empirical research is quantitative or qualitative

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What is "empirical research".

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Empirical research  is based on observed and measured phenomena and derives knowledge from actual experience rather than from theory or belief. 

How do you know if a study is empirical? Read the subheadings within the article, book, or report and look for a description of the research "methodology."  Ask yourself: Could I recreate this study and test these results?

Key characteristics to look for:

• Specific research questions  to be answered
• Definition of the  population, behavior, or   phenomena  being studied
• Description of the  process  used to study this population or phenomena, including selection criteria, controls, and testing instruments (such as surveys)

Another hint: some scholarly journals use a specific layout, called the "IMRaD" format, to communicate empirical research findings. Such articles typically have 4 components:

• Introduction : sometimes called "literature review" -- what is currently known about the topic -- usually includes a theoretical framework and/or discussion of previous studies
• Methodology:  sometimes called "research design" --  how to recreate the study -- usually describes the population, research process, and analytical tools
• Results : sometimes called "findings"  --  what was learned through the study -- usually appears as statistical data or as substantial quotations from research participants
• Discussion : sometimes called "conclusion" or "implications" -- why the study is important -- usually describes how the research results influence professional practices or future studies
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Empirical Research: Definition, Methods, Types and Examples

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Empirical research: Definition

Empirical research: origin, quantitative research methods, qualitative research methods, steps for conducting empirical research, empirical research methodology cycle, advantages of empirical research, disadvantages of empirical research, why is there a need for empirical research.

Empirical research is defined as any research where conclusions of the study is strictly drawn from concretely empirical evidence, and therefore “verifiable” evidence.

This empirical evidence can be gathered using quantitative market research and  qualitative market research  methods.

For example: A research is being conducted to find out if listening to happy music in the workplace while working may promote creativity? An experiment is conducted by using a music website survey on a set of audience who are exposed to happy music and another set who are not listening to music at all, and the subjects are then observed. The results derived from such a research will give empirical evidence if it does promote creativity or not.

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You must have heard the quote” I will not believe it unless I see it”. This came from the ancient empiricists, a fundamental understanding that powered the emergence of medieval science during the renaissance period and laid the foundation of modern science, as we know it today. The word itself has its roots in greek. It is derived from the greek word empeirikos which means “experienced”.

In today’s world, the word empirical refers to collection of data using evidence that is collected through observation or experience or by using calibrated scientific instruments. All of the above origins have one thing in common which is dependence of observation and experiments to collect data and test them to come up with conclusions.

LEARN ABOUT: Causal Research

Types and methodologies of empirical research

Empirical research can be conducted and analysed using qualitative or quantitative methods.

• Quantitative research : Quantitative research methods are used to gather information through numerical data. It is used to quantify opinions, behaviors or other defined variables . These are predetermined and are in a more structured format. Some of the commonly used methods are survey, longitudinal studies, polls, etc
• Qualitative research:   Qualitative research methods are used to gather non numerical data.  It is used to find meanings, opinions, or the underlying reasons from its subjects. These methods are unstructured or semi structured. The sample size for such a research is usually small and it is a conversational type of method to provide more insight or in-depth information about the problem Some of the most popular forms of methods are focus groups, experiments, interviews, etc.

Data collected from these will need to be analysed. Empirical evidence can also be analysed either quantitatively and qualitatively. Using this, the researcher can answer empirical questions which have to be clearly defined and answerable with the findings he has got. The type of research design used will vary depending on the field in which it is going to be used. Many of them might choose to do a collective research involving quantitative and qualitative method to better answer questions which cannot be studied in a laboratory setting.

LEARN ABOUT: Qualitative Research Questions and Questionnaires

Quantitative research methods aid in analyzing the empirical evidence gathered. By using these a researcher can find out if his hypothesis is supported or not.

• Survey research: Survey research generally involves a large audience to collect a large amount of data. This is a quantitative method having a predetermined set of closed questions which are pretty easy to answer. Because of the simplicity of such a method, high responses are achieved. It is one of the most commonly used methods for all kinds of research in today’s world.

Previously, surveys were taken face to face only with maybe a recorder. However, with advancement in technology and for ease, new mediums such as emails , or social media have emerged.

For example: Depletion of energy resources is a growing concern and hence there is a need for awareness about renewable energy. According to recent studies, fossil fuels still account for around 80% of energy consumption in the United States. Even though there is a rise in the use of green energy every year, there are certain parameters because of which the general population is still not opting for green energy. In order to understand why, a survey can be conducted to gather opinions of the general population about green energy and the factors that influence their choice of switching to renewable energy. Such a survey can help institutions or governing bodies to promote appropriate awareness and incentive schemes to push the use of greener energy.

Learn more: Renewable Energy Survey Template Descriptive Research vs Correlational Research

• Experimental research: In experimental research , an experiment is set up and a hypothesis is tested by creating a situation in which one of the variable is manipulated. This is also used to check cause and effect. It is tested to see what happens to the independent variable if the other one is removed or altered. The process for such a method is usually proposing a hypothesis, experimenting on it, analyzing the findings and reporting the findings to understand if it supports the theory or not.

For example: A particular product company is trying to find what is the reason for them to not be able to capture the market. So the organisation makes changes in each one of the processes like manufacturing, marketing, sales and operations. Through the experiment they understand that sales training directly impacts the market coverage for their product. If the person is trained well, then the product will have better coverage.

• Correlational research: Correlational research is used to find relation between two set of variables . Regression analysis is generally used to predict outcomes of such a method. It can be positive, negative or neutral correlation.

LEARN ABOUT: Level of Analysis

For example: Higher educated individuals will get higher paying jobs. This means higher education enables the individual to high paying job and less education will lead to lower paying jobs.

• Longitudinal study: Longitudinal study is used to understand the traits or behavior of a subject under observation after repeatedly testing the subject over a period of time. Data collected from such a method can be qualitative or quantitative in nature.

For example: A research to find out benefits of exercise. The target is asked to exercise everyday for a particular period of time and the results show higher endurance, stamina, and muscle growth. This supports the fact that exercise benefits an individual body.

• Cross sectional: Cross sectional study is an observational type of method, in which a set of audience is observed at a given point in time. In this type, the set of people are chosen in a fashion which depicts similarity in all the variables except the one which is being researched. This type does not enable the researcher to establish a cause and effect relationship as it is not observed for a continuous time period. It is majorly used by healthcare sector or the retail industry.

For example: A medical study to find the prevalence of under-nutrition disorders in kids of a given population. This will involve looking at a wide range of parameters like age, ethnicity, location, incomes  and social backgrounds. If a significant number of kids coming from poor families show under-nutrition disorders, the researcher can further investigate into it. Usually a cross sectional study is followed by a longitudinal study to find out the exact reason.

• Causal-Comparative research : This method is based on comparison. It is mainly used to find out cause-effect relationship between two variables or even multiple variables.

For example: A researcher measured the productivity of employees in a company which gave breaks to the employees during work and compared that to the employees of the company which did not give breaks at all.

LEARN ABOUT: Action Research

Some research questions need to be analysed qualitatively, as quantitative methods are not applicable there. In many cases, in-depth information is needed or a researcher may need to observe a target audience behavior, hence the results needed are in a descriptive analysis form. Qualitative research results will be descriptive rather than predictive. It enables the researcher to build or support theories for future potential quantitative research. In such a situation qualitative research methods are used to derive a conclusion to support the theory or hypothesis being studied.

LEARN ABOUT: Qualitative Interview

• Case study: Case study method is used to find more information through carefully analyzing existing cases. It is very often used for business research or to gather empirical evidence for investigation purpose. It is a method to investigate a problem within its real life context through existing cases. The researcher has to carefully analyse making sure the parameter and variables in the existing case are the same as to the case that is being investigated. Using the findings from the case study, conclusions can be drawn regarding the topic that is being studied.

For example: A report mentioning the solution provided by a company to its client. The challenges they faced during initiation and deployment, the findings of the case and solutions they offered for the problems. Such case studies are used by most companies as it forms an empirical evidence for the company to promote in order to get more business.

• Observational method:   Observational method is a process to observe and gather data from its target. Since it is a qualitative method it is time consuming and very personal. It can be said that observational research method is a part of ethnographic research which is also used to gather empirical evidence. This is usually a qualitative form of research, however in some cases it can be quantitative as well depending on what is being studied.

For example: setting up a research to observe a particular animal in the rain-forests of amazon. Such a research usually take a lot of time as observation has to be done for a set amount of time to study patterns or behavior of the subject. Another example used widely nowadays is to observe people shopping in a mall to figure out buying behavior of consumers.

• One-on-one interview: Such a method is purely qualitative and one of the most widely used. The reason being it enables a researcher get precise meaningful data if the right questions are asked. It is a conversational method where in-depth data can be gathered depending on where the conversation leads.

For example: A one-on-one interview with the finance minister to gather data on financial policies of the country and its implications on the public.

• Focus groups: Focus groups are used when a researcher wants to find answers to why, what and how questions. A small group is generally chosen for such a method and it is not necessary to interact with the group in person. A moderator is generally needed in case the group is being addressed in person. This is widely used by product companies to collect data about their brands and the product.

For example: A mobile phone manufacturer wanting to have a feedback on the dimensions of one of their models which is yet to be launched. Such studies help the company meet the demand of the customer and position their model appropriately in the market.

• Text analysis: Text analysis method is a little new compared to the other types. Such a method is used to analyse social life by going through images or words used by the individual. In today’s world, with social media playing a major part of everyone’s life, such a method enables the research to follow the pattern that relates to his study.

For example: A lot of companies ask for feedback from the customer in detail mentioning how satisfied are they with their customer support team. Such data enables the researcher to take appropriate decisions to make their support team better.

Sometimes a combination of the methods is also needed for some questions that cannot be answered using only one type of method especially when a researcher needs to gain a complete understanding of complex subject matter.

We recently published a blog that talks about examples of qualitative data in education ; why don’t you check it out for more ideas?

Since empirical research is based on observation and capturing experiences, it is important to plan the steps to conduct the experiment and how to analyse it. This will enable the researcher to resolve problems or obstacles which can occur during the experiment.

Step #1: Define the purpose of the research

This is the step where the researcher has to answer questions like what exactly do I want to find out? What is the problem statement? Are there any issues in terms of the availability of knowledge, data, time or resources. Will this research be more beneficial than what it will cost.

Before going ahead, a researcher has to clearly define his purpose for the research and set up a plan to carry out further tasks.

Step #2 : Supporting theories and relevant literature

The researcher needs to find out if there are theories which can be linked to his research problem . He has to figure out if any theory can help him support his findings. All kind of relevant literature will help the researcher to find if there are others who have researched this before, or what are the problems faced during this research. The researcher will also have to set up assumptions and also find out if there is any history regarding his research problem

Step #3: Creation of Hypothesis and measurement

Before beginning the actual research he needs to provide himself a working hypothesis or guess what will be the probable result. Researcher has to set up variables, decide the environment for the research and find out how can he relate between the variables.

Researcher will also need to define the units of measurements, tolerable degree for errors, and find out if the measurement chosen will be acceptable by others.

Step #4: Methodology, research design and data collection

In this step, the researcher has to define a strategy for conducting his research. He has to set up experiments to collect data which will enable him to propose the hypothesis. The researcher will decide whether he will need experimental or non experimental method for conducting the research. The type of research design will vary depending on the field in which the research is being conducted. Last but not the least, the researcher will have to find out parameters that will affect the validity of the research design. Data collection will need to be done by choosing appropriate samples depending on the research question. To carry out the research, he can use one of the many sampling techniques. Once data collection is complete, researcher will have empirical data which needs to be analysed.

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Step #5: Data Analysis and result

Data analysis can be done in two ways, qualitatively and quantitatively. Researcher will need to find out what qualitative method or quantitative method will be needed or will he need a combination of both. Depending on the unit of analysis of his data, he will know if his hypothesis is supported or rejected. Analyzing this data is the most important part to support his hypothesis.

Step #6: Conclusion

A report will need to be made with the findings of the research. The researcher can give the theories and literature that support his research. He can make suggestions or recommendations for further research on his topic.

A.D. de Groot, a famous dutch psychologist and a chess expert conducted some of the most notable experiments using chess in the 1940’s. During his study, he came up with a cycle which is consistent and now widely used to conduct empirical research. It consists of 5 phases with each phase being as important as the next one. The empirical cycle captures the process of coming up with hypothesis about how certain subjects work or behave and then testing these hypothesis against empirical data in a systematic and rigorous approach. It can be said that it characterizes the deductive approach to science. Following is the empirical cycle.

• Observation: At this phase an idea is sparked for proposing a hypothesis. During this phase empirical data is gathered using observation. For example: a particular species of flower bloom in a different color only during a specific season.
• Induction: Inductive reasoning is then carried out to form a general conclusion from the data gathered through observation. For example: As stated above it is observed that the species of flower blooms in a different color during a specific season. A researcher may ask a question “does the temperature in the season cause the color change in the flower?” He can assume that is the case, however it is a mere conjecture and hence an experiment needs to be set up to support this hypothesis. So he tags a few set of flowers kept at a different temperature and observes if they still change the color?
• Deduction: This phase helps the researcher to deduce a conclusion out of his experiment. This has to be based on logic and rationality to come up with specific unbiased results.For example: In the experiment, if the tagged flowers in a different temperature environment do not change the color then it can be concluded that temperature plays a role in changing the color of the bloom.
• Testing: This phase involves the researcher to return to empirical methods to put his hypothesis to the test. The researcher now needs to make sense of his data and hence needs to use statistical analysis plans to determine the temperature and bloom color relationship. If the researcher finds out that most flowers bloom a different color when exposed to the certain temperature and the others do not when the temperature is different, he has found support to his hypothesis. Please note this not proof but just a support to his hypothesis.
• Evaluation: This phase is generally forgotten by most but is an important one to keep gaining knowledge. During this phase the researcher puts forth the data he has collected, the support argument and his conclusion. The researcher also states the limitations for the experiment and his hypothesis and suggests tips for others to pick it up and continue a more in-depth research for others in the future. LEARN MORE: Population vs Sample

LEARN MORE: Population vs Sample

There is a reason why empirical research is one of the most widely used method. There are a few advantages associated with it. Following are a few of them.

• It is used to authenticate traditional research through various experiments and observations.
• This research methodology makes the research being conducted more competent and authentic.
• It enables a researcher understand the dynamic changes that can happen and change his strategy accordingly.
• The level of control in such a research is high so the researcher can control multiple variables.
• It plays a vital role in increasing internal validity .

Even though empirical research makes the research more competent and authentic, it does have a few disadvantages. Following are a few of them.

• Such a research needs patience as it can be very time consuming. The researcher has to collect data from multiple sources and the parameters involved are quite a few, which will lead to a time consuming research.
• Most of the time, a researcher will need to conduct research at different locations or in different environments, this can lead to an expensive affair.
• There are a few rules in which experiments can be performed and hence permissions are needed. Many a times, it is very difficult to get certain permissions to carry out different methods of this research.
• Collection of data can be a problem sometimes, as it has to be collected from a variety of sources through different methods.

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Empirical research is important in today’s world because most people believe in something only that they can see, hear or experience. It is used to validate multiple hypothesis and increase human knowledge and continue doing it to keep advancing in various fields.

For example: Pharmaceutical companies use empirical research to try out a specific drug on controlled groups or random groups to study the effect and cause. This way, they prove certain theories they had proposed for the specific drug. Such research is very important as sometimes it can lead to finding a cure for a disease that has existed for many years. It is useful in science and many other fields like history, social sciences, business, etc.

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With the advancement in today’s world, empirical research has become critical and a norm in many fields to support their hypothesis and gain more knowledge. The methods mentioned above are very useful for carrying out such research. However, a number of new methods will keep coming up as the nature of new investigative questions keeps getting unique or changing.

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Empirical Research: Defining, Identifying, & Finding

Defining empirical research, what is empirical research, quantitative or qualitative.

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Calfee & Chambliss (2005)  (UofM login required) describe empirical research as a "systematic approach for answering certain types of questions."  Those questions are answered "[t]hrough the collection of evidence under carefully defined and replicable conditions" (p. 43). 

The evidence collected during empirical research is often referred to as "data." 

Characteristics of Empirical Research

Emerald Publishing's guide to conducting empirical research identifies a number of common elements to empirical research: 

• A  research question , which will determine research objectives.
• A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.
• The gathering of  primary data , which is then analysed.
• A particular  methodology  for collecting and analysing the data, such as an experiment or survey.
• The limitation of the data to a particular group, area or time scale, known as a sample [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.
• The ability to  recreate  the study and test the results. This is known as  reliability .
• The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Empirical research methodologies can be described as quantitative, qualitative, or a mix of both (usually called mixed-methods).

