research web meaning

What is Research? Definition, Types, Methods and Process

By Nick Jain

Published on: July 25, 2023

Table of Contents

What is Research?

Types of research methods, research process: how to conduct research, top 10 best practices for conducting research in 2023.

Research is defined as a meticulous and systematic inquiry process designed to explore and unravel specific subjects or issues with precision. This methodical approach encompasses the thorough collection, rigorous analysis, and insightful interpretation of information, aiming to delve deep into the nuances of a chosen field of study. By adhering to established research methodologies, investigators can draw meaningful conclusions, fostering a profound understanding that contributes significantly to the existing knowledge base. This dedication to systematic inquiry serves as the bedrock of progress, steering advancements across sciences, technology, social sciences, and diverse disciplines. Through the dissemination of meticulously gathered insights, scholars not only inspire collaboration and innovation but also catalyze positive societal change.

In the pursuit of knowledge, researchers embark on a journey of discovery, seeking to unravel the complexities of the world around us. By formulating clear research questions, researchers set the course for their investigations, carefully crafting methodologies to gather relevant data. Whether employing quantitative surveys or qualitative interviews, data collection lies at the heart of every research endeavor. Once the data is collected, researchers meticulously analyze it, employing statistical tools or thematic analysis to identify patterns and draw meaningful insights. These insights, often supported by empirical evidence, contribute to the collective pool of knowledge, enriching our understanding of various phenomena and guiding decision-making processes across diverse fields. Through research, we continually refine our understanding of the universe, laying the foundation for innovation and progress that shape the future.

Research embodies the spirit of curiosity and the pursuit of truth. Here are the key characteristics of research:

• Systematic Approach: Research follows a well-structured and organized approach, with clearly defined steps and methodologies. It is conducted in a systematic manner to ensure that data is collected, analyzed, and interpreted in a logical and coherent way.
• Objective and Unbiased: Research is objective and strives to be free from bias or personal opinions. Researchers aim to gather data and draw conclusions based on evidence rather than preconceived notions or beliefs.
• Empirical Evidence: Research relies on empirical evidence obtained through observations, experiments, surveys, or other data collection methods. This evidence serves as the foundation for drawing conclusions and making informed decisions.
• Clear Research Question or Problem: Every research study begins with a specific research question or problem that the researcher aims to address. This question provides focus and direction to the entire research process.
• Replicability: Good research should be replicable, meaning that other researchers should be able to conduct a similar study and obtain similar results when following the same methods.
• Transparency and Ethics: Research should be conducted with transparency, and researchers should adhere to ethical guidelines and principles. This includes obtaining informed consent from participants, ensuring confidentiality, and avoiding any harm to participants or the environment.
• Generalizability: Researchers often aim for their findings to be generalizable to a broader population or context. This means that the results of the study can be applied beyond the specific sample or situation studied.
• Logical and Critical Thinking: Research involves critical thinking to analyze and interpret data, identify patterns, and draw meaningful conclusions. Logical reasoning is essential in formulating hypotheses and designing the study.
• Contribution to Knowledge: The primary purpose of research is to contribute to the existing body of knowledge in a particular field. Researchers aim to expand understanding, challenge existing theories, or propose new ideas.
• Peer Review and Publication: Research findings are typically subject to peer review by experts in the field before being published in academic journals or presented at conferences. This process ensures the quality and validity of the research.
• Iterative Process: Research is often an iterative process, with findings from one study leading to new questions and further research. It is a continuous cycle of discovery and refinement.
• Practical Application: While some research is theoretical in nature, much of it aims to have practical applications and real-world implications. It can inform policy decisions, improve practices, or address societal challenges.

These key characteristics collectively define research as a rigorous and valuable endeavor that drives progress, knowledge, and innovation in various disciplines.

Research methods refer to the specific approaches and techniques used to collect and analyze data in a research study. There are various types of research methods, and researchers often choose the most appropriate method based on their research question, the nature of the data they want to collect, and the resources available to them. Some common types of research methods include:

1. Quantitative Research: Quantitative research methods focus on collecting and analyzing quantifiable data to draw conclusions. The key methods for conducting quantitative research are:

Surveys- Conducting structured questionnaires or interviews with a large number of participants to gather numerical data.

Experiments-Manipulating variables in a controlled environment to establish cause-and-effect relationships.

Observational Studies- Systematically observing and recording behaviors or phenomena without intervention.

Secondary Data Analysis- Analyzing existing datasets and records to draw new insights or conclusions.

2. Qualitative Research: Qualitative research employs a range of information-gathering methods that are non-numerical, and are instead intellectual in order to provide in-depth insights into the research topic. The key methods are:

Interviews- Conducting in-depth, semi-structured, or unstructured interviews to gain a deeper understanding of participants’ perspectives.

Focus Groups- Group discussions with selected participants to explore their attitudes, beliefs, and experiences on a specific topic.

Ethnography- Immersing in a particular culture or community to observe and understand their behaviors, customs, and beliefs.

Case Studies- In-depth examination of a single individual, group, organization, or event to gain comprehensive insights.

3. Mixed-Methods Research: Combining both quantitative and qualitative research methods in a single study to provide a more comprehensive understanding of the research question.

4. Cross-Sectional Studies: Gathering data from a sample of a population at a specific point in time to understand relationships or differences between variables.

5. Longitudinal Studies: Following a group of participants over an extended period to examine changes and developments over time.

6. Action Research: Collaboratively working with stakeholders to identify and implement solutions to practical problems in real-world settings.

7. Case-Control Studies: Comparing individuals with a particular outcome (cases) to those without the outcome (controls) to identify potential causes or risk factors.

8. Descriptive Research: Describing and summarizing characteristics, behaviors, or patterns without manipulating variables.

9. Correlational Research: Examining the relationship between two or more variables without inferring causation.

10. Grounded Theory: An approach to developing theory based on systematically gathering and analyzing data, allowing the theory to emerge from the data.

11. Surveys and Questionnaires: Administering structured sets of questions to a sample population to gather specific information.

12. Meta-Analysis: A statistical technique that combines the results of multiple studies on the same topic to draw more robust conclusions.

Researchers often choose a research method or a combination of methods that best aligns with their research objectives, resources, and the nature of the data they aim to collect. Each research method has its strengths and limitations, and the choice of method can significantly impact the findings and conclusions of a study.

Learn more: What is Research Design?

Conducting research involves a systematic and organized process that follows specific steps to ensure the collection of reliable and meaningful data. The research process typically consists of the following steps:

Step 1. Identify the Research Topic

Choose a research topic that interests you and aligns with your expertise and resources. Develop clear and focused research questions that you want to answer through your study.

Step 2. Review Existing Research

Conduct a thorough literature review to identify what research has already been done on your chosen topic. This will help you understand the current state of knowledge, identify gaps in the literature, and refine your research questions.

Step 3. Design the Research Methodology

Determine the appropriate research methodology that suits your research questions. Decide whether your study will be qualitative , quantitative , or a mix of both (mixed methods). Also, choose the data collection methods, such as surveys, interviews, experiments, observations, etc.

Step 4. Select the Sample and Participants

If your study involves human participants, decide on the sample size and selection criteria. Obtain ethical approval, if required, and ensure that participants’ rights and privacy are protected throughout the research process.

Step 5. Information Collection

Collect information and data based on your chosen research methodology. Qualitative research has more intellectual information, while quantitative research results are more data-oriented. Ensure that your data collection process is standardized and consistent to maintain the validity of the results.

Step 6. Data Analysis

Analyze the data you have collected using appropriate statistical or qualitative research methods . The type of analysis will depend on the nature of your data and research questions.

Step 7. Interpretation of Results

Interpret the findings of your data analysis. Relate the results to your research questions and consider how they contribute to the existing knowledge in the field.

Step 8. Draw Conclusions

Based on your interpretation of the results, draw meaningful conclusions that answer your research questions. Discuss the implications of your findings and how they align with the existing literature.

Step 9. Discuss Limitations

Acknowledge and discuss any limitations of your study. Addressing limitations demonstrates the validity and reliability of your research.

Step 10. Make Recommendations

If applicable, provide recommendations based on your research findings. These recommendations can be for future research, policy changes, or practical applications.

Step 11. Write the Research Report

Prepare a comprehensive research report detailing all aspects of your study, including the introduction, methodology, results, discussion, conclusion, and references.

Step 12. Peer Review and Revision

If you intend to publish your research, submit your report to peer-reviewed journals. Revise your research report based on the feedback received from reviewers.

Make sure to share your research findings with the broader community through conferences, seminars, or other appropriate channels, this will help contribute to the collective knowledge in your field of study.

Remember that conducting research is a dynamic process, and you may need to revisit and refine various steps as you progress. Good research requires attention to detail, critical thinking, and adherence to ethical principles to ensure the quality and validity of the study.

Learn more: What is Primary Market Research?

Best practices for conducting research remain rooted in the principles of rigor, transparency, and ethical considerations. Here are the essential best practices to follow when conducting research in 2023:

1. Research Design and Methodology

• Carefully select and justify the research design and methodology that aligns with your research questions and objectives.
• Ensure that the chosen methods are appropriate for the data you intend to collect and the type of analysis you plan to perform.
• Clearly document the research design and methodology to enhance the reproducibility and transparency of your study.

2. Ethical Considerations

• Obtain approval from relevant research ethics committees or institutional review boards, especially when involving human participants or sensitive data.
• Prioritize the protection of participants’ rights, privacy, and confidentiality throughout the research process.
• Provide informed consent to participants, ensuring they understand the study’s purpose, risks, and benefits.

3. Data Collection

• Ensure the reliability and validity of data collection instruments, such as surveys or interview protocols.
• Conduct pilot studies or pretests to identify and address any potential issues with data collection procedures.

4. Data Management and Analysis

• Implement robust data management practices to maintain the integrity and security of research data.
• Transparently document data analysis procedures, including software and statistical methods used.
• Use appropriate statistical techniques to analyze the data and avoid data manipulation or cherry-picking results.

5. Transparency and Open Science

• Embrace open science practices, such as pre-registration of research protocols and sharing data and code openly whenever possible.
• Clearly report all aspects of your research, including methods, results, and limitations, to enhance the reproducibility of your study.

6. Bias and Confounders

• Be aware of potential biases in the research process and take steps to minimize them.
• Consider and address potential confounding variables that could affect the validity of your results.

7. Peer Review

• Seek peer review from experts in your field before publishing or presenting your research findings.
• Be receptive to feedback and address any concerns raised by reviewers to improve the quality of your study.

8. Replicability and Generalizability

• Strive to make your research findings replicable, allowing other researchers to validate your results independently.
• Clearly state the limitations of your study and the extent to which the findings can be generalized to other populations or contexts.

9. Acknowledging Funding and Conflicts of Interest

• Disclose any funding sources and potential conflicts of interest that may influence your research or its outcomes.

10. Dissemination and Communication

• Effectively communicate your research findings to both academic and non-academic audiences using clear and accessible language.
• Share your research through reputable and open-access platforms to maximize its impact and reach.

By adhering to these best practices, researchers can ensure the integrity and value of their work, contributing to the advancement of knowledge and promoting trust in the research community.

Learn more: What is Consumer Research?

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Home Market Research

What is Research: Definition, Methods, Types & Examples

The search for knowledge is closely linked to the object of study; that is, to the reconstruction of the facts that will provide an explanation to an observed event and that at first sight can be considered as a problem. It is very human to seek answers and satisfy our curiosity. Let’s talk about research.

Content Index

What is Research?

What are the characteristics of research.

• Comparative analysis chart

Qualitative methods

Quantitative methods, 8 tips for conducting accurate research.

Research is the careful consideration of study regarding a particular concern or research problem using scientific methods. According to the American sociologist Earl Robert Babbie, “research is a systematic inquiry to describe, explain, predict, and control the observed phenomenon. It involves inductive and deductive methods.”

Inductive methods analyze an observed event, while deductive methods verify the observed event. Inductive approaches are associated with qualitative research , and deductive methods are more commonly associated with quantitative analysis .

