar research project. Alternatively, you can usually find a relevant research group within your University to talk to. They can explain in more detail their experiences and suggest what your PhD programme could involve with respect to daily routines and challenges.

Look at advertised PhD Programmes

Use our Search tool , or look on University PhD listing pages to identify advertised PhD programmes for ideas.

• What kind of PhD research topics are available?
• Are these similar to your ideas?
• Are you interested in any of these topics?
• What do these programmes entail?

The popularity of similar PhD programmes to your proposed topic is a good indicator that universities see value in the research area. The final bullet point is perhaps the most valuable takeaway from looking at advertised listings. Review what similar programmes involve, and whether this is something you would like to do. If so, a similar research topic would allow you to do this.

Writing a Research Proposal

As part of the PhD application process , you may be asked to summarise your proposed research topic in a research proposal. This is a document which summarises your intended research and will include the title of your proposed project, an Abstract, Background and Rationale, Research Aims and Objectives, Research Methodology, Timetable, and a Bibliography. If you are required to submit this document then read our guidance on how to write a research proposal for your PhD application.

Browse PhDs Now

Join thousands of students.

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• CAREER NEWS
• 06 June 2022

Share your experiences of graduate-student life with Nature

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Chris Woolston is a freelance writer in Billings, Montana.

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Nature wants PhD and master’s students to participate in a survey that will explore their experiences and career progression. Created in partnership with Shift Learning, a London-based research consultancy, the global survey will run throughout June and into July. The survey questions, aimed at students in science, technology, engineering and mathematics (STEM) fields, is available in English, Chinese, French, Portuguese and Spanish.

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NIH Protein Biotechnology Training Program Research Symposium

Join us in celebrating 35 years of our NIH Protein Biotechnology Training Program by attending our annual Research Symposium on Thursday, April 4, from 8:30 a.m. to 5 p.m. in the Elson Floyd Cultural Center on the Pullman campus. This year our symposium theme is “Breaking the PhD Mold” where we will be celebrating unique, inspirational, and diverse PhD journeys that do not follow the traditional academic/industry routes. We will be enjoying poster and oral presentations from current program trainees and external speakers.

Make sure you register at the link below, spots are limited and preference will be given to our program trainees and faculty. Registration includes free breakfast and lunch. We hope to see you there!

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Regions & Countries

Methodology: 2022-23 survey of asian americans.

The survey analysis is drawn from a national cross-sectional survey conducted for Pew Research Center by Westat. The sampling design of the survey was an address-based sampling (ABS) approach, supplemented by list samples, to reach a nationally representative group of respondents. The survey was fielded July 5, 2022, through Jan. 27, 2023. Self-administered screening interviews were conducted with a total of 36,469 U.S. adults either online or by mail, resulting in 7,006 interviews with Asian American adults. It is these 7,006 Asian Americans who are the focus of this report. After accounting for the complex sample design and loss of precision due to weighting, the margin of sampling error for these respondents is plus or minus 2.1 percentage points at the 95% level of confidence.

The survey was administered in two stages. In the first stage, a short screening survey was administered to a national sample of U.S. adults to collect basic demographics and determine a respondent’s eligibility for the extended survey of Asian Americans. Screener respondents were considered eligible for the extended survey if they self-identified as Asian (alone or in combination with any other race or ethnicity). Note that all individuals who self-identified as Asian were asked to complete the extended survey.

To maintain consistency with the Census Bureau’s definition of “Asian,” individuals responding as Asian but who self-identified with origins that did not meet the bureau’s official standards prior to the 2020 decennial census were considered ineligible and were not asked to complete the extended survey or were removed from the final sample. Those excluded were people solely of Southwest Asian descent (e.g., Lebanese, Saudi), those with Central Asian origins (e.g., Afghan, Uzbek) as well as various other non-Asian origins. The impact of excluding these groups is small, as together they represent about 1%-2% of the national U.S. Asian population, according to a Pew Research Center analysis of the 2021 American Community Survey.

Eligible survey respondents were asked in the extended survey how they identified ethnically (for example: Chinese, Filipino, Indian, Korean, Vietnamese, or some other ethnicity with a write-in option). Note that survey respondents were asked about their ethnicity rather than nationality. For example, those classified as Chinese in the survey are those self-identifying as of Chinese ethnicity, rather than necessarily being a citizen or former citizen of the People’s Republic of China. Since this is an ethnicity, classification of survey respondents as Chinese also includes those who are Taiwanese.

The research plan for this project was submitted to Westat’s institutional review board (IRB), which is an independent committee of experts that specializes in helping to protect the rights of research participants. Due to the minimal risks associated with this questionnaire content and the population of interest, this research underwent an expedited review and received approval (approval #FWA 00005551).