Ruane (2016)  (UofM login required) gets at the basic differences in approach between quantitative and qualitative research:

• Quantitative research  -- an approach to documenting reality that relies heavily on numbers both for the measurement of variables and for data analysis (p. 33).
• Qualitative research  -- an approach to documenting reality that relies on words and images as the primary data source (p. 33).

Both quantitative and qualitative methods are empirical . If you can recognize that a research study is quantitative or qualitative study, then you have also recognized that it is empirical study. 

Below are information on the characteristics of quantitative and qualitative research. This video from Scribbr also offers a good overall introduction to the two approaches to research methodology: 

Characteristics of Quantitative Research 

Researchers test hypotheses, or theories, based in assumptions about causality, i.e. we expect variable X to cause variable Y. Variables have to be controlled as much as possible to ensure validity. The results explain the relationship between the variables. Measures are based in pre-defined instruments.

Examples: experimental or quasi-experimental design, pretest & post-test, survey or questionnaire with closed-ended questions. Studies that identify factors that influence an outcomes, the utility of an intervention, or understanding predictors of outcomes. 

Characteristics of Qualitative Research

Researchers explore “meaning individuals or groups ascribe to social or human problems (Creswell & Creswell, 2018, p3).” Questions and procedures emerge rather than being prescribed. Complexity, nuance, and individual meaning are valued. Research is both inductive and deductive. Data sources are multiple and varied, i.e. interviews, observations, documents, photographs, etc. The researcher is a key instrument and must be reflective of their background, culture, and experiences as influential of the research.

Examples: open question interviews and surveys, focus groups, case studies, grounded theory, ethnography, discourse analysis, narrative, phenomenology, participatory action research.

Calfee, R. C. & Chambliss, M. (2005). The design of empirical research. In J. Flood, D. Lapp, J. R. Squire, & J. Jensen (Eds.),  Methods of research on teaching the English language arts: The methodology chapters from the handbook of research on teaching the English language arts (pp. 43-78). Routledge.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=125955&site=eds-live&scope=site .

Creswell, J. W., & Creswell, J. D. (2018).  Research design: Qualitative, quantitative, and mixed methods approaches  (5th ed.). Thousand Oaks: Sage.

How to... conduct empirical research . (n.d.). Emerald Publishing.  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research .

Scribbr. (2019). Quantitative vs. qualitative: The differences explained  [video]. YouTube.  https://www.youtube.com/watch?v=a-XtVF7Bofg .

Ruane, J. M. (2016).  Introducing social research methods : Essentials for getting the edge . Wiley-Blackwell.  http://ezproxy.memphis.edu/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1107215&site=eds-live&scope=site .  

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Empirical Research: Quantitative & Qualitative

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What's the Difference Between Qualitative and Quantitative?

Distinguishing quantitative & qualitative methods, word clues to identify methods.

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What’s the Difference Between Qualitative and Quantitative Methods?

From: Suter, W. N. (2012). Qualitative Data, Analysis, and Design. In  Introduction to educational research: A critical thinking approach . SAGE Publications, Inc., www.galileo.usg.edu/redirect?inst=pie1&url=https://dx.doi.org/10.4135/9781483384443

The words in this table can be used to evaluate whether an article tends more toward the quantitative or qualitative domain. Well-written article abstracts will contain words like these to succinctly characterize the article's content.

Adapted from: McMillan, J. H. (2012).  Educational research: Fundamentals for the consumer  (6th ed.). Boston, MA: Pearson.

Search SAGE Research Methods for resources about qualitative methods

Search SAGE Research Methods for resources about quantitative methods

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What is Empirical Research? Definition, Methods, Examples

Appinio Research · 09.02.2024 · 35min read

Ever wondered how we gather the facts, unveil hidden truths, and make informed decisions in a world filled with questions? Empirical research holds the key.

In this guide, we'll delve deep into the art and science of empirical research, unraveling its methods, mysteries, and manifold applications. From defining the core principles to mastering data analysis and reporting findings, we're here to equip you with the knowledge and tools to navigate the empirical landscape.

What is Empirical Research?

Empirical research is the cornerstone of scientific inquiry, providing a systematic and structured approach to investigating the world around us. It is the process of gathering and analyzing empirical or observable data to test hypotheses, answer research questions, or gain insights into various phenomena. This form of research relies on evidence derived from direct observation or experimentation, allowing researchers to draw conclusions based on real-world data rather than purely theoretical or speculative reasoning.

Characteristics of Empirical Research

Empirical research is characterized by several key features:

• Observation and Measurement : It involves the systematic observation or measurement of variables, events, or behaviors.
• Data Collection : Researchers collect data through various methods, such as surveys, experiments, observations, or interviews.
• Testable Hypotheses : Empirical research often starts with testable hypotheses that are evaluated using collected data.
• Quantitative or Qualitative Data : Data can be quantitative (numerical) or qualitative (non-numerical), depending on the research design.
• Statistical Analysis : Quantitative data often undergo statistical analysis to determine patterns , relationships, or significance.
• Objectivity and Replicability : Empirical research strives for objectivity, minimizing researcher bias . It should be replicable, allowing other researchers to conduct the same study to verify results.
• Conclusions and Generalizations : Empirical research generates findings based on data and aims to make generalizations about larger populations or phenomena.

Importance of Empirical Research

Empirical research plays a pivotal role in advancing knowledge across various disciplines. Its importance extends to academia, industry, and society as a whole. Here are several reasons why empirical research is essential:

• Evidence-Based Knowledge : Empirical research provides a solid foundation of evidence-based knowledge. It enables us to test hypotheses, confirm or refute theories, and build a robust understanding of the world.
• Scientific Progress : In the scientific community, empirical research fuels progress by expanding the boundaries of existing knowledge. It contributes to the development of theories and the formulation of new research questions.
• Problem Solving : Empirical research is instrumental in addressing real-world problems and challenges. It offers insights and data-driven solutions to complex issues in fields like healthcare, economics, and environmental science.
• Informed Decision-Making : In policymaking, business, and healthcare, empirical research informs decision-makers by providing data-driven insights. It guides strategies, investments, and policies for optimal outcomes.
• Quality Assurance : Empirical research is essential for quality assurance and validation in various industries, including pharmaceuticals, manufacturing, and technology. It ensures that products and processes meet established standards.
• Continuous Improvement : Businesses and organizations use empirical research to evaluate performance, customer satisfaction, and product effectiveness. This data-driven approach fosters continuous improvement and innovation.
• Human Advancement : Empirical research in fields like medicine and psychology contributes to the betterment of human health and well-being. It leads to medical breakthroughs, improved therapies, and enhanced psychological interventions.
• Critical Thinking and Problem Solving : Engaging in empirical research fosters critical thinking skills, problem-solving abilities, and a deep appreciation for evidence-based decision-making.

Empirical research empowers us to explore, understand, and improve the world around us. It forms the bedrock of scientific inquiry and drives progress in countless domains, shaping our understanding of both the natural and social sciences.

How to Conduct Empirical Research?

So, you've decided to dive into the world of empirical research. Let's begin by exploring the crucial steps involved in getting started with your research project.

1. Select a Research Topic

Selecting the right research topic is the cornerstone of a successful empirical study. It's essential to choose a topic that not only piques your interest but also aligns with your research goals and objectives. Here's how to go about it:

• Identify Your Interests : Start by reflecting on your passions and interests. What topics fascinate you the most? Your enthusiasm will be your driving force throughout the research process.
• Brainstorm Ideas : Engage in brainstorming sessions to generate potential research topics. Consider the questions you've always wanted to answer or the issues that intrigue you.
• Relevance and Significance : Assess the relevance and significance of your chosen topic. Does it contribute to existing knowledge? Is it a pressing issue in your field of study or the broader community?
• Feasibility : Evaluate the feasibility of your research topic. Do you have access to the necessary resources, data, and participants (if applicable)?

2. Formulate Research Questions

Once you've narrowed down your research topic, the next step is to formulate clear and precise research questions . These questions will guide your entire research process and shape your study's direction. To create effective research questions:

• Specificity : Ensure that your research questions are specific and focused. Vague or overly broad questions can lead to inconclusive results.
• Relevance : Your research questions should directly relate to your chosen topic. They should address gaps in knowledge or contribute to solving a particular problem.
• Testability : Ensure that your questions are testable through empirical methods. You should be able to gather data and analyze it to answer these questions.
• Avoid Bias : Craft your questions in a way that avoids leading or biased language. Maintain neutrality to uphold the integrity of your research.

3. Review Existing Literature

Before you embark on your empirical research journey, it's essential to immerse yourself in the existing body of literature related to your chosen topic. This step, often referred to as a literature review, serves several purposes:

• Contextualization : Understand the historical context and current state of research in your field. What have previous studies found, and what questions remain unanswered?
• Identifying Gaps : Identify gaps or areas where existing research falls short. These gaps will help you formulate meaningful research questions and hypotheses.
• Theory Development : If your study is theoretical, consider how existing theories apply to your topic. If it's empirical, understand how previous studies have approached data collection and analysis.
• Methodological Insights : Learn from the methodologies employed in previous research. What methods were successful, and what challenges did researchers face?

4. Define Variables

Variables are fundamental components of empirical research. They are the factors or characteristics that can change or be manipulated during your study. Properly defining and categorizing variables is crucial for the clarity and validity of your research. Here's what you need to know:

• Independent Variables : These are the variables that you, as the researcher, manipulate or control. They are the "cause" in cause-and-effect relationships.
• Dependent Variables : Dependent variables are the outcomes or responses that you measure or observe. They are the "effect" influenced by changes in independent variables.
• Operational Definitions : To ensure consistency and clarity, provide operational definitions for your variables. Specify how you will measure or manipulate each variable.
• Control Variables : In some studies, controlling for other variables that may influence your dependent variable is essential. These are known as control variables.

Understanding these foundational aspects of empirical research will set a solid foundation for the rest of your journey. Now that you've grasped the essentials of getting started, let's delve deeper into the intricacies of research design.

Empirical Research Design

Now that you've selected your research topic, formulated research questions, and defined your variables, it's time to delve into the heart of your empirical research journey – research design . This pivotal step determines how you will collect data and what methods you'll employ to answer your research questions. Let's explore the various facets of research design in detail.

Types of Empirical Research

Empirical research can take on several forms, each with its own unique approach and methodologies. Understanding the different types of empirical research will help you choose the most suitable design for your study. Here are some common types:

• Experimental Research : In this type, researchers manipulate one or more independent variables to observe their impact on dependent variables. It's highly controlled and often conducted in a laboratory setting.
• Observational Research : Observational research involves the systematic observation of subjects or phenomena without intervention. Researchers are passive observers, documenting behaviors, events, or patterns.
• Survey Research : Surveys are used to collect data through structured questionnaires or interviews. This method is efficient for gathering information from a large number of participants.
• Case Study Research : Case studies focus on in-depth exploration of one or a few cases. Researchers gather detailed information through various sources such as interviews, documents, and observations.
• Qualitative Research : Qualitative research aims to understand behaviors, experiences, and opinions in depth. It often involves open-ended questions, interviews, and thematic analysis.
• Quantitative Research : Quantitative research collects numerical data and relies on statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys.

Your choice of research type should align with your research questions and objectives. Experimental research, for example, is ideal for testing cause-and-effect relationships, while qualitative research is more suitable for exploring complex phenomena.

Experimental Design

Experimental research is a systematic approach to studying causal relationships. It's characterized by the manipulation of one or more independent variables while controlling for other factors. Here are some key aspects of experimental design:

• Control and Experimental Groups : Participants are randomly assigned to either a control group or an experimental group. The independent variable is manipulated for the experimental group but not for the control group.
• Randomization : Randomization is crucial to eliminate bias in group assignment. It ensures that each participant has an equal chance of being in either group.
• Hypothesis Testing : Experimental research often involves hypothesis testing. Researchers formulate hypotheses about the expected effects of the independent variable and use statistical analysis to test these hypotheses.

Observational Design

Observational research entails careful and systematic observation of subjects or phenomena. It's advantageous when you want to understand natural behaviors or events. Key aspects of observational design include:

• Participant Observation : Researchers immerse themselves in the environment they are studying. They become part of the group being observed, allowing for a deep understanding of behaviors.
• Non-Participant Observation : In non-participant observation, researchers remain separate from the subjects. They observe and document behaviors without direct involvement.
• Data Collection Methods : Observational research can involve various data collection methods, such as field notes, video recordings, photographs, or coding of observed behaviors.

Survey Design

Surveys are a popular choice for collecting data from a large number of participants. Effective survey design is essential to ensure the validity and reliability of your data. Consider the following:

• Questionnaire Design : Create clear and concise questions that are easy for participants to understand. Avoid leading or biased questions.
• Sampling Methods : Decide on the appropriate sampling method for your study, whether it's random, stratified, or convenience sampling.
• Data Collection Tools : Choose the right tools for data collection, whether it's paper surveys, online questionnaires, or face-to-face interviews.

Case Study Design

Case studies are an in-depth exploration of one or a few cases to gain a deep understanding of a particular phenomenon. Key aspects of case study design include:

• Single Case vs. Multiple Case Studies : Decide whether you'll focus on a single case or multiple cases. Single case studies are intensive and allow for detailed examination, while multiple case studies provide comparative insights.
• Data Collection Methods : Gather data through interviews, observations, document analysis, or a combination of these methods.

Qualitative vs. Quantitative Research

In empirical research, you'll often encounter the distinction between qualitative and quantitative research . Here's a closer look at these two approaches:

• Qualitative Research : Qualitative research seeks an in-depth understanding of human behavior, experiences, and perspectives. It involves open-ended questions, interviews, and the analysis of textual or narrative data. Qualitative research is exploratory and often used when the research question is complex and requires a nuanced understanding.
• Quantitative Research : Quantitative research collects numerical data and employs statistical analysis to draw conclusions. It involves structured questionnaires, experiments, and surveys. Quantitative research is ideal for testing hypotheses and establishing cause-and-effect relationships.

Understanding the various research design options is crucial in determining the most appropriate approach for your study. Your choice should align with your research questions, objectives, and the nature of the phenomenon you're investigating.

Data Collection for Empirical Research

Now that you've established your research design, it's time to roll up your sleeves and collect the data that will fuel your empirical research. Effective data collection is essential for obtaining accurate and reliable results.

Sampling Methods

Sampling methods are critical in empirical research, as they determine the subset of individuals or elements from your target population that you will study. Here are some standard sampling methods:

• Random Sampling : Random sampling ensures that every member of the population has an equal chance of being selected. It minimizes bias and is often used in quantitative research.
• Stratified Sampling : Stratified sampling involves dividing the population into subgroups or strata based on specific characteristics (e.g., age, gender, location). Samples are then randomly selected from each stratum, ensuring representation of all subgroups.
• Convenience Sampling : Convenience sampling involves selecting participants who are readily available or easily accessible. While it's convenient, it may introduce bias and limit the generalizability of results.
• Snowball Sampling : Snowball sampling is instrumental when studying hard-to-reach or hidden populations. One participant leads you to another, creating a "snowball" effect. This method is common in qualitative research.
• Purposive Sampling : In purposive sampling, researchers deliberately select participants who meet specific criteria relevant to their research questions. It's often used in qualitative studies to gather in-depth information.

The choice of sampling method depends on the nature of your research, available resources, and the degree of precision required. It's crucial to carefully consider your sampling strategy to ensure that your sample accurately represents your target population.

Data Collection Instruments

Data collection instruments are the tools you use to gather information from your participants or sources. These instruments should be designed to capture the data you need accurately. Here are some popular data collection instruments:

• Questionnaires : Questionnaires consist of structured questions with predefined response options. When designing questionnaires, consider the clarity of questions, the order of questions, and the response format (e.g., Likert scale , multiple-choice).
• Interviews : Interviews involve direct communication between the researcher and participants. They can be structured (with predetermined questions) or unstructured (open-ended). Effective interviews require active listening and probing for deeper insights.
• Observations : Observations entail systematically and objectively recording behaviors, events, or phenomena. Researchers must establish clear criteria for what to observe, how to record observations, and when to observe.
• Surveys : Surveys are a common data collection instrument for quantitative research. They can be administered through various means, including online surveys, paper surveys, and telephone surveys.
• Documents and Archives : In some cases, data may be collected from existing documents, records, or archives. Ensure that the sources are reliable, relevant, and properly documented.

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Data Collection Procedures

Data collection procedures outline the step-by-step process for gathering data. These procedures should be meticulously planned and executed to maintain the integrity of your research.

