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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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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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Science, health, and public trust.
September 8, 2021
Explaining How Research Works
We’ve heard “follow the science” a lot during the pandemic. But it seems science has taken us on a long and winding road filled with twists and turns, even changing directions at times. That’s led some people to feel they can’t trust science. But when what we know changes, it often means science is working.
Explaining the scientific process may be one way that science communicators can help maintain public trust in science. Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle.
Questions about how the world works are often investigated on many different levels. For example, scientists can look at the different atoms in a molecule, cells in a tissue, or how different tissues or systems affect each other. Researchers often must choose one or a finite number of ways to investigate a question. It can take many different studies using different approaches to start piecing the whole picture together.
Sometimes it might seem like research results contradict each other. But often, studies are just looking at different aspects of the same problem. Researchers can also investigate a question using different techniques or timeframes. That may lead them to arrive at different conclusions from the same data.
Using the data available at the time of their study, scientists develop different explanations, or models. New information may mean that a novel model needs to be developed to account for it. The models that prevail are those that can withstand the test of time and incorporate new information. Science is a constantly evolving and self-correcting process.
Scientists gain more confidence about a model through the scientific process. They replicate each other’s work. They present at conferences. And papers undergo peer review, in which experts in the field review the work before it can be published in scientific journals. This helps ensure that the study is up to current scientific standards and maintains a level of integrity. Peer reviewers may find problems with the experiments or think different experiments are needed to justify the conclusions. They might even offer new ways to interpret the data.
It’s important for science communicators to consider which stage a study is at in the scientific process when deciding whether to cover it. Some studies are posted on preprint servers for other scientists to start weighing in on and haven’t yet been fully vetted. Results that haven't yet been subjected to scientific scrutiny should be reported on with care and context to avoid confusion or frustration from readers.
We’ve developed a one-page guide, "How Research Works: Understanding the Process of Science" to help communicators put the process of science into perspective. We hope it can serve as a useful resource to help explain why science changes—and why it’s important to expect that change. Please take a look and share your thoughts with us by sending an email to [email protected].
Below are some additional resources:
- Discoveries in Basic Science: A Perfectly Imperfect Process
- When Clinical Research Is in the News
- What is Basic Science and Why is it Important?
- What is a Research Organism?
- What Are Clinical Trials and Studies?
- Basic Research – Digital Media Kit
- Decoding Science: How Does Science Know What It Knows? (NAS)
- Can Science Help People Make Decisions ? (NAS)
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What is Research? – Purpose of Research
- By DiscoverPhDs
- September 10, 2020
The purpose of research is to enhance society by advancing knowledge through the development of scientific theories, concepts and ideas. A research purpose is met through forming hypotheses, collecting data, analysing results, forming conclusions, implementing findings into real-life applications and forming new research questions.
What is Research
Simply put, research is the process of discovering new knowledge. This knowledge can be either the development of new concepts or the advancement of existing knowledge and theories, leading to a new understanding that was not previously known.
As a more formal definition of research, the following has been extracted from the Code of Federal Regulations :
While research can be carried out by anyone and in any field, most research is usually done to broaden knowledge in the physical, biological, and social worlds. This can range from learning why certain materials behave the way they do, to asking why certain people are more resilient than others when faced with the same challenges.
The use of ‘systematic investigation’ in the formal definition represents how research is normally conducted – a hypothesis is formed, appropriate research methods are designed, data is collected and analysed, and research results are summarised into one or more ‘research conclusions’. These research conclusions are then shared with the rest of the scientific community to add to the existing knowledge and serve as evidence to form additional questions that can be investigated. It is this cyclical process that enables scientific research to make continuous progress over the years; the true purpose of research.
What is the Purpose of Research
From weather forecasts to the discovery of antibiotics, researchers are constantly trying to find new ways to understand the world and how things work – with the ultimate goal of improving our lives.
The purpose of research is therefore to find out what is known, what is not and what we can develop further. In this way, scientists can develop new theories, ideas and products that shape our society and our everyday lives.
Although research can take many forms, there are three main purposes of research:
- Exploratory: Exploratory research is the first research to be conducted around a problem that has not yet been clearly defined. Exploration research therefore aims to gain a better understanding of the exact nature of the problem and not to provide a conclusive answer to the problem itself. This enables us to conduct more in-depth research later on.
- Descriptive: Descriptive research expands knowledge of a research problem or phenomenon by describing it according to its characteristics and population. Descriptive research focuses on the ‘how’ and ‘what’, but not on the ‘why’.
- Explanatory: Explanatory research, also referred to as casual research, is conducted to determine how variables interact, i.e. to identify cause-and-effect relationships. Explanatory research deals with the ‘why’ of research questions and is therefore often based on experiments.
Characteristics of Research
There are 8 core characteristics that all research projects should have. These are:
- Empirical – based on proven scientific methods derived from real-life observations and experiments.
- Logical – follows sequential procedures based on valid principles.
- Cyclic – research begins with a question and ends with a question, i.e. research should lead to a new line of questioning.
- Controlled – vigorous measures put into place to keep all variables constant, except those under investigation.
- Hypothesis-based – the research design generates data that sufficiently meets the research objectives and can prove or disprove the hypothesis. It makes the research study repeatable and gives credibility to the results.
- Analytical – data is generated, recorded and analysed using proven techniques to ensure high accuracy and repeatability while minimising potential errors and anomalies.
- Objective – sound judgement is used by the researcher to ensure that the research findings are valid.
- Statistical treatment – statistical treatment is used to transform the available data into something more meaningful from which knowledge can be gained.
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Types of Research
Research can be divided into two main types: basic research (also known as pure research) and applied research.
Basic Research
Basic research, also known as pure research, is an original investigation into the reasons behind a process, phenomenon or particular event. It focuses on generating knowledge around existing basic principles.
Basic research is generally considered ‘non-commercial research’ because it does not focus on solving practical problems, and has no immediate benefit or ways it can be applied.
While basic research may not have direct applications, it usually provides new insights that can later be used in applied research.
Applied Research
Applied research investigates well-known theories and principles in order to enhance knowledge around a practical aim. Because of this, applied research focuses on solving real-life problems by deriving knowledge which has an immediate application.
Methods of Research
Research methods for data collection fall into one of two categories: inductive methods or deductive methods.
Inductive research methods focus on the analysis of an observation and are usually associated with qualitative research. Deductive research methods focus on the verification of an observation and are typically associated with quantitative research.
Qualitative Research
Qualitative research is a method that enables non-numerical data collection through open-ended methods such as interviews, case studies and focus groups .
It enables researchers to collect data on personal experiences, feelings or behaviours, as well as the reasons behind them. Because of this, qualitative research is often used in fields such as social science, psychology and philosophy and other areas where it is useful to know the connection between what has occurred and why it has occurred.
Quantitative Research
Quantitative research is a method that collects and analyses numerical data through statistical analysis.
It allows us to quantify variables, uncover relationships, and make generalisations across a larger population. As a result, quantitative research is often used in the natural and physical sciences such as engineering, biology, chemistry, physics, computer science, finance, and medical research, etc.
What does Research Involve?
Research often follows a systematic approach known as a Scientific Method, which is carried out using an hourglass model.
A research project first starts with a problem statement, or rather, the research purpose for engaging in the study. This can take the form of the ‘ scope of the study ’ or ‘ aims and objectives ’ of your research topic.
Subsequently, a literature review is carried out and a hypothesis is formed. The researcher then creates a research methodology and collects the data.
The data is then analysed using various statistical methods and the null hypothesis is either accepted or rejected.
In both cases, the study and its conclusion are officially written up as a report or research paper, and the researcher may also recommend lines of further questioning. The report or research paper is then shared with the wider research community, and the cycle begins all over again.
Although these steps outline the overall research process, keep in mind that research projects are highly dynamic and are therefore considered an iterative process with continued refinements and not a series of fixed stages.
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Daniel is a third year PhD student at the University of York. His research is based around self-play training in multiagent systems; training AIs on a game such that they improve overtime.
Frances recently completed her PhD at the University of Bristol. Her research investigated the causes and consequences of hazardous lava-water interactions.
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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.
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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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How Research Works
Have you ever wondered what it means to “follow the science?” Sometimes it may seem like what’s true one day changes the next. But when what we know changes, it often means science is working.
Research helps us understand the world through careful testing. Each advance builds on past discoveries. This process can take a long time. But the end result is a better understanding of the world around us.
In general, the scientific process follows many steps. First, scientists start with a question. They look at past research to see what others have learned. Different scientists have diverse skills and training. They each bring their own approaches and ideas. And they design new experiments to test their ideas.
Next, scientists perform their experiments and collect data. Then, they evaluate what their findings might mean. This often leads them to new questions and ideas to test.
The next step is to share their data and ideas with other scientists. Other experts can give new perspectives or point out problems.
It’s natural to want answers. But it’s important not to draw conclusions based on a single study. Scientists start to form conclusions only after looking at many studies over time. Sometimes, even these conclusions change with more evidence. Science is an evolving process. But it’s the best way we have to seek out answers.
NIH has created a one-page guide to explain more about how research works. Find the guide in English or Spanish .
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Overview of research process.
The Research Process
Anything you write involves organization and a logical flow of ideas, so understanding the logic of the research process before beginning to write is essential. Simply put, you need to put your writing in the larger context—see the forest before you even attempt to see the trees.
In this brief introductory module, we’ll review the major steps in the research process, conceptualized here as a series of steps within a circle, with each step dependent on the previous one. The circle best depicts the recursive nature of the process; that is, once the process has been completed, the researcher may begin again by refining or expanding on the initial approach, or even pioneering a completely new approach to solving the problem.
Identify a Research Problem
You identify a research problem by first selecting a general topic that’s interesting to you and to the interests and specialties of your research advisor. Once identified, you’ll need to narrow it. For example, if teenage pregnancy is your general topic area, your specific topic could be a comparison of how teenage pregnancy affects young fathers and mothers differently.
Review the Literature
Find out what’s being asked or what’s already been done in the area by doing some exploratory reading. Discuss the topic with your advisor to gain additional insights, explore novel approaches, and begin to develop your research question, purpose statement, and hypothesis(es), if applicable.
Determine Research Question
A good research question is a question worth asking; one that poses a problem worth solving. A good question should:
- Be clear . It must be understandable to you and to others.
- Be researchable . It should be capable of developing into a manageable research design, so data may be collected in relation to it. Extremely abstract terms are unlikely to be suitable.
- Connect with established theory and research . There should be a literature on which you can draw to illuminate how your research question(s) should be approached.
- Be neither too broad nor too narrow. See Appendix A for a brief explanation of the narrowing process and how your research question, purpose statement, and hypothesis(es) are interconnected.
Appendix A Research Questions, Purpose Statement, Hypothesis(es)
Develop Research Methods
Once you’ve finalized your research question, purpose statement, and hypothesis(es), you’ll need to write your research proposal—a detailed management plan for your research project. The proposal is as essential to successful research as an architect’s plans are to the construction of a building.
See Appendix B to view the basic components of a research proposal.
Appendix B Components of a Research Proposal
Collect & Analyze Data
In Practical Research–Planning and Design (2005, 8th Edition), Leedy and Ormrod provide excellent advice for what the researcher does at this stage in the research process. The researcher now
- collects data that potentially relate to the problem,
- arranges the data into a logical organizational structure,
- analyzes and interprets the data to determine their meaning,
- determines if the data resolve the research problem or not, and
- determines if the data support the hypothesis or not.
Document the Work
Because research reports differ by discipline, the most effective way for you to understand formatting and citations is to examine reports from others in your department or field. The library’s electronic databases provide a wealth of examples illustrating how others in your field document their research.
Communicate Your Research
Talk with your advisor about potential local, regional, or national venues to present your findings. And don’t sell yourself short: Consider publishing your research in related books or journals.
Refine/Expand, Pioneer
Earlier, we emphasized the fact that the research process, rather than being linear, is recursive—the reason we conceptualized the process as a series of steps within a circle. At this stage, you may need to revisit your research problem in the context of your findings. You might also investigate the implications of your work and identify new problems or refine your previous approach.
The process then begins anew . . . and you’ll once again move through the series of steps in the circle.
Continue to Module Two
Appendix C - Key Research Terms
Module 1: Introduction: What is Research?
Learning Objectives
By the end of this module, you will be able to:
- Explain how the scientific method is used to develop new knowledge
- Describe why it is important to follow a research plan
The Scientific Method consists of observing the world around you and creating a hypothesis about relationships in the world. A hypothesis is an informed and educated prediction or explanation about something. Part of the research process involves testing the hypothesis , and then examining the results of these tests as they relate to both the hypothesis and the world around you. When a researcher forms a hypothesis, this acts like a map through the research study. It tells the researcher which factors are important to study and how they might be related to each other or caused by a manipulation that the researcher introduces (e.g. a program, treatment or change in the environment). With this map, the researcher can interpret the information he/she collects and can make sound conclusions about the results.
Research can be done with human beings, animals, plants, other organisms and inorganic matter. When research is done with human beings and animals, it must follow specific rules about the treatment of humans and animals that have been created by the U.S. Federal Government. This ensures that humans and animals are treated with dignity and respect, and that the research causes minimal harm.
No matter what topic is being studied, the value of the research depends on how well it is designed and done. Therefore, one of the most important considerations in doing good research is to follow the design or plan that is developed by an experienced researcher who is called the Principal Investigator (PI). The PI is in charge of all aspects of the research and creates what is called a protocol (the research plan) that all people doing the research must follow. By doing so, the PI and the public can be sure that the results of the research are real and useful to other scientists.
Module 1: Discussion Questions
- How is a hypothesis like a road map?
- Who is ultimately responsible for the design and conduct of a research study?
- How does following the research protocol contribute to informing public health practices?
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What does a researcher do?
Would you make a good researcher? Take our career test and find your match with over 800 careers.
What is a Researcher?
A researcher is trained to conduct systematic and scientific investigations in a particular field of study. Researchers use a variety of techniques to collect and analyze data to answer research questions or test hypotheses. They are responsible for designing studies, collecting data, analyzing data, and interpreting the results. Researchers may work in a wide range of fields, including science, medicine, engineering, social sciences, humanities, and many others.
To become a researcher, individuals usually need to obtain a graduate degree in their chosen field of study. They may also need to gain experience working as an assistant or intern in a research setting before becoming a full-fledged researcher. Researchers may work in academic or industrial settings, or they may work independently as consultants or freelance researchers. Regardless of the setting, researchers play a vital role in advancing knowledge and finding solutions to real-world problems.
What does a Researcher do?
Researchers are essential to the advancement of knowledge in various fields, including science, technology, medicine, social sciences, and humanities. Their work involves conducting systematic investigations to gather data, analyze it, and draw meaningful conclusions. Through their research, they can identify new problems and challenges, develop innovative solutions, and test hypotheses to validate theories.
Researchers also play a critical role in improving existing practices and policies, identifying gaps in knowledge, and creating new avenues for future research. They provide valuable insights and information that can inform decision-making, shape public opinion, and drive progress in society.
Duties and Responsibilities The duties and responsibilities of researchers can vary depending on the field of study and the type of research being conducted. However, here are some common duties and responsibilities that researchers are typically expected to fulfill:
- Develop research proposals: Developing a research proposal typically involves identifying a research question or problem, reviewing the relevant literature, selecting appropriate research methods and techniques, and outlining the expected outcomes of the research. Researchers must also ensure that their proposal aligns with the funding agency's objectives and guidelines.
- Conduct literature reviews: Literature reviews involve searching for and reviewing existing research papers, articles, books, and other relevant publications to identify gaps in knowledge and to build upon previous research. Researchers must ensure that they are using credible and reliable sources of information and that their review is comprehensive.
- Collect and analyze data: Collecting and analyzing data is a key aspect of research. This may involve designing and conducting experiments, surveys, interviews, or observations. Researchers must ensure that their data collection methods are valid and reliable, and that their analysis is appropriate and accurate.
- Ensure ethical considerations: Research ethics involve ensuring that the research is conducted in a manner that protects the rights, welfare, and dignity of all participants, as well as the environment. Researchers must obtain informed consent from human participants, ensure that animal research is conducted ethically and humanely, and comply with relevant regulations and guidelines.
- Communicate research findings: Researchers must communicate their research findings clearly and effectively to a range of audiences, including academic peers, policymakers, and the general public. This may involve writing research papers, presenting at conferences, and producing reports or other materials.
- Manage research projects: Managing a research project involves planning, organizing, and coordinating resources, timelines, and budgets to ensure that the project is completed on time and within budget. Researchers must ensure that they have the necessary resources, such as funding, personnel, and equipment, and that they are managing these resources effectively.