Research is conducted with a purpose to:

• Identify potential and new customers
• Understand existing customers
• Set pragmatic goals
• Develop productive market strategies
• Address business challenges
• Put together a business expansion plan
• Identify new business opportunities
• Good research follows a systematic approach to capture accurate data. Researchers need to practice ethics and a code of conduct while making observations or drawing conclusions.
• The analysis is based on logical reasoning and involves both inductive and deductive methods.
• Real-time data and knowledge is derived from actual observations in natural settings.
• There is an in-depth analysis of all data collected so that there are no anomalies associated with it.
• It creates a path for generating new questions. Existing data helps create more research opportunities.
• It is analytical and uses all the available data so that there is no ambiguity in inference.
• Accuracy is one of the most critical aspects of research. The information must be accurate and correct. For example, laboratories provide a controlled environment to collect data. Accuracy is measured in the instruments used, the calibrations of instruments or tools, and the experiment’s final result.

What is the purpose of research?

There are three main purposes:

• Exploratory: As the name suggests, researchers conduct exploratory studies to explore a group of questions. The answers and analytics may not offer a conclusion to the perceived problem. It is undertaken to handle new problem areas that haven’t been explored before. This exploratory data analysis process lays the foundation for more conclusive data collection and analysis.

LEARN ABOUT: Descriptive Analysis

• Descriptive: It focuses on expanding knowledge on current issues through a process of data collection. Descriptive research describe the behavior of a sample population. Only one variable is required to conduct the study. The three primary purposes of descriptive studies are describing, explaining, and validating the findings. For example, a study conducted to know if top-level management leaders in the 21st century possess the moral right to receive a considerable sum of money from the company profit.

LEARN ABOUT: Best Data Collection Tools

• Explanatory: Causal research or explanatory research is conducted to understand the impact of specific changes in existing standard procedures. Running experiments is the most popular form. For example, a study that is conducted to understand the effect of rebranding on customer loyalty.

Here is a comparative analysis chart for a better understanding:

It begins by asking the right questions and choosing an appropriate method to investigate the problem. After collecting answers to your questions, you can analyze the findings or observations to draw reasonable conclusions.

When it comes to customers and market studies, the more thorough your questions, the better the analysis. You get essential insights into brand perception and product needs by thoroughly collecting customer data through surveys and questionnaires . You can use this data to make smart decisions about your marketing strategies to position your business effectively.

To make sense of your study and get insights faster, it helps to use a research repository as a single source of truth in your organization and manage your research data in one centralized data repository .

Types of research methods and Examples

Research methods are broadly classified as Qualitative and Quantitative .

Both methods have distinctive properties and data collection methods.

Qualitative research is a method that collects data using conversational methods, usually open-ended questions . The responses collected are essentially non-numerical. This method helps a researcher understand what participants think and why they think in a particular way.

Types of qualitative methods include:

• One-to-one Interview
• Focus Groups
• Ethnographic studies
• Text Analysis

Quantitative methods deal with numbers and measurable forms . It uses a systematic way of investigating events or data. It answers questions to justify relationships with measurable variables to either explain, predict, or control a phenomenon.

Types of quantitative methods include:

• Survey research
• Descriptive research
• Correlational research

LEARN MORE: Descriptive Research vs Correlational Research

Remember, it is only valuable and useful when it is valid, accurate, and reliable. Incorrect results can lead to customer churn and a decrease in sales.

It is essential to ensure that your data is:

• Valid – founded, logical, rigorous, and impartial.
• Accurate – free of errors and including required details.
• Reliable – other people who investigate in the same way can produce similar results.
• Timely – current and collected within an appropriate time frame.
• Complete – includes all the data you need to support your business decisions.

Gather insights

• Identify the main trends and issues, opportunities, and problems you observe. Write a sentence describing each one.
• Keep track of the frequency with which each of the main findings appears.
• Make a list of your findings from the most common to the least common.
• Evaluate a list of the strengths, weaknesses, opportunities, and threats identified in a SWOT analysis .
• Prepare conclusions and recommendations about your study.
• Act on your strategies
• Look for gaps in the information, and consider doing additional inquiry if necessary
• Plan to review the results and consider efficient methods to analyze and interpret results.

Review your goals before making any conclusions about your study. Remember how the process you have completed and the data you have gathered help answer your questions. Ask yourself if what your analysis revealed facilitates the identification of your conclusions and recommendations.

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Research Basics

• What Is Research?
• Types of Research
• Secondary Research | Literature Review
• Developing Your Topic
• Primary vs. Secondary Sources
• Evaluating Sources
• Responsible Conduct of Research
• Additional Help

A good working definition of research might be:

Research is the deliberate, purposeful, and systematic gathering of data, information, facts, and/or opinions for the advancement of personal, societal, or overall human knowledge.

Based on this definition, we all do research all the time. Most of this research is casual research. Asking friends what they think of different restaurants, looking up reviews of various products online, learning more about celebrities; these are all research.

Formal research includes the type of research most people think of when they hear the term “research”: scientists in white coats working in a fully equipped laboratory. But formal research is a much broader category that just this. Most people will never do laboratory research after graduating from college, but almost everybody will have to do some sort of formal research at some point in their careers.

So What Do We Mean By “Formal Research?”

Casual research is inward facing: it’s done to satisfy our own curiosity or meet our own needs, whether that’s choosing a reliable car or figuring out what to watch on TV. Formal research is outward facing. While it may satisfy our own curiosity, it’s primarily intended to be shared in order to achieve some purpose. That purpose could be anything: finding a cure for cancer, securing funding for a new business, improving some process at your workplace, proving the latest theory in quantum physics, or even just getting a good grade in your Humanities 200 class.

What sets formal research apart from casual research is the documentation of where you gathered your information from. This is done in the form of “citations” and “bibliographies.” Citing sources is covered in the section "Citing Your Sources."

Formal research also follows certain common patterns depending on what the research is trying to show or prove. These are covered in the section “Types of Research.”

• Next: Types of Research >>
• Last Updated: Dec 21, 2023 3:49 PM
• URL: https://guides.library.iit.edu/research_basics

Conducting Internet Research

Considerations for participant protections when conducting internet research.

If an activity falls under the category of human subjects research, it is regulated by the federal government and Teachers College (TC) Institutional Review Board (IRB). TC IRB has provided a guide to help researchers determine if their activities can be considered human subjects research.

Internet research is a common practice of using Internet information, especially free information on the World Wide Web or Internet-based resources (e.g., discussion forums, social media), in research. This guide will cover considerations pertaining to participant protections when conducting Internet research, including:

• Private versus public spaces for exempt research
• Identifiable data available in public databases
• Minimizing risks when using sensitive Internet data
• Common Internet research approaches

The following information is from an NIH videocast . ( Odwanzy, L. (2014, May 8). Conducting Internet Research: Challenges and Strategies for IRBs [Video]. VideoCast NIH. https://videocast.nih.gov/summary.asp?Live=13932&bhcp=1 )  

Private Versus Public Spaces for Exempt Research

Federal regulations define a category of human subjects research that is exempt from IRB review as:  

“ Research that only includes interactions involving educational tests (cognitive, diagnostic, aptitude, achievement), survey procedures, interview procedures, or observation of public behavior (including visual or auditory recording) .” 

With regards to online information, if the data is publicly available (such as Census data or labor statistics), it is usually not considered human subjects research. However, if the data includes identifiable information—meaning the data can be linked back to a specific individual—then it may need to undergo IRB review. Additionally, de-identified data pulled from a private source, such as data provided by a company, may also be considered human subjects research.

Public behavior is any behavior that a subject would or could perform in public without special devices or interventions. Public behavior on the Internet, however, is more difficult to pinpoint. Federal regulations indicate that an environment may be private if a reasonable user would consider their interactions in that environment to be private. To help identify public behavior on the Internet, consider:

• Typically, posts on a private or password-protected social media profile or site are not considered public behavior.
• Even if a website is publicly available, the information on the website may be protected by other measures (e.g., community guidelines, terms of use, etc.).
• Sites that require users to pay for access to their content (e.g., purchasing a dataset) are not always considered private, even if the information is behind a paywall.
• Discussions and chats on public forums, news broadcasts, and free podcasts or videos are typically considered public communications. 
• Emails and person-to-person chat messages are often private, rather than public, communications.
• However, institutions may dictate that any activity on their devices (e.g., a company laptop or phone) is subject to review. In these cases, the institutions can limit an individual’s privacy.
• Some websites explicitly state that the interactions on their site are not to be used for research purposes.
• Other sites may not explicitly refuse research activities, but they may require users to be respectful of others’ experiences. Depending on the website, “respect” may have a variety of meanings, including respect of user privacy.
• Expectations of privacy may not always equate to the reality of privacy. 
• For example, individuals may share personal information on an open forum because there is an expectation within the community that other users will respect their privacy. However, the community guidelines may not explicitly state that their website is private.
• Forums and websites directed towards youth may require extra precautions, as the youth may be on the website with or without their guardian’s permission.
• If a user shares media on a private profile, but then that media becomes publicly available through re-posts, the media should still be considered private. It is likely that a reasonable user would expect shares on private profiles to remain private. 
• A site may only be open to certain types of users based on demographics or life experiences (e.g., cancer survivors, support groups for addiction, etc.). In these cases, a reasonable user may expect greater privacy based on the types of users they expect to interact with.

TC IRB will determine whether an Internet environment is private or public based on the IRB protocol submission.

Identifiable Data in Public Datasets

Identifiable data is information or records about a research participant that allows others to identify that person. Names, social security numbers, and bank account numbers are considered personal identifiers  and are protected under the Health Insurance Portability and Accountability Act of 1996 (HIPAA). TC IRB has a blog posted on Understanding Identifiable Data that further explains the different types of identifiers. Data that includes personal identifiers does not fall under the Exempt category.  

Other types of participant information may include indirect identifiers , such as birthdate, age, ethnicity, gender, etc. Taken alone, these pieces of information are not enough to identify any single participant. However, researchers have shown that certain combinations of these identifiers may identify participants. For example, Sweeny (2000) demonstrated that 87% of the United States population could be uniquely identified based solely on their ZIP code, gender, and date of birth.

It is important to remember that while data may be publicly available, it may still contain identifiable information. In these cases, the IRB will decide the risk to participants on a case-by-case basis. With Internet information, consider these to be possible identifiers:  

Users may include their partial or full name in a username. When collecting usernames from a site, researchers should consider replacing usernames with pseudonyms.

IP addresses are unique identifiers for devices. Researchers should be wary of pairing IP addresses with other information.

Purchase Habits

With the surge in online shopping, individuals’ unique online purchase habits are shown to be possible identifiers. 

Digital Images, Audio, & Video

Photos, audio recordings, or videos of an individual are typically considered identifiable, unless the images or audio are ascertained in a way that protects the subject’s identity.

Avatars or Profile Pictures

Although avatars and profile pictures may not include real photos of the user, it is possible that they were chosen because of a resemblance to the user.

Keystroke Dynamics or Typing Biometrics

The detailed information of an individual’s timing and rhythm when typing on a keyboard is a unique identifier. "Keystroke rhythm" measures when each key is pressed and released while a user is typing. These rhythm combinations are as unique to an individual as a fingerprint or a signature.

Minimizing Risk When Using Sensitive Internet Data 

In cases where sensitive Internet data must be used for research purposes, researchers should take precautions to ensure the safety and privacy of participants. The nature of online research increases risk to participants in some areas. Researchers should develop a plan to minimize risk in the following areas:

• Reduced Participant Contact : when research is conducted over the Internet, researchers have limited or no direct contact with subjects. This makes it more difficult for researchers to gauge subjects' reactions to the study interventions. 
• Researchers should think through multiple possibilities for interventions, debriefing, and follow-up, if applicable.
• Researcher and TC IRB contact information should be presented on the informed consent before beginning the study. This will ensure that participants know whom to contact if they have questions or concerns.
• Breach of Confidentiality: when storing or collecting data on devices connected to the Internet, there is a heightened risk for identifiable participant data to be leaked. 
• TC IRB has published a Data Security Plan  outlining best practices for securing and transmitting data. Researchers should implement these practices as they apply to their specific study.
• In the case of a breach of confidentiality, researchers must file an adverse event with TC IRB.  