• Training : If you have a research team, ensure that they are trained in data collection methods and protocols. Consistency in data collection is crucial.
• Pilot Testing : Before launching your data collection, conduct a pilot test with a small group to identify any potential problems with your instruments or procedures. Make necessary adjustments based on feedback.
• Data Recording : Establish a systematic method for recording data. This may include timestamps, codes, or identifiers for each data point.
• Data Security : Safeguard the confidentiality and security of collected data. Ensure that only authorized individuals have access to the data.
• Data Storage : Properly organize and store your data in a secure location, whether in physical or digital form. Back up data to prevent loss.

Ethical Considerations

Ethical considerations are paramount in empirical research, as they ensure the well-being and rights of participants are protected.

• Informed Consent : Obtain informed consent from participants, providing clear information about the research purpose, procedures, risks, and their right to withdraw at any time.
• Privacy and Confidentiality : Protect the privacy and confidentiality of participants. Ensure that data is anonymized and sensitive information is kept confidential.
• Beneficence : Ensure that your research benefits participants and society while minimizing harm. Consider the potential risks and benefits of your study.
• Honesty and Integrity : Conduct research with honesty and integrity. Report findings accurately and transparently, even if they are not what you expected.
• Respect for Participants : Treat participants with respect, dignity, and sensitivity to cultural differences. Avoid any form of coercion or manipulation.
• Institutional Review Board (IRB) : If required, seek approval from an IRB or ethics committee before conducting your research, particularly when working with human participants.

Adhering to ethical guidelines is not only essential for the ethical conduct of research but also crucial for the credibility and validity of your study. Ethical research practices build trust between researchers and participants and contribute to the advancement of knowledge with integrity.

With a solid understanding of data collection, including sampling methods, instruments, procedures, and ethical considerations, you are now well-equipped to gather the data needed to answer your research questions.

Empirical Research Data Analysis

Now comes the exciting phase of data analysis, where the raw data you've diligently collected starts to yield insights and answers to your research questions. We will explore the various aspects of data analysis, from preparing your data to drawing meaningful conclusions through statistics and visualization.

Data Preparation

Data preparation is the crucial first step in data analysis. It involves cleaning, organizing, and transforming your raw data into a format that is ready for analysis. Effective data preparation ensures the accuracy and reliability of your results.

• Data Cleaning : Identify and rectify errors, missing values, and inconsistencies in your dataset. This may involve correcting typos, removing outliers, and imputing missing data.
• Data Coding : Assign numerical values or codes to categorical variables to make them suitable for statistical analysis. For example, converting "Yes" and "No" to 1 and 0.
• Data Transformation : Transform variables as needed to meet the assumptions of the statistical tests you plan to use. Common transformations include logarithmic or square root transformations.
• Data Integration : If your data comes from multiple sources, integrate it into a unified dataset, ensuring that variables match and align.
• Data Documentation : Maintain clear documentation of all data preparation steps, as well as the rationale behind each decision. This transparency is essential for replicability.

Effective data preparation lays the foundation for accurate and meaningful analysis. It allows you to trust the results that will follow in the subsequent stages.

Descriptive Statistics

Descriptive statistics help you summarize and make sense of your data by providing a clear overview of its key characteristics. These statistics are essential for understanding the central tendencies, variability, and distribution of your variables. Descriptive statistics include:

• Measures of Central Tendency : These include the mean (average), median (middle value), and mode (most frequent value). They help you understand the typical or central value of your data.
• Measures of Dispersion : Measures like the range, variance, and standard deviation provide insights into the spread or variability of your data points.
• Frequency Distributions : Creating frequency distributions or histograms allows you to visualize the distribution of your data across different values or categories.

Descriptive statistics provide the initial insights needed to understand your data's basic characteristics, which can inform further analysis.

Inferential Statistics

Inferential statistics take your analysis to the next level by allowing you to make inferences or predictions about a larger population based on your sample data. These methods help you test hypotheses and draw meaningful conclusions. Key concepts in inferential statistics include:

• Hypothesis Testing : Hypothesis tests (e.g., t-tests, chi-squared tests) help you determine whether observed differences or associations in your data are statistically significant or occurred by chance.
• Confidence Intervals : Confidence intervals provide a range within which population parameters (e.g., population mean) are likely to fall based on your sample data.
• Regression Analysis : Regression models (linear, logistic, etc.) help you explore relationships between variables and make predictions.
• Analysis of Variance (ANOVA) : ANOVA tests are used to compare means between multiple groups, allowing you to assess whether differences are statistically significant.

Inferential statistics are powerful tools for drawing conclusions from your data and assessing the generalizability of your findings to the broader population.

Qualitative Data Analysis

Qualitative data analysis is employed when working with non-numerical data, such as text, interviews, or open-ended survey responses. It focuses on understanding the underlying themes, patterns, and meanings within qualitative data. Qualitative analysis techniques include:

• Thematic Analysis : Identifying and analyzing recurring themes or patterns within textual data.
• Content Analysis : Categorizing and coding qualitative data to extract meaningful insights.
• Grounded Theory : Developing theories or frameworks based on emergent themes from the data.
• Narrative Analysis : Examining the structure and content of narratives to uncover meaning.

Qualitative data analysis provides a rich and nuanced understanding of complex phenomena and human experiences.

Data Visualization

Data visualization is the art of representing data graphically to make complex information more understandable and accessible. Effective data visualization can reveal patterns, trends, and outliers in your data. Common types of data visualization include:

• Bar Charts and Histograms : Used to display the distribution of categorical or discrete data.
• Line Charts : Ideal for showing trends and changes in data over time.
• Scatter Plots : Visualize relationships and correlations between two variables.
• Pie Charts : Display the composition of a whole in terms of its parts.
• Heatmaps : Depict patterns and relationships in multidimensional data through color-coding.
• Box Plots : Provide a summary of the data distribution, including outliers.
• Interactive Dashboards : Create dynamic visualizations that allow users to explore data interactively.

Data visualization not only enhances your understanding of the data but also serves as a powerful communication tool to convey your findings to others.

As you embark on the data analysis phase of your empirical research, remember that the specific methods and techniques you choose will depend on your research questions, data type, and objectives. Effective data analysis transforms raw data into valuable insights, bringing you closer to the answers you seek.

How to Report Empirical Research Results?

At this stage, you get to share your empirical research findings with the world. Effective reporting and presentation of your results are crucial for communicating your research's impact and insights.

1. Write the Research Paper

Writing a research paper is the culmination of your empirical research journey. It's where you synthesize your findings, provide context, and contribute to the body of knowledge in your field.

• Title and Abstract : Craft a clear and concise title that reflects your research's essence. The abstract should provide a brief summary of your research objectives, methods, findings, and implications.
• Introduction : In the introduction, introduce your research topic, state your research questions or hypotheses, and explain the significance of your study. Provide context by discussing relevant literature.
• Methods : Describe your research design, data collection methods, and sampling procedures. Be precise and transparent, allowing readers to understand how you conducted your study.
• Results : Present your findings in a clear and organized manner. Use tables, graphs, and statistical analyses to support your results. Avoid interpreting your findings in this section; focus on the presentation of raw data.
• Discussion : Interpret your findings and discuss their implications. Relate your results to your research questions and the existing literature. Address any limitations of your study and suggest avenues for future research.
• Conclusion : Summarize the key points of your research and its significance. Restate your main findings and their implications.
• References : Cite all sources used in your research following a specific citation style (e.g., APA, MLA, Chicago). Ensure accuracy and consistency in your citations.
• Appendices : Include any supplementary material, such as questionnaires, data coding sheets, or additional analyses, in the appendices.

Writing a research paper is a skill that improves with practice. Ensure clarity, coherence, and conciseness in your writing to make your research accessible to a broader audience.

2. Create Visuals and Tables

Visuals and tables are powerful tools for presenting complex data in an accessible and understandable manner.

• Clarity : Ensure that your visuals and tables are clear and easy to interpret. Use descriptive titles and labels.
• Consistency : Maintain consistency in formatting, such as font size and style, across all visuals and tables.
• Appropriateness : Choose the most suitable visual representation for your data. Bar charts, line graphs, and scatter plots work well for different types of data.
• Simplicity : Avoid clutter and unnecessary details. Focus on conveying the main points.
• Accessibility : Make sure your visuals and tables are accessible to a broad audience, including those with visual impairments.
• Captions : Include informative captions that explain the significance of each visual or table.

Compelling visuals and tables enhance the reader's understanding of your research and can be the key to conveying complex information efficiently.

3. Interpret Findings

Interpreting your findings is where you bridge the gap between data and meaning. It's your opportunity to provide context, discuss implications, and offer insights. When interpreting your findings:

• Relate to Research Questions : Discuss how your findings directly address your research questions or hypotheses.
• Compare with Literature : Analyze how your results align with or deviate from previous research in your field. What insights can you draw from these comparisons?
• Discuss Limitations : Be transparent about the limitations of your study. Address any constraints, biases, or potential sources of error.
• Practical Implications : Explore the real-world implications of your findings. How can they be applied or inform decision-making?
• Future Research Directions : Suggest areas for future research based on the gaps or unanswered questions that emerged from your study.

Interpreting findings goes beyond simply presenting data; it's about weaving a narrative that helps readers grasp the significance of your research in the broader context.

With your research paper written, structured, and enriched with visuals, and your findings expertly interpreted, you are now prepared to communicate your research effectively. Sharing your insights and contributing to the body of knowledge in your field is a significant accomplishment in empirical research.

Examples of Empirical Research

To solidify your understanding of empirical research, let's delve into some real-world examples across different fields. These examples will illustrate how empirical research is applied to gather data, analyze findings, and draw conclusions.

Social Sciences

In the realm of social sciences, consider a sociological study exploring the impact of socioeconomic status on educational attainment. Researchers gather data from a diverse group of individuals, including their family backgrounds, income levels, and academic achievements.

Through statistical analysis, they can identify correlations and trends, revealing whether individuals from lower socioeconomic backgrounds are less likely to attain higher levels of education. This empirical research helps shed light on societal inequalities and informs policymakers on potential interventions to address disparities in educational access.

Environmental Science

Environmental scientists often employ empirical research to assess the effects of environmental changes. For instance, researchers studying the impact of climate change on wildlife might collect data on animal populations, weather patterns, and habitat conditions over an extended period.

By analyzing this empirical data, they can identify correlations between climate fluctuations and changes in wildlife behavior, migration patterns, or population sizes. This empirical research is crucial for understanding the ecological consequences of climate change and informing conservation efforts.

Business and Economics

In the business world, empirical research is essential for making data-driven decisions. Consider a market research study conducted by a business seeking to launch a new product. They collect data through surveys, focus groups, and consumer behavior analysis.

By examining this empirical data, the company can gauge consumer preferences, demand, and potential market size. Empirical research in business helps guide product development, pricing strategies, and marketing campaigns, increasing the likelihood of a successful product launch.

Psychological studies frequently rely on empirical research to understand human behavior and cognition. For instance, a psychologist interested in examining the impact of stress on memory might design an experiment. Participants are exposed to stress-inducing situations, and their memory performance is assessed through various tasks.

By analyzing the data collected, the psychologist can determine whether stress has a significant effect on memory recall. This empirical research contributes to our understanding of the complex interplay between psychological factors and cognitive processes.

These examples highlight the versatility and applicability of empirical research across diverse fields. Whether in medicine, social sciences, environmental science, business, or psychology, empirical research serves as a fundamental tool for gaining insights, testing hypotheses, and driving advancements in knowledge and practice.

Conclusion for Empirical Research

Empirical research is a powerful tool for gaining insights, testing hypotheses, and making informed decisions. By following the steps outlined in this guide, you've learned how to select research topics, collect data, analyze findings, and effectively communicate your research to the world. Remember, empirical research is a journey of discovery, and each step you take brings you closer to a deeper understanding of the world around you. Whether you're a scientist, a student, or someone curious about the process, the principles of empirical research empower you to explore, learn, and contribute to the ever-expanding realm of knowledge.

How to Collect Data for Empirical Research?

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Appinio is more than just a market research platform; it's a catalyst for transforming the way you approach empirical research, making it exciting, intuitive, and seamlessly integrated into your decision-making process.

Here's why Appinio is the go-to solution for empirical research:

• From Questions to Insights in Minutes : With Appinio's streamlined process, you can go from formulating your research questions to obtaining actionable insights in a matter of minutes, saving you time and effort.
• Intuitive Platform for Everyone : No need for a PhD in research; Appinio's platform is designed to be intuitive and user-friendly, ensuring that anyone can navigate and utilize it effectively.
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Qualitative vs Quantitative Research Methods & Data Analysis

Saul Mcleod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul Mcleod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

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Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

On This Page:

What is the difference between quantitative and qualitative?

The main difference between quantitative and qualitative research is the type of data they collect and analyze.

Quantitative research collects numerical data and analyzes it using statistical methods. The aim is to produce objective, empirical data that can be measured and expressed in numerical terms. Quantitative research is often used to test hypotheses, identify patterns, and make predictions.

Qualitative research , on the other hand, collects non-numerical data such as words, images, and sounds. The focus is on exploring subjective experiences, opinions, and attitudes, often through observation and interviews.

Qualitative research aims to produce rich and detailed descriptions of the phenomenon being studied, and to uncover new insights and meanings.

Quantitative data is information about quantities, and therefore numbers, and qualitative data is descriptive, and regards phenomenon which can be observed but not measured, such as language.

What Is Qualitative Research?

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data, such as language. Qualitative research can be used to understand how an individual subjectively perceives and gives meaning to their social reality.

Qualitative data is non-numerical data, such as text, video, photographs, or audio recordings. This type of data can be collected using diary accounts or in-depth interviews and analyzed using grounded theory or thematic analysis.

Qualitative research is multimethod in focus, involving an interpretive, naturalistic approach to its subject matter. This means that qualitative researchers study things in their natural settings, attempting to make sense of, or interpret, phenomena in terms of the meanings people bring to them. Denzin and Lincoln (1994, p. 2)

Interest in qualitative data came about as the result of the dissatisfaction of some psychologists (e.g., Carl Rogers) with the scientific study of psychologists such as behaviorists (e.g., Skinner ).

Since psychologists study people, the traditional approach to science is not seen as an appropriate way of carrying out research since it fails to capture the totality of human experience and the essence of being human.  Exploring participants’ experiences is known as a phenomenological approach (re: Humanism ).

Qualitative research is primarily concerned with meaning, subjectivity, and lived experience. The goal is to understand the quality and texture of people’s experiences, how they make sense of them, and the implications for their lives.

Qualitative research aims to understand the social reality of individuals, groups, and cultures as nearly as possible as participants feel or live it. Thus, people and groups are studied in their natural setting.

Some examples of qualitative research questions are provided, such as what an experience feels like, how people talk about something, how they make sense of an experience, and how events unfold for people.

Research following a qualitative approach is exploratory and seeks to explain ‘how’ and ‘why’ a particular phenomenon, or behavior, operates as it does in a particular context. It can be used to generate hypotheses and theories from the data.

Qualitative Methods

There are different types of qualitative research methods, including diary accounts, in-depth interviews , documents, focus groups , case study research , and ethnography.

The results of qualitative methods provide a deep understanding of how people perceive their social realities and in consequence, how they act within the social world.

The researcher has several methods for collecting empirical materials, ranging from the interview to direct observation, to the analysis of artifacts, documents, and cultural records, to the use of visual materials or personal experience. Denzin and Lincoln (1994, p. 14)

Here are some examples of qualitative data:

Interview transcripts : Verbatim records of what participants said during an interview or focus group. They allow researchers to identify common themes and patterns, and draw conclusions based on the data. Interview transcripts can also be useful in providing direct quotes and examples to support research findings.

Observations : The researcher typically takes detailed notes on what they observe, including any contextual information, nonverbal cues, or other relevant details. The resulting observational data can be analyzed to gain insights into social phenomena, such as human behavior, social interactions, and cultural practices.

Unstructured interviews : generate qualitative data through the use of open questions.  This allows the respondent to talk in some depth, choosing their own words.  This helps the researcher develop a real sense of a person’s understanding of a situation.

Diaries or journals : Written accounts of personal experiences or reflections.

Notice that qualitative data could be much more than just words or text. Photographs, videos, sound recordings, and so on, can be considered qualitative data. Visual data can be used to understand behaviors, environments, and social interactions.

Qualitative Data Analysis

Qualitative research is endlessly creative and interpretive. The researcher does not just leave the field with mountains of empirical data and then easily write up his or her findings.

Qualitative interpretations are constructed, and various techniques can be used to make sense of the data, such as content analysis, grounded theory (Glaser & Strauss, 1967), thematic analysis (Braun & Clarke, 2006), or discourse analysis.