- Collaborate with others: Collaboration is an important aspect of research, and researchers often work with other researchers, academic institutions, funding agencies, and industry partners to achieve research objectives. Collaboration can help to facilitate the sharing of resources, expertise, and knowledge.
- Stay up-to-date with developments in their field: Research is an evolving field, and researchers must stay up-to-date with the latest developments and trends in their field to ensure that their research remains relevant and impactful. This may involve attending conferences, workshops, and seminars, reading academic journals and other publications, and participating in professional development opportunities.
Types of Researchers There are many types of researchers, depending on their areas of expertise, research methods, and the types of questions they seek to answer. Here are some examples:
- Basic Researchers: These researchers focus on understanding fundamental concepts and phenomena in a particular field. Their work may not have immediate practical applications, but it lays the groundwork for applied research.
- Applied Researchers: These researchers seek to apply basic research findings to real-world problems and situations. They may work in fields such as engineering, medicine, or psychology.
- Clinical Researchers: These researchers conduct studies with human subjects to better understand disease, illness, and treatment options. They may work in hospitals, universities, or research institutes.
- Epidemiologists : These researchers study the spread and distribution of disease in populations, and work to develop strategies for disease prevention and control.
- Social Scientists: These researchers study human behavior and society, using methods such as surveys, experiments, and observations. They may work in fields such as psychology, sociology, or anthropology.
- Natural Scientists: These researchers study the natural world, including the physical, chemical, and biological processes that govern it. They may work in fields such as physics, chemistry, or biology.
- Data Scientists : These researchers use statistical and computational methods to analyze large datasets and derive insights from them. They may work in fields such as machine learning, artificial intelligence, or business analytics.
- Policy Researchers: These researchers study policy issues, such as healthcare, education, or environmental regulations, and work to develop evidence-based policy recommendations. They may work in government agencies, think tanks, or non-profit organizations.
What is the workplace of a Researcher like?
The workplace of a researcher can vary greatly depending on the field and area of study. Researchers can work in a variety of settings, including academic institutions, government agencies, non-profit organizations, and private companies.
In academic settings, researchers often work in universities or research institutions, conducting experiments and analyzing data to develop new theories and insights into various fields of study. They may also teach courses and mentor students in their area of expertise.
In government agencies, researchers may work on projects related to public policy, health, and safety. They may be responsible for conducting research to support the development of new regulations or programs, analyzing data to assess the effectiveness of existing policies, or providing expertise on specific issues.
Non-profit organizations often employ researchers to study social and environmental issues, such as poverty, climate change, and human rights. These researchers may conduct surveys and collect data to understand the impact of various programs and initiatives, and use this information to advocate for policy changes or other interventions.
Private companies also employ researchers, particularly in industries such as technology and healthcare. These researchers may be responsible for developing new products, improving existing technologies, or conducting market research to understand consumer preferences and behaviors.
Regardless of the setting, researchers typically spend a significant amount of time conducting research, analyzing data, and communicating their findings through presentations, reports, and publications. They may also collaborate with other researchers or professionals in their field, attend conferences and workshops, and stay up-to-date with the latest research and developments in their area of expertise.
Frequently Asked Questions
Academic writer vs researcher.
An academic writer is someone who produces written material for academic purposes, such as research papers, essays, and other scholarly works. Academic writers may work as freelance writers, editors, or as staff writers for academic institutions or publishers.
On the other hand, a researcher is someone who conducts original research to generate new knowledge or validate existing knowledge. Researchers may work in academic settings, government agencies, private companies, or non-profit organizations. They typically design and execute experiments, surveys, or other data collection methods, analyze the data, and draw conclusions based on their findings.
While there may be some overlap between the skills required for academic writing and research, they are distinct activities with different goals. Academic writers often rely on the research of others to support their arguments, while researchers generate new knowledge through their own experiments and data analysis. However, academic writers may also be researchers who write about their own research findings.
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Passing the Baton – The future of HSR is in Good Hands, but Much Work Remains
Outgoing President and CEO, Dr. Lisa Simpson, reflects on her AcademyHealth tenure and the work that needs to be done to advance evidence into policy and practice.
It is hard for me to believe that 13 years have passed since I began as President and CEO of AcademyHealth. As I reflect on my time as President and CEO and prepare to welcome Aaron Carroll to the role next week, I am both pleased at the progress we have made and humbled at how much more needs to be done to truly advance evidence into policy and practice. I have learned so much along the way, and I will be forever grateful for the opportunity to work with so many talented colleagues and collaborators.
Every step of my career prepared me for this role, and each informed my approach to leading and advocating for this field. From generating evidence to advance better policy for children’s health and services to working to apply that evidence in federal and state policy and programs, and from serving in leadership at the lead federal agency for health services research to advocating for funding, I came to AcademyHealth passionate about our mission and well versed in the opportunities and challenges of our field. As I step down to reflect and refresh, I want to leave you with some reflections from my time as President and CEO and highlight some areas of impact that require our ongoing commitment.
Health Services Research (HSR) Must Continue to Move Toward Solutions and Implementation
Thanks to decades of HSR findings , we have learned the “epidemiology” of care – who does and does not get health care and its safety, quality, outcomes , and costs. We have come to understand better the root causes of these patterns and the profound links to economic and social factors. However, we have made far less progress actually using this knowledge to develop, test, and implement effective policies, programs, and services. AcademyHealth's mission always energizes me because it focuses on action; on supporting our field to have an impact. Through our state networks , learning communities, and partnerships AcademyHealth is continually developing better ways to support the use of evidence. This is important work that needs sustained investment of time, talent, and resources.
The Journey Toward Diversity, Equity, Inclusion and Accessibility Must Continue Apace
I published my first paper documenting disparities during my post-doctoral program at the Institute for Health Policy Studies in 1991. Despite many studies since then, it is only in recent years that I have begun to fully grasp how limited many of our dominant research frameworks and methods are in supporting truly actionable and equitable HSR. For me personally, it has been an area of growth and continued learning. As a field we must do better. AcademyHealth has made a public commitment to driving this work forward. An integral part of our success will be how welcoming our broad community is to differing voices, perspectives, and ways of knowing. Addressing systemic challenges requires effective, systemic responses. From our methods and data, to training and workforce development, incentives, narratives, and funding, the work of DEIA must continue at every level of our field. Understanding the complexity of these commitments and the need to uplift what works, AcademyHealth recently launched a new award to recognize organizations or teams that have created a more diverse, inclusive, just, and welcoming experience and sense of community for their employees and/or members.
Data and technology are transforming society, health care and science
Each day brings a new report on how the “ fourth industrial revolution ” is affecting our daily lives. Less well chronicled – and understood – are the fundamental shifts that are occurring in science. The growth in data from electronic health records has led to many new insights into patient outcomes and this is now being further expanded by a flood of patient reported data from wearables and other applications. Advances in generative AI over the last 18 months are enabling efficiencies in research conduct while also presenting ethical challenges at each step of the research process. The pace of improvement in these AI models is no less than mind-boggling yet research remains a slow and deliberative process. Can the field of HSR keep up? I think HSR has two important roles to play: first, in evaluating the impact of new data and technology on health, care, and equity and second, in using these new tools to improve the relevance, speed, and quality of HSR itself. AcademyHealth has an important role to play here.
Every system is perfectly designed to achieve the results it gets
While this is an adage I learned from quality improvement, it aptly describes one of the challenges of HSR. Our field is an applied one and should be more directly linked to measurable improvements in care and health. Yet, researchers are not rewarded for impact, for patient engagement, or for community based participatory research as much as they are for publications and more funding. We must move the incentives toward social impact – and the biggest incentive is money. Federal research funders, including the National Institutes of Health (NIH), the Patient Centered Outcomes Research Institute, the Agency for Healthcare Research and Quality, and the Veterans Administration Health Services Research Program have a critical leadership role to play, and can affect significant change through their funding practices. Each has launched initiatives to drive implementation and impact, yet the proportion of total funding on these is small compared to total research funding. This must change.
The context for our work has become more challenging
When I came to the nation’s capital for my first policy job (working on health reform in the Clinton administration), I quickly learned that the national policy community is rich with experts and knowledgeable individuals eager to use evidence and work together to improve health and health care –even if they disagree on the most appropriate policy solutions. As Senator Daniel Patrick Moynihan once famously said, “You are entitled to your opinion. But you are not entitled to your own facts.” Sadly, this has changed dramatically. Misinformation and disinformation have become commonplace in policy debates and trust in science and institutions has eroded. Polarization in our political and public discourse has further weakened our ability to come together and develop solutions. It is in this environment that AcademyHealth’s nonpartisan stance is essential. We advocate for our field and its ability to do its work effectively through more funding and better research policies but do not take political positions on critical health policy topics (e.g. the Dobbs decision reversing Roe v Wade, the Medicaid unwinding). I’ll admit that distinction has been difficult to hold at times, particularly as policy decisions exacerbate existing inequities and challenge our mission to improve health and health care for all . Into this challenge, AcademyHealth and the field it represents must continue to lead with evidence, educate for understanding, and strive to inform policy debates in nonpartisan, mission-aligned ways.
Finally, I want to thank all of you for being part of this ride. The membership, sponsors, supporters, and most importantly, our staff have made the last 13 years among the most fulfilling of my career. Working together with you has been an honor and a privilege.
I leave AcademyHealth proud of the work we have done together and confident in its future. You can always find me on LinkedIn . Please stay in touch!
Working nine to thrive
At a glance.
- Health can be meaningfully modified by factors outside traditional healthcare systems, including work factors.
- Employers have considerable opportunities to improve health through six modifiable drivers: social interaction, mindsets and beliefs, productive activity, stress, economic security, and sleep.
- Globally, improving employee health and well-being could create $3.7 trillion to $11.7 trillion in economic value.
Imagine a world in which employers make evidence-based investments in the health of their employees. In return, they reap a manifold benefit to those investments: their employees thrive, their business thrives, and the societies in which they operate thrive. There's a positive opportunity that arises when employers address the inherent interconnectedness between work and health.
The 23 drivers of health
The McKinsey Health Institute has identified 23 drivers of health across six categories. 1 Lars Hartenstein and Tom Latkovic, “ The secret to great health? Escaping the healthcare matrix ,” McKinsey Health Institute, December 20, 2022. All of them are considered modifiable]:[[footnote 2]
- physical inputs: diet, supplementation, and substance use
- movement: mobility, exercise, and sleep
- daily living: productive activity, social interaction, content consumption, and hygiene
- exposure: nature, atmosphere, sensory stimulation, materials, and stress
- state of being: mindsets and beliefs, body composition, physical security, and economic security
- healthcare: vaccination, detection and diagnosis, clinical intervention, adherence
Together, the drivers of health have a broad influence on holistic health (mental, physical, social, and spiritual health) and apply to settings beyond the workplace. Other research on holistic health has explored a smaller ecosystem of factors that are directly measurable within an organization. 3 Jacqueline Brassey, Brad Herbig, Barbara Jeffery, and Drew Ungerman, “ Reframing employee health: Moving beyond burnout to holistic health ,” McKinsey Health Institute, November 2, 2023; Sanne Magnan, “Social determinants of health 101 for health care: Five plus five,” National Academy of Medicine, October 9, 2017.
The McKinsey Health Institute (MHI) has previously identified 23 drivers of health (see sidebar “The 23 drivers of health”). 1 Lars Hartenstein and Tom Latkovic, “ The secret to great health? Escaping the healthcare matrix ,” McKinsey Health Institute, December 20, 2022. Employment can greatly influence some of these drivers, such as social interaction and sleep. In this article, we zoom in on six drivers of health that employers can influence and could be wise to support. By improving employees’ health, employers could add trillions of dollars to the global economy and have a positive impact on society. When employers and employees work together to improve modifiable drivers of health, everyone benefits.
Modifiable drivers of health in the workplace: What does the research say?
Six modifiable drivers of health in the workplace—social interaction, mindsets and beliefs, productive activity, stress, economic security, and sleep—were identified from the growing body of research that connects the dots among drivers of health and the workplace. Researchers are building a greater understanding of how employers can address modifiable drivers to create change in favor of optimal employee health.
Considering that the average person spends a third of their life at work (more than 90,000 hours in a lifetime), 2 “How many hours does the average person work per week?,” FreshBooks, April 17, 2023. employment can be a critical piece of the puzzle when working toward the goal of improving global health. MHI analyzed 26 workplace factors to understand how they influence a range of health- and work-related outcomes across 30 countries. 3 Jacqueline Brassey, Brad Herbig, Barbara Jeffery, and Drew Ungerman, “ Reframing employee health: Moving beyond burnout to holistic health ,” McKinsey Health Institute, November 2, 2023. In this article, any McKinsey Health Institute research not otherwise cited comes from this source. That research showed there are important differences between the workplace factors that lead to poor health and those that lead to good health. Our analysis found that employee self-efficacy, adaptability, and feelings of belonging at work were top predictors of good health, whereas toxic workplace behaviors, role ambiguity, and role conflict at work were top predictors of poor health.
Previously, researchers at the University of Oxford’s Wellbeing Research Centre analyzed data from more than 15 million employees on their well-being and the underlying workplace factors driving it. 4 “How to use the Work Wellbeing Score on Indeed company pages,” Indeed, May 1, 2023. The researchers identified and tested 11 factors, including compensation, flexibility, purpose, inclusion, achievement, support, trust, belonging, management, and learning. The three top factors for the companies that scored best on well-being were feeling energized, belonging, and trust. Interestingly, they are different from the top drivers that employees think will make them happy and drive well-being at work: pay and flexibility. 5 “The key drivers of workplace wellbeing: Tapping into the hidden gems of happiness,” Indeed, July 6, 2021.
Together, all the research led us to identify six drivers of health that employers can most easily influence.
Employers can improve employee health through six modifiable drivers
Our analysis shows that employers can effect significant change through six modifiable drivers of health: social interaction, mindsets and beliefs, productive activity, stress, economic security, and sleep. 6 We recognize that employers can influence other modifiable drivers of health not specifically addressed here (for example, diet and mobility) but are focusing this article on the drivers most likely to create considerable opportunities for employers to improve health.
Social interaction
The positive effects of regular social interactions on health have been widely reported. For instance, a study reviewing mortality rates has documented an average 50 percent increase in likelihood for survival if participants have strong social relationships. 1 Julianne Holt-Lunstad, J. Bradley Layton, and Timothy B. Smith, “Social relationships and mortality risk: A meta-analytic review, PLOS Medicine , July 2010, Volume 7, Number 7. Furthermore, social integration during childhood is related to lower blood pressure and body mass index in adulthood. 2 For more, see Jenny M. Cundiff and Karen A. Matthews, “Friends with health benefits: The long-term benefits of early peer social integration for blood pressure and obesity in midlife,” Psychological Science , May 2018, Volume 29, Number 5.
Social interactions at work experienced by employees strongly influence health and workplace outcomes. Feeling connected at work is associated with greater innovation, engagement, and quality of work—and may be especially impactful for those with smaller social networks outside of their jobs. 3 For more, see Our epidemic of loneliness and isolation: The U.S. Surgeon General’s advisory on the healing effects of social connection and community , US Office of the Surgeon General, May 3, 2023. MHI’s 2023 research shows experiencing toxic workplace behavior is a strong predictor of negative health outcomes at work, including loneliness at work, the intention to leave an organization, and burnout symptoms.
Toxic workplace behavior is a critical workplace driver to combat. If left unaddressed, it can mitigate the benefits of any health and well-being initiatives pursued. Examples of interventions to counter toxic workplace behavior include establishing a zero-tolerance policy for it and creating anonymous feedback processes through which employees can report it—which also normalizes a culture of providing concrete, specific feedback to colleagues. 4 For more, see Amy Gallo, “How to manage a toxic employee,” Harvard Business Review , October 3, 2016, and Deepa Purushothaman and Lisen Stromberg, “Leaders, Stop Rewarding Toxic Rock Stars,” Harvard Business Review , April 20, 2022.
Meanwhile, experiencing psychological safety on a team and support from coworkers and managers predicts positive health outcomes, including better holistic health . In 2023, MIT Sloan School of Management researchers outlined proven social-health initiatives that helped managers build psychological safety on their teams. 5 Chris Rider et al., “Proven tactics for improving teams’ psychological safety,” MIT Sloan Management Review , March 27, 2023. They included training managers to use one-on-one meetings to increase employee individuation 6 “Individuation” refers to treating employees as unique individuals. by asking employees what was important to them and where they needed support. Another use of the meetings was to remove blockers for employees by helping them prioritize among tasks. Interestingly, individuation has been shown to increase psychological safety the most when psychological safety is relatively low, while removing blockers is more effective when psychological safety is relatively high.
