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What is Research? Definition, Types, Methods, and Examples

Academic research is a methodical way of exploring new ideas or understanding things we already know. It involves gathering and studying information to answer questions or test ideas and requires careful thinking and persistence to reach meaningful conclusions. Let’s try to understand what research is.   

Table of Contents

Why is research important?    

Whether it’s doing experiments, analyzing data, or studying old documents, research helps us learn more about the world. Without it, we rely on guesswork and hearsay, often leading to mistakes and misconceptions. By using systematic methods, research helps us see things clearly, free from biases. (1)   

What is the purpose of research?  

In the real world, academic research is also a key driver of innovation. It brings many benefits, such as creating valuable opportunities and fostering partnerships between academia and industry. By turning research into products and services, science makes meaningful improvements to people’s lives and boosts the economy. (2)(3)  

What are the characteristics of research?    

The research process collects accurate information systematically. Logic is used to analyze the collected data and find insights. Checking the collected data thoroughly ensures accuracy. Research also leads to new questions using existing data.   

Accuracy is key in research, which requires precise data collection and analysis. In scientific research, laboratories ensure accuracy by carefully calibrating instruments and controlling experiments. Every step is checked to maintain integrity, from instruments to final results. Accuracy gives reliable insights, which in turn help advance knowledge.   

Types of research    

The different forms of research serve distinct purposes in expanding knowledge and understanding:    

• Exploratory research ventures into uncharted territories, exploring new questions or problem areas without aiming for conclusive answers. For instance, a study may delve into unexplored market segments to better understand consumer behaviour patterns.   
• Descriptive research delves into current issues by collecting and analyzing data to describe the behaviour of a sample population. For instance, a survey may investigate millennials’ spending habits to gain insights into their purchasing behaviours.   
• Explanatory research, also known as causal research, seeks to understand the impact of specific changes in existing procedures. An example might be a study examining how changes in drug dosage over some time improve patients’ health.   
• Correlational research examines connections between two sets of data to uncover meaningful relationships. For instance, a study may analyze the relationship between advertising spending and sales revenue.   
• Theoretical research deepens existing knowledge without attempting to solve specific problems. For example, a study may explore theoretical frameworks to understand the underlying principles of human behaviour.   
• Applied research focuses on real-world issues and aims to provide practical solutions. An example could be a study investigating the effectiveness of a new teaching method in improving student performance in schools.  (4)

Types of research methods

• Qualitative Method: Qualitative research gathers non-numerical data through interactions with participants. Methods include one-to-one interviews, focus groups, ethnographic studies, text analysis, and case studies. For example, a researcher interviews cancer patients to understand how different treatments impact their lives emotionally.    
• Quantitative Method: Quantitative methods deal with numbers and measurable data to understand relationships between variables. They use systematic methods to investigate events and aim to explain or predict outcomes. For example, Researchers study how exercise affects heart health by measuring variables like heart rate and blood pressure in a large group before and after an exercise program. (5)  

Basic steps involved in the research process    

Here are the basic steps to help you understand the research process:   

• Choose your topic: Decide the specific subject or area that you want to study and investigate. This decision is the foundation of your research journey.   
• Find information: Look for information related to your research topic. You can search in journals, books, online, or ask experts for help.   
• Assess your sources: Make sure the information you find is reliable and trustworthy. Check the author’s credentials and the publication date.   
• Take notes: Write down important information from your sources that you can use in your research.   
• Write your paper: Use your notes to write your research paper. Broadly, start with an introduction, then write the body of your paper, and finish with a conclusion.   
• Cite your sources: Give credit to the sources you used by including citations in your paper.   
• Proofread: Check your paper thoroughly for any errors in spelling, grammar, or punctuation before you submit it. (6)

How to ensure research accuracy?  

Ensuring accuracy in research is a mix of several essential steps:    

• Clarify goals: Start by defining clear objectives for your research. Identify your research question, hypothesis, and variables of interest. This clarity will help guide your data collection and analysis methods, ensuring that your research stays focused and purposeful.   
• Use reliable data: Select trustworthy sources for your information, whether they are primary data collected by you or secondary data obtained from other sources. For example, if you’re studying climate change, use data from reputable scientific organizations with transparent methodologies.   
• Validate data: Validate your data to ensure it meets the standards of your research project. Check for errors, outliers, and inconsistencies at different stages, such as during data collection, entry, cleaning, or analysis.    
• Document processes: Documenting your data collection and analysis processes is essential for transparency and reproducibility. Record details such as data collection methods, cleaning procedures, and analysis techniques used. This documentation not only helps you keep track of your research but also enables others to understand and replicate your work.   
• Review results: Finally, review and verify your research findings to confirm their accuracy and reliability. Double-check your analyses, cross-reference your data, and seek feedback from peers or supervisors. (7) 

Research is crucial for better understanding our world and for social and economic growth. By following ethical guidelines and ensuring accuracy, researchers play a critical role in driving this progress, whether through exploring new topics or deepening existing knowledge.   

References:  

• Why is Research Important – Introductory Psychology – Washington State University  
• The Role Of Scientific Research In Driving Business Innovation – Forbes  
• Innovation – Royal Society  
• Types of Research – Definition & Methods – Bachelor Print  
• What Is Qualitative vs. Quantitative Study? – National University  
• Basic Steps in the Research Process – North Hennepin Community College  
• Best Practices for Ensuring Data Accuracy in Research – LinkedIn  

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Based on 21+ years of experience in academia, Researcher.Life All Access empowers researchers to put their best research forward and move closer to success. Explore our top AI Tools pack, AI Tools + Publication Services pack, or Build Your Own Plan. Find everything a researcher needs to succeed, all in one place –  Get All Access now starting at just $17 a month !    

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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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Introduction and definitions

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research and development

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• Corporate Finance Institiute - Research and Development
• Business LibreTexts - An Introduction to Research and Development
• Econlib - Research and Development
• The Canadian Encyclopedia - Industrial Research and Development
• University of California at Berkeley - Econometrics Laboratory - Research and Development
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research and development , in industry , two intimately related processes by which new products and new forms of old products are brought into being through technological innovation .

Research and development, a phrase unheard of in the early part of the 20th century, has since become a universal watchword in industrialized nations. The concept of research is as old as science; the concept of the intimate relationship between research and subsequent development, however, was not generally recognized until the 1950s. Research and development is the beginning of most systems of industrial production. The innovations that result in new products and new processes usually have their roots in research and have followed a path from laboratory idea, through pilot or prototype production and manufacturing start-up, to full-scale production and market introduction. The foundation of any innovation is an invention . Indeed, an innovation might be defined as the application of an invention to a significant market need. Inventions come from research—careful, focused, sustained inquiry, frequently trial and error. Research can be either basic or applied, a distinction that was established in the first half of the 20th century.

Basic research is defined as the work of scientists and others who pursue their investigations without conscious goals, other than the desire to unravel the secrets of nature. In modern programs of industrial research and development, basic research (sometimes called pure research) is usually not entirely “pure”; it is commonly directed toward a generalized goal, such as the investigation of a frontier of technology that promises to address the problems of a given industry. An example of this is the research being done on gene splicing or cloning in pharmaceutical company laboratories.

Applied research carries the findings of basic research to a point where they can be exploited to meet a specific need, while the development stage of research and development includes the steps necessary to bring a new or modified product or process into production. In Europe , the United States , and Japan the unified concept of research and development has been an integral part of economic planning , both by government and by private industry.

The first organized attempt to harness scientific skill to communal needs took place in the 1790s, when the young revolutionary government in France was defending itself against most of the rest of Europe. The results were remarkable. Explosive shells, the semaphore telegraph, the captive observation balloon, and the first method of making gunpowder with consistent properties all were developed during this period.

The lesson was not learned permanently, however, and another half century was to pass before industry started to call on the services of scientists to any serious extent. At first the scientists consisted of only a few gifted individuals. Robert W. Bunsen, in Germany, advised on the design of blast furnaces. William H. Perkin, in England, showed how dyes could be synthesized in the laboratory and then in the factory. William Thomson (Lord Kelvin), in Scotland, supervised the manufacture of telecommunication cables. In the United States, Leo H. Baekeland, a Belgian, produced Bakelite, the first of the plastics. There were inventors, too, such as John B. Dunlop, Samuel Morse, and Alexander Graham Bell , who owed their success more to intuition , skill, and commercial acumen than to scientific understanding.

While industry in the United States and most of western Europe was still feeding on the ideas of isolated individuals, in Germany a carefully planned effort was being mounted to exploit the opportunities that scientific advances made possible. Siemens, Krupp, Zeiss, and others were establishing laboratories and, as early as 1900, employed several hundred people on scientific research. In 1870 the Physicalische Technische Reichsanstalt (Imperial Institute of Physics and Technology) was set up to establish common standards of measurement throughout German industry. It was followed by the Kaiser Wilhelm Gesellschaft (later renamed the Max Planck Society for the Advancement of Science), which provided facilities for scientific cooperation between companies.

In the United States, the Cambria Iron Company set up a small laboratory in 1867, as did the Pennsylvania Railroad in 1875. The first case of a laboratory that spent a significant part of its parent company’s revenues was that of the Edison Electric Light Company, which employed a staff of 20 in 1878. The U.S. National Bureau of Standards was established in 1901, 31 years after its German counterpart, and it was not until the years immediately preceding World War I that the major American companies started to take research seriously. It was in this period that General Electric , Du Pont, American Telephone & Telegraph, Westinghouse, Eastman Kodak, and Standard Oil set up laboratories for the first time.

Except for Germany, progress in Europe was even slower. When the National Physical Laboratory was founded in England in 1900, there was considerable public comment on the danger to Britain’s economic position of German dominance in industrial research, but there was little action. Even in France, which had an outstanding record in pure science , industrial penetration was negligible.

World War I produced a dramatic change. Attempts at rapid expansion of the arms industry in the belligerent as well as in most of the neutral countries exposed weaknesses in technology as well as in organization and brought an immediate appreciation of the need for more scientific support. The Department of Scientific and Industrial Research in the United Kingdom was founded in 1915, and the National Research Council in the United States in 1916. These bodies were given the task of stimulating and coordinating the scientific support to the war effort, and one of their most important long-term achievements was to convince industrialists, in their own countries and in others, that adequate and properly conducted research and development were essential to success.

At the end of the war the larger companies in all the industrialized countries embarked on ambitious plans to establish laboratories of their own; and, in spite of the inevitable confusion in the control of activities that were novel to most of the participants, there followed a decade of remarkable technical progress. The automobile, the airplane, the radio receiver, the long-distance telephone, and many other inventions developed from temperamental toys into reliable and efficient mechanisms in this period. The widespread improvement in industrial efficiency produced by this first major injection of scientific effort went far to offset the deteriorating financial and economic situation.

The economic pressures on industry created by the Great Depression reached crisis levels by the early 1930s, and the major companies started to seek savings in their research and development expenditure. It was not until World War II that the level of effort in the United States and Britain returned to that of 1930. Over much of the European continent the depression had the same effect, and in many countries the course of the war prevented recovery after 1939. In Germany Nazi ideology tended to be hostile to basic scientific research, and effort was concentrated on short-term work.

The picture at the end of World War II provided sharp contrasts. In large parts of Europe industry had been devastated, but the United States was immensely stronger than ever before. At the same time the brilliant achievements of the men who had produced radar, the atomic bomb , and the V-2 rocket had created a public awareness of the potential value of research that ensured it a major place in postwar plans. The only limit was set by the shortage of trained persons and the demands of academic and other forms of work.

Since 1945 the number of trained engineers and scientists in most industrial countries has increased each year. The U.S. effort has stressed aircraft, defense, space, electronics , and computers. Indirectly, U.S. industry in general has benefited from this work, a situation that compensates in part for the fact that in specifically nonmilitary areas the number of persons employed in the United States is lower in relation to population than in a number of other countries.

Outside the air, space, and defense fields the amount of effort in different industries follows much the same pattern in different countries, a fact made necessary by the demands of international competition. (An exception was the former Soviet Union , which devoted less R and D resources to nonmilitary programs than most other industrialized nations.) An important point is that countries like Japan, which have no significant aircraft or military space industries, have substantially more manpower available for use in the other sectors. The preeminence of Japan in consumer electronics, cameras, and motorcycles and its strong position in the world automobile market attest to the success of its efforts in product innovation and development.

The Research Whisperer

Just like the thesis whisperer – but with more money, what is research.

We all know what research is – it’s the thing we do when we want to find something out. It is what we are trained to do in a PhD program. It’s what comes before development.

The wonderful people at Wordnet define research as

Noun: systematic investigation to establish facts; a search for knowledge. Verb: attempt to find out in a systematically and scientific manner; inquire into.

An etymologist might tell us that it comes from the Old French word cerchier , to search , with re- expressing intensive force. I guess it is saying that before 1400 in France, research meant to search really hard.

If I was talking to a staff member at my university, though, I would say that searching hard was scholarship . The difference? Research has to have an element of discovering something new, of creating knowledge. While a literature search is one important part of a research project, it isn’t research in and of itself. It is scholarship.

Don’t take my word for it. In Australian universities, we define research this way:

Research is defined as the creation of new knowledge and/or the use of existing knowledge in a new and creative way so as to generate new concepts, methodologies and understandings. This could include synthesis and analysis of previous research to the extent that it leads to new and creative outcomes. This definition of research is consistent with a broad notion of research and experimental development (R&D) as comprising of creative work undertaken on a systematic basis in order to increase the stock of knowledge, including knowledge of humanity, culture and society, and the use of this stock of knowledge to devise new applications This definition of research encompasses pure and strategic basic research, applied research and experimental development. Applied research is original investigation undertaken to acquire new knowledge but directed towards a specific, practical aim or objective (including a client-driven purpose).

Drawn from the 2012 Higher Education Research Data Collection (HERDC) specifications for the collection of 2011 data .

What research sounds like

Sometimes, however, you don’t want to talk about ‘Research ‘ . If you are applying to a philanthropic foundation, for example, they may not be interested in your new knowledge so much as the impact that your work will have, your capacity to help them to solve a problem. Industry partners may also be wary of the ‘R’ word. “Don’t bank your business on someone’s PhD”, they will say (and I would wholeheartedly agree).

This creates something of a quandary, as the government gives us money based on how much research income we bring in. They audit our claims, so everything we say is research has to actually be research. So, it helps to flag it as research, even if you don’t say it explicitly.

Instead, you might talk about innovation , or about experimentation . You could describe the element of risk associated with discovery . Investigation might lead to analysis . There might be tests that you will undertake to prove your hypothesis . You could just say that this work is original and has never been done before. You could talk about what new knowledge your work will lead to.

You might describe a new method or a new data source that will lead to a breakthrough or an incremental improvement over current practice. You could make it clear that it is the precursor to development , in the sense of ‘research and development’.