For example, thematic analysis is a qualitative approach that involves identifying implicit or explicit ideas within the data. Themes will often emerge once the data has been coded.

Key Features

• Events can be understood adequately only if they are seen in context. Therefore, a qualitative researcher immerses her/himself in the field, in natural surroundings. The contexts of inquiry are not contrived; they are natural. Nothing is predefined or taken for granted.
• Qualitative researchers want those who are studied to speak for themselves, to provide their perspectives in words and other actions. Therefore, qualitative research is an interactive process in which the persons studied teach the researcher about their lives.
• The qualitative researcher is an integral part of the data; without the active participation of the researcher, no data exists.
• The study’s design evolves during the research and can be adjusted or changed as it progresses. For the qualitative researcher, there is no single reality. It is subjective and exists only in reference to the observer.
• The theory is data-driven and emerges as part of the research process, evolving from the data as they are collected.

Limitations of Qualitative Research

• Because of the time and costs involved, qualitative designs do not generally draw samples from large-scale data sets.
• The problem of adequate validity or reliability is a major criticism. Because of the subjective nature of qualitative data and its origin in single contexts, it is difficult to apply conventional standards of reliability and validity. For example, because of the central role played by the researcher in the generation of data, it is not possible to replicate qualitative studies.
• Also, contexts, situations, events, conditions, and interactions cannot be replicated to any extent, nor can generalizations be made to a wider context than the one studied with confidence.
• The time required for data collection, analysis, and interpretation is lengthy. Analysis of qualitative data is difficult, and expert knowledge of an area is necessary to interpret qualitative data. Great care must be taken when doing so, for example, looking for mental illness symptoms.

Advantages of Qualitative Research

• Because of close researcher involvement, the researcher gains an insider’s view of the field. This allows the researcher to find issues that are often missed (such as subtleties and complexities) by the scientific, more positivistic inquiries.
• Qualitative descriptions can be important in suggesting possible relationships, causes, effects, and dynamic processes.
• Qualitative analysis allows for ambiguities/contradictions in the data, which reflect social reality (Denscombe, 2010).
• Qualitative research uses a descriptive, narrative style; this research might be of particular benefit to the practitioner as she or he could turn to qualitative reports to examine forms of knowledge that might otherwise be unavailable, thereby gaining new insight.

What Is Quantitative Research?

Quantitative research involves the process of objectively collecting and analyzing numerical data to describe, predict, or control variables of interest.

The goals of quantitative research are to test causal relationships between variables , make predictions, and generalize results to wider populations.

Quantitative researchers aim to establish general laws of behavior and phenomenon across different settings/contexts. Research is used to test a theory and ultimately support or reject it.

Quantitative Methods

Experiments typically yield quantitative data, as they are concerned with measuring things.  However, other research methods, such as controlled observations and questionnaires , can produce both quantitative information.

For example, a rating scale or closed questions on a questionnaire would generate quantitative data as these produce either numerical data or data that can be put into categories (e.g., “yes,” “no” answers).

Experimental methods limit how research participants react to and express appropriate social behavior.

Findings are, therefore, likely to be context-bound and simply a reflection of the assumptions that the researcher brings to the investigation.

There are numerous examples of quantitative data in psychological research, including mental health. Here are a few examples:

Another example is the Experience in Close Relationships Scale (ECR), a self-report questionnaire widely used to assess adult attachment styles .

The ECR provides quantitative data that can be used to assess attachment styles and predict relationship outcomes.

Neuroimaging data : Neuroimaging techniques, such as MRI and fMRI, provide quantitative data on brain structure and function.

This data can be analyzed to identify brain regions involved in specific mental processes or disorders.

For example, the Beck Depression Inventory (BDI) is a clinician-administered questionnaire widely used to assess the severity of depressive symptoms in individuals.

The BDI consists of 21 questions, each scored on a scale of 0 to 3, with higher scores indicating more severe depressive symptoms. 

Quantitative Data Analysis

Statistics help us turn quantitative data into useful information to help with decision-making. We can use statistics to summarize our data, describing patterns, relationships, and connections. Statistics can be descriptive or inferential.

Descriptive statistics help us to summarize our data. In contrast, inferential statistics are used to identify statistically significant differences between groups of data (such as intervention and control groups in a randomized control study).

• Quantitative researchers try to control extraneous variables by conducting their studies in the lab.
• The research aims for objectivity (i.e., without bias) and is separated from the data.
• The design of the study is determined before it begins.
• For the quantitative researcher, the reality is objective, exists separately from the researcher, and can be seen by anyone.
• Research is used to test a theory and ultimately support or reject it.

Limitations of Quantitative Research

• Context: Quantitative experiments do not take place in natural settings. In addition, they do not allow participants to explain their choices or the meaning of the questions they may have for those participants (Carr, 1994).
• Researcher expertise: Poor knowledge of the application of statistical analysis may negatively affect analysis and subsequent interpretation (Black, 1999).
• Variability of data quantity: Large sample sizes are needed for more accurate analysis. Small-scale quantitative studies may be less reliable because of the low quantity of data (Denscombe, 2010). This also affects the ability to generalize study findings to wider populations.
• Confirmation bias: The researcher might miss observing phenomena because of focus on theory or hypothesis testing rather than on the theory of hypothesis generation.

Advantages of Quantitative Research

• Scientific objectivity: Quantitative data can be interpreted with statistical analysis, and since statistics are based on the principles of mathematics, the quantitative approach is viewed as scientifically objective and rational (Carr, 1994; Denscombe, 2010).
• Useful for testing and validating already constructed theories.
• Rapid analysis: Sophisticated software removes much of the need for prolonged data analysis, especially with large volumes of data involved (Antonius, 2003).
• Replication: Quantitative data is based on measured values and can be checked by others because numerical data is less open to ambiguities of interpretation.
• Hypotheses can also be tested because of statistical analysis (Antonius, 2003).

Antonius, R. (2003). Interpreting quantitative data with SPSS . Sage.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated approach to research design, measurement and statistics . Sage.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology . Qualitative Research in Psychology , 3, 77–101.

Carr, L. T. (1994). The strengths and weaknesses of quantitative and qualitative research : what method for nursing? Journal of advanced nursing, 20(4) , 716-721.

Denscombe, M. (2010). The Good Research Guide: for small-scale social research. McGraw Hill.

Denzin, N., & Lincoln. Y. (1994). Handbook of Qualitative Research. Thousand Oaks, CA, US: Sage Publications Inc.

Glaser, B. G., Strauss, A. L., & Strutzel, E. (1968). The discovery of grounded theory; strategies for qualitative research. Nursing research, 17(4) , 364.

Minichiello, V. (1990). In-Depth Interviewing: Researching People. Longman Cheshire.

Punch, K. (1998). Introduction to Social Research: Quantitative and Qualitative Approaches. London: Sage

Further Information

• Designing qualitative research
• Methods of data collection and analysis
• Introduction to quantitative and qualitative research
• Checklists for improving rigour in qualitative research: a case of the tail wagging the dog?
• Qualitative research in health care: Analysing qualitative data
• Qualitative data analysis: the framework approach
• Using the framework method for the analysis of
• Qualitative data in multi-disciplinary health research
• Content Analysis
• Grounded Theory
• Thematic Analysis

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Empirical Research: A Comprehensive Guide for Academics 

Empirical research relies on gathering and studying real, observable data. The term ’empirical’ comes from the Greek word ’empeirikos,’ meaning ‘experienced’ or ‘based on experience.’ So, what is empirical research? Instead of using theories or opinions, empirical research depends on real data obtained through direct observation or experimentation. 

Why Empirical Research?

Empirical research plays a key role in checking or improving current theories, providing a systematic way to grow knowledge across different areas. By focusing on objectivity, it makes research findings more trustworthy, which is critical in research fields like medicine, psychology, economics, and public policy. In the end, the strengths of empirical research lie in deepening our awareness of the world and improving our capacity to tackle problems wisely. 1,2  

Qualitative and Quantitative Methods

There are two main types of empirical research methods – qualitative and quantitative. 3,4 Qualitative research delves into intricate phenomena using non-numerical data, such as interviews or observations, to offer in-depth insights into human experiences. In contrast, quantitative research analyzes numerical data to spot patterns and relationships, aiming for objectivity and the ability to apply findings to a wider context. 

Steps for Conducting Empirical Research

When it comes to conducting research, there are some simple steps that researchers can follow. 5,6  

• Create Research Hypothesis:  Clearly state the specific question you want to answer or the hypothesis you want to explore in your study. 
• Examine Existing Research:  Read and study existing research on your topic. Understand what’s already known, identify existing gaps in knowledge, and create a framework for your own study based on what you learn. 
• Plan Your Study:  Decide how you’ll conduct your research—whether through qualitative methods, quantitative methods, or a mix of both. Choose suitable techniques like surveys, experiments, interviews, or observations based on your research question. 
• Develop Research Instruments:  Create reliable research collection tools, such as surveys or questionnaires, to help you collate data. Ensure these tools are well-designed and effective. 
• Collect Data:  Systematically gather the information you need for your research according to your study design and protocols using the chosen research methods. 
• Data Analysis:  Analyze the collected data using suitable statistical or qualitative methods that align with your research question and objectives. 
• Interpret Results:  Understand and explain the significance of your analysis results in the context of your research question or hypothesis. 
• Draw Conclusions:  Summarize your findings and draw conclusions based on the evidence. Acknowledge any study limitations and propose areas for future research. 

Advantages of Empirical Research

Empirical research is valuable because it stays objective by relying on observable data, lessening the impact of personal biases. This objectivity boosts the trustworthiness of research findings. Also, using precise quantitative methods helps in accurate measurement and statistical analysis. This precision ensures researchers can draw reliable conclusions from numerical data, strengthening our understanding of the studied phenomena. 4  

Disadvantages of Empirical Research

While empirical research has notable strengths, researchers must also be aware of its limitations when deciding on the right research method for their study.4 One significant drawback of empirical research is the risk of oversimplifying complex phenomena, especially when relying solely on quantitative methods. These methods may struggle to capture the richness and nuances present in certain social, cultural, or psychological contexts. Another challenge is the potential for confounding variables or biases during data collection, impacting result accuracy.  

Tips for Empirical Writing

In empirical research, the writing is usually done in research papers, articles, or reports. The empirical writing follows a set structure, and each section has a specific role. Here are some tips for your empirical writing. 7   

• Define Your Objectives:  When you write about your research, start by making your goals clear. Explain what you want to find out or prove in a simple and direct way. This helps guide your research and lets others know what you have set out to achieve. 
• Be Specific in Your Literature Review:  In the part where you talk about what others have studied before you, focus on research that directly relates to your research question. Keep it short and pick studies that help explain why your research is important. This part sets the stage for your work. 
• Explain Your Methods Clearly : When you talk about how you did your research (Methods), explain it in detail. Be clear about your research plan, who took part, and what you did; this helps others understand and trust your study. Also, be honest about any rules you follow to make sure your study is ethical and reproducible. 
• Share Your Results Clearly : After doing your empirical research, share what you found in a simple way. Use tables or graphs to make it easier for your audience to understand your research. Also, talk about any numbers you found and clearly state if they are important or not. Ensure that others can see why your research findings matter. 
• Talk About What Your Findings Mean:  In the part where you discuss your research results, explain what they mean. Discuss why your findings are important and if they connect to what others have found before. Be honest about any problems with your study and suggest ideas for more research in the future. 
• Wrap It Up Clearly:  Finally, end your empirical research paper by summarizing what you found and why it’s important. Remind everyone why your study matters. Keep your writing clear and fix any mistakes before you share it. Ask someone you trust to read it and give you feedback before you finish. 

References:  

• Empirical Research in the Social Sciences and Education, Penn State University Libraries. Available online at  https://guides.libraries.psu.edu/emp  
• How to conduct empirical research, Emerald Publishing. Available online at  https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research  
• Empirical Research: Quantitative & Qualitative, Arrendale Library, Piedmont University. Available online at  https://library.piedmont.edu/empirical-research  
• Bouchrika, I.  What Is Empirical Research? Definition, Types & Samples  in 2024. Research.com, January 2024. Available online at  https://research.com/research/what-is-empirical-research  
• Quantitative and Empirical Research vs. Other Types of Research. California State University, April 2023. Available online at  https://libguides.csusb.edu/quantitative  
• Empirical Research, Definitions, Methods, Types and Examples, Studocu.com website. Available online at  https://www.studocu.com/row/document/uganda-christian-university/it-research-methods/emperical-research-definitions-methods-types-and-examples/55333816  
• Writing an Empirical Paper in APA Style. Psychology Writing Center, University of Washington. Available online at  https://psych.uw.edu/storage/writing_center/APApaper.pdf  

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• How to Write a Scientific Paper in 10 Steps 
• What is a Literature Review? How to Write It (with Examples)
• What is an Argumentative Essay? How to Write It (With Examples)
• Ethical Research Practices For Research with Human Subjects

Ethics in Science: Importance, Principles & Guidelines 

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Quantitative vs. Qualitative Research: The Differences Explained

From Scribbr 

Empirical Research

What is empirical research.

"Empirical research is research that is based on observation and measurement of phenomena, as directly experienced by the researcher. The data thus gathered may be compared against a theory or hypothesis, but the results are still based on real life experience. The data gathered is all primary data, although secondary data from a literature review may form the theoretical background."

Characteristics of Empirical Research

Emerald Publishing's  guide to conducting empirical research  identifies a number of common elements to empirical research: 

A  research question , which will determine research objectives.

A particular and planned  design  for the research, which will depend on the question and which will find ways of answering it with appropriate use of resources.

The gathering of  primary data , which is then analysed.

A particular  methodology  for collecting and analysing the data, such as an experiment or survey.

The limitation of the data to a particular group, area or time scale, known as a  sample  [emphasis added]: for example, a specific number of employees of a particular company type, or all users of a library over a given time scale. The sample should be somehow representative of a wider population.

The ability to  recreate  the study and test the results. This is known as  reliability .

The ability to  generalize  from the findings to a larger sample and to other situations.

If you see these elements in a research article, you can feel confident that you have found empirical research. Emerald's guide goes into more detail on each element. 

Emerald Publishing (n.d.). How to... conduct empirical research. https://www.emeraldgrouppublishing.com/how-to/research-methods/conduct-empirical-research-l 

• Quantitative vs. Qualitative
• Data Collection Methods
• Analyzing Data

When collecting and analyzing data,  quantitative research  deals with numbers and statistics, while  qualitative research  deals with words and meanings. Both are important for gaining different kinds of knowledge.

Quantitative research

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions.

Qualitative research

Common qualitative methods include interviews with open-ended questions, observations described in words, and literature reviews that explore concepts and theories.

Streefkerk, R. (2022, February 7). Qualitative vs. quantitative research: Differences, examples & methods. Scibbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/ 

Quantitative and qualitative data can be collected using various methods. It is important to use a  data collection  method that will help answer your research question(s).

Many data collection methods can be either qualitative or quantitative. For example, in surveys, observations or  case studies , your data can be represented as numbers (e.g. using rating scales or counting frequencies) or as words (e.g. with open-ended questions or descriptions of what you observe).

However, some methods are more commonly used in one type or the other.

Quantitative data collection methods

• Surveys :  List of closed or multiple choice questions that is distributed to a  sample  (online, in person, or over the phone).
• Experiments :  Situation in which  variables  are controlled and manipulated to establish cause-and-effect relationships.
• Observations:  Observing subjects in a natural environment where variables can’t be controlled.

Qualitative data collection methods

• Interviews : Asking open-ended questions verbally to respondents.
• Focus groups:  Discussion among a group of people about a topic to gather opinions that can be used for further research.
• Ethnography : Participating in a community or organization for an extended period of time to closely observe culture and behavior.
• Literature review :  Survey of published works by other authors.

When to use qualitative vs. quantitative research

A rule of thumb for deciding whether to use qualitative or quantitative data is:

• Use quantitative research if you want to  confirm or test something  (a theory or hypothesis)
• Use qualitative research if you want to  understand something  (concepts, thoughts, experiences)

For most  research topics  you can choose a qualitative, quantitative or  mixed methods approach . Which type you choose depends on, among other things, whether you’re taking an  inductive vs. deductive research approach ; your  research question(s) ; whether you’re doing  experimental ,  correlational , or  descriptive research ; and practical considerations such as time, money, availability of data, and access to respondents.

Streefkerk, R. (2022, February 7).  Qualitative vs. quantitative research: Differences, examples & methods.  Scibbr. https://www.scribbr.com/methodology/qualitative-quantitative-research/ 

Qualitative or quantitative data by itself can’t prove or demonstrate anything, but has to be analyzed to show its meaning in relation to the research questions. The method of analysis differs for each type of data.