Mindsets and beliefs
Research, including MHI analysis, has demonstrated a connection between positive mindsets and beliefs and better health experience. 1 For more, see Mathias Allemand, Patrick L. Hill, and Brent W. Roberts, “Examining the pathways between gratitude and self-rated physical health across adulthood,” Personality and Individual Differences , January 2013, Volume 54, Number 1; Lisa A. Williams and Monica Y. Bartlett, “Warm thanks: Gratitude expression facilitates social affiliation in new relationships via perceived warmth,” Emotion , February 2015, Volume 15, Number 1; and David S. Yeager et al., “A synergistic mindsets intervention protects adolescents from stress,” Nature , July 2022, Volume 607, Number 7,919. This includes the positive effects of a growth mindset on mental health and the benefits of gratitude on physical health. Positive mindsets and beliefs in the workplace are also greatly influential in good holistic health.
In fact, good holistic health isn’t achieved by completely avoiding workplace stressors. Instead, it can be maintained through creating positive experiences at work, such as experiencing high self-efficacy, high adaptability, a feeling of meaning, and a feeling of belonging at work. For example, an individual may be able to tolerate the stress of a looming deadline on a big project if they believe that they have the support of their team.
Employers can foster meaning and belonging by engaging employees through compelling storytelling and fostering a connection to an organization’s mission. Purpose-driven companies that excel at this grow two times faster than their competitors do and achieve gains in employee satisfaction, employee retention, and consumer trust. 2 Scott Mautz, “Patagonia has only 4 percent employee turnover because they value this 1 thing so much,” Inc. , March 30, 2019; Graham Staplehurst, “The evolution of purpose,” Kantar, August 27, 2020; “This is what work-life balance looks like at a company with 100% retention of moms,” Quartz, October 16, 2016. Some of these outcomes may be attributed to employees who are intrinsically motivated and able to maintain better well-being over time, creating a positive performance loop. 3 For more, see Emma L. Bradshaw et al., “A meta-analysis of the dark side of the American dream: Evidence for the universal wellness costs of prioritizing extrinsic over intrinsic goals,” Journal of Personality and Social Psychology , April 2023, Volume 124, Number 4. Additionally, employee self-efficacy and adaptability are capabilities that can be cultivated among employees to make a more resilient and healthy workforce. 4 For more, see Jacqueline Brassey, Aaron De Smet, and Michiel Kruyt, Deliberate Calm: How to Learn and Lead in a Volatile World , New York, NY: HarperCollins Publishers, 2022.
Productive activity
Productive activity includes employment- and nonemployment-related activities. Examples include volunteering, caregiving, spending time on hobbies, worshiping, spending time on activism, playing music, and traveling.
Employment has been linked to improved life expectancy. 1 For more, see “Relationship between employment and health,” Health Foundation, October 5, 2022. According to MHI research, one of the top contributors to productivity at work is an individual’s sense of self-efficacy—an employee’s belief that they can cope with difficult or changing situations. Self-efficacy can be improved through interventions, suggesting that employers can target self-efficacy to improve employee productivity. 2 For more, see Jacqueline Brassey et al., “Emotional flexibility and general self-efficacy: A pilot training intervention study with knowledge workers,” PLOS One , 2020, Volume 15, Number 10.
Furthermore, employers have the opportunity to help the people in their communities connect to meaningful and productive activities that support their long-term health and well-being. Enjoyable leisure activities are also associated with improved psychosocial and physical measures that support good health and well-being, including greater life satisfaction and engagement and lower rates of depression, blood pressure, cortisol, and physical function. 3 For more, see Sarah D. Pressman et al., “Association of enjoyable leisure activities with psychological and physical well-being,” Psychosomatic Medicine , September 2009, Volume 71, Number 7.
In discussing workplace stressors, it’s important to acknowledge that stress itself isn’t necessarily a bad thing, as it’s actually needed to learn, grow, and develop. 1 For more, see R. B. Zajonc, “Social facilitation,” Science , July 1965, Volume 149, Number 3,681. Optimal levels of stress can contribute to better performance. After that point, the benefits diminish into worse well-being because of the excessive demands of high stress and lack of replenishment of energy resources. The employer’s role is to ensure that employees are stimulated, challenged, and motivated—but not overwhelmed—by the demands they experience in the workplace.
Chronically elevated levels of stress can increase the risk of cardiovascular disease, neurodegenerative disease, and metabolic disease. 2 For more, see Fan Tian et al., “Association of stress-related disorders with subsequent risk of all-cause and cause-specific mortality: A population-based and sibling-controlled cohort study,” Lancet Regional Health–Europe , May 2022, Volume 18. Job strain and effort–reward imbalance can predict several common mental disorders. 3 For more, see Bridget Candy and Stephen Stansfeld, “Psychosocial work environment and mental health—a meta-analytic review,” Scandinavian Journal of Work, Environment & Health , December 2006, Volume 32, Number 6. Additionally, MHI research shows that an increase in workplace demands is the driver most predictive of burnout and distress symptoms at work.
Some jobs are high in demand by structure. For example, some organizations have seasonal or other cyclical patterns in work demand. In these situations, interventions should focus on building in recovery time so that employees can regain their energy after high-demand periods.
Economic security
Economic opportunity and economic security can influence many facets of health and productivity. For example, high-income individuals are five times more likely than low-income individuals to report strong health. 1 Steven H. Woolf et al., How are income and wealth linked to health and longevity? , a joint report from Urban Institute and Virginia Commonwealth University, April 13, 2015. Employees who are struggling financially are more likely than others to experience signs of poor mental health that might affect their ability to function at work. 2 For more, see Lu Fan and Soomin Ryu, “The relationship between financial worries and psychological distress among U.S. adults,” Journal of Family and Economic Issues , 2023, Volume 44, Number 1. A lack of job stability links with poor mental health, as well as poor physical well-being (for example, cardiovascular disease). 3 For more, see Susan J. Ashford, Guo-Hua Huang, and Cynthia Lee, “Job insecurity and the changing workplace: Recent developments and the future trends in job insecurity research,” Annual Review of Organizational Psychology and Organizational Behavior , January 2018, Volume 5; Imma Cortès-Franch et al., “Employment stability and mental health in Spain: Towards understanding the influence of gender and partner/marital status,” BMC Public Health , April 2018, Volume 18, Number 1; Marnie Dobson, Paul Landsbergis, and Peter L. Schnall, “Globalization, work, and cardiovascular disease,” International Journal of Health Services , October 2016, Volume 46, Number 4; and Jose A. Llosa et al., “Job insecurity and mental health: A meta-analytical review of the consequences of precarious work in clinical disorders,” Anales de Psicología , 2018, Volume 34, Number 2. Any short-term rise in employee performance fueled by job insecurity is often negated by the additional burden on employee physical and mental health. 4 Mindy Shoss et al., “Job insecurity harms both employees and employers,” Harvard Business Review , September 6, 2022.
MHI research shows that the greatest contributor to employees’ feelings of financial insecurity is whether they are paid sufficiently to cover their basic needs. While what it takes to feel economically secure is unique to each person, employers can reduce feelings of financial insecurity by ensuring that compensation covers basic needs.
There’s a strong association between sleep hours and both employee health and workplace outcomes. The cost to employers when employees have insufficient or poor-quality sleep can be substantial.
Employees with untreated insomnia cost employers an average of $2,280 more annually than employees without untreated insomnia because of absenteeism, “presenteeism,” poor performance, and increased incidents of accident and injury. 1 Ronald C. Kessler et al., “Insomnia and the performance of US workers: Results from the America Insomnia Survey,” Sleep , September 2011, Volume 34, Number 9. According to the MHI 2023 survey, 31 percent of employees across the world average fewer than seven hours of sleep per night. Although everyone has unique needs, this falls below the ballpark number of hours recommended to maintain good health. 2 We acknowledge that every individual is different, but there are some indications of recommended average hours of sleep that may benefit health. For more, see Eric J. Olson, “How many hours of sleep are enough for good health?,” Mayo Foundation for Medical Education and Research (MFMER), February 21, 2023. Researchers have shown severe sleep loss can even lead to death, as our bodies conduct necessary reparative processes when we sleep. 3 For more, see Alexandra Vaccaro et al., “Sleep loss can cause death through accumulation of reactive oxygen species in the gut,” Cell , June 2020, Volume 181, Number 6.
The MHI survey found that one of the main contributors to an employee’s average number of sleep hours is the experienced volume of work required of them. Furthermore, one of the top contributors to an employee’s satisfaction with their sleep is their ability to adjust to unexpected changes. This may suggest that employee programs that look to improve adaptability may in turn improve employees’ satisfaction with their sleep.
Employers have additional interventions they can consider if their employees are struggling with getting consistent, high-quality sleep. They include creating work environments with ample natural light and access to healthy foods, limiting or disabling employees from being online after hours, creating incentives for employees who prioritize sleep, and encouraging and rewarding leaders who model the prioritization of sleep over work.
Many employers are already investing in employee health and well-being, but we would encourage them to reflect on where they currently provide support and if they might want to change resources or add more interventions. For example, many employee assistance programs (EAPs) provide coverage of interventions for factors such as stress and economic security but less coverage of those for factors such as social interactions at work. Additionally, while EAPs are widely available, they tend to be underused by employees and focus on a reactive instead of a proactive approach to health. 7 For more, see James Kenney, “Why most employee assistance programs don't work,” Forbes , July 6, 2022; and Stephen Sokoler, “Reimagining traditional employee assistance programs,” Forbes , March 17, 2023.
In rethinking a workplace strategy on employee health and well-being, current EAP offerings can be useful starting points for action but are unlikely to be the full solution. They are also unlikely, by themselves, to yield the ROI that employers increasingly expect. Strengthening the measurement of intervention outcomes may also help guide an organization’s overall investment strategy.
In rethinking a workplace strategy on employee health and well-being, current EAP offerings can be useful starting points for action but are unlikely to be the full solution.
Improving global employee health can create trillions of dollars of economic value
It makes good business sense to invest in employee health and well-being. We estimate that the total global opportunity for optimizing employee health and well-being is $3.7 trillion to $11.7 trillion, which is equivalent to raising global GDP by 4 to 12 percent. Together, high- and middle-income economies represent 95 percent of this total opportunity (exhibit).
While it may not be feasible in the near term to bring all employees everywhere to optimal well-being, capturing just 10 percent of the total opportunity could yield up to $1.17 trillion of annual value and raise the global GDP by more than 1 percent (see sidebar “Business case methodology”).
Business case methodology
To size the economic value that could be created if addressing health and well-being at the global level, we first established the metric for all economies by summing the positive economic effects of increased employee attraction, productivity, and retention with the savings created if absenteeism, attrition, and “presenteeism” were reduced.
Using a similar methodology, we sized the economic value of medium- and high-income economies. We calculated the economic value of low-income economies by subtracting that of the medium- and high-income economies from that of all economies. However, there’s low confidence in current estimates for low-income economies because of insufficient and unreliable data collected in these countries.
The economic value possible by addressing each driver was calculated as follows:
- attrition: the total turnover multiplied by the cost of turnover per employee
- absenteeism: the estimated number of working days lost because of work-related ill health and nonfatal workplace injuries multiplied by the average daily pay
- presenteeism: the estimated number of productive days lost when employees are present at work but can’t be fully productive multiplied by the average daily pay
- attraction: the total premium by employees for employers with an above-average happiness score multiplied by the total turnover
- productivity: the increase in productivity associated with increased well-being multiplied by the average value of productivity
- retention: the total turnover multiplied by the benefit of retention
In addition to contributing to increased productivity at work, our calculations indicate that investing in employee health and well-being provides a positive opportunity for attracting and retaining talent. As noted in McKinsey research, employees facing mental-health and well-being challenges are four times more likely than others to want to leave their organizations . 8 Patrick Guggenberger, Dana Maor, Michael Park, and Patrick Simon, “ The State of Organizations 2023: Ten shifts transforming organizations ,” McKinsey, April 26, 2023.
Better health correlates with higher productivity across countries and workplace settings and is also strongly correlated with workforce participation at all ages. 9 For more, see Dan Chisholm et al., “Scaling-up treatment of depression and anxiety: A global return on investment analysis,” Lancet Psychiatry , May 2016, Volume 3, Number 5; Clément S. Bellet, Jan-Emmanuel De Neve, and George Ward, “Does employee happiness have an impact on productivity?," Management Science , May 11, 2023; Miriam Dickinson, Kathryn Rost, and Jeffrey L. Smith, “The effect of improving primary care depression management on employee absenteeism and productivity: A randomized trial,” Medical Care , December 2004, Volume 42, Number 12; and “ Prioritizing health: A prescription for prosperity ,” McKinsey Global Institute, July 8, 2020. Every 1 to 3 percent increase in global workforce participation is worth a further $1.4 billion to $4.2 billion, 10 Assumes additional labor force is employed at the same unemployment rate and generates the same average GDP per employee as the current labor force. benefiting employees, their health, the societies in which they live, and government finances. 11 For more, see Lixin Cai, “The relationship between health and labour force participation: Evidence from a panel data simultaneous equation model,” Labour Economics , January 2010, Volume 17, Number 1.
To capture these economic benefits fully, employers need to move from a sole focus of protecting against incidental risk and illness to helping employees achieve more optimal health. This is particularly important when considering that employees move along a continuum of health over time and may draw upon different workplace resources throughout their employment with a company. Ultimately, a focus on improving health could lead to a virtuous circle of positive change, as employees gain health literacy, and employers in turn respond to employee health concerns.
To capture the economic benefits of good health fully, employers need to move from a sole focus of protecting against incidental risk and illness to helping employees achieve more optimal health.
Acting now also reduces future brand and business risk. In Australia, a lawsuit resulted in a fine for an organization that tolerated a toxic workplace culture. 12 For more, see Naomi Neilson, “Court Services Victoria fined $380k for ‘toxic’ workplace,” Lawyers Weekly, October 19, 2023. Recently, the European Union adopted the European Sustainability Reporting Standards, requiring organizations by law to report on working conditions such as working time, social dialogue, and work–life balance. As employees develop higher standards for what is tolerable in the workplace, more pushback and litigation may be possible.
Furthermore, investors such as asset managers, private equity companies, and venture capitalists are increasingly weighing environmental, social, and governance (ESG) considerations in their investment decisions. They are guided by ESG ratings released by various agencies and standards issued by the International Sustainability Standards Board.
Improving employee health and well-being involves more than just employers
We have highlighted practical examples of how employers can play a role in changing norms and catalyzing innovation around employee health and well-being. However, employers alone can’t complete this task. Employees, policy makers, and local governments will need to help.
Employees can play a role in their own health by taking advantage of the workplace resources that do exist and helping cultivate a community and culture of healthy practices among colleagues. They can make their desires known to employers as a means of holding leaders accountable for responding to the health needs and aspirations of their workforces. These might include benefits such as paid parental leave and caregiving support, which aim to help employees balance work and family responsibilities while tending to their own overall health and well-being.
Policy and decision makers may want to consider a variety of ways to protect and promote employee health. Possibilities include mandating upper limits on total working hours, health coverage paid by employers, and employee access to therapy and other psychological resources. 13 For more, see Richard Layard, “Wellbeing as the goal of policy,” LSE Public Policy Review , December 2021, Volume 2, Number 2. Enhancing standards and transparency could enable employees to make informed choices about their employment while also allowing policy makers to audit progress on a wider scale.
Through investment in public health (such as funding and grants), policy makers can encourage and enable employers to take employee health seriously and professionalize how they track the impact of their initiatives on employee health and well-being. Finally, policy and decision makers can lead by example in acting to promote their own employees’ health. This may be done in partnership with both private and other public sector employers, such as those that play a critical role in educating individuals about health—school systems, healthcare systems, and community programs—down to the city level.
City governments can play an important role in unlocking positive health outcomes. Given that most large employers are concentrated in cities, there’s a unique opportunity for companies and employees to come together to set broader aspirations on health and identify targeted interventions to pursue jointly.
Employment can and does have a profound impact on health, both positive and negative. Adapting how and where people work to support optimal employee health could result in billions of employees and their families around the world living longer, higher-quality lives—and simultaneously benefiting their employers and the societies in which they live.