It really helps if you are doing something new .

What research looks like

Sometimes, it isn’t what you say, but what you do. If your work will lead to a patent, book or book chapter, refereed journal article or conference publication, or an artwork or exhibition (in the case of creative outputs), then it almost always fulfills the definition no matter what you call it.

What research isn’t

Sometimes, you can see a thing more clearly by describing what it isn’t.

Research isn’t teaching. Don’t get me wrong – you can research teaching, just like you can research anything else. However, teaching itself is generally regarded as the synthesis and transfer of existing knowledge. Generally, the knowledge has to exist before you can teach it. Most of the time, you aren’t creating new knowledge as you teach. Some lecturers may find that their students create strange new ‘knowledge’ in their assignments, but making stuff up doesn’t count as research either.

Research isn’t scholarship. As I said at the start, a literature search is an important aspect of the research process but it isn’t research in and of itself. Scholarship (the process of being a scholar) generally describes surveying existing knowledge. You might be looking for new results that you hadn’t read before, or you might be synthesizing the information for your teaching practice. Either way, you aren’t creating new knowledge, you are reviewing what already exists.

Research isn’t encyclopaedic. Encyclopedias, by and large, seek to present a synthesis of existing knowledge. Collecting and publishing existing knowledge isn’t research, as it doesn’t create new knowledge.

Research isn’t just data-gathering. Data-gathering is a vital part of research, but it doesn’t lead to new knowledge without some analysis, some further work. Just collecting the data doesn’t count, unless you do something else with it.

Research isn’t just about methodology. Just because you are using mice, or interviewing people, or using a High Performance Liquid Chromatograph (HPLC) doesn’t mean you are doing research. You might be, if you are using a new data set or using the method in a new way or testing a new hypothesis. However, if you are using the same method, on the same data, exploring the same question, then you will almost certainly get the same results. And that is repetition, not research.

Research isn’t repetition, except in some special circumstances. If you are doing the same thing that someone else has already done, then generally that isn’t research unless you are specifically trying to prove or disprove their work. What’s the difference? Repeating an experiment from 1400 isn’t research. You know what the result will be before your start – it has already been verified many times before. Repeating an experiment reported last year probably is research because the original result can’t be relied upon until it is verified.

Is development research? Development (as in ‘research and development’) may or may not be classified as research, depending on the type of risk involved. Sometimes, the two are inextricably linked: the research leads to the development and the development refines the research. At other times, you are creating something new, but it is a new product or process, not new knowledge. It is based on new knowledge, rather than creating new knowledge. If the risk involved is a business risk, rather than intellectual risk, then the knowledge is already known.

Help me out here – what are your favourite words that signal research?

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26 comments.

currently, im doing postgraduate education for both social science and technological science. i can’t help but to feel slightly amused by your assertion ..

“Don’t bank your business on someone’s PhD”, they will say (and I would wholeheartedly agree).

this is quite true when you’re doing phd for social science. however, if your phd is technologically inclined, the business entity who intends to commercialize it, may have to bank on your research for success.

illustrating this would not be a feat.

are you using google? well, did you know that google was actually a phd research? if they hadn’t banked on page’s and brin’s research, there wouldn’t be google today, would it? presently, it is rumoured that google and microsoft are competing for phd graduates from ivy leagues and what not.

personally, i’ve met a couple of ‘technopreneurs’ who have successfully commercialized their phd research. though they may not be as successful as google, financially speaking, their achievement should not be trivialized.

Thanks, pikir kool.

You are right, of course. I’m a big fan of businesses who provide scholarships for PhD students. It is a great way for the student to get funded, and for the business to get a bit of an edge.

‘Chercher’, the modern French word for chercier means to explore or get. Re-chercher adds the concept of re- or ‘again’ to indicate looking-again, usually on the basis of evidence or experience pointing to the object of the search being in a particular place, hence to ‘search really hard’. French-speaking individuals will ‘rechercher’ a criminal on the run, ‘rechercher’ the more probable destinations of a friend who is out shopping, and so forth. I agree Australian businesses consider PhD graduates are overpriced ‘scholars’ and ‘technicians’ trained to avoid risk, hold similar opinions, and assume as little responsibility for group/enterprise outcomes as possible. What shocks me is your suggestion graduates should misinform potential employers by suggesting they might be able to innovate, discover, and lead the business toward new markets and technologies by simply choosing hot button words. In France, universities are centres of ‘learning’ where individuals experience a rich intellectual environment that the government believes ‘develops’ curiosity, opens up new horizons, tests principles to live by, and rewards leadership. The ‘elitist’ French haut écoles are often criticised by Anglo-saxon countries, but I say the learning environment, which – by the way – focuses less on methodology, reflects human diversity (unique identity). The Australian system is based on an equal opportunity social objective and is funded to produce an intellectual resource pipeline .

Hello Gordon

Thanks for your information on ‘Chercher’.

I was not trying to suggest that anybody misinform anybody else with the use of words, hot button or otherwise, but I can understand how you read it that way.

I wrote that section, in part, as a guide to staff who are trying to satisfy two audiences – the people who are providing funding and the government auditors who are deciding what is counted as research. The easiest way to satisfy the government auditors that something is research is to call it ‘research’. However, in some funding situations, that simply isn’t appropriate. One way forward is to describe the work using words other than ‘research’ that signal to the auditors that the work satisfies the criteria for research.

I’m afraid that I’m not experienced enough with research in France to reply to your comparison of the French and Australian research training environments. I work within the Australian environment, and try to do the best job that I can.

Thank you for this post – very relevant for me right now and thought-provoking. I’m 13 months into my PhD investigating communication designers’ engagement with research and I’m astounded that there is so little consensus in academic literature (not to mention in professional practice) about what legitimate research is.

It seems that any definition or criteria for research that I find, I can also find an example of research that contradicts it. For example, in your post you note “data gathering is a vital part of research” but when I included this in my definition, a highly respected scholar in my field pointed out that research in his own field of Philosophy did not involve data gathering, yet he believed constituted research. So I’m still thinking about it : )

Your philosopher is right, of course. Some researchers are working with ideas and recombining them, reworking them, creating new ideas.

I deal with applied research, mostly, and I guess my definition reflects that.

I would love to see your definition when you are done.

Your article is rad. It shaped the whole concept of research in my mind. And I think that it exactly is a ‘re- search’, where you will be searching the facts again & again, on grass root level, following a sequence of systematic processes to reach a novel & efficient conclusion .

Thanks. Glad I could help, anonymouswailer.

Thank you for the post on ‘What is Research?’ Interesting and useful posts and comments. Since I am considering naming a blog page The Synthesist, I got off on a tangent relating to the words thesis, synthesis, etc. A couple thoughts …

I think you may be undervaluing the function of “synthesis” when it is only referred to in relation to encyclopedic summaries of existing knowledge, I think true synthesis is when 2 or more ideas combine to create a new idea. I also learned, when I served a literacy tutor, that “synthesis” is considered to be a more sophisticated learned literacy skill than “analysis,” which I thought was interesting. We live in analysis culture, creating deep silos of knowledge, with few strong horizontal threads that truly support “learning.”

Interesting comment on French value of learning as the highest human capacity. Not feeling that here in America.

Also, I was hoping to see in your answer of what research IS, a reference to the importance of questions and question formation.

Thanks– Amy

I’m prompted to comment by Amy’s:

After a long time working outside of academia I’m returning to begin a Masters in Disaster Communication and Resilience; I’m still at that early stage of being excited by ideas, and not quite ready to decide on a research topic. What I am sure of is that, in the area of disaster (post-typhoon for example) one of the biggest challenges is that the specialists don’t feel comfortable talking to each other and therefore need the generalist communicators / networkers to listen to what they are on about, develop a general understanding of what they are saying, and link them together with people in other specialist areas whose work might be strikingly different but potentially have enormous potential for synergy/ synthesis.

And I doubt that any research is being done on this.

[…] What is research? by Jonathan O’Donnell […]

This is perhaps a slightly different point of view/perspective from a reasonably long career in applied research, and I am now enrolled in a Doctorate program.

What I find really interesting is pondering where does ‘innovation’ especially in terms of various forms of professional practice or creative endeavour actually come from, if not from ‘research’ as you describe it above? (I often heard and still hear people in industry or the professional practice word using the word ‘research’ to describe an often fairly informal literature search to back up what they have already decided to do in practice – but that is probably another story.)

However, I often wonder where do the ideas for ‘innovation’ actually come from?

When they are drawn from research conclusions (or initially drove the research question) this probably makes that particular research more valuable from a funder point of view.

But it kind of begs the question as to what comes or should come first especially in terms of good applied research.

And then finally, where does creativity come in – especially when deciding what to research, and how to interpret the data and conclusions from the research?

I am off to think of some more concrete examples and to ponder the nexus between research – innovation and creativity.

BTW love this discussion so far!

The nexus between research, innovation and creativity is a great topic! If you are interested in writing it up as a blog post, let me know. We’d be happy to consider it for a guest post on the Research Whisperer.

Jonathan Let me think about it – this has provoked my thinking about the issues but not sure if I am there yet in terms of writing a post about it. I will let you know! Jane

Well, it certainly was interesting to see this comment thread come back to me after three years.

I was about to reply to this person named Amy who said she was going to start a blog called The Synthesist to tell her that I had myself started a blog called Neon Synthesist.

Then I realized it was myself. Strange mirror of time. In 2014, I discovered there was a rock band called Synthesist and named it Neon Synthesist instead, since it tends to be provocative.

There are some fun posts there like “What is an Idea?” and “Why Philosophy Isn’t Dead” and, funding researchers might like, a four-part series called “The Philanthropy Games” … but alas this page will probably go away. No subscribers that I could tell.

http://www.neonsynthesist.blogspot.com

Cheers! Amy

[…] (2012) what is research [online] available from < https://theresearchwhisperer.wordpress.com/2012/09/18/what-is-research/ > [09 march […]

[…] O’Donnell, J. (2012, September 18). What is research? [Blog post]. Retrieved from https://theresearchwhisperer.wordpress.com/2012/09/18/what-is-research/ […]

[…] For more discussion on the question “What is Research”, please see “What is Research?”, Study.com, available from https://study.com/academy/lesson/what-is-research-definition-purpose-typical-researchers.html . See also “What is Research?”, The Research Whisperer, available from https://researchwhisperer.org/2012/09/18/what-is-research/ . […]

I am enriched with the discussions. Thanks.

Thanks, Raton Kumar. I’m glad that you found it useful. Jonathan

[…] For wiser words on research than mine, CLICK HERE. […]

Research is creating new knowledge through systematic investigation and analysis of data. Research leads to development but not in all cases and Repetition of a research already done can be said valid only when we try to prove or disprove it. It sounds great!!!

Research is the effort done by an individual or group of people, to explore something new. It can be an effort to prove the same matter but applying new methods, it also can be done to prove a different findings.

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What Is Research, and Why Do People Do It?

• Open Access
• First Online: 03 December 2022

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• James Hiebert 6 ,
• Jinfa Cai 7 ,
• Stephen Hwang 7 ,
• Anne K Morris 6 &
• Charles Hohensee 6  

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

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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.

You have full access to this open access chapter,  Download chapter PDF

Part I. What Is Research?

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

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

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

Exercise 1.1

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

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

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

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

Exercise 1.2

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

Creating an Image of Scientific Inquiry

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

Descriptor 1. Experience Carefully Planned in Advance

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

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

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

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

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

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

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

Exercise 1.3

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

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

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

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

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

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

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

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

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

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

Doing Scientific Inquiry

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

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

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

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

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

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

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

Exercise 1.4

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

Unpacking the Terms Formulating, Testing, and Revising Hypotheses

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.5

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

Exercise 1.6

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

Learning from Doing Scientific Inquiry

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

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

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

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

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

Part II. Why Do Educators Do Research?

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

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

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

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

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

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

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

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

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

Exercise 1.7

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

Part III. Conducting Research as a Practice of Failing Productively

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

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

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

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

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

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

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

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

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

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

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

Exercise 1.8

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

Exercise 1.9

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

Part IV. Preview of Chap. 2

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

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

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* Research Basics *

• Introduction

So What Do We Mean By “Formal Research?”

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A good working definition of research might be:

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

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

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

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

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

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

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

• 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
• Self-serving bias
• Information bias

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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Home » Research – Types, Methods and Examples

Research – Types, Methods and Examples

Table of Contents

Definition:

Research refers to the process of investigating a particular topic or question in order to discover new information , develop new insights, or confirm or refute existing knowledge. It involves a systematic and rigorous approach to collecting, analyzing, and interpreting data, and requires careful planning and attention to detail.

History of Research

The history of research can be traced back to ancient times when early humans observed and experimented with the natural world around them. Over time, research evolved and became more systematic as people sought to better understand the world and solve problems.

In ancient civilizations such as those in Greece, Egypt, and China, scholars pursued knowledge through observation, experimentation, and the development of theories. They explored various fields, including medicine, astronomy, and mathematics.

During the Middle Ages, research was often conducted by religious scholars who sought to reconcile scientific discoveries with their faith. The Renaissance brought about a renewed interest in science and the scientific method, and the Enlightenment period marked a major shift towards empirical observation and experimentation as the primary means of acquiring knowledge.

The 19th and 20th centuries saw significant advancements in research, with the development of new scientific disciplines and fields such as psychology, sociology, and computer science. Advances in technology and communication also greatly facilitated research efforts.

Today, research is conducted in a wide range of fields and is a critical component of many industries, including healthcare, technology, and academia. The process of research continues to evolve as new methods and technologies emerge, but the fundamental principles of observation, experimentation, and hypothesis testing remain at its core.