Analyzing quantitative data

Quantitative data is based on numbers. Simple math or more advanced  statistical analysis  is used to discover commonalities or patterns in the data. The results are often reported in graphs and tables.

Applications such as Excel, SPSS, or R can be used to calculate things like:

• Average scores
• The number of times a particular answer was given
• The  correlation or causation  between two or more variables
• The  reliability and validity  of the results

Analyzing qualitative data

Qualitative data is more difficult to analyze than quantitative data. It consists of text, images or videos instead of numbers.

Some common approaches to analyzing qualitative data include:

• Qualitative content analysis : Tracking the occurrence, position and meaning of words or phrases
• Thematic analysis : Closely examining the data to identify the main themes and patterns
• Discourse analysis : Studying how communication works in social contexts

Comparison of Research Processes

Creswell, J. W., & Creswell, J. D. (2018).  Research design : qualitative, quantitative, and mixed methods approaches  (Fifth). SAGE Publications.

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Quantitative and Empirical Research vs. Other Types of Research: Quantitative Research

• Quantitative Research
• Other Types of Research
• What are Scholarly Journals?

     P rofessors often want you to use scholarly journal articles for your assignments.

     Sometimes, they will require you to use scholarly journal articles that contain quantitative research .

DEFINITIONS

QUANTITATIVE

     Quantitative research looks at factors that can actually be measured in some way, in other words, quantified . It produces numerical results that can be analyzed statistically.

     Quantitative research commonly involves experimentation, surveys, or questionnaires in the context of a large, randomly selected group.

     The term  empirical research  is often used as a synonym for quantitative research, but strictly speaking, empirical research is simply any form of research based upon direct observation. It might also be quantitative, but it might not.

PLEASE NOTE: Some professors use these two terms interchangeably.  When this occurs, they are usually referring to articles that fit the quantitative description above.

HINT: Don't use the words "quantitative" or "empirical" in your keyword searches.  They usually do not appear in article titles, abstracts, or subject words.  Instead, check the articles you find to see if some sort of numerical measuring and statistical analysis is present along with the characteristics listed on the right.

CHARACTERISTICS OF QUANTITATIVE RESEARCH

      W atch for these features when determining if an article has quantitative research. They may appear in the abstract, or you may need to skim the text of the article to find them.

• Introduction : a statement of background or purpose (what was being studied and why). May review prior studies on the same topic.
• Description of the design and/or method of the study (the experimental group or sample , control, variables, number of test subjects, test conditions, etc.)
• Results , or report of the findings (in numeric form as tables, charts, or graphs, etc., often with statistical analysis)
• Conclusions that can be drawn from the results (may be labeled  discussion or significance )
• Footnotes and/or a bibliography
• Author credentials (degrees earned, where they work, etc.)  

SAMPLE QUANTITATIVE RESEARCH ARTICLES

• Relations Between Trait Impulsivity, Behavioral Impulsivity, Physiological Arousal, and Risky Sexual Behavior Among Young Men
• Nocturnal Heart Rate Variability in Patients Treated with Cognitive–Behavioral Therapy for Insomnia.
• Characterisation of Mainstream and Passive Vapors Emitted by Selected Electronic Cigarettes

• Next: Other Types of Research >>
• Last Updated: Apr 6, 2023 8:16 AM
• URL: https://libguides.csusb.edu/quantitative

Research Basics - All Subjects

• Getting Started
• Evaluating Resources
• Generating Search Terms
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• Subject Guides: Find your Major This link opens in a new window
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• Qualitative, Quantitative & Empirical Research

Quantitative Research

Qualitative Research

Empirical Studies

• An empirical study is research derived from actual observation or experimentation.
• The written articles resulting from empirical studies undergo a rigorous review by experts in the field of study prior to being published in journals.
• After passing this review the articles are published in a scholarly, peer-reviewed, or academic journal.
• Empirical study articles will generally contain the following features: Abstract - This is a summary of the article. Introduction - This is often identified as the hypothesis of the study and describes the researcher's intent.            Method - A description of how the research was conducted. Results - A description of the findings obtained as a result of the research. Most often answers the hypothesis. Conclusion - A description of how/if the findings were successful and the impact made as a result. References - A detailed listing of all resources cited in the article that support the written work.              

Mixed Methods Research

Mixed Methods Research uses strategies from both qualitative and quantitative research processes to provide a greater understanding of the subject matter.

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• What is empirical research: Methods, types & examples

Defne Çobanoğlu

Having opinions on matters based on observation is okay sometimes. Same as having theories on the subject you want to solve. However, some theories need to be tested. Just like Robert Oppenheimer says, “Theory will take you only so far .” 

In that case, when you have your research question ready and you want to make sure it is correct, the next step would be experimentation. Because only then you can test your ideas and collect tangible information. Now, let us start with the empirical research definition:

• What is empirical research?

Empirical research is a research type where the aim of the study is based on finding concrete and provable evidence . The researcher using this method to draw conclusions can use both quantitative and qualitative methods. Different than theoretical research, empirical research uses scientific experimentation and investigation. 

Using experimentation makes sense when you need to have tangible evidence to act on whatever you are planning to do. As the researcher, you can be a marketer who is planning on creating a new ad for the target audience, or you can be an educator who wants the best for the students. No matter how big or small, data gathered from the real world using this research helps break down the question at hand. 

• When to use empirical research?

Empirical research methods are used when the researcher needs to gather data analysis on direct, observable, and measurable data. Research findings are a great way to make grounded ideas. Here are some situations when one may need to do empirical research:

1. When quantitative or qualitative data is needed

There are times when a researcher, marketer, or producer needs to gather data on specific research questions to make an informed decision. And the concrete data gathered in the research process gives a good starting point.

2. When you need to test a hypothesis

When you have a hypothesis on a subject, you can test the hypothesis through observation or experiment. Making a planned study is a great way to collect information and test whether or not your hypothesis is correct.

3. When you want to establish causality

Experimental research is a good way to explore whether or not there is any correlation between two variables. Researchers usually establish causality by changing a variable and observing if the independent variable changes accordingly.

• Types of empirical research

The aim of empirical research is to collect information about a subject from the people by doing experimentation and other data collection methods. However, the methods and data collected are divided into two groups: one collects numerical data, and the other one collects opinion-like data. Let us see the difference between these two types:

Quantitative research

Quantitative research methods are used to collect data in a numerical way. Therefore, the results gathered by these methods will be numbers, statistics, charts, etc. The results can be used to quantify behaviors, opinions, and other variables. Quantitative research methods are surveys, questionnaires, and experimental research.

Qualitiative research

Qualitative research methods are not used to collect numerical answers, instead, they are used to collect the participants’ reasons, opinions, and other meaningful aspects. Qualitative research methods include case studies, observations, interviews, focus groups, and text analysis.

• 5 steps to conduct empirical research

Necessary steps for empirical research

When you want to collect direct and concrete data on a subject, empirical research is a great way to go. And, just like every other project and research, it is best to have a clear structure in mind. This is even more important in studies that may take a long time, such as experiments that take years. Let us look at a clear plan on how to do empirical research:

1. Define the research question

The very first step of every study is to have the question you will explore ready. Because you do not want to change your mind in the middle of the study after investing and spending time on the experimentation.

2. Go through relevant literature

This is the step where you sit down and do a desk research where you gather relevant data and see if other researchers have tried to explore similar research questions. If so, you can see how well they were able to answer the question or what kind of difficulties they faced during the research process.

3. Decide on the methodology

Once you are done going through the relevant literature, you can decide on which method or methods you can use. The appropriate methods are observation, experimentation, surveys, interviews, focus groups, etc.

4. Do data analysis

When you get to this step, it means you have successfully gathered enough data to make a data analysis. Now, all you need to do is look at the data you collected and make an informed analysis.

5. Conclusion

This is the last step, where you are finished with the experimentation and data analysis process. Now, it is time to decide what to do with this information. You can publish a paper and make informed decisions about whatever your goal is.

• Empirical research methodologies

Some essential methodologies to conduct empirical research

The aim of this type of research is to explore brand-new evidence and facts. Therefore, the methods should be primary and gathered in real life, directly from the people. There is more than one method for this goal, and it is up to the researcher to use which one(s). Let us see the methods of empirical research: 

• Observation

The method of observation is a great way to collect information on people without the effect of interference. The researcher can choose the appropriate area, time, or situation and observe the people and their interactions with one another. The researcher can be just an outside observer or can be a participant as an observer or a full participant.

• Experimentation

The experimentation process can be done in the real world by intervening in some elements to unify the environment for all participants. This method can also be done in a laboratory environment. The experimentation process is good for being able to change the variables according to the aim of the study.

The case study method is done by making an in-depth analysis of already existing cases. When the parameters and variables are similar to the research question at hand, it is wise to go through what was researched before.

• Focus groups

The case study method is done by using a group of individuals or multiple groups and using their opinions, characteristics, and responses. The scientists gather the data from this group and generalize it to the whole population.

Surveys are an effective way to gather data directly from people. It is a systematic approach to collecting information. If it is done in an online setting as an online survey , it would be even easier to reach out to people and ask their opinions in open-ended or close-ended questions.

Interviews are similar to surveys as you are using questions to collect information and opinions of the people. Unlike a survey, this process is done face-to-face, as a phone call, or as a video call.

• Advantages of empirical research

Empirical research is effective for many reasons, and helps researchers from numerous fields. Here are some advantages of empirical research to have in mind for your next research:

• Empirical research improves the internal validity of the study.
• Empirical evidence gathered from the study is used to authenticate the research question.
• Collecting provable evidence is important for the success of the study.
• The researcher is able to make informed decisions based on the data collected using empirical research.
• Disadvantages of empirical research

After learning about the positive aspects of empirical research, it is time to mention the negative aspects. Because this type may not be suitable for everyone and the researcher should be mindful of the disadvantages of empirical research. Here are the disadvantages of empirical research:

• As it is similar to other research types, a case study where experimentation is included will be time-consuming no matter what. It has more steps and variables than concluding a secondary research.
• There are a lot of variables that need to be controlled and considered. Therefore, it may be a challenging task to be mindful of all the details.
• Doing evidence-based research can be expensive if you need to complete it on a large scale.
• When you are conducting an experiment, you may need some waivers and permissions.
• Frequently asked questions about empirical research

Empirical research is one of the many research types, and there may be some questions in mind about its similarities and differences to other research types.

Is empirical research qualitative or quantitative?

The data collected by empirical research can be qualitative, quantitative, or a mix of both. It is up to the aim of researcher to what kind of data is needed and searched for.

Is empirical research the same as quantitative research?

As quantitative research heavily relies on data collection methods of observation and experimentation, it is, in nature, an empirical study. Some professors may even use the terms interchangeably. However, that does not mean that empirical research is only a quantitative one.

What is the difference between theoretical and empirical research?

Empirical studies are based on data collection to prove theories or answer questions, and it is done by using methods such as observation and experimentation. Therefore, empirical research relies on finding evidence that backs up theories. On the other hand, theoretical research relies on theorizing on empirical research data and trying to make connections and correlations.

What is the difference between conceptual and empirical research?

Conceptual research is about thoughts and ideas and does not involve any kind of experimentation. Empirical research, on the other hand, works with provable data and hard evidence.

What is the difference between empirical vs applied research?

Some scientists may use these two terms interchangeably however, there is a difference between them. Applied research involves applying theories to solve real-life problems. On the other hand, empirical research involves the obtaining and analysis of data to test hypotheses and theories.

• Final words

Empirical research is a good means when the goal of your study is to find concrete data to go with. You may need to do empirical research when you need to test a theory, establish causality, or need qualitative/quantitative data. For example, you are a scientist and want to know if certain colors have an effect on people’s moods, or you are a marketer and want to test your theory on ad places on websites. 

In both scenarios, you can collect information by using empirical research methods and make informed decisions afterward. These are just the two of empirical research examples. This research type can be applied to many areas of work life and social sciences. Lastly, for all your research needs, you can visit forms.app to use its many useful features and over 1000 form and survey templates!

Defne is a content writer at forms.app. She is also a translator specializing in literary translation. Defne loves reading, writing, and translating professionally and as a hobby. Her expertise lies in survey research, research methodologies, content writing, and translation.

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Quantitative research is generally empirical in nature; it relies upon observation and in some cases, experimentation. Quantitative research is usually highly structured, with results which have numerical values. These results can be compared with other number-based results. Sometimes, generalisations of wider populations in the form of hypotheses can be estimated with the collected data. Data is often analysed with statistical methods to avoid bias. 

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Qualitative vs Empirical Research: Difference and Comparison

Empirical research draws its conclusions strictly from ’empirical’ or ‘verifiable’ evidence.

This research analysis mode can be further bifurcated into two subsets known as qualitative and quantitative research, where the former deals with a general description of the studied phenomenon. In contrast, the latter is based on numerical analysis.

Key Takeaways Data type: Qualitative research focuses on non-numerical data like opinions, emotions, and experiences, while empirical research emphasizes measurable, numerical data. Methods: Qualitative research employs interviews, observations, and content analysis, while empirical research uses experiments, surveys, and quantitative data analysis. Goal: Qualitative research aims to understand human behavior and underlying motivations, whereas empirical research seeks to establish cause-and-effect relationships and test hypotheses.

Qualitative vs Empirical Research

The difference between qualitative and empirical research is misinterpreted as the practical method being the independent research method dealing with numerical data and facts. In contrast, the qualitative approach is concerned with the descriptive analysis and opinions of the subjects.

But, empirical research is not an independent category and entails both qualitative and quantitative methods as subcategories.

The empirical method gathers information from the numerical data by working on the predetermined notions. The qualitative approach also incorporates non-numerical data, such as interviews, focus groups, etc., to get a clearer idea of the subject.

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• What is Qualitative Research? | Definition, Methods, Examples, Pros vs Cons
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Comparison Table

What is qualitative research.

The qualitative method is the research practice that considers the human subject to develop an understanding of human and social behaviour and to find a way to feel and react.

The advantages of Qualitative Research are as follows:

• It allows for exploring the hidden research approaches, which numerical data alone cannot do.
• Along with the research analysis, it allows for the contradictions to suggest the social reality of individuals.
• It follows no conventional standards; therefore, data duplication is impossible.

Along with the advantages, this research method has certain drawbacks as well, such as:

• Sampling size is not enough to provide the true reflection of ideas that the researcher wants to carry forward.
• A personal bias towards certain notions consciously or unconsciously influences it.

What is Empirical Research?

Empirical research is the analytical form of research method which collects information based on numerical data by employing statistical, mathematical, and numerical techniques from descriptive (qualitative) sources.

Surveys, focus groups, polls, experimental research, etc., are some of the elements of the analysis that it considers.

The advantages of Empirical research are as follows:

• One can easily collect more samples in less time by doing it quantitatively.
• Researchers can focus on specified cultural realities they want to explore.
• Most of the time, it doesn’t require direct interactions with the subjects.
• It can be performed in a lab-like setting or on some naturalistic grounds.

Along with the advantages, this research method also has certain drawbacks, such as:   

• This kind of research is very time-consuming and demands patience.
• It can also prove expensive because the researcher will conduct the study at different locations.

Main Differences Between Qualitative and Empirical Research

• The elements of analysis in Qualitative research are interviews, group discussions, case studies, etc., whereas, in Empirical research, the key elements are surveys, focus groups, interviews, etc.
• The qualitative mode of analysis is multi-method in focus. Therefore, it allows ambiguities and contradictions, but the Empirical method works by measuring things and drawing explanatory inferences. Therefore, it doesn’t always provide space for contradictions.

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• v.32(4); 2006 Apr

Empirical research in bioethical journals. A quantitative analysis

The objective of this research is to analyse the evolution and nature of published empirical research in the fields of medical ethics and bioethics.

Retrospective quantitative study of nine peer reviewed journals in the field of bioethics and medical ethics ( Bioethics , Cambridge Quarterly of Healthcare Ethics , Hastings Center Report , Journal of Clinical Ethics , Journal of Medical Ethics , Kennedy Institute of Ethics Journal , Nursing Ethics , Christian Bioethics, and Theoretical Medicine and Bioethics ).

In total, 4029 articles published between 1990 and 2003 were retrieved from the journals studied. Over this period, 435 (10.8%) studies used an empirical design. The highest percentage of empirical research articles appeared in Nursing Ethics (n = 145, 39.5%), followed by the Journal of Medical Ethics (n = 128, 16.8%) and the Journal of Clinical Ethics (n = 93, 15.4%). These three journals account for 84.1% of all empirical research in bioethics published in this period. The results of the χ 2 test for two independent samples for the entire dataset indicate that the period 1997–2003 presented a higher number of empirical studies (n = 309) than did the period 1990–1996 (n = 126). This increase is statistically significant (χ 2  = 49.0264, p<.0001). Most empirical studies employed a quantitative paradigm (64.6%, n = 281). The main topic of research was prolongation of life and euthanasia (n = 68).