Jacqueline Brassey is a coleader of employee health at the McKinsey Health Institute (MHI) and a senior fellow in McKinsey’s Luxembourg office, Barbara Jeffery is a coleader of employee health at MHI and a partner in the London office, Lars Hartenstein is a global leader of MHI and a senior fellow in the Paris office, and Patrick Simon is a senior partner in the Berlin office.
The authors wish to thank Erica Coe, Aaron De Smet, Martin Dewhurst, Arne Gast, Brad Herbig, Anna Hextall, Ashini Kothari, Tom Latkovic, May Lim, Robyn Macrae, Dana Maor, Roxy Merkand, Hannah Mirman, Lucy Pérez, and Brooke Weddle for their contributions to this article.
This article was edited by Hannah Buchdahl, an associate editor in the Washington, DC, office.
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Methodology
Research Methods | Definitions, Types, Examples
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design . When planning your methods, there are two key decisions you will make.
First, decide how you will collect data . Your methods depend on what type of data you need to answer your research question :
- Qualitative vs. quantitative : Will your data take the form of words or numbers?
- Primary vs. secondary : Will you collect original data yourself, or will you use data that has already been collected by someone else?
- Descriptive vs. experimental : Will you take measurements of something as it is, or will you perform an experiment?
Second, decide how you will analyze the data .
- For quantitative data, you can use statistical analysis methods to test relationships between variables.
- For qualitative data, you can use methods such as thematic analysis to interpret patterns and meanings in the data.
Table of contents
Methods for collecting data, examples of data collection methods, methods for analyzing data, examples of data analysis methods, other interesting articles, frequently asked questions about research methods.
Data is the information that you collect for the purposes of answering your research question . The type of data you need depends on the aims of your research.
Qualitative vs. quantitative data
Your choice of qualitative or quantitative data collection depends on the type of knowledge you want to develop.
For questions about ideas, experiences and meanings, or to study something that can’t be described numerically, collect qualitative data .
If you want to develop a more mechanistic understanding of a topic, or your research involves hypothesis testing , collect quantitative data .
You can also take a mixed methods approach , where you use both qualitative and quantitative research methods.
Primary vs. secondary research
Primary research is any original data that you collect yourself for the purposes of answering your research question (e.g. through surveys , observations and experiments ). Secondary research is data that has already been collected by other researchers (e.g. in a government census or previous scientific studies).
If you are exploring a novel research question, you’ll probably need to collect primary data . But if you want to synthesize existing knowledge, analyze historical trends, or identify patterns on a large scale, secondary data might be a better choice.
Descriptive vs. experimental data
In descriptive research , you collect data about your study subject without intervening. The validity of your research will depend on your sampling method .
In experimental research , you systematically intervene in a process and measure the outcome. The validity of your research will depend on your experimental design .
To conduct an experiment, you need to be able to vary your independent variable , precisely measure your dependent variable, and control for confounding variables . If it’s practically and ethically possible, this method is the best choice for answering questions about cause and effect.
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Your data analysis methods will depend on the type of data you collect and how you prepare it for analysis.
Data can often be analyzed both quantitatively and qualitatively. For example, survey responses could be analyzed qualitatively by studying the meanings of responses or quantitatively by studying the frequencies of responses.
Qualitative analysis methods
Qualitative analysis is used to understand words, ideas, and experiences. You can use it to interpret data that was collected:
- From open-ended surveys and interviews , literature reviews , case studies , ethnographies , and other sources that use text rather than numbers.
- Using non-probability sampling methods .
Qualitative analysis tends to be quite flexible and relies on the researcher’s judgement, so you have to reflect carefully on your choices and assumptions and be careful to avoid research bias .
Quantitative analysis methods
Quantitative analysis uses numbers and statistics to understand frequencies, averages and correlations (in descriptive studies) or cause-and-effect relationships (in experiments).
You can use quantitative analysis to interpret data that was collected either:
- During an experiment .
- Using probability sampling methods .
Because the data is collected and analyzed in a statistically valid way, the results of quantitative analysis can be easily standardized and shared among researchers.
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- Chi square test of independence
- Statistical power
- Descriptive statistics
- Degrees of freedom
- Pearson correlation
- Null hypothesis
- Double-blind study
- Case-control study
- Research ethics
- Data collection
- Hypothesis testing
- Structured interviews
Research bias
- Hawthorne effect
- Unconscious bias
- Recall bias
- Halo effect
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Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.
Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.
In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .
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.
The research methods you use depend on the type of data you need to answer your research question .
- If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
- If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
- If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.
Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.
Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).
In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .
In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.
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Doing more but learning less: Addressing the risks of AI in research
A rtificial intelligence (AI) is widely heralded for its potential to enhance productivity in scientific research. But with that promise come risks that could narrow scientists' ability to better understand the world, according to a new paper co-authored by a Yale anthropologist.
Some future AI approaches, the authors argue, could constrict the questions researchers ask, the experiments they perform, and the perspectives that come to bear on scientific data and theories.
All told, these factors could leave people vulnerable to "illusions of understanding" in which they believe they comprehend the world better than they do.
The Perspective article is published in Nature .
"There is a risk that scientists will use AI to produce more while understanding less," said co-author Lisa Messeri, an anthropologist in Yale's Faculty of Arts and Sciences. "We're not arguing that scientists shouldn't use AI tools, but we're advocating for a conversation about how scientists will use them and suggesting that we shouldn't automatically assume that all uses of the technology, or the ubiquitous use of it, will benefit science."
The paper, co-authored by Princeton cognitive scientist M. J. Crockett, sets a framework for discussing the risks involved in using AI tools throughout the scientific research process, from study design through peer review.
"We hope this paper offers a vocabulary for talking about AI's potential epistemic risks," Messeri said.
Added Crockett, "To understand these risks, scientists can benefit from work in the humanities and qualitative social sciences."
Messeri and Crockett classified proposed visions of AI spanning the scientific process that are currently creating buzz among researchers into four archetypes:
- In study design, they argue, "AI as Oracle" tools are imagined as being able to objectively and efficiently search, evaluate, and summarize massive scientific literatures, helping researchers to formulate questions in their project's design stage.
- In data collection, "AI as Surrogate" applications, it is hoped, allow scientists to generate accurate stand-in data points, including as a replacement for human study participants, when data is otherwise too difficult or expensive to obtain.
- In data analysis, "AI as Quant" tools seek to surpass the human intellect's ability to analyze vast and complex datasets.
- And "AI as Arbiter" applications aim to objectively evaluate scientific studies for merit and replicability, thereby replacing humans in the peer-review process.
The authors warn against treating AI applications from these four archetypes as trusted partners, rather than simply tools, in the production of scientific knowledge. Doing so, they say, could make scientists susceptible to illusions of understanding, which can crimp their perspectives and convince them that they know more than they do.
The efficiencies and insights that AI tools promise can weaken the production of scientific knowledge by creating "monocultures of knowing," in which researchers prioritize the questions and methods best suited to AI over other modes of inquiry, Messeri and Crockett state. A scholarly environment of that kind leaves researchers vulnerable to what they call "illusions of exploratory breadth," where scientists wrongly believe that they are exploring all testable hypotheses, when they are only examining the narrower range of questions that can be tested through AI.
For example, "Surrogate" AI tools that seem to accurately mimic human survey responses could make experiments that require measurements of physical behavior or face-to-face interactions increasingly unpopular because they are slower and more expensive to conduct, Crockett said.
The authors also describe the possibility that AI tools become viewed as more objective and reliable than human scientists, creating a "monoculture of knowers" in which AI systems are treated as a singular, authoritative, and objective knower in place of a diverse scientific community of scientists with varied backgrounds, training, and expertise. A monoculture, they say, invites "illusions of objectivity" where scientists falsely believe that AI tools have no perspective or represent all perspectives when, in truth, they represent the standpoints of the computer scientists who developed and trained them.
"There is a belief around science that the objective observer is the ideal creator of knowledge about the world," Messeri said. "But this is a myth. There has never been an objective 'knower,' there can never be one, and continuing to pursue this myth only weakens science."
There is substantial evidence that human diversity makes science more robust and creative, the authors add.
"Acknowledging that science is a social practice that benefits from including diverse standpoints will help us realize its full potential," Crockett said. "Replacing diverse standpoints with AI tools will set back the clock on the progress we've made toward including more perspectives in scientific work."
It is important to remember AI's social implications, which extend far beyond the laboratories where it is being used in research, Messeri said.
"We train scientists to think about technical aspects of new technology," she said. "We don't train them nearly as well to consider the social aspects, which is vital to future work in this domain."
More information: Lisa Messeri et al, Artificial intelligence and illusions of understanding in scientific research, Nature (2024). DOI: 10.1038/s41586-024-07146-0
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- v.34; 2021 Dec
What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire
Anne pignault.
1 Université de Lorraine, Psychology & Neuroscience Laboratory (2LPN, EA7489), 23 boulevard Albert 1er, 54000 Nancy, France
Claude Houssemand
2 University of Luxembourg, Department of Education and Social Work, Institute for Lifelong Learning & Guidance (LLLG), 2 Avenue de l’Université, L-4365 Esch-sur-Alzette, Luxembourg
Associated Data
The datasets generated and/or analyzed during the current study are available from the corresponding author.
Considering the recent and current evolution of work and the work context, the meaning of work is becoming an increasingly relevant topic in research in the social sciences and humanities, particularly in psychology. In order to understand and measure what contributes to the meaning of work, Morin constructed a 30-item questionnaire that has become predominant and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, it has been validated only in part.
Meaning of work questionnaire was conducted in French with 366 people (51.3% of women; age: ( M = 39.11, SD = 11.25); 99.2% of whom were employed with the remainder retired). Three sets of statistical analyses were run on the data. Exploratory and confirmatory factor analysis were conducted on independent samples.
The questionnaire described a five-factor structure. These dimensions (Success and Recognition at work and of work, α = .90; Usefulness, α = .88; Respect for work, α = .88; Value from and through work, α = .83; Remuneration, α = .85) are all attached to a general second-order latent meaning of work factor (α = .96).
Conclusions
Validation of the scale, and implications for health in the workplace and career counseling practices, are discussed.
Introduction
Since the end of the 1980s, many studies have been conducted to explore the meaning of work, particularly in psychology (Rosso, Dekas, & Wrzesniewski, 2010 ). A review of the bibliographical data in PsychInfo shows that between 1974 and 2006, 183 studies addressed this topic (Morin, 2006 ). This scholarly interest was primarily triggered by Sverko and Vizek-Vidovic’s ( 1995 ) article, which identified the approaches and models that have been used and their main results.
Whereas early studies on the meaning of work introduced the concept and its theoretical underpinnings (e.g., Harpaz, 1986 ; Harpaz & Fu, 2002 ; Morin, 2003 ; MOW International Research team, 1987 ), later research tried to connect this aspect of work with other psychological dimensions or individual perceptions of the work context (e.g., Harpaz & Meshoulam, 2010 ; Morin, 2008 ; Morin, Archambault, & Giroux, 2001 ; Rosso et al., 2010 ; Wrzesniewski, Dutton, & Debebe, 2003 ). Nevertheless, scholars, particularly those in organizational and occupational psychology, soon found it difficult to precisely identify the meaning of work because it changes in accordance with the conceptualizations of different researchers, the theoretical models used to describe it, and the tools that are available to measure it for individuals and for groups.
This article first seeks to clarify the concept of the meaning of work (definitions and models) before bringing up certain problems involved in its measurement and the diversity in how the concept has been used. Then the paper focuses on a particular meaning of work measurement tool developed in Canada, which is now widely used in French-speaking countries. At the beginning of the twenty-first century, Morin et al. ( 2001 ) developed a 30-item questionnaire to better determine the dimensions that give meaning to a person’s work. The statistical analyses needed to determine the reliability and validity of Morin et al.’s meaning of work questionnaire have never been completed. Indeed, some changes were made to the initial scale, and the analyses only based on homogenous samples of workers in different professional sectors. Thus and even though the meaning of work scale is used quite frequently, both researchers and practitioners have been unsure about whether or not to trust its results. The main objective of the present study was thus to provide a psychometric validation of Morin et al.’s meaning of work scale and to uncover its latent psychological structure.
Meaning of work: from definition to measurement
Meaning of work: what is it.
As many scholars have found, the concept of the meaning of work is not easy to define (e.g., Rosso et al., 2010 ). In terms of theory, it has been defined differently in different academic fields. In psychology, it refers to an individual’s interpretations of his/her actual experiences and interactions at work (Ros, Schwartz, & Surkiss, 1999 ). From a sociological point of view, it involves assessing meaning in reference to a system of values (Rosso et al., 2010 ). In this case, its definition depends on cultural or social differences, which make explaining this concept even more complex (e.g., Morse & Weiss, 1955 ; MOW International Research team, 1987 ; Steers & Porter, 1979 ; Sverko & Vizek-Vidovic, 1995 ).
At a conceptual level, the meaning of work has been defined in three different ways (Morin, 2003 ). First, it can refer to the meaning of work attached to an individual’s representations of work and the values he/she attributes to that work (Morse & Weiss, 1955 ; MOW International Research team, 1987 ). Second, it can refer to a personal preference for work as defined by the intentions that guide personal action (Super & Sverko, 1995 ). Third, it can be understood as consistency between oneself and one’s work, similar to a balance in one’s personal relationship with work (Morin & Cherré, 2004 ).
With respect to terms, some differences exist because the meaning of work is considered an individual’s interpretation of what work means or of the role it plays in one’s life (Pratt & Ashforth, 2003 ). Yet this individual perception is also influenced by the environment and the social context (Wrzesniewski et al., 2003 ). The psychological literature on the meaning of work has primarily examined its positive aspects, even though work experiences can be negative or neutral. This partiality about the nature of the meaning of work in research has led to some confusion in the literature between this concept and that of meaningful , which refers to the extent to which work has personal significance (a quantity) and seems to depend on positive elements (Steger, Dik, & Duffy, 2012 ). A clearer demarcation should be made between these terms in order to specify the exact sense of the meaning of work: “This would reserve ‘meaning’ for instances in which authors are referring to what work signifies (the type of meaning), rather than the amount of significance attached to the work” (Rosso et al., 2010 , p. 95).
The original idea of the meaning of work refers to the central importance of work for people, beyond the simple behavioral activity through which it occurs. Drawing on various historical references, certain authors present work as an essential driver of human life; these scholars then seek to understand how work is fundamental (e.g., Morin, 2006 ; Sverko & Vizek-Vidovic, 1995 ). The concept of the meaning of work is connected to the centrality of work for the individual and consequently fulfills four different important functions: economic (to earn a living), social (to interact with others), prestige (social position), and psychological (identity and recognition). In this view, the centrality of work is based on an ensemble of personal and social values that differ between individuals as well as between cultures, economic climates, and occupations (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ).
Meaning of work: which theoretical model?
The first theoretical model for the meaning of work was based on research in the MOW project (MOW International Research team, 1987 ), considered the “most empirically rigorous research ever undertaken to understand, both within and between countries, the meanings people attach to their work roles” (Brief, 1991 , p. 176). This view suggests that the meaning of work is based on five principal theoretical dimensions: work centrality as a life role, societal norms regarding work, valued work outcomes, importance of work goals, and work-role identification. A series of studies on this theory was conducted in Israel (Harpaz, 1986 ; Harpaz & Fu, 2002 ; Harpaz & Meshoulam, 2010 ), complementing the work of the MOW project (MOW International Research team, 1987 ). Harpaz ( 1986 ) empirically identified six latent factors that represent the meaning of work: work centrality, entitlement norm, obligation norm, economic orientation, interpersonal relations, and expressive orientation.
Another theoretical model on the importance of work in a person’s life was created by Sverko in 1989 . This approach takes into account the interactions among certain work values (the importance of these values and the perception of possible achievements through work), which depend on a process of socialization. The ensemble is then moderated by an individual’s personal experiences with work. In the same vein, Rosso et al. ( 2010 ) tried to create an exhaustive model of the sources that influence the meaning of work. This model is built around two major dimensions: Self-Others (individual vs. other individuals, groups, collectives, organizations, and higher powers) and Agency-Communion (the drives to differentiate, separate, assert, expand, master, and create vs. the drives to contact, attach, connect, and unite). This theoretical framework describes four major pathways to the meaning of work: individuation (autonomy, competence, and self-esteem), contribution (perceived impact, significance, interconnection, and self-abnegation), self-connection (self-concordance, identity affirmation, and personal engagement), and unification (value systems, social identification, and connectedness).
Lastly, a more recent model (Lips-Wiersma & Wright, 2012 ) converges with the theory suggested by Rosso et al. ( 2010 ) but distinguishes two dimensions: Self-Others versus Being-Doing. This model describes four pathways to meaningful work: developing the inner self, unity with others, service to others, and expressing one’s full potential.