Types of Research

Types of Research are as follows:

• Applied Research : This type of research aims to solve practical problems or answer specific questions, often in a real-world context.
• Basic Research : This type of research aims to increase our understanding of a phenomenon or process, often without immediate practical applications.
• Experimental Research : This type of research involves manipulating one or more variables to determine their effects on another variable, while controlling all other variables.
• Descriptive Research : This type of research aims to describe and measure phenomena or characteristics, without attempting to manipulate or control any variables.
• Correlational Research: This type of research examines the relationships between two or more variables, without manipulating any variables.
• Qualitative Research : This type of research focuses on exploring and understanding the meaning and experience of individuals or groups, often through methods such as interviews, focus groups, and observation.
• Quantitative Research : This type of research uses numerical data and statistical analysis to draw conclusions about phenomena or populations.
• Action Research: This type of research is often used in education, healthcare, and other fields, and involves collaborating with practitioners or participants to identify and solve problems in real-world settings.
• Mixed Methods Research : This type of research combines both quantitative and qualitative research methods to gain a more comprehensive understanding of a phenomenon or problem.
• Case Study Research: This type of research involves in-depth examination of a specific individual, group, or situation, often using multiple data sources.
• Longitudinal Research: This type of research follows a group of individuals over an extended period of time, often to study changes in behavior, attitudes, or health outcomes.
• Cross-Sectional Research : This type of research examines a population at a single point in time, often to study differences or similarities among individuals or groups.
• Survey Research: This type of research uses questionnaires or interviews to gather information from a sample of individuals about their attitudes, beliefs, behaviors, or experiences.
• Ethnographic Research : This type of research involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices.
• Historical Research : This type of research investigates events or phenomena from the past using primary sources, such as archival records, newspapers, and diaries.
• Content Analysis Research : This type of research involves analyzing written, spoken, or visual material to identify patterns, themes, or messages.
• Participatory Research : This type of research involves collaboration between researchers and participants throughout the research process, often to promote empowerment, social justice, or community development.
• Comparative Research: This type of research compares two or more groups or phenomena to identify similarities and differences, often across different countries or cultures.
• Exploratory Research : This type of research is used to gain a preliminary understanding of a topic or phenomenon, often in the absence of prior research or theories.
• Explanatory Research: This type of research aims to identify the causes or reasons behind a particular phenomenon, often through the testing of theories or hypotheses.
• Evaluative Research: This type of research assesses the effectiveness or impact of an intervention, program, or policy, often through the use of outcome measures.
• Simulation Research : This type of research involves creating a model or simulation of a phenomenon or process, often to predict outcomes or test theories.

Data Collection Methods

• Surveys : Surveys are used to collect data from a sample of individuals using questionnaires or interviews. Surveys can be conducted face-to-face, by phone, mail, email, or online.
• Experiments : Experiments involve manipulating one or more variables to measure their effects on another variable, while controlling for other factors. Experiments can be conducted in a laboratory or in a natural setting.
• Case studies : Case studies involve in-depth analysis of a single case, such as an individual, group, organization, or event. Case studies can use a variety of data collection methods, including interviews, observation, and document analysis.
• Observational research : Observational research involves observing and recording the behavior of individuals or groups in a natural setting. Observational research can be conducted covertly or overtly.
• Content analysis : Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Ethnography : Ethnography involves immersion in a cultural group or community to understand their way of life, beliefs, values, and practices. Ethnographic research can use a range of data collection methods, including observation, interviews, and document analysis.
• Secondary data analysis : Secondary data analysis involves using existing data from sources such as government agencies, research institutions, or commercial organizations. Secondary data can be used to answer research questions, without collecting new data.
• Focus groups: Focus groups involve gathering a small group of people together to discuss a topic or issue. The discussions are usually guided by a moderator who asks questions and encourages discussion.
• Interviews : Interviews involve one-on-one conversations between a researcher and a participant. Interviews can be structured, semi-structured, or unstructured, and can be conducted in person, by phone, or online.
• Document analysis : Document analysis involves collecting and analyzing written documents, such as reports, memos, and emails. Document analysis can be used to study organizational communication, policy documents, and other forms of written material.

Data Analysis Methods

Data Analysis Methods in Research are as follows:

• Descriptive statistics : Descriptive statistics involve summarizing and describing the characteristics of a dataset, such as mean, median, mode, standard deviation, and frequency distributions.
• Inferential statistics: Inferential statistics involve making inferences or predictions about a population based on a sample of data, using methods such as hypothesis testing, confidence intervals, and regression analysis.
• Qualitative analysis: Qualitative analysis involves analyzing non-numerical data, such as text, images, or audio, to identify patterns, themes, or meanings. Qualitative analysis can be used to study subjective experiences, social norms, and cultural practices.
• Content analysis: Content analysis involves analyzing written, spoken, or visual material to identify patterns, themes, or messages. Content analysis can be used to study media, social media, or other forms of communication.
• Grounded theory: Grounded theory involves developing a theory or model based on empirical data, using methods such as constant comparison, memo writing, and theoretical sampling.
• Discourse analysis : Discourse analysis involves analyzing language use, including the structure, function, and meaning of words and phrases, to understand how language reflects and shapes social relationships and power dynamics.
• Network analysis: Network analysis involves analyzing the structure and dynamics of social networks, including the relationships between individuals and groups, to understand social processes and outcomes.

Research Methodology

Research methodology refers to the overall approach and strategy used to conduct a research study. It involves the systematic planning, design, and execution of research to answer specific research questions or test hypotheses. The main components of research methodology include:

• Research design : Research design refers to the overall plan and structure of the study, including the type of study (e.g., observational, experimental), the sampling strategy, and the data collection and analysis methods.
• Sampling strategy: Sampling strategy refers to the method used to select a representative sample of participants or units from the population of interest. The choice of sampling strategy will depend on the research question and the nature of the population being studied.
• Data collection methods : Data collection methods refer to the techniques used to collect data from study participants or sources, such as surveys, interviews, observations, or secondary data sources.
• Data analysis methods: Data analysis methods refer to the techniques used to analyze and interpret the data collected in the study, such as descriptive statistics, inferential statistics, qualitative analysis, or content analysis.
• Ethical considerations: Ethical considerations refer to the principles and guidelines that govern the treatment of human participants or the use of sensitive data in the research study.
• Validity and reliability : Validity and reliability refer to the extent to which the study measures what it is intended to measure and the degree to which the study produces consistent and accurate results.

Applications of Research

Research has a wide range of applications across various fields and industries. Some of the key applications of research include:

• Advancing scientific knowledge : Research plays a critical role in advancing our understanding of the world around us. Through research, scientists are able to discover new knowledge, uncover patterns and relationships, and develop new theories and models.
• Improving healthcare: Research is instrumental in advancing medical knowledge and developing new treatments and therapies. Clinical trials and studies help to identify the effectiveness and safety of new drugs and medical devices, while basic research helps to uncover the underlying causes of diseases and conditions.
• Enhancing education: Research helps to improve the quality of education by identifying effective teaching methods, developing new educational tools and technologies, and assessing the impact of various educational interventions.
• Driving innovation: Research is a key driver of innovation, helping to develop new products, services, and technologies. By conducting research, businesses and organizations can identify new market opportunities, gain a competitive advantage, and improve their operations.
• Informing public policy : Research plays an important role in informing public policy decisions. Policy makers rely on research to develop evidence-based policies that address societal challenges, such as healthcare, education, and environmental issues.
• Understanding human behavior : Research helps us to better understand human behavior, including social, cognitive, and emotional processes. This understanding can be applied in a variety of settings, such as marketing, organizational management, and public policy.

Importance of Research

Research plays a crucial role in advancing human knowledge and understanding in various fields of study. It is the foundation upon which new discoveries, innovations, and technologies are built. Here are some of the key reasons why research is essential:

• Advancing knowledge: Research helps to expand our understanding of the world around us, including the natural world, social structures, and human behavior.
• Problem-solving: Research can help to identify problems, develop solutions, and assess the effectiveness of interventions in various fields, including medicine, engineering, and social sciences.
• Innovation : Research is the driving force behind the development of new technologies, products, and processes. It helps to identify new possibilities and opportunities for improvement.
• Evidence-based decision making: Research provides the evidence needed to make informed decisions in various fields, including policy making, business, and healthcare.
• Education and training : Research provides the foundation for education and training in various fields, helping to prepare individuals for careers and advancing their knowledge.
• Economic growth: Research can drive economic growth by facilitating the development of new technologies and innovations, creating new markets and job opportunities.

When to use Research

Research is typically used when seeking to answer questions or solve problems that require a systematic approach to gathering and analyzing information. Here are some examples of when research may be appropriate:

• To explore a new area of knowledge : Research can be used to investigate a new area of knowledge and gain a better understanding of a topic.
• To identify problems and find solutions: Research can be used to identify problems and develop solutions to address them.
• To evaluate the effectiveness of programs or interventions : Research can be used to evaluate the effectiveness of programs or interventions in various fields, such as healthcare, education, and social services.
• To inform policy decisions: Research can be used to provide evidence to inform policy decisions in areas such as economics, politics, and environmental issues.
• To develop new products or technologies : Research can be used to develop new products or technologies and improve existing ones.
• To understand human behavior : Research can be used to better understand human behavior and social structures, such as in psychology, sociology, and anthropology.

Characteristics of Research

The following are some of the characteristics of research:

• Purpose : Research is conducted to address a specific problem or question and to generate new knowledge or insights.
• Systematic : Research is conducted in a systematic and organized manner, following a set of procedures and guidelines.
• Empirical : Research is based on evidence and data, rather than personal opinion or intuition.
• Objective: Research is conducted with an objective and impartial perspective, avoiding biases and personal beliefs.
• Rigorous : Research involves a rigorous and critical examination of the evidence and data, using reliable and valid methods of data collection and analysis.
• Logical : Research is based on logical and rational thinking, following a well-defined and logical structure.
• Generalizable : Research findings are often generalized to broader populations or contexts, based on a representative sample of the population.
• Replicable : Research is conducted in a way that allows others to replicate the study and obtain similar results.
• Ethical : Research is conducted in an ethical manner, following established ethical guidelines and principles, to ensure the protection of participants’ rights and well-being.
• Cumulative : Research builds on previous studies and contributes to the overall body of knowledge in a particular field.

Advantages of Research

Research has several advantages, including:

• Generates new knowledge: Research is conducted to generate new knowledge and understanding of a particular topic or phenomenon, which can be used to inform policy, practice, and decision-making.
• Provides evidence-based solutions : Research provides evidence-based solutions to problems and issues, which can be used to develop effective interventions and strategies.
• Improves quality : Research can improve the quality of products, services, and programs by identifying areas for improvement and developing solutions to address them.
• Enhances credibility : Research enhances the credibility of an organization or individual by providing evidence to support claims and assertions.
• Enables innovation: Research can lead to innovation by identifying new ideas, approaches, and technologies.
• Informs decision-making : Research provides information that can inform decision-making, helping individuals and organizations make more informed and effective choices.
• Facilitates progress: Research can facilitate progress by identifying challenges and opportunities and developing solutions to address them.
• Enhances understanding: Research can enhance understanding of complex issues and phenomena, helping individuals and organizations navigate challenges and opportunities more effectively.
• Promotes accountability : Research promotes accountability by providing a basis for evaluating the effectiveness of policies, programs, and interventions.
• Fosters collaboration: Research can foster collaboration by bringing together individuals and organizations with diverse perspectives and expertise to address complex issues and problems.

Limitations of Research

Some Limitations of Research are as follows:

• Cost : Research can be expensive, particularly when large-scale studies are required. This can limit the number of studies that can be conducted and the amount of data that can be collected.
• Time : Research can be time-consuming, particularly when longitudinal studies are required. This can limit the speed at which research findings can be generated and disseminated.
• Sample size: The size of the sample used in research can limit the generalizability of the findings to larger populations.
• Bias : Research can be affected by bias, both in the design and implementation of the study, as well as in the analysis and interpretation of the data.
• Ethics : Research can present ethical challenges, particularly when human or animal subjects are involved. This can limit the types of research that can be conducted and the methods that can be used.
• Data quality: The quality of the data collected in research can be affected by a range of factors, including the reliability and validity of the measures used, as well as the accuracy of the data entry and analysis.
• Subjectivity : Research can be subjective, particularly when qualitative methods are used. This can limit the objectivity and reliability of the findings.
• Accessibility : Research findings may not be accessible to all stakeholders, particularly those who are not part of the academic or research community.
• Interpretation : Research findings can be open to interpretation, particularly when the data is complex or contradictory. This can limit the ability of researchers to draw firm conclusions.
• Unforeseen events : Unexpected events, such as changes in the environment or the emergence of new technologies, can limit the relevance and applicability of research findings.

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Definition of research

 (Entry 1 of 2)

Definition of research  (Entry 2 of 2)

transitive verb

intransitive verb

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

Examples of research in a Sentence

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

Word History

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

1577, in the meaning defined at sense 3

1588, in the meaning defined at transitive sense 1

Phrases Containing research

• marketing research
• market research
• operations research
• oppo research

research and development

• research park
• translational research

Dictionary Entries Near research

Cite this entry.

“Research.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/research. Accessed 28 Aug. 2024.

Kids Definition

Kids definition of research.

Kids Definition of research  (Entry 2 of 2)

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What is Research?

Research is a process of systematic inquiry that entails collection of data; documentation of critical information; and analysis and interpretation of that data/information, in accordance with suitable methodologies set by specific professional fields and academic disciplines.

Research is conducted to...

• Evaluate the validity of a hypothesis or an interpretive framework.
• To assemble a body of substantive knowledge and findings for sharing them in appropriate manners.
• To help generate questions for further inquiries.

If you would like further examples of specific ways different schools at Hampshire think about research, see: School Definitions of Research » What is "research" that needs to be reviewed and approved by the Institutional Review Board at Hampshire before proceeding?  Research should be reviewed by the IRB only when human subjects are involved, and the term research should be considered under a more narrow definition. Specifically, when the researcher is conducting research as outlined above AND has direct interaction with participants or data linked to personal identifiers , it should always fall under the purview of the IRB. Even if you have not directly collected the data yourself, as the researcher, your research may fall under the purview of the IRB. In reviewing such research, the IRB is concerned with the methodology of data collection in the "field" (e.g. collection, experimentation, interview, participant observation, etc.) and the use of the data.  The broader validity of the hypotheses or research questions, and the quality of inferences that may result (unless, of course, the research methodologies severely compromise the data collection and data usage directly), is not something they will be evaluating.

What if I am using information that is already available?

If you are doing research that is limited to secondary analysis of data, records, or specimens that are either publicly available, de-identified, or otherwise impossible to be linked to personal identities, you may still need IRB approval to do your project. Sometimes a data use agreement between the researcher and the data custodian may still be required to verify that the researcher will not have access to identifying codes.  This "de-linking" of data from personal identifiers  allows the IRB to make this determination. Regardless, you should submit an IRB proposal so the IRB can determine whether your project needs IRB review, and if so, the type of review required. For specifics of what research should be reviewed by the IRB and the category of review required, see the flow chart and examples provided .

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Work Motivation: The Roles of Individual Needs and Social Conditions

Thuy thi diem vo.

1 Department of Business Administration, National Taiwan University of Science and Technology, No. 43, Section 4, Keelung Road, Da’an District, Taipei City 106335, Taiwan; wt.ude.tsutn.liam@31880701d (T.T.D.V.); wt.ude.tsutn.liam@nehcwc (C.-W.C.)

Kristine Velasquez Tuliao

2 Graduate Institute of Human Resource Management, National Central University, No. 300, Zhongda Road, Zhongli District, Taoyuan City 320317, Taiwan

Chung-Wen Chen

Associated data.

The data that support this study are publicly available.