Common Internet Research Approaches

The Secretary’s Advisory Committee on Human Research Protections (SACHRP) has provided examples of common Internet research practices. These include elements of research conducted over the Internet. Below are possible examples of Internet research where human subjects may be involved:  

• Existing datasets (secondary data analysis)
• Social media/blog posts
• Chat room interactions  
• Amazon Mechanical Turk
• Social media
• Patterns on social media or websites
• Evolution of privacy issues
• Spread of false information
• Online shopping patterns and personalized digital marketing
• Online interventions such as “nudging"

Increased Internet use for research requires researchers and IRBs to become familiar with Internet research-related topics and concerns. Research submitted to the IRB will be reviewed on a case-by-case basis. The Institutional Review Board at Teachers College will make the final determination of whether a study requires review. Researchers should email  [email protected] if they have any questions or concerns about their study design and whether it should be IRB reviewed.

Institutional Review Board

Address: Russell Hall, Room 13

* Phone: 212-678-4105 * Email:   [email protected]

Appointments are available by request . Make sure to have your IRB protocol number (e.g., 19-011) available.  If you are unable to access any of the downloadable resources, please contact  OASID via email [email protected] .

Doing Research: A New Researcher’s Guide pp 1–15 Cite as

What Is Research, and Why Do People Do It?

• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  
• Open Access
• First Online: 03 December 2022

14k Accesses

Part of the book series: Research in Mathematics Education ((RME))

Abstractspiepr Abs1

Every day people do research as they gather information to learn about something of interest. In the scientific world, however, research means something different than simply gathering information. Scientific research is characterized by its careful planning and observing, by its relentless efforts to understand and explain, and by its commitment to learn from everyone else seriously engaged in research. We call this kind of research scientific inquiry and define it as “formulating, testing, and revising hypotheses.” By “hypotheses” we do not mean the hypotheses you encounter in statistics courses. We mean predictions about what you expect to find and rationales for why you made these predictions. Throughout this and the remaining chapters we make clear that the process of scientific inquiry applies to all kinds of research studies and data, both qualitative and quantitative.

Download chapter PDF

Part I. What Is Research?

Have you ever studied something carefully because you wanted to know more about it? Maybe you wanted to know more about your grandmother’s life when she was younger so you asked her to tell you stories from her childhood, or maybe you wanted to know more about a fertilizer you were about to use in your garden so you read the ingredients on the package and looked them up online. According to the dictionary definition, you were doing research.

Recall your high school assignments asking you to “research” a topic. The assignment likely included consulting a variety of sources that discussed the topic, perhaps including some “original” sources. Often, the teacher referred to your product as a “research paper.”

Were you conducting research when you interviewed your grandmother or wrote high school papers reviewing a particular topic? Our view is that you were engaged in part of the research process, but only a small part. In this book, we reserve the word “research” for what it means in the scientific world, that is, for scientific research or, more pointedly, for scientific inquiry .

Exercise 1.1

Before you read any further, write a definition of what you think scientific inquiry is. Keep it short—Two to three sentences. You will periodically update this definition as you read this chapter and the remainder of the book.

This book is about scientific inquiry—what it is and how to do it. For starters, scientific inquiry is a process, a particular way of finding out about something that involves a number of phases. Each phase of the process constitutes one aspect of scientific inquiry. You are doing scientific inquiry as you engage in each phase, but you have not done scientific inquiry until you complete the full process. Each phase is necessary but not sufficient.

In this chapter, we set the stage by defining scientific inquiry—describing what it is and what it is not—and by discussing what it is good for and why people do it. The remaining chapters build directly on the ideas presented in this chapter.

A first thing to know is that scientific inquiry is not all or nothing. “Scientificness” is a continuum. Inquiries can be more scientific or less scientific. What makes an inquiry more scientific? You might be surprised there is no universally agreed upon answer to this question. None of the descriptors we know of are sufficient by themselves to define scientific inquiry. But all of them give you a way of thinking about some aspects of the process of scientific inquiry. Each one gives you different insights.

Exercise 1.2

As you read about each descriptor below, think about what would make an inquiry more or less scientific. If you think a descriptor is important, use it to revise your definition of scientific inquiry.

Creating an Image of Scientific Inquiry

We will present three descriptors of scientific inquiry. Each provides a different perspective and emphasizes a different aspect of scientific inquiry. We will draw on all three descriptors to compose our definition of scientific inquiry.

Descriptor 1. Experience Carefully Planned in Advance

Sir Ronald Fisher, often called the father of modern statistical design, once referred to research as “experience carefully planned in advance” (1935, p. 8). He said that humans are always learning from experience, from interacting with the world around them. Usually, this learning is haphazard rather than the result of a deliberate process carried out over an extended period of time. Research, Fisher said, was learning from experience, but experience carefully planned in advance.

This phrase can be fully appreciated by looking at each word. The fact that scientific inquiry is based on experience means that it is based on interacting with the world. These interactions could be thought of as the stuff of scientific inquiry. In addition, it is not just any experience that counts. The experience must be carefully planned . The interactions with the world must be conducted with an explicit, describable purpose, and steps must be taken to make the intended learning as likely as possible. This planning is an integral part of scientific inquiry; it is not just a preparation phase. It is one of the things that distinguishes scientific inquiry from many everyday learning experiences. Finally, these steps must be taken beforehand and the purpose of the inquiry must be articulated in advance of the experience. Clearly, scientific inquiry does not happen by accident, by just stumbling into something. Stumbling into something unexpected and interesting can happen while engaged in scientific inquiry, but learning does not depend on it and serendipity does not make the inquiry scientific.

Descriptor 2. Observing Something and Trying to Explain Why It Is the Way It Is

When we were writing this chapter and googled “scientific inquiry,” the first entry was: “Scientific inquiry refers to the diverse ways in which scientists study the natural world and propose explanations based on the evidence derived from their work.” The emphasis is on studying, or observing, and then explaining . This descriptor takes the image of scientific inquiry beyond carefully planned experience and includes explaining what was experienced.

According to the Merriam-Webster dictionary, “explain” means “(a) to make known, (b) to make plain or understandable, (c) to give the reason or cause of, and (d) to show the logical development or relations of” (Merriam-Webster, n.d. ). We will use all these definitions. Taken together, they suggest that to explain an observation means to understand it by finding reasons (or causes) for why it is as it is. In this sense of scientific inquiry, the following are synonyms: explaining why, understanding why, and reasoning about causes and effects. Our image of scientific inquiry now includes planning, observing, and explaining why.

We need to add a final note about this descriptor. We have phrased it in a way that suggests “observing something” means you are observing something in real time—observing the way things are or the way things are changing. This is often true. But, observing could mean observing data that already have been collected, maybe by someone else making the original observations (e.g., secondary analysis of NAEP data or analysis of existing video recordings of classroom instruction). We will address secondary analyses more fully in Chap. 4 . For now, what is important is that the process requires explaining why the data look like they do.

We must note that for us, the term “data” is not limited to numerical or quantitative data such as test scores. Data can also take many nonquantitative forms, including written survey responses, interview transcripts, journal entries, video recordings of students, teachers, and classrooms, text messages, and so forth.

Exercise 1.3

What are the implications of the statement that just “observing” is not enough to count as scientific inquiry? Does this mean that a detailed description of a phenomenon is not scientific inquiry?

Find sources that define research in education that differ with our position, that say description alone, without explanation, counts as scientific research. Identify the precise points where the opinions differ. What are the best arguments for each of the positions? Which do you prefer? Why?

Descriptor 3. Updating Everyone’s Thinking in Response to More and Better Information

This descriptor focuses on a third aspect of scientific inquiry: updating and advancing the field’s understanding of phenomena that are investigated. This descriptor foregrounds a powerful characteristic of scientific inquiry: the reliability (or trustworthiness) of what is learned and the ultimate inevitability of this learning to advance human understanding of phenomena. Humans might choose not to learn from scientific inquiry, but history suggests that scientific inquiry always has the potential to advance understanding and that, eventually, humans take advantage of these new understandings.

Before exploring these bold claims a bit further, note that this descriptor uses “information” in the same way the previous two descriptors used “experience” and “observations.” These are the stuff of scientific inquiry and we will use them often, sometimes interchangeably. Frequently, we will use the term “data” to stand for all these terms.

An overriding goal of scientific inquiry is for everyone to learn from what one scientist does. Much of this book is about the methods you need to use so others have faith in what you report and can learn the same things you learned. This aspect of scientific inquiry has many implications.

One implication is that scientific inquiry is not a private practice. It is a public practice available for others to see and learn from. Notice how different this is from everyday learning. When you happen to learn something from your everyday experience, often only you gain from the experience. The fact that research is a public practice means it is also a social one. It is best conducted by interacting with others along the way: soliciting feedback at each phase, taking opportunities to present work-in-progress, and benefitting from the advice of others.

A second implication is that you, as the researcher, must be committed to sharing what you are doing and what you are learning in an open and transparent way. This allows all phases of your work to be scrutinized and critiqued. This is what gives your work credibility. The reliability or trustworthiness of your findings depends on your colleagues recognizing that you have used all appropriate methods to maximize the chances that your claims are justified by the data.

A third implication of viewing scientific inquiry as a collective enterprise is the reverse of the second—you must be committed to receiving comments from others. You must treat your colleagues as fair and honest critics even though it might sometimes feel otherwise. You must appreciate their job, which is to remain skeptical while scrutinizing what you have done in considerable detail. To provide the best help to you, they must remain skeptical about your conclusions (when, for example, the data are difficult for them to interpret) until you offer a convincing logical argument based on the information you share. A rather harsh but good-to-remember statement of the role of your friendly critics was voiced by Karl Popper, a well-known twentieth century philosopher of science: “. . . if you are interested in the problem which I tried to solve by my tentative assertion, you may help me by criticizing it as severely as you can” (Popper, 1968, p. 27).

A final implication of this third descriptor is that, as someone engaged in scientific inquiry, you have no choice but to update your thinking when the data support a different conclusion. This applies to your own data as well as to those of others. When data clearly point to a specific claim, even one that is quite different than you expected, you must reconsider your position. If the outcome is replicated multiple times, you need to adjust your thinking accordingly. Scientific inquiry does not let you pick and choose which data to believe; it mandates that everyone update their thinking when the data warrant an update.

Doing Scientific Inquiry

We define scientific inquiry in an operational sense—what does it mean to do scientific inquiry? What kind of process would satisfy all three descriptors: carefully planning an experience in advance; observing and trying to explain what you see; and, contributing to updating everyone’s thinking about an important phenomenon?

We define scientific inquiry as formulating , testing , and revising hypotheses about phenomena of interest.

Of course, we are not the only ones who define it in this way. The definition for the scientific method posted by the editors of Britannica is: “a researcher develops a hypothesis, tests it through various means, and then modifies the hypothesis on the basis of the outcome of the tests and experiments” (Britannica, n.d. ).

Notice how defining scientific inquiry this way satisfies each of the descriptors. “Carefully planning an experience in advance” is exactly what happens when formulating a hypothesis about a phenomenon of interest and thinking about how to test it. “ Observing a phenomenon” occurs when testing a hypothesis, and “ explaining ” what is found is required when revising a hypothesis based on the data. Finally, “updating everyone’s thinking” comes from comparing publicly the original with the revised hypothesis.

Doing scientific inquiry, as we have defined it, underscores the value of accumulating knowledge rather than generating random bits of knowledge. Formulating, testing, and revising hypotheses is an ongoing process, with each revised hypothesis begging for another test, whether by the same researcher or by new researchers. The editors of Britannica signaled this cyclic process by adding the following phrase to their definition of the scientific method: “The modified hypothesis is then retested, further modified, and tested again.” Scientific inquiry creates a process that encourages each study to build on the studies that have gone before. Through collective engagement in this process of building study on top of study, the scientific community works together to update its thinking.

Before exploring more fully the meaning of “formulating, testing, and revising hypotheses,” we need to acknowledge that this is not the only way researchers define research. Some researchers prefer a less formal definition, one that includes more serendipity, less planning, less explanation. You might have come across more open definitions such as “research is finding out about something.” We prefer the tighter hypothesis formulation, testing, and revision definition because we believe it provides a single, coherent map for conducting research that addresses many of the thorny problems educational researchers encounter. We believe it is the most useful orientation toward research and the most helpful to learn as a beginning researcher.