Throughout this methodology statement, the terms “extended survey” and “extended questionnaire” refer to the extended survey of Asian Americans that is the focus of this report, and “eligible adults” and “eligible respondents” refer to those individuals who met its eligibility criteria, unless otherwise noted.

Sample design

The survey had a complex sample design constructed to maximize efficiency in reaching Asian American adults while also supporting reliable, national estimates for the population as a whole and for the five largest ethnic groups (Chinese, Filipino, Indian, Korean and Vietnamese). Asian American adults include those who self-identify as Asian, either alone or in combination with other races or Hispanic identity.

The main sample frame of the 2022-2023 Asian American Survey is an address-based sample (ABS). The ABS frame of addresses was derived from the USPS Computerized Delivery Sequence file. It is maintained by Marketing Systems Group (MSG) and is updated monthly. MSG geocodes their entire ABS frame, so block, block group, and census tract characteristics from the decennial census and the American Community Survey (ACS) could be appended to addresses and used for sampling and data collection.

All addresses on the ABS frame were geocoded to a census tract. Census tracts were then grouped into three strata based on the density of Asian American adults, defined as the proportion of Asian American adults among all adults in the tract. The three strata were defined as:

• High density: Tracts with an Asian American adult density of 10% or higher
• Medium density: Tracts with a density 3% to less than 10%
• Low density: Tracts with a density less than 3%

Mailing addresses in census tracts from the lowest density stratum, strata 3, were excluded from the sampling frame. As a result, the frame excluded 54.1% of the 2020 census tracts, 49.1% of the U.S. adult population, including 9.1% of adults who self-identified as Asian alone or in combination with other races or Hispanic ethnicity. For the largest five Asian ethnic subgroups, Filipinos had the largest percentage of excluded adults, with 6.8%, while Indians had the lowest with 4.2% of the adults. Addresses were then sampled from the two remaining strata. This stratification and the assignment of differential sampling rates to the strata were critical design components because of the rareness of the Asian American adult population.

Despite oversampling of the high- and medium-density Asian American strata in the ABS sample, the ABS sample was not expected to efficiently yield the required number of completed interviews for some ethnic subgroups. Therefore, the ABS sample was supplemented with samples from the specialized surname list frames maintained by the MSG. These list frames identify households using commercial databases linked to addresses and telephone numbers. The individuals’ surnames in these lists could be classified by likely ethnic origin. Westat requested MSG to produce five list frames: Chinese, Filipino, Indian, Korean and Vietnamese. The lists were subset to include only cases with a mailing address. Addresses sampled from the lists, unlike those sampled from the ABS frame, were not limited to high- and medium-density census tracts.

Once an address was sampled from either the ABS frame or the surname lists, an invitation was mailed to the address. The invitation requested that the adult in the household with the next birthday complete the survey.

Data collection

To maximize response, the survey used a sequential mixed-mode protocol in which sampled households were first directed to respond online and later mailed a paper version of the questionnaire if they did not respond online.

The first mailing was a letter introducing the survey and providing the information necessary (URL and unique PIN) for online response. A pre-incentive of $2 was included in the mailing. This and remaining screener recruitment letters focused on the screener survey, without mentioning the possibility of eligibility for a longer survey and associated promised incentive, since most people would only be asked to complete the short screening survey. It was important for all households to complete the screening survey, not just those who identify as Asian American. As such, the invitation did not mention that the extended survey would focus on topics surrounding the Asian American experience. The invitation was generic to minimize the risk of nonresponse bias due to topic salience bias.

After one week, Westat sent a postcard reminder to all sampled individuals, followed three weeks later by a reminder letter to nonrespondents. Approximately 8.5 weeks after the initial mailing, Westat sent nonrespondents a paper version screening survey, which was a four-page booklet (one folded 11×17 paper) and a postage-paid return envelope in addition to the cover letter. If no response was obtained from those four mailings, no further contact was made.

Eligible adults who completed the screening interview on the web were immediately asked to continue with the extended questionnaire. If an eligible adult completed the screener online but did not complete the extended interview, Westat sent them a reminder letter. This was performed on a rolling basis when it had been at least one week since the web breakoff. Names were not collected until the end of the web survey, so these letters were addressed to “Recent Participant.”

If an eligible respondent completed a paper screener, Westat mailed them the extended survey and a postage-paid return envelope. This was sent weekly as completed paper screeners arrived. Westat followed these paper mailings with a reminder postcard. Later, Westat sent a final paper version via FedEx to eligible adults who had not completed the extended interview online or by paper.

A pre-incentive of $2 (in the form of two $1 bills) was sent to all sampled addresses with the first letter, which provided information about how to complete the survey online. This and subsequent screener invitations only referred to the pre-incentive without reference to the possibility of later promised incentives.