Work motivation plays a vital role in the development of organizations, as it increases employee productivity and effectiveness. To expand insights into individuals’ work motivation, the authors investigated the influence of individuals’ competence, autonomy, and social relatedness on their work motivation. Additionally, the country-level moderating factors of those individual-level associations were examined. Hierarchical linear modeling (HLM) was used to analyze data from 32,614 individuals from 25 countries, obtained from the World Values Survey (WVS). Findings showed that autonomy and social relatedness positively impacted work motivation, while competence negatively influenced work motivation. Moreover, the individual-level associations were moderated by the country-level religious affiliation, political participation, humane orientation, and in-group collectivism. Contributions, practical implications, and directions for further research were then discussed.

1. Introduction

Work motivation is considered an essential catalyst for the success of organizations, as it promotes employees’ effective performance. To achieve an organization’s objectives, the employer depends on the performance of their employees [ 1 ]. However, insufficiently motivated employees perform poorly despite being skillful [ 1 , 2 ]. Employers, therefore, need their employees to work with complete motivation rather than just showing up at their workplaces [ 3 ]. Work motivation remains a vital factor in organizational psychology, as it helps explain the causes of individual conduct in organizations [ 4 ]. Consequently, studies on the factors that encourage work motivation can contribute to the theoretical underpinnings on the roots of individual and practical social conditions that optimize individuals’ performance and wellness [ 5 ].

Several decades of research have endeavored to explain the dynamics that initiate work-related behavior. The primary factor examining this aspect is motivation, as it explains why individuals do what they do [ 6 ]. The basic psychological needs have represented a vital rationalization of individual differences in work motivation. Psychological needs are considered natural psychological nutrients and humans’ inner resources. They have a close relationship with individual conduct and have a strong explicit meaning for work performance [ 7 , 8 ]. Different needs are essential drivers of individual functioning due to the satisfaction derived from dealing with them [ 9 ]. In addition to individual-level antecedents, the social context has also been regarded to have implications for work motivation. Social exchange and interaction among individuals accentuate the importance of work motivation as something to be studied with consideration of contextual factors [ 10 ].

Significant contributions have been made to the socio-psychological perspective of work motivation ( Table 1 ). However, current literature shows three deficiencies. First, over 150 papers utilize the key approaches of psychological needs to justify motivational processes in the workplace [ 11 ], which justifies the vital role of psychological needs in interpreting individual work motivation. The association between psychological needs and work motivation has often been implicitly assumed; however, the influence of psychological needs on work motivation has been inadequately tested [ 8 ]. The verification of the extent and the direction of influence will provide a better understanding of, and offer distinct implications for, the facilitation of work motivation. In examining the influence of psychological needs on work motivation, this paper mainly focuses on the intrinsic aspect of motivation. The study of Alzahrani et al. (2018) [ 12 ] argued that although intrinsic motivation is more efficient than extrinsic motivation, researchers have mostly neglected it.

Several investigated predictors of work motivation in general and intrinsic motivation in particular.

Second, there is no study examining the country-level moderating effects of social conditions and national cultures on individual relationships between psychological needs and work motivation. Pinder (2014) [ 20 ] argued that contextual practices could influence variables at the individual level. Culture is a crucial factor influencing motivation [ 15 , 16 , 17 , 18 ]. Researchers (e.g., [ 19 ]) have further suggested that both the proximal social situations (e.g., workgroup) and the distal social situations (e.g., cultural values) in which humans operate influence their need for satisfaction and their motivation type. Intrinsic motivation interacts with prosocial motivation in judging work performance [ 21 ]. By including the social conditions in the framework, prosocial motivation is considered. Prosocial motivation refers to the desire to help and promote the welfare of others [ 22 , 23 ]. The study of Shao et al. (2019) [ 24 ] proposed that prosocial motivation promotes employee engagement in particular organizational tasks. Researchers often consider prosocial motivation as a pattern of intrinsic motivation [ 23 ]. This implies that when intrinsic motivation is investigated, prosocial motivation should be examined together to obtain a comprehensive understanding.

Third, there are few studies using a considerable number of cross-national samples to investigate factors influencing work motivation. A cross-cultural analysis makes the findings more objective by minimizing individual bias towards any particular culture. Therefore, the examination of the study is crucial to expanding insights on the influence of social situations on the individual associations between psychological needs and work motivation.

2. Literature Review and Hypothesis Development

2.1. work motivation: a conceptual background.

Work motivation is considered “a set of energetic forces that originate both within as well as beyond an individual’s being, to initiate work-related behavior, and to determine its form direction intensity and duration” [ 20 ]. Nicolescu and Verboncu (2008) [ 25 ] argued that work motivation contributes directly and indirectly to employees’ performance. Additionally, research (e.g., [ 26 ]) has postulated that work motivation could be seen as a source of positive energy that leads to employees’ self-recognition and self-fulfillment. Therefore, work motivation is an antecedent of the self-actualization of individuals and the achievement of organizations.

Literature has identified several models of work motivation. One of the primary models is Maslow’s (1954) [ 27 ] need hierarchy theory, which proposes that humans fulfill a set of needs, including physiological, safety and security, belongingness, esteem, and self-actualization. Additionally, Herzberg’s (1966) [ 28 ] motivation-hygiene theory proposed that work motivation is mainly influenced by the job’s intrinsic challenge and provision of opportunities for recognition and reinforcement. More contemporary models also emerged. For instance, the study of Nicolescu and Verboncu (2008) [ 25 ] has categorized the types of motivation into four pairs, including positive-negative, intrinsic-extrinsic, cognitive-affective, and economic-moral spiritual. Additionally, Ryan and Deci [ 29 ] focused on intrinsic motivation and extrinsic motivation.

With the existence of numerous factors that relate to work motivation, this paper mainly focuses on intrinsic motivation. Previous research found that emotional intelligence and interpersonal relationship quality predict individuals’ intrinsic motivation [ 14 ]. Additionally, the study of Lin (2020) [ 13 ] argued that personal factors, including age, gender, educational level, living setting, health status, and family support, impact people’s intrinsic motivation. To understand more about intrinsic motivation, the authors examined individuals’ psychological needs. Fulfillment of the basic needs is related to wellness and effective performance [ 7 ]. Since intrinsic motivation results in high-quality creativity, recognizing the factors influencing intrinsic motivation is important [ 5 ].

Although a significant number of important contributions have been made regarding intrinsic motivation, self-determination theory is of particular significance for this study. Self-determination theory (SDT) postulates that all humans possess a variety of basic psychological needs. One of the primary crucial needs is the need for competence [ 30 , 31 ], which makes individuals feel confident and effective in their actions. Additionally, the need for autonomy [ 32 ] is one of the important psychological needs, which makes people satisfied with optimal wellness and good performance obtained as a result of their own decisions. Moreover, SDT proposed the crucial importance of interpersonal relationships and how social forces can influence thoughts, emotions, and behaviors [ 33 ]. This means that the psychological need for social relatedness [ 34 ] also plays a significant role in human’s psychological traits. Individuals need to be cared for by others and care for others to perceive belongingness. The need for relatedness can motivate people to behave more socially [ 35 ].

Prior research (e.g., [ 36 ]) has explored self-determination theory and related theories as approaches to work motivation and organizational behavior. The study of Van den Broeck et al. (2010) [ 37 ] emphasized grasping autonomy, competence, and relatedness at workplaces. This paper contributes to the exhaustive understanding of intrinsic work motivation influenced by further examining the impact of these three factors on work motivation as well as the moderating effects of social contexts.

2.2. Main Effect

2.2.1. individuals’ competence and work motivation.

Competence is “the collective learning in the organization, especially how to coordinate diverse production skills and integrate multiple streams of technologies” [ 38 ]. The study of Hernández-March et al. (2009) [ 39 ] argued that a stronger competence was commonly found in university graduates rather than those without higher education. Competence has been considered a significant factor of work motivation that enhances productivity and profits. Harter’s (1983) [ 40 ] model of motivation proposed that competence enhances motivation because competence promotes flexibility for individuals [ 41 ]. Likewise, Patall et al. (2014) [ 42 ] indirectly argued that competence positively affects work motivation. Individuals become more engaged in activities that demonstrate their competence [ 6 ]. When people perceive that they are competent enough to attain goals, they generally feel confident and concentrate their efforts on achieving their objectives as soon as possible for their self-fulfillment.

Individuals’ competence positively relates to their work motivation.

2.2.2. Individuals’ Autonomy and Work Motivation

Autonomy is viewed as “self-determination, self-rule, liberty of rights, freedom of will and being one’s own person” [ 43 ]. Reeve (2006) [ 44 ] argued that autonomy is a primary theoretical approach in the study of human motivation and emotion. Autonomy denotes that certain conduct is performed with a sense of willingness [ 30 ]. Several researchers (e.g., [ 45 ]) investigated the positive relationship between individuals’ autonomy and work motivation. When humans are involved in actions because of their interest, they fully perform those activities volitionally [ 36 ]. Dickinson (1995) [ 46 ] also proposed that autonomous individuals are more highly motivated, and autonomy breeds more effective outcomes. Moreover, when individuals have a right to make their own decisions, they tend to be more considerate and responsible for those decisions, as they need to take accountability for their actions. Bandura (1991) [ 47 ] has argued that humans’ ability to reflect, react, and direct their actions motivates them for future purposes. Therefore, autonomy motivates individuals to work harder and overcome difficulties to achieve their objectives.

Individuals’ autonomy positively relates to their work motivation.

2.2.3. Individuals’ Social Relatedness and Work Motivation

The psychological need for social relatedness occurs when an individual has a sense of being secure, related to, or understood by others in the social environment [ 48 ]. The relatedness need is fulfilled when humans experience the feeling of close relationships with others [ 49 ]. Researchers (e.g., [ 34 ]) have postulated that the need for relatedness reflects humans’ natural tendency to feel associated with others, such as being a member of any social groups, or to love and care as well as be loved and cared for. Prior studies have shown that social relatedness strongly impacts motivation [ 50 , 51 , 52 ]. Social relatedness offers people many opportunities to communicate with others, making them more motivated at the workplace, aligning them with the group’s shared objectives. Marks (1974) [ 53 ] suggested that social relatedness encourages individuals to focus on community welfare as a reference for their behavior, resulting in enhanced work motivation. Moreover, when individuals feel that they relate to and are cared for by others, their motivation can be maximized since their relatedness need is fulfilled [ 54 ]. Therefore, establishing close relationships with others plays a vital role in promoting human motivation [ 55 ]. When people perceive that they are cared for and loved by others, they tend to create positive outcomes for common benefits to deserve the kindness received, thereby motivating them to work harder.

Individuals’ social relatedness positively relates to their work motivation.

Aside from exploring the influence of psychological needs on work motivation, this paper also considers country-level factors. Previous research (e.g., [ 56 ]) has examined the influence of social institutions and national cultures on work motivation. However, the moderating effects of country-level factors have to be investigated, given the contextual impacts on individual needs, attitudes, and behavior. Although social conditions provide the most common interpretation for nation-level variance in individual work behaviors [ 57 ], few cross-national studies examine social conditions and individual work behaviors [ 56 ]. Hence, this paper investigates the moderating effects, including religious affiliation, political participation, humane orientation, and in-group collectivism, on the psychological needs-work motivation association.

A notable theory to explain the importance of contextual factors in work motivation that is customarily linked with SDT is the concept of prosocial motivation. Prosocial motivation suggests that individuals have the desire to expend efforts in safeguarding and promoting others’ well-being [ 58 , 59 ]. It is proposed that prosocial motivation strengthens endurance, performance, and productivity, as well as generates creativity that encourages individuals to develop valuable and novel ideas [ 21 , 60 ]. Prosocial motivation is found to interact with intrinsic motivation in influencing positive work outcomes [ 21 , 61 ]. However, there are few studies examining the effects of prosocial motivation on work motivation [ 62 ].

Utilizing the concept of prosocial motivation and examining it on a country-level, this paper suggests that prosocial factors promote basic psychological needs satisfaction that reinforces motivational processes at work. Therefore, prosocial behaviors and values may enhance the positive impact of individuals’ basic psychological needs, including competence, autonomy, and social relatedness, on work motivation.

2.3. Moderating Effects

2.3.1. religious affiliation.

Religions manifest values that are usually employed as grounds to investigate what is right and wrong [ 63 ]. Religious affiliation is considered prosocial because it satisfies the need for belongingness and upholds collective well-being through gatherings to worship, seek assistance, and offer comfort within religious communities. Hence, religious affiliation promotes the satisfaction of individuals’ psychological needs, which directs motivation at work and life in general. Research (e.g., [ 64 ]) has argued that religious affiliation is an essential motivational component given its impact on psychological processes. The study of Simon and Primavera (1972) [ 65 ] investigated the relationship between religious affiliation and work motivation. To humans characterized by competence, autonomy, and social relatedness, attachment to religious principles increases their motivation to accomplish organizational goals. Religious membership will increase the influence of psychological needs on work motivation. The tendency of individuals affiliated with any religion to be demotivated is lower compared to those who are not. Individuals with religious affiliations also tend to work harder as the virtue of hard work is aligned with religious principles. Accordingly, religious affiliation may enhance the positive association between individuals’ psychological needs and work motivation.

2.3.2. Political Participation

Political participation, indicated by people’s voting habits, plays a crucial role in ensuring citizens’ well-being and security [ 66 ]. Political participation encourages shared beliefs and collective goals among individuals [ 67 ]. The communication and interaction among people help them grasp the government’s developmental strategies, motivating them to work harder. Political participation is a collective pursuit that makes societal members feel more confident, socially related, and motivated at work to achieve communal targets. Increased political participation reinforces effective public policy to enhance its members’ welfare, congruent with the perspectives of prosocial motivation. The prosocial values and behaviors derived from political participation satisfy human needs and interact positively with intrinsic motivation. Therefore, political participation may strengthen the positive influence of individuals’ competence, autonomy, and social relatedness on work motivation. Conversely, poor political participation is perceived as a separation from the society that may lead to demotivation. In a society with poor political participation, an individualistic mentality is encouraged, thereby decreasing the desire to pursue cooperative endeavors.