A final clarification of our definition is that it applies equally to qualitative and quantitative research. This is a familiar distinction in education that has generated much discussion. You might think our definition favors quantitative methods over qualitative methods because the language of hypothesis formulation and testing is often associated with quantitative methods. In fact, we do not favor one method over another. In Chap. 4 , we will illustrate how our definition fits research using a range of quantitative and qualitative methods.

Exercise 1.4

Look for ways to extend what the field knows in an area that has already received attention by other researchers. Specifically, you can search for a program of research carried out by more experienced researchers that has some revised hypotheses that remain untested. Identify a revised hypothesis that you might like to test.

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

To get a full sense of the definition of scientific inquiry we will use throughout this book, it is helpful to spend a little time with each of the key terms.

We first want to make clear that we use the term “hypothesis” as it is defined in most dictionaries and as it used in many scientific fields rather than as it is usually defined in educational statistics courses. By “hypothesis,” we do not mean a null hypothesis that is accepted or rejected by statistical analysis. Rather, we use “hypothesis” in the sense conveyed by the following definitions: “An idea or explanation for something that is based on known facts but has not yet been proved” (Cambridge University Press, n.d. ), and “An unproved theory, proposition, or supposition, tentatively accepted to explain certain facts and to provide a basis for further investigation or argument” (Agnes & Guralnik, 2008 ).

We distinguish two parts to “hypotheses.” Hypotheses consist of predictions and rationales . Predictions are statements about what you expect to find when you inquire about something. Rationales are explanations for why you made the predictions you did, why you believe your predictions are correct. So, for us “formulating hypotheses” means making explicit predictions and developing rationales for the predictions.

“Testing hypotheses” means making observations that allow you to assess in what ways your predictions were correct and in what ways they were incorrect. In education research, it is rarely useful to think of your predictions as either right or wrong. Because of the complexity of most issues you will investigate, most predictions will be right in some ways and wrong in others.

By studying the observations you make (data you collect) to test your hypotheses, you can revise your hypotheses to better align with the observations. This means revising your predictions plus revising your rationales to justify your adjusted predictions. Even though you might not run another test, formulating revised hypotheses is an essential part of conducting a research study. Comparing your original and revised hypotheses informs everyone of what you learned by conducting your study. In addition, a revised hypothesis sets the stage for you or someone else to extend your study and accumulate more knowledge of the phenomenon.

We should note that not everyone makes a clear distinction between predictions and rationales as two aspects of hypotheses. In fact, common, non-scientific uses of the word “hypothesis” may limit it to only a prediction or only an explanation (or rationale). We choose to explicitly include both prediction and rationale in our definition of hypothesis, not because we assert this should be the universal definition, but because we want to foreground the importance of both parts acting in concert. Using “hypothesis” to represent both prediction and rationale could hide the two aspects, but we make them explicit because they provide different kinds of information. It is usually easier to make predictions than develop rationales because predictions can be guesses, hunches, or gut feelings about which you have little confidence. Developing a compelling rationale requires careful thought plus reading what other researchers have found plus talking with your colleagues. Often, while you are developing your rationale you will find good reasons to change your predictions. Developing good rationales is the engine that drives scientific inquiry. Rationales are essentially descriptions of how much you know about the phenomenon you are studying. Throughout this guide, we will elaborate on how developing good rationales drives scientific inquiry. For now, we simply note that it can sharpen your predictions and help you to interpret your data as you test your hypotheses.

Hypotheses in education research take a variety of forms or types. This is because there are a variety of phenomena that can be investigated. Investigating educational phenomena is sometimes best done using qualitative methods, sometimes using quantitative methods, and most often using mixed methods (e.g., Hay, 2016 ; Weis et al. 2019a ; Weisner, 2005 ). This means that, given our definition, hypotheses are equally applicable to qualitative and quantitative investigations.

Hypotheses take different forms when they are used to investigate different kinds of phenomena. Two very different activities in education could be labeled conducting experiments and descriptions. In an experiment, a hypothesis makes a prediction about anticipated changes, say the changes that occur when a treatment or intervention is applied. You might investigate how students’ thinking changes during a particular kind of instruction.

A second type of hypothesis, relevant for descriptive research, makes a prediction about what you will find when you investigate and describe the nature of a situation. The goal is to understand a situation as it exists rather than to understand a change from one situation to another. In this case, your prediction is what you expect to observe. Your rationale is the set of reasons for making this prediction; it is your current explanation for why the situation will look like it does.

You will probably read, if you have not already, that some researchers say you do not need a prediction to conduct a descriptive study. We will discuss this point of view in Chap. 2 . For now, we simply claim that scientific inquiry, as we have defined it, applies to all kinds of research studies. Descriptive studies, like others, not only benefit from formulating, testing, and revising hypotheses, but also need hypothesis formulating, testing, and revising.

One reason we define research as formulating, testing, and revising hypotheses is that if you think of research in this way you are less likely to go wrong. It is a useful guide for the entire process, as we will describe in detail in the chapters ahead. For example, as you build the rationale for your predictions, you are constructing the theoretical framework for your study (Chap. 3 ). As you work out the methods you will use to test your hypothesis, every decision you make will be based on asking, “Will this help me formulate or test or revise my hypothesis?” (Chap. 4 ). As you interpret the results of testing your predictions, you will compare them to what you predicted and examine the differences, focusing on how you must revise your hypotheses (Chap. 5 ). By anchoring the process to formulating, testing, and revising hypotheses, you will make smart decisions that yield a coherent and well-designed study.

Exercise 1.5

Compare the concept of formulating, testing, and revising hypotheses with the descriptions of scientific inquiry contained in Scientific Research in Education (NRC, 2002 ). How are they similar or different?

Exercise 1.6

Provide an example to illustrate and emphasize the differences between everyday learning/thinking and scientific inquiry.

Learning from Doing Scientific Inquiry

We noted earlier that a measure of what you have learned by conducting a research study is found in the differences between your original hypothesis and your revised hypothesis based on the data you collected to test your hypothesis. We will elaborate this statement in later chapters, but we preview our argument here.

Even before collecting data, scientific inquiry requires cycles of making a prediction, developing a rationale, refining your predictions, reading and studying more to strengthen your rationale, refining your predictions again, and so forth. And, even if you have run through several such cycles, you still will likely find that when you test your prediction you will be partly right and partly wrong. The results will support some parts of your predictions but not others, or the results will “kind of” support your predictions. A critical part of scientific inquiry is making sense of your results by interpreting them against your predictions. Carefully describing what aspects of your data supported your predictions, what aspects did not, and what data fell outside of any predictions is not an easy task, but you cannot learn from your study without doing this analysis.

Analyzing the matches and mismatches between your predictions and your data allows you to formulate different rationales that would have accounted for more of the data. The best revised rationale is the one that accounts for the most data. Once you have revised your rationales, you can think about the predictions they best justify or explain. It is by comparing your original rationales to your new rationales that you can sort out what you learned from your study.

Suppose your study was an experiment. Maybe you were investigating the effects of a new instructional intervention on students’ learning. Your original rationale was your explanation for why the intervention would change the learning outcomes in a particular way. Your revised rationale explained why the changes that you observed occurred like they did and why your revised predictions are better. Maybe your original rationale focused on the potential of the activities if they were implemented in ideal ways and your revised rationale included the factors that are likely to affect how teachers implement them. By comparing the before and after rationales, you are describing what you learned—what you can explain now that you could not before. Another way of saying this is that you are describing how much more you understand now than before you conducted your study.

Revised predictions based on carefully planned and collected data usually exhibit some of the following features compared with the originals: more precision, more completeness, and broader scope. Revised rationales have more explanatory power and become more complete, more aligned with the new predictions, sharper, and overall more convincing.

Part II. Why Do Educators Do Research?

Doing scientific inquiry is a lot of work. Each phase of the process takes time, and you will often cycle back to improve earlier phases as you engage in later phases. Because of the significant effort required, you should make sure your study is worth it. So, from the beginning, you should think about the purpose of your study. Why do you want to do it? And, because research is a social practice, you should also think about whether the results of your study are likely to be important and significant to the education community.

If you are doing research in the way we have described—as scientific inquiry—then one purpose of your study is to understand , not just to describe or evaluate or report. As we noted earlier, when you formulate hypotheses, you are developing rationales that explain why things might be like they are. In our view, trying to understand and explain is what separates research from other kinds of activities, like evaluating or describing.

One reason understanding is so important is that it allows researchers to see how or why something works like it does. When you see how something works, you are better able to predict how it might work in other contexts, under other conditions. And, because conditions, or contextual factors, matter a lot in education, gaining insights into applying your findings to other contexts increases the contributions of your work and its importance to the broader education community.

Consequently, the purposes of research studies in education often include the more specific aim of identifying and understanding the conditions under which the phenomena being studied work like the observations suggest. A classic example of this kind of study in mathematics education was reported by William Brownell and Harold Moser in 1949 . They were trying to establish which method of subtracting whole numbers could be taught most effectively—the regrouping method or the equal additions method. However, they realized that effectiveness might depend on the conditions under which the methods were taught—“meaningfully” versus “mechanically.” So, they designed a study that crossed the two instructional approaches with the two different methods (regrouping and equal additions). Among other results, they found that these conditions did matter. The regrouping method was more effective under the meaningful condition than the mechanical condition, but the same was not true for the equal additions algorithm.

What do education researchers want to understand? In our view, the ultimate goal of education is to offer all students the best possible learning opportunities. So, we believe the ultimate purpose of scientific inquiry in education is to develop understanding that supports the improvement of learning opportunities for all students. We say “ultimate” because there are lots of issues that must be understood to improve learning opportunities for all students. Hypotheses about many aspects of education are connected, ultimately, to students’ learning. For example, formulating and testing a hypothesis that preservice teachers need to engage in particular kinds of activities in their coursework in order to teach particular topics well is, ultimately, connected to improving students’ learning opportunities. So is hypothesizing that school districts often devote relatively few resources to instructional leadership training or hypothesizing that positioning mathematics as a tool students can use to combat social injustice can help students see the relevance of mathematics to their lives.

We do not exclude the importance of research on educational issues more removed from improving students’ learning opportunities, but we do think the argument for their importance will be more difficult to make. If there is no way to imagine a connection between your hypothesis and improving learning opportunities for students, even a distant connection, we recommend you reconsider whether it is an important hypothesis within the education community.

Notice that we said the ultimate goal of education is to offer all students the best possible learning opportunities. For too long, educators have been satisfied with a goal of offering rich learning opportunities for lots of students, sometimes even for just the majority of students, but not necessarily for all students. Evaluations of success often are based on outcomes that show high averages. In other words, if many students have learned something, or even a smaller number have learned a lot, educators may have been satisfied. The problem is that there is usually a pattern in the groups of students who receive lower quality opportunities—students of color and students who live in poor areas, urban and rural. This is not acceptable. Consequently, we emphasize the premise that the purpose of education research is to offer rich learning opportunities to all students.

One way to make sure you will be able to convince others of the importance of your study is to consider investigating some aspect of teachers’ shared instructional problems. Historically, researchers in education have set their own research agendas, regardless of the problems teachers are facing in schools. It is increasingly recognized that teachers have had trouble applying to their own classrooms what researchers find. To address this problem, a researcher could partner with a teacher—better yet, a small group of teachers—and talk with them about instructional problems they all share. These discussions can create a rich pool of problems researchers can consider. If researchers pursued one of these problems (preferably alongside teachers), the connection to improving learning opportunities for all students could be direct and immediate. “Grounding a research question in instructional problems that are experienced across multiple teachers’ classrooms helps to ensure that the answer to the question will be of sufficient scope to be relevant and significant beyond the local context” (Cai et al., 2019b , p. 115).

As a beginning researcher, determining the relevance and importance of a research problem is especially challenging. We recommend talking with advisors, other experienced researchers, and peers to test the educational importance of possible research problems and topics of study. You will also learn much more about the issue of research importance when you read Chap. 5 .

Exercise 1.7

Identify a problem in education that is closely connected to improving learning opportunities and a problem that has a less close connection. For each problem, write a brief argument (like a logical sequence of if-then statements) that connects the problem to all students’ learning opportunities.