Conclusions

We conclude that the proportion of empirical research in the nine journals increased steadily from 5.4% in 1990 to 15.4% in 2003. It is likely that the importance of empirical methods in medical ethics and bioethics will continue to increase.

In the last decade, an intense debate has challenged theologians, philosophers, healthcare scientists, social scientists, and medical scientists to clarify what value they attribute to empirical evidence in ethical reflection. 1 , 2 , 3 At a fundamental level, this debate focuses on the regard that medical ethics holds for empirical approaches and, at a methodological level, it questions what this connection or integration between ethics and empirical research means in practice.

State of the art

Notwithstanding the intense debate on the relationship between empirical and normative perspectives in bioethics, only two studies have described the evolution and nature of published empirical research in the fields of medical ethics and bioethics. 4 , 5 Sugarman et al 4 set out to describe the inclusion of empirical literature in the field of medical ethics during the 1980s. These authors used Bioethicsline (a database maintained by the National Reference Center for Bioethics, first issued in 1979), which was, at that time, the most comprehensive resource for identifying references of relevance to bioethics. Because Bioethicsline later was subsumed by Medline (Database from the National Library of Medicine, United States), Sugarman 5 developed a new set of search criteria to identify empirical and ethics postings in the period 1980–1999 by using keywords and Boolean operators in Medline. This last study, however, presents serious shortcomings. Because the publications were not reviewed individually to ensure that they indeed represented a report of ethics or empirical research, it is likely that this research overestimates the number of truly relevant postings. Furthermore, this study retrieved no information about differences in journals, the respondents studied, and the methods used. 5 In order to present an overview of the entire field of bioethics research and to report on a range of characteristics of that research and how they change over time, we have undertaken a new quantitative study for the period 1990–2003, with the aim of confronting the methodological shortcomings of the previous studies.

By means of a quantitative study of peer reviewed journals in the fields of medical ethics and bioethics for the period 1990–2003, this article aims to examine four hypotheses concerning empirical research in bioethics.

Firstly, ever since Brody 6 and Arnold and Forrow 7 stated respectively that a novel form of scholarship in bioethics and a “new form of ethics paper” had appeared, various authors have claimed that ethicists' interest in empirical data continues to grow. 8 As a consequence, our first objective is to analyse whether the number of empirical‐ethical publications in selected peer reviewed journals in the field of bioethics and medical ethics really has been increasing and whether significant differences regarding this issue appear between these journals in the field of bioethics and medical ethics.

Secondly, we want to analyse the methods used in these empirical studies. Many commentators highlight the value of qualitative research methods for bioethics, which are said to be particularly well suited to understanding values, personal perspectives, experiences, and contextual circumstances. 9 Some empirical scientists working in the field of bioethics state that the method par excellence for conducting socially and culturally cognisant and sensitive bioethical research is ethnography and its qualitative methodology. 10 , 11 , 12 Thus, the second objective of our research is to assess whether this “positive” relationship between bioethics and the qualitative methodologies can be confirmed by a factual analysis.

Thirdly, we aim to identify the subjects of research in these empirical studies. The relationship between physicians, their patients, and society at large has undergone significant changes in recent times. The paternalistic relationship between physician and patient has—for example, been modified considerably in favour of the patient. 13 Given the increasing attention to the autonomy and rights of the patient, it could be expected that more studies with patients than with healthcare providers would have been undertaken in the period we studied. After all, empirical research is one of the ways to hear and involve patients in the medical setting. As a result, the third objective of our study is to analyse the hypothesis that in the last decade patients were the most frequent subject of study in empirical research in bioethics.

Fourthly, we aim to determine the topics in bioethics that have been studied empirically. According to some estimates, less than 10% of the world's biomedical research and development funds are dedicated to addressing problems that are responsible for 90% of the world's burden of disease. 14 Leigh Turner 15 has also stated recently that the agenda of bioethicists is biased toward ethical problems that affect wealthy developed nations. He points out that bioethicists are reluctant to study important health issues facing people in poor countries and impoverished regions, instead addressing “sexy” topics, such as embryonic stem cell research, germline gene therapy, and therapeutic and reproductive cloning.

Our research focuses on a set of peer reviewed journals (dating from 1990 to 2003) that are explicitly dedicated to bioethical issues in the context of health care and biomedicine and that are still active in 2003. The journals were selected after comparing the lists of journals indexed by Medline, Fangerau, 16 the French Centre de documentation en éthique des sciences de la vie et de la santé de l'Institut National de la Santé et de la Recherche Médicale, and the German Reference Centre for Ethics in the Life Sciences. All research publications (excluding news, articles from the editors, interviews, letters, (invited) commentaries or (invited) replies to articles and cases) in peer reviewed journals indexed by the four databases were retrieved. This guaranteed that they were international journals. These journals were: Bioethics , Cambridge Quarterly of Healthcare Ethics , Hastings Center Report , Journal of Clinical Ethics , Journal of Medical Ethics , Kennedy Institute of Ethics Journal , Nursing Ethics , Christian Bioethics , and Theoretical Medicine and Bioethics . The two journals that were not present for the full period (1990–2003) in a Belgian library were not included in our analysis ( HEC Forum and the Bulletin of Medical Ethics ). Medline was used to obtain all electronic citations of the articles published in these journals. To verify their reliability, all journals were searched by hand and compared to the electronic dataset. Microsoft Access was used to create a template for data collection and coding. Articles were coded for the following criteria: number of authors; present occupation of author(s); countries of author(s); funding; research design; research subject, and research topic (in the case of empirical research). To guarantee reliability of data collection, the coding scheme was pilot tested by two independent researchers. Following a discussion to resolve inconsistencies, the coding scheme was refined and then used for our review of the entire dataset. An article was considered to include empirical research if there was evidence for collection and analysis of data. Hereby we understand the systematic investigation of a particular problem in the field of bioethics by using a research methodology (such as a qualitative or a quantitative approach) that has methodological roots in the social sciences with the aim to generate, analyse, and interpret reliable and valid information or data. As we were studying journals in the fields of bioethics and medical ethics, we assumed that all articles were relevant for bioethical reflection. Both investigators subsequently judged each of the citations to determine whether they fitted our definition of empirical research in medical ethics, in terms of the method used and the subject of research. Any differences of opinion were discussed in order to come to a single interpretation. The abstract was used to code the article; in the event that the abstract did not reveal the information sought, the entire paper was subsequently retrieved and read. Data were analysed in SAS 9.1.2 using the non‐parametric χ 2 test for independent samples.

Prevalence of empirical research in bioethics

In total, 4029 articles published between 1990 and 2003 were retrieved from the nine bioethical journals under study. As we can observe in table 1 ​ 1, , over this period of time, 435 (10.8%) studies used an empirical design. In 3594 articles (89.2%) there was no evidence of data collection and analysis. The period studied shows an increase in the number of publications with an empirical design. Although only 5.4% of the total number of publications indexed in 1990 were empirical, they amounted to 15.3% in 2003. The results of the χ 2 test for two independent samples for the entire dataset (table 2 ​ 2) ) indicate that the period 1997–2003 presented a higher number of empirical studies (n = 309) than did the period 1990–1996 (n = 126). This increase is statistically significant (χ 2  = 49.0264, p<.0001).

NA, not applicable

Choice of journal

Table 1 ​ 1 shows that the highest percentage of empirical research articles appeared in Nursing Ethics (n = 145, 39.5% of the publications in this journal), followed by the Journal of Medical Ethics (n = 128, 16.8%) and the Journal of Clinical Ethics (n = 93, 15.4%). These three journals together account for 84.13% of all empirical research in bioethics published from 1990 to 2003 in the nine selected journals.

On the other hand, some journals rarely or never published empirical research over this period—for example the Hastings Center Report (n = 5, 0.9% of the publications in this journal), Kennedy Institute of Ethics Journal (n = 3, 1%), and Christian Bioethics (n = 0, 0%). Figure 1 ​ 1 shows that the increase in articles with an empirical research design is most noticeable in Nursing Ethics . At the time of this journal's inception in 1994, it counted 12.5% of empirical publications in its journal (n = 3), while in 2003, it counted 60% (n = 27) of empirical publications. Because Nursing Ethics clearly seems to influence the evolution of the number of empirical studies in our dataset, we compared the results of a χ 2 test with and without this journal (table 2 ​ 2). ). The dataset without Nursing Ethics still presents significantly more empirical studies in the period 1997–2003 (n = 186) than from 1990–1996 (n = 104), but has a lower χ 2 value (χ 2  = 15,4169, p<.0001).

Figure 1  Percentage of publications with an empirical design, by publication year and by journal (with in total more than 5% of empirical publications [see also table 1 ​ 1 ]).

Choice of paradigm and data collection method

As we can see in table 3 ​ 3, , most empirical studies employed a quantitative paradigm (n = 281, 64.6%). The proportion of articles based on a qualitative design was 32.2% (n = 140). A minority combined quantitative and qualitative paradigms (3.2%, n = 14). Of all empirical studies, more than half of the articles with a qualitative design were published in Nursing Ethics (n = 74, 52.9%). More than half of the articles with a quantitative design were published in the Journal of Medical Ethics and the Journal of Clinical Ethics (n = 170, 60.5%). As we can observe in table 3 ​ 3, , more than half of the empirical studies used a descriptive survey (n = 233, 53.56%), which typically sought to ascertain respondents' perspectives or experiences with a specified subject in a predetermined structured manner at a single point in time (by self administered questionnaire or structured [telephone] interview). Other, much less frequent, quantitative methods included longitudinal surveys (n = 13, 3%), which followed a group of respondents over time; (observational) prospective studies (n = 15, 3.5%), which observed interaction and quantified particular behaviours; and (observational) retrospective studies (n = 11, 2.5%), based on a quantitative study of materials, such as patient records, public documents, informed consent forms, etc. The most frequently used qualitative research method was the in depth interview (n = 89, 20.5%).

Sources of data

The bioethical empirical research that we analysed drew on a wide variety of data sources (table 4 ​ 4). ). The sources cited most frequently were nursing staff (n = 98, 22.5%), patients (n = 97, 22.3%), and physicians (n = 92, 22.1%). Some groups were much more likely to be the subject of quantitative research than of qualitative research, such as the general public (21 hits in quantitative research, 0 in qualitative research: 21/0; this represents 7.5% of the publications with a quantitative design and 0% of the publications with a qualitative design); students (41/2; 14.6% v 1.4%); ethicists and ethics committees (22/2; 7.8% v 1.4%), and physicians (71/19; 25.3% v 13.6%). Nursing staff (54/40; 19.2% v 28.6%) and patients (61/35; 21.7% v 25%) have been studied more in publications with a qualitative design.

Some publications studied more than one subject. As a consequence, percentages will be higher than 100%.

Choice of research topic

The publication's abstract was used to classify its main research topic as well as second and third topic if appropriate. Our classification system was based on a refined version of the library classification scheme of the National Reference Center for Bioethics Literature. 1 Thirty three topics were identified in the published research, as depicted in table 5 ​ 5. . The main topic of research was prolongation of life and euthanasia (n = 68—for example, artificial feeding, DNR orders, persistent vegetative state, euthanasia), closely followed by theoretical perspectives on ethics and bioethics (n = 58—for example, sensitivity to and identification of ethical problems, ethical reasoning) and informed consent and patient participation in decision making (n = 42—for example, informed consent in general, parental consent, competence, or substituted judgment). These three main topics remained dominant when the second and third topics of research were included.

The research described in each article was classified by its main topic. If available, second and third topics were recorded. The number of instances of each topic as main, second, or third topic is shown.

Sugarman et al 4 observed that during the 1980s, on average 3.4% of the 663 postings retrieved from Bioethicsline were based on empirical research. The proportion of empirical research in the total postings increased steadily from 1.5% in 1980 to over 5% in 1989. Even though our dataset is not the same, our results match those of Sugarman et al 's study remarkably well. In 1990, we observed a total of 5.4% of empirical postings, which increased steadily to 15.4% in 2003. Overall, the prevalence of empirical research articles in the field of bioethics increased gradually from 1980 until 2003. Our research shows that the hypothesis of Sugarman's 5 second study—that empirical research in ethics represents approximately 25% of these postings—overestimates the number of empirical postings in the field of bioethics and medical ethics.

We observed differences, however, among the journals studied. Three journals accounted for more than 80% of the empirical publications: significantly more articles with an empirical design were accepted for publication in Nursing Ethics , the Journal of Clinical Ethics , and the Journal of Medical Ethics . The 1997 change in the notice for contributors to the Journal of Medical Ethics is noteworthy. For the first time, the editors issued guidelines for submitting reports based on empirical research.

From 1980 until 2003, the number of empirical publications in journals of bioethics and medical ethics increased steadily. It is not clear, however, whether this emergence reflects the fact that journals in bioethics and medical ethics were more disposed to publish empirical studies or the fact that more ethics related empirical studies were realised and submitted in general (and were reported more frequently in journals of medical ethics and bioethics as a consequence). Our sample was limited to international peer reviewed periodicals dedicated to medical ethics and bioethics. It would be interesting to include an overview of the number, design, and topic of empirical studies in bioethics and medical ethics that have been published in all medical journals. This would not be easy, however, considering the difficulty of identifying ethics related empirical studies in journals that are not typically ethics related, and the sheer number of medical journals in circulation.

On the basis of the increase observed in this study in both the absolute number of empirical/ethical studies and the proportion of these studies compared to the total number of bioethics papers, we advance the hypothesis that more ethics related empirical studies are carried out and reported. The increase in empirical studies confirms previous research 17 that medical ethics and bioethics as a field and the journals in this field are more open to empirical approaches than in years past. In that study we described (a) how a theory driven bioethics that did not sufficiently take practical reality into account has been criticised, (b) how clinical ethics has increased the awareness of empirical research in bioethics, and (c) how the paradigm of evidence based approaches has been taken up by the vocabulary of bioethics. This has certainly led to a greater openness toward empirical studies in bioethics on the part of several institutes for medical ethics and [in] some editorial boards of journals in the fields of bioethics and medical ethics.

In Sugarman et al 's research, 4 only a small minority of the empirical studies in the period 1980–1989 used a qualitative design (n = 18, 3%). By contrast, our research for the period 1990–2003 revealed a proportion of 32.2% (n = 140) for qualitative studies. More than half of these studies appeared in Nursing Ethics . This remarkable growth in the number of qualitative studies was caused partially by the fact that qualitative studies are increasingly recognised as important to understanding the richness and complexities of health care. 18 The discipline of nursing made a particularly important contribution to the high proportion of qualitative studies. In a quantitative evaluation of the qualitative studies published in 170 core clinical journals during 2000, McKibbon and Gadd 19 showed that most qualitative studies were published in nursing journals and only rarely in high ranked medical and clinical journals. Qualitative studies have been embraced in nursing research and practice. 19 When confronted recently by proponents of evidence based movements, some nursing scholars argued strongly for nursing to defend the importance of qualitative research. 20 It is not surprising that nursing ethics espouses significantly more qualitative research. 21 As a result, the substantial number of qualitative studies is not representative of bioethics and medical ethics in general, but reflects the methodological inclination of research in the field of nursing sciences. 22

Although we expected an overwhelming presence of patients as research subject, as was observed already by Sugarman et al in 1980, 4 our research shows an almost equal division of responses from nursing staff, patients, and physicians. Contrary to what we expected, patients are not the most studied group in empirical research in the field of medical ethics and bioethics. Healthcare professionals (nursing staff, physicians, and caregivers) are much more studied than patients, probably because they can be more easily studied as group—that is, they are more accessible for researchers in the field of health care.

With regard to the topic of research, ethical problems related to the end of life have been the most common subject of study: these include prolongation of life and euthanasia (n = 68); living wills and advance directives (n = 12); health care for the aged (n = 10); care for the dying patient (n = 9); (assisted) suicide (n = 4), and death and dying in general (n = 3). In addition, a great deal of research concerns problems and issues that deal with ethics as a profession and discipline, such as theoretical perspectives on ethics and bioethics (n = 58), ethics committees and ethics consultation (n = 30), and ethics education (n = 34). In fact, empirical studies in bioethics commonly address ethical issues that arise in wealthy developed countries, while problems that are more relevant to developing countries—international justice and human rights; HIV and AIDS; health care for minorities; population growth; world hunger; patents; basic health care; food; safe water, and sanitation—are only rarely studied empirically. An explanation might be that some topics are difficult or less useful to study empirically. Future research is needed to map all the different topics studied in the field of bioethics and medical ethics. This epidemiology of bioethics 23 could indicate the degree to which bioethics is biased toward some ethical issues and neglects other relevant topics of study.