2.3.3. Humane Orientation

GLOBE characterizes humane orientation as “the degree to which an organization or society encourages and rewards individuals for being fair, altruistic, generous, caring, and kind to others” [ 68 ]. Research (e.g., [ 69 , 70 ]) has argued that a high humane orientation encourages members to develop a strong sense of belonging, commit to fair treatment, and manifest benevolence. The desire to help others or enhance others’ well-being indicates prosocial values and behaviors [ 71 , 72 ]. Since humane orientation is correlated with philanthropy and promotes good relations, this cultural value may enhance work motivation. Fairness, which is derived from a humane-oriented society, is one of the most vital influences on work motivation [ 1 ]. Moreover, altruism, promoted by humane-oriented societies, encourages individuals to sacrifice individual interests for shared benefits. Altruism then encourages attachment to others’ welfare and increases resources needed for prosocial behaviors such as work [ 73 , 74 ]. Members of humane-oriented countries view work in a positive light—it is an opportunity for them to perform altruistic behaviors and engage in collective actions. Therefore, people are more likely to work harder for common interests in humane-oriented societies. In such conditions, individuals with competence, autonomy, and social relatedness will be more motivated to work. By contrast, a less humane-oriented society gives prominence to material wealth and personal enjoyment [ 75 ]. Although this may be perceived as a positive influence on the association between psychological needs and work motivation, such an individualistic mindset works against the prosocial factors that further motivate individuals.

2.3.4. In-Group Collectivism

House et al. (2004) [ 68 ] defined in-group collectivism as “the degree to which individuals express pride, loyalty, and cohesiveness in their organizations or families”. Collectivistic cultures indicate the need for individuals to rely on group membership for identification [ 76 ]. High collectivism enhances equity, solidarity, loyalty, and encouragement [ 77 , 78 ]. Humans living in a collectivist culture are interdependent and recognize their responsibilities towards each other [ 79 ]. In-group collectivism transfers the concepts of social engagement, interdependence with others, and care for the group over the self (e.g., [ 79 , 80 , 81 ], thereby motivating individuals to work harder for the common interests. Oyserman et al. (2002) [ 82 ] have further argued that individualistic values encourage an independent personality, whereas collectivistic values form an interdependent one. Therefore, in-group collectivism is a prosocial value that emphasizes the importance of reciprocal relationships and encourages people to work harder to benefit the group. By contrast, low collectivism promotes individual interests and personal well-being while neglecting the value of having strong relations with others [ 70 ]. Considering that in-group collectivism promotes individuals’ prosocial behaviors of individuals, people who are competent, autonomous, and socially related to collective societies are less likely to be demotivated at the workplace. Consequently, in-group collectivism may intensify the positive influence of individuals’ competence, autonomy, and social relatedness on their work motivation.

(a–d): The positive relationship between individuals’ competence and their work motivation is enhanced as religious affiliation (a), political participation (b), humane orientation (c), and in-group collectivism (d) increase.

(a–d): The positive relationship between individuals’ autonomy and their work motivation is enhanced as religious affiliation (a), political participation (b), humane orientation (c), and in-group collectivism (d) increase.

(a–d): The positive relationship between individuals’ social relatedness and their work motivation is enhanced as religious affiliation (a), political participation (b), humane orientation (c), and in-group collectivism (d) increase.

3.1. Sample

The data came from the seventh wave (2017–2021) of the World Values Survey (WVS) [ 83 ], which examines humans’ beliefs and values. This survey is performed every five years to explore changes in people’s values and perceptions. Face-to-face interviews, or phone interviews for remote areas, were conducted by local organizations. Almost 90 percent of the world’s population is represented in the WVS. At least 1000 individuals were selected as respondents to exhibit each nation’s population. Further information regarding the WVS can be reached at the WVS website ( http://www.worldvaluessurvey.org , accessed on 14 October 2021).

The samples of this study were based on the availability of national-level data for the moderators and individual-level data for the measures of independent and dependent variables. Respondents without answers on the individual measures and corresponding country-level data were excluded from the analysis. The final data included 32,614 respondents in 25 countries aged 18 and above. The 25 countries included Argentina, Australia, Brazil, China, Colombia, Ecuador, Egypt, Germany, Greece, Guatemala, Hong Kong, Indonesia, Iran, Japan, Kazakhstan, Malaysia, Mexico, New Zealand, Philippines, Russia, South Korea, Taiwan, Thailand, Turkey, and the USA.

3.2. Dependent Variable

Consistent with previous researchers (e.g., [ 84 ]), the authors used four items to gauge individual work motivation, namely “Indicate how important work is in your life”, “People who do not work turn lazy”, “Work is a duty towards society”, and “Work should always come first, even if it means less spare”. The first item was measured on a scale from 1 to 4, in which lower scores indicate a higher level of work importance. The other three items were gauged on a scale from 1 to 5 (1 indicating strongly agree and 5 indicating strongly disagree). The scores for each item were reverse coded, and the mean scores were computed so that higher scores indicate greater work motivation.

3.3. Independent Variables

The independent variables of this study include individuals’ competence, autonomy, and social relatedness. First, people’s competence was measured by the item “What is the highest educational level that you attained” on a scale from 0 to 8, in which higher scores indicate a higher level of educational attainment. The authors used the item to gauge individual competence, as a capacity for learning is highlighted in the examination of competence [ 39 ]. Second, a scale from 1 to 10 was utilized to measure the item “How much freedom of choice and control”, which represented individual autonomy (1 indicating no choice at all and 10 indicating a great deal of choice). The authors used the item to gauge people’s autonomy as this item indicates the degree to which individual can make their own decisions. Finally, the individual’s social relatedness was gauged by twelve items, representing twelve types of organizations where individuals are active/inactive members or do not belong. The twelve items were measured on a scale from 0 to 2 (0 indicating do not belong, 1 indicating inactive member, and 2 indicating active member). The mean score of the twelve items represents the individual’s social relatedness. The membership in organizations represents social relatedness, as this indicates the reciprocal relationship between the individual and the organization through their mutual rights, responsibilities, and obligations towards each other [ 85 ].

3.4. Moderators

The four country-level moderators in this study were religious affiliation, political participation, humane orientation, and in-group collectivism. Similar to prior research (e.g., [ 86 ]), the authors used the percentage of the country’s population with religious affiliation obtained from Pew Research Center 2015 [ 87 ]. Secondly, the index of voter turnout collected from the International Institute for Democracy and Electoral Assistance [ 88 ] was utilized to gauge political participation. Voting habits are an indicator of an individual’s presence in their country’s life, and a nation with a high index of voter turnout illustrates its substantial degree of political participation [ 89 ]. Finally, two cultural values, including humane orientation and in-group collectivism, were obtained from the GLOBE study [ 68 ]. The authors used scores on cultural practices as the moderators for this study because they indicate the actual behaviors as “the way things are done in this culture” [ 68 ].

3.5. Control Variables

Several individual-level and country-level elements related to the dependent variable were considered control variables. The effects of gender, marital status, age, and income level were accounted for, as these four variables are basic personal factors that may impact individual’s motivation [ 90 ]. Gender (1 indicating male and 0 indicating female) and marital status (1 indicating married and 0 indicating other status) were dummy coded. Moreover, age was measured in years, while income level was gauged using a scale from 1 representing the lowest group to 10 representing the highest group. Along with the above individual-level controls, education and family strength were treated as country-level control variables. Education and family are primary institutions that shape individuals’ motivation [ 91 , 92 ]. Similar to prior researchers (e.g., [ 93 ]), education was computed as two-thirds of the adult literacy rate attained from the UNESCO Institute for Statistics 2020 [ 94 ] and one-third of the mean years of schooling obtained from the Human Development Report 2020 [ 95 ]. This score is commonly approved as representing access to education in a country [ 42 ]. Regarding family strength, the score was quantified by the ratio of divorces to marriages per 1000 members of the population consistent with previous researchers (e.g., [ 93 ]). The data was obtained from the United Nations Demographic Yearbook [ 96 ].

3.6. Measurement and Analysis

To perform the descriptive statistics, cross-level correlations, scale reliability, confirmatory factor analysis, convergent validity, and discriminant validity, the authors utilized SPSS software.

The framework of this study considers independent variables, dependent variables, and moderators at different levels. Thus, the authors used a hierarchical linear model (HLM) [ 97 ] to test the hypotheses. HLM was defined as a “complex form of ordinary least squares (OLS) regression that is used to analyze variance in the outcome variables when the predictor variables are at varying hierarchical levels” [ 98 ]. This technique evaluates the impacts of higher-level outcomes on lower-level ones while preserving an appropriate degree of analysis [ 99 ]. HLM has been employed in several cross-level studies (e.g., [ 100 , 101 ]).

Table 2 presents a matrix of correlations and sample statistics from the individual-level to country-level variables. Table 3 and Table 4 report convergent and discriminant validity test results, respectively. Finally, Table 5 illustrates results for hypotheses testing using HLM. Three models are presented in the table: those of individual-level main effects and control variables (Model 1), those of country-level main effects (Model 2), and country-level moderating effects (Model 3).

Descriptive statistics, cross-level correlations and scale reliability a,b,c .

a   n = 32,614 level 1; n = 25, level 2. b * p < 0.05, ** p < 0.01. c The reliability found in the parentheses is expressed as Cronbach’s alpha for scales with ≥four items.

Convergent validity.

Discriminant validity—Fornell and Larcker’s criterion.

* p < 0.05.

HLM results: (The DV is work motivation) a,b .

a , n = 32,614 level 1; n = 25, level 2. b , †, p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.

For the confirmatory factor analysis, previous research (e.g., [ 102 , 103 , 104 ]) suggested that analysis of each variable requires at least three items. Factor analysis using statistical software will provide imprecise results if there are fewer than three items per variable [ 105 ]. Therefore, the authors only performed Confirmatory Factor Analysis (CFA) for social relatedness and work motivation.

To assess the measurement, convergent and discriminant validity were tested. Composite Reliability (CR) and Average Variance Extracted (AVE) were performed to illustrate convergent validity. The study of Hair et al. (2019) [ 106 ] suggested that CR is required to be above a threshold of 0.7. On the other hand, the AVE value should be higher than a threshold of 0.5 [ 107 ]. As shown in Table 3 , CR is acceptable while AVE is slightly lower than a threshold of 0.5. Despite the limitation of AVE, the acceptable result of the discriminant validity is achieved. The discriminant validity was tested using Fornell and Larcker (1981)’s criterion [ 107 ]. This proposes that the square root of the AVE of any latent variable should be higher than its correlation with any other construct. The result of the discriminant validity test indicates that all the two latent constructs have a square root of AVE higher than its correlation with the other construct, as presented in Table 4 .

The authors argued that individuals’ competence (H1), autonomy (H2), and social relatedness (H3) positively relate to their work motivation. However, the findings only supported H2 (β2 = 0.036, p < 0.001) and H3 (β3 = 0.042, p < 0.001). In contrast, the findings presented that H1 was also significant, but in the opposite direction compared with our original prediction. The result suggests that individuals’ competence negatively relates to their work motivation.

In Hypotheses 4a–d, we proposed that higher levels of religious affiliation (4a), political participation (4b), humane orientation (4c), and in-group collectivism (4d) strengthen the relationship described in H1. However, the results only demonstrated support for the two hypotheses, H4c (γ13 = 0.032, p < 0.001) and H4d (γ14 = 0.042, p < 0.001). In contrast, the findings presented that H4a was also significant, but opposite our initial prediction. This different result proposes that a higher level of religious affiliation weakens the association between individuals’ competence and work motivation.

In Hypotheses 5a–d, the authors argued that the higher levels of religious affiliation (5a), political participation (5b), humane orientation (5c), and in-group collectivism (5d) enhance the positive relationship between individuals’ autonomy and their work motivation. However, the results only supported the two hypotheses H5b (γ22 = 0.012, p < 0.05) and H5c (γ23 = 0.012, p < 0.1), while H5a and H5d were not significant.

In Hypotheses 6a–d, the authors argued that the higher levels of religious affiliation (6a), political participation (6b), humane orientation (6c), and in-group collectivism (6d) enhance the positive relationship between individuals’ social relatedness and their work motivation. However, the results only supported H6c (γ33 = 0.019, p < 0.01). In contrast, the findings indicated that H6d was also significant, but in the opposite direction compared to our initial hypothesis. The different result suggests that higher in-group collectivism weakens the positive association between individuals’ social relatedness and work motivation. Figure 1 , Figure 2 , Figure 3 , Figure 4 and Figure 5 represent the significant moderators of the associations examined.

The association between competence and work motivation at different levels of humane orientation.

The association between competence and work motivation at different levels of in-group collectivism.

The association between autonomy and work motivation at different levels of political participation.

The association between autonomy and work motivation at different levels of humane orientation.

The association between social relatedness and work motivation at different levels of humane orientation.

Regarding the statistical results of the control variables, gender, marital status, and age consistently indicated significant positive relationships with work motivation across three models. On the other hand, family strength indicated a significant negative association to work motivation only in Model 1.

5. Discussion

The study’s objective was to examine the influence of individuals’ competence, autonomy, and social relatedness on their work motivation, as well as the impact of country-level moderators, including religious affiliation, political participation, humane orientation, and in-group collectivism on their relationships. Seven primary findings are crucial in this research. First, people’s autonomy and social relatedness positively relate to their work motivation. This result is in line with the findings of prior researchers (e.g., [ 45 , 52 ]), postulating that humans’ autonomy and social relatedness breeds work motivation. The study of Theurer et al. (2018) [ 108 ] argued that, among motivational elements, autonomy had been found to greatly predict positive work motivation. When people feel they have enough control over their activities, they are more confident and motivated to work. Along with autonomy, humans’ social relatedness promotes communal benefits, thereby motivating people to work harder for their organization. Second, the association between individual competence and work motivation is moderated by cultural values, including humane orientation and in-group collectivism. The findings are consistent with the viewpoints of prior researchers (e.g., [ 69 , 70 , 77 , 78 ]), namely that a society with higher levels of humane orientation and in-group collectivism strengthens altruism, solidarity, loyalty, and the encouragement of individuals, which results in work motivation. Consequently, there will be an increase in the differences in individuals’ competence and work motivation if they live in a society with greater humane orientation and in-group collectivism. Third, political participation and humane orientation moderate the relationship between individual autonomy and work motivation. These results are in line with the investigations of prior researchers (e.g., [18,45), which found that social circumstances and cultural practices promote people’s motivation. Accordingly, the differences in individuals’ autonomy based on their work motivation will be enhanced if they belong to nations with higher political participation and humane orientation. Fourth, the association between social relatedness and work motivation is moderated by humane orientation. Accordingly, in a humane-oriented society, the differences in individuals’ social relatedness based on their work motivation will be strengthened.