Part III. Conducting Research as a Practice of Failing Productively

Scientific inquiry involves formulating hypotheses about phenomena that are not fully understood—by you or anyone else. Even if you are able to inform your hypotheses with lots of knowledge that has already been accumulated, you are likely to find that your prediction is not entirely accurate. This is normal. Remember, scientific inquiry is a process of constantly updating your thinking. More and better information means revising your thinking, again, and again, and again. Because you never fully understand a complicated phenomenon and your hypotheses never produce completely accurate predictions, it is easy to believe you are somehow failing.

The trick is to fail upward, to fail to predict accurately in ways that inform your next hypothesis so you can make a better prediction. Some of the best-known researchers in education have been open and honest about the many times their predictions were wrong and, based on the results of their studies and those of others, they continuously updated their thinking and changed their hypotheses.

A striking example of publicly revising (actually reversing) hypotheses due to incorrect predictions is found in the work of Lee J. Cronbach, one of the most distinguished educational psychologists of the twentieth century. In 1955, Cronbach delivered his presidential address to the American Psychological Association. Titling it “Two Disciplines of Scientific Psychology,” Cronbach proposed a rapprochement between two research approaches—correlational studies that focused on individual differences and experimental studies that focused on instructional treatments controlling for individual differences. (We will examine different research approaches in Chap. 4 ). If these approaches could be brought together, reasoned Cronbach ( 1957 ), researchers could find interactions between individual characteristics and treatments (aptitude-treatment interactions or ATIs), fitting the best treatments to different individuals.

In 1975, after years of research by many researchers looking for ATIs, Cronbach acknowledged the evidence for simple, useful ATIs had not been found. Even when trying to find interactions between a few variables that could provide instructional guidance, the analysis, said Cronbach, creates “a hall of mirrors that extends to infinity, tormenting even the boldest investigators and defeating even ambitious designs” (Cronbach, 1975 , p. 119).

As he was reflecting back on his work, Cronbach ( 1986 ) recommended moving away from documenting instructional effects through statistical inference (an approach he had championed for much of his career) and toward approaches that probe the reasons for these effects, approaches that provide a “full account of events in a time, place, and context” (Cronbach, 1986 , p. 104). This is a remarkable change in hypotheses, a change based on data and made fully transparent. Cronbach understood the value of failing productively.

Closer to home, in a less dramatic example, one of us began a line of scientific inquiry into how to prepare elementary preservice teachers to teach early algebra. Teaching early algebra meant engaging elementary students in early forms of algebraic reasoning. Such reasoning should help them transition from arithmetic to algebra. To begin this line of inquiry, a set of activities for preservice teachers were developed. Even though the activities were based on well-supported hypotheses, they largely failed to engage preservice teachers as predicted because of unanticipated challenges the preservice teachers faced. To capitalize on this failure, follow-up studies were conducted, first to better understand elementary preservice teachers’ challenges with preparing to teach early algebra, and then to better support preservice teachers in navigating these challenges. In this example, the initial failure was a necessary step in the researchers’ scientific inquiry and furthered the researchers’ understanding of this issue.

We present another example of failing productively in Chap. 2 . That example emerges from recounting the history of a well-known research program in mathematics education.

Making mistakes is an inherent part of doing scientific research. Conducting a study is rarely a smooth path from beginning to end. We recommend that you keep the following things in mind as you begin a career of conducting research in education.

First, do not get discouraged when you make mistakes; do not fall into the trap of feeling like you are not capable of doing research because you make too many errors.

Second, learn from your mistakes. Do not ignore your mistakes or treat them as errors that you simply need to forget and move past. Mistakes are rich sites for learning—in research just as in other fields of study.

Third, by reflecting on your mistakes, you can learn to make better mistakes, mistakes that inform you about a productive next step. You will not be able to eliminate your mistakes, but you can set a goal of making better and better mistakes.

Exercise 1.8

How does scientific inquiry differ from everyday learning in giving you the tools to fail upward? You may find helpful perspectives on this question in other resources on science and scientific inquiry (e.g., Failure: Why Science is So Successful by Firestein, 2015).

Exercise 1.9

Use what you have learned in this chapter to write a new definition of scientific inquiry. Compare this definition with the one you wrote before reading this chapter. If you are reading this book as part of a course, compare your definition with your colleagues’ definitions. Develop a consensus definition with everyone in the course.

Part IV. Preview of Chap. 2

Now that you have a good idea of what research is, at least of what we believe research is, the next step is to think about how to actually begin doing research. This means how to begin formulating, testing, and revising hypotheses. As for all phases of scientific inquiry, there are lots of things to think about. Because it is critical to start well, we devote Chap. 2 to getting started with formulating hypotheses.

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Hiebert, J., Cai, J., Hwang, S., Morris, A.K., Hohensee, C. (2023). What Is Research, and Why Do People Do It?. In: Doing Research: A New Researcher’s Guide. Research in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-031-19078-0_1

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

• disquisition
• examination
• exploration
• inquisition
• investigation
• delve (into)
• inquire (into)
• investigate
• look (into)

Examples of research in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'research.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

Middle French recerche , from recercher to go about seeking, from Old French recerchier , from re- + cerchier, sercher to search — more at search

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• translational research
• operations research
• research park

research and development

• market research
• marketing research
• oppo research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 19 Mar. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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World Wide Web: Definition, history and facts

The World Wide Web was created by British scientist Tim Berners-Lee.

• Creating WWW.

Additional resources

Bibliography.

Before the invention of the World Wide Web (WWW), the earliest internet users were mainly researchers and military personnel. The network was complicated and, although it was possible to share files and messages, the interface was not user-friendly. 

In 1993, a researcher at CERN called Tim Berners-Lee started building a layer on top of the internet to make it easier to access, according to the World Wide Web Foundation . 

Berners-Lee's idea was to make information available as pages, written in a shared language called Hypertext Markup Language (HTML). This eventually became the World Wide Web, which is the platform used by billions of internet users around the world.

Creating the World Wide Web

After completing his first-class degree in physics at Oxford University, Berners-Lee moved on to become a scientist at CERN, the European Organization for Nuclear Research in 1989, according to the World Wide Web Consortium ( W3C ). 

That same year, Berners-Lee published a paper titled "Information Management: A Proposal", in which he suggested the combination of hypertext and the internet for an information management system.

In this initial proposal for the World Wide Web, Berners-Lee described the shortcomings of the then-current system at CERN in allowing scientists access to their information and documentation. Though the internet had been around for a decade, the information had limited accessibility. 

Berners-Lee set out to connect both the internet and a web-structured platform to revolutionise data sharing. To achieve this he created the Hypertext Transfer Protocol (HTTP), Uniform Resource Identifier (URI) and Hypertext Makeup Language (HTML), the building blocks for internet browsing that remain in use today, according to CERN .

Created to better serve CERN scientists and assist those across the globe with their research, Berners-Lee launched the first website, http:// info.cern.ch , in 1990. This new way to obtain information was something Berners-Lee wanted the entire world to have access to. He decided to make the World Wide Web an open and royalty- free software, allowing it to grow beyond academia. 

By 1994 there were around 3,000 websites in existence, according to the World Economic Forum . After such a roaring success, Berners-Lee created W3C, a web standards organisation that also develops web specifications, guidelines, software and tools. With the continued success of the iconic ‘www.’, Berners-Lee founded the World Wide Web Foundation in 2009, an organisation working to deliver digital equality to the world.

The development of the world wide web has meant that anyone can add to the internet, creating their own pages and sharing their own content. No-one owns the internet, according to the journal Educational Technology , although big tech companies wield a lot of its power. 

It is simply a collection of interlinked networks managed by companies, governments, research organizations, and individuals. Google, Microsoft, Amazon and others have changed the way it works, but so too have amateurs creating content from their homes.

After the invention of the world wide web, users continued to expand the internet, sharing bigger and more complicated content. In 1993, there were fewer than 150 websites on the internet, now there are almost two billion, according to Internet Live Stats . This ever-growing web of connections has completely changed the way that people live, work, and interact.

For more information about the invention of the World Wide Web check out " Weaving the Web: The Past, Present and Future of the World Wide Web by its Inventor ", by Tim Berners-Lee and the World Wide Web Consortium (W3C) .

• " World Wide Web ", accessed March 2022. 
• CERN, " The birth of the web ", accessed March 2022. 
• World Wide Web Foundation, " History of the Web ", accessed March 2022. 
• Jeremy Galbreath, " The Internet: Past, Present, and Future ", Educational Technology, Volume 37, 1997. 
• World Wide Web Consortium, " Longer Biography ", accessed March 2022. 
• "Internet Live Stats, " Total number of websites ", accessed March 2022.  

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Scott is a staff writer for How It Works magazine and has previously written for other science and knowledge outlets, including BBC Wildlife magazine, World of Animals magazine, Space.com and All About History magazine . Scott has a masters in science and environmental journalism and a bachelor's degree in conservation biology degree from the University of Lincoln in the U.K. During his academic and professional career, Scott has participated in several animal conservation projects, including English bird surveys, wolf monitoring in Germany and leopard tracking in South Africa. 

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World Leaders in Research-Based User Experience

Web research: believe the data.

July 10, 1999 1999-07-10

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By now, we know a good deal about users' behavior on the Web. For example, they demand fast download and are extremely impatient and want immediate support for their own goals. Even so, most websites are slow, internally-driven, and do not focus on solving the users' problems.

Do not ignore research: it can improve your site by several hundred percent. Even when a specific project does not have data from its exact customers using its exact site, it should use general data about the average behavior of Web users with common sites. Well, maybe your site is so unique and different that people will behave differently when they visit your site than when they visit other sites. But I wouldn't bet the farm on it. If you have data showing that you are different, then OK, but in the absence of specific data, it's best to follow the general rules.

In This Article:

Example 1: delay customer registration, example 2: effectiveness of web marketing, more research needed.

Customer registration forms are the bane of attracting new customers. It's like posting an armed guard at the door to a department store and only letting people in the store after they show two forms of ID and suffer nosy questions about their family tree.

Web users often turn away rather than having to register. Quite simply, it's not worth peoples' time to answer all your questions. Even if we get a Web-wide standard for one-click registration, users will resent being asked to register. Every click is a burden for busy Web users, but more important, users don't like parting with their personal data before they have developed a sense of trust in the site.

• Customer registration (and anything else that gets in the way of allowing users to focus on what they want to do) should be minimized and made as easy and fast as possible; make sure that all such forms are usability tested.
• Comply with any widely used mechanisms for allowing users to enter their data once and reuse it across the Web. Single log-in is a known usability requirement for all other systems, and we need it on the Web as well.
• Postpone registration as far as possible into the usage process: if you ask too early before you have established your value to a new customer, you will simply turn away the prospect.

It is common for sites that violate these rules to discover that 1/3 or more of their "customer" registrations are bogus. Unless you believe that Donald Duck is a very frequent user of these sites.

Qualitative studies have long shown that demanding that users register hurts usability and makes users turn away . (Offering guest checkout has long been a key guideline for e-commerce design.) But how much does it hurt?

At the annual conference of the Usability Professionals' Association  earlier this month, Marie Tahir from Intuit described the usability process used in developing several versions of QuickenMortgage.com (a mortgage site). Too-early customer registration requirement posed a major problem in the earlier version of the site. After this finding, the site was redesigned to allow users to enter valuable areas of the site without having to register. Registration was postponed to a later stage where it was truly necessary to know the user's personal data in order to provide a mortgage. As a result, usage doubled .

Two lessons:

• Yes, usability works. When you fix a part of your site that users can't or won't use, you will get immediate results.
• The magnitude of usability improvements is usually large. This is not a matter of increasing use by a few percent. It is common for usability efforts to result in a hundred percent or more increase in traffic or sales .

In a recent study reported by Internet World and Iconocast , Forrester surveyed Web marketing executives to find out how much they used various means of promoting their site and how effective these methods had been. The results show a negative correlation between the effectiveness and use of Web marketing methods (see figure). In other words, the less results you get from a Web marketing method, the more it is used .