In the nine peer reviewed journals from the field of bioethics and medical ethics, we observed a clear emergence of empirical research. The nine journals were chosen on the basis that they were present in the four databases listed. Important journals, however, such as the American Journal of Bioethics, the Journal of Medicine and Philosophy, Developing World Bioethics, Ethics and Medicine, Medicine Health Care and Philosophy did not fit our selection criteria and were excluded from analysis. Although we did not intentionally limit our research to English language journals, journals as Ethik in der Medizin and other non‐English language journals could not be included for the same reason. Further research should address our research objectives in this larger group of journals.

Furthermore, ethics related empirical research is published in other medical journals, which number over 40,000 worldwide. 24 Future research should address the presence and characteristics of empirical research in bioethics and medical ethics in the contexts of these publications.

Empirical research is an accepted and growing component of the medical ethics and bioethics literature. We believe that bioethics could benefit significantly from intense discussion and interaction between the empirical and normative perspectives. The two perspectives should interact with and learn from each other, in spite of their different backgrounds and characteristics. Reflecting on the relationship between empirical and normative perspectives is a fundamental issue that has implications for defining research priorities; applying for grants; teaching bioethics to students, and being involved in ethical decision making. It is likely that the importance of empirical methods in medical ethics and bioethics can only be expected to increase.

Acknowledgements

The authors wish to thank Professor Dr Heiner Fangerau for providing the list of journals on medical ethics that he developed in his study and Eveline de Vos for her help in the data collection.

1 http://www.georgetown.edu/research/nrcbl/nrc/index.htm (last accessed 21 Jun 2005).

• Open access
• Published: 11 April 2024

The role of champions in the implementation of technology in healthcare services: a systematic mixed studies review

• Sissel Pettersen 1 ,
• Hilde Eide 2 &
• Anita Berg 1  

BMC Health Services Research volume  24 , Article number:  456 ( 2024 ) Cite this article

239 Accesses

Metrics details

Champions play a critical role in implementing technology within healthcare services. While prior studies have explored the presence and characteristics of champions, this review delves into the experiences of healthcare personnel holding champion roles, as well as the experiences of healthcare personnel interacting with them. By synthesizing existing knowledge, this review aims to inform decisions regarding the inclusion of champions as a strategy in technology implementation and guide healthcare personnel in these roles.

A systematic mixed studies review, covering qualitative, quantitative, or mixed designs, was conducted from September 2022 to March 2023. The search spanned Medline, Embase, CINAHL, and Scopus, focusing on studies published from 2012 onwards. The review centered on health personnel serving as champions in technology implementation within healthcare services. Quality assessments utilized the Mixed Methods Appraisal Tool (MMAT).

From 1629 screened studies, 23 were included. The champion role was often examined within the broader context of technology implementation. Limited studies explicitly explored experiences related to the champion role from both champions’ and health personnel’s perspectives. Champions emerged as promoters of technology, supporting its adoption. Success factors included anchoring and selection processes, champions’ expertise, and effective role performance.

The specific tasks and responsibilities assigned to champions differed across reviewed studies, highlighting that the role of champion is a broad one, dependent on the technology being implemented and the site implementing it. Findings indicated a correlation between champion experiences and organizational characteristics. The role’s firm anchoring within the organization is crucial. Limited evidence suggests that volunteering, hiring newly graduated health personnel, and having multiple champions can facilitate technology implementation. Existing studies predominantly focused on client health records and hospitals, emphasizing the need for broader research across healthcare services.

Conclusions

With a clear mandate, dedicated time, and proper training, health personnel in champion roles can significantly contribute professional, technological, and personal competencies to facilitate technology adoption within healthcare services. The review finds that the concept of champions is a broad one and finds varied definitions of the champion role concept. This underscores the importance of describing organizational characteristics, and highlights areas for future research to enhance technology implementation strategies in different healthcare settings with support of a champion.

Peer Review reports

Digital health technologies play a transformative role in healthcare service systems [ 1 , 2 ]. The utilization of technology and digitalization is essential for ensuring patient safety, delivering high quality, cost-effective, and sustainable healthcare services [ 3 , 4 ]. The implementation of technology in healthcare services is a complex process that demands systematic changes in roles, workflows, and service provision [ 5 , 6 ].

The successful implementation of new technologies in healthcare services relies on the adaptability of health professionals [ 7 , 8 , 9 ]. Champions have been identified as a key factor in the successful implementation of technology among health personnel [ 10 , 11 , 12 ]. However, they have rarely been studied as an independent strategy; instead, they are often part of a broader array of strategies in implementation studies (e.g., Hudson [ 13 ], Gullslett and Bergmo [ 14 ]). Prior research has frequently focused on determining the presence or absence of champions [ 10 , 12 , 15 ], as well as investigating the characteristics of individuals assuming the champion role (e.g., George et al. [ 16 ], Shea and Belden [ 17 ]).

Recent reviews on champions [ 18 , 19 , 20 ] have studied their effects on adherence to guidelines, implementation of innovations and facilitation of evidence-based practice. While these reviews suggest that having champions yields positive effects, they underscore the importance for studies that offer detailed insights into the champion’s role concerning specific types of interventions.

There is limited understanding of the practical role requirements and the actual experiences of health personnel in performing the champion role in the context of technology implementation within healthcare services. Further, this knowledge is needed to guide future research on the practical, professional, and relational prerequisites for health personnel in this role and for organizations to successfully employ champions as a strategy in technology implementation processes.

This review seeks to synthesize the existing empirical knowledge concerning the experiences of those in the champion role and the perspectives of health personnel involved in technology implementation processes. The aim is to contribute valuable insights that enhance our understanding of practical role requirements, the execution of the champion role, and best practices in this domain.

The term of champions varies [ 10 , 19 ] and there is a lack of explicit conceptualization of the term ‘champion’ in the implementation literature [ 12 , 18 ]. Various terms for individuals with similar roles also exist in the literature, such as implementation leader, opinion leader, facilitator, change agent, superuser and facilitator. For the purpose of this study, we have adopted the terminology utilized in the recent review by Rigby, Redley and Hutchinson [ 21 ] collectively referring to these roles as ‘champions’. This review aims to explore the experiences of health personnel in their role as champions and the experiences of health personnel interacting with them in the implementation of technology in the healthcare services.

Prior review studies on champions in healthcare services have employed various designs [ 10 , 18 , 19 , 20 ]. In this review, we utilized a comprehensive mixed studies search to identify relevant empirical studies [ 22 ]. The search was conducted utilizing the Preferred Reporting Items for Systematic and Meta-Analysis (PRISMA) guidelines, ensuring a transparent and comprehensive overview that can be replicated or updated by others [ 23 ]. The study protocol is registered in PROSPERO (ID CRD42022335750), providing a more comprehensive description of the methods [ 24 ]. A systematic mixed studies review, examining research using diverse study designs, is well-suited for synthesizing existing knowledge and identifying gaps by harnessing the strengths of both qualitative and quantitative methods [ 22 ]. Our search encompassed qualitative, quantitative, and mixed methods design to capture experiences with the role of champions in technology implementation.

Search strategy and study selection

Search strategy.

The first author, in collaboration with a librarian, developed the search strategy based on initial searches to identify appropriate terms and truncations that align with the eligibility criteria. The search was constructed utilizing a combination of MeSH terms and keywords related to technology, implementation, champion, and attitudes/experiences. Conducted in August/September 2022, the search encompassed four databases: Medline, Embase, CINAHL, and Scopus, with an updated search conducted in March 2023. The full search strategy for Medline is provided in Appendix  1 . The searches in Embase, CINAHL and Scopus employed the same strategy, with adopted terms and phrases to meet the requirements of each respective database.

Eligibility criteria

We included all empirical studies employing qualitative, quantitative, and mixed methods designs that detailed the experiences and/or attitudes of health personnel regarding the champions role in the implementation of technology in healthcare services. Articles in the English language published between 2012 and 2023 were considered. The selected studies involved technology implemented or adapted within healthcare services.

Conference abstract and review articles were excluded from consideration. Articles published prior 2012 were excluded as a result of the rapid development of technology, which could impact the experiences reported. Furthermore, articles involving surgical technology and pre-implementation studies were also excluded, as the focus was on capturing experiences and attitudes from the adoption and daily use of technology. The study also excluded articles that involved champions without clinical health care positions.

Study selection

A total of 1629 studies were identified and downloaded from the selected databases, with Covidence [ 25 ] utilized as a software platform for screening. After removing 624 duplicate records, all team members collaborated to calibrate the screening process utilizing the eligibility criteria on the initial 50 studies. Subsequently, the remaining abstracts were independently screened by two researchers, blinded to each other, to ensure adherence to the eligibility criteria. Studies were included if the title and abstract included the term champion or its synonyms, along with technology in healthcare services, implementation, and health personnel’s experiences or attitudes. Any discrepancies were resolved through consensus among all team members. A total of 949 abstracts were excluded for not meeting this inclusion condition. During the initial search, 56 remaining studies underwent full-text screening, resulting in identification of 22 studies qualified for review.

In the updated search covering the period September 2022 to March 2023, 64 new studies were identified. Of these, 18 studies underwent full-text screening, and one study was included in our review. The total number of included studies is 23. The PRISMA flowchart (Fig.  1 ) illustrates the process.

Flow Chart illustrating the study selection and screening process

Data extraction

The research team developed an extraction form for the included studies utilizing an Excel spreadsheet. Following data extraction, the information included the Name of Author(s) Year of publication, Country/countries, Title of the article, Setting, Aim, Design, Participants, and Sample size of the studies, Technology utilized in healthcare services, name/title utilized to describe the Champion Role, how the studies were analyzed and details of Attitude/Experience with the role of champion. Data extraction was conducted by SP, and the results were deliberated in a workshop with the other researchers AB, and HE until a consensus was reached. Any discrepancies were resolved through discussions. The data extraction was categorized into three categories: qualitative, quantitative, and mixed methods, in preparation for quality appraisal.

Quality appraisal

The MMAT [ 26 ] was employed to assess the quality of the 23 included studies. Specifically designed for mixed studies reviews, the MMAT allows for the appraisal of the methodological quality of studies falling into five categories. The studies in our review encompassed qualitative, quantitative descriptive, and mixed methods studies. The MMAT begins with two screening questions to confirm the empirical nature of this study. Subsequently, all studies were categorized by type and evaluated utilizing specific criteria based on their research methods, with ratings of ‘Yes,’ ‘No’ or ‘Can’t tell.’ The MMAT discourages overall scores in favor of providing a detailed explanation for each criterion. Consequently, we did not rely on the MMAT’s overall methodical quality scores and continued to include all 23 studies for our review. Two researchers independently scored the studies, and any discrepancies were discussed among all team members until a consensus was reached. The results of the MMAT assessments are provided in Appendix  2 .

Data synthesis

Based on discussions of this material, additional tables were formulated to present a comprehensive overview of the study characteristics categorized by study design, study settings, technology included, and descriptions/characteristics of the champion role. To capture attitudes and experiences associated with the champion role, the findings from the included studies were translated into narrative texts [ 22 ]. Subsequently, the reviewers worked collaboratively to conduct a thematic analysis, drawing inspiration from Braun and Clarke [ 27 ]. Throughout the synthesis process, multiple meetings were conducted to discern and define the emerging themes and subthemes.

The adopting of new technology in healthcare services can be perceived as both an event and a process. According to Iqbal [ 28 ], experience is defined as the knowledge and understanding gained after an event or the process of living through or undergoing an event. This review synthesizes existing empirical knowledge regarding the experiences of occupying the champion role, and the perspectives of health personnel interacting with champions in technology implementation processes.

Study characteristics

The review encompassed a total of 23 studies, and an overview of these studies is presented in Table  1 . Of these, fourteen studies employed a qualitative design, four had quantitative design, and five utilized a mixed method design. The geographical distribution revealed that the majority of studies were conducted in the USA (8), followed by Australia (5), England (4), Canada (2), Norway (2), Ireland (1), and Malaysia (1). In terms of settings, 11 studies were conducted in hospitals, five in primary health care, three in home-based care settings, and four in a mixed settings where two or more settings collaborated. Various technologies were employed across these studies, with client health records (7) and telemedicine (5) being the most frequently utilized. All studies included experiences from champions or health personnel collaborating with champions in their respective healthcare services. Only three studies had the champion role as a main objective [ 29 , 30 , 31 ]. The remaining studies described champions as one of the strategies in technology implementation processes, including 10 evaluation studies (including feasibility studies [ 32 , 33 , 34 ] and one cost-benefit study [ 30 ]).

Several studies underscored the importance of champions for successful implementation [ 29 , 30 , 31 , 34 , 35 , 36 , 37 , 38 , 40 , 41 , 42 , 43 , 49 ]. Four studies specifically highlighted champions as a key factor for success [ 34 , 36 , 37 , 43 ], and one study went further to describe champions as the most important factor for successful implementation [ 39 ]. Additionally, one study associated champions with reduced labor cost [ 30 ].

Thin descriptions, yet clear expectations for technology champions’ role and -attributes

The analyses revealed that the concept of champions in studies pertaining to technology implementation in healthcare services varies, primarily as a result of the diversity of terms utilized to describe the role combined with short role descriptions. Nevertheless, the studies indicated clear expectations for the champion’s role and associated attributes.

The term champion

The term champion was expressed in 20 different forms across the 23 studies included in our review. Three studies utilized multiple terms within the same study [ 32 , 47 , 48 ] and 15 different authors [ 29 , 32 , 33 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 46 , 47 , 50 ] employed the term with different compositions (Table  1 ). Furthermore, four authors utilized the term Super user [ 30 , 31 , 49 , 51 ], while four authors employed the terms Facilitator [ 38 ], IT clinician [ 48 ], Leader [ 45 ], and Manager [ 34 ], each in combination with more specific terms (such as local opinion leaders, IT nurse, or practice manager).

Most studies associated champion roles with specific professions. In seven studies, the professional title was explicitly linked to the concept of champions, such as physician champions or clinical nurse champions, or through the strategic selection of specific professions [ 29 , 33 , 36 , 40 , 43 , 47 , 50 ]. Additionally, some studies did not specify professions, but utilized terms like clinicians [ 45 ] or health professionals [ 41 ].

All included articles portray the champion’s role as facilitating implementation and daily use of technology among staff. In four studies, the champion’s role was not elaborated beyond indicating that the individual holding the role is confident with an interest in technology [ 35 , 41 , 42 , 44 ]. The champion’s role was explicitly examined in six studies [ 29 , 30 , 31 , 33 , 46 , 50 ]. Furthermore, seven studies described the champion in both the methods and results [ 32 , 36 , 38 , 47 , 48 , 49 , 51 ]. In ten of the studies, champions were solely mentioned in the results [ 34 , 35 , 37 , 39 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ].

Eight studies provided a specific description or definition of the champion [ 29 , 30 , 31 , 32 , 38 , 48 , 49 , 50 ]. The champion’s role was described as involving training in the specific technology, being an expert on the technology, providing support and assisting peers when needed. In some instance, the champion had a role in leading the implementation [ 50 ], while in other situations, the champion operated as a mediator [ 48 ].

The champions tasks

In the included studies, the champion role encompassed two interrelated facilitators tasks: promoting the technology and supporting others in adopting the technology in their daily practice. Promoting the technology involved encouraging staff adaptation [ 32 , 34 , 35 , 37 , 40 , 41 , 49 ], generally described as being enthusiastic about the technology [ 32 , 35 , 37 , 41 , 48 ], influencing the attitudes and beliefs of colleagues [ 42 , 45 ] and legitimizing the introduction of the technology [ 42 , 46 , 48 ]. Supporting others in technology adaption involved training and teaching [ 31 , 35 , 38 , 40 , 51 ], as well as providing technical support [ 30 , 31 , 39 , 43 , 49 ] and social support [ 49 ]. Only four studies reported that the champions received their own training to enable them able to support their colleagues [ 30 , 31 , 39 , 48 ]. Furthermore, eight studies [ 32 , 34 , 38 , 40 , 48 , 49 , 50 , 51 ], specified that the champion role included leadership and management responsibilities, mentioning tasks such as planning, organizing, coordinating, and mediating technology adaption without providing further details.