The remaining findings were contrary to the original propositions. Pinder (2014) [ 20 ] argued that it is possible to find that contextual practices can influence variables at the individual level in the opposite prediction in motivation research. Fifth, individuals’ competence negatively influences their work motivation. This finding proposes that more competent individuals are less motivated at work. One possible interpretation of this opposite result is that, when the majority of the organization members recognize individuals’ competence, these individuals may perceive that it is not necessary to devote most of their time and energy to work anymore. These individuals may believe that no matter how unwillingly they perform, they are still competent enough because of their prior achievements. Additionally, competent individuals recognize that they have already sacrificed their enjoyment of life for their previous successes; therefore, they tend to offset this by investing their valuable time in other aspects. This is consistent with other researchers’ investigations (e.g., [ 109 ]), which found that low-skilled individuals are more often compelled to engage in regular work activities and are more easily motivated than others. By contrast, highly competent individuals tend to be motivated by challenging tasks and improving themselves through further education. Sixth, the relationship between competence and work motivation is negatively moderated by religious affiliation. This finding suggests that religious affiliation weakens the association between individuals’ competence and work motivation. One possible explanation for this finding is that strong religious beliefs are the foundation for virtuous living [ 110 ]. Individuals with religious affiliation usually employ religious principles to guide their behavior, regardless of their competence. In other words, both competent and incompetent individuals tend to be more motivated at the workplace if they are affiliated with any religion, thereby diminishing the influence of competence in work motivation. Seventh, the relationship between social relatedness and work motivation is negatively moderated by in-group collectivism. This result proposes that a higher degree of in-group collectivism weakens the association between individuals’ social relatedness and work motivation. One possible explanation for this is that, under an in-group collective society, people put more weight on mutual relationships and encourage acts that may build up the solidarity of groups. Since in-group collectivism is viewed as a social attachment in which people emphasize the group over the self (e.g., [ 79 , 80 , 81 ]), individuals are fairly conscious of their responsibility to the group regardless of their social relatedness. Both socially related and unrelated individuals belonging to in-group collective cultures tend to work harder for common goals. Accordingly, the influence of individuals’ social relatedness on their work motivation is reduced.

6. Limitations and Future Research

Despite its significant contributions, this study has its limitations. The use of secondary data represents the fact that the data collection process was beyond the authors’ control. However, the collection of cross-national data is time-consuming and costly. The authors used the available data but strove for the efficient use of multilevel data. The secondary data also limited the measurement of individual-level factors based on the available data. Moreover, it is quite complex to gauge an individual’s work motivation appropriately, since personal work motivation may not be one-dimensional. Nevertheless, the authors made efforts to employ the measurements utilized by prior research. Moreover, it is complicated to measure social factors such as political participation. There are challenges in investigating social contexts due to the absence of direct measurements [ 111 ]. This compels the authors to identify substitute measurements for this study. Finally, this study covered 25 samples from 25 countries with different characteristics. Despite the attempt of this study to include the most relevant social conditions in the framework, the influence of other national differences and cultural sensitivities were not considered.

This paper directs further research considering that several frameworks and approaches should be employed to better examine motivation [ 112 ]. First, as some of the results were opposite to the original propositions based on the theoretical foundations employed, combining different concepts and approaches is necessary to enhance perspectives of psychological needs and social issues. For instance, the relationship between competence and work motivation can be further investigated by employing other theories to understand their association better. Similarly, the moderating effects of social contexts such as religious affiliation and in-group collectivism should be further examined to obtain a more in-depth comprehension of the roles of contextual circumstances and cultural values in individual-level relationships. Additionally, self-determination theory and the concept of prosocial motivation may be used to explore motivation towards specific behavior in organizations, such as organizational citizenship and proactive behaviors. Organizational context, such as rewards, training, and culture, can be considered as part of the framework to enhance the conception of work motivation.

7. Conclusions

This study has utilized a multilevel framework to examine the influence of psychological needs and social context on work motivation. Through this research, a deeper understanding of the roles of competence, autonomy, and social relatedness, as well as social situations and cultural values on work motivation, is achieved. The contrary findings call for integrating other concepts and approaches towards a more comprehensive knowledge of work motivation.

Along with the theoretical contribution, the study’s findings offer practical implications. The satisfaction of psychological needs promotes self-motivation, which creates positive outcomes. Hence, organizations can provide programs and activities to promote employees’ autonomy and social relatedness as this will enhance their work motivation. Employee empowerment can be advocated by encouraging them to make their own decisions at the workplace, providing constructive criticisms rather than instilling the fear of failure. Additionally, managers should encourage solidarity, support, and mutual care among employees. Putting more weight on employees’ fulfillment of needs will further increase employees’ motivation, thereby diminishing costs related to stress or turnover [ 50 ]. To establish a novel mechanism towards promoting work motivation in the entire nation, the government should pay attention to the political structure and conditions that encourage citizens’ participation. Additionally, a culture of humane orientation should be promoted in the workplace and society so that solidarity, kind assistance, and altruism among communities as well as among individuals can be strengthened. For instance, teamwork should be encouraged for employees to help each other overcome difficulties at the workplace or share responsibilities with their colleagues. This will motivate people to work harder for collective goals, contributing to the development of organizations.

Author Contributions

Conceptualization, T.T.D.V. and K.V.T.; data collection, T.T.D.V.; methodology, T.T.D.V. and K.V.T.; formal analysis, T.T.D.V. and K.V.T.; resources, K.V.T. and C.-W.C.; writing-original draft, T.T.D.V. and K.V.T.; writing-review, editing & proofreading, T.T.D.V., K.V.T. and C.-W.C.; visualization, K.V.T.; supervision, K.V.T. and C.-W.C.; project administration, K.V.T. All authors have read and agreed to the published version of the manuscript.

This paper does not receive funding from any individuals or organizations.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Psychology: Research and Review

• Open access
• Published: 04 January 2021

What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire

• Anne Pignault   ORCID: orcid.org/0000-0001-7946-3793 1 &
• Claude Houssemand 2  

Psicologia: Reflexão e Crítica volume  34 , Article number:  2 ( 2021 ) Cite this article

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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 1 ).

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 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.

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 3 presents the items, which were designed and administered to the participants in French.

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 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 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.

Table 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 5 shows the EFA results, which described a five-factor structure.

Nevertheless, the correlation matrix for the latent factors obtained by the EFA (see Table 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.

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 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.

Figure 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 8 highlights possible overlaps.

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 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.

Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author.

Abbreviations

Confirmatory factor analyses

Comparative Fit Index

Exploratory factor analyses

Luxembourg Agency for Research Integrity

• Meaning of work

Tucker Lewis Index of factoring reliability

Root mean square error of approximation

Standardized root mean square residual

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Pignault, A., Houssemand, C. What factors contribute to the meaning of work? A validation of Morin’s Meaning of Work Questionnaire. Psicol. Refl. Crít. 34 , 2 (2021). https://doi.org/10.1186/s41155-020-00167-4

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• Structure validation
• Psychometrics
• Organizational psychology

• Desk Research: Definition, Types, Application, Pros & Cons

If you are looking for a way to conduct a research study while optimizing your resources, desk research is a great option. Desk research uses existing data from various sources, such as books, articles, websites, and databases, to answer your research questions. 

Let’s explore desk research methods and tips to help you select the one for your research.

What Is Desk Research?

Desk research, also known as secondary research or documentary research, is a type of research that relies on data that has already been collected and published by others. Its data sources include public libraries, websites, reports, surveys, journals, newspapers, magazines, books, podcasts, videos, and other sources. 

When performing desk research, you are not gathering new information from primary sources such as interviews, observations, experiments, or surveys. The information gathered will then be used to make informed decisions.

The most common use cases for desk research are market research , consumer behavior , industry trends , and competitor analysis .

How Is Desk Research Used?

Here are the most common use cases for desk research:

• Exploring a new topic or problem
• Identifying existing knowledge gaps
• Reviewing the literature on a specific subject
• Finding relevant data and statistics
• Analyzing trends and patterns
• Evaluating competitors and market trends
• Supporting or challenging hypotheses
• Validating or complementing primary research

Types of Desk Research Methods

There are two main types of desk research methods: qualitative and quantitative. 

• Qualitative Desk Research 

Analyzing non-numerical data, such as texts, images, audio, or video. Here are some examples of qualitative desk research methods:

Content analysis – Examining the content and meaning of texts, such as articles, books, reports, or social media posts. It uses data to help you identify themes, patterns, opinions, attitudes, emotions, or biases.

Discourse analysis – Studying the use of language and communication in texts, such as speeches, interviews, conversations, or documents. It helps you understand how language shapes reality, influences behavior, constructs identities, creates power relations, and more.

Narrative analysis – Analyzing the stories and narratives that people tell in texts, such as biographies, autobiographies, memoirs, or testimonials. This allows you to explore how people make sense of their experiences, express their emotions, construct their identities, or cope with challenges.

• Quantitative Desk Research

Analyzing numerical data, such as statistics, graphs, charts, or tables. 

Here are common examples of quantitative desk research methods:

Statistical analysis : This method involves applying mathematical techniques and tools to numerical data, such as percentages ratios, averages, correlations, or regressions.

You can use statistical analysis to measure, describe, compare, or test relationships in the data.

Meta-analysis : Combining and synthesizing the results of multiple studies on a similar topic or question. Meta-analysis can help you increase the sample size, reduce the margin of error, or identify common findings or discrepancies in data.

Trend analysis : This method involves examining the changes and developments in numerical data over time, such as sales, profits, prices, or market share. It helps you identify patterns, cycles, fluctuations, or anomalies. 

Examples of Desk Research

Here are some real-life examples of desk research questions:

• What are the current trends and challenges in the fintech industry?
• How do Gen Z consumers perceive money and financial services?
• What are the best practices for conducting concept testing for a new fintech product?
• Documentary on World War II and its effect on Austria as a country

You can use the secondary data sources listed below to answer these questions:

Industry reports and publications

• Market research surveys and studies
• Academic journals and papers
• News articles and blogs
• Podcasts and videos
• Social media posts and reviews
• Government and non-government agencies

How to Choose the Best Type of Desk Research

The main factors for selecting a desk research method are:

• Research objective and question
• Budget and deadlines
• Data sources availability and accessibility.
• Quality and reliability of data sources
• Your data analysis skills

Let’s say your research question requires an in-depth analysis of a particular topic, a literature review may be the best method. But if the research question requires analysis of large data sets, you can use trend analysis.

Differences Between Primary Research and Desk Research

The main difference between primary research and desk research is the source of data. Primary research uses data that is collected directly from the respondents or participants of the study. Desk research uses data that is collected by someone else for a different purpose.

Another key difference is the cost and time involved. Primary research is usually more expensive, time-consuming, and resource-intensive than desk research. However, it can also provide you with more specific, accurate, and actionable data that is tailored to your research goal and question.

The best practice is to use desk-based research before primary research; it refines the scope of the work and helps you optimize resources.

Read Also – Primary vs Secondary Research Methods: 15 Key Differences

How to Conduct a Desk Research

Here are the four main steps to conduct desk research:

• Define Research Goal and Question

What do you want to achieve with your desk research? What problem do you want to solve or what opportunity do you want to explore? What specific question do you want to answer with your desk research?

• Identify and Evaluate Data Sources

Where can you find relevant data for your desk research? How relevant and current are the data sources for your research? How consistent and comparable are they with each other? 

You can evaluate your data sources based on factors such as- 

– Authority: Who is the author or publisher of the data source? What are their credentials and reputation? Are they experts or credible sources on the topic?

– Accuracy: How accurate and precise is the data source? Does it contain any errors or mistakes? Is it supported by evidence or references?

– Objectivity: How objective and unbiased is the data source? Does it present facts or opinions? Does it have any hidden agenda or motive?

– Coverage: How comprehensive and complete is the data source? Does it cover all aspects of your topic? Does it provide enough depth and detail?

– Currency: How current and up-to-date is the data source? When was it published or updated? Is it still relevant to your topic?

• Collect and Analyze Your Data

How can you collect your data efficiently and effectively? What tools or techniques can you use to organize and analyze your data? How can you interpret your data with your research goal and question?

• Present and Report Your Findings

How can you communicate your findings clearly and convincingly? What format or medium can you use to accurately record your findings?

You can use spreadsheets, presentation slides, charts, infographics, and more.

Advantages of Desk Research

• Cost Effective

It is cheaper and faster than primary research, you don’t have to collect new data or report them. You can simply analyze and leverage your findings to make deductions.

• Prevents Effort Duplication

Desk research provides you with a broad and thorough overview of the research topic and related issues. This helps to avoid duplication of efforts and resources by using existing data.

• Improves Data Validity

Using desk research, you can compare and contrast various perspectives and opinions on the same topic. This enhances the credibility and validity of your research by referencing authoritative sources.

• Identify Data Trends and Patterns

 It helps you to identify new trends and patterns in the data that may not be obvious from primary research. This can help you see knowledge and research gaps to offer more effective solutions.

Disadvantages of Desk Research

• Outdated Information

One of the main challenges of desk research is that the data may not be relevant, accurate, or up-to-date for the specific research question or purpose. Desk research relies on data that was collected for a different reason or context, which may not match the current needs or goals of the researcher.

• Limited Scope

Another limitation of desk research is that it may not provide enough depth or insight into qualitative aspects of the market, such as consumer behavior, preferences, motivations, or opinions. 

Data obtained from existing sources may be biased or incomplete due to the agenda or perspective of the source.

Read More – Research Bias: Definition, Types + Examples

• Data Inconsistencies

It may also be inconsistent or incompatible with other data sources due to different definitions or methodologies.

• Legal and Technical Issues

Desk research data may also be difficult to access or analyze due to legal, ethical, or technical issues.

How to Use Desk Research Effectively

Here are some tips on how to use desk research effectively:

• Define the research problem and objectives clearly and precisely.
• Identify and evaluate the sources of secondary data carefully and critically.
• Compare and contrast different sources of data to check for consistency and reliability.
• Use multiple sources of data to triangulate and validate the findings.
• Supplement desk research with primary research when exploring deeper issues.
• Cite and reference the sources of data properly and ethically.

Desk research should not be used as a substitute for primary research, but rather as a complement or supplement. Combine it with primary research methods, such as surveys, interviews, observations, experiments, and others to obtain a more complete and accurate picture of your research topic.

Desk research is a cost-effective tool for gaining insights into your research topic. Although it has limitations, if you choose the right method and carry out your desk research effectively, you will save a lot of time, money, and effort that primary research would require.

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• desk research
• market research
• primary vs secondary research
• research bias
• secondary research
• Moradeke Owa

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Research Implications | Definition, Examples & Tips

Introduction

What are research implications, why discuss research implications, types of implications in research, how do you present research implications.

Every scientific inquiry is built on previous studies and lays the groundwork for future research. The latter is where discussion of research implications lies. Researchers are expected not only to present what their findings suggest about the phenomenon being studied but also what the findings mean in a broader context.

In this article, we'll explore the nature of research implications as a means for contextualizing the findings of qualitative research and the foundation it sets for further research.

Research implications include any kind of discussion of what a particular study means for its research field and in general terms. Researchers write implications to lay out future research studies, make research recommendations based on proposed theoretical developments, and discuss practical and technological implications that can be applied in the real world.

To put it another way, research implications are intended to answer the question "what does this research mean?". Research implications look forward and out. Once findings are presented and discussed, the researcher lays out what the findings mean in a broader context and how they could guide subsequent research.