Without claiming to be exhaustive, this brief presentation of the theoretical models of the meaning of work underscores the difficulty in precisely defining this concept, the diversity of possible approaches to identifying its contours, and therefore implicitly addresses the various tools designed to measure it.
Measuring the meaning of work
Various methodologies have been used to better determine the concept of the meaning of work and to grasp what it involves in practice. The tools examined below have been chosen because of their different methodological approaches.
One of the first kinds of measurements was developed by the international MOW project (MOW International Research team, 1987 ). In this study, England and Harpaz ( 1990 ) and Ruiz-Quintanilla and England ( 1994 ) used 14 defining elements to assess agreement on the perception of work of 11 different sample groups questioned between 1989 and 1992. These elements, resulting from the definition of work given by the MOW project and studied by applying multivariate analyses and textual content analyses ( When do you consider an activity as working ? Choose four statements from the list below which best define when an activity is “ working,” MOW International Research team, 1987 ), can be grouped into four distinct heuristic categories (Table (Table1 1 ).
Items used to define the concept of work
These items were taken from Ruiz-Quintanilla and England ( 1994 ). The letter in front of each item corresponds to the initial order of the items (MOW International Research team, 1987 )
Similarly, England ( 1991 ) studied changes in the meaning of work in the USA between 1982 and 1989. He used four different methodological approaches to the meaning of work: societal norms about work, importance of work goals, work centrality, and definition of work by the labor force. In the wake of these studies, others developed scales to measure the centrality of work in people’s lives, either for the general population (e.g., Warr, 2008 ) or for specific subpopulations such as unemployed people, on the basis of a rather similar conceptualization of the meaning of work (McKee-Ryan, Song, Wanberg, & Kinicki, 2005 ; Wanberg, 2012 ).
Finally, Wrzesniewski, McCauley, Rozin, and Schwartz ( 1997 ) developed a rather unusual method for evaluating people’s relationships with their work. Although not directly connected to research on the meaning of work, this study and the questionnaire they used ( University of Pennsylvania Work-Life Questionnaire ) addressed some of the same concepts. Above all, they employed the concepts in a very particular way that combined psychological scales, scenarios, and sociodemographic questions. Through these scenarios (Table (Table2) 2 ) and the extent to which the respondents felt like the described characters, their relationship to work was described as either a Job, a Career, or a Calling.
Scenarios used to measure the relationship to work
These scenarios were taken from Wrzesniewski et al. ( 1997 , p. 24)
This presentation of certain tools for measuring the meaning of work reveals a variety of methodological approaches. Nevertheless, whereas certain methods have adopted a rather traditional psychological approach, others are often difficult to use for various reasons such as their psychometrics (e.g., the use of only one item to measure a concept; England, 1991 ; Wrzesniewski et al., 1997 ) or for practical reasons (e.g., the participants were asked questions that pertained not only to their individual assessment of work but also to various other parts of their lives; England, 1991 ; Warr, 2008 ). This diversity in the possible uses of the meaning of work makes it difficult to select a tool to measure it.
In French-speaking countries (Canada and Europe primarily), the previously mentioned scale created by Morin et al. ( 2001 ) has predominated and has repeatedly been used in research in occupational psychology and by practitioners in the field. Nevertheless, there has not been a complete validation of the scale (i.e., different forms of the same tool, only the use of exploratory factor analyses, and no similar structures found) that was the motivation for the current study.
The present study
The present article conceives of the meaning of work as representing a certain consistency between what an individual wants out of work and the individual’s perception, lived or imagined, of his/her work. It thus corresponds to the third definition of the meaning of work presented above—consistency between oneself and one's work (Morin & Cherré, 2004 ). This definition is strictly limited to the meaning given to work and the personal significance of this work from the activities that the work implies. Within this conceptual framework, some older studies adopted a slightly different cognitive conception, in which individuals constantly seek a balance between themselves and their environment, and any imbalance triggers a readjustment through which the person attempts to stabilize his/her cognitive state (e.g., Heider, 1946 ; Osgood & Tannenbaum, 1955 ). Here, the meaning of work must be considered a means for maintaining psychological harmony despite the destabilizing events that work might involve. In this view, meaning is viewed as an effect or a product of the activity (Brief & Nord, 1990 ) and not as a permanent or fixed state. It then becomes a result of person-environment fit and falls within the theory of work adjustment (Dawis, Lofquist, & Weiss, 1968 ).
Within this framework, a series of recurring and interdependent studies should be noted (e.g., Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ) because they have attempted to measure the coherence that a person finds in the relation between the person’s self and his/her work and thus implicitly the meaning of that work. Therefore, these studies make it possible to understand the meaning of work in greater detail, meaning that it could be used in practice through a self-evaluation questionnaire. The level of coherence is considered the degree of similarity between the characteristics of work that the person attributes meaning to and the characteristics that he/she perceives in his/her present work (Aronsson, Bejerot, & Häremstam, 1999 ; Morin & Cherré, 2004 ). Based on semi-structured interviews and on older research related to the quality of life at work (Hackman & Oldham, 1976 ; Ketchum & Trist, 1992 ), a model involving 14 characteristics was developed: the usefulness of work, the social contribution of work, rationalization of the tasks, workload, cooperation, salary, the use of skills, learning opportunities, autonomy, responsibilities, rectitude of social and organizational practices, the spirit of service, working conditions, and, finally, recognition and appreciation (Morin, 2006 ; Morin & Cherré, 1999 ). Then, based on this model, a 30-item questionnaire was developed to offer more precise descriptions of these dimensions. Table Table3 3 presents the items, which were designed and administered to the participants in French.
Items from the meaning of work scale by Morin with their theoretical dimensions and exploratory factor analyses
P personal power at work, U usefulness of work, R success at work, A autonomy at work, S safety, E ethics, UT usefulness of work, VP personal value, EF personal efficacy, ET ethics of work, RT rationalization of work, IE personal influence
(*) = French version. 1 = Morin and Cherré ( 1999 ). 2 = Morin et al. ( 2001 ) and Morin ( 2003 ). 3 = Morin and Cherré ( 2004 )
Some studies for structurally validating this questionnaire have been conducted over the years (e.g., Morin, 2003 , 2006 , 2008 ; Morin & Cherré, 2004 ). However, their results were not very precise or comparable. For example, the number of latent factors in the meaning of work scale structure varied (e.g., six or eight factors: Morin, 2003 ; six factors: Morin, 2006 ; Morin & Cherré, 2004 ), the sample groups were not completely comparable (especially with respect to occupations), and finally, items were added or removed or their phrasing was changed (e.g., 30 and 33 items: Morin, 2003 ; 30 items: Morin, 2006 ; 26 items: Morin, 2008 ). Yet the most prominent methodological problem was that only exploratory analyses (most often a principal component analysis with varimax rotation) had been applied. This scale was entirely relevant from a theoretical point of view because it offered a more specific definition of the meaning of work than other scales and, mainly, because some subdimensions appeared to be linked with anxiety, depression, irritability, cognitive problems, psychological distress, and subjective well-being (Morin et al., 2001 ). It was also relevant from a practical point of view because it was short and did not take much time to complete. However, its use was questionable because it had never been validated psychometrically, and a consistent latent psychological structure had not been identified across studies.
As an example, two models representing the structure of the 30-item scale are presented in Table Table3 3 (Morin et al., 2001 ; Morin, 2003 , for the first model; Morin & Cherré, 2004 , for the second one). This table presents the items, the meaning of work dimensions they are theoretically related to, and the solution from the principal component analysis in each study. These analyses revealed that the empirical and theoretical structures of this tool are not stable and that the latent structure suffers from the insufficient use of statistical methods. In particular, there was an important difference found between the two models in previous studies (Morin et al., 2001 ; Morin & Cherré, 2004 ). Only the “usefulness of work” dimension was found to be identical, comprised of the same items in both models. Other dimensions had a maximum of only three items in common. Therefore, it is very difficult to utilize this tool both in practice and diagnostically, and complementary studies must be conducted. Even though there are techniques for replicating explanatory analyses (e.g., Osborne, 2012 ), such techniques could not be used here because not all the necessary information was given (e.g., all factor loadings, communalities). This is why collecting new data appeared to be the only way to analyze the scale.
More recently, two studies (which applied a new 25-item meaningful work questionnaire ) were developed on the basis of Morin’s scale (Bendassolli & Borges-Andrade, 2013 ; Bendassolli, Borges-Andrade, Coelho Alves, & de Lucena Torres, 2015 ). Even though the concepts of the “meaning of work” and “meaningful work” are close, the two scales are formally and theoretically different and do not evaluate the same construct.
The purpose of the present study was thus to determine the structure of original Morin’s 30-item scale (Morin, 2003 ; Morin & Cherré, 2004 ) by using an exploratory approach as well as confirmatory statistical methods (structural equation modeling) and in so doing, to address the lacunae in previous research discussed above. The end goal was thus to identify the structure of the scale statistically so that it can be used empirically in both academic and professional fields. Indeed, as mentioned previously, this scale is of particular interest to researchers because its design is not limited to measuring a general meaning of work for each individual; it can also be used to evaluate discrepancies or a convergence between a person’s own personal meaning of work and a specific work context (e.g., tasks, relations with others, autonomy). Finally, and with respect to previous results, the scale could be a potential predictor of professional well-being and psychological distress at work (Morin et al., 2001 ).
Participants
The questionnaire was conducted with 366 people who were mainly resident in Paris and the surrounding regions in France. The gender distribution was almost equal; 51.3% of the respondents were women. The respondents’ ages ranged from 19 to 76 years ( M = 39.11, SD = 11.25). The large majority of people were employed (99.2%). Twenty percent worked in medical and paramedical fields, 26% in retail and sales, and 17% in human resources (the other respondents worked in education, law, communication, reception, banking, and transportation). Seventy percent had fewer than 10 years of seniority in their current job ( M = 8.64, SD = 9.65). Only three people were retired (0.8%).
Morin’s 30-item meaning of work questionnaire (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ) along with sociodemographic questions (i.e., sex, age, job activities, and seniority at work) were conducted in French through an online platform. Answers to the meaning of work questionnaire were given on a 5-point Likert scale ranging from 1 ( strongly disagree ) to 5 ( strongly agree ).
Participants were recruited through various professional online social networks. This method does not provide for a true random sample but, owing to it resulting in a potentially larger range of respondents, it enlarges the heterogeneousness of the participants, even if it cannot ensure representativeness (Barberá & Zeitzoff, 2018 ; Hoblingre Klein, 2018 ). This point seems important because very homogenous samples were used in previous studies, especially with regard to professions.
Participants were volunteers, and were given the option of being able to stop the survey at any time. They received no compensation and no individual feedback. Participants were informed of these conditions before filling out the questionnaire. Oral and informed consent was obtained from all participants. Moreover, the Luxembourg Agency for Research Integrity (LARI on which the researchers in this study depend) specified that according to Code de la santé publique—Article L1123-7, it appears that France does not require research ethics committee [Les Comités de Protection des Personnes (CPP)] approval if the research is non-biomedical, non-interventional, observational, and does not collect personal health information, and thus CNR approval was not required.
Participants had to answer each question in order to submit the questionnaire: If one item was not answered, the respondent was not allowed to proceed to the next question. Thus, the database has no missing data. An introduction presented the subject of the study and its goals and guaranteed the participant’s anonymity. Researchers’ e-mail addresses were given, and participants were informed that they could contact the researchers for more information.
Data analyses
Three sets of statistical analyses were run on the data:
- Analysis of the items, using traditional true score theory and item response theory, for verifying the psychometric qualities (using mainly R package “psych”). The main objectives of this part of analysis were to better understand the variability of respondents’ answers, to compute the discriminatory power of items, and to verify the distribution of items by using every classical descriptive indicator (mean, standard-deviation, skewness, and kurtosis), corrected item-total correlations, and functions of responses for distributions.
- An exploratory factor analysis (EFA) with an oblimin rotation in order to define the latent structure of the meaning of work questionnaire, performed with the R packages “psych” and “GPArotation”. The structure we retained was based on adequation fits of various solutions (TLI, RMSEA and SRMR, see “List of abbreviations” section at the end of the article), and the use of R package “EFAtools” which helps to determine the adequate number of factors to retain for the EFA solution. Finally, this part of the analysis was concluded using calculations of internal consistency for each factor found in the scale.
- A confirmatory factor analysis using the R package Lavaan and based on the results of the EFA, in order to verify that the latent structure revealed in Step c was valid and relevant for this meaning of work scale. The adequation between data and latent structure was appreciated on the basis of CFI, TLI, RMSEA, and SRMR (see “Abbreviations” section).
For step a, the responses of the complete sample were considered. For steps b and c, 183 subjects were selected randomly for each analysis from the total study sample. Thus, two subsamples comprised of completely different participants were used, one for the EFA in step b and one for the CFA in step c.
Because of the ordinal measurement of the responses and its small number of categories (5-point Likert), none of the items can be normally distributed. This point was verified in step a of the analyses. Thus, the data did not meet the necessary assumptions for applying factor analyses with conventional estimators such as maximum likelihood (Li, 2015 ; Lubke & Muthén, 2004 ). Therefore, because the variables were measured on ordinal scales, it was most appropriate to apply the EFA and CFA analyses to the polychoric correlation matrix (Carroll, 1961 ). Then, to reduce the effects of the specific item distributions of the variables used in the factor analyses, a minimum residuals extraction (MINRES; Harman, 1960 ; Jöreskog, 2003 ) was used for the EFA, and a weighted least squares estimator with degrees of freedom adjusted for means and variances (WLSMV) was used for the CFA as recommended psychometric studies (Li, 2015 ; Muthén, 1984 ; Muthén & Kaplan, 1985 ; Muthén & Muthén, 2010 ; Yang, Nay, & Hoyle, 2010 ; Yu, 2002 ).
The size of samples for the different analyses has been taken into consideration. A model structure analysis with 30 observed variables needs a recommended minimum sample of 100 participants for 6 latent variables, and 200 for 5 latent variables (Soper, 2019 ). The samples used in the present research corresponded to these a priori calculations.
Finally, according to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), acceptable and excellent model fits are indicated by CFI and TLI values greater than .90 and .95, respectively, by RMSEA values smaller than .08 (acceptable) and .06 (excellent), respectively, and SRMR values smaller than .08.
Item analyses
The main finding was the limited amount of variability in the answers to each item. Indeed, as Table Table4 4 shows, respondents usually and mainly chose the answers agree and strongly agree , as indicated by the column of cumulated percentages of these response modalities (%). Thus, for all items, the average answer was higher than 4, except for item 11, the median was 4, and skewness and kurtosis indicators confirmed a systematic skewed on the left leptokurtic distribution. This lack of variability in the participants’ responses and the high average scores indicate nearly unanimous agreement with the propositions made about the meaning of work in the questionnaire.
Distribution and analysis of the 30 items of the scale
M average of the answers to the item, SD standard deviation of the answers to the item, Med median, % cumulated percentages of answers 4 ( agree ) and 5 ( strongly agree ) for each item, skew skewness, kurt kurtosis, rit corrected item-total correlations
Table Table4 4 also shows that the items had good discriminatory power, expressed by corrected item-total correlations (calculated with all items) which were above .40 for all items. Finally, item analyses were concluded through the application of item response theory (Excel tools using the eirt add in; Valois, Houssemand, Germain, & Belkacem, 2011 ) which confirmed, by analyses of item characteristic curves (taking into account that item response theory models are parametric and assume that the item responses distributions follow a logistic function, Rasch, 1980 ; Streiner, Norman, & Cairney, 2015 , p. 297), the psychometric quality of each item and their link to an identical latent dimension. These different results confirmed the interest in keeping all items of the questionnaire in order to measure the work-meaning construct.
Exploratory analyses of the scale
A five-factor solution was identified. This solution explained 58% of the total variance in the responses of the scale items; the TLI was .885, the RMSEA was .074, and the SRMR was .04. The structure revealed by this analysis was relatively simple (saturation of one main factor for each item; Thurstone, 1947 ), and the communality of each item was high, except for item 11. The solution we retained presented the best adequation fits and the most conceptual explanation concerning the latent factors. Additionally, the “EFAtools” R package confirmed the appropriateness of the chosen solution. Table Table5 5 shows the EFA results, which described a five-factor structure.