Respondents who completed the screening survey and were found eligible were offered a promised incentive of $10 to go on and complete the extended survey. All participants who completed the extended web survey were offered their choice of a $10 Amazon.com gift code instantly or $10 cash mailed. All participants who completed the survey via paper were mailed a $10 cash incentive.

In December 2022 a mailing was added for eligible respondents who had completed a screener questionnaire, either by web or paper but who had not yet completed the extended survey. It was sent to those who had received their last mailing in the standard sequence at least four weeks earlier. It included a cover letter, a paper copy of the extended survey, and a business reply envelope, and was assembled in a 9×12 envelope with a $1 bill made visible through the envelope window.

In the last month of data collection, an additional mailing was added to boost the number of Vietnamese respondents. A random sample of 4,000 addresses from the Vietnamese surname list and 2,000 addresses from the ABS frame who were flagged as likely Vietnamese were sent another copy of the first invitation letter, which contained web login credentials but no paper copy of the screener. This was sent in a No. 10 envelope with a wide window and was assembled with a $1 bill visible through the envelope window.

The mail and web screening and extended surveys were developed in English and translated into Chinese (Simplified and Traditional), Hindi, Korean, Tagalog and Vietnamese. For web, the landing page was displayed in English initially but included banners at the top and bottom of the page that allowed respondents to change the displayed language. Once in the survey, a dropdown button at the top of each page was available to respondents to toggle between languages.

The paper surveys were also formatted into all six languages. Recipients thought to be more likely to use a specific language option, based on supplemental information in the sampling frame or their address location, were sent a paper screener in that language in addition to an English screener questionnaire. Those receiving a paper extended instrument were sent the extended survey in the language in which the screener was completed. For web, respondents continued in their selected language from the screener.

Weighting and variance estimation

Household-level weighting.

The first step in weighting was creating a base weight for each sampled mailing address to account for its probability of selection into the sample. The base weight for mailing address k is called BW k and is defined as the inverse of its probability of selection. The ABS sample addresses had a probability of selection based on the stratum from which they were sampled. The supplemental samples (i.e., Chinese, Filipino, Indian, Korean and Vietnamese surname lists) also had a probability of selection from the list frames. Because all of the addresses in the list frames are also included in the ABS frame, these addresses had multiple opportunities for these addresses to be selected, and the base weights include an adjustment to account for their higher probability of selection.

Each sampled mailing address was assigned to one of four categories according to its final screener disposition. The categories were 1) household with a completed screener interview, 2) household with an incomplete screener interview, 3) ineligible (i.e., not a household, which were primarily postmaster returns), and 4) addresses for which status was unknown (i.e., addresses that were not identified as undeliverable by the USPS but from which no survey response was received).

The second step in the weighting process was adjusting the base weight to account for occupied households among those with unknown eligibility (category 4). Previous ABS studies have found that about 13% of all addresses in the ABS frame were either vacant or not home to anyone in the civilian, non-institutionalized adult population. For this survey, it was assumed that 87% of all sampled addresses from the ABS frame were eligible households. However, this value was not appropriate for the addresses sampled from the list frames, which were expected to have a higher proportion of households as these were maintained lists. For the list samples, the occupied household rate was computed as the proportion of list cases in category 3 compared to all resolved list cases (i.e., the sum of categories 1 through 3). The base weights for the share of category 4 addresses (unknown eligibility) assumed to be eligible were then allocated to cases in categories 1 and 2 (known households) so that the sum of the combined category 1 and 2 base weights equaled the number of addresses assumed to be eligible in each frame. The category 3 ineligible addresses were given a weight of zero.

The next step was adjusting for nonresponse for households without a completed screener interview to create a final household weight. This adjustment allocated the weights of nonrespondents (category 2) to those of respondents (category 1) within classes defined by the cross-classification of sampling strata, census region, and sample type (e.g., ABS and list supplemental samples). Those classes with fewer than 50 sampled addresses or large adjustment factors were collapsed with nearby cells within the sample type. Given the large variance in the household weights among the medium density ABS stratum, final household weights for addresses within this stratum were capped at 300.

Weighting of extended survey respondents

The extended interview nonresponse adjustment began by assigning each case that completed the screener interview to one of three dispositions: 1) eligible adult completed the extended interview; 2) eligible adult did not complete the extended interview; and 3) not eligible for the extended interview.

An initial adult base weight was calculated for the cases with a completed extended interview as the product of the truncated number of adults in the household (max value of 3) and the household weight. This adjustment accounted for selecting one adult in each household.