An aspect of academic writing that's related to implications is the discussion of the study's limitations. These limitations differ from implications in that they explore already acknowledged shortcomings in a study (e.g., a small sample size, an inherent weakness in a chosen methodological approach), but these limitations can also suggest how future research could address these shortcomings. Both the implications and recommendations are often coupled with limitations in a discussion section to explain the significance of the study's contributions to scientific knowledge.

Strictly speaking, there is a fine line between limitations and implications, one that a traditional approach to the scientific method may not adequately explore. Under the scientific method, the product of any research study addresses its research questions or confirms or challenges its expected outcomes. Fulfilling just this task, however, may overlook a more important step in the research process in terms of demonstrating significance.

One of the more famous research examples can provide useful insight. Galileo's experiments with falling objects allowed him to answer questions raised by Aristotle's understanding about gravity affecting objects of different weights. Galileo had something of a hypothesis - objects should fall at the same speed regardless of weight - based on a critique of then-current scientific knowledge - Aristotle's assertion about gravity - that he wanted to test in research. By conducting different experiments using inclines and pendulums (and supposedly one involving falling objects from the Tower of Pisa), he established a new understanding about gravity and its relationship (or lack thereof) to the weight of objects.

Discussion of that experiment focused on how the findings challenged Aristotle's understanding of physics. It did not, however, pose the next logical question: Why would an object like a feather fall at a much slower rate of descent than an object like a hammer if weight was not a factor?

Galileo's experiment and other similar experiments laid the groundwork for experiments on air resistance, most famously the Apollo 15 experiment on the moon where a feather and hammer fell at the same rate in a vacuum, absent any air resistance. The limitation Galileo had at the time was the inability to create a vacuum to test any theories about gravity and air resistance. The implications of his experiments testing Aristotle's claims include the call to further research that could eventually confirm or challenge his understanding of falling objects.

In formal scientific research, particularly in academic settings where peer review is an essential component, contemporary researchers are supposed to do more than simply report their findings. They are expected to engage in critical reflection in placing their research findings in a broader context. The peer review process in research publication often assesses the quality of a research paper by its ability to detail the significance of a given research study. Without an explicit description of the implications in research, readers may not necessarily know what importance the study and its findings holds for them.

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Breaking down the kinds of implications that your research findings might have will be useful in crafting a clearer and more persuasive presentation. More important than saying that the findings are compelling is arguing in what aspects the findings should prove useful.

There are different types of implications, and the type you should emphasize depends on your target audience.

Theoretical implications

When research findings present novel scientific knowledge, it should have an influence on existing theories by affirming, contradicting, or contextualizing them. This can mean the proposal of a brand new theoretical framework or developments to a existing one.

Keep in mind that, in qualitative research , researchers will often contextualize a theory rather than confirm or refute it. This means that a theory or conceptual framework that is applied to an unfamiliar context (e.g., a theory about adolescent development in a study involving graduate students) will undergo some sort of transformation due to the new analysis.

New understandings will likely develop more complex descriptions of theories as they are interpreted and re-interpreted in new contexts. The discussion of theoretical implications here requires researchers to consider how new theoretical developments might be applied to new data in future research.

Practical implications

More applied forums are interested in how a study's findings can be used in the real world. New developments in psychology could yield discussion of applications in psychiatry, while research in physics can lead to technological innovations in engineering and architecture. While some researchers focus on developing theory, others conduct research to generate actionable insights and tangible results for stakeholders.

Education research, for example, may present pathways to a new teaching method or assessment of learining outcomes. Theories about how students passively and actively develop expertise in subject-matter knowledge could eventually prompt scholars and practitioners to change existing pedagogies and materials that account for more novel understandings of teaching and learning.

Exploring the practical dimensions of research findings may touch on political implications such as policy recommendations, marketable technologies, or novel approaches to existing methods or processes. Discussion of implications along these lines is meant to promote further research and activity in the field to support these practical developments.

Methodological implications

Qualitative research methods are always under constant development and innovation. Moreover, applying research methods in new contexts or for novel research inquiries can lead to unanticipated results that might cause a researcher to reflect on and iterate on their methods of data collection and analysis .

Critical reflections on research methods are not meant to assert that the study was conducted without the necessary rigor . However, rigorous and transparent researchers are expected to argue that further iterations of the research that address any methodological gaps can only bolster the persuasiveness of the findings or generate richer insights.

There are many possible avenues for implications in terms of innovating on methodology. Does the nature of your interview questions change when interviewing certain populations? Should you change certain practices when collecting data in an ethnography to establish rapport with research participants ? How does the use of technology influence the collection and analysis of data?

All of these questions are worth discussing, with the answers providing useful guidance to those who want to base their own study design on yours. As a result, it's important to devote some space in your paper or presentation to how you conducted your study and what you would do in future iterations of your study to bolster its research rigor.

Presenting research implications or writing research implications in a research paper is a matter of answering the following question: Why should scholars read or pay attention to your research? Especially in the social sciences, the potential impact of a study is not always a foregone conclusion. In other words, to make the findings as insightful and persuasive to your audience as they are to you, you need to persuade them beyond the presentation of the analysis and the insights generated.

Here are a few main principles to achieve this task. In broad terms, they focus on what the findings mean to you, what it should mean to others, and what those impacts might mean in context.

Establish importance

Academic research writing tends to follow a structure that narrates a study from the researcher's motivation to conduct the research to why the research's findings matter. While there's seldom a strict requirement for sections in a paper or presentation, understanding commonly used patterns in academic writing will point out where the research implications are discussed.

If you look at a typical research paper abstract in a peer-reviewed journal , for example, you might find that the last sentence or two explicitly establishes why the research is useful to motivate readers to look at the paper more deeply. In the body of the paper, this is further explained in detail towards the end of the introduction and discussion sections and in the conclusion section. These areas are where you should focus on detailing the research implications and explaining how you perceive the impact of your study.

It's essential that you use these spaces to highlight why the findings matter to you. As mentioned earlier, this impact should never be assumed to be understood. Rather, you should explain in detail how your initial motivation to conduct the research has been satisfied and how you might use what you have learned from the research in theoretical and practical terms.

Tailor to your audience

Research is partly about sharing expertise and partly about understanding your audience. Scientific knowledge is generated through consensus, and the more that the researcher ensures their implications are understood by their audience, the more it will resonate in the field.

A good strategy for tailoring your research paper to a particular journal is to read its articles for the implications that are explored in the research. Applied journals will focus on more practical implications while more theoretical publications will emphasize theoretical or conceptual frameworks for other scholars to rely on. As a result, there's no need to detail every single possible implication from your study; simply describing those implications that are most relevant to your audience is often sufficient.

Provide useful examples

One of the easier ways to persuade readers of the potential implications of your research is to provide concrete examples that are simple to understand.

Think about a study that interviews children, for example, where the methodological implications dwell on establishing an emotional connection before collecting data. This might include practical considerations such as bringing toys or conducting the interview in a setting familiar to them like their classroom so they are comfortable during data collection. Explicitly detailing this example can guide scholars in useful takeaways for their research design.

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Effectiveness of 1,500 global climate policies ranked for first time

The world can take a major step to meeting the goals of the Paris Climate Accord by focusing on 63 cases where climate policies have had the most impact, new research has revealed. The findings have been published today in Science .

Our results inform contentious policy debates in three main ways. First, we show evidence for the effectiveness of policy mixes. Second our findings highlight that successful policy mixes vary across sectors and that policy-makers should focus on sector-specific best practices. Third our results stress that effective policies vary with economic development. Study co-author Dr Moritz Schwarz , an Associate at the Climate Econometrics Programme at the University of Oxford

The study, led by Climate Econometricians at the University of Oxford, the Potsdam Institute for Climate Impact Research (PIK), and the Mercator Research Institute on Global Commons and Climate Change (MCC), analysed 1,500 observed policies documented in a novel, high quality, OECD climate policy database for effectiveness. It is the first time a global dataset of policies has been compared and ranked in this way.

Using a methodology developed by Climate Econometrics at The Institute for New Economic Thinking at the Oxford Martin School (INET Oxford), the researchers measured ‘emission breaks’ that followed policy interventions. The break detection methodology, called indicator saturation estimation, developed at Climate Econometrics, allows break indicators for all possible dates to be examined objectively using a variant of machine learning.

The results were sobering: Across four sectors, 41 countries, two decades and 1,500 policies, only 63 successful policy interventions with large effects were identified, which reduced total emissions between 0.6 and 1.8 Gt CO2.

However, the authors say the good news is that policymakers can learn from the 63 effective cases where climate policies had led to meaningful reductions to get back on track.

The researchers have made the data available to policy-makers across the world, and have produced a sector by sector, country by country data visualisation in a dashboard .

Overall, the Team concluded:

• Climate policies are more effective as part of a mix:  In most cases, effect sizes of climate policies are larger if a policy instrument is part of a policy mix rather than implemented alone –for example combining carbon pricing with a subsidy.
• Developed and developing countries have different climate policy needs:  In developed countries, carbon pricing stands out as an effective policy, whereas in developing countries, regulation is the most powerful policy.
• The Paris emissions gap can be closed:  Focusing on the 63 cases of effective climate policies would close the current emissions gap to meet the Paris Targets by 26% -41%, a significant contribution.

Scaling up good practice policies identified in this study to other sectors and other parts of the world can in the short term be a powerful climate mitigation strategy…The dashboard that we make available to policy-makers provides an accessible platform to conduct country-by-country, sector-by-sector comparisons and to find a suitable policy mix for different situations. Study co-author Professor Felix Pretis , Co-Director of the Climate Econometrics Programme at Nuffield College, University of Oxford

Study co-author Ebba Mark , researcher at the Calleva Project at INET Oxford, said the world needed to get back on track to meeting the Paris Climate Accord targets. ‘Meeting the Paris Climate objectives necessitates decisive policy action and this research shows the way. Data from the UN estimates that there remains a median emissions gap of 23 billion tonnes of CO2 equivalent by 2030 . The persistence of this emissions gap is caused not only by an ambition gap but also a gap in the outcomes that adopted policies achieve in terms of emissions reductions.’

What works: Examples from the UK and USA

The country by country analysis showed that the UK has made very successful progress in the electricity sector, with two adjacent breaks detected following the mid-2013 introduction of a carbon price floor that imposed a minimum price for UK power producers. However, the study did not find in other UK sectors any major emission reductions following a policy intervention beyond what would be expected based on long-term economic and population dynamics.

The US has managed to reduce carbon emissions in the transport sector following actions taken in the aftermath of the financial crisis. While successful policy implementation in the transport sector is generally difficult and hence can be viewed as a positive example for the climate policy globally, the lack of any further climate policy successes in other sectors points to huge remaining challenges in the power sector or industry.

Dr Anupama Sen ,  Head of Policy Engagement at the Oxford Smith School of Enterprise and the Environment said: ' In more than 80%  of investments the total lifetime cost of a clean technology is considerably lower than that of a fossil technology. While the new UK government’s policies are moving in the right direction, they need to go further and faster to unlock these lower costs. New Oxford research now provides evidence that an optimal mix of policies can achieve this, and rapidly lower a country’s emissions.'

Further analysis can be found in INET Oxford’s accompanying Insight brief .

The study ‘Climate policies that achieved major emission reductions: Global evidence from two decades’ has been published in Science .

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What is burnout?

Signs and symptoms of burnout, the difference between stress and burnout, burnout vs. depression, stages of burnout, causes of burnout, how to deal with burnout, tip 1: turn to other people, tip 2: reframe the way you look at work, tip 3: reevaluate your priorities, tip 4: make exercise a priority, tip 5: support your mood and energy levels with a healthy diet, burnout symptoms, treatment, and tips on how to deal.

If constant stress has you feeling helpless, disillusioned, and completely exhausted, you may be on the road to burnout. Learn what you can do to regain your balance and feel positive and hopeful again.

Burnout is a state of emotional, physical, and mental exhaustion caused by excessive and prolonged stress . It occurs when you feel overwhelmed, emotionally drained, and unable to meet constant demands. As the stress continues, you begin to lose the interest and motivation that led you to take on a certain role in the first place.

Burnout reduces productivity and saps your energy, leaving you feeling increasingly helpless, hopeless, cynical, and resentful. Eventually, you may feel like you have nothing more to give.

The negative effects of burnout spill over into every area of life—including your home, work, and social life. Burnout can also cause long-term changes to your body that make you vulnerable to illnesses like colds and flu. Because of its many consequences, it’s important to deal with burnout right away.

Are you on the road to burnout?

You may be on the road to burnout if:

• Every  day is a bad day.
• Caring about your work or home life seems like a total waste of energy.
• You’re exhausted all the time.
• The majority of your day is spent on tasks you find either mind-numbingly dull or overwhelming.
• You feel like nothing you do makes a difference or is appreciated.

Most of us have days when we feel helpless, overloaded, or unappreciated—when dragging ourselves out of bed requires the determination of Hercules. If you feel like this most of the time, however, you may be burned out.

Burnout is a gradual process. It doesn’t happen overnight, but it can creep up on you. The signs and symptoms are subtle at first, but become worse as time goes on. Think of the early symptoms as red flags that something is wrong that needs to be addressed. If you pay attention and actively reduce your stress , you can prevent a major breakdown. If you ignore them, you’ll eventually burn out.

Physical signs and symptoms of burnout

• Feeling tired and drained most of the time.
• Lowered immunity, frequent illnesses.
• Frequent headaches or muscle pain.
• Change in appetite or sleep habits.

Emotional signs and symptoms of burnout

• Sense of failure and self-doubt.
• Feeling helpless, trapped, and defeated.
• Detachment, feeling alone in the world.
• Loss of motivation. Increasingly cynical and negative outlook.
• Decreased satisfaction and sense of accomplishment.

Behavioral signs and symptoms of burnout

• Withdrawing from responsibilities.
• Isolating from others.
• Procrastinating, taking longer to get things done.
• Using food, drugs, or alcohol to cope .
• Taking frustrations out on others.
• Skipping work or coming in late and leaving early.

Burnout may be the result of unrelenting stress, but it isn’t the same as too much stress. Stress, by and large, involves too much: too many pressures that demand too much of you physically and mentally. However, stressed people can still imagine that if they can just get everything under control, they’ll feel better.

Burnout, on the other hand, is about not enough. Being burned out means feeling empty and mentally exhausted, devoid of motivation, and beyond caring. People experiencing burnout often don’t see any hope of positive change in their situations. If excessive stress feels like you’re drowning in responsibilities, burnout is a sense of being all dried up. And while you’re usually aware of being under a lot of stress, you don’t always notice burnout when it happens.