Loadings and communalities of the 30 items from the meaning of work scale
EFA with five factors, oblimin rotation. Bold = loading ≥ .30. h 2 = communality
Nevertheless, the correlation matrix for the latent factors obtained by the EFA (see Table Table6) 6 ) suggested the existence of a general second-order meaning of work factor, because the five factors were significantly correlated each with others. This result could be described as the existence of a general meaning of work factor, which alone would explain 44% of the total variance in the responses.
Correlations between the latent factors from the EFA, Cronbach’s alpha, and McDonald omega for each dimension and general factor
F1: success and recognition at work and from work; F2: usefulness; F3: respect; F4: value from and through work; F5: remuneration; general: total scale
Internal consistency of latent factors of the scale
The internal consistency of each latent factor, estimated by Cronbach alpha and McDonald omega, was high (above .80) and very high for the entire scale (α = .96 and ω = .97). Thus, for S uccess and Recognition at work and from work ’ s factor ω was .93, for Usefulness ’s factor ω was .92, for Respect ’s factor ω was .91, for Value from and through work ’s factor ω was slightly lower and equal to .85, and finally for Remuneration ’ s factor for which ω was .87.
Confirmatory factor analyses of the scale
In order to improve the questionnaire, we applied a CFA to this five-factor model to improve the model fit and refine the latent dimensions of the questionnaire. We used CFA to (a) determine the relevance of this latent five-factor structure and (b) confirm the relevance of a general second-order meaning-of-work factor. Although this procedure might appear redundant at first glance, it enabled us to select a definitive latent structure in which each item represents only one latent factor (simple structure; Thurstone, 1947 ), whereas the EFA that was computed in the previous step showed that certain items loaded on several factors. The CFA also easily verified the existence of a second-order latent meaning of work factor (the first-order loadings were .894, .920, .873, .892, and .918, respectively). Thus, this CFA was computed to complement the previous analyses by refining the latent model proposed for the questionnaire.
According to conventional rules of thumb (Hu & Bentler, 1999 ; Kline, 2011 ), although the RMSEA value for the five-factor model was somewhat too high, the CFI and TLI values were excellent (χ 2 = 864.72, df = 400, RMSEA = .080, CFI = .989, TLI = .988). Table Table7 7 presents the adequation fits for both solutions: a model with 5 first-order factors (as EFA suggests), and a model with 5 first-order factors and 1 second-order factor.
Solutions of confirmatory factor analyses
χ 2 Chi-square, df degrees of freedom, CFI comparative fit index, TLI Tucker-Lewis Index of factoring reliability, RMSEA root mean square error of approximation, SRMR standardized root mean square residual
Figure Figure1 1 shows the model after the confirmatory test. This analysis confirmed the existence of a simple structure with five factors for the meaning of work scale and with a general, second-order factor of the meaning of work as suggested by the previous EFA.
Standardized solution of the structural model of the Meaning of Work Scale
The objective of this study was to verify the theoretical and psychometric structure of the meaning of work scale developed by Morin in recent years (Morin, 2003 ; Morin et al., 2001 ; Morin & Cherré, 2004 ). This scale has the advantages of being rather short, of proposing a multidimensional structure for the meaning of work, and of making it possible to assess the coherence between the aspects of work that are personally valued and the actual characteristics of the work environment. Thus, it can be used diagnostically or to guide individuals. To establish the structure of this scale, we analyzed deeply the items, and we implemented exploratory and confirmatory factor analyses, which we believe the scale’s authors had not carried out sufficiently. Moreover, we used a broad range of psychometric evaluation methods (traditional true score theory, item response theory, EFA, and structural equation modeling) to test the validity of the scale.
Item analyses confirmed results found in previous studies in which the meaning-of-work scale was administered. The majority of respondents agreed with the proposals of the questionnaire. Thus, this lack of variability is not specific to the present research and its sample (e.g., Morin & Cherré, 2004 ). Nevertheless, this finding can be explained by different reasons (which could be studied by other research) such as social desirability and the importance of work norms in industrial societies, or a lack of control regarding response bias.
The various versions of the latent structure of the scale proposed by the authors were not confirmed by the statistical analyses seen here. It nevertheless appears that this tool for assessing the meaning of work can describe and measure five different dimensions, all attached to a general factor. The first factor (F1), composed of nine items, is a dimension of recognition and success (e.g., item 17: work where your skills are recognized ; item 19: work where your results are recognized ; item 24: work that enables you to achieve the goals that you set for yourself ). It should thus be named Success and Recognition at work and from work and is comparable to dimensions from previous studies (personal success, Morin et al., 2001 ; social influence, Morin & Cherré, 2004 ). The second factor (F2), composed of seven items, is a dimension that represents the usefulness of work for an individual, whether that usefulness is social (e.g., Item 22: work that gives you the opportunity to serve others ) or personal (e.g., Item 28: work that enables you to be fulfilled ). It can be interpreted in terms of the Usefulness of work and generally corresponds to dimensions of the same name in earlier models (Morin, 2003 ; Morin & Cherré, 2004 ), although the definition used here is more precise. The third factor (F3), described by four items, refers to the Respect dimension of work (e.g., Item 5: work that respects human values ) and corresponds in part to the factors highlighted in prior studies (respect and rationalization of work, Morin, 2003 ; Morin & Cherré, 2004 ). The fourth factor (F4), composed of four items, refers to the personal development dimension and Value from and through work (e.g., Item 2: work that enables you to learn or to improve ). It is in some ways similar to autonomy and effectiveness, described by the authors of the scale (Morin, 2003 ; Morin & Cherré, 2004 ). Finally, the fifth and final factor (F5), with six items, highlights the financial and, more important, personal benefits sought or received from work. This includes physical and material safety and the enjoyment of work (e.g., item 14: work you enjoy doing ). This dimension of Remuneration partially converges with the aspects of personal values related to work described in previous research (Morin et al., 2001 ). Although the structure of the scale highlighted here differed from previous studies, some theoretical elements were nevertheless consistent with each other. To be convinced of this, the Table Table8 8 highlights possible overlaps.
Final structure the items of the meaning of work scale by Morin and their theoretical dimensions
1 = Previous dimensions of Morin et al. ( 2001 ) and Morin ( 2003 ). 2 = Morin and Cherré ( 1999 )
A second important result of this study is the highlighting of a second-order factor by the statistical analyses carried out. This latent second-level factor refers to the existence of a general meaning of work dimension. This unitary conception of the meaning of work, subdivided into different linked facets, is not in contradiction with the different theories related to this construct. Thus, Ros et al. ( 1999 ) defined the meaning of work as a personal interpretation of experiences and interaction at work. This view of meaning of work can confer it a unitary functionality for maintaining psychological harmony, despite the destabilizing events that are often a feature of work. It must be considered as a permanent process of work adjustment or work adaptation. In order to be effective, this adjustment needs to remain consistent and to be globally oriented toward the cognitive balance between the reality of work and the meaning attributed to it. Thus, it has to keep a certain coherence which would explain the unitary conception of the meaning of work.
In addition to the purely statistical results of this study, whereas some partial overlap was found between the structural model in this study and structural models from previous work, this paper provides a much-needed updating and improvement of these dimensions, as we examined several theoretical meaning of work models in order to explain them psychologically. Indeed, the dimensions defined here as Success and Recognition , Usefulness , Respect , Value , and Remuneration from the meaning of work scale by Morin et al. ( 2001 ) have some strong similarities to other theoretical models on the meaning of work, even though the authors of the scale referred to these models only briefly. For example, the dimensions work centrality as a life role , societal norms regarding work , valued work outcomes , importance of work goals , and work-role identification (MOW International Research team, 1987 ) concur with the model described in the present study. In the same manner, the model by Rosso et al. ( 2010 ) has some similarities to the present structure, and there is a conceptual correspondence between the five dimensions found here and those from their study ( individuation , contribution , self-connection , and unification ). Finally, Baumeister’s ( 1991 ), Morin and Cherré’s ( 2004 ), and Sommer, Baumeister, and Stillman ( 2012 ) studies presented similar findings on the meaning of important life experiences for individuals; they described four essential needs that make such experiences coherent and reasonable ( purpose , efficacy - control , rectitude , and self - worth ). It is obvious that the parallels noted here were fostered by the conceptual breadth of the dimensions as defined in these models. In future research, much more precise definitions are needed. To do so, it will be essential to continue running analyses to test for construct validity by establishing convergent validity between the dimensions of the various existing meaning of work scales.
It is also interesting to note the proximity between the dimensions described here and those examined in studies on the dimensions that characterize the work context (Pignault & Houssemand, 2016 ) or in Karasek’s ( 1979 ) and Siegrist’s ( 1996 ) well-known models, for example, which determined the impact of work on health, stress, and well-being. These studies were able to clearly show how dimensions related to autonomy, support, remuneration, and esteem either contribute to health or harm it. These dimensions, which give meaning to work in a manner that is similar to the dimensions highlighted in the current study (Recognition, Value, and Remuneration in particular), are also involved in health. Thus, it would be interesting to verify the relations between these dimensions and measures of work health.
Thus, the conceptual dimensions of the meaning of work, as defined by Morin ( 2003 ) and Morin and Cherré ( 1999 ), remained of strong theoretical importance even if, at the empirical level, the scale created on this basis did not correspond exactly. The present study has had the modest merit of showing this interest, and also of proposing a new structure of the facets of this general dimension. One of the major interests of this research can be found in the possible better interpretations that this scale will enable to make. As mentioned above, the Morin’s scale is very frequently used in practice (e.g., in state employment agencies or by Human Resources departments), and the divergent models of previous studies could lead to individual assessments of the meaning of work diverging, depending on the reading grid chosen. Showing that a certain similarity in the structures of the meaning of work exists, and that a general factor of the meaning of work could be considered, the results of the current research can contribute to more precise use of this tool.
At this stage and in conclusion, it may be interesting to consider the reasons for the variations between the structures of the scale highlighted by the different studies. There were obviously the different changes applied to the different versions of the scale, but beyond that, three types of explanation could emerge. At the level of methods, the statistics used by the studies varied greatly, and could explain the variations observed. At the level of the respondents, work remains one of the most important elements of life in our societies. A certain temptation to overvalue its importance and purposes could be at the origin of the broad acceptance of all the proposals of the questionnaire, and the strong interactions between the sub-dimensions. Finally, at the theoretical level, if, as our study showed, a general dimension of meaning of work seems to exist, all the items, all the facets and all the first order factors of the scale, are strongly interrelated at each respective level. As well, small variations in the distribution of responses could lead to variations of the structure.
The principal contribution of this study is undoubtedly the use of confirmatory methods to test the descriptive models that were based on Morin’s scale (Morin, 2003 , 2006 ; Morin & Cherré, 1999 , 2004 ). The principal results confirm that the great amount of interest in this scale is not without merit and suggest its validity for use in research, both by practitioners (e.g., career counselors and Human Resources departments) and diagnostically. The results show a tool that assesses a general dimension and five subdimensions of the meaning of work with a 30-item questionnaire that has strong psychometric qualities. Conceptual differences from previous exploratory studies were brought to light, even though there were also certain similarities. Thus, the objectives of this study were met.
Limitations
As with any research, this study also has a certain number of limitations. The first is the sample size used for statistical analyses. Even if the research design respected the general criteria for these kind of analyses (Soper, 2019 ), it will be necessary to repeat the study with larger samples. The second is the cultural and social character of the meaning of work, which was not addressed in this study because the sample was comprised of people working in France. They can thus be compared with those in Morin’s studies ( 2003 ) because of the linguistic proximity (French) of the samples, but differences in the structure of the scale could be due to cultural differences between America and Europe. Nevertheless, other different international populations should be questioned about their conception of the meaning of work in order to measure the impact of cultural and social aspects (England, 1991 ; England & Harpaz, 1990 ; Roe & Ester, 1999 ; Ruiz-Quintanilla & England, 1994 ; Topalova, 1994 ; Zanders, 1993 ). In the same vein, a third limitation involves the homogeneity of the respondents’ answers. Indeed, there was quasi-unanimous agreement with all of the items describing work (see Table Table4 4 and previous results, Morin & Cherré, 2004 ). It is worth examining whether this lack of variance results from a work norm that is central and promoted in industrialized countries as it might mask broader interindividual differences. Thus, this study’s protocol should be repeated with other samples from different cultures. Finally, a fourth limitation that was mentioned previously involves the validity of the scale. Concerning the content validity and because some items loaded similarly different factors, it could be interesting to verify the wording content of the items, and potentially modify or replace some of them. The purpose of the present study was not to change the content of the scale but to suggest how future studies could analyze this point. Concerning the construct validity, this first phase of validation needs to be followed by other phases that involve tests of convergent validity between the existing meaning of work scales as well as tests of discriminant validity in order to confirm the existence of the meaning of work construct examined here. In such studies, the centrality of work (Warr, 2008 ; Warr, Cook, & Wall, 1979 ) should be used to confirm the validity of the meaning of work scale. Other differential, individual, and psychological variables related to work (e.g., performance, motivation, well-being) should also be introduced in order to expand the understanding of whether relations exist between the set of psychological concepts involved in work and individuals’ jobs.
Acknowledgements
Not applicable.
Abbreviations
Authors’ contributions.
Both the authors are responsible for study conceptualization, data collection, data preparation, data analysis and report writing. The original questionnaire is a public one. No permission is required. The author(s) read and approved the final manuscript.
No funding.
Availability of data and materials
Ethics approval and consent to participate.
Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The Luxembourg Agency for Research Integrity (LARI) specifies that according to Code de la santé publique - Article L1123-7, it appears that France does not require research ethics committee (Les Comités de Protection des Personnes (CPP)) approval if the research is non-biomedical, non-interventional, observational, and does not collect personal health information. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. At the beginning of the questionnaire, the participants had to give their consent that the data could be used for research purposes, and they had to consent to the publication of the results of the study. Participation was voluntary and confidential. No potentially identifiable human images or data is presented in this study.
Competing interests
The authors declare that they have no competing interests.
Publisher’s Note
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Contributor Information
Anne Pignault, Email: [email protected] .
Claude Houssemand, Email: [email protected] .
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Regions & Countries
5. electoral reform and direct democracy.
Free and fair elections are a critical element of a healthy democratic system . And in many of the 24 countries surveyed, reforming how elections and the electoral system work is a key priority. People want both large-scale, systemic changes – such as switching from first-past-the-post to proportional representation – as well as smaller-scale issues like making Election Day a holiday.
Many people link these changes to greater citizen representation, whether it’s because they allow people to vote more easily or because their votes can be more readily and accurately converted into representation.
But some people take it even a step further, arguing for their country to have more direct democracy . Particularly in France and Germany, where direct democracy is the second-most suggested change, people want to have more chances to vote via referenda on topics that matter to them.
Electoral reform
Changing the electoral system appears in the top five ranked issues in seven of the 24 countries surveyed. In Canada, Nigeria and the UK, the issue ranks second among the 17 substantive topics coded.
In six countries, those who do not support the governing party or parties are more likely to mention electoral reform than those who do support such parties. In the UK, for example, where electoral reform is ranked second only to politicians, 17% of those who do not support the ruling Conservative Party mention electoral reform, compared with 6% of Conservative Party supporters. (For more information on how we classify governing party supporters, refer to Appendix D .)
However, in the U.S. and Israel, this pattern is reversed: Those who do support the governing parties are more likely than those who do not to mention electoral reform as an improvement to democracy.
“People should have the right to choose their leaders through a free and fair election.” Woman, 20, Nigeria
Across the countries surveyed, people want to see a wide range of electoral reforms. Some of these focus on the logistics of casting votes – how and when people vote , and who is eligible . Others focus more on changing the electoral system , referencing issues like electoral thresholds and gerrymandering. And some emphasize the need to ensure free and fair elections . In Nigeria and Brazil, people who are not confident that their recent national elections were conducted fairly and accurately (as asked in a separate question in Brazil, Kenya and Nigeria) are more likely to bring up electoral reform.
Logistics of casting votes
Some of the calls for electoral reform center specifically on how ballots are cast. For example, some see benefits to electronic voting options over paper ballots, especially as a tool to protect elections: “Use modernized technology to help in security of the voting system,” said one Kenyan woman. Others see electronic ballots as an issue of convenience, particularly if it means one can vote from the comfort of their own house. As one Canadian man put it: “I think people should be able to vote electronically, using the internet and telephone instead of going to a polling station. It makes it more convenient.”
Still, in some places that have electronic voting, respondents raise concerns about this method. “End the electronic ballot box,” said a Brazilian woman. A man in India expressed his preference for paper ballots : “The use of electronic voting machines should be stopped and bring paper ballots back so that transparent democracy will be seen.”