I have said since 1997 that advertising doesn't work on the Web . My original analysis was based on qualitative studies of user behavior, but there is now much quantitative data to support the conclusion:

• eye-tracking studies  find that users never even see the ads
• click-through rates dropping from 2% to 0.5% in a few years
• sales data from many sites showing that they usually don't sell a lot to those few users who do click through — paying customers usually arrive in other ways

The dropping click-through rate may be the single-most striking set of data in all Web research because the trend line is so clear and has been so consistent over the last three years. Continue the trend line out a few years (it will hit 0.1% by the end of 2000) and the conclusions are clear:

• Don't build Internet business models that rely on sites getting substantial advertising revenues — except for huge sites: Yahoo can survive its current monetizing quotient (MQ) of 0.4 cents per page view because they have 310 million page views per day; you can't.
• Don't make online advertising the center of the marketing plan for your own site - instead combine offline advertising with Internet-appropriate marketing methods like affiliate programs and email (to customers who ask to be notified; never send spam if you want a reputation as a reliable and high-trust  site).

Many marketers are in denial of the plain-spoken message communicated by the data. They would like advertising to succeed on the Web just like it was successful in the old media they know how to deal with. So they keep running banners even though they don't work. I say, let's believe the data and move on.

I recently asked the marketing director for a major e-commerce site why they didn't have an affiliate program , even though this way of encouraging inbound links is known to be one of the best marketing methods on the Internet (the top scorer in the Forrester study). The answer was basically that the software effort to develop an affiliates program was too burdensome. Many others probably have similar reactions, which could explain why the best marketing method is the least used. This is a major opportunity for somebody to develop an utterly simple way for websites to set up affiliates programs. But even with current technology, the cost to have a geek or two handle the software side of the marketing program is much less than most spending on less-effective methods.

Unfortunately, there is still too little publicly available research on Web usability. Some proprietary research exists and I am gratified by the growth in the number of companies that are running their own studies. But we also need published results since many of the findings do generalize. For example, several studies have shown that the Back button is the second-most used feature  on the Web (after clicking on links), and this finding is a great counter-argument against the people who want to open new browser windows because "users won't find their way back otherwise." Oh, yes, they will.

I particularly encourage universities to conduct more research into Web use. Soon, it will be too late to study the transition from the pre-Web to the post-Web world. The control group of non-wired people will vanish. Hurry.

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Identifying Research Fronts in the Web of Science: From metrics to meaning

Martin Szomszor

Director, Institute for Scientific Information

The latest Global Research Report from the Institute for Scientific Information™ (ISI) uses clusters of citations from recently published papers to show the hot and emerging scientific topics that are currently attracting exceptional attention. The ability to find these fronts and to track emerging specialty areas of scientific research, together with the key players at the epicenter of the front, provides a distinct advantage for those who fund, monitor, support and advance the conduct of research, often in the face of finite resources.

By examining Research Fronts in the Web of Science™, it is both possible and desirable to find the foci of innovation and chance. For companies looking to create revolutionary products, the ability to identify and access a stream of innovative exploratory research and the identification of its key players as potential collaborators provides many opportunities to turn published research into a compelling competitive advantage with societal and economic impact.

Identifying Research Fronts in the Web of Science: From metrics to meaning uses science mapping and data visualization to highlight their value by using familiar examples – CRISPR, 2D Materials, and Machine Learning. It also includes testimonials from the Chinese Academy of Sciences (CAS) and the Japan Science & Technology Agency (JST) have used Research Fronts to inform horizon scanning, funding focus, and research program analysis.

The new report also highlights the value of Research Fronts in providing new and complementary evaluation approaches as the ability to run computational assessment on large data sets together with advances in data visualization paves the way to modernize research conduct and measurement.

“The ISI encourages researchers and managers to perform deeper evaluations to inform and improve research evaluation and highlight topics with economic and societal impact.”

Scores and ranks have their uses but are limited in revealing many aspects of research activity and different dimensions of contributions and fuller, more informative types of assessment are now possible. The insights offered go far beyond the information derived from more traditional research performance metrics.

The report highlights the use and value of Research Fronts for:

• Researchers. The identification of a Research Front may help to suggest how a research career might be shaped. An author, by locating their current activity, can see how close her work is to a Research Front.
• Institutional research managers. A research manager can determine the distribution of institutional output across the knowledge landscape, filtering for recent or longer time windows, and then assess the relationship of their research clusters to Research Fronts. They can also make a comparative evaluation with competitor institutions.
• Research funders. By identifying the distribution of publications arising from funded projects, a research agency can see whether its investments are producing work located in or near Research Fronts, or perhaps redirect funding to projects addressing emerging fronts and topics gathering exceptional peer attention. It also provides valuable information in identifying new and unpredictable scientific and technological discoveries for the basis of new industries, and with the greatest promise of innovation.
• Policy makers . The distribution of a national portfolio in the research landscape will be of interest both for international comparisons and for the extent to which the country is engaging with Research Fronts, especially in areas related to policy priorities.
• Publishers. The landscape location of a journal’s contents can be seen not only in the context of broad disciplines but in relation to Research Fronts as topics of exceptional current interest. Where appropriate, editorial policies can be adjusted accordingly.

ISI encourages analysts engaged in research assessment and research policy to consider the citation network as more than a tool for metrics, but as an evolving structure that reflects the changing discourse of research. Download the full copy of the report here.

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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.

Prevent plagiarism. Run a free check.

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

Types of quantitative research designs

Quantitative designs can be split into four main types.

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

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

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

Types of qualitative research designs

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

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

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

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

Defining the population

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

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

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

• Sampling methods

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

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

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

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

Case selection in qualitative research

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

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

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

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

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

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

Survey methods

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

Observation methods

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

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

Other methods of data collection

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

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

Secondary data

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

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

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

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

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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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What is Web Design?

Web design refers to the design of websites. It usually refers to the user experience aspects of website development rather than software development. Web design used to be focused on designing websites for desktop browsers; however, since the mid-2010s, design for mobile and tablet browsers has become ever-increasingly important.

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A web designer works on a website's appearance, layout, and, in some cases, content .

Appearance relates to the colors, typography, and images used.

Layout refers to how information is structured and categorized. A good web design is easy to use, aesthetically pleasing, and suits the user group and brand of the website.

A well-designed website is simple and communicates clearly to avoid confusing users. It wins and fosters the target audience's trust, removing as many potential points of user frustration as possible.

Responsive and adaptive design are two common ways to design websites that work well on both desktop and mobile.

What is Responsive Web Design?

© Interaction Design Foundation, CC BY-SA 4.0

Responsive Web Design (a.k.a. "Responsive" or "Responsive Design") is an approach to designing web content that appears regardless of the resolution governed by the device. It’s typically accomplished with viewport breakpoints (resolution cut-offs for when content scales to that view). The viewports should adjust logically on tablets, phones, and desktops of any resolution.

In responsive design, you can define rules for how the content flows and how the layout changes based on the size range of the screen.

Responsive designs respond to changes in browser width by adjusting the placement of design elements to fit in the available space. If you open a responsive site on the desktop and change the browser window's size, the content will dynamically rearrange itself to fit the browser window. The site checks for the available space on mobile phones and then presents itself in the ideal arrangement.

Best Practices and Considerations for Responsive Design

With responsive design, you design for flexibility in every aspect—images, text and layouts. So, you should:

Take the mobile-first approach —start the product design process for mobile devices first instead of desktop devices.

Create fluid grids and images .

Prioritize the use of Scalable Vector Graphics (SVGs). These are an XML-based file format for 2D graphics, which supports interactivity and animations.

Include three or more breakpoints (layouts for three or more devices).

Prioritize and hide content to suit users’ contexts . Check your visual hierarchy and use progressive disclosure and navigation drawers to give users needed items first. Keep nonessential items (nice-to-haves) secondary.

Aim for minimalism .

Apply design patterns to maximize ease of use for users in their contexts and quicken their familiarity: e.g., the column drop pattern fits content to many screen types.

Aim for accessibility .

What is Adaptive Web Design?

Adaptive design is similar to responsive design—both are approaches for designing across a diverse range of devices; the difference lies in how the tailoring of the content takes place.

In the case of responsive design, all content and functionality are the same for every device. Therefore, a large-screen desktop and smartphone browser displays the same content. The only difference is in the layout of the content. 

In this video, CEO of Experience Dynamics, Frank Spillers, explains the advantages of adaptive design through a real-life scenario.

Adaptive design takes responsiveness up a notch. While responsive design focuses on just the device, adaptive design considers both the device and the user’s context. This means that you can design context-aware experiences —a web application's content and functionality can look and behave very differently from the version served on the desktop.

For example, if an adaptive design detects low bandwidth or the user is on a mobile device instead of a desktop device, it might not load a large image (e.g., an infographic). Instead, it might show a smaller summary version of the infographic.

Another example could be to detect if the device is an older phone with a smaller screen. The website can show larger call-to-action buttons than usual.

Accessibility for Web Design

“The power of the Web is in its universality. Access by everyone regardless of disability is an essential aspect.” —Tim Berners-Lee, W3C Director and inventor of the World Wide Web

Web accessibility means making websites and technology usable for people with varying abilities and disabilities. An accessible website ensures that all users, regardless of their abilities, can perceive, understand, navigate, and interact with the web.

In this video, William Hudson, CEO of Syntagm, discusses the importance of accessibility and provides tips on how to make websites more accessible.

The World Wide Web Consortium (W3C) lists a few basic considerations for web accessibility:

Provide sufficient contrast between foreground and background . For example, black or dark gray text on white is easier to read than gray text on a lighter shade of gray. Use color contrast checkers to test the contrast ratio between your text and background colors to ensure people can easily see your content.

Don’t use color alone to convey information . For example, use underlines for hyperlinked text in addition to color so that people with colorblindness can still recognize a link, even if they can’t differentiate between the hyperlink and regular text.

Ensure that interactive elements are easy to identify . For example, show different styles for links when the user hovers over them or focuses using the keyboard.

Provide clear and consistent navigation options . Use consistent layouts and naming conventions for menu items to prevent confusion. For example, if you use breadcrumbs, ensure they are consistently in the same position across different web pages.

Ensure that form elements include clearly associated labels . For example, place form labels to the left of a form field (for left-to-right languages) instead of above or inside the input field to reduce errors.

Provide easily identifiable feedback . If feedback (such as error messages) is in fine print or a specific color, people with lower vision or colorblindness will find it harder to use the website. Make sure such feedback is clear and easy to identify. For example, you can offer options to navigate to different errors.

Use headings and spacing to group related content. Good visual hierarchy (through typography, whitespace and grid layouts) makes it easy to scan content.

Create designs for different viewport sizes . Ensure your content scales up (to larger devices) and down (to fit smaller screens). Design responsive websites and test them thoroughly. 

Include image and media alternatives in your design . Provide transcripts for audio and video content and text alternatives for images. Ensure the alternative text on images conveys meaning and doesn’t simply describe the image. If you use PDFs, make sure they, too, are accessible.

Provide controls for content that starts automatically . Allow users to pause animations or video content that plays automatically.

These practices not only make a website easier to access for people with disabilities but also for usability in general for everyone.

Learn More about Web Design

Learn how to apply the principles of user-centered design in the course Web Design for Usability . 

For more on adaptive and responsive design, take the Mobile UX Design: The Beginner's Guide course. 

See W3C’s Designing for Web Accessibility for practical tips on implementing accessibility.

Questions related to Web Design

Designing a web page involves creating a visual layout and aesthetic.

Start by defining the purpose and target audience of your page.

Understand the type of content and what actions the user will perform on the web page.

Sketch ideas and create wireframes or mockups of the layout.

Select a color scheme, typography, and imagery that align with your brand identity.

Use design software like Figma or Sketch to create the design.

Finally, gather feedback and make necessary revisions before handing off the development design.

In each step, remember to keep the user experience and accessibility considerations foremost. Here’s why Accessibility Matters: 

The salary of web designers varies widely based on experience, location, and skill set. As of our last update, the average salary for a Web Designer in the United States is reported to be approximately $52,691 per year, according to Glassdoor. However, this figure can range from around $37,000 for entry-level positions to over $73,000 for experienced designers. It is crucial to mention that salaries may differ significantly by region, company size, and individual qualifications. For the most up-to-date and region-specific salary information, visit Glassdoor .