Desirable champion attributes

To effectively fulfill their role, champions should ideally possess clinical expertise and experience [ 29 , 35 , 38 , 40 , 48 ], stay professionally updated [ 37 , 48 ], and possess knowledge of the organization and workflows [ 29 , 34 , 46 ]. They should have the ability to understand and communicate effectively with healthcare personnel [ 31 , 32 , 46 , 49 ] and be proficient in IT language [ 51 ]. Moreover, champions should demonstrate a general technological interest and competence, and competence, along with specific knowledge of the technology to be implemented [ 32 , 37 , 49 ]. It is also emphasized that they should command formal and/or informal respect and authority in the organization [ 36 , 45 ], be accessible to others [ 39 , 43 ], possess leadership qualities [ 34 , 37 , 38 , 46 ], and understand and balance the needs of stakeholders [ 43 ]. Lastly, the champions should be enthusiastic promoters of the technology, engaging and supporting others [ 31 , 32 , 33 , 34 , 37 , 39 , 40 , 41 , 43 , 49 ], while also effectively coping with cultural resistance to change [ 31 , 46 ].

Anchoring and recruiting for the champion role

The champions were organized differently within services, holding various positions in the organizations, and being recruited for the role in different ways.

Anchoring the champion role

The champion’s role is primarily anchored at two levels: the management level and/or the clinical level, with two studies having champions at both levels [ 34 , 49 ]. Those working with the management actively participated in the planning of the technology implementation [ 29 , 36 , 40 , 41 , 45 ]. Serving as advisors to management, they leveraged their clinical knowledge to guide the implementation in alignment with the necessities and possibilities of daily work routines in the clinics. Champions in this capacity experienced having a clear formal position that enabled them to fulfil their role effectively [ 29 , 40 ]. Moreover, these champions served as bridge builders between the management and department levels [ 36 , 45 ], ensuring the necessary flow of information in both directions.

Champions anchored at the clinic level played a pivotal role in the practical implementation and facilitation of the daily use of technology [ 31 , 33 , 35 , 37 , 38 , 43 , 48 , 51 ]. Additionally, these champions actively participated in meetings with senior management to discuss the technology and its implementation in the clinic. This position conferred potential influence over health personnel [ 33 , 35 ]. Champions at the clinic level facilitated collaboration between employees, management, and suppliers [ 48 ]. Fontaine et al. [ 36 ] identified respected champions at the clinical level, possessing authority and formal support from all leadership levels, as the most important factor for success.

Only one study reported that the champions received additional compensation for their role [ 36 ], while another study mentioned champions having dedicated time to fulfil their role [ 46 ]. The remaining studies did not provide this information.

Recruiting for the role as champion

Several studies have reported different experiences regarding the management’s selection of champions. A study highlighted the distinctions between a volunteered role and an appointed champion’s role [ 31 ]. Some studies underscored that appointed champions were chosen based on technological expertise and skills [ 41 , 48 , 51 ]. Moreover, the selection criteria included champions’ interest in the specific technology [ 42 ] or experiential skills [ 40 ]. The remaining studies did not provide this information.

While the champion role was most frequently held by health personnel with clinical experience, one study deviated by hiring 150 newly qualified nurses as champions [ 30 ] for a large-scale implementation of an Electronic Health Record (EHR). Opting for clinical novices assisted in reducing implementation costs, as it avoided disrupting daily tasks and interfering with daily operations. According to Bullard [ 30 ], these super-user nurses became highly sought after post-implementation as a result of their technological confidence and competence.

Reported experiences of champions and health personnel

Drawing from the experiences of both champions and health personnel, it is essential for a champion to possess a combination of general knowledge and specific champion characteristics. Furthermore, champions are required to collaborate with individuals both within and outside the organization. The subsequent paragraphs delineate these experiences, categorizing them into four subsets: champions’ contextual knowledge and expertise, preferred performance of the champion role, recognizing that a champion alone is insufficient, and distinguishing between reactive and proactive champions.

Champions’ contextual knowledge and know-how

Health personnel with experience interacting with champions emphasized that a champion must be familiar with the department and its daily work routines [ 35 , 40 ]. Knowledge of the department’s daily routines made it easier for champions to facilitate the adaptation of technology. However, there was a divergence of opinions on whether champions were required to possess extensive clinical experience to fulfil their role. In most studies, having an experienced and competent clinician as a champion instilled a sense of confidence among health personnel. Conversely, Bullard’s study [ 30 ] exhibited that health personnel were satisfied with newly qualified nurses in the role of champion, despite their initial skepticism.

It is a generally expected that champions should possess technological knowledge beyond that of other health professionals [ 37 , 41 ]. Some health personnel perceived the champions as uncritical promoters of technology, with the impression that health personnel were being compelled to utilize technology [ 46 ]. Champions could also overestimate the readiness of health personnel to implement a technology, especially during the early phases of the implementation process [ 32 ]. Regardless of whether the champion is at the management level or the clinic level, champions themselves have acknowledged the importance of providing time and space for innovation. Moreover, the recruitment of champions should span all levels of the organization [ 34 , 46 ]. Furthermore, champions must be familiar with daily work routines, work tools, and work surfaces [ 38 , 40 , 43 ].

Preferable performance of the champion role

The studies identified several preferable characteristics of successful champions. Health personnel favored champions utilizing positive words when discussing technology and exhibiting positive attitudes while facilitating and adapting it [ 33 , 34 , 37 , 38 , 41 , 46 ]. Additionally, champions who were enthusiastic and engaging were considered good role models for the adoption of technology. Successful champions were perceived as knowledgeable and adept problem solvers who motivated and supported health personnel [ 41 , 43 , 44 , 48 ]. They were also valued for being available and responding promptly when contacted [ 42 ]. Health professionals noted that champions perceived as competent garnered respect in the organization [ 40 ]. Moreover, some health personnel felt that some certain champions wielded a greater influence based on how they encouraged the use of the system [ 48 ]. It was also emphasized that health personnel needed to feel it was safe to provide feedback to champions, especially when encountering difficulties or uncertainties [ 49 ].

A champion is not enough

The role of champions proved to be more demanding than expected [ 29 , 31 , 38 ], involving tasks such as handling an overwhelming number of questions or actively participating in the installation process to ensure the technology functions effectively in the department [ 29 ]. Regardless of the organizational characteristics or the champion’s profile, appointing the champion as a “solo implementation agent” is deemed unsuitable. If the organization begins with one champion, it is recommended that this individual promptly recruits others into the role [ 42 ].

Health personnel, reliant on champions’ expertise, found it beneficial to have champions in all departments, and these champions had to be actively engaged in day-to-day operations [ 31 , 33 , 34 , 37 ]. Champions themselves also noted that health personnel increased their technological expertise through their role as champions in the department [ 39 ].

Furthermore, the successful implementation of technology requires the collaboration of various professions and support functions, a task that cannot be solely addressed by a champion [ 29 , 43 , 48 ]. In Orchard et. al.‘s study [ 34 ], champions explicitly emphasized the necessity of support from other personnel in the organization, such as those responsible for the technical aspects and archiving routines, to provide essential assistance.

According to health personnel, the role of champions is vulnerable in case they become sick or leave their position [ 42 , 51 ]. In some of the included studies, only one or a few hold the position of champion [ 37 , 38 , 42 , 48 ]. Two studies observed that their implementations were not completed because champions left or reassigned for various reasons [ 32 , 51 ]. The health professionals in the study by Owens and Charles [ 32 ] expressed that champions must be replaced in such cases. Further, the study of Olsen et al., 2021 [ 42 ] highlights the need for quicky building a champion network within the organization.

Reactive and proactive champions

Health personnel and champions alike noted that champions played both a reactive and proactive role. The proactive role entailed facilitating measures such as training and coordination [ 31 , 32 , 33 , 34 , 37 , 39 , 40 , 41 , 43 , 48 , 49 ] as initiatives to generate enthusiasm for the technology [ 31 , 32 , 33 , 34 , 35 , 37 , 39 , 40 , 41 , 43 , 49 ]. On the other hand, the reactive role entailed hands-on support and troubleshooting [ 30 , 31 , 39 , 43 , 49 ].

In a study presenting experiences from both health personnel and champions, Yuan et al. [ 31 ] found that personnel observed differences in the assistance provided by appointed and self-chosen champions. Appointed champions demonstrated the technology, answered questions from health personnel, but quickly lost patience and track of employees who had received training [ 31 ]. Health personnel perceived that self-chosen champions were proactive and well-prepared to facilitate the utilization of technology, communicating with the staff as a group and being more competent in utilizing the technology in daily practice [ 31 ]. Health personnel also noted that volunteer champions were supportive, positive, and proactive in promoting the technology, whereas appointed champions acted on request and had a more reactive approach [ 31 ].

This review underscores the breadth of the concept of champion and the significant variation in the champion’s role in implementation of technology in healthcare services. This finding supports the results from previous reviews [ 10 , 18 , 19 , 20 ]. The majority of studies meeting our inclusion criteria did not specifically focus on the experiences of champions and health personnel regarding the champion role, with the exception of studies by Bullard [ 30 ], Gui et al. [ 29 ], Helmer-Smith et al. [ 33 ], Hogan-Murphy et al. [ 46 ], Rea et al. [ 50 ], and Yuan et al. [ 31 ].

The 23 studies encompassed in this review utilized 20 different terms for the champion role. In most studies, the champion’s role was briefly described in terms of the duties it entailed or should entail. This may be linked to the fact that the role of champions was not the primary focus of the study, but rather one of the strategies in the implementation process being investigated. This result reinforces the conclusions drawn by Miech et al. [ 10 ] and Shea et al. [ 12 ] regarding the lack of united understandings of the concept. Furthermore, in Santos et al.‘s [ 19 ] review, champions were only operationalized through presence or absence in 71.4% of the included studies. However, our review finds that there is a consistent and shared understanding that champions should promote and support technology implementation.

Several studies advocate for champions as an effective and recommended strategy for implementing technology [ 30 , 31 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 42 , 43 , 45 , 46 ]. However, we identified that few studies exclusively explore health personnel`s experiences within the champion role when implementing technology in healthcare services.

This suggests a general lack of information essential for understanding the pros, cons, and prerequisites for champions as a strategy within this field of knowledge. However, this review identifies, on a general basis, the types of support and structures required for champions to perform their role successfully from the perspectives of health personnel, contributing to Shea’s conceptual model [ 12 ].

Regarding the organization of the role, this review identified champions holding both formal appointed and informal roles, working in management or clinical settings, being recruited for their clinical and/or technological expertise, and either volunteering or being hired with specific benefits for the role. Regardless of these variations, anchoring the role is crucial for both the individuals holding the champion role and the health personnel interacting with them. Anchoring, in this context, is associated with the clarity of the role’s content and a match between role expectations and opportunities for fulfilment. Furthermore, the role should be valued by the management, preferably through dedicated time and/or salary support [ 34 , 36 , 46 ]. Additionally, our findings indicate that relying on a “solo champion” is vulnerable to issues such as illness, turnover, excessive workload, and individual champion performance [ 32 , 37 ]. Based on these insights, it appears preferable to appoint multiple champions, with roles at both management and clinical levels [ 33 ].

Some studies have explored the selection of champions and its impact on role performance, revealing diverse experiences [ 30 , 31 ]. Notably, Bullard [ 30 ], stands out for emphasizing long clinical experience, and hiring newly trained nurses as superusers to facilitate the use of electronic health records. Despite facing initial reluctance, these newly trained nurses gradually succeeded in their roles. This underscores the importance of considering contextual factors in the champion selection [ 30 , 52 ]. In Bullard’s study [ 30 ], the collaboration between newly trained nurses as digital natives and clinical experienced health personnel proved beneficial, highlighting the need to align champion selection with the organization’s needs based on personal characteristics. This finding aligns with Melkas et al.‘s [ 9 ] argument that implementing technology requires a deeper understanding of users, access to contextual know-how, and health personnel’s tacit knowledge.

To meet role expectations and effectively leverage their professional and technological expertise, champions should embody personal qualities such as the ability to engage others, take a leadership role, be accessible, supportive, and communicate clearly. These qualities align with the key attributes for change in healthcare champions described by Bonawitz et al. [ 15 ]. These attributes include influence, ownership, physical presence, persuasiveness, grit, and a participative leadership style (p.5). These findings suggest that the active performance of the role, beyond mere presence, is crucial for champions to be a successful strategy in technology implementation. Moreover, the recruitment process is not inconsequential. Identifying the right person for the role and providing them with adequate training, organizational support, and dedicated time to fulfill their responsibilities emerge as an important factor based on the insights from champions and health personnel.

Strengths and limitations

While this study benefits from identifying various terms associated with the role of champions, it acknowledges the possibility of missing some studies as a result of diverse descriptions of the role. Nonetheless, a notable strength of the study lies in its specific focus on the health personnel’s experiences in holding the champion role and the broader experiences of health personnel concerning champions in technology implementation within healthcare services. This approach contributes valuable insights into the characteristics of experiences and attitudes toward the role of champions in implementing technology. Lastly, the study emphasizes the relationship between the experiences with the champion role and the organizational setting’s characteristics.

The champion role was frequently inadequately defined [ 30 , 33 , 34 , 35 , 36 , 37 , 39 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 51 ], aligning with previous reviews [ 17 , 19 , 21 ]. As indicated by van Laere and Aggestam [ 52 ], this lack of clarity complicates the identification and comparison of champions across studies. Studies that lacking a distinct definition of the champion’s role were consequently excluded. Only studies written in English were included, introducing the possibility of overlooking relevant studies based on our chosen terms for identifying the champion’s role. Most of the included studies focused on technology implementation in a general context, with champions being just one of several measures. This approach resulted in scant descriptions, as champions were often discussed in the results, discussion, or implications sections rather than being the central focus of the research.

As highlighted by Hall et al. [ 18 ]., methodological issues and inadequate reporting in studies of the champion role create challenges for conducting high-quality reviews, introducing uncertainty around the findings. We have adopted a similar approach to Santos et al. [ 19 ], including all studies even when some issues were identified during the quality assessment. Our review shares the same limitations as previous review by Santos et al. [ 19 ] on the champion role.

Practical implications, policy, and future research

The findings emphasize the significance of the relationship between experiences with the champion role and characteristics of organizational settings as crucial factors for success in the champion role. Clear anchoring of the role within the organization is vital and may impact routines, workflows, staffing, and budgets. Despite limited evidence on the experience of the champion’s role, volunteering, hiring newly graduated health personnel, and appointing more than one champion are identified as facilitators of technology implementation. This study underscores the need for future empirical research including clear descriptions of the champion roles, details on study settings and the technologies to be adopted. This will enable the determination of outcomes and success factors in holding champions in technology implementation processes, transferability of knowledge between contexts and technologies as well as enhance the comparability of studies. Furthermore, there is a need for studies to explore experiences with the champion role, preferably from the perspective of multiple stakeholders, as well as focus on the champion role within various healthcare settings.

This study emphasizes that champions can hold significant positions when provided with a clear mandate, dedicated time, and training, contributing their professional, technological, and personal competencies to expedite technology adoption within services. It appears to be an advantage if the health personnel volunteer or apply for the role to facilitate engaged and proactive champions. The implementation of technology in healthcare services demands efforts from the entire service, and the experiences highlighted in this review exhibits that champions can play an important role. Consequently, empirical studies dedicated to the champion role, employing robust designs based current knowledge, are still needed to provide solid understanding of how champions can be a successful initiative when implementing technology in healthcare services.

Data availability

This review relies exclusively on previously published studies. The datasets supporting the conclusions of this article are included within the article and its supplementary files: Description and characteristics of included studies in Table  1 , Study characteristics. The search strategy is provided in Appendix  1 , and the Critical Appraisal Summary of included studies utilizing MMAT is presented in Appendix  2 .

Abbreviations

Electronic Health Record

Implementation Outcomes Framework

Preferred Reporting Items for Systematics and Meta-Analysis

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Acknowledgements

We would like to thank the librarian Malin E. Norman, at Nord university, for her assistance in the development of the search, as well as guidance regarding the scientific databases.

This study is a part of a PhD project undertaken by the first author, SP, and funded by Nord University, Norway. This research did not receive any specific grant from funding agencies in the public, commercial, as well as not-for-profit sectors.

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The first author/SP has been the project manager and was mainly responsible for all phases of the study. The second and third authors HE and AB have contributed to screening, quality assessment, analysis and discussion of findings. Drafting of the final manuscript has been a collaboration between the first/SP and third athor/AB. The final manuscript has been approved by all authors.

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Correspondence to Sissel Pettersen .

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Pettersen, S., Eide, H. & Berg, A. The role of champions in the implementation of technology in healthcare services: a systematic mixed studies review. BMC Health Serv Res 24 , 456 (2024). https://doi.org/10.1186/s12913-024-10867-7

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Received : 19 June 2023

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DOI : https://doi.org/10.1186/s12913-024-10867-7

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