For some Americans, increased access to absentee or mail-in voting is a specific electoral change they want to see: “Making vote-by-mail standard in every state, giving voters time to vote at their convenience, rather than having to miss work. It also gives them the time to research candidates at their leisure.” Others in the U.S. oppose mail-in voting : “Stop voter fraud! Go back to voting on Election Day. Enough with this all-month voting and mail-in votes,” wrote one American woman. “Stop mail-in ballots unless for military or another exempt person,” echoed a man. There are large partisan divides in U.S. views of voting methods , and more Democrats cast absentee votes than Republicans.
When people vote
People also see the need to change the frequency of elections . Some request fewer elections so that officeholders spend less of their term campaigning for reelection: One Australian man wanted to “lengthen the period between federal elections to five years.” Others want to see more elections, like a Canadian woman who said, “Do not have an election every four years; it should be every two years,” or a Nigerian woman who wanted her government to “conduct elections every two years, or frequently.” One South African woman went so far as to say, “Elections should be held every year.”
Some in the U.S. (where national elections are held on the first Tuesday after the first Monday of November) call for making Election Day a holiday . The U.S. is one of few advanced economies that does not hold elections over the weekend or designate the day a national holiday. For example, one American man said, “Create a national voting holiday to ensure every American has a chance to vote.” Another person said, “Eliminate voter suppression. Make Election Day a national holiday. Make voting as easy as mailing a letter.”
Who gets to vote
Making changes to who is allowed to participate in elections is another means people see to improve their democracy. For example, some want to alter the age at which citizens become eligible to cast their votes . For those who want to lower it, the argument centers around allowing more young people to participate in elections: “Lowering the voting age to 16, now young people have more stake in the game,” suggested a Canadian man. An American man had a similar opinion, saying, “I think lowering the age for voting would help democracy, because many teens as young as 16 already have views about policies in the U.S.”
Not all are in favor of lowering the voting age, however. As one Swedish man put it: “Raise the voting age. People at 18 need to take their electoral mandate more seriously.”
“There should be a voter’s license, and voters should take a civics test. Informed voting is the crux of democracy.” Man, 76, Italy
Others feel voters need to pass a knowledge test in order to cast a vote. “The right to vote should be bound by educational attainment,” said a man in Hungary. An Italian man said, “Those who want to vote should pass a test of general culture before the elections.” And a woman in Sweden was specific on this policy: “One should know what you’re voting for, a little mini test so you know what you’re voting for. A driver’s license to vote.” (For more on perceived citizen responsibility, read Chapter 4 .)
In some countries, though, there are calls to protect people’s existing right to vote . In the U.S., where voter suppression has become an electoral issue, several people were vocal about protecting the right to vote. “Abolish state laws that restrict voters’ rights,” suggested one American man. An Australian man focused specifically on protecting voting rights for Aboriginal people: “Ensure Indigenous voters have the opportunity to vote in all circumstances.” Certain respondents even want to enfranchise new types of voters: “Open the right to vote to all permanent residents, such as all Europeans who live in France,” said one French woman.
Mandatory voting
“To oblige every citizen to vote and influence according to law.” Man, 68, Israel
Respondents in some places went as far as suggesting that voting in elections and referenda be required as a means to improve democracy. One Greek woman said, “All citizens should be forced to vote on very important laws and decisions for the country.” A man in the Netherlands saw mandatory voting as a way to improve voter turnout: “Compulsory voting should be reintroduced. For provincial council elections, turnout is only 50% to 60%. Introducing compulsory voting could improve this.”
Still, not everyone who lives in a country that has mandatory voting approves of it. “Don’t make it compulsory to vote for someone. That way, the people who really care will have their vote and those who don’t care won’t just pick the first person on the sheet or the one with the best name with no idea who they are voting for,” said one Australian woman. Another Australian shared a similar view: “I would like to see the scrapping of compulsory voting, as this will mean political parties will need to work harder for votes.” And, in Argentina, where voting is mandatory for most citizens, some respondents called for its overhaul – “that voting is not compulsory.”
Changing the electoral system
“Election law reform. Stop voting by region and switch to a national election where one can choose the winner based on the highest number of votes nationwide.” Woman, 63, Japan
People also call for a different style of voting than they currently have. For example, some focus on implementing a first-past-the-post voting system (in which people vote for a single candidate and the candidate with the most votes wins). As one Australian man put it: “Introduce first-past-the-post voting , dispensing with preferential voting, as the minor parties are making every government difficult to operate.”
Other people value proportional representation , a system where politicians hold the number of seats proportional to their party’s support in the voting population. “Reintroduce the proportional representation voting system and ensure accountability by elected officials,” said a South African man. And a French woman said, “All representatives should be elected by proportional representation.”
Some expressed frustration with ballots listing a choice of parties instead of specific candidates , as in the case of a Swedish man who said, “Direct election of people, not parties. It is better to vote for a person, you know what they think.” An Australian agreed: “Enhancing the electoral process for Australians to vote for candidates, and less for their parties.”
There are also calls for things like ranked-choice voting (“Ranked-choice voting would limit extremism.”) and two-round voting (“The kind of two-round voting system would improve democracy.”).
But no one system necessarily satisfies everyone. In some countries that already have first-past-the-post voting, for example, there are requests to eliminate it: “Get rid of first-past-the-post. The electoral system needs reform so that the representation by popular votes should have some weight,” said one man in Canada. One Japanese woman said, “Abolish the single-seat constituency system ,” referring to a type of voting that includes first-past-the-post, where one winner represents one electoral district.
Electoral threshold
“The electoral threshold should be raised, there should be fewer and larger parties.” Man, 82, Netherlands
Changes to the electoral threshold , or the minimum share of votes needed for a candidate or party to provide representation, is suggested by some as a way to improve democracy – particularly among those who live in countries with low thresholds and fragmented party systems. In Israel, where the 3.25% electoral threshold leads to many parties participating in each election, one woman said, “Significantly increase the electoral threshold.”
This sentiment is echoed in the Netherlands, where the 0.67% threshold is the lowest in the world . One Dutch man said, “I think a high electoral threshold would be good. This could lead to less fragmentation and speed up decision-making.” Another Dutch man saw this change as a means to improve the overall quality of elections: “Raise the electoral threshold, so that there will be more substance. That way not everyone can just start a party.” The Dutch survey was conducted prior to November 2023 elections , in which the far-right Party for Freedom (PVV) won the most seats in the House of Representatives.
Making all votes count – or count more
Revising the borders of electoral districts is a reform some think could help increase voter representation. Gerrymandering , for example – a term coined in the U.S. to describe the practice of drawing electoral district boundaries in a way that creates an advantage for one party over another – is something that people in multiple countries flagged as a problem. For example, an Australian man said, “If we were to ban gerrymandering then each political group would have an equal chance to be elected.” In the U.S., one man said, “It would help if we got rid of gerrymandering and the Electoral College and things that suppress the majority.”
For others, voter representation is not just about physical electoral districts, but about correcting a perceived imbalance in the value of each vote . A 38-year-old Japanese man suggested “equalizing the value of votes from young people versus those of the elderly. Young people should be entitled to two votes.” This issue was also brought up in Spain: “The best thing would be one person, one vote. That is, that all votes were worth the same, that they were not counted by autonomous communities,” said one man.
The U.S. Electoral College
The Electoral College – the process by which U.S. presidential elections are decided – is a major focus of electoral reform for many Americans. One man’s response summarized this stance: “Abolition of the Electoral College to allow for direct representation of individual voters rather than allowing certain states to be overrepresented compared to their population size.”
Most of the U.S. respondents who mention the Electoral College are against the process, like one woman who said, “We need to do away with the Electoral College. It was a good idea, but now it doesn’t make sense.” For many, it’s an issue of unequal representation: “The Electoral College should go away, and potentially change how senators are allotted. Sparsely populated areas have too much influence while tens of millions of city residents essentially have no say,” said another woman.
Free and fair elections
“Have transparent voting and respect who wins. And the one who loses should help the one who won and move on.” Man, 38, Argentina
People also call for more election integrity . For example, some feel there should be more transparency: “More openness in general election, no corruption, collusion or nepotism,” said a woman in Indonesia. Or, as a Nigerian man put it: “Let us have a free and fair election with transparency.” People are concerned about this issue in advanced economies as well, with one Canadian man saying, “Election integrity needs to be improved, and no outside interference.”
Others emphasize the importance of respecting election results . “Accept when a candidate loses the election and when a candidate is elected,” said a man in Brazil. An Israeli man put it simply: “Respect the results of the elections.”
“Monitor the processes more, so that there is no miscount.” Woman, 23, Mexico
Improving electoral monitoring , or the use of unbiased observers to ensure that elections are free and fair , is also a key change people want: “Supervision over the counting of votes,” as a woman in Israel said.
In Mexico, where President Andrés Manuel López Obrador has sought controversial election reforms that many believe will weaken the country’s National Electoral Institute (INE), there are specific calls to “strengthen the INE instead of wanting to destroy it,” as one man said.
A Nigerian man expressed his wish for a better institutional oversight, saying, “The electoral commission should be independent and free from interference from the ruling party.” Nigeria’s electoral commission faced criticism during the February 2023 presidential election and was accused of delaying election results .
Direct democracy
“Consult the French people more often through referendums about important issues, life-changing issues.” Woman, 49, France
For some, a form of government where the public votes directly on proposed legislation or policies is a solution to fixing democracy.
This sentiment is particularly common in European countries: In France, Germany, Greece and the Netherlands, it appears in the top five topics mentioned.
In most other countries, it is less of a priority.
In a handful of countries (Australia, Canada, France, Greece, the Netherlands and the UK), those who do not support the governing party or coalition are more likely to mention direct democracy.
French people stand out as particularly likely to mention direct democracy
In France, direct democracy is the second-most mentioned change people want to see. French people on the ideological left are more likely to bring up this topic than those on the right. Additionally, French adults who believe most elected officials don’t care what people like them think ( as asked in a separate question ) are twice as likely to mention direct democracy as those who say most officials care what they think.
Some in France specifically reference Article 49.3 of the French Constitution , under which the government can push legislation through the National Assembly with no legislative vote: “Article 49.3, which had been established for certain situations, is being used to force through unpopular measures,” said one man. The survey was fielded in France between February and April, a period during which Article 49.3 was used to implement controversial pension reforms . Another French man criticizing Article 49.3 saw direct democracy as a clear solution, saying, “Take into account the opinion of citizens in the form of a referendum. Ask for the citizens’ opinions to avoid passing laws in the form of 49.3.”
The Swiss model
Switzerland’s political system – in which the public is able to vote directly on constitutional initiatives and policy referenda – is perceived positively by others around the world, many of whom want their own country to emulate this model. For example, one Canadian woman said, “If people could vote on important issues like in Switzerland and make decisions on important laws, that’s a true democracy there.”
“More public participation on single important topics, just like the referendums in Switzerland.” Man, 55, Germany
This viewpoint is particularly widespread across European respondents; many want their country’s democracy to resemble Switzerland’s. “It would be a good idea to go back and make decisions much more collegially, like the Swiss system,” said a French man. And a Swedish woman said, “More referenda on nuclear power, sexuality, NATO and the EU. Like Switzerland, which has referendums on many issues.” (The survey was conducted prior to Sweden joining NATO in March 2024.)
Respondents in many countries highlight the benefits of more referenda , or instances where the public votes directly on an issue. For some, a key factor is the frequency of voting . One Kenyan man responded, “Citizens should have a referendum at least once in a while to decide on major issues that affect the country.” And a German woman asked that “more referendums take place.”
“More citizen participation in real decision-making. In other countries, referendums are held expressing opinions on different issues, not like here where they vote every four years.” Man, 41, Spain
In other cases, referenda are seen as opportunities for the government to seek the public’s approval . A Mexican man explained, “Before becoming legal, reforms should pass through a citizen filter and popular consultation.” This sometimes includes ensuring that more marginalized voices get a chance to weigh in. For example, one Israeli man said, “When enacting any law, there should be a referendum where all citizens vote, whether Arabs or Jews.” And an Australian woman wished to see more perspectives reflected, calling for “more direct democracy, and more opportunities for influence by poor, multicultural and minority groups.”
In the UK, where a controversial June 2016 referendum resulted in the UK departing the European Union (known as Brexit), some still express support for direct democracy. A British woman suggested, “We need to put down more questions more polls for the public to choose new policies, new laws.” One British man even noted that a referendum could undo Brexit: “We should have a referendum that is truly reflective about Brexit and rejoining the EU.” But other Britons are more wary of direct democracy: One man said, “We should not allow the general public to make critical decisions. The general public should not be allowed to make economic decisions, for example, Brexit.”
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The ZIP Code Shift: Why Many Americans No Longer Live Where They Work
A new study shows that white-collar employees who can work remotely now live roughly twice as far from their offices as they did prepandemic.
By Emma Goldberg
In 2020, Virginia Martin lived two and a half miles from her office. Today, the distance between her work and home is 156.
Ms. Martin, 37, used to live in Durham, N.C., and drove about 10 minutes to her job as a librarian at Duke. After the onset of remote work, Ms. Martin got her boss’s blessing to return to her hometown, Richmond, Va., in March 2022, so she could raise her two young children with help from family.
As an ’80s-born “child of AIM,” Ms. Martin said of AOL instant messaging, it hadn’t been hard for her to maintain co-worker friendships online. She drives back to the office several times a year for events, most recently for the December holiday party.
Ms. Martin is part of today’s growing ZIP code shift: She is one of the millions of Americans who, thanks to remote and hybrid work, no longer lives close to where she works.
Many Americans now live roughly twice as far from their offices as they did prepandemic. That’s according to a new study , set to be released this week, from economists at Stanford and Gusto, a payroll provider, using data from Gusto. The economists studied employee and employer address data from nearly 6,000 employers across the country and found that the average distance between people’s homes and workplaces rose to 27 miles in 2023 from 10 miles in 2019, more than doubling.
The share of people who live 50 or more miles from where they work rose sevenfold during the pandemic, climbing to 5.5 percent in 2023 from 0.8 percent in 2019. These trends have proved resilient even as employees return to the office, according to the researchers.
This phenomenon — expanding the distance between work and home — has been driven primarily by white-collar workers whose jobs can be done remotely, according to the study. It is one largely concentrated among people who earn more than $100,000 and work in jobs like tech, finance, law, marketing and accounting. Workers who earn under $50,000 a year, and those who work in jobs that cannot be done remotely like retail, health care and manufacturing (the majority of the work force), have barely budged in their average distance from work.
The workers moving away from city centers are often people in their 30s and 40s, who have young children and may want larger homes, rather than those in their 20s and 60s. The group also includes a significant number of workers who were newly hired during the pandemic — which means employers most likely expanded their hiring radius as they embraced hybrid work.
Urban scholars argue that the new data illustrates a longstanding American tradition of high-income earners leaving urban housing markets in pursuit of bigger homes in the suburbs.
“We like big houses, and we like big cars,” said Richard Florida, an expert on cities and author of “The New Urban Crisis.” “It’s part of our post-World War II DNA.”
But remote and hybrid work has supercharged this trend.
A small portion of the work force (around 12 percent now, compared with roughly 50 percent at the peak of Covid lockdowns) is still able to work entirely remotely. Some chose to leave pricey housing markets like San Francisco or New York in favor of new hometowns , sometimes called “Zoom towns.” Others who are working in hybrid environments, in which they have to go to the office only two or three days a week, moved and accepted lengthier “ super commutes ” in exchange for cheaper housing and more space.
Verna Coleman is one of those super commuters. Ms. Coleman, 41, works for a media company in New York. Before the pandemic, she lived in Brooklyn and went into the office five days a week. In 2020, after remote work started, she bought a house in Cincinnati, where she grew up and wants to raise her two children.
Now Ms. Coleman commutes into her Manhattan office for three days every other week, and leases a small apartment in Harlem.
“It’s only an hour-and-a-half flight, so I frequently cite to people it’s a shorter flight than driving across the George Washington Bridge and sitting in traffic for two and a half hours,” she said. “I take a 6 a.m. flight from Cincinnati, and I’m normally at my desk before 9.”
Some days are more challenging, though — including last week, with foggy skies causing flight delays. “We create the options we have to for our kids and to maintain our careers,” she added.
But the effects of this shift on cities have been troubling, many economists argue, as urban leaders struggle to revive the downtown areas sapped of some workers who used to eat, drink and shop there.
And business leaders are grappling with both the downsides and the blessings of their newly dispersed work forces.