Burnout and depression can also be difficult to tell apart, and some of the symptoms can overlap. For example, whether you’re depressed or burned out, you might feel exhausted or have a hard time focusing. Burnout can also be a risk factor for depression . However, the two conditions have important differences.

Researchers have used several models to chart the development of burnout symptoms. For example, one model follows 12 stages, starting with a desire to prove oneself in a specific task and then advancing to unhealthier behaviors, such as neglecting self-care. Eventually, this leads toward the later stages, including feelings of emptiness and depression.

Another model simplifies burnout progression to five stages:

5 stages of burnout

Stage 1 (Honeymoon Phase): You feel committed to an endeavor, whether you’ve just gotten a new job, a promotion, enrolled in a class, or started parenting or caregiving. You’re ready to accept new responsibilities and eager to prove yourself. You may feel creative, productive, and energized.

Stage 2 (Stress Onset): As the stress of your new responsibilities begins to take its toll, you start to neglect your self-care needs. Your sleep quality diminishes. Anxiety shows up more often, along with irritability, headaches, and fatigue. You become less productive, have a harder time focusing, and try to avoid making decisions.

Stage 3 (Chronic Stress): You’re consistently tired and feel cynical or apathetic. Social issues can also crop up. You may withdraw from coworkers or feel resentful toward your loved ones. You might frequently procrastinate or use drugs or alcohol to self-medicate , even as you deny the problem.

Stage 4 (Burnout): At this point, you feel pessimistic about the future and obsessed with any problems that crop up. You’re neglecting your personal health, and that comes with physical problems like gastrointestinal issues and chronic headaches. You’re plagued by self-doubt and look to socially isolate yourself.

Stage 5 (Habitual Burnout): Your sense of well-being reaches a low. You’re always sad and mentally and physically fatigued. Depression may develop here.

Burnout often stems from your job. But anyone who feels overworked and undervalued is at risk for burnout, from the hardworking office worker who hasn’t had a vacation in years, to the frazzled stay-at-home mom tending to kids, housework, and an aging parent .

But burnout is not caused solely by stressful work or too many responsibilities. Other factors contribute to burnout, including your lifestyle and personality traits. In fact, what you do in your downtime and how you look at the world can play just as big of a role in causing overwhelming stress as work or home demands.

Work-related causes of burnout

• Feeling like you have little or no control over your work.
• Lack of recognition or reward for good work.
• Unclear or overly demanding job expectations.
• Doing work that’s monotonous or unchallenging.
• Working in a chaotic or high-pressure environment.

Lifestyle causes of burnout

• Working too much, without enough time for socializing or relaxing.
• Lack of close, supportive relationships.
• Taking on too many responsibilities, without enough help from others.
• Not getting enough sleep.

Personality traits can contribute to burnout

• Perfectionistic tendencies; nothing is ever good enough.
• Pessimistic view of yourself and the world.
• The need to be in control; reluctance to delegate to others.
• High-achieving, Type A personality.

Whether you recognize the warning signs of impending burnout or you’re already past the breaking point, trying to push through the exhaustion and continuing as you have been will only cause further emotional and physical damage. Now is the time to pause and change direction by learning how you can help yourself overcome burnout and feel healthy and positive again.

Dealing with burnout requires the “Three R” approach:

Recognize. Watch for the warning signs of burnout.

Reverse. Undo the damage by seeking support and managing stress.

Resilience. Build your resilience to stress by taking care of your physical and emotional health.

The following tips for preventing or dealing with burnout can help you cope with symptoms and regain your energy, focus, and sense of well-being.

When you’re burned out, problems seem insurmountable, everything looks bleak, and it’s difficult to muster up the energy to care, let alone take action to help yourself. But you have a lot more control over stress than you may think. There are positive steps you can take to deal with overwhelming stress and get your life back into balance. One of the most effective is to reach out to others.

Social contact is nature’s antidote to stress and talking face to face with a good listener is one of the fastest ways to calm your nervous system and relieve stress. The person you talk to doesn’t have to be able to “fix” your stressors; they just have to be a good listener, someone who’ll listen attentively without becoming distracted or expressing judgment.

[Read: Social Support for Stress Relief]

Reach out to those closest to you, such as your partner, family, and friends. Opening up won’t make you a burden to others. In fact, most friends and loved ones will be flattered that you trust them enough to confide in them, and it will only strengthen your friendship. Try not to think about what’s burning you out and make the time you spend with loved ones positive and enjoyable.

Be more sociable with your coworkers. Developing friendships with people you work with can help buffer you from stress at work . When you take a break, for example, instead of directing your attention to your smartphone, try engaging your colleagues. Or schedule social events together after work.

Limit your contact with negative people. Hanging out with negative-minded people who do nothing but complain will only drag down your mood and outlook. If you have to work with a negative person, try to limit the amount of time you spend together.

Connect with a cause or a community group that is personally meaningful to you. Joining a religious, social, or support group can give you a place to talk to like-minded people about how to deal with daily stress—and to make new friends. If your line of work has a professional association, you can attend meetings and interact with others coping with the same workplace demands. You can also find virtual support groups through some online therapy platforms .

Find new friends. If you don’t feel that you have anyone to turn to, it’s never too late to build new friendships and expand your social network.

The power of giving

Being helpful to others delivers immense pleasure and can help to significantly reduce stress as well as broaden your social circle.

While it’s important not to take on too much when you’re facing overwhelming stress, helping others doesn’t have to involve a lot of time or effort. Even small things like a kind word or friendly smile can make you feel better and help lower stress both for you and the other person.

Speak to a Licensed Therapist

BetterHelp is an online therapy service that matches you to licensed, accredited therapists who can help with depression, anxiety, relationships, and more. Take the assessment and get matched with a therapist in as little as 48 hours.

Whether you have a job that leaves you rushed off your feet or one that is monotonous and unfulfilling, the most effective way to combat job burnout is to quit and find a job you love instead. Of course, for many of us changing job or career is far from being a practical solution, we’re grateful just to have work that pays the bills. Whatever your situation, though, there are still steps you can take to improve your state of mind.

Try to find some value in your work.  Even in some mundane jobs, you can often focus on how your role helps others, for example, or provides a much-needed product or service. Focus on aspects of the job that you do enjoy, even if it’s just chatting with your coworkers at lunch. Changing your attitude towards your job can help you regain a sense of purpose and control.

Find balance in your life. If you hate your job, look for meaning and satisfaction elsewhere in your life: in your family, friends, hobbies, or voluntary work . Focus on the parts of your life that bring you joy.

[Read: Mental Health in the Workplace]

Make friends at work. Having strong ties in the workplace can help reduce monotony and counter the effects of burnout. Having friends to chat and joke with during the day can help relieve stress from an unfulfilling or demanding job, improve your job performance, or simply get you through a rough day.

Take time off. If burnout seems inevitable, try to take a complete break from work. Go on vacation, use up your sick days, ask for a temporary leave-of-absence, anything to remove yourself from the situation. Use the time away to recharge your batteries and pursue other methods of recovery.

Burnout is an undeniable sign that something important in your life is not working. Take time to think about your hopes, goals, and dreams. Are you neglecting something that is truly important to you? This can be an opportunity to rediscover what really makes you happy and to slow down and give yourself time to rest, reflect, and heal.

Set boundaries.  Don’t overextend yourself. Learn how to say “no” to requests on your time. If you find this difficult, remind yourself that saying “no” allows you to say “yes” to the commitments you want to make.

Take a daily break from technology.  Set a time each day when you completely disconnect. Put away your laptop,  turn off your phone , and stop checking email or social media .

Nourish your creative side.  Creativity is a powerful antidote to burnout. Try something new, start a fun project, or resume a favorite hobby. Choose activities that have nothing to do with work or whatever is causing your stress.

Set aside relaxation time.  Relaxation techniques  such as yoga, meditation, and deep breathing activate the body’s relaxation response, a state of restfulness that is the opposite of the stress response.

Get plenty of sleep.  Feeling tired can exacerbate burnout by causing you to think irrationally. Keep your cool in stressful situations by  getting a good night’s sleep .

Boost your ability to stay on task

If you’re having trouble following through with these self-help tips to prevent or overcome burnout, HelpGuide’s free Emotional Intelligence Toolkit can help.

• Learn how to reduce stress in the moment.
• Manage troublesome thoughts and feelings.
• Motivate yourself to take the steps that can relieve stress and burnout.
• Improve your relationships at work and home.
• Rediscover joy and meaning that make work and life worthwhile.
• Increase your overall health and happiness.

Even though it may be the last thing you feel like doing when you’re burned out, exercise is a powerful antidote to stress and burnout. It’s also something you can do right now to boost your mood.

Aim to exercise for 30 minutes or more per day or break that up into short, 10-minute bursts of activity. A 10-minute walk can improve your mood for two hours.

Rhythmic exercise, where you move both your arms and legs, is a hugely effective way to lift your mood, increase energy, sharpen focus, and relax both the mind and body. Try walking , running, weight training, swimming, martial arts, or even dancing.

To maximize stress relief, instead of continuing to focus on your thoughts, focus on your body and how it feels as you move: the sensation of your feet hitting the ground, for example, or the wind on your skin.

What you put in your body can have a huge impact on your mood and energy levels throughout the day.

Minimize sugar and refined carbs.  You may crave sugary snacks or comfort foods such as pasta or French fries, but these  refined carbs can quickly lead to a crash in mood and energy.

Reduce your high intake of foods that can adversely affect your mood , such as caffeine, unhealthy fats, and foods with chemical preservatives or hormones.

Eat more Omega-3 fatty acids to give your mood a boost. The best Omega-3 sources are fatty fish (salmon, herring, mackerel, anchovies, sardines), seaweed, flaxseed, and walnuts.

Avoid nicotine. Smoking when you’re feeling stressed may seem calming, but nicotine is a powerful stimulant, leading to higher, not lower, levels of anxiety.

Drink alcohol in moderation. Alcohol temporarily reduces worry, but too much can cause anxiety as it wears off.

Since it’s not a diagnosable medical condition, burnout is a term that’s widely misused. But if you recognize the symptoms of burnout, such as feeling mentally, emotionally, and physically exhausted, it’s critical you pause, reevaluate your priorities, and make changes in your life. With the right treatment and support, you can recover from burnout, regain your energy and enthusiasm, and feel more hopeful.

More Information

• Prevention - Prevent burnout by building your resilience to stress and adversity. (American Psychological Association)
• Trauma- and Stressor-Related Disorders. (2013). In Diagnostic and Statistical Manual of Mental Disorders . American Psychiatric Association. Link
• “ICD-11 for Mortality and Morbidity Statistics.” Accessed November 16, 2021. Link
• Maslach, Christina, and Michael P. Leiter. “Understanding the Burnout Experience: Recent Research and Its Implications for Psychiatry.” World Psychiatry 15, no. 2 (June 2016): 103–11. Link
• Koutsimani, Panagiota, Anthony Montgomery, and Katerina Georganta. “The Relationship Between Burnout, Depression, and Anxiety: A Systematic Review and Meta-Analysis.” Frontiers in Psychology 10 (March 13, 2019): 284. Link
• Salvagioni, Denise Albieri Jodas, Francine Nesello Melanda, Arthur Eumann Mesas, Alberto Durán González, Flávia Lopes Gabani, and Selma Maffei de Andrade. “Physical, Psychological and Occupational Consequences of Job Burnout: A Systematic Review of Prospective Studies.” PLOS ONE 12, no. 10 (October 4, 2017): e0185781. Link
• Information, National Center for Biotechnology, U. S. National Library of Medicine 8600 Rockville Pike, Bethesda MD, and 20894 Usa. Depression: What Is Burnout? InformedHealth.Org [Internet] . Institute for Quality and Efficiency in Health Care (IQWiG), 2020. Link
• Roskam, Isabelle, Marie-Emilie Raes, and Moïra Mikolajczak. “Exhausted Parents: Development and Preliminary Validation of the Parental Burnout Inventory.” Frontiers in Psychology 8 (2017): 163. Link
• Heinemann, Linda V., and Torsten Heinemann. “Burnout Research: Emergence and Scientific Investigation of a Contested Diagnosis.” SAGE Open 7, no. 1 (January 1, 2017): 2158244017697154. Link
• Bianchi, Renzo, Irvin Sam Schonfeld, and Eric Laurent. “Is It Time to Consider the ‘Burnout Syndrome’ A Distinct Illness?” Frontiers in Public Health 3 (June 8, 2015): 158. Link
• Ahola, Kirsi, Salla Toppinen-Tanner, and Johanna Seppänen. “Interventions to Alleviate Burnout Symptoms and to Support Return to Work among Employees with Burnout: Systematic Review and Meta-Analysis.” Burnout Research 4 (March 1, 2017): 1–11. Link
• Ruisoto, Pablo, Marina R. Ramírez, Pedro A. García, Belén Paladines-Costa, Silvia L. Vaca, and Vicente J. Clemente-Suárez. “Social Support Mediates the Effect of Burnout on Health in Health Care Professionals.” Frontiers in Psychology 11 (2021): 3867. Link
• Salmon, P. “Effects of Physical Exercise on Anxiety, Depression, and Sensitivity to Stress: A Unifying Theory.” Clinical Psychology Review 21, no. 1 (February 2001): 33–61. Link
• Toussaint, Loren, Quang Anh Nguyen, Claire Roettger, Kiara Dixon, Martin Offenbächer, Niko Kohls, Jameson Hirsch, and Fuschia Sirois. “Effectiveness of Progressive Muscle Relaxation, Deep Breathing, and Guided Imagery in Promoting Psychological and Physiological States of Relaxation.” Evidence-Based Complementary and Alternative Medicine 2021 (July 3, 2021): e5924040. Link
• De Hert, S. (2020). Burnout in Healthcare Workers: Prevalence, Impact and Preventative Strategies. Local and Regional Anesthesia , 13 , 171–183. Link
• Depression: Learn More – What is burnout? – InformedHealth.org—NCBI Bookshelf . (n.d.). Retrieved May 28, 2024, from Link
• Schonfeld, I. S., Bianchi, R., & Palazzi, S. (2018). What is the difference between depression and burnout? An ongoing debate. Rivista Di Psichiatria , 53 (4), 218–219. Link

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Uganda National MPox Situation report #001, 23-AUG-2024

Uganda confirmed the first cases of Mpox on 24th July 2024 following the confirmation of two case-patients from Kasese District, Bwera Hospital by the Uganda Virus Research Institute (UVRI) through a routine sentinel surveillance system. These two cases were detected among six case-patients with symptoms consistent with the Mpox case definition. The two index cases were treated and discharged. Nationwide surveillance for mpox continues alongside routine surveillance.

This reporting week, two cases were reported from two districts (Amuru, Mayuge) which are outside the index district (Kasese). Today marks 29 days of responding to the Mpox outbreak and four days since the last confirmed case. This is the first national SitRep.