The final step in the adult weighting was calibrating the adult weights for those who completed the extended interview so that the calibrated weights (i.e., the estimated number of adults) aligned with benchmarks for non-institutionalized Asian adults from the 2016-2020 American Community Surveys Public Use Microdata Sample (PUMS). Specifically, raking was used to calibrate the weights on the following dimensions:

• Ethnic group (Chinese, Filipino, Indian, Japanese, Korean, Vietnamese, other single Asian ethnicities, and multiple Asian ethnicities)
• Collapsed ethnic group (Chinese, Filipino, Indian, Korean, Vietnamese, all other single and multiple Asian ethnicities) by age group
• Collapsed ethnic group by sex
• Collapsed ethnic group by census region
• Collapsed ethnic group by education
• Collapsed ethnic group by housing tenure
• Collapsed ethnic group by nativity
• Income group by number of persons in the household

The control totals used in raking were based on the entire population of Asian American adults (including those who live in the excluded stratum) to correct for both extended interview nonresponse and undercoverage from excluding the low-density stratum in the ABS frame.

Variance estimation

Response rates.

Westat assigned all sampled cases a result code for their participation in the screener, and then they assigned a result for the extended questionnaire for those who were eligible for the survey of Asian Americans. Two of the dispositions warrant some discussion. One is the category “4.313 No such address.” This category is for addresses that were returned by the U.S. Postal Service as not being deliverable. This status indicates the address, which was on the USPS Delivery Sequence File at the time of sampling, currently is not occupied or no longer exists. The second category is “4.90 Other.” This category contains 588 addresses that were never mailed because they had a drop count of greater than four. Drop points are addresses with multiple households that share the same address. The information available in the ABS frame on drop points is limited to the number of drop points at the address, without information on the type of households at the drop point, or how they should be labeled for mailing purposes. In this survey, all drop points were eligible for sampling, but only those with drop point counts of four or fewer were mailed. Westat treated drop point counts of five or more as out of scope, and no mailing was done for those addresses.

Westat used the disposition results to compute response rates consistent with AAPOR definitions. The response rates are weighted by the base weight to account for the differential sampling in this survey. The AAPOR RR3 response rate to the screening interview was 17.0%. 2  The RR1 response rate to the extended Asian American interview (77.9%) is the number of eligible adults completing the questionnaire over the total sampled for that extended questionnaire. The overall response rate is the product of the screener response rate and the conditional response rate for the extended questionnaire. The overall response rate for the Asian American sample in the Pew Research Center survey was 13.3% (17.0% x 77.9%).

Analysis of Asians living in poverty

Survey analysis of Asian adults living in poverty is based on 561 respondents of the 2022-23 survey of Asian Americans whose approximate family income falls at or below the 2022 federal poverty line published by the U.S. Department of Health and Human Services (HHS).

The survey asked respondents to choose their family income brackets in the 12 months prior to the survey. These income brackets were converted into dollars in the following ways:

• For those reporting a family income of less than $12,500, $12,499 was used as a proxy for their family income.
• For respondents reporting income brackets that are between $12,500 and $149,999, the midpoint of the selected income bracket was used as a proxy. For example, if they chose “$12,500 to $14,999,” $13,750 was used.
• For respondents reporting a family income of $150,000 or more, $150,000 was used.

The survey also asked respondents how many adults ages 18 or older live in their household including themselves, from one to 10 adults. Additionally, the survey asked how many children under 18 live in their household, from zero to 10 children. These responses were used to calculate their total family (household) size. Asian adults were categorized as “living near or below the poverty line” if their approximate family income, after being adjusted for family size, falls at or below 100% of the 2022 federal poverty line. Respondents with a household size of four were categorized as “living near or below the poverty line” if their approximate family income is $27,750 or less. 3 All Asian adults whose approximate family income is $12,499 were categorized as “living near or below” the poverty line regardless of family size, since those respondents have an income under the 2022 federal poverty line for a family of one. All Asian adults who meet the criteria above are used for the analysis of Asians in poverty, irrespective of their status as students or not.

A number of sensitivity checks were performed to test the robustness of the findings, and the main conclusions were consistently upheld. These sensitivity checks included using the poverty thresholds published by the Census Bureau instead of the poverty line published by HHS to define Asians in poverty, and excluding full-time students from the analysis even if their family income falls at or below the poverty line.

• For additional details on jackknife replication, refer to Rust, K.F., and J.N.K. Rao. 1996. “ Variance estimation for complex surveys using replication techniques .” Statistical Methods in Medical Research. ↩
• The weighted share of unscreened households assumed to be eligible for the screener interview (occupied “e”) was 87%. ↩
• The U.S. Department of Health and Human Services has separate poverty guidelines for the 48 contiguous states and the District of Columbia, Alaska and Hawaii. For all respondents in the 2022-23 survey of Asian Americans, poverty status was determined by applying the federal poverty line for the 48 contiguous states and D.C., regardless of respondents’ state of residence. ↩

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