A video game company in Boulder, Colo., called Serenity Forge, adopted a hybrid policy in 2021. The company’s founder, Zhenghua Yang, gets nostalgic for prepandemic days when people hung out at the office over potlucks and Ping-Pong — but also notices that his employees now seem to have a healthier balance between family and professional life.
Noah Lang, chief executive of a benefits platform called Stride, took remote work as a prompt to cut his company’s San Francisco office lease and move his own family out of the city to a house in Marin County.
Being able to hire employees in cities all over the country has been helpful to his business, he said, because Stride provides benefits to gig workers all over America and needs to understand customer experiences far beyond the Bay Area.
“We’re trying to help people who are low- to moderate-income hardworking Americans who in a lot of cases are not in the tech scene,” Mr. Lang said. “They’re not in this bubble of San Francisco.”
The stream of workers, like Mr. Lang, trading cities for suburbia has bred fears among economists about the possibility of a doom loop : Fewer workers commute downtown, which means less business for shops and a diminished sense of safety, which means even fewer people want to commute downtown. Average weekly foot traffic in downtown areas is still three-quarters what it was prepandemic, according to an analysis of mobile device activity in downtown areas by researchers at the University of Toronto.
But many argue that city leaders are up to the challenge of reimagining urban business centers in response to these demographic changes. Mr. Florida, for example, advises city leaders to make their downtown areas into tourist destinations, or even destinations for people who work at home and then socialize in the city. One study of 26 American downtown areas, published last year, found that on average, visitors made up 61 percent of foot traffic in city centers and residents just 11 percent.
“The future of downtown lies much more in becoming an entertainment and culture and amenity and sports center,” Mr. Florida said.
And in the far-flung areas where office workers have set down new roots, urbanists hope that economic activity will follow.
“People are social animals,” said Dan Luscher, who runs the 15 Minute City project, which researches the concept of a city where all amenities are accessible within a 15-minute walking distance. “The person that moves to Tahoe, they’re going to look for a community there. They’re going to be making that place more vibrant. The activity will shift, but it doesn’t go away.”
Emma Goldberg is a business reporter covering workplace culture and the ways work is evolving in a time of social and technological change. More about Emma Goldberg
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The powerful computational system of IBM’s quantum computers could help manage complex calculations that may soon be too difficult for computers that use traditional silicon processors. (Photo/Connie Zhou for IBM)
IBM agreement boosts USC’s quantum computing leadership
The collaboration with IBM — part of USC President Carol Folt’s Frontiers of Computing “moonshot” — establishes USC’s IBM Quantum Innovation Center and propels USC researchers and students toward new discoveries.
USC has a new agreement with IBM that boosts quantum research by scientists and students and will reinforce the university’s status as a leader in quantum research and a top trainer of the nation’s tech workforce.
“Our new partnership with IBM to expand quantum computing at USC will boost research and innovation in this field and marks a major milestone for our Frontiers of Computing initiative,” USC President Carol Folt said. “By opening the first IBM Quantum Innovation Center on the West Coast, USC is inviting top researchers and game-changers in industry to join us in shaping the future of quantum computing.”
The agreement also accelerates the university’s efforts to achieve the research and education objectives of Folt’s Frontiers of Computing “moonshot,” a more than $1 billion initiative that supports ethical advancement in areas such as artificial intelligence, robotics and quantum computing.
USC started official operations as an IBM Quantum Innovation Center on Feb. 1, giving the university’s researchers cloud access to IBM quantum systems.
“We are excited to collaborate with USC, not only to help advance their research interests, but to prepare their students to join a rapidly growing quantum workforce,” said Jay Gambetta , IBM Fellow and vice president, IBM Quantum. “And with increasing industry interest, USC will play an important connector role as an IBM Quantum Innovation Center, providing a path for organizations to jointly develop algorithms and use cases for practical applications of quantum computing.”
Across the USC Viterbi School of Engineering and the USC Dornsife College of Letters, Arts and Sciences, researchers note that the collaboration greatly increases USC’s quantum capabilities for research. The powerful computational system of IBM’s quantum computers could help them manage complex calculations that may soon be too difficult for ordinary, “classical” computers that use traditional silicon processors.
“The new IBM Quantum Innovation Center at USC will be a pillar in our ability to do cutting-edge research in the area of quantum computing and to train future scientists who will be making exciting discoveries,” said Daniel Lidar , the director of the USC Center for Quantum Information Science and Technology who will also direct USC’s IBM Quantum Innovation Center.
Until recently, USC had been using IBM’s open access plan.
“Anyone in the world can run experiments via the cloud using a limited set of IBM’s quantum computers,” said Lidar, a professor of multiple disciplines — electrical and computer engineering, chemistry, physics and astronomy at USC Viterbi and USC Dornsife, and the holder of the Viterbi Professorship. “Now, thanks to the IBM Quantum Innovation Center, we can run our experiments on a wider array of the cutting-edge quantum computers that IBM makes available.
“The remarkable innovation that underpins quantum computing is the result of decades of knowledge exchange between industry leaders like IBM and academic researchers at universities like USC,” USC Dornsife Dean Amber Miller said. “Building on this long tradition of collaboration, we will work together to accelerate the quantum revolution and deepen our fundamental understanding of the world.”
Quantum computing shows promise as a revolutionary technology because, in theory, its processing speeds far exceed those of classical computers, particularly in solving especially difficult computational problems. Although the technology has not yet crossed the threshold where it demonstrates an overwhelming advantage, many scientists think that day will soon come.
“The quantum realm is an exciting and intriguing next frontier in computing, communications and sensing. USC has anticipated this emergence and has been at its forefront for more than a decade with the assembly of a strong group of faculty who advanced academic quantum computing and communications both on campus and at our own Information Sciences Institute,” said Yannis C. Yortsos , dean of USC Viterbi.
“Today, we are taking another significant step with the partnership with IBM Quantum,” Yortsos added. “It will further catalyze our pioneering research in leveraging quantum phenomena for technology and in educating the next generation of engineering and science students in the fascinating quantum world.”
Quantum computing: A new era of research and innovation
When it happens, the development of applications with a quantum advantage could affect multiple industries. With quantum computing, scientists expect to achieve potential breakthroughs in drug discovery, energy-efficient electronics and energy storage. Quantum also may be the key to breakthrough advancements in machine learning that could address issues in areas such as sustainability and image processing.
“With the proliferation of artificial intelligence and machine learning problems, and the need to assemble training data for those areas, we are going to have to deal with larger and larger datasets,” said Mahta Moghaddam , vice dean of research for USC Viterbi and a Distinguished Professor of electrical and computer engineering.
“Current computers are going to have limitations as the size and diversity of datasets grow, so eventually we are going to need the quantum solutions to make those AI problems solvable,” said Moghaddam, who holds the Ming Hsieh Chair in Electrical and Computer Engineering-Electrophysics.
The IBM agreement is the latest milestone for the university in its quantum journey. The university is already a proven leader in quantum, ranked among the top five programs in quantum information systems, as reported by The Quantum Insider , with particular strength in quantum computing, quantum cryptography and quantum information theory. Its other strength is quantum error correction, an essential aspect in the quest to enable quantum computers to realize their computational advantage despite their susceptibility to errors due to external disturbances.
“This agreement reinforces USC’s leadership in quantum information science,” said Moh El-Naggar , the USC Dornsife divisional dean of the physical sciences and mathematics who helped facilitate the agreement. “Our faculty experts were ahead of their time in applying emerging quantum computers to address grand challenges in health and energy. We strategically designed this agreement to now position USC as a hub for future industry partnerships that benefit from our expertise and prioritized access to quantum hardware.”
Some scientists believe it is not long — perhaps less than 10 years — before quantum computers are used to solve some computational problems that are unsolvable for CPUs or GPUs. Last summer, IBM reported a crucial breakthrough in a Nature paper, demonstrating the ability of today’s quantum systems to operate at utility scale — the point at which quantum computers can serve as scientific tools to explore new classes of problems beyond brute-force, classical simulation of quantum mechanics.
“The universality of the IBM quantum systems means that they are designed to support running any calculation — just like you can on an ordinary laptop or desktop — of course, with added quantum power,” Lidar said. “For example, last year, a former student (now at IBM) and I reported the first example of an algorithmic quantum speedup: We used IBM’s quantum computers to solve a guessing problem faster than is possible using any classical computer.”
When quantum computers surpass classic computers in the ability to solve certain complex problems, this may be accompanied not just by revolutionary computational speedups, but also by massive reductions in energy consumption, Lidar said.
“Once you cool everything down, the computation expends little energy, and that is very different from the way that ordinary classical computers operate,” said Lidar, who envisions a solution soon. “Combining this with quantum speedup means that a large-scale quantum computer will eventually consume far less energy than a classical supercomputer.”
Tomorrow’s tech jobs will be in quantum computing
The IBM agreement provides USC with access to tools to conduct advanced research and train students at the vanguard of computing advancement and research. “Access to IBM’s quantum systems invigorates the work of 20 or so faculty across USC Dornsife and USC Viterbi,” said Stephen Bradforth , USC Dornsife Dean Miller’s senior advisor for research strategy and development.
The new quantum center at USC also includes opportunities for jobs and innovations, as well as new business opportunities for students who graduate from the university, particularly in STEM.
“This resource positions our doctoral students and postdocs in math, physics, chemistry and computational biology at the leading edge in designing algorithms to tackle hard computational problems in each of these disciplines in totally new ways,” Bradforth said.
More employers, including government agencies, are seeking graduates with quantum science and technology degrees in both the public and private sectors, and the matter of educating and training the quantum workforce is a concern highlighted in recent federal reports. The White House and National Quantum Initiative in 2022 mapped a path to building a quantum workforce, and the federal government continues to seek input from research and higher education industries to create a pipeline that can meet the rising national need for quantum.
Quantum technology is valued in the billions of dollars and its potential is even greater. A report by McKinsey & Co. last April noted that annual quantum technology startup investments hit $2.35 billion before it issued the report. The company then estimated that the quantum technology sector could reach $106 billion in value by 2040.
With foresight in 2020, USC spearheaded the launch of a master’s program in quantum information systems. Two cohorts have graduated and landed jobs at tech giants, including Amazon, Google and, of course, IBM. USC graduate students and postdocs also have landed research positions at national laboratories and faculty positions at Duke University, Cornell University and the University of New Mexico, as well as many other universities worldwide.
“Students who graduate from our programs tend to do very well in terms of being placed in either industry or academia,” Lidar said.
USC Dornsife and USC Viterbi leaders expect even more interest in quantum coursework and scholarship now that the university is an IBM Quantum Innovation Center. And they are ready for a new wave of students and research.
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Artistic research, also seen as 'practice-based research', can take form when creative works are considered both the research and the object of research itself. It is the debatable body of thought which offers an alternative to purely scientific methods in research in its search for knowledge and truth.
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.".
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.
Placing research in the bigger context of its field and where it fits into the scientific process can help people better understand and interpret new findings as they emerge. A single study usually uncovers only a piece of a larger puzzle. Questions about how the world works are often investigated on many different levels.
The purpose of research is to further understand the world and to learn how this knowledge can be applied to better everyday life. It is an integral part of problem solving. Although research can take many forms, there are three main purposes of research: Exploratory: Exploratory research is the first research to be conducted around a problem ...
Step 4: Create a research design. The research design is a practical framework for answering your research questions. It involves making decisions about the type of data you need, the methods you'll use to collect and analyze it, and the location and timescale of your research. There are often many possible paths you can take to answering ...
A research paper is a piece of academic writing that provides analysis, interpretation, and argument based on in-depth independent research. Research papers are similar to academic essays, but they are usually longer and more detailed assignments, designed to assess not only your writing skills but also your skills in scholarly research ...
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 ...
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.
First, scientists start with a question. They look at past research to see what others have learned. Different scientists have diverse skills and training. They each bring their own approaches and ideas. And they design new experiments to test their ideas. Next, scientists perform their experiments and collect data.
Research conducted for the purpose of contributing towards science by the systematic collection, interpretation and evaluation of data and that, too, in a planned manner is called scientific research: a researcher is the one who conducts this research. The results obtained from a small group through scientific studies are socialised, and new ...
The Research Process. Anything you write involves organization and a logical flow of ideas, so understanding the logic of the research process before beginning to write is essential. Simply put, you need to put your writing in the larger context—see the forest before you even attempt to see the trees. In this brief introductory module, we ...
1. Earn a bachelor's degree. To become a researcher, you first need to pursue a bachelor's degree. A general degree in clinical research will provide an excellent base for a career as a researcher. If your field of interest is medical research, you can complete a bachelor's degree in chemistry, medicine or biology.
Doing research is stimulating and fulfilling work. Scientists make discoveries to build knowledge and solve problems, and they work with other dedicated researchers. Research is a highly complex activity, so it takes years for beginning researchers to learn everything they need to know to do science well. Part of this large body of knowledge is ...
3) Clarity in expression: The researcher(s) should be able to state a clear academic argument, which will serve as the basis for the research team's work. A new research stream may face an uphill battle, especially if it is seen as challenging conventional thinking; the team needs to anticipate this response and be prepared to defend why it is ...
Research is a process to discover new knowledge. In the Code of Federal Regulations (45 CFR 46.102 (d)) pertaining to the protection of human subjects research is defined as: "A systematic investigation (i.e., the gathering and analysis of information) designed to develop or contribute to generalizable knowledge.".
A 2016 report by professional-networking service LinkedIn notes that 41% of research professionals — compared with 37% across all sectors — say that they are mainly driven by purpose rather ...
A researcher is trained to conduct systematic and scientific investigations in a particular field of study. Researchers use a variety of techniques to collect and analyze data to answer research questions or test hypotheses. They are responsible for designing studies, collecting data, analyzing data, and interpreting the results. Researchers may work in a wide range of fields, including ...
Peer Review is defined as "a process of subjecting an author's scholarly work, research or ideas to the scrutiny of others who are experts in the same field" ( 1 ). Peer review is intended to serve two primary purposes. Firstly, it acts as a filter to ensure that only high quality research is published, especially in reputable journals ...
Common research skills necessary for a variety of jobs include attention to detail, time management, and problem solving. Here we explore what research skills are, examples of in-demand research skills, how you can improve and use research skills at work, and how to highlight your research skills during the job search process.
Health Services Research (HSR) Must Continue to Move Toward Solutions and Implementation . ... The context for our work has become more challenging When I came to the nation's capital for my first policy job (working on health reform in the Clinton administration), I quickly learned that the national policy community is rich with experts and ...
Research, including MHI analysis, has demonstrated a connection between positive mindsets and beliefs and better health experience. 1 For more, see Mathias Allemand, Patrick L. Hill, and Brent W. Roberts, "Examining the pathways between gratitude and self-rated physical health across adulthood," Personality and Individual Differences, January 2013, Volume 54, Number 1; Lisa A. Williams and ...
Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Artificial intelligence (AI) is widely heralded for its potential to enhance productivity in scientific research. But with that promise come risks that could narrow scientists' ability to better ...
Michigan's work force is aging, and the government must recognize what this trend means. From 2000 to 2020, the share of employed adults who were over 60 more than doubled, and this is expected to increase by 2030, according to Peter Berg, professor and director of the School of Human Resources and Labor Relations.. Discussion also included the issues and programs that the government needs ...
Introduction. Since the end of the 1980s, many studies have been conducted to explore the meaning of work, particularly in psychology (Rosso, Dekas, & Wrzesniewski, 2010).A review of the bibliographical data in PsychInfo shows that between 1974 and 2006, 183 studies addressed this topic (Morin, 2006).This scholarly interest was primarily triggered by Sverko and Vizek-Vidovic's article, which ...
Free and fair elections are a critical element of a healthy democratic system.And in many of the 24 countries surveyed, reforming how elections and the electoral system work is a key priority. People want both large-scale, systemic changes - such as switching from first-past-the-post to proportional representation - as well as smaller-scale issues like making Election Day a holiday.
The pace of change in the development of Artificial Intelligence is breathtaking, and we are rapidly delegating more and more tasks to it. In this talk two philosophers explore some aspects of these trends: the role of AI in democratic decision making, and its role in a range of areas where human control has so far seemed essential, such as in the military and in criminal justice.
In 2020, Virginia Martin lived two and a half miles from her office. Today, the distance between her work and home is 156. Ms. Martin, 37, used to live in Durham, N.C., and drove about 10 minutes ...
The IBM agreement provides USC with access to tools to conduct advanced research and train students at the vanguard of computing advancement and research. "Access to IBM's quantum systems invigorates the work of 20 or so faculty across USC Dornsife and USC Viterbi," said Stephen Bradforth , USC Dornsife Dean Miller's senior advisor for ...