To become a web designer, you should start by understanding design principles, usability best practices, color theory, and typography. Next, learn the essential tools like Adobe Photoshop, Illustrator, and Sketch. Familiarize yourself with web design languages such as HTML, CSS, and JavaScript. It's important to create a portfolio of your top work to impress potential employers. Additionally, consider taking online courses to enhance your knowledge and skills. 

Interaction Design Foundation offers a comprehensive UI Designer Learning Path that can help you become proficient in user interface design, a key component of web design. Lastly, continuously practice web design, seek feedback, and stay up-to-date with the latest trends and technologies.

The role of a web designer entails the task of designing a website's visual design and layout of a website, which includes the site's appearance, structure, navigation, and accessibility. They select color palettes, create graphics, choose fonts, and layout content to create an aesthetically pleasing, user-friendly, and accessible design. Web designers also work closely with web developers to verify that the design is technically feasible and implemented correctly. They may be involved in user experience design, ensuring the website is intuitive, accessible, and easy to use. Additionally, web designers must be aware of designer bias, as discussed in this video. 

Ultimately, a web designer's goal is to create a visually appealing, functional, accessible, and positive user experience.

Web design and coding are closely related, but they are not the same. Web design involves creating the visual elements and layout of a website, while coding involves translating these designs into a functional website using programming languages like HTML, CSS, and JavaScript. Typically, dedicated web developers translate the designs to code. Several design tools can also export code directly.

Although some web designers also have coding skills, it is not a requirement for all web design roles. However, having a basic understanding of coding can be beneficial for a web designer as it helps in creating designs that are both aesthetically pleasing and technically feasible.

Responsive web design guarantees that a website adapts its format to fit any screen size across different devices and screen sizes, from desktops to tablets to mobile phones. It includes the site to the device's resolution, supports device switching and increases accessibility and SEO-friendliness.

As Frank Spillers, CEO of Experience Dynamics mentions in this video, responsive design is a default, and not an optional feature because everyone expects mobile optimization. This approach is vital for Google's algorithm, which prioritizes responsive sites.

To learn web design, start by understanding its fundamental principles, such as color theory, typography, and layout. Practice designing websites, get feedback, and iterate on your designs. Enhance your skills by taking online courses, attending workshops, and reading articles. 

Consider the Interaction Design Foundation's comprehensive UI Designer learning path for essential skills and knowledge. If you're interested in expanding your skill set, consider exploring UX design as an alternative. The article " How to Change Your Career from Web Design to UX Design " on the IxDF Blog offers insightful guidance. Start your journey today!

Absolutely, web design is a rewarding career choice. It offers creative freedom, a chance to solve real-world problems, and a growing demand for skilled professionals. With the digital world expanding, businesses seek qualified web designers to create user-friendly and visually appealing websites. Additionally, web design offers diverse job opportunities, competitive salaries, and the option to work freelance or in-house. Continuously evolving technology ensures that web design remains a dynamic and future-proof career.

Web design and front-end development are related but distinct disciplines. Web design involves creating the visual layout and aesthetics of a website, focusing on user experience, graphics, and overall look. Front-end development, on the other hand, involves implementing the design into a functional website using coding languages like HTML, CSS, and JavaScript. While there is overlap, and many professionals have skills in both areas, web design is more creative, and front-end development is more technical.

In this Master Class webinar, Szymon Adamiak of Hype4 shares his top tips for smooth designer-developer relationships, based on years of working as a front-end developer with teams of designers on various projects.

Yes and no! A web page is a type of user interface—it is the touchpoint between a business and the user. People interact with web pages. They may fill out a form, or simply navigate from one page to another. A web designer must also be familiar with UI design best practices to ensure the website is usable.

That said, in practice, the term UI is most often associated with applications. Unlike web pages, which tend to be more static and are closely related to branding and communication, applications (on both web and mobile) allow users to manipulate data and perform tasks.. 

UI design, as explained in this video above, involves visualizing and creating the interface of an application, focusing on aesthetics, user experience, and overall look. To learn more, check our UI Design Learning Path .

A modal in web design is a secondary window that appears above the primary webpage, focusing on specific content and pausing interaction with the main page. It's a common user interface design pattern used to solve interface problems by showing contextual information when they matter. 

The video above explains the importance of designing good UI patterns to enhance user experience and reduce usability issues. Modals are crucial for successful user-centered design and product development like other UI patterns.

In web design, CMS refers to a Content Management System. It is software used to create and manage digital content. 

The video above implies that the content, including those managed by a CMS, is crucial in every stage of the user experience, from setup to engagement. The top 10 CMS in 2023 are the following:

Magento (more focused on e-commerce)

Squarespace

Shopify (more focused on e-commerce)

The popularity and usage of CMS platforms can vary over time, and there may be new players in the market since our last update. 

Literature on Web Design

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

Learn more about Web Design

Take a deep dive into Web Design with our course Mobile UX Design: The Beginner's Guide .

In the “ Build Your Portfolio” project, you’ll find a series of practical exercises that will give you first-hand experience with the methods we cover. You will build on your project in each lesson so once you have completed the course you will have a thorough case study for your portfolio.

Mobile User Experience Design: Introduction , has been built on evidence-based research and practice. It is taught by the CEO of ExperienceDynamics.com, Frank Spillers, author, speaker and internationally respected Senior Usability practitioner.

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• Cambridge Dictionary +Plus

Meaning of research in English

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• He has dedicated his life to scientific research.
• He emphasized that all the people taking part in the research were volunteers .
• The state of Michigan has endowed three institutes to do research for industry .
• I'd like to see the research that these recommendations are founded on.
• It took months of painstaking research to write the book .
• absorptive capacity
• dream something up
• modularization
• nanotechnology
• non-imitative
• operations research
• think outside the box idiom
• think something up
• uninventive
• study What do you plan on studying in college?
• major US She majored in philosophy at Harvard.
• cram She's cramming for her history exam.
• revise UK I'm revising for tomorrow's test.
• review US We're going to review for the test tomorrow night.
• research Scientists are researching possible new treatments for cancer.
• The amount of time and money being spent on researching this disease is pitiful .
• We are researching the reproduction of elephants .
• She researched a wide variety of jobs before deciding on law .
• He researches heart disease .
• The internet has reduced the amount of time it takes to research these subjects .
• adjudication
• analytically
• interpretable
• interpretive
• interpretively
• investigate
• reinvestigate
• reinvestigation
• risk assessment
• run over/through something
• run through something

You can also find related words, phrases, and synonyms in the topics:

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Research | intermediate english, research | business english, examples of research, collocations with research.

These are words often used in combination with research .

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Translations of research

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Word of the Day

a servant or someone who behaves like one by obeying someone else's orders or by doing unpleasant work for them

Renowned and celebrated (Words meaning ‘famous’)

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Information Retrieval and the Web

The science surrounding search engines is commonly referred to as information retrieval, in which algorithmic principles are developed to match user interests to the best information about those interests.

Google started as a result of our founders' attempt to find the best matching between the user queries and Web documents, and do it really fast. During the process, they uncovered a few basic principles: 1) best pages tend to be those linked to the most; 2) best description of a page is often derived from the anchor text associated with the links to a page. Theories were developed to exploit these principles to optimize the task of retrieving the best documents for a user query.

Search and Information Retrieval on the Web has advanced significantly from those early days: 1) the notion of ""information"" has greatly expanded from documents to much richer representations such as images, videos, etc., 2) users are increasingly searching on their Mobile devices with very different interaction characteristics from search on the Desktops; 3) users are increasingly looking for direct information, such as answers to a question, or seeking to complete tasks, such as appointment booking. Through our research, we are continuing to enhance and refine the world's foremost search engine by aiming to scientifically understand the implications of those changes and address new challenges that they bring.

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Literature review

A general guide on how to conduct and write a literature review.

Please check course or programme information and materials provided by teaching staff, including your project supervisor, for subject-specific guidance.

What is a literature review?

A literature review is a piece of academic writing demonstrating knowledge and understanding of the academic literature on a specific topic placed in context.  A literature review also includes a critical evaluation of the material; this is why it is called a literature review rather than a literature report. It is a process of reviewing the literature, as well as a form of writing.

To illustrate the difference between reporting and reviewing, think about television or film review articles.  These articles include content such as a brief synopsis or the key points of the film or programme plus the critic’s own evaluation.  Similarly the two main objectives of a literature review are firstly the content covering existing research, theories and evidence, and secondly your own critical evaluation and discussion of this content. 

Usually a literature review forms a section or part of a dissertation, research project or long essay.  However, it can also be set and assessed as a standalone piece of work.

What is the purpose of a literature review?

…your task is to build an argument, not a library. Rudestam, K.E. and Newton, R.R. (1992) Surviving your dissertation: A comprehensive guide to content and process. California: Sage, p49.

In a larger piece of written work, such as a dissertation or project, a literature review is usually one of the first tasks carried out after deciding on a topic.  Reading combined with critical analysis can help to refine a topic and frame research questions.  Conducting a literature review establishes your familiarity with and understanding of current research in a particular field before carrying out a new investigation. After doing a literature review, you should know what research has already been done and be able to identify what is unknown within your topic.

When doing and writing a literature review, it is good practice to:

• summarise and analyse previous research and theories;
• identify areas of controversy and contested claims;
• highlight any gaps that may exist in research to date.

Conducting a literature review

Focusing on different aspects of your literature review can be useful to help plan, develop, refine and write it.  You can use and adapt the prompt questions in our worksheet below at different points in the process of researching and writing your review.  These are suggestions to get you thinking and writing.

Developing and refining your literature review (pdf)

Developing and refining your literature review (Word)

Developing and refining your literature review (Word rtf)

Writing a literature review has a lot in common with other assignment tasks.  There is advice on our other pages about thinking critically, reading strategies and academic writing.  Our literature review top tips suggest some specific things you can do to help you submit a successful review.

Literature review top tips (pdf)

Literature review top tips (Word rtf)

Our reading page includes strategies and advice on using books and articles and a notes record sheet grid you can use.

Reading at university

The Academic writing page suggests ways to organise and structure information from a range of sources and how you can develop your argument as you read and write.

Academic writing

The Critical thinking page has advice on how to be a more critical researcher and a form you can use to help you think and break down the stages of developing your argument.

Critical thinking

As with other forms of academic writing, your literature review needs to demonstrate good academic practice by following the Code of Student Conduct and acknowledging the work of others through citing and referencing your sources.  

Good academic practice

As with any writing task, you will need to review, edit and rewrite sections of your literature review.  The Editing and proofreading page includes tips on how to do this and strategies for standing back and thinking about your structure and checking the flow of your argument.

Editing and proofreading

Guidance on literature searching from the University Library

The Academic Support Librarians have developed LibSmart I and II, Learn courses to help you develop and enhance your digital research skills and capabilities; from getting started with the Library to managing data for your dissertation.

Searching using the library’s DiscoverEd tool: DiscoverEd

Finding resources in your subject: Subject guides

The Academic Support Librarians also provide one-to-one appointments to help you develop your research strategies.

1 to 1 support for literature searching and systematic reviews

Advice to help you optimise use of Google Scholar, Google Books and Google for your research and study: Using Google

Managing and curating your references

A referencing management tool can help you to collect and organise and your source material to produce a bibliography or reference list. 

Referencing and reference management

Information Services provide access to Cite them right online which is a guide to the main referencing systems and tells you how to reference just about any source (EASE log-in may be required).

Cite them right

Published study guides

There are a number of scholarship skills books and guides available which can help with writing a literature review.  Our Resource List of study skills guides includes sections on Referencing, Dissertation and project writing and Literature reviews.

Study skills guides

More From Forbes

The power of ai and data-as-a-service: how next-gen web scraping is redefining research in 2024.

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Pavlo Zinkovskyi is the co-founder and CTO of Infatica.io , which offers a wide range of proxy support for residential and mobile needs.

Research is a cornerstone of human progress, which holds significant value in business, the public sector and academia. However, gathering of trustworthy information was once a laborious and time-consuming task spanning months or years. The advent of web scraping tools has revolutionized this practice, ensuring rapid and high-quality data collection. This evolution will be further propelled by AI, user-friendly interfaces akin to ChatGPT and data-as-a-service.

Exploring Web Scraping

Web scraping involves an automated extraction of publicly available data from websites. It requires two parts, namely a crawler and a scraper. The former is an algorithm that searches for particular data across the web, while the latter is a tool created to extract it. Such solutions leverage protocols like HTTP or API calls.

Web scraping can handle diverse types of data, which is particularly valuable for businesses seeking insights into market trends, consumer behavior and competitor activities, as well as for academia and the public sector.

The global market for web scraping has expanded exponentially in recent years. One of the fresh reports shows that the industry was valued at $4.9 billion in 2023 and is expected to grow with an impressive CAGR of 28% till 2032. As for the global web scraping software market size, it has likely already exceeded $800 million and is far from reaching its true potential. It is estimated to reach over $1.8 billion by 2030 and is fueled by the increasing reliance on data-driven decision-making across industries.

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In the business landscape, the e-commerce industry is one of the largest consumers of web scraped data. It holds a market share of around 25%, according to my company's research .

Industry professionals leverage scraping tools to automate price tracking of specific goods, such as electronics, housing and food, and calculate the consumer price index. This data aids in adjusting pricing strategies and optimizing product offerings.

Moreover, web scraping enables marketers to monitor the same products sold under different conditions, such as during promotional periods. It can also collect data on product reviews, customer ratings and feedback. This all helps to analyze consumer behavior and sheds light on how external factors impact purchasing decisions. This, in turn, helps their marketing strategies.

Public Sector

In the public sector, web scraping has become a powerful tool, especially in investigative journalism or political research. It can be used to track political developments, public sentiments, etc. Moreover, journalists can uncover hidden information and contribute to more detailed and informed reporting. Indicatively, the Centre for Investigative Journalism, which provides training to journalists and researchers since 2003, is offering an extensive workshop in web scraping.

Government agencies can utilize web scraping to monitor compliance, track economic indicators and gather data for policy formulation. The ability to access real-time data from the web ensures that policies are based on the most current information available.

Researchers use scraping tools to extract and analyze big data from various sources, supplementing traditional datasets. This helps to test and validate hypotheses while creating new research questions. No surprise, Brown University offers its students a web scraping toolkit for the local library, and Wharton University partners with a third-party provider to meet its researchers' needs.

Social scientists can employ web scraping to study online interactions and sentiments. This way, they get insights into societal trends and attitudes. In healthcare research, it can extract data from medical journals, clinical trials and patient forums to get a clearer understanding of healthcare dynamics.

Prominent examples of using alternative data for scientific research include depression and anxiety studies based on public social media behavior or studies related to the pandemic. One of them utilized Google Trends data on Covid-19 to forecast the future trends of daily new cases, cumulative cases and deaths for India, USA and U.K.

Next-Gen Web Scraping

Ai-powered data collection.

The future of web scraping is intricately tied to the advancements in AI and ML technologies. In 2024, scraping tools will become more intelligent and the need for manual intervention is diminishing. AI-driven scrapers can fully comprehend HTML pages and extract necessary information with unparalleled precision.

Emerging scraping tools can navigate through website changes in real time. They adapt on the fly to alterations in layout and content structure. This not only enhances reliability of data extraction but reduces maintenance overhead.

User-Focused Design

Due to the rise in the popularity of conversational AI chatbots, such as ChatGPT (which is already being used by over 100 million people a week), customers now seek more intuitive and user-friendly interfaces in other services too.

This trend extends to web scrapers, with the industry moving toward more intuitive tools. They allow users to communicate through simple dialogue. This human-centric design enhances usability, attracting people with varying levels of expertise in tech.

Data-as-a-Service Surge

Finally, companies are moving away from purchasing scraping tools to acquiring pre-processed and well-organized data. This helps to reduce costs.

To adapt to this trend, providers are transitioning towards data-as-a-service models. The latter are in demand in data management. The DaaS market size is growing rapidly. According to the most recent data , market size was valued at approximately $4.9 billion in 2022 and is expected to reach around $18.7 billion by 2032.

Legal And Ethical Dilemmas

This shift also underscores legal and ethical considerations. Mass adoption of web scraping tools in business raises some important questions. Ethically, while collecting data from websites, businesses may gather sensitive information about individuals without their consent. This may lead to concerns over data privacy from customers or stakeholders. Moreover, web scraping can contribute to unfair competition if companies use scraped data to gain an advantage over competitors or manipulate markets. All of this can lead to potential lawsuits.

There are some other legal issues as well. For instance, companies potentially can violate copyrights when scraping data from websites without proper authorization. Many websites have terms of service or use agreements that explicitly prohibit automated data collection. Therefore, it's crucial for businesses to recognize potential issues associated with web scraping and adopt tools in an ethical manner to avoid concerns and unwanted consequences.

Companies today focus on data privacy regulations and emphasize the importance of transparent and ethical data acquisition methods. Collaborative relationships between web scraper developers and businesses are essential to fully comply with all legal requirements.

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Regions & Countries

3. christianity’s place in politics, and ‘christian nationalism’.

Most Americans express support for the principle of separation of church and state. And few say they think the federal government should declare Christianity to be the official religion of the United States.

But many Americans do think that even though the U.S. shouldn’t officially be declared a Christian country, the federal government should promote Christian moral values. And half of U.S. adults say they think the Bible should have at least some influence in U.S. laws, including 28% who say that if the Bible conflicts with the will of the people, the Bible should have more sway.

Fewer than half of U.S. adults say they have ever heard or read anything about Christian nationalism, including 5% who say they have a favorable view of it and 25% who say they have an unfavorable view.

How much influence should the Bible have on U.S. laws?

U.S. adults are divided over the amount of influence the Bible should have on the country’s laws. About half of adults (49%) say the Bible should have “a great deal” (23%) or “some” influence (26%), while 51% say it should have “not much” or “none at all.”

This is the third time we’ve asked this question in the last four years, and responses have remained fairly steady over that time .

White evangelical Protestants are more likely than adults in most other groups to say the Bible should have at least some influence on U.S. laws (86%) – including 55% who say the Bible should have “a great deal” of influence. A majority of Hispanic Protestants (78%) and Black Protestants (74%) also think the Bible should hold at least some influence on the country’s laws.

By contrast, 80% of religiously unaffiliated adults, 79% of Jews and 57% of Muslims say the Bible should not have influence on the laws of the United States. This includes 84% of atheists and 78% of agnostics who say the Bible should have no influence at all.

There also are large political divides on this topic. While 67% of Republicans and Republican leaners say the Bible should influence U.S. laws at least some, only 32% of Democrats and Democratic leaners share this opinion.

Younger adults and college graduates are less likely than other adults to say that the Bible should have at least some influence on U.S. laws.

What should happen when the Bible and the will of the people conflict?

Respondents who said the Bible should have at least some influence on U.S. laws were asked a follow-up question: When the Bible and the will of the people conflict with each other, which should have more influence?

Overall, 28% of U.S. adults say the Bible should have influence over U.S. laws and that it should take priority over the will of the people if the two conflict, while 19% say the Bible should have influence but that the will of the people should take precedence.

White evangelical Protestants and Hispanic Protestants are more likely than those in other religious groups to say the Bible should carry more weight in U.S. laws than the will of the people – 64% and 61%, respectively, say the Bible should have more influence on laws when the Bible and the will of the people conflict. And 49% of Black Protestants voice this opinion.

Among Catholics, 24% say the Bible should have more influence than the will of the people if the two conflict, 23% say the will of the people should take precedence over the Bible, and 50% say the Bible should have little or no influence on U.S. laws.

Republicans are much more likely than Democrats to say the Bible should have more influence than the will of the people when the two conflict (42% vs. 16%).

How much influence does the Bible have on U.S. laws today?

Distinct from their preferences on how much influence the Bible should have on U.S. laws, a majority of adults (57%) say they think the Bible currently does have at least some influence on this country’s laws.

Atheists (86%) and agnostics (83%) are far more likely than people in other religious groups to say the Bible has influence on U.S. laws.

And 73% of Jewish respondents say the Bible has a great deal of or some influence over today’s laws.

Black Protestants are the only group in which a clear majority says the Bible does not currently have much influence on the country’s laws.

Democrats are significantly more likely than Republicans to think the Bible has at least some influence on today’s laws (67% vs. 48%).

Should the government stop enforcing church-state separation?

Just over half of Americans say the federal government should enforce the separation of church and state (55%) – virtually unchanged from when we asked this question three years ago .

Meanwhile, 16% of U.S. adults say the government should stop enforcing church-state separation. And 28% of Americans say they have no opinion on this question or that neither option represents their views.

Almost all atheists (95%) say church-state separation should continue to be enforced by the federal government. Agnostics (89%) and Jews (84%) also are widely in favor of continued enforcement.

On the other hand, White evangelical Protestants are almost equally divided on this question: 35% say they favor federal enforcement of church-state separation, 31% say the government should stop enforcing this separation, and 32% choose neither of these options.

White evangelical Protestants are more likely than any of the other religious groups in this analysis to say the government should stop enforcing church-state separation.

Republicans are about twice as likely as Democrats to say the federal government should stop enforcing church-state separation (23% vs. 10%). But Republicans express more support for separation of church and state than opposition to it (43% vs. 23%).

Meanwhile, a clear majority of Democrats support the government enforcing the separation of church and state (68%).

Americans with a college degree are significantly more likely than other adults to say the federal government should enforce the separation of church and state.

Should the federal government declare Christianity the country’s official religion?

Survey respondents were asked to pick which of three statements best aligns with their views:

• The federal government should declare Christianity the official religion of the United States.
• The federal government should not declare Christianity the official religion of the United States, but it should promote Christian moral values.
• The federal government should not declare Christianity the official religion of the United States, and it should not promote Christian moral values.

An overwhelming majority of Americans – 83% – say the government should not declare Christianity the official religion of the country. Only 13% of Americans support declaring Christianity as the national religion.  (In our 2021 survey, a different question found a similar result on this topic.)

Another 44% of U.S. adults say the government should not declare the U.S. a Christian nation but should promote Christian values.

The remaining 39% do not want the government to promote Christian values or to declare a Christian nation.

A slim majority of Christians say they want the government to promote Christian values without declaring an official religion. In other religious groups, respondents most commonly say the government should neither declare a Christian nation nor promote Christian values. Atheists (90%) are particularly likely to fall in this camp.

While relatively few people say the federal government should declare Christianity the official religion of the U.S., this view is somewhat more common among White evangelical Protestants, Black Protestants and Hispanic Protestants. About a quarter in each group expresses this opinion.

Most Republicans (57%) say the federal government should promote Christian moral values but not declare the U.S. a Christian nation, while most Democrats (58%) say the government should not promote Christian values or declare the U.S a Christian nation.

Republicans also are more likely than Democrats to say Christianity should be declared the official national religion (21% vs. 7%).

Young adults are more likely than older adults to say that the government should neither declare Christianity the country’s official religion nor promote Christian moral values.

Do Americans know about ‘Christian nationalism’?

A slim majority of U.S. adults (54%) say they have heard or read “nothing at all” about “Christian nationalism” – the same share who said this when we asked this question two years ago .

Among the 45% who have heard anything about Christian nationalism, relatively few say they’ve heard “a great deal” (6%) or “quite a bit” (9%). More Americans say they’ve heard or read “some” (16%) or “a little” (14%) about Christian nationalism.

Most atheists, agnostics and Jews have heard at least a little about Christian nationalism. By contrast, 60% of Christians say they have heard or read nothing at all about it.

Views of Christian nationalism

Respondents who had heard or read anything about Christian nationalism were then asked a follow-up question: All in all, do you have a favorable or unfavorable view of Christian nationalism?

Overall, 25% of U.S. adults say they have heard of Christian nationalism and have an unfavorable view of it. Far fewer adults say they have a favorable view of Christian nationalism (5%).

There are no religious groups in which more people have a favorable than unfavorable view of Christian nationalism.

And some religious groups are particularly likely to hold an unfavorable view. For instance, 49% of Jewish respondents have an unfavorable view of Christian nationalism, while 1% say they have a favorable view.

Democrats are far more likely than Republicans to have heard about Christian nationalism and to have an unfavorable view of it. Most Republicans say they have never heard of Christian nationalism.

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About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .