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• What Is Qualitative Research? | Methods & Examples

What Is Qualitative Research? | Methods & Examples

Published on June 19, 2020 by Pritha Bhandari . Revised on June 22, 2023.

Qualitative research involves collecting and analyzing non-numerical data (e.g., text, video, or audio) to understand concepts, opinions, or experiences. It can be used to gather in-depth insights into a problem or generate new ideas for research.

Qualitative research is the opposite of quantitative research , which involves collecting and analyzing numerical data for statistical analysis.

Qualitative research is commonly used in the humanities and social sciences, in subjects such as anthropology, sociology, education, health sciences, history, etc.

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Table of contents

Approaches to qualitative research, qualitative research methods, qualitative data analysis, advantages of qualitative research, disadvantages of qualitative research, other interesting articles, frequently asked questions about qualitative research.

Qualitative research is used to understand how people experience the world. While there are many approaches to qualitative research, they tend to be flexible and focus on retaining rich meaning when interpreting data.

Common approaches include grounded theory, ethnography , action research , phenomenological research, and narrative research. They share some similarities, but emphasize different aims and perspectives.

Note that qualitative research is at risk for certain research biases including the Hawthorne effect , observer bias , recall bias , and social desirability bias . While not always totally avoidable, awareness of potential biases as you collect and analyze your data can prevent them from impacting your work too much.

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Each of the research approaches involve using one or more data collection methods . These are some of the most common qualitative methods:

• Observations: recording what you have seen, heard, or encountered in detailed field notes.
• Interviews:  personally asking people questions in one-on-one conversations.
• Focus groups: asking questions and generating discussion among a group of people.
• Surveys : distributing questionnaires with open-ended questions.
• Secondary research: collecting existing data in the form of texts, images, audio or video recordings, etc.
• You take field notes with observations and reflect on your own experiences of the company culture.
• You distribute open-ended surveys to employees across all the company’s offices by email to find out if the culture varies across locations.
• You conduct in-depth interviews with employees in your office to learn about their experiences and perspectives in greater detail.

Qualitative researchers often consider themselves “instruments” in research because all observations, interpretations and analyses are filtered through their own personal lens.

For this reason, when writing up your methodology for qualitative research, it’s important to reflect on your approach and to thoroughly explain the choices you made in collecting and analyzing the data.

Qualitative data can take the form of texts, photos, videos and audio. For example, you might be working with interview transcripts, survey responses, fieldnotes, or recordings from natural settings.

Most types of qualitative data analysis share the same five steps:

• Prepare and organize your data. This may mean transcribing interviews or typing up fieldnotes.
• Review and explore your data. Examine the data for patterns or repeated ideas that emerge.
• Develop a data coding system. Based on your initial ideas, establish a set of codes that you can apply to categorize your data.
• Assign codes to the data. For example, in qualitative survey analysis, this may mean going through each participant’s responses and tagging them with codes in a spreadsheet. As you go through your data, you can create new codes to add to your system if necessary.
• Identify recurring themes. Link codes together into cohesive, overarching themes.

There are several specific approaches to analyzing qualitative data. Although these methods share similar processes, they emphasize different concepts.

Qualitative research often tries to preserve the voice and perspective of participants and can be adjusted as new research questions arise. Qualitative research is good for:

• Flexibility

The data collection and analysis process can be adapted as new ideas or patterns emerge. They are not rigidly decided beforehand.

• Natural settings

Data collection occurs in real-world contexts or in naturalistic ways.

• Meaningful insights

Detailed descriptions of people’s experiences, feelings and perceptions can be used in designing, testing or improving systems or products.

• Generation of new ideas

Open-ended responses mean that researchers can uncover novel problems or opportunities that they wouldn’t have thought of otherwise.

Researchers must consider practical and theoretical limitations in analyzing and interpreting their data. Qualitative research suffers from:

• Unreliability

The real-world setting often makes qualitative research unreliable because of uncontrolled factors that affect the data.

• Subjectivity

Due to the researcher’s primary role in analyzing and interpreting data, qualitative research cannot be replicated . The researcher decides what is important and what is irrelevant in data analysis, so interpretations of the same data can vary greatly.

• Limited generalizability

Small samples are often used to gather detailed data about specific contexts. Despite rigorous analysis procedures, it is difficult to draw generalizable conclusions because the data may be biased and unrepresentative of the wider population .

• Labor-intensive

Although software can be used to manage and record large amounts of text, data analysis often has to be checked or performed manually.

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

• Chi square goodness of fit test
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

There are five common approaches to qualitative research :

• Grounded theory involves collecting data in order to develop new theories.
• Ethnography involves immersing yourself in a group or organization to understand its culture.
• Narrative research involves interpreting stories to understand how people make sense of their experiences and perceptions.
• Phenomenological research involves investigating phenomena through people’s lived experiences.
• Action research links theory and practice in several cycles to drive innovative changes.

Data collection is the systematic process by which observations or measurements are gathered in research. It is used in many different contexts by academics, governments, businesses, and other organizations.

There are various approaches to qualitative data analysis , but they all share five steps in common:

• Prepare and organize your data.
• Review and explore your data.
• Develop a data coding system.
• Assign codes to the data.
• Identify recurring themes.

The specifics of each step depend on the focus of the analysis. Some common approaches include textual analysis , thematic analysis , and discourse analysis .

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Qualitative Research : Definition

Qualitative research is the naturalistic study of social meanings and processes, using interviews, observations, and the analysis of texts and images.  In contrast to quantitative researchers, whose statistical methods enable broad generalizations about populations (for example, comparisons of the percentages of U.S. demographic groups who vote in particular ways), qualitative researchers use in-depth studies of the social world to analyze how and why groups think and act in particular ways (for instance, case studies of the experiences that shape political views).   

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What is Qualitative Research? Methods, Types, Approaches and Examples

Qualitative research is a type of method that researchers use depending on their study requirements. Research can be conducted using several methods, but before starting the process, researchers should understand the different methods available to decide the best one for their study type. The type of research method needed depends on a few important criteria, such as the research question, study type, time, costs, data availability, and availability of respondents. The two main types of methods are qualitative research and quantitative research. Sometimes, researchers may find it difficult to decide which type of method is most suitable for their study. Keeping in mind a simple rule of thumb could help you make the correct decision. Quantitative research should be used to validate or test a theory or hypothesis and qualitative research should be used to understand a subject or event or identify reasons for observed patterns.  

Qualitative research methods are based on principles of social sciences from several disciplines like psychology, sociology, and anthropology. In this method, researchers try to understand the feelings and motivation of their respondents, which would have prompted them to select or give a particular response to a question. Here are two qualitative research examples :  

• Two brands (A & B) of the same medicine are available at a pharmacy. However, Brand A is more popular and has higher sales. In qualitative research , the interviewers would ideally visit a few stores in different areas and ask customers their reason for selecting either brand. Respondents may have different reasons that motivate them to select one brand over the other, such as brand loyalty, cost, feedback from friends, doctor’s suggestion, etc. Once the reasons are known, companies could then address challenges in that specific area to increase their product’s sales.  
• A company organizes a focus group meeting with a random sample of its product’s consumers to understand their opinion on a new product being launched.  

Table of Contents

What is qualitative research? 1

Qualitative research is the process of collecting, analyzing, and interpreting non-numerical data. The findings of qualitative research are expressed in words and help in understanding individuals’ subjective perceptions about an event, condition, or subject. This type of research is exploratory and is used to generate hypotheses or theories from data. Qualitative data are usually in the form of text, videos, photographs, and audio recordings. There are multiple qualitative research types , which will be discussed later.  

Qualitative research methods 2

Researchers can choose from several qualitative research methods depending on the study type, research question, the researcher’s role, data to be collected, etc.  

The following table lists the common qualitative research approaches with their purpose and examples, although there may be an overlap between some.  

Types of qualitative research 3,4

The data collection methods in qualitative research are designed to assess and understand the perceptions, motivations, and feelings of the respondents about the subject being studied. The different qualitative research types include the following:  

• In-depth or one-on-one interviews : This is one of the most common qualitative research methods and helps the interviewers understand a respondent’s subjective opinion and experience pertaining to a specific topic or event. These interviews are usually conversational and encourage the respondents to express their opinions freely. Semi-structured interviews, which have open-ended questions (where the respondents can answer more than just “yes” or “no”), are commonly used. Such interviews can be either face-to-face or telephonic, and the duration can vary depending on the subject or the interviewer. Asking the right questions is essential in this method so that the interview can be led in the suitable direction. Face-to-face interviews also help interviewers observe the respondents’ body language, which could help in confirming whether the responses match.  
• Document study/Literature review/Record keeping : Researchers’ review of already existing written materials such as archives, annual reports, research articles, guidelines, policy documents, etc.  
• Focus groups : Usually include a small sample of about 6-10 people and a moderator, to understand the participants’ opinion on a given topic. Focus groups ensure constructive discussions to understand the why, what, and, how about the topic. These group meetings need not always be in-person. In recent times, online meetings are also encouraged, and online surveys could also be administered with the option to “write” subjective answers as well. However, this method is expensive and is mostly used for new products and ideas.  
• Qualitative observation : In this method, researchers collect data using their five senses—sight, smell, touch, taste, and hearing. This method doesn’t include any measurements but only the subjective observation. For example, “The dessert served at the bakery was creamy with sweet buttercream frosting”; this observation is based on the taste perception.  

Qualitative research : Data collection and analysis

• Qualitative data collection is the process by which observations or measurements are gathered in research.  
• The data collected are usually non-numeric and subjective and could be recorded in various methods, for instance, in case of one-to-one interviews, the responses may be recorded using handwritten notes, and audio and video recordings, depending on the interviewer and the setting or duration.  
• Once the data are collected, they should be transcribed into meaningful or useful interpretations. An experienced researcher could take about 8-10 hours to transcribe an interview’s recordings. All such notes and recordings should be maintained properly for later reference.  
• Some interviewers make use of “field notes.” These are not exactly the respondents’ answers but rather some observations the interviewer may have made while asking questions and may include non-verbal cues or any information about the setting or the environment. These notes are usually informal and help verify respondents’ answers.  

2. Qualitative data analysis 

• This process involves analyzing all the data obtained from the qualitative research methods in the form of text (notes), audio-video recordings, and pictures.  
• Text analysis is a common form of qualitative data analysis in which researchers examine the social lives of the participants and analyze their words, actions, etc. in specific contexts. Social media platforms are now playing an important role in this method with researchers analyzing all information shared online.   

There are usually five steps in the qualitative data analysis process: 5

• Prepare and organize the data  
• Transcribe interviews  
• Collect and document field notes and other material  
• Review and explore the data  
• Examine the data for patterns or important observations  
• Develop a data coding system  
• Create codes to categorize and connect the data  
• Assign these codes to the data or responses  
• Review the codes  
• Identify recurring themes, opinions, patterns, etc.  
• Present the findings  
• Use the best possible method to present your observations  

The following table 6 lists some common qualitative data analysis methods used by companies to make important decisions, with examples and when to use each. The methods may be similar and can overlap.  

Characteristics of qualitative research methods 4

• Unstructured raw data : Qualitative research methods use unstructured, non-numerical data , which are analyzed to generate subjective conclusions about specific subjects, usually presented descriptively, instead of using statistical data.  
• Site-specific data collection : In qualitative research methods , data are collected at specific areas where the respondents or researchers are either facing a challenge or have a need to explore. The process is conducted in a real-world setting and participants do not need to leave their original geographical setting to be able to participate.  
• Researchers’ importance : Researchers play an instrumental role because, in qualitative research , communication with respondents is an essential part of data collection and analysis. In addition, researchers need to rely on their own observation and listening skills during an interaction and use and interpret that data appropriately.  
• Multiple methods : Researchers collect data through various methods, as listed earlier, instead of relying on a single source. Although there may be some overlap between the qualitative research methods , each method has its own significance.  
• Solving complex issues : These methods help in breaking down complex problems into more useful and interpretable inferences, which can be easily understood by everyone.  
• Unbiased responses : Qualitative research methods rely on open communication where the participants are allowed to freely express their views. In such cases, the participants trust the interviewer, resulting in unbiased and truthful responses.  
• Flexible : The qualitative research method can be changed at any stage of the research. The data analysis is not confined to being done at the end of the research but can be done in tandem with data collection. Consequently, based on preliminary analysis and new ideas, researchers have the liberty to change the method to suit their objective.  

When to use qualitative research   4

The following points will give you an idea about when to use qualitative research .  

• When the objective of a research study is to understand behaviors and patterns of respondents, then qualitative research is the most suitable method because it gives a clear insight into the reasons for the occurrence of an event.  
• A few use cases for qualitative research methods include:  
• New product development or idea generation  
• Strengthening a product’s marketing strategy  
• Conducting a SWOT analysis of product or services portfolios to help take important strategic decisions  
• Understanding purchasing behavior of consumers  
• Understanding reactions of target market to ad campaigns  
• Understanding market demographics and conducting competitor analysis  
• Understanding the effectiveness of a new treatment method in a particular section of society  

A qualitative research method case study to understand when to use qualitative research 7

Context : A high school in the US underwent a turnaround or conservatorship process and consequently experienced a below average teacher retention rate. Researchers conducted qualitative research to understand teachers’ experiences and perceptions of how the turnaround may have influenced the teachers’ morale and how this, in turn, would have affected teachers’ retention.  

Method : Purposive sampling was used to select eight teachers who were employed with the school before the conservatorship process and who were subsequently retained. One-on-one semi-structured interviews were conducted with these teachers. The questions addressed teachers’ perspectives of morale and their views on the conservatorship process.  

Results : The study generated six factors that may have been influencing teachers’ perspectives: powerlessness, excessive visitations, loss of confidence, ineffective instructional practices, stress and burnout, and ineffective professional development opportunities. Based on these factors, four recommendations were made to increase teacher retention by boosting their morale.  

Advantages of qualitative research 1

• Reflects real-world settings , and therefore allows for ambiguities in data, as well as the flexibility to change the method based on new developments.  
• Helps in understanding the feelings or beliefs of the respondents rather than relying only on quantitative data.  
• Uses a descriptive and narrative style of presentation, which may be easier to understand for people from all backgrounds.  
• Some topics involving sensitive or controversial content could be difficult to quantify and so qualitative research helps in analyzing such content.  
• The availability of multiple data sources and research methods helps give a holistic picture.  
• There’s more involvement of participants, which gives them an assurance that their opinion matters, possibly leading to unbiased responses.   

Disadvantages of qualitative research 1

• Large-scale data sets cannot be included because of time and cost constraints.  
• Ensuring validity and reliability may be a challenge because of the subjective nature of the data, so drawing definite conclusions could be difficult.  
• Replication by other researchers may be difficult for the same contexts or situations.  
• Generalization to a wider context or to other populations or settings is not possible.  
• Data collection and analysis may be time consuming.  
• Researcher’s interpretation may alter the results causing an unintended bias.  

Differences between qualitative research and quantitative research 1

Frequently asked questions on qualitative research  

Q: how do i know if qualitative research is appropriate for my study  .

A: Here’s a simple checklist you could use:  

• Not much is known about the subject being studied.  
• There is a need to understand or simplify a complex problem or situation.  
• Participants’ experiences/beliefs/feelings are required for analysis.  
• There’s no existing hypothesis to begin with, rather a theory would need to be created after analysis.  
• You need to gather in-depth understanding of an event or subject, which may not need to be supported by numeric data.  

Q: How do I ensure the reliability and validity of my qualitative research findings?  

A: To ensure the validity of your qualitative research findings you should explicitly state your objective and describe clearly why you have interpreted the data in a particular way. Another method could be to connect your data in different ways or from different perspectives to see if you reach a similar, unbiased conclusion.   

To ensure reliability, always create an audit trail of your qualitative research by describing your steps and reasons for every interpretation, so that if required, another researcher could trace your steps to corroborate your (or their own) findings. In addition, always look for patterns or consistencies in the data collected through different methods.  

Q: Are there any sampling strategies or techniques for qualitative research ?   

A: Yes, the following are few common sampling strategies used in qualitative research :  

1. Convenience sampling  

Selects participants who are most easily accessible to researchers due to geographical proximity, availability at a particular time, etc.  

2. Purposive sampling  

Participants are grouped according to predefined criteria based on a specific research question. Sample sizes are often determined based on theoretical saturation (when new data no longer provide additional insights).  

3. Snowball sampling  

Already selected participants use their social networks to refer the researcher to other potential participants.  

4. Quota sampling  

While designing the study, the researchers decide how many people with which characteristics to include as participants. The characteristics help in choosing people most likely to provide insights into the subject.  

Q: What ethical standards need to be followed with qualitative research ?  

A: The following ethical standards should be considered in qualitative research:  

• Anonymity : The participants should never be identified in the study and researchers should ensure that no identifying information is mentioned even indirectly.  
• Confidentiality : To protect participants’ confidentiality, ensure that all related documents, transcripts, notes are stored safely.  
• Informed consent : Researchers should clearly communicate the objective of the study and how the participants’ responses will be used prior to engaging with the participants.  

Q: How do I address bias in my qualitative research ?  

  A: You could use the following points to ensure an unbiased approach to your qualitative research :  

• Check your interpretations of the findings with others’ interpretations to identify consistencies.  
• If possible, you could ask your participants if your interpretations convey their beliefs to a significant extent.  
• Data triangulation is a way of using multiple data sources to see if all methods consistently support your interpretations.  
• Contemplate other possible explanations for your findings or interpretations and try ruling them out if possible.  
• Conduct a peer review of your findings to identify any gaps that may not have been visible to you.  
• Frame context-appropriate questions to ensure there is no researcher or participant bias.

We hope this article has given you answers to the question “ what is qualitative research ” and given you an in-depth understanding of the various aspects of qualitative research , including the definition, types, and approaches, when to use this method, and advantages and disadvantages, so that the next time you undertake a study you would know which type of research design to adopt.  

References:  

• McLeod, S. A. Qualitative vs. quantitative research. Simply Psychology [Accessed January 17, 2023]. www.simplypsychology.org/qualitative-quantitative.html    
• Omniconvert website [Accessed January 18, 2023]. https://www.omniconvert.com/blog/qualitative-research-definition-methodology-limitation-examples/  
• Busetto L., Wick W., Gumbinger C. How to use and assess qualitative research methods. Neurological Research and Practice [Accessed January 19, 2023] https://neurolrespract.biomedcentral.com/articles/10.1186/s42466-020-00059  
• QuestionPro website. Qualitative research methods: Types & examples [Accessed January 16, 2023]. https://www.questionpro.com/blog/qualitative-research-methods/  
• Campuslabs website. How to analyze qualitative data [Accessed January 18, 2023]. https://baselinesupport.campuslabs.com/hc/en-us/articles/204305675-How-to-analyze-qualitative-data  
• Thematic website. Qualitative data analysis: Step-by-guide [Accessed January 20, 2023]. https://getthematic.com/insights/qualitative-data-analysis/  
• Lane L. J., Jones D., Penny G. R. Qualitative case study of teachers’ morale in a turnaround school. Research in Higher Education Journal . https://files.eric.ed.gov/fulltext/EJ1233111.pdf  
• Meetingsnet website. 7 FAQs about qualitative research and CME [Accessed January 21, 2023]. https://www.meetingsnet.com/cme-design/7-faqs-about-qualitative-research-and-cme     
• Qualitative research methods: A data collector’s field guide. Khoury College of Computer Sciences. Northeastern University. https://course.ccs.neu.edu/is4800sp12/resources/qualmethods.pdf  

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What is qualitative research?

Qualitative research is a process of naturalistic inquiry that seeks an in-depth understanding of social phenomena within their natural setting. It focuses on the "why" rather than the "what" of social phenomena and relies on the direct experiences of human beings as meaning-making agents in their every day lives. Rather than by logical and statistical procedures, qualitative researchers use multiple systems of inquiry for the study of human phenomena including biography, case study, historical analysis, discourse analysis, ethnography, grounded theory, and phenomenology.

University of Utah College of Nursing, (n.d.). What is qualitative research? [Guide] Retrieved from  https://nursing.utah.edu/research/qualitative-research/what-is-qualitative-research.php#what 

The following video will explain the fundamentals of qualitative research.

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Qualitative Research Definition

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Table of Contents

Qualitative research seeks to understand people’s experiences and perspectives by studying social organizations and human behavior. Data in qualitative studies focuses on people’s beliefs and emotional responses. Qualitative data is especially helpful when a company wants to know how customers feel about a product or service, such as in user experience (UX) design or marketing . 

Researchers use qualitative approaches to “determine answers to research questions on human behavior and the cultural values that drive our thinking and behavior,” says Margaret J. King, director at The Center for Cultural Studies & Analysis in Philadelphia.

Data in qualitative research typically can’t be assessed mathematically — the data is not sets of numbers or quantifiable information. Rather, it’s collections of images, words, notes on behaviors, descriptions of emotions, and historical context. Data is collected through observations, interviews, surveys, focus groups, and secondary research. 

However, a qualitative study needs a “clear research question at its base,” notes King, and the research needs to be “observed, categorized, compared, and evaluated (along a scale or by a typology chart) by reference to a baseline in order to determine an outcome with value as new and reliable information.”

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Who Uses Qualitative Research?

Researchers in social sciences and humanities often use qualitative research methods, especially in specific areas of study like anthropology, history, education, and sociology. 

Qualitative methods are also applicable in business, technology , and marketing spaces. For example, product managers use qualitative research to understand how target audiences respond to their products. They may use focus groups to gain insights from potential customers on product prototypes and improvements or surveys from existing customers to understand what changes users want to see. 

Other careers that may involve qualitative research include: 

• Marketing analyst
• UX and UI analyst
• Market researcher
• Statistician
• Business analyst
• Data analyst
• Research assistant
• Claims investigator

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Good research begins with a question, and this question informs the approach used by qualitative researchers. 

Grounded Theory

Grounded theory is an inductive approach to theory development. In many forms of research, you begin with a hypothesis and then test it to see if you’re correct. In grounded theory, though, you go in without any assumptions and rely on the data you collect to form theories. You start with an open question about a phenomenon you are studying and collect and analyze data until you can form a fully-fledged theory from the information. 

Example: A company wants to improve its brand and marketing strategies. The company performs a grounded theory approach to solving this problem by conducting interviews and surveys with past, current, and prospective customers. The information gathered from these methods helps the company understand what type of branding and marketing their customer-base likes and dislikes, allowing the team to inductively craft a new brand and marketing strategy from the data. 

Action Research

Action research is one part study and one part problem-solving . Through action research, analysts investigate a problem or weakness and develop practical solutions. The process of action research is cyclical —- researchers assess solutions for efficiency and effectiveness, and create further solutions to correct any issues found. 

Example: A manager notices her employees struggle to cooperate on group projects. She carefully reviews how team members interact with each other and asks them all to respond to a survey about communication. Through the survey and study, she finds that guidelines for group projects are unclear. After changing the guidelines, she reviews her team again to see if there are any changes to their behavior.  

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Phenomenological Research

Phenomenological research investigates a phenomenon in depth, looking at people’s experiences and understanding of the situation. This sort of study is primarily descriptive and seeks to broaden understanding around a specific incident and the people involved. Researchers in phenomenological studies must be careful to set aside any biases or assumptions because the information used should be entirely from the subjects themselves. 

Example : A researcher wants to better understand the lived experience of college students with jobs. The purpose of this research is to gain insights into the pressures of college students who balance studying and working at the same time. The researcher conducts a series of interviews with several college students, learning about their past and current situations. Through the first few interviews, the researcher builds a relationship with the students. Later discussions are more targeted, with questions prompting the students to discuss their emotions surrounding both work and school and the difficulties and benefits arising from their situation. The researcher then analyzes these interviews, and identifies shared themes to contextualize the experiences of the students.

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Ethnography

Ethnography is an immersive study of a particular culture or community. Through ethnographic research, analysts aim to learn about a group’s conventions, social dynamics, and cultural norms. Some researchers use active observation methods, finding ways to integrate themselves into the culture as much as possible. Others use passive observation, watching closely from the outside but not fully immersing themselves. 

Example: A company hires an external researcher to learn what their company’s culture is actually like. The researcher studies the social dynamics of the employees and may even look at how these employees interact with clients and with each other outside of the office. The goal is to deliver a comprehensive report of the company’s culture and the social dynamics of its employees.

Case Studies

A case study is a type of in-depth analysis of a situation. Case studies can focus on an organization, belief system, event, person, or action. The goal of a case study is to understand the phenomenon and put it in a real-world context. Case studies are also commonly used in marketing and sales to highlight the benefits of a company’s products or services. 

Example: A business performs a case study of its competitors’ strategies. This case study aims to show why the company should adopt a specific business strategy. The study looks at each competitor’s business structure, marketing campaigns, product offerings, and historical growth trends. Then, using this data on other businesses, the researcher can theorize how that strategy would benefit their company.

>>MORE: Learn how companies use case study interviews to assess candidates’ research and problem-solving skills. 

Qualitative research methods are great for generating new ideas. The exploratory nature of qualitative research means uncovering unexpected information, which often leads to new theories and further research topics. Additionally, qualitative findings feel meaningful. These studies focus on people, emotions, and societies and may feel closer to their communities than quantitative research that relies on more mathematical and logical data. 

However, qualitative research can be unreliable at times. It’s difficult to replicate qualitative studies since people’s opinions and emotions can change quickly. For example, a focus group has a lot of variables that can affect the outcome, and that same group, asked the same questions a year later, may have entirely different responses. The data collection can also be difficult and time-consuming with qualitative research. Ultimately, interviewing people, reviewing surveys, and understanding and explaining human emotions can be incredibly complex.

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While qualitative research deals with data that isn’t easily manipulated by mathematics, quantitative research almost exclusively involves numbers and numerical data. Quantitative studies aim to find concrete details, like units of time, percentages, or statistics. 

Besides the types of data used, a core difference between quantitative and qualitative research is the idea of control and replication. 

“Qualitative is less subject to control (as in lab studies) and, therefore, less statistically measurable than quantitative approaches,” says King.

One person’s interview about a specific topic can have completely different responses than every other person’s interview since there are so many variables in qualitative research. On the other hand, quantitative studies can often be replicated. For instance, when testing the effects of a new medication, quantifiable data, like blood test results, can be repeated. Qualitative data, though, like how people feel about the medication, may differ from person to person and from moment to moment.

JPMorgan Quantitative Research

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You can show your experience with qualitative research on your resume in your skills or work experience sections and your cover letter . 

• In your skills section , you can list types of qualitative research you are skilled at, like conducting interviews, performing grounded theory research, or crafting case studies. 
• In your work or internship experience descriptions , you can highlight specific examples, like talking about a time you used action research to solve a complex issue at your last job. 
• In your cover letter , you can discuss in-depth qualitative research projects you’ve completed. For instance, say you spent a summer conducting ethnographic research or a whole semester running focus groups to get feedback on a product. You can talk about these experiences in your cover letter and note how these skills make you a great fit for the job. 

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An Overview of Qualitative Research Methods

Direct Observation, Interviews, Participation, Immersion, Focus Groups

• Research, Samples, and Statistics
• Key Concepts
• Major Sociologists
• News & Issues
• Recommended Reading
• Archaeology

Qualitative research is a type of social science research that collects and works with non-numerical data and that seeks to interpret meaning from these data that help understand social life through the study of targeted populations or places.

People often frame it in opposition to quantitative research , which uses numerical data to identify large-scale trends and employs statistical operations to determine causal and correlative relationships between variables.

Within sociology, qualitative research is typically focused on the micro-level of social interaction that composes everyday life, whereas quantitative research typically focuses on macro-level trends and phenomena.

Key Takeaways

Methods of qualitative research include:

• observation and immersion
• open-ended surveys
• focus groups
• content analysis of visual and textual materials
• oral history

Qualitative research has a long history in sociology and has been used within it for as long as the field has existed.

This type of research has long appealed to social scientists because it allows the researchers to investigate the meanings people attribute to their behavior, actions, and interactions with others.

While quantitative research is useful for identifying relationships between variables, like, for example, the connection between poverty and racial hate, it is qualitative research that can illuminate why this connection exists by going directly to the source—the people themselves.

Qualitative research is designed to reveal the meaning that informs the action or outcomes that are typically measured by quantitative research. So qualitative researchers investigate meanings, interpretations, symbols, and the processes and relations of social life.

What this type of research produces is descriptive data that the researcher must then interpret using rigorous and systematic methods of transcribing, coding, and analysis of trends and themes.

Because its focus is everyday life and people's experiences, qualitative research lends itself well to creating new theories using the inductive method , which can then be tested with further research.

Qualitative researchers use their own eyes, ears, and intelligence to collect in-depth perceptions and descriptions of targeted populations, places, and events.

Their findings are collected through a variety of methods, and often a researcher will use at least two or several of the following while conducting a qualitative study:

• Direct observation : With direct observation, a researcher studies people as they go about their daily lives without participating or interfering. This type of research is often unknown to those under study, and as such, must be conducted in public settings where people do not have a reasonable expectation of privacy. For example, a researcher might observe the ways in which strangers interact in public as they gather to watch a street performer.
• Open-ended surveys : While many surveys are designed to generate quantitative data, many are also designed with open-ended questions that allow for the generation and analysis of qualitative data. For example, a survey might be used to investigate not just which political candidates voters chose, but why they chose them, in their own words.
• Focus group : In a focus group, a researcher engages a small group of participants in a conversation designed to generate data relevant to the research question. Focus groups can contain anywhere from 5 to 15 participants. Social scientists often use them in studies that examine an event or trend that occurs within a specific community. They are common in market research, too.
• In-depth interviews : Researchers conduct in-depth interviews by speaking with participants in a one-on-one setting. Sometimes a researcher approaches the interview with a predetermined list of questions or topics for discussion but allows the conversation to evolve based on how the participant responds. Other times, the researcher has identified certain topics of interest but does not have a formal guide for the conversation, but allows the participant to guide it.
• Oral history : The oral history method is used to create a historical account of an event, group, or community, and typically involves a series of in-depth interviews conducted with one or multiple participants over an extended period.
• Participant observation : This method is similar to observation, however with this one, the researcher also participates in the action or events to not only observe others but to gain the first-hand experience in the setting.
• Ethnographic observation : Ethnographic observation is the most intensive and in-depth observational method. Originating in anthropology, with this method, a researcher fully immerses themselves into the research setting and lives among the participants as one of them for anywhere from months to years. By doing this, the researcher attempts to experience day-to-day existence from the viewpoints of those studied to develop in-depth and long-term accounts of the community, events, or trends under observation.
• Content analysis : This method is used by sociologists to analyze social life by interpreting words and images from documents, film, art, music, and other cultural products and media. The researchers look at how the words and images are used, and the context in which they are used to draw inferences about the underlying culture. Content analysis of digital material, especially that generated by social media users, has become a popular technique within the social sciences.

While much of the data generated by qualitative research is coded and analyzed using just the researcher's eyes and brain, the use of computer software to do these processes is increasingly popular within the social sciences.

Such software analysis works well when the data is too large for humans to handle, though the lack of a human interpreter is a common criticism of the use of computer software.

Pros and Cons

Qualitative research has both benefits and drawbacks.

On the plus side, it creates an in-depth understanding of the attitudes, behaviors, interactions, events, and social processes that comprise everyday life. In doing so, it helps social scientists understand how everyday life is influenced by society-wide things like social structure , social order , and all kinds of social forces.

This set of methods also has the benefit of being flexible and easily adaptable to changes in the research environment and can be conducted with minimal cost in many cases.

Among the downsides of qualitative research is that its scope is fairly limited so its findings are not always widely able to be generalized.

Researchers also have to use caution with these methods to ensure that they do not influence the data in ways that significantly change it and that they do not bring undue personal bias to their interpretation of the findings.

Fortunately, qualitative researchers receive rigorous training designed to eliminate or reduce these types of research bias.

• How to Conduct a Sociology Research Interview
• What Is Participant Observation Research?
• Immersion Definition: Cultural, Language, and Virtual
• Definition and Overview of Grounded Theory
• The Differences Between Indexes and Scales
• Pros and Cons of Secondary Data Analysis
• Social Surveys: Questionnaires, Interviews, and Telephone Polls
• The Different Types of Sampling Designs in Sociology
• Principal Components and Factor Analysis
• Sociology Explains Why Some People Cheat on Their Spouses
• Deductive Versus Inductive Reasoning
• Data Sources For Sociological Research
• How to Construct an Index for Research
• A Review of Software Tools for Quantitative Data Analysis
• Constructing a Deductive Theory
• Scales Used in Social Science Research

• Open access
• Published: 27 May 2020

How to use and assess qualitative research methods

• Loraine Busetto   ORCID: orcid.org/0000-0002-9228-7875 1 ,
• Wolfgang Wick 1 , 2 &
• Christoph Gumbinger 1  

Neurological Research and Practice volume  2 , Article number:  14 ( 2020 ) Cite this article

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This paper aims to provide an overview of the use and assessment of qualitative research methods in the health sciences. Qualitative research can be defined as the study of the nature of phenomena and is especially appropriate for answering questions of why something is (not) observed, assessing complex multi-component interventions, and focussing on intervention improvement. The most common methods of data collection are document study, (non-) participant observations, semi-structured interviews and focus groups. For data analysis, field-notes and audio-recordings are transcribed into protocols and transcripts, and coded using qualitative data management software. Criteria such as checklists, reflexivity, sampling strategies, piloting, co-coding, member-checking and stakeholder involvement can be used to enhance and assess the quality of the research conducted. Using qualitative in addition to quantitative designs will equip us with better tools to address a greater range of research problems, and to fill in blind spots in current neurological research and practice.

The aim of this paper is to provide an overview of qualitative research methods, including hands-on information on how they can be used, reported and assessed. This article is intended for beginning qualitative researchers in the health sciences as well as experienced quantitative researchers who wish to broaden their understanding of qualitative research.

What is qualitative research?

Qualitative research is defined as “the study of the nature of phenomena”, including “their quality, different manifestations, the context in which they appear or the perspectives from which they can be perceived” , but excluding “their range, frequency and place in an objectively determined chain of cause and effect” [ 1 ]. This formal definition can be complemented with a more pragmatic rule of thumb: qualitative research generally includes data in form of words rather than numbers [ 2 ].

Why conduct qualitative research?

Because some research questions cannot be answered using (only) quantitative methods. For example, one Australian study addressed the issue of why patients from Aboriginal communities often present late or not at all to specialist services offered by tertiary care hospitals. Using qualitative interviews with patients and staff, it found one of the most significant access barriers to be transportation problems, including some towns and communities simply not having a bus service to the hospital [ 3 ]. A quantitative study could have measured the number of patients over time or even looked at possible explanatory factors – but only those previously known or suspected to be of relevance. To discover reasons for observed patterns, especially the invisible or surprising ones, qualitative designs are needed.

While qualitative research is common in other fields, it is still relatively underrepresented in health services research. The latter field is more traditionally rooted in the evidence-based-medicine paradigm, as seen in " research that involves testing the effectiveness of various strategies to achieve changes in clinical practice, preferably applying randomised controlled trial study designs (...) " [ 4 ]. This focus on quantitative research and specifically randomised controlled trials (RCT) is visible in the idea of a hierarchy of research evidence which assumes that some research designs are objectively better than others, and that choosing a "lesser" design is only acceptable when the better ones are not practically or ethically feasible [ 5 , 6 ]. Others, however, argue that an objective hierarchy does not exist, and that, instead, the research design and methods should be chosen to fit the specific research question at hand – "questions before methods" [ 2 , 7 , 8 , 9 ]. This means that even when an RCT is possible, some research problems require a different design that is better suited to addressing them. Arguing in JAMA, Berwick uses the example of rapid response teams in hospitals, which he describes as " a complex, multicomponent intervention – essentially a process of social change" susceptible to a range of different context factors including leadership or organisation history. According to him, "[in] such complex terrain, the RCT is an impoverished way to learn. Critics who use it as a truth standard in this context are incorrect" [ 8 ] . Instead of limiting oneself to RCTs, Berwick recommends embracing a wider range of methods , including qualitative ones, which for "these specific applications, (...) are not compromises in learning how to improve; they are superior" [ 8 ].

Research problems that can be approached particularly well using qualitative methods include assessing complex multi-component interventions or systems (of change), addressing questions beyond “what works”, towards “what works for whom when, how and why”, and focussing on intervention improvement rather than accreditation [ 7 , 9 , 10 , 11 , 12 ]. Using qualitative methods can also help shed light on the “softer” side of medical treatment. For example, while quantitative trials can measure the costs and benefits of neuro-oncological treatment in terms of survival rates or adverse effects, qualitative research can help provide a better understanding of patient or caregiver stress, visibility of illness or out-of-pocket expenses.

How to conduct qualitative research?

Given that qualitative research is characterised by flexibility, openness and responsivity to context, the steps of data collection and analysis are not as separate and consecutive as they tend to be in quantitative research [ 13 , 14 ]. As Fossey puts it : “sampling, data collection, analysis and interpretation are related to each other in a cyclical (iterative) manner, rather than following one after another in a stepwise approach” [ 15 ]. The researcher can make educated decisions with regard to the choice of method, how they are implemented, and to which and how many units they are applied [ 13 ]. As shown in Fig.  1 , this can involve several back-and-forth steps between data collection and analysis where new insights and experiences can lead to adaption and expansion of the original plan. Some insights may also necessitate a revision of the research question and/or the research design as a whole. The process ends when saturation is achieved, i.e. when no relevant new information can be found (see also below: sampling and saturation). For reasons of transparency, it is essential for all decisions as well as the underlying reasoning to be well-documented.

Iterative research process

While it is not always explicitly addressed, qualitative methods reflect a different underlying research paradigm than quantitative research (e.g. constructivism or interpretivism as opposed to positivism). The choice of methods can be based on the respective underlying substantive theory or theoretical framework used by the researcher [ 2 ].

Data collection

The methods of qualitative data collection most commonly used in health research are document study, observations, semi-structured interviews and focus groups [ 1 , 14 , 16 , 17 ].

Document study

Document study (also called document analysis) refers to the review by the researcher of written materials [ 14 ]. These can include personal and non-personal documents such as archives, annual reports, guidelines, policy documents, diaries or letters.

Observations

Observations are particularly useful to gain insights into a certain setting and actual behaviour – as opposed to reported behaviour or opinions [ 13 ]. Qualitative observations can be either participant or non-participant in nature. In participant observations, the observer is part of the observed setting, for example a nurse working in an intensive care unit [ 18 ]. In non-participant observations, the observer is “on the outside looking in”, i.e. present in but not part of the situation, trying not to influence the setting by their presence. Observations can be planned (e.g. for 3 h during the day or night shift) or ad hoc (e.g. as soon as a stroke patient arrives at the emergency room). During the observation, the observer takes notes on everything or certain pre-determined parts of what is happening around them, for example focusing on physician-patient interactions or communication between different professional groups. Written notes can be taken during or after the observations, depending on feasibility (which is usually lower during participant observations) and acceptability (e.g. when the observer is perceived to be judging the observed). Afterwards, these field notes are transcribed into observation protocols. If more than one observer was involved, field notes are taken independently, but notes can be consolidated into one protocol after discussions. Advantages of conducting observations include minimising the distance between the researcher and the researched, the potential discovery of topics that the researcher did not realise were relevant and gaining deeper insights into the real-world dimensions of the research problem at hand [ 18 ].

Semi-structured interviews

Hijmans & Kuyper describe qualitative interviews as “an exchange with an informal character, a conversation with a goal” [ 19 ]. Interviews are used to gain insights into a person’s subjective experiences, opinions and motivations – as opposed to facts or behaviours [ 13 ]. Interviews can be distinguished by the degree to which they are structured (i.e. a questionnaire), open (e.g. free conversation or autobiographical interviews) or semi-structured [ 2 , 13 ]. Semi-structured interviews are characterized by open-ended questions and the use of an interview guide (or topic guide/list) in which the broad areas of interest, sometimes including sub-questions, are defined [ 19 ]. The pre-defined topics in the interview guide can be derived from the literature, previous research or a preliminary method of data collection, e.g. document study or observations. The topic list is usually adapted and improved at the start of the data collection process as the interviewer learns more about the field [ 20 ]. Across interviews the focus on the different (blocks of) questions may differ and some questions may be skipped altogether (e.g. if the interviewee is not able or willing to answer the questions or for concerns about the total length of the interview) [ 20 ]. Qualitative interviews are usually not conducted in written format as it impedes on the interactive component of the method [ 20 ]. In comparison to written surveys, qualitative interviews have the advantage of being interactive and allowing for unexpected topics to emerge and to be taken up by the researcher. This can also help overcome a provider or researcher-centred bias often found in written surveys, which by nature, can only measure what is already known or expected to be of relevance to the researcher. Interviews can be audio- or video-taped; but sometimes it is only feasible or acceptable for the interviewer to take written notes [ 14 , 16 , 20 ].

Focus groups

Focus groups are group interviews to explore participants’ expertise and experiences, including explorations of how and why people behave in certain ways [ 1 ]. Focus groups usually consist of 6–8 people and are led by an experienced moderator following a topic guide or “script” [ 21 ]. They can involve an observer who takes note of the non-verbal aspects of the situation, possibly using an observation guide [ 21 ]. Depending on researchers’ and participants’ preferences, the discussions can be audio- or video-taped and transcribed afterwards [ 21 ]. Focus groups are useful for bringing together homogeneous (to a lesser extent heterogeneous) groups of participants with relevant expertise and experience on a given topic on which they can share detailed information [ 21 ]. Focus groups are a relatively easy, fast and inexpensive method to gain access to information on interactions in a given group, i.e. “the sharing and comparing” among participants [ 21 ]. Disadvantages include less control over the process and a lesser extent to which each individual may participate. Moreover, focus group moderators need experience, as do those tasked with the analysis of the resulting data. Focus groups can be less appropriate for discussing sensitive topics that participants might be reluctant to disclose in a group setting [ 13 ]. Moreover, attention must be paid to the emergence of “groupthink” as well as possible power dynamics within the group, e.g. when patients are awed or intimidated by health professionals.

Choosing the “right” method

As explained above, the school of thought underlying qualitative research assumes no objective hierarchy of evidence and methods. This means that each choice of single or combined methods has to be based on the research question that needs to be answered and a critical assessment with regard to whether or to what extent the chosen method can accomplish this – i.e. the “fit” between question and method [ 14 ]. It is necessary for these decisions to be documented when they are being made, and to be critically discussed when reporting methods and results.

Let us assume that our research aim is to examine the (clinical) processes around acute endovascular treatment (EVT), from the patient’s arrival at the emergency room to recanalization, with the aim to identify possible causes for delay and/or other causes for sub-optimal treatment outcome. As a first step, we could conduct a document study of the relevant standard operating procedures (SOPs) for this phase of care – are they up-to-date and in line with current guidelines? Do they contain any mistakes, irregularities or uncertainties that could cause delays or other problems? Regardless of the answers to these questions, the results have to be interpreted based on what they are: a written outline of what care processes in this hospital should look like. If we want to know what they actually look like in practice, we can conduct observations of the processes described in the SOPs. These results can (and should) be analysed in themselves, but also in comparison to the results of the document analysis, especially as regards relevant discrepancies. Do the SOPs outline specific tests for which no equipment can be observed or tasks to be performed by specialized nurses who are not present during the observation? It might also be possible that the written SOP is outdated, but the actual care provided is in line with current best practice. In order to find out why these discrepancies exist, it can be useful to conduct interviews. Are the physicians simply not aware of the SOPs (because their existence is limited to the hospital’s intranet) or do they actively disagree with them or does the infrastructure make it impossible to provide the care as described? Another rationale for adding interviews is that some situations (or all of their possible variations for different patient groups or the day, night or weekend shift) cannot practically or ethically be observed. In this case, it is possible to ask those involved to report on their actions – being aware that this is not the same as the actual observation. A senior physician’s or hospital manager’s description of certain situations might differ from a nurse’s or junior physician’s one, maybe because they intentionally misrepresent facts or maybe because different aspects of the process are visible or important to them. In some cases, it can also be relevant to consider to whom the interviewee is disclosing this information – someone they trust, someone they are otherwise not connected to, or someone they suspect or are aware of being in a potentially “dangerous” power relationship to them. Lastly, a focus group could be conducted with representatives of the relevant professional groups to explore how and why exactly they provide care around EVT. The discussion might reveal discrepancies (between SOPs and actual care or between different physicians) and motivations to the researchers as well as to the focus group members that they might not have been aware of themselves. For the focus group to deliver relevant information, attention has to be paid to its composition and conduct, for example, to make sure that all participants feel safe to disclose sensitive or potentially problematic information or that the discussion is not dominated by (senior) physicians only. The resulting combination of data collection methods is shown in Fig.  2 .

Possible combination of data collection methods

Attributions for icons: “Book” by Serhii Smirnov, “Interview” by Adrien Coquet, FR, “Magnifying Glass” by anggun, ID, “Business communication” by Vectors Market; all from the Noun Project

The combination of multiple data source as described for this example can be referred to as “triangulation”, in which multiple measurements are carried out from different angles to achieve a more comprehensive understanding of the phenomenon under study [ 22 , 23 ].

Data analysis

To analyse the data collected through observations, interviews and focus groups these need to be transcribed into protocols and transcripts (see Fig.  3 ). Interviews and focus groups can be transcribed verbatim , with or without annotations for behaviour (e.g. laughing, crying, pausing) and with or without phonetic transcription of dialects and filler words, depending on what is expected or known to be relevant for the analysis. In the next step, the protocols and transcripts are coded , that is, marked (or tagged, labelled) with one or more short descriptors of the content of a sentence or paragraph [ 2 , 15 , 23 ]. Jansen describes coding as “connecting the raw data with “theoretical” terms” [ 20 ]. In a more practical sense, coding makes raw data sortable. This makes it possible to extract and examine all segments describing, say, a tele-neurology consultation from multiple data sources (e.g. SOPs, emergency room observations, staff and patient interview). In a process of synthesis and abstraction, the codes are then grouped, summarised and/or categorised [ 15 , 20 ]. The end product of the coding or analysis process is a descriptive theory of the behavioural pattern under investigation [ 20 ]. The coding process is performed using qualitative data management software, the most common ones being InVivo, MaxQDA and Atlas.ti. It should be noted that these are data management tools which support the analysis performed by the researcher(s) [ 14 ].

From data collection to data analysis

Attributions for icons: see Fig. 2 , also “Speech to text” by Trevor Dsouza, “Field Notes” by Mike O’Brien, US, “Voice Record” by ProSymbols, US, “Inspection” by Made, AU, and “Cloud” by Graphic Tigers; all from the Noun Project

How to report qualitative research?

Protocols of qualitative research can be published separately and in advance of the study results. However, the aim is not the same as in RCT protocols, i.e. to pre-define and set in stone the research questions and primary or secondary endpoints. Rather, it is a way to describe the research methods in detail, which might not be possible in the results paper given journals’ word limits. Qualitative research papers are usually longer than their quantitative counterparts to allow for deep understanding and so-called “thick description”. In the methods section, the focus is on transparency of the methods used, including why, how and by whom they were implemented in the specific study setting, so as to enable a discussion of whether and how this may have influenced data collection, analysis and interpretation. The results section usually starts with a paragraph outlining the main findings, followed by more detailed descriptions of, for example, the commonalities, discrepancies or exceptions per category [ 20 ]. Here it is important to support main findings by relevant quotations, which may add information, context, emphasis or real-life examples [ 20 , 23 ]. It is subject to debate in the field whether it is relevant to state the exact number or percentage of respondents supporting a certain statement (e.g. “Five interviewees expressed negative feelings towards XYZ”) [ 21 ].

How to combine qualitative with quantitative research?

Qualitative methods can be combined with other methods in multi- or mixed methods designs, which “[employ] two or more different methods [ …] within the same study or research program rather than confining the research to one single method” [ 24 ]. Reasons for combining methods can be diverse, including triangulation for corroboration of findings, complementarity for illustration and clarification of results, expansion to extend the breadth and range of the study, explanation of (unexpected) results generated with one method with the help of another, or offsetting the weakness of one method with the strength of another [ 1 , 17 , 24 , 25 , 26 ]. The resulting designs can be classified according to when, why and how the different quantitative and/or qualitative data strands are combined. The three most common types of mixed method designs are the convergent parallel design , the explanatory sequential design and the exploratory sequential design. The designs with examples are shown in Fig.  4 .

Three common mixed methods designs

In the convergent parallel design, a qualitative study is conducted in parallel to and independently of a quantitative study, and the results of both studies are compared and combined at the stage of interpretation of results. Using the above example of EVT provision, this could entail setting up a quantitative EVT registry to measure process times and patient outcomes in parallel to conducting the qualitative research outlined above, and then comparing results. Amongst other things, this would make it possible to assess whether interview respondents’ subjective impressions of patients receiving good care match modified Rankin Scores at follow-up, or whether observed delays in care provision are exceptions or the rule when compared to door-to-needle times as documented in the registry. In the explanatory sequential design, a quantitative study is carried out first, followed by a qualitative study to help explain the results from the quantitative study. This would be an appropriate design if the registry alone had revealed relevant delays in door-to-needle times and the qualitative study would be used to understand where and why these occurred, and how they could be improved. In the exploratory design, the qualitative study is carried out first and its results help informing and building the quantitative study in the next step [ 26 ]. If the qualitative study around EVT provision had shown a high level of dissatisfaction among the staff members involved, a quantitative questionnaire investigating staff satisfaction could be set up in the next step, informed by the qualitative study on which topics dissatisfaction had been expressed. Amongst other things, the questionnaire design would make it possible to widen the reach of the research to more respondents from different (types of) hospitals, regions, countries or settings, and to conduct sub-group analyses for different professional groups.

How to assess qualitative research?

A variety of assessment criteria and lists have been developed for qualitative research, ranging in their focus and comprehensiveness [ 14 , 17 , 27 ]. However, none of these has been elevated to the “gold standard” in the field. In the following, we therefore focus on a set of commonly used assessment criteria that, from a practical standpoint, a researcher can look for when assessing a qualitative research report or paper.

Assessors should check the authors’ use of and adherence to the relevant reporting checklists (e.g. Standards for Reporting Qualitative Research (SRQR)) to make sure all items that are relevant for this type of research are addressed [ 23 , 28 ]. Discussions of quantitative measures in addition to or instead of these qualitative measures can be a sign of lower quality of the research (paper). Providing and adhering to a checklist for qualitative research contributes to an important quality criterion for qualitative research, namely transparency [ 15 , 17 , 23 ].

Reflexivity

While methodological transparency and complete reporting is relevant for all types of research, some additional criteria must be taken into account for qualitative research. This includes what is called reflexivity, i.e. sensitivity to the relationship between the researcher and the researched, including how contact was established and maintained, or the background and experience of the researcher(s) involved in data collection and analysis. Depending on the research question and population to be researched this can be limited to professional experience, but it may also include gender, age or ethnicity [ 17 , 27 ]. These details are relevant because in qualitative research, as opposed to quantitative research, the researcher as a person cannot be isolated from the research process [ 23 ]. It may influence the conversation when an interviewed patient speaks to an interviewer who is a physician, or when an interviewee is asked to discuss a gynaecological procedure with a male interviewer, and therefore the reader must be made aware of these details [ 19 ].

Sampling and saturation

The aim of qualitative sampling is for all variants of the objects of observation that are deemed relevant for the study to be present in the sample “ to see the issue and its meanings from as many angles as possible” [ 1 , 16 , 19 , 20 , 27 ] , and to ensure “information-richness [ 15 ]. An iterative sampling approach is advised, in which data collection (e.g. five interviews) is followed by data analysis, followed by more data collection to find variants that are lacking in the current sample. This process continues until no new (relevant) information can be found and further sampling becomes redundant – which is called saturation [ 1 , 15 ] . In other words: qualitative data collection finds its end point not a priori , but when the research team determines that saturation has been reached [ 29 , 30 ].

This is also the reason why most qualitative studies use deliberate instead of random sampling strategies. This is generally referred to as “ purposive sampling” , in which researchers pre-define which types of participants or cases they need to include so as to cover all variations that are expected to be of relevance, based on the literature, previous experience or theory (i.e. theoretical sampling) [ 14 , 20 ]. Other types of purposive sampling include (but are not limited to) maximum variation sampling, critical case sampling or extreme or deviant case sampling [ 2 ]. In the above EVT example, a purposive sample could include all relevant professional groups and/or all relevant stakeholders (patients, relatives) and/or all relevant times of observation (day, night and weekend shift).

Assessors of qualitative research should check whether the considerations underlying the sampling strategy were sound and whether or how researchers tried to adapt and improve their strategies in stepwise or cyclical approaches between data collection and analysis to achieve saturation [ 14 ].

Good qualitative research is iterative in nature, i.e. it goes back and forth between data collection and analysis, revising and improving the approach where necessary. One example of this are pilot interviews, where different aspects of the interview (especially the interview guide, but also, for example, the site of the interview or whether the interview can be audio-recorded) are tested with a small number of respondents, evaluated and revised [ 19 ]. In doing so, the interviewer learns which wording or types of questions work best, or which is the best length of an interview with patients who have trouble concentrating for an extended time. Of course, the same reasoning applies to observations or focus groups which can also be piloted.

Ideally, coding should be performed by at least two researchers, especially at the beginning of the coding process when a common approach must be defined, including the establishment of a useful coding list (or tree), and when a common meaning of individual codes must be established [ 23 ]. An initial sub-set or all transcripts can be coded independently by the coders and then compared and consolidated after regular discussions in the research team. This is to make sure that codes are applied consistently to the research data.

Member checking

Member checking, also called respondent validation , refers to the practice of checking back with study respondents to see if the research is in line with their views [ 14 , 27 ]. This can happen after data collection or analysis or when first results are available [ 23 ]. For example, interviewees can be provided with (summaries of) their transcripts and asked whether they believe this to be a complete representation of their views or whether they would like to clarify or elaborate on their responses [ 17 ]. Respondents’ feedback on these issues then becomes part of the data collection and analysis [ 27 ].

Stakeholder involvement

In those niches where qualitative approaches have been able to evolve and grow, a new trend has seen the inclusion of patients and their representatives not only as study participants (i.e. “members”, see above) but as consultants to and active participants in the broader research process [ 31 , 32 , 33 ]. The underlying assumption is that patients and other stakeholders hold unique perspectives and experiences that add value beyond their own single story, making the research more relevant and beneficial to researchers, study participants and (future) patients alike [ 34 , 35 ]. Using the example of patients on or nearing dialysis, a recent scoping review found that 80% of clinical research did not address the top 10 research priorities identified by patients and caregivers [ 32 , 36 ]. In this sense, the involvement of the relevant stakeholders, especially patients and relatives, is increasingly being seen as a quality indicator in and of itself.

How not to assess qualitative research

The above overview does not include certain items that are routine in assessments of quantitative research. What follows is a non-exhaustive, non-representative, experience-based list of the quantitative criteria often applied to the assessment of qualitative research, as well as an explanation of the limited usefulness of these endeavours.

Protocol adherence

Given the openness and flexibility of qualitative research, it should not be assessed by how well it adheres to pre-determined and fixed strategies – in other words: its rigidity. Instead, the assessor should look for signs of adaptation and refinement based on lessons learned from earlier steps in the research process.

Sample size

For the reasons explained above, qualitative research does not require specific sample sizes, nor does it require that the sample size be determined a priori [ 1 , 14 , 27 , 37 , 38 , 39 ]. Sample size can only be a useful quality indicator when related to the research purpose, the chosen methodology and the composition of the sample, i.e. who was included and why.

Randomisation

While some authors argue that randomisation can be used in qualitative research, this is not commonly the case, as neither its feasibility nor its necessity or usefulness has been convincingly established for qualitative research [ 13 , 27 ]. Relevant disadvantages include the negative impact of a too large sample size as well as the possibility (or probability) of selecting “ quiet, uncooperative or inarticulate individuals ” [ 17 ]. Qualitative studies do not use control groups, either.

Interrater reliability, variability and other “objectivity checks”

The concept of “interrater reliability” is sometimes used in qualitative research to assess to which extent the coding approach overlaps between the two co-coders. However, it is not clear what this measure tells us about the quality of the analysis [ 23 ]. This means that these scores can be included in qualitative research reports, preferably with some additional information on what the score means for the analysis, but it is not a requirement. Relatedly, it is not relevant for the quality or “objectivity” of qualitative research to separate those who recruited the study participants and collected and analysed the data. Experiences even show that it might be better to have the same person or team perform all of these tasks [ 20 ]. First, when researchers introduce themselves during recruitment this can enhance trust when the interview takes place days or weeks later with the same researcher. Second, when the audio-recording is transcribed for analysis, the researcher conducting the interviews will usually remember the interviewee and the specific interview situation during data analysis. This might be helpful in providing additional context information for interpretation of data, e.g. on whether something might have been meant as a joke [ 18 ].

Not being quantitative research

Being qualitative research instead of quantitative research should not be used as an assessment criterion if it is used irrespectively of the research problem at hand. Similarly, qualitative research should not be required to be combined with quantitative research per se – unless mixed methods research is judged as inherently better than single-method research. In this case, the same criterion should be applied for quantitative studies without a qualitative component.

The main take-away points of this paper are summarised in Table 1 . We aimed to show that, if conducted well, qualitative research can answer specific research questions that cannot to be adequately answered using (only) quantitative designs. Seeing qualitative and quantitative methods as equal will help us become more aware and critical of the “fit” between the research problem and our chosen methods: I can conduct an RCT to determine the reasons for transportation delays of acute stroke patients – but should I? It also provides us with a greater range of tools to tackle a greater range of research problems more appropriately and successfully, filling in the blind spots on one half of the methodological spectrum to better address the whole complexity of neurological research and practice.

Availability of data and materials

Not applicable.

Abbreviations

Endovascular treatment

Randomised Controlled Trial

Standard Operating Procedure

Standards for Reporting Qualitative Research

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Busetto, L., Wick, W. & Gumbinger, C. How to use and assess qualitative research methods. Neurol. Res. Pract. 2 , 14 (2020). https://doi.org/10.1186/s42466-020-00059-z

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• 1 University of Nebraska Medical Center
• 2 GDB Research and Statistical Consulting
• 3 GDB Research and Statistical Consulting/McLaren Macomb Hospital
• PMID: 29262162
• Bookshelf ID: NBK470395

Qualitative research is a type of research that explores and provides deeper insights into real-world problems. Instead of collecting numerical data points or intervening or introducing treatments just like in quantitative research, qualitative research helps generate hypothenar to further investigate and understand quantitative data. Qualitative research gathers participants' experiences, perceptions, and behavior. It answers the hows and whys instead of how many or how much. It could be structured as a standalone study, purely relying on qualitative data, or part of mixed-methods research that combines qualitative and quantitative data. This review introduces the readers to some basic concepts, definitions, terminology, and applications of qualitative research.

Qualitative research, at its core, asks open-ended questions whose answers are not easily put into numbers, such as "how" and "why." Due to the open-ended nature of the research questions, qualitative research design is often not linear like quantitative design. One of the strengths of qualitative research is its ability to explain processes and patterns of human behavior that can be difficult to quantify. Phenomena such as experiences, attitudes, and behaviors can be complex to capture accurately and quantitatively. In contrast, a qualitative approach allows participants themselves to explain how, why, or what they were thinking, feeling, and experiencing at a particular time or during an event of interest. Quantifying qualitative data certainly is possible, but at its core, qualitative data is looking for themes and patterns that can be difficult to quantify, and it is essential to ensure that the context and narrative of qualitative work are not lost by trying to quantify something that is not meant to be quantified.

However, while qualitative research is sometimes placed in opposition to quantitative research, where they are necessarily opposites and therefore "compete" against each other and the philosophical paradigms associated with each other, qualitative and quantitative work are neither necessarily opposites, nor are they incompatible. While qualitative and quantitative approaches are different, they are not necessarily opposites and certainly not mutually exclusive. For instance, qualitative research can help expand and deepen understanding of data or results obtained from quantitative analysis. For example, say a quantitative analysis has determined a correlation between length of stay and level of patient satisfaction, but why does this correlation exist? This dual-focus scenario shows one way in which qualitative and quantitative research could be integrated.

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• Introduction
• Issues of Concern
• Clinical Significance
• Enhancing Healthcare Team Outcomes
• Review Questions

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Qualitative vs Quantitative Research Methods & Data Analysis

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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

• Mixed methods research
• 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

• Open access
• Published: 27 July 2024

A qualitative study of the barriers and facilitators impacting the implementation of a quality improvement program for emergency departments: SurgeCon

• Nahid Rahimipour Anaraki 1 ,
• Meghraj Mukhopadhyay 1 ,
• Jennifer Jewer 2 ,
• Christopher Patey 3 ,
• Paul Norman 4 ,
• Oliver Hurley 1 ,
• Holly Etchegary 5 &
• Shabnam Asghari 1 , 6  

BMC Health Services Research volume  24 , Article number:  855 ( 2024 ) Cite this article

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Metrics details

The implementation of intervention programs in Emergency Departments (EDs) is often fraught with complications due to the inherent complexity of the environment. Hence, the exploration and identification of barriers and facilitators prior to an implementation is imperative to formulate context-specific strategies to ensure the tenability of the intervention.

In assessing the context of four EDs prior to the implementation of SurgeCon, a quality improvement program for ED efficiency and patient satisfaction, this study identifies and explores the barriers and facilitators to successful implementation from the perspective of the healthcare providers, patients, researchers, and decision-makers involved in the implementation.

Two rural and two urban Canadian EDs with 24/7 on-site physician support.

Data were collected prior to the implementation of SurgeCon, by means of qualitative and quantitative methods consisting of semi-structured interviews with 31 clinicians (e.g., physicians, nurses, and managers), telephone surveys with 341 patients, and structured observations from four EDs. The interpretive description approach was utilized to analyze the data gathered from interviews, open-ended questions of the survey, and structured observations.

A set of five facilitator-barrier pairs were extracted. These key facilitator-barrier pairs were: (1) management and leadership, (2) available resources, (3) communications and networks across the organization, (4) previous intervention experiences, and (5) need for change.

Improving our understanding of the barriers and facilitators that may impact the implementation of a healthcare quality improvement intervention is of paramount importance. This study underscores the significance of identifing the barriers and facilitators of implementating an ED quality improvement program and developing strategies to overcome the barriers and enhance the facilitators for a successful implementations. We propose a set of strategies for hospitals when implementing such interventions, these include: staff training, champion selection, communicating the value of the intervention, promoting active engagement of ED staff, assigning data recording responsibilities, and requiring capacity analysis.

Trial registration

ClinicalTrials.gov. NCT04789902. 10/03/2021.

Peer Review reports

Introduction

Research motivation.

Wait times and overcrowding in emergency departments pose a severe national challenge for Canada as it has one of the highest wait times as compared to similarly industrialized countries [ 1 ]. This issue has persistently worsened as the number of emergency department (ED) visits in Canada has been increasing steadily over the past decade. From 2010–2011 to 2019–2020, the number of ED visits increased from approximately 6.7 million to 7.6 million, representing an average annual increase of 1.2%. Furthermore, the number of reported ED visits rose to almost 14.9 million in 2021–2022 from 11.7 million in 2020–2021[ 2 ]. The typical duration of a visit to an ED is around 3.5 h, which poses a risk to patients since prolonged waiting times in EDs have been associated with sub-optimal patient outcomes [ 3 , 4 , 5 , 6 , 7 , 8 , 9 ], and to the increased likelihood of adverse events [ 10 ]. To address this issue, SurgeCon, a quality improvement program, was devised to address the lack of integration, sustainability and logistical issues which negatively impact wait times in EDs [ 11 , 12 , 13 ]. SurgeCon delivers its quality improvement program through a department level management platform that encompasses three key elements: the installation and configuration of a tailored eHealth system, organizational restructuring, and the establishment of a patient-centric environment. SurgeCon aims to go beyond simply improving wait times; it seeks to optimize ED efficiency while providing a high standard of care for patients and promoting communication among clinicians.

Implementation of such a multidimensional quality improvement program in the dynamic and complex organizational structure of EDs, requires exploring barriers and facilitators prior to the respective implementation to formulate a set of strategies to enhance facilitators and overcome barriers, which may lead to a redesign of the program itself. Previous research in this area either lack the inclusion of strategies to overcome barriers or solely concentrate on eHealth adoption and implementation while neglecting considerations towards restructuring the organization of EDs and improving communication among clinicians. Barriers are factors that inhibit the implementation of practice change [ 14 ], while facilitators are factors that make the implementation easier [ 15 ]. Schreiweis et al. (2019) identified 76 barriers and 268 facilitators of implementation of eHealth services in health care out of 38 articles published between 2007 to 2018 from 12 different countries [ 16 ]. The most frequent barriers were categorized in three categories: individuals (e.g., poor digital health literacy), environmental and organizational (e.g., problems with financing eHealth solutions), and technical (e.g., lack of necessary devices). Also, some of the most stated facilitators were as follows: individuals (e.g., improvement in communication), environmental and organizational (e.g., involvement of all relevant stakeholders), and technical (e.g., ease of use). A limited number of studies have been conducted in an ED setting. For instance, Gyamfi et al., (2017) along with Kirk et al. (2016) and MacWilliams et al. (2017) explored relevant facilitators (e.g., capacity building, involvement and moral support of management and implementers, training and motivation, and environmental context and resources) and barriers (e.g., financial resources, data entry errors, shortage of human resources, and logistical constraints) that influence the implementation of eHealth services (e.g., Electronic Medical Records and screening tools) in EDs in Denmark, Ghana, and Canada (Ontario and Nova Scotia) [ 17 , 18 , 19 ]. Gyamfi et al. (2017) and Kirk et al. (2016) utilized semi-structured interviews, while MacWilliams et al. (2017) utilized focus groups for their data collection (these findings were then thematically analyzed) [ 17 , 18 , 19 ]. MacWilliams et al. (2017) also proposed suggestions to overcome barriers to implementation of Electronic Medical Records in EDs such as providing sufficient logistics (e.g., computers and accessories, reliable internet), rewarding staff, and regular staff training [ 19 ].

The aforementioned literature, along with other related literature, does not encompass the exploration of barriers and facilitators prior to the implementation of a large-scale quality improvement program that targets not only technical (i.e., eHealth system), but also structural (i.e., restructuring the ED organization and fostering patient-centric environment) and human (i.e., promoting communication across clinicians) aspects of the healthcare system. In fact, the objective of the quality improvement program in this study is not only to improve wait time, but also patient satisfaction, provider satisfaction, and the quality of care provided in EDs. As such, the SurgeCon program is connected to multiple dimensions related to patient outcomes within EDs. This study aims to explore barriers and facilitators prior to the implementation of SurgeCon in two rural and two urban Canadian EDs and formulate a set of strategies to overcome barriers and enhance facilitators. The findings identify areas of change for practitioners and policymakers [ 20 ]. This study is based on in-depth semi-structured interviews with clinicians, telephone interviews with patients, and structured observations in four EDs.

SurgeCon: a quality improvement program

SurgeCon includes three components: implementing an eHealth system to automate an action-based surge capacity plan, restructuring the ED organization and workflow, and fostering a more patient-centric environment. SurgeCon’s eHealth system predicts surge levels which sets appropriate automated workflows in motion to enact proactive measures to improve patient flow and associated outcomes. Crucially, eHealth interventions are generally reported to have a positive impact on patient care [ 21 ] wherein its impact ranges from an increase in the availability of patient information, enhanced communication between healthcare workers, improved healthcare accessibility, and reduced patient wait times [ 13 , 22 , 23 ]. The evolution of eHealth services can be attributed to solving critical challenges faced by healthcare institutions around the world, such as wait times and overcrowding which are significant challenges for EDs globally [ 24 , 25 ]. In addition to SurgeCon's eHealth system, a comprehensive approach to improving ED efficiency is also provided by including a patient flow course for frontline nurses and physicians. This course focuses on patient-centeredness and introduces process improvement strategies such as enhancing collaboration between physicians and mid-level providers like nurse practitioners, prioritizing stable patients based on factors beyond just acuity, and aiming to decrease the duration between a patient's arrival and their first assessment by a physician. Furthermore, SurgeCon's implementation process aims to improve the patient experience while in the department. This involves identifying problem areas that could negatively impact a patient's physical and mental well-being, such as their comfort level, ease of navigation, cleanliness of the department, clutter in the ED, and other related factors.

Study design

We employed a mixed-method approach at the technique level, incorporating semi-structured interviews, a structured questionnaire, and structured observation to collect data. To analyze the data, we adopted an interpretive description approach, as outlined by Thorne et al. (1997) [ 26 ]. This approach entails situating the findings within the current body of knowledge and drawing upon the contributions of other scholars, as highlighted by Mitchell and Cody (1993) [ 27 ]. This study aims to provide rich descriptive information on the key barriers and facilitators based on the language of the people involved, which inherently requires some degree of interpretation. The existing knowledge is not an organizing structure, rather, it serves as a foundational framework, providing a starting point and acts as an appropriate platform “upon which the design logic and the inductive reasoning in interpreting meanings within the data can be judged” [ 26 ].

Study context

The implementation of SurgeCon in this study follows a stepped-wedge cluster trial design, specifically focusing on EDs within Category A hospitals. These hospitals offer round-the-clock physician coverage in their EDs. All the hospitals involved in the study are located within the same jurisdiction, operating under the same governance and management structure. The two rural intervention sites in this trial are similar in size, each with a capacity of 8 ED beds. They have a staff roster consisting of approximately 6–10 physicians and 12 nurses, divided into two teams that work on rotating schedules.

One of the urban sites is an acute care facility that provides services to the entire province. The other urban site has 15 beds and shares a physician roster of approximately 40 physicians with the other urban site. Each ED at the urban sites is staffed with 55 and 70 nurses, respectively. Both urban sites offer a wide range of inpatient and outpatient services, including several tertiary services (Table  1 ).

Data collection

Prior to the implementation of the SurgeCon intervention we conducted semi-structured, in-depth interviews with a total of 31 clinicians. This cohort comprised of 20 clinicians from rural EDs and 11 from urban EDs. This included 12 nurses, 9 physicians, 7 managers, 2 patient care facilitators, and 1 program coordinator with 1 to 32 years of work experience in EDs with 69% of participants identifying as female. The interview questions were informed by the Consolidated Framework for Implementation Research (CFIR), Organization Readiness for Knowledge Translation (OR4KT) domains, and the clinical/content expertise of the team. The recruitment continued until data saturation was achieved [ 28 ].

Data on patient satisfaction and patient-reported experiences with ED wait times were collected through telephone surveys that took place from March 1, 2021, to August 31, 2021. In total, 341 patients who visited one of the four selected EDs were interviewed, with 136 coming from rural EDs and 205 from urban EDs. The mean age was 55.7 (SD = 16.8) with 66% of participants identifying as female. We analyzed open-ended questions that specifically targeted patients' experiences while receiving care at the selected EDs and gathered their suggestions for improving the ED environment. Patients' insights confirmed our findings regarding resources, communication, and the necessity for change. The interview guide adapted questions from previously validated questionnaires which include the Ontario Emergency Department Patient Experience of Care Survey , the CIHI Canadian Patient Experiences Survey , the Press Ganey Emergency Department Survey , and the NHS Accident and Emergency Department Questionnaire .

Structured observations were conducted by research team members who were also healthcare staff and had special permission to visit each of the sites which were locked-down and only accessible to authorized ED personnel and patients due to COVID-19 pandemic restrictions. A ‘Site Assessment Checklist’ was used to assess each of the four EDs in terms of the ED’s available resources (e.g., medical, human, and technological), staff communication, pervious experiences of intervention, staff readiness and tension for change. The checklist was developed through a Delphi approach which included the input of research team members, ED staff, and patients who selected key criteria to assess the EDs.

The data collected and referenced in this analysis stems from an innovative pragmatic cluster randomized trial designed to evaluate the effects of SurgeCon, an ED management platform, on wait times and patient satisfaction. The subset of data that was considered relevant to our analysis was collected from March 2021 to December 2022. All data used in this study were collected prior to the implementation of SurgeCon at the four EDs selected for the cluster randomized trial. Even though each dataset was gathered and analyzed independently, they were considered complementary to each other instead of being mutually exclusive.

Data analysis

Data from in-depth interviews, surveys, and structured observations was analyzed according to an interpretative description approach, while utilizing constant comparative analysis. Each set of data was repeatedly read by a qualitative researcher to comprehend the overall phenomena with questions such as “what is happening here? and “what am I learning about this?”, to become familiar with the data, to identify the potential themes or patterns and to achieve a broader insight about the phenomena [ 26 , 28 , 29 , 30 ]. The data was then coded in a broad manner and continually compared and examined for similarities, differences, and relationships to help formulate major themes. A set of five facilitator-barrier pairs was extracted in this study.

All stages in the coding process were conducted by a qualitative researcher and were then categorically reviewed by members of the team to reach a consensus. The data analysis process started with the exploration of semi-structured interview data, which then progressed to include structured observation, and ended with the comprehensive analysis of the data gathered through surveys. Data extracted from semi-structured, in-depth interviews with clinicians served as the primary source for exploring barriers and facilitators before implementation. However, structured observations and survey data were integrated to offer additional clarity and act as auxiliary and confirmatory sources. Data collected from different stakeholders produced complementary results that captured multidimensional interpretations of the topic. The integrated blend of findings collected from various stakeholders through disparate methods not only explains multiple dimensions of the phenomena but also targets different audiences. In this study, data triangulation (gathering data at different times from various sources), investigator triangulation (multiple researchers study the topic of interest), and methodological triangulation (utilizing multiple methods) were utilized as cross-validation checks [ 31 , 32 ].

The barriers and facilitators to the implementation of SurgeCon fell into five themes, each of which plays a dual role of a barrier and facilitator (see Fig.  1 ). These key pairings were: (1) management and leadership, (2) available resources, (3) communications and networks across the organization, (4) previous intervention experiences, and (5) need for change. No significant differences were observed in terms of barriers and facilitators between the groups (i.e., rural and urban EDs) or among providers, patients, and observer inputs. While observer inputs provided insight on all categories, the patients’ input had the most influence on the following categories; available resources, communications and networks, and the need for change. In the following sections, we discuss each of these barrier and facilitator pairs.

Process of Identifying Barriers and Facilitators toward Formulating Strategies

Management and leadership

The overarching management and leadership EDs was anticipated to be one of the most important facilitators of the SurgeCon implementation. Having a receptive, accessible, and supportive senior manager who is continually engaged with all aspects of the transition phase paired with an effective management system where the staff are involved in the decision-making process, was perceived to stimulate positive managerial-clinical communications along with an increasing likelihood for the positive reception of an implementation program. Active early involvement, support, and engagement of managers in two EDs were deemed crucial facilitators to fostering a nurturing and motivating environment that encourages physicians and nurses to proactively engage in the implementation process. Data from the observations served as confirmation of the involvement of both management and staff as well.

“I can converse openly and there is an open-door policy. Furthermore, just in terms of communication, there is always a timely response and the manager is very proactive” [Healthcare provider] “The site manager, the direct manager of the staff, comes every morning to the department to see what was happening last night. If there is any new issue, [the manager offers assistance and any logistical resolutions] that can be done or offered immediately. Additionally, they have free access to the director and to the manager through email. The manager’s office is just a few meters away from them, so they can just reach them at any time. For the doctors, the situation is also the same” [Healthcare provider]

However, management and leadership could also pose barriers to a successful implementation. Barriers such as low manager participation and contribution, unreceptive and inaccessible managers, low staff autonomy and involvement in decision-making, and the lack of staff consultation all emerged in the analysis.

“You know a couple of years ago with the previous manager, everything was unilaterally implemented. As in, it was put forward and we had to strictly abide by it irrespective of what we felt the outcome was going to be. There were several instances where you had to accept what was told to you and consequently, there was very little room for discussion or negotiation.” [Healthcare provider]

When working in a small ED with limited staff turnover and a long-standing team who are familiar with daily routines and operations, it was deemed integral for managers to involve and engage frontline ED staff in the decision-making process while also managing strategies for running the department. Failure to give staff autonomy in their roles was anticipated to be a barrier to a successful implementation within this framework.

“The emergency department was say anywhere from 98-99% senior. So, when you got a small department that is pretty much occupied by senior staff, it runs itself. Most of us have been nursing for 30 plus years. So, we know how the system works; we know what we have to do; we know how to solve problems; we are familiar with critical thinking to get issues resolved. However, this other manager was always critiquing us, and certainly not in a constructive manner”. [Healthcare provider]

Amplifying these issues was the fact that there was a history of struggling with unapproachable, autocratic and unavailable managers in the ED. It left the clinicians with sentiments of neglect and varying overdue demands and expectations. This in-turn caused a “toxic environment” which was percived as a critical barrier to the successfull implementation:

“But it really was like I said before, a toxic environment which placed everybody in on a defensive stance at all times and people did not want to go to work and more crucially, people did not like to work. If they did statistics on it, I am sure there was a huge spike in sick leave as people were just not wanting to go to work. That's the bottom line.” [Healthcare provider]

Available resources

Availability of resources was considered as a critical facet for the implementation of SurgeCon. As such, disparate resources, that crossed human and medical resources and several other silos (e.g., space constraint), were anticipated to be necessary considerations to ensure the long-term tenability of the SurgeCon intervention. Participants at all four EDs unanimously identified excess workload, and staff shortages, and absence of opportunities to ease workloads as the most significant anticipated barriers to implementation. To incorporate the new implementation system, not only was it asserted that all clinicians need to be available and have sufficient time to attend a staff training program, but they also need to regularly entering and updating SurgeCon data. Virtually all participants anticipated that the lack of human resources (i.e., insufficient medical staff) would be a crucial barrier.

“Human resources can be a bit harder to come by because nurses are often treated as a commodity. There is so much overtime at the current time and requires increased staff.” [Healthcare provider] “I think more family doctors are needed to lower the congestion in the ED.” [Patient] “Need more staff. Patient asked multiple times to be taken to the bathroom after being left alone in a wheelchair... She asked again hours later and received no help so she peed in her wheelchair fully clothed and left without seeing a doctor due to embarrassment and such a lack of help.” [Patient] “if we don't have enough staff or if we don't have enough beds. To me it don't matter what you're doing, it’s not going to work. It's going to be harder for it to work if you don't have the resources.” [Healthcare provider]

Other than staff shortages, high staff turnover rates were cited as another anticipated barrier to implementation. The high level of staff turnover adversely impacted the level of communication among staff, and was also perceived as a significant challenge with regards to training and accommodating necessary implementation activities.

“We have a lot of new nurses that are just coming out of program. So, helping mentor them with an overwhelmed emergency department is difficult as they are also trying to get their footing within the emergency department, and learn new skills and tasks. I find communications a bit lacking right now because we have so much new staff and they're just trying to get their footing and learn. In doing so, it is hard to have that communication. Like everyone helps wherever they can but you're also trying to, within that time, train your new staff as well. It's kind of a bit hectic.” [Healthcare provider] “Rapid turnover of staff at HSC. So some of the staff have been through process improvement while many others have not.” [Observer]

Insufficient admission space (e.g., inadequate number of beds) and the lack of physical space and rooms in EDs were often identified by clinicians as the primary cause of backlogs and overcrowding in EDs. These factors were anticipated to be barriers to the implementation process as they affect patient admissions, transfers, discharges, as well as the restructuring of the ED organization and workflow.

“Some of the barriers would certainly be the inability to have free or vacant beds to transfer patients out of or transporting patients out of our department to a tertiary care facility.” [Healthcare provider] “There needs to be more beds and seating arrangements.” [Patient] “There is no current space adequate enough to run the flow center model.” [Observer] “Rooms are sticky at times; space is small and overpopulated.” [Observer]

Communications and Networks Across the Organization

In order to ensure the successful adoption of SurgeCon, intra and inter-departmental communication was deemed to be a critical factor. Consistent and frequent communication between clinicians, particularly among physicians and nurses, is necessary to execute implementation activities successfully. However, this theme received mixed evaluations by participants. Poor communication and fragmented relationships between nurses and physicians, and lack of teamwork among staff emerged as significant barriers to the implementation of SurgeCon. In all four EDs, it was observed that physicians and nurses do not have any formal joint meetings and there was scarce communication between different units within EDs. The lack of shared multidisciplinary meetings in EDs decreased the chance of developing mutual understanding and commitment, building empathy and awareness toward each other’s challenges, and enhancing unity and teamwork.

“There seems to be a huge miscommunication between staff, mainly to do with rules surrounding COVID.” [Patient] “We do not sit down at the same table. There are family practice meetings, there are student emergency doc meetings and then, there are nursing meetings; you are not set at the same table. So, I cannot realistically know, feel nor empathize with anybody else’s needs if I am not even aware of them. We are never really made aware of that stuff.” [Healthcare provider] “More communication between staff and patients would be very useful as most people will be more patient and understanding.” [Patient]

Even in the case of personal conflicts and tensions arising between nurses and physicians, formal meetings of managers were considered as a predominate strategy to resolve the respective issues rather than directly involving staff. While the lack of intergroup (i.e., nurses and physicians) communication was evaluated as a barrier, participants positively evaluated intragroup communication, citing regular weekly formal meetings and informal daily meetings when necessary. Furthermore, nurses at one of the sites participated in a Facebook group to share their concerns.

“There is a Facebook group… it was outlined that they are short a nurse, and they are looking for an extra nurse to come in. So, they posted that on the Facebook group in hopes that somebody will see it and come to their rescue.” [Healthcare provider]

In general, a collaborative, supportive, receptive and cooperative environment were considered as a facilitator to implementation. The staff valued a culture of support, transparency, and availability. Also, it was assessed that working in a small ED, where the clinicians are familiar with one another more intimately and for a prolonged duration of time, positively fostered teamwork and supportive communication.

“One main ED unit and there seemed to be good communication and in the smaller sites its quite easy to communicate” [Observer]

Another barrier under this construct was identified as the lack of communication and dialogue between staff in two different units within the EDs. As these units operated independently, the minimal contact and communication between them became routine. Communication between the two units was restricted to the end of the shift and pertained primarily to handing-over patients. When problems arose, the most common means of communication to resolve or discuss the issue was conducted via email.

“We’re taking care of the patients in unit one or unit two, and someone else is taking care of the patients in the other unit. So, I don't really talk to the other person. So, the only time when we communicate is around handover. So that's often sort of one we're saying, “Well, I am leaving, so you take over this patient.” [Healthcare provider] “When we asked staff if they felt the areas of the departments communicated well together they said yes but while we watched it certainly seemed like all the areas functioned independently of each other. NO situational awareness.” [Observer]

A common concern among participants pertained to the lack of engagement and involvement of other departments in the hospital in the implementation process of the intervention. The participants seemed to believe that the implementation could not be successful if other departments and stakeholders in the hospital have no intention to participate. Given the interconnectedness of a hospital’s departments, an intervention aimed to improve ED patient flow must also comprise meaningful engagement from external departments and must be prioritized at all levels of the organization rather than having the ED treated as an individual entity.

“We've done a lot of improvements. For instance, our stroke process or STEMI process, those are things that we've implemented within our department to help streamline that category of patient, that were more focused on just the ED which were more successful. We haven't been able to be successful because of the barriers that lie outside of our department which are a little bit more systems or like, organizational wide. It becomes harder because maybe there's been an unwillingness to participate or not seeing the value because a lot of people don't see what it is like in our department all the time. So, they think that it's just value for us as opposed to value for them as well.” [Healthcare provider]

Another potential barrier to implementation was the anticipated lack of physician participation in the implementation process. Nurses constantly emphasized the crucial role of physicians in the uptake of the intervention and furthermore desired assurance that the physicians will be well-informed about the implementation and will not be disengaged during the process.

“I think physicians are older, more experienced positions or maybe just set in their ways and are less open to change. Some of the physician group will be more resistant.” [Healthcare provider]

Despite the busy clinical environments, the success in the development and undertaking of the implementation hinged on constant and regular communication, including routine informal and formal meetings, that took place between the research team and clinicians. Although in-person meetings were preferable, due to COVID-19 pandemic related restrictions, videoconferencing was replaced to facilitate communication. Scheduling and arranging a meeting with clinicians because of the heavy workload, busy clinical schedule and demands was deemed as extremely challenging and proposed a critical barrier to implementation. Additionally, some of the research members do not have a direct line of communication with clinicians if not through internal facilitators or champions– i.e., nurse practitioners. Although a champion or facilitator demonstrated knowledge about the workload of clinicians which facilitated the scheduling of meetings, the lack of direct communication and in-person meetings seemed to be a critical barrier to implementation as the level of social engagement and connectedness between research staff and medical staff was adversely impacted.

Previous intervention experiences

The previous experiences of staff members in implementing other interventions were evaluated as mostly positive by clinicians and researchers who conducted structured observations. However, some barriers were reported as well. The prior positive experiences of interventions were reported by the study participants, such as with X32 Healthcare’s Online Staffing Optimization project. In general, participants reported that the X32 project resulted in improved workflow efficiency, simplified and organized patient assessments, prioritized triage, and reduced wait times. These positive experiences with past interventions seemed to positively shape the participants perceptions of the SurgeCon implementation.

“The X32 program was overall an effective program in my opinion. We did implement a lot of changes, overall infrastructure changes- the way that we introduce patients into our department and get them through the department to finally get them discharged. After the X32 program, we've seen dramatic improvements and changes versus the way that we were doing it.” [Healthcare provider]

However, there were also negative perceptions of past intervenstions, for example, a lack of communication between researchers and staff, and the lack of follow-up evaluations to meet the contextually specific needs of the EDs.

“Initially, there was a fair bit of communication between staff, the researchers and the end users but after it was implemented, I don't think there was any follow-up or any review of the X32.” [Healthcare provider]

The perception of inadequacies or unsuccessful outcomes from prior intervention efforts appeared to influence the study participants' perceptions of the implementation of SurgeCon and was seen to be a potential barrier to future implementations. This historical context of past initiatives not meeting their intended goals created scepticism and resistance towards embracing the new SurgeCon program.

“SurgeCon is new to us, but we've tried lots of different things over the years, and they've all failed. We've all put work into it… we'll try something, and we'll get all motivated to do it- we'll try it for six months, and everything that we've done falls apart inevitably.” [Healthcare provider] “Many previous wait time related interventions over the past number of years and front line staff report mostly failures with staff reverting to old ways.” [Observer]

Need for change

Tension for change is considerd as an important concept for leaders seeking to improve performance in their organizations. It is a mechanism that created the energy and motivation needed to mobilize human beings into action. Although dissatisfaction with the current approach was the most common perspective as described from patients and providers in four EDs; this was considered concurrently as a strong motivation and potential barrier for clinicians to actively engage in the implementation process. Dissatisfaction with long wait-times and poor workflow was perceived as a major aspect of motivation; the most endorsed facilitator was found to be the perception of necessity of the intervention to rectify deficiencies in wait-time and workflow efficiency. Clinicians valued the change and deemed it as urgently necessary and beneficial. They valued the intervention and possessed an intrinsic inclination towards change as they had long-lasting concerns about the wait-time and workflow; they anticipated that SurgeCon might help to resolve the issues faced in EDs. Thus, clinicians in these EDs collectively valued the intervention and demonstrated an appreciation for the actions taken, which was seen to be one of the more crucial facilitators and implementation drivers.

“I had to wait for 7 and a half hours which felt ridiculously long, even though there were not a lot of other people waiting.” [Patient] “We have been waiting for 2 days because there were no in-patient beds available.” [Patient] “The most important motivation is improving the quality of management for the patients and then, that will be reflected to the wellbeing of the patient as well as the smooth flow of the patient within the department. So, if there is any new idea that can facilitate this- they usually are very eager to adapt and undertake it.” [Healthcare provider]

The participants frequently felt that the staff struggled to deal with the confusion arising from technological limitations in communicating information about wait times and the availability of medical resources. Several complaints were made regarding complications in scheduling appointments, inconsistent wait times, and misallocation of scarce resources which diminished the overall efficiency of the ED. These issue was considered motivating factors for the implementation of SurgeCon.

“The sites lacked a digital patient tracking system that resulted in communication lapses between units.” [Observer] “[Our province] is far behind in technology compared to other provinces.” [Patient]

Participants expressed some dissatisfaction with the planned implementation as a result of not having enough time to participate, staff shortages, and heavy workloads. Two of the selected EDs were found to be particularly affected by this issue, which posed a significant obstacle even before the implementation which involved conducting pre-implementation in-depth interviews. The implementation of the quality improvement program would go ahead as planned, albeit with poor engagement and support from ED staff. Consequently, this lack of involvement might hinder the intervention from reaching their full potential.

“I think that's going to be the biggest challenge is just getting them on board. Just the word “change” or “implementation” right now is a bit challenging.” [Healthcare provider] “I mean morale in the past few years… it’s not in a good place and I think it's because of the increased business, and staff feel like they're burning out, so it's not that they don't do a good job. We need more resources.” [Healthcare provider]

Two of the EDs chosen for this study had rejected previous intervention attempts, (e.g., X32 Healthcare’s Online Staffing Optimalization), which implies that the organizational climate might not be change-oriented. This phenomenon, other than dissatisfaction, was rooted in being resistant to changes (including technological changes) while conforming to the existing status-quo and being reluctant to adopt the consulted changes suggested from outside of the organization. To the participants, interventions meant novel systems, processes and skills which inherently implied altering the quondam workplace routine to adopt a newer system. While ED staff constantly struggled with the internal forces for change (e.g., heavy workload, staffing issues, and long wait time), they were not receptive to the external research team’s attempts at initiating change through the implementation of the intervention. This extended to not only external stimuli for change, but also propositions for change initiated by insiders which were not mobilized in either of the urban sites.

Repeated resistance to technological changes expressed by staff in general. [Observer] “It was unknown- you hear this company from outside is going to come in and fix your emergency department. A lot of people felt like, ‘Well, why do we need an outside company? Why don’t they just speak to the staff that actually works there to see how they could fix it?’ We knew what needed to be fixed but I kind of felt amused as to why did an external entity do it when they didn't ask the people that worked in a department first.” [Healthcare provider] “I feel like change is a big thing for people personally and professionally. So, it is just going to take a while for people to get used to it and, it's something new that’s breaking our old routine of how we did things. I feel those will be some barriers. Technology is going to be a challenge and like I said, it’s a big change.” [Healthcare provider]

During the pandemic, it became evident that engaging ED staff in implementation activities across all four EDs will create a challenging environment. Frontline staff had to manage exhaustion, frustration, burnout, isolation, and a higher volume of sick patients, making change initiation difficult. Clinicians often lacked the energy to participate in pre-implementation interviews, despite compensation and other offered incentives. In describing their experiences, one participant states:

“We're just basically keeping our heads above water at this point.” [Healthcare provider]

Low motivation to participate was caused due to feeling burdened by a heavy workload, COVID-19 regulations and subsequent procedure alterations. Thus, these dismayed clinicians struggled with the pandemic and thereby, served as another major barrier to the intervention.

“With this pandemic, there's constant policy changes, procedure changes, and they're frustrated with it. So, if you want to bring in something else, even though it's going to help them a lot of times- they're resistant because it's just something else on their ‘To Do List’ and they don't want to be bothered with having to learn something else.” [Healthcare provider].

Summary of findings

Given the high rate of failure in translating evidence into practice in health care services and the challenges of implementing eHealth interventions [ 33 , 34 ], it is necessary to assess barriers and facilitators prior to implementation to attain a successful implementation. This study found five facilitator-barrier pairs that were perceived to influence the successful implementation of SurgeCon in the four EDs in our study.

Management and leadership structures were the first facilitator-barrier pair. Such structures play a critical role in the integration and maintenance of innovative implementations in hospital settings[ 35 ]. The findings of Bonawitz et al. (2020) suggest that ineffective management and leadership serve as barriers to change in healthcare institutions [ 36 ]. Management systems that effectively encourage the involvement of health care providers in making ED-related decisions and support proactive managers are perceived to be crucial facilitators, as evidenced by the findings of this study, while disengaged managers and lack of staff autonomy are perceived as critical barriers. The findings observed in this study parallel those observed by Manca et al. (2018) [ 37 ], who found that participative leadership, which seeps into control-oriented management, poses a significant barrier to the dynamics presented by the organizational culture toward change. Furthermore, the lack of top-management sponsorship and presence-based culture presented a recurring barrier to the adoption of innovation in healthcare institutions. Our data suggest that early engagement of managers in implementation procedures and applying a participative leadership style that promotes active engagement of staff may facilitate successful implementation. This is supported by Bonawitz et al. (2020) who found a participative leadership style to be a critical component in successfully implementing change in a healthcare setting [ 36 ].

Available resources is the second facilitator-barrier pair. According to de Wit et al. (2018), implementing system-wide changes requires substantial prerequisite committed hospital resources [ 38 ]. However, tailoring a strategy may permit circumventing change management projects that require committing substantial additional resources [ 39 ]. Furthermore, Barnett et al. (2011) express that the influence of human-based resources is integral in the process of developing, establishing, and diffusing innovations in healthcare institutions [ 40 ]. However, the Canadian Institute for Health Information (2021) points out the stark shortages and increasing staff turnover rates in medical staff within the Canadian healthcare system [ 24 ]. With a perpetually changing and constrained workforce, any pursuit to adopt an implementation will intrinsically face initial challenges. Additionally, de Wit et al. (2018) provide a comprehensive overview of the critical resources prior to initiating change: depending on the idiosyncratic details of implementation, educational resources need to be made available (with minimal barriers to accessing them), along with committed hospital resources in the form of financial, staffing, and other resources [ 38 ]. Furthermore, a lack of medical resources negatively impacts patient admissions, patient transfer delays, cancellation of surgeries, or early discharges [ 41 ]. Inadequate financial, technological, human, and medical resources were consistently identified as anticipated barriers across all four ED sites. Although implementing SurgeCon does not require substantial additional resources and the ED sites are provided with the technological equipment and educational requirements prior to the intervention, the shortage of medical staff and lack of medical resources remain potentially significant barriers, as found by this study.

The third facilitator-barrier pair is communications and networks across the organization. Considering the insights gained from previous studies on leadership structures in healthcare institutions, communication is a critical symptom of a participative leadership structure [ 35 , 36 , 42 , 43 ]. It is repeatedly established that teamwork, trust and other parameters of the respective organizational climate are founded by the principles of the underlying leadership structure. According to our study however, even in the participative leadership structure which embraces engagement and involvement of staff, ED environments suffer a lack of communication between nurses and physicians and between different ED units. While the minimal formal and informal discussions that occur between physicians and nurses may meet the basic requirements for professional standards, they are not fully cognisant of each other’s concerns and challenges. To fully engage and participate in the implementation of an intervention, collaboration between all ED staff is required. Lack of communication, dialogue, and teamwork among staff is recognized as an anticipated barrier to successful implementation. Conversely, constant communication and dialogue between the research staff and healthcare provider is considered as a practice that would facilitate the intervention’s implementation. However, in our case, due to COVID-19 restrictions, almost all communications were transferred from in-person to a virtual medium. Being overwhelmed by COVID-19 regulatory demands, staff shortages and burdensome workloads, clinicians were not left with enough energy and time to participate in pre-implementation on-line interviews.

The fourth facilitator-barrier pair, previous intervention experiences, were also anticipated to impact the SurgeCon implementation. Hamilton et al. (2010) found that prior experience with change efforts contributed to readiness for change in healthcare institutions [ 44 ]. As such, it is expressed that previous experience with interventions contributes to calibrating an appropriate organizational climate that is conducive to change. Previous experience greatly assists in establishing the appropriate steps and instilling confidence to create a ripe organizational climate for the implementation [ 45 ]. Zapka et al. (2013) express the need for reviews of past experiences of change as a necessary element to sustain the implementation [ 46 ]. The findings of this study with regard to previous experience of interventions and its potential to make a positive or negative impact on future interventions parallel those observed by previous scholars. Our data reveals that the negative perceptions of past intervenstions (e.g., lack of follow-up evaluations), was considered a notable obstacle to the implementation of SurgeCon.

The fifth facilitator-barrier pair was the need for change. Grol (2013) illustrates the importance of the perception of necessity in successfully adopting an intervention, particularly in a healthcare environment. Institutions with a positive perception of the necessity of an intervention are more likely to adopt and sustain an implementation [ 47 ]. Tension for change in implementation science is defined as the proclivity for shareholders to perceive the current situation as requiring a change or intolerable [ 48 , 49 , 50 ]. Our findings illustrated that dissatisfaction with the current system, with long wait times and poor workflow in EDs, was perceived as a necessity for urgent change and intervention. However, the perception of the necessity of the intervention does not necessarily imply valuing or practicing the change requirements. Our study supports findings of the inverse relationship between staff burnout and motivation to support an intervention [ 51 , 52 ]. When considering the drastic national rise in burnout experienced by healthcare workers in Canada [ 53 ], the current healthcare environment is not conducive to change. Lack of time, staff shortages, and heavy workload coupled with COVID-19 fatigue and burnout did not leave clinicians with sufficient energy to even participate in pre-implementation interviews, let alone in interest in being actively involved in the intervention. Additionally, this study found that using new technology and altering the workplace routines were perceived as barriers to change among clinicians. Regardless of the high level of dissatisfaction and staff workload, clinicians were still resistant to the interventions proposed by external sources.

Strategies for overcoming barriers and enhancing facilitators

Identifying and evaluating barriers and facilitators alone is only the first step in enhancing the probability of successful implementations of eHealth interventions such as SurgeCon. It is also important to formulate a set of strategies for hospitals to overcome the identified barriers and enhance the facilitators (Fig.  1 ). The recommended strategies—staff training, frontline champions, performance data review, communicating the value of the intervention, encouraging active engagement of ED staff, assigning an individual to regularly record data, and requiring capacity analysis—aim to address and overcome barriers while capitalizing on facilitators. These multi-faceted strategies were identified through discussions with decision makers, clinicians, patients, and research team members as well as lessons learned from SurgeCon’s implementation at the pilot site.

To elaborate on the specific components: It is crucial that a majority of ED staff attend a training on paitient flow and have ED leadership participate in software configuration to adjust and tailor SurgeCon’s the digital eHealth platform to their ED. Attending training sessions facilitates the adoption of quality improvement initiatives and patient flow strategies included within the SurgeCon platform and encourages ED staff to become actively engaged with the implementation process. This process is essential to foster an active participation and discussion between all tiers of staff which may not routinely transpire. The training course needs to actively engage frontline staff and must include the following modules: Interactive Simulation, SurgeCon eHealth Platform, and Patient Centeredness modules. The aim of the Interactive Simulation module is to provide insights into the rationale of connecting the software to process improvement and elucidate its procedure in a practical setting using ED-based scenarios. Since the module will be interactive, it allows for greater clarity to ensure that learning outcomes are achieved. The SurgeCon eHealth Platform module will assist ED staff in becoming familiar with the digital whiteboard application. This includes learning how the system collects and reports information, how to interpret and respond to system notices and warnings, and how to customize the dashboard to create a site-specific, adaptive version of SurgeCon that addresses the unique needs of their ED. The Patient Centeredness module comprises an educational session which reinforces the core importance of values pertaining to patient care across the following topics: providing quality ED care to all patients regardless of urgency; treating patients with respect; and considering the patient’s visit to an ED as always of vital necessity.

Having a dedicated frontline champion who is selected by ED management and trained by the implementation team can help ensure effective communication and facilitate the implementation process. These individuals can act as a liaison between ED staff and the research team, providing ongoing support and addressing any questions or concerns that may arise. In addition, they can provide valuable feedback to the research team regarding technical issues or challenges encountered during implementation which can help inform adjustments and improvements to the intervention. Ultimately, having frontline champions who are invested in the success of the intervention can contribute to a more seamless and effective implementation process.

Continuous performance reporting plays a crucial role in enhancing the operational efficiency and effectiveness of EDs and contributes to the development of improved operational strategies by providing meaningful data. In this study, the research protocol involves prominently displaying department-level data in the ED, such as at nursing stations, and providing individual-level performance reports to physicians at the participating sites. However, in the post-COVID era, EDs have been experiencing staffing shortages, which have necessitated changes in the reporting protocols of this study, particularly regarding key performance indicator (KPI) data. The KPIs examined in this study include the time to physician initial assessment (PIA), the length of stay in the ED (LOS), and the rate of patients leaving the ED without being seen by a physician (LWBS). These KPIs are widely recognized as the gold standard for evaluating ED performance. However, these indicators assume consistent operating conditions, and the reliability of using them as the primary method for assessing department efficiency diminishes in the presence of staff shortages. Providing individual physicians with performance reports may serve as a reminder of the operational challenges they have faced rather than providing a fair assessment of their ability to efficiently manage patient flow in their department. As a result, the research team decided to recommend aggregated department-level performance reports. Ultimately, the primary goal is to increase physician motivation to utilize SurgeCon by demonstrating its capacity to reduce door-to-doctor time, which is a critical metric for assessing standards of emergency care and efficiency.

It is important to the research team to communicate the importance and value of SurgeCon by presenting a successful implementation in the pilot site to raise awareness about the prospective results and enhance motivation for the adoption of the intervention. Additionally, implementing interventions is a “collective action” which necessitates a commitment to the process by all members. As Weiner (2009, p. 2) [ 54 ] states “implementing complex organizational changes involves collective action by many people, each of whom contributes something to the implementation effort […] problems arise when some feel committed to implementation, but others do not.” To stimulate engagement, compensation (i.e., full payment for attendance including travel and meals) offers for participating in training sessions and interviews; refreshments, in the form of snacks and beverages, were also provided at every training session. Furthermore, assigning an individual whose primary role is to manually enter data that cannot be automated into SurgeCon’s eHealth system, and using demand and capacity analysis to determine staffing models that will benefit the department are among the suggested strategies to overcome several of the encountered barriers to implementation.

Conclusion and implications

Successfully implementing eHealth systems goes beyond addressing technological aspects alone. It requires a thorough exploration of potential barriers and facilitators and the development of strategies to overcome barriers and enhance the facilitators. SurgeCon aims to enhance quality standards, improve efficiency, and increase satisfaction among both patients and providers in EDs. However, implementing such a quality improvement initiative in EDs presents challenges. Therefore, identifying these barriers and facilitators is crucial for developing tailored implementation strategies that are contextually relevant. This approach helps to ensure a smooth and sustainable transition, leading to long-term success and optimal performance. This study extends the findings in relevant literature by indentifying these facilitator-barrier pairs and providing a set of strategies to overcome the barriers and enhance the facilitators in the implementation of a large-scale quality improvement program. In investigating the factors associated with the successful adoption of SurgeCon, a broader consideration of the barriers and facilitators can be derived. Understanding these factors can assist in identifying obstacles and motivators that enable the sustainability and effectiveness of interventions at other EDs; this is critical given the high failure rate of ED quality improvement programs.

Effective management and leadership structures and participative leadership styles that encourage staff involvement and proactive management may facilitate ED implementations. Emphasis on the allocation of sufficient hospital resources (i.e., technological, human, and medical) and effective communication and collaboration are essential for fostering a supportive and cohesive work environment, thus facilitating such interventions. Those with positive perceptions of the need for the intervention are more likely to adopt and sustain implementation efforts, and previous experiences with interventions and the perception of the need for an intervention emerged as influential factors in the readiness for change.

This study strategically incorporates triangulation. By doing so, it addresses inherent blind spots and biases in each method, enhances the validation of data, and offers diverse perspectives on the topic. This triangulation not only validates findings but also contributes to a more comprehensive and calibrated understanding of the phenomena under investigation. Furthermore, this study involves a multi-disciplinary planning and implementation team to comprehensively study the various facilitators and barriers prior to implementation.

This study, like any rigorous research endeavor, is not exempt from limitations, and it is essential to openly acknowledge these factors to provide a transparent understanding of the study's scope. While our study gains insights from four diverse EDs, it is crucial to note a limitation in its context-specific nature. Our primary focus revolves around understanding barriers and facilitators before implementing the SurgeCon quality improvement program in Canadian EDs. Findings may lack broad generalizability. However, our emphasis on transferability urges researchers to assess the applicability of insights in similar settings, fostering a nuanced understanding. In this study, the data collector observed potential social desirability tendencies among participants. To address this, we made efforts to assure participants of anonymity and confidentiality, provided clear communication about the study's purpose and data use, and incorporated strategies like follow-up questions. Additionally, we encouraged participants to share examples to illustrate their responses, aiming to mitigate potential response bias [ 55 ]. Finally, the study, conducted within a specific timeframe, must consider the dynamic healthcare landscape. The advent of COVID-19 brought rapid changes to healthcare policies, ED protocols, and overall healthcare delivery. Acknowledging this evolving context during and after data collection is crucial for interpreting the study's findings in the broader context of a changing healthcare system.

The findings of this study will guide future initiatives for the implementation of quality improvement programs within the complex environment of EDs by identifying facilitators and barriers prior to implementation to ensure they are continually considered during the design phase of an intervention. We propose that it is important to examine these factors before implementing such systems so that the implementation can be designed and managed to address the multivariate impact they may impose.

Availability of data and materials

The datasets generated and/or analysed during the current study are not publicly available to protect the confidentiality of participants’ data but are available from the corresponding author upon reasonable request.

Abbreviations

• Emergency department

Consolidated Framework for Implementation Research

Newfoundland and Labrador

Organization Readiness for Knowledge Translation

Key performance indicator

Physician initial assessment

Length of stay

Leaving the ED without being seen

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What is Qualitative in Qualitative Research

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What is qualitative research? If we look for a precise definition of qualitative research, and specifically for one that addresses its distinctive feature of being “qualitative,” the literature is meager. In this article we systematically search, identify and analyze a sample of 89 sources using or attempting to define the term “qualitative.” Then, drawing on ideas we find scattered across existing work, and based on Becker’s classic study of marijuana consumption, we formulate and illustrate a definition that tries to capture its core elements. We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. This formulation is developed as a tool to help improve research designs while stressing that a qualitative dimension is present in quantitative work as well. Additionally, it can facilitate teaching, communication between researchers, diminish the gap between qualitative and quantitative researchers, help to address critiques of qualitative methods, and be used as a standard of evaluation of qualitative research.

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What is Qualitative in Research

Unsettling definitions of qualitative research, what is “qualitative” in qualitative research why the answer does not matter but the question is important.

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If we assume that there is something called qualitative research, what exactly is this qualitative feature? And how could we evaluate qualitative research as good or not? Is it fundamentally different from quantitative research? In practice, most active qualitative researchers working with empirical material intuitively know what is involved in doing qualitative research, yet perhaps surprisingly, a clear definition addressing its key feature is still missing.

To address the question of what is qualitative we turn to the accounts of “qualitative research” in textbooks and also in empirical work. In his classic, explorative, interview study of deviance Howard Becker ( 1963 ) asks ‘How does one become a marijuana user?’ In contrast to pre-dispositional and psychological-individualistic theories of deviant behavior, Becker’s inherently social explanation contends that becoming a user of this substance is the result of a three-phase sequential learning process. First, potential users need to learn how to smoke it properly to produce the “correct” effects. If not, they are likely to stop experimenting with it. Second, they need to discover the effects associated with it; in other words, to get “high,” individuals not only have to experience what the drug does, but also to become aware that those sensations are related to using it. Third, they require learning to savor the feelings related to its consumption – to develop an acquired taste. Becker, who played music himself, gets close to the phenomenon by observing, taking part, and by talking to people consuming the drug: “half of the fifty interviews were conducted with musicians, the other half covered a wide range of people, including laborers, machinists, and people in the professions” (Becker 1963 :56).

Another central aspect derived through the common-to-all-research interplay between induction and deduction (Becker 2017 ), is that during the course of his research Becker adds scientifically meaningful new distinctions in the form of three phases—distinctions, or findings if you will, that strongly affect the course of his research: its focus, the material that he collects, and which eventually impact his findings. Each phase typically unfolds through social interaction, and often with input from experienced users in “a sequence of social experiences during which the person acquires a conception of the meaning of the behavior, and perceptions and judgments of objects and situations, all of which make the activity possible and desirable” (Becker 1963 :235). In this study the increased understanding of smoking dope is a result of a combination of the meaning of the actors, and the conceptual distinctions that Becker introduces based on the views expressed by his respondents. Understanding is the result of research and is due to an iterative process in which data, concepts and evidence are connected with one another (Becker 2017 ).

Indeed, there are many definitions of qualitative research, but if we look for a definition that addresses its distinctive feature of being “qualitative,” the literature across the broad field of social science is meager. The main reason behind this article lies in the paradox, which, to put it bluntly, is that researchers act as if they know what it is, but they cannot formulate a coherent definition. Sociologists and others will of course continue to conduct good studies that show the relevance and value of qualitative research addressing scientific and practical problems in society. However, our paper is grounded in the idea that providing a clear definition will help us improve the work that we do. Among researchers who practice qualitative research there is clearly much knowledge. We suggest that a definition makes this knowledge more explicit. If the first rationale for writing this paper refers to the “internal” aim of improving qualitative research, the second refers to the increased “external” pressure that especially many qualitative researchers feel; pressure that comes both from society as well as from other scientific approaches. There is a strong core in qualitative research, and leading researchers tend to agree on what it is and how it is done. Our critique is not directed at the practice of qualitative research, but we do claim that the type of systematic work we do has not yet been done, and that it is useful to improve the field and its status in relation to quantitative research.

The literature on the “internal” aim of improving, or at least clarifying qualitative research is large, and we do not claim to be the first to notice the vagueness of the term “qualitative” (Strauss and Corbin 1998 ). Also, others have noted that there is no single definition of it (Long and Godfrey 2004 :182), that there are many different views on qualitative research (Denzin and Lincoln 2003 :11; Jovanović 2011 :3), and that more generally, we need to define its meaning (Best 2004 :54). Strauss and Corbin ( 1998 ), for example, as well as Nelson et al. (1992:2 cited in Denzin and Lincoln 2003 :11), and Flick ( 2007 :ix–x), have recognized that the term is problematic: “Actually, the term ‘qualitative research’ is confusing because it can mean different things to different people” (Strauss and Corbin 1998 :10–11). Hammersley has discussed the possibility of addressing the problem, but states that “the task of providing an account of the distinctive features of qualitative research is far from straightforward” ( 2013 :2). This confusion, as he has recently further argued (Hammersley 2018 ), is also salient in relation to ethnography where different philosophical and methodological approaches lead to a lack of agreement about what it means.

Others (e.g. Hammersley 2018 ; Fine and Hancock 2017 ) have also identified the treat to qualitative research that comes from external forces, seen from the point of view of “qualitative research.” This threat can be further divided into that which comes from inside academia, such as the critique voiced by “quantitative research” and outside of academia, including, for example, New Public Management. Hammersley ( 2018 ), zooming in on one type of qualitative research, ethnography, has argued that it is under treat. Similarly to Fine ( 2003 ), and before him Gans ( 1999 ), he writes that ethnography’ has acquired a range of meanings, and comes in many different versions, these often reflecting sharply divergent epistemological orientations. And already more than twenty years ago while reviewing Denzin and Lincoln’ s Handbook of Qualitative Methods Fine argued:

While this increasing centrality [of qualitative research] might lead one to believe that consensual standards have developed, this belief would be misleading. As the methodology becomes more widely accepted, querulous challengers have raised fundamental questions that collectively have undercut the traditional models of how qualitative research is to be fashioned and presented (1995:417).

According to Hammersley, there are today “serious treats to the practice of ethnographic work, on almost any definition” ( 2018 :1). He lists five external treats: (1) that social research must be accountable and able to show its impact on society; (2) the current emphasis on “big data” and the emphasis on quantitative data and evidence; (3) the labor market pressure in academia that leaves less time for fieldwork (see also Fine and Hancock 2017 ); (4) problems of access to fields; and (5) the increased ethical scrutiny of projects, to which ethnography is particularly exposed. Hammersley discusses some more or less insufficient existing definitions of ethnography.

The current situation, as Hammersley and others note—and in relation not only to ethnography but also qualitative research in general, and as our empirical study shows—is not just unsatisfactory, it may even be harmful for the entire field of qualitative research, and does not help social science at large. We suggest that the lack of clarity of qualitative research is a real problem that must be addressed.

Towards a Definition of Qualitative Research

Seen in an historical light, what is today called qualitative, or sometimes ethnographic, interpretative research – or a number of other terms – has more or less always existed. At the time the founders of sociology – Simmel, Weber, Durkheim and, before them, Marx – were writing, and during the era of the Methodenstreit (“dispute about methods”) in which the German historical school emphasized scientific methods (cf. Swedberg 1990 ), we can at least speak of qualitative forerunners.

Perhaps the most extended discussion of what later became known as qualitative methods in a classic work is Bronisław Malinowski’s ( 1922 ) Argonauts in the Western Pacific , although even this study does not explicitly address the meaning of “qualitative.” In Weber’s ([1921–-22] 1978) work we find a tension between scientific explanations that are based on observation and quantification and interpretative research (see also Lazarsfeld and Barton 1982 ).

If we look through major sociology journals like the American Sociological Review , American Journal of Sociology , or Social Forces we will not find the term qualitative sociology before the 1970s. And certainly before then much of what we consider qualitative classics in sociology, like Becker’ study ( 1963 ), had already been produced. Indeed, the Chicago School often combined qualitative and quantitative data within the same study (Fine 1995 ). Our point being that before a disciplinary self-awareness the term quantitative preceded qualitative, and the articulation of the former was a political move to claim scientific status (Denzin and Lincoln 2005 ). In the US the World War II seem to have sparked a critique of sociological work, including “qualitative work,” that did not follow the scientific canon (Rawls 2018 ), which was underpinned by a scientifically oriented and value free philosophy of science. As a result the attempts and practice of integrating qualitative and quantitative sociology at Chicago lost ground to sociology that was more oriented to surveys and quantitative work at Columbia under Merton-Lazarsfeld. The quantitative tradition was also able to present textbooks (Lundberg 1951 ) that facilitated the use this approach and its “methods.” The practices of the qualitative tradition, by and large, remained tacit or was part of the mentoring transferred from the renowned masters to their students.

This glimpse into history leads us back to the lack of a coherent account condensed in a definition of qualitative research. Many of the attempts to define the term do not meet the requirements of a proper definition: A definition should be clear, avoid tautology, demarcate its domain in relation to the environment, and ideally only use words in its definiens that themselves are not in need of definition (Hempel 1966 ). A definition can enhance precision and thus clarity by identifying the core of the phenomenon. Preferably, a definition should be short. The typical definition we have found, however, is an ostensive definition, which indicates what qualitative research is about without informing us about what it actually is :

Qualitative research is multimethod in focus, involving an interpretative, 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. Qualitative research involves the studied use and collection of a variety of empirical materials – case study, personal experience, introspective, life story, interview, observational, historical, interactional, and visual texts – that describe routine and problematic moments and meanings in individuals’ lives. (Denzin and Lincoln 2005 :2)

Flick claims that the label “qualitative research” is indeed used as an umbrella for a number of approaches ( 2007 :2–4; 2002 :6), and it is not difficult to identify research fitting this designation. Moreover, whatever it is, it has grown dramatically over the past five decades. In addition, courses have been developed, methods have flourished, arguments about its future have been advanced (for example, Denzin and Lincoln 1994) and criticized (for example, Snow and Morrill 1995 ), and dedicated journals and books have mushroomed. Most social scientists have a clear idea of research and how it differs from journalism, politics and other activities. But the question of what is qualitative in qualitative research is either eluded or eschewed.

We maintain that this lacuna hinders systematic knowledge production based on qualitative research. Paul Lazarsfeld noted the lack of “codification” as early as 1955 when he reviewed 100 qualitative studies in order to offer a codification of the practices (Lazarsfeld and Barton 1982 :239). Since then many texts on “qualitative research” and its methods have been published, including recent attempts (Goertz and Mahoney 2012 ) similar to Lazarsfeld’s. These studies have tried to extract what is qualitative by looking at the large number of empirical “qualitative” studies. Our novel strategy complements these endeavors by taking another approach and looking at the attempts to codify these practices in the form of a definition, as well as to a minor extent take Becker’s study as an exemplar of what qualitative researchers actually do, and what the characteristic of being ‘qualitative’ denotes and implies. We claim that qualitative researchers, if there is such a thing as “qualitative research,” should be able to codify their practices in a condensed, yet general way expressed in language.

Lingering problems of “generalizability” and “how many cases do I need” (Small 2009 ) are blocking advancement – in this line of work qualitative approaches are said to differ considerably from quantitative ones, while some of the former unsuccessfully mimic principles related to the latter (Small 2009 ). Additionally, quantitative researchers sometimes unfairly criticize the first based on their own quality criteria. Scholars like Goertz and Mahoney ( 2012 ) have successfully focused on the different norms and practices beyond what they argue are essentially two different cultures: those working with either qualitative or quantitative methods. Instead, similarly to Becker ( 2017 ) who has recently questioned the usefulness of the distinction between qualitative and quantitative research, we focus on similarities.

The current situation also impedes both students and researchers in focusing their studies and understanding each other’s work (Lazarsfeld and Barton 1982 :239). A third consequence is providing an opening for critiques by scholars operating within different traditions (Valsiner 2000 :101). A fourth issue is that the “implicit use of methods in qualitative research makes the field far less standardized than the quantitative paradigm” (Goertz and Mahoney 2012 :9). Relatedly, the National Science Foundation in the US organized two workshops in 2004 and 2005 to address the scientific foundations of qualitative research involving strategies to improve it and to develop standards of evaluation in qualitative research. However, a specific focus on its distinguishing feature of being “qualitative” while being implicitly acknowledged, was discussed only briefly (for example, Best 2004 ).

In 2014 a theme issue was published in this journal on “Methods, Materials, and Meanings: Designing Cultural Analysis,” discussing central issues in (cultural) qualitative research (Berezin 2014 ; Biernacki 2014 ; Glaeser 2014 ; Lamont and Swidler 2014 ; Spillman 2014). We agree with many of the arguments put forward, such as the risk of methodological tribalism, and that we should not waste energy on debating methods separated from research questions. Nonetheless, a clarification of the relation to what is called “quantitative research” is of outmost importance to avoid misunderstandings and misguided debates between “qualitative” and “quantitative” researchers. Our strategy means that researchers, “qualitative” or “quantitative” they may be, in their actual practice may combine qualitative work and quantitative work.

In this article we accomplish three tasks. First, we systematically survey the literature for meanings of qualitative research by looking at how researchers have defined it. Drawing upon existing knowledge we find that the different meanings and ideas of qualitative research are not yet coherently integrated into one satisfactory definition. Next, we advance our contribution by offering a definition of qualitative research and illustrate its meaning and use partially by expanding on the brief example introduced earlier related to Becker’s work ( 1963 ). We offer a systematic analysis of central themes of what researchers consider to be the core of “qualitative,” regardless of style of work. These themes – which we summarize in terms of four keywords: distinction, process, closeness, improved understanding – constitute part of our literature review, in which each one appears, sometimes with others, but never all in the same definition. They serve as the foundation of our contribution. Our categories are overlapping. Their use is primarily to organize the large amount of definitions we have identified and analyzed, and not necessarily to draw a clear distinction between them. Finally, we continue the elaboration discussed above on the advantages of a clear definition of qualitative research.

In a hermeneutic fashion we propose that there is something meaningful that deserves to be labelled “qualitative research” (Gadamer 1990 ). To approach the question “What is qualitative in qualitative research?” we have surveyed the literature. In conducting our survey we first traced the word’s etymology in dictionaries, encyclopedias, handbooks of the social sciences and of methods and textbooks, mainly in English, which is common to methodology courses. It should be noted that we have zoomed in on sociology and its literature. This discipline has been the site of the largest debate and development of methods that can be called “qualitative,” which suggests that this field should be examined in great detail.

In an ideal situation we should expect that one good definition, or at least some common ideas, would have emerged over the years. This common core of qualitative research should be so accepted that it would appear in at least some textbooks. Since this is not what we found, we decided to pursue an inductive approach to capture maximal variation in the field of qualitative research; we searched in a selection of handbooks, textbooks, book chapters, and books, to which we added the analysis of journal articles. Our sample comprises a total of 89 references.

In practice we focused on the discipline that has had a clear discussion of methods, namely sociology. We also conducted a broad search in the JSTOR database to identify scholarly sociology articles published between 1998 and 2017 in English with a focus on defining or explaining qualitative research. We specifically zoom in on this time frame because we would have expect that this more mature period would have produced clear discussions on the meaning of qualitative research. To find these articles we combined a number of keywords to search the content and/or the title: qualitative (which was always included), definition, empirical, research, methodology, studies, fieldwork, interview and observation .

As a second phase of our research we searched within nine major sociological journals ( American Journal of Sociology , Sociological Theory , American Sociological Review , Contemporary Sociology , Sociological Forum , Sociological Theory , Qualitative Research , Qualitative Sociology and Qualitative Sociology Review ) for articles also published during the past 19 years (1998–2017) that had the term “qualitative” in the title and attempted to define qualitative research.

Lastly we picked two additional journals, Qualitative Research and Qualitative Sociology , in which we could expect to find texts addressing the notion of “qualitative.” From Qualitative Research we chose Volume 14, Issue 6, December 2014, and from Qualitative Sociology we chose Volume 36, Issue 2, June 2017. Within each of these we selected the first article; then we picked the second article of three prior issues. Again we went back another three issues and investigated article number three. Finally we went back another three issues and perused article number four. This selection criteria was used to get a manageable sample for the analysis.

The coding process of the 89 references we gathered in our selected review began soon after the first round of material was gathered, and we reduced the complexity created by our maximum variation sampling (Snow and Anderson 1993 :22) to four different categories within which questions on the nature and properties of qualitative research were discussed. We call them: Qualitative and Quantitative Research, Qualitative Research, Fieldwork, and Grounded Theory. This – which may appear as an illogical grouping – merely reflects the “context” in which the matter of “qualitative” is discussed. If the selection process of the material – books and articles – was informed by pre-knowledge, we used an inductive strategy to code the material. When studying our material, we identified four central notions related to “qualitative” that appear in various combinations in the literature which indicate what is the core of qualitative research. We have labeled them: “distinctions”, “process,” “closeness,” and “improved understanding.” During the research process the categories and notions were improved, refined, changed, and reordered. The coding ended when a sense of saturation in the material arose. In the presentation below all quotations and references come from our empirical material of texts on qualitative research.

Analysis – What is Qualitative Research?

In this section we describe the four categories we identified in the coding, how they differently discuss qualitative research, as well as their overall content. Some salient quotations are selected to represent the type of text sorted under each of the four categories. What we present are examples from the literature.

Qualitative and Quantitative

This analytic category comprises quotations comparing qualitative and quantitative research, a distinction that is frequently used (Brown 2010 :231); in effect this is a conceptual pair that structures the discussion and that may be associated with opposing interests. While the general goal of quantitative and qualitative research is the same – to understand the world better – their methodologies and focus in certain respects differ substantially (Becker 1966 :55). Quantity refers to that property of something that can be determined by measurement. In a dictionary of Statistics and Methodology we find that “(a) When referring to *variables, ‘qualitative’ is another term for *categorical or *nominal. (b) When speaking of kinds of research, ‘qualitative’ refers to studies of subjects that are hard to quantify, such as art history. Qualitative research tends to be a residual category for almost any kind of non-quantitative research” (Stiles 1998:183). But it should be obvious that one could employ a quantitative approach when studying, for example, art history.

The same dictionary states that quantitative is “said of variables or research that can be handled numerically, usually (too sharply) contrasted with *qualitative variables and research” (Stiles 1998:184). From a qualitative perspective “quantitative research” is about numbers and counting, and from a quantitative perspective qualitative research is everything that is not about numbers. But this does not say much about what is “qualitative.” If we turn to encyclopedias we find that in the 1932 edition of the Encyclopedia of the Social Sciences there is no mention of “qualitative.” In the Encyclopedia from 1968 we can read:

Qualitative Analysis. For methods of obtaining, analyzing, and describing data, see [the various entries:] CONTENT ANALYSIS; COUNTED DATA; EVALUATION RESEARCH, FIELD WORK; GRAPHIC PRESENTATION; HISTORIOGRAPHY, especially the article on THE RHETORIC OF HISTORY; INTERVIEWING; OBSERVATION; PERSONALITY MEASUREMENT; PROJECTIVE METHODS; PSYCHOANALYSIS, article on EXPERIMENTAL METHODS; SURVEY ANALYSIS, TABULAR PRESENTATION; TYPOLOGIES. (Vol. 13:225)

Some, like Alford, divide researchers into methodologists or, in his words, “quantitative and qualitative specialists” (Alford 1998 :12). Qualitative research uses a variety of methods, such as intensive interviews or in-depth analysis of historical materials, and it is concerned with a comprehensive account of some event or unit (King et al. 1994 :4). Like quantitative research it can be utilized to study a variety of issues, but it tends to focus on meanings and motivations that underlie cultural symbols, personal experiences, phenomena and detailed understanding of processes in the social world. In short, qualitative research centers on understanding processes, experiences, and the meanings people assign to things (Kalof et al. 2008 :79).

Others simply say that qualitative methods are inherently unscientific (Jovanović 2011 :19). Hood, for instance, argues that words are intrinsically less precise than numbers, and that they are therefore more prone to subjective analysis, leading to biased results (Hood 2006 :219). Qualitative methodologies have raised concerns over the limitations of quantitative templates (Brady et al. 2004 :4). Scholars such as King et al. ( 1994 ), for instance, argue that non-statistical research can produce more reliable results if researchers pay attention to the rules of scientific inference commonly stated in quantitative research. Also, researchers such as Becker ( 1966 :59; 1970 :42–43) have asserted that, if conducted properly, qualitative research and in particular ethnographic field methods, can lead to more accurate results than quantitative studies, in particular, survey research and laboratory experiments.

Some researchers, such as Kalof, Dan, and Dietz ( 2008 :79) claim that the boundaries between the two approaches are becoming blurred, and Small ( 2009 ) argues that currently much qualitative research (especially in North America) tries unsuccessfully and unnecessarily to emulate quantitative standards. For others, qualitative research tends to be more humanistic and discursive (King et al. 1994 :4). Ragin ( 1994 ), and similarly also Becker, ( 1996 :53), Marchel and Owens ( 2007 :303) think that the main distinction between the two styles is overstated and does not rest on the simple dichotomy of “numbers versus words” (Ragin 1994 :xii). Some claim that quantitative data can be utilized to discover associations, but in order to unveil cause and effect a complex research design involving the use of qualitative approaches needs to be devised (Gilbert 2009 :35). Consequently, qualitative data are useful for understanding the nuances lying beyond those processes as they unfold (Gilbert 2009 :35). Others contend that qualitative research is particularly well suited both to identify causality and to uncover fine descriptive distinctions (Fine and Hallett 2014 ; Lichterman and Isaac Reed 2014 ; Katz 2015 ).

There are other ways to separate these two traditions, including normative statements about what qualitative research should be (that is, better or worse than quantitative approaches, concerned with scientific approaches to societal change or vice versa; Snow and Morrill 1995 ; Denzin and Lincoln 2005 ), or whether it should develop falsifiable statements; Best 2004 ).

We propose that quantitative research is largely concerned with pre-determined variables (Small 2008 ); the analysis concerns the relations between variables. These categories are primarily not questioned in the study, only their frequency or degree, or the correlations between them (cf. Franzosi 2016 ). If a researcher studies wage differences between women and men, he or she works with given categories: x number of men are compared with y number of women, with a certain wage attributed to each person. The idea is not to move beyond the given categories of wage, men and women; they are the starting point as well as the end point, and undergo no “qualitative change.” Qualitative research, in contrast, investigates relations between categories that are themselves subject to change in the research process. Returning to Becker’s study ( 1963 ), we see that he questioned pre-dispositional theories of deviant behavior working with pre-determined variables such as an individual’s combination of personal qualities or emotional problems. His take, in contrast, was to understand marijuana consumption by developing “variables” as part of the investigation. Thereby he presented new variables, or as we would say today, theoretical concepts, but which are grounded in the empirical material.

Qualitative Research

This category contains quotations that refer to descriptions of qualitative research without making comparisons with quantitative research. Researchers such as Denzin and Lincoln, who have written a series of influential handbooks on qualitative methods (1994; Denzin and Lincoln 2003 ; 2005 ), citing Nelson et al. (1992:4), argue that because qualitative research is “interdisciplinary, transdisciplinary, and sometimes counterdisciplinary” it is difficult to derive one single definition of it (Jovanović 2011 :3). According to them, in fact, “the field” is “many things at the same time,” involving contradictions, tensions over its focus, methods, and how to derive interpretations and findings ( 2003 : 11). Similarly, others, such as Flick ( 2007 :ix–x) contend that agreeing on an accepted definition has increasingly become problematic, and that qualitative research has possibly matured different identities. However, Best holds that “the proliferation of many sorts of activities under the label of qualitative sociology threatens to confuse our discussions” ( 2004 :54). Atkinson’s position is more definite: “the current state of qualitative research and research methods is confused” ( 2005 :3–4).

Qualitative research is about interpretation (Blumer 1969 ; Strauss and Corbin 1998 ; Denzin and Lincoln 2003 ), or Verstehen [understanding] (Frankfort-Nachmias and Nachmias 1996 ). It is “multi-method,” involving the collection and use of a variety of empirical materials (Denzin and Lincoln 1998; Silverman 2013 ) and approaches (Silverman 2005 ; Flick 2007 ). It focuses not only on the objective nature of behavior but also on its subjective meanings: individuals’ own accounts of their attitudes, motivations, behavior (McIntyre 2005 :127; Creswell 2009 ), events and situations (Bryman 1989) – what people say and do in specific places and institutions (Goodwin and Horowitz 2002 :35–36) in social and temporal contexts (Morrill and Fine 1997). For this reason, following Weber ([1921-22] 1978), it can be described as an interpretative science (McIntyre 2005 :127). But could quantitative research also be concerned with these questions? Also, as pointed out below, does all qualitative research focus on subjective meaning, as some scholars suggest?

Others also distinguish qualitative research by claiming that it collects data using a naturalistic approach (Denzin and Lincoln 2005 :2; Creswell 2009 ), focusing on the meaning actors ascribe to their actions. But again, does all qualitative research need to be collected in situ? And does qualitative research have to be inherently concerned with meaning? Flick ( 2007 ), referring to Denzin and Lincoln ( 2005 ), mentions conversation analysis as an example of qualitative research that is not concerned with the meanings people bring to a situation, but rather with the formal organization of talk. Still others, such as Ragin ( 1994 :85), note that qualitative research is often (especially early on in the project, we would add) less structured than other kinds of social research – a characteristic connected to its flexibility and that can lead both to potentially better, but also worse results. But is this not a feature of this type of research, rather than a defining description of its essence? Wouldn’t this comment also apply, albeit to varying degrees, to quantitative research?

In addition, Strauss ( 2003 ), along with others, such as Alvesson and Kärreman ( 2011 :10–76), argue that qualitative researchers struggle to capture and represent complex phenomena partially because they tend to collect a large amount of data. While his analysis is correct at some points – “It is necessary to do detailed, intensive, microscopic examination of the data in order to bring out the amazing complexity of what lies in, behind, and beyond those data” (Strauss 2003 :10) – much of his analysis concerns the supposed focus of qualitative research and its challenges, rather than exactly what it is about. But even in this instance we would make a weak case arguing that these are strictly the defining features of qualitative research. Some researchers seem to focus on the approach or the methods used, or even on the way material is analyzed. Several researchers stress the naturalistic assumption of investigating the world, suggesting that meaning and interpretation appear to be a core matter of qualitative research.

We can also see that in this category there is no consensus about specific qualitative methods nor about qualitative data. Many emphasize interpretation, but quantitative research, too, involves interpretation; the results of a regression analysis, for example, certainly have to be interpreted, and the form of meta-analysis that factor analysis provides indeed requires interpretation However, there is no interpretation of quantitative raw data, i.e., numbers in tables. One common thread is that qualitative researchers have to get to grips with their data in order to understand what is being studied in great detail, irrespective of the type of empirical material that is being analyzed. This observation is connected to the fact that qualitative researchers routinely make several adjustments of focus and research design as their studies progress, in many cases until the very end of the project (Kalof et al. 2008 ). If you, like Becker, do not start out with a detailed theory, adjustments such as the emergence and refinement of research questions will occur during the research process. We have thus found a number of useful reflections about qualitative research scattered across different sources, but none of them effectively describe the defining characteristics of this approach.

Although qualitative research does not appear to be defined in terms of a specific method, it is certainly common that fieldwork, i.e., research that entails that the researcher spends considerable time in the field that is studied and use the knowledge gained as data, is seen as emblematic of or even identical to qualitative research. But because we understand that fieldwork tends to focus primarily on the collection and analysis of qualitative data, we expected to find within it discussions on the meaning of “qualitative.” But, again, this was not the case.

Instead, we found material on the history of this approach (for example, Frankfort-Nachmias and Nachmias 1996 ; Atkinson et al. 2001), including how it has changed; for example, by adopting a more self-reflexive practice (Heyl 2001), as well as the different nomenclature that has been adopted, such as fieldwork, ethnography, qualitative research, naturalistic research, participant observation and so on (for example, Lofland et al. 2006 ; Gans 1999 ).

We retrieved definitions of ethnography, such as “the study of people acting in the natural courses of their daily lives,” involving a “resocialization of the researcher” (Emerson 1988 :1) through intense immersion in others’ social worlds (see also examples in Hammersley 2018 ). This may be accomplished by direct observation and also participation (Neuman 2007 :276), although others, such as Denzin ( 1970 :185), have long recognized other types of observation, including non-participant (“fly on the wall”). In this category we have also isolated claims and opposing views, arguing that this type of research is distinguished primarily by where it is conducted (natural settings) (Hughes 1971:496), and how it is carried out (a variety of methods are applied) or, for some most importantly, by involving an active, empathetic immersion in those being studied (Emerson 1988 :2). We also retrieved descriptions of the goals it attends in relation to how it is taught (understanding subjective meanings of the people studied, primarily develop theory, or contribute to social change) (see for example, Corte and Irwin 2017 ; Frankfort-Nachmias and Nachmias 1996 :281; Trier-Bieniek 2012 :639) by collecting the richest possible data (Lofland et al. 2006 ) to derive “thick descriptions” (Geertz 1973 ), and/or to aim at theoretical statements of general scope and applicability (for example, Emerson 1988 ; Fine 2003 ). We have identified guidelines on how to evaluate it (for example Becker 1996 ; Lamont 2004 ) and have retrieved instructions on how it should be conducted (for example, Lofland et al. 2006 ). For instance, analysis should take place while the data gathering unfolds (Emerson 1988 ; Hammersley and Atkinson 2007 ; Lofland et al. 2006 ), observations should be of long duration (Becker 1970 :54; Goffman 1989 ), and data should be of high quantity (Becker 1970 :52–53), as well as other questionable distinctions between fieldwork and other methods:

Field studies differ from other methods of research in that the researcher performs the task of selecting topics, decides what questions to ask, and forges interest in the course of the research itself . This is in sharp contrast to many ‘theory-driven’ and ‘hypothesis-testing’ methods. (Lofland and Lofland 1995 :5)

But could not, for example, a strictly interview-based study be carried out with the same amount of flexibility, such as sequential interviewing (for example, Small 2009 )? Once again, are quantitative approaches really as inflexible as some qualitative researchers think? Moreover, this category stresses the role of the actors’ meaning, which requires knowledge and close interaction with people, their practices and their lifeworld.

It is clear that field studies – which are seen by some as the “gold standard” of qualitative research – are nonetheless only one way of doing qualitative research. There are other methods, but it is not clear why some are more qualitative than others, or why they are better or worse. Fieldwork is characterized by interaction with the field (the material) and understanding of the phenomenon that is being studied. In Becker’s case, he had general experience from fields in which marihuana was used, based on which he did interviews with actual users in several fields.

Grounded Theory

Another major category we identified in our sample is Grounded Theory. We found descriptions of it most clearly in Glaser and Strauss’ ([1967] 2010 ) original articulation, Strauss and Corbin ( 1998 ) and Charmaz ( 2006 ), as well as many other accounts of what it is for: generating and testing theory (Strauss 2003 :xi). We identified explanations of how this task can be accomplished – such as through two main procedures: constant comparison and theoretical sampling (Emerson 1998:96), and how using it has helped researchers to “think differently” (for example, Strauss and Corbin 1998 :1). We also read descriptions of its main traits, what it entails and fosters – for instance, an exceptional flexibility, an inductive approach (Strauss and Corbin 1998 :31–33; 1990; Esterberg 2002 :7), an ability to step back and critically analyze situations, recognize tendencies towards bias, think abstractly and be open to criticism, enhance sensitivity towards the words and actions of respondents, and develop a sense of absorption and devotion to the research process (Strauss and Corbin 1998 :5–6). Accordingly, we identified discussions of the value of triangulating different methods (both using and not using grounded theory), including quantitative ones, and theories to achieve theoretical development (most comprehensively in Denzin 1970 ; Strauss and Corbin 1998 ; Timmermans and Tavory 2012 ). We have also located arguments about how its practice helps to systematize data collection, analysis and presentation of results (Glaser and Strauss [1967] 2010 :16).

Grounded theory offers a systematic approach which requires researchers to get close to the field; closeness is a requirement of identifying questions and developing new concepts or making further distinctions with regard to old concepts. In contrast to other qualitative approaches, grounded theory emphasizes the detailed coding process, and the numerous fine-tuned distinctions that the researcher makes during the process. Within this category, too, we could not find a satisfying discussion of the meaning of qualitative research.

Defining Qualitative Research

In sum, our analysis shows that some notions reappear in the discussion of qualitative research, such as understanding, interpretation, “getting close” and making distinctions. These notions capture aspects of what we think is “qualitative.” However, a comprehensive definition that is useful and that can further develop the field is lacking, and not even a clear picture of its essential elements appears. In other words no definition emerges from our data, and in our research process we have moved back and forth between our empirical data and the attempt to present a definition. Our concrete strategy, as stated above, is to relate qualitative and quantitative research, or more specifically, qualitative and quantitative work. We use an ideal-typical notion of quantitative research which relies on taken for granted and numbered variables. This means that the data consists of variables on different scales, such as ordinal, but frequently ratio and absolute scales, and the representation of the numbers to the variables, i.e. the justification of the assignment of numbers to object or phenomenon, are not questioned, though the validity may be questioned. In this section we return to the notion of quality and try to clarify it while presenting our contribution.

Broadly, research refers to the activity performed by people trained to obtain knowledge through systematic procedures. Notions such as “objectivity” and “reflexivity,” “systematic,” “theory,” “evidence” and “openness” are here taken for granted in any type of research. Next, building on our empirical analysis we explain the four notions that we have identified as central to qualitative work: distinctions, process, closeness, and improved understanding. In discussing them, ultimately in relation to one another, we make their meaning even more precise. Our idea, in short, is that only when these ideas that we present separately for analytic purposes are brought together can we speak of qualitative research.

Distinctions

We believe that the possibility of making new distinctions is one the defining characteristics of qualitative research. It clearly sets it apart from quantitative analysis which works with taken-for-granted variables, albeit as mentioned, meta-analyses, for example, factor analysis may result in new variables. “Quality” refers essentially to distinctions, as already pointed out by Aristotle. He discusses the term “qualitative” commenting: “By a quality I mean that in virtue of which things are said to be qualified somehow” (Aristotle 1984:14). Quality is about what something is or has, which means that the distinction from its environment is crucial. We see qualitative research as a process in which significant new distinctions are made to the scholarly community; to make distinctions is a key aspect of obtaining new knowledge; a point, as we will see, that also has implications for “quantitative research.” The notion of being “significant” is paramount. New distinctions by themselves are not enough; just adding concepts only increases complexity without furthering our knowledge. The significance of new distinctions is judged against the communal knowledge of the research community. To enable this discussion and judgements central elements of rational discussion are required (cf. Habermas [1981] 1987 ; Davidsson [ 1988 ] 2001) to identify what is new and relevant scientific knowledge. Relatedly, Ragin alludes to the idea of new and useful knowledge at a more concrete level: “Qualitative methods are appropriate for in-depth examination of cases because they aid the identification of key features of cases. Most qualitative methods enhance data” (1994:79). When Becker ( 1963 ) studied deviant behavior and investigated how people became marihuana smokers, he made distinctions between the ways in which people learned how to smoke. This is a classic example of how the strategy of “getting close” to the material, for example the text, people or pictures that are subject to analysis, may enable researchers to obtain deeper insight and new knowledge by making distinctions – in this instance on the initial notion of learning how to smoke. Others have stressed the making of distinctions in relation to coding or theorizing. Emerson et al. ( 1995 ), for example, hold that “qualitative coding is a way of opening up avenues of inquiry,” meaning that the researcher identifies and develops concepts and analytic insights through close examination of and reflection on data (Emerson et al. 1995 :151). Goodwin and Horowitz highlight making distinctions in relation to theory-building writing: “Close engagement with their cases typically requires qualitative researchers to adapt existing theories or to make new conceptual distinctions or theoretical arguments to accommodate new data” ( 2002 : 37). In the ideal-typical quantitative research only existing and so to speak, given, variables would be used. If this is the case no new distinction are made. But, would not also many “quantitative” researchers make new distinctions?

Process does not merely suggest that research takes time. It mainly implies that qualitative new knowledge results from a process that involves several phases, and above all iteration. Qualitative research is about oscillation between theory and evidence, analysis and generating material, between first- and second -order constructs (Schütz 1962 :59), between getting in contact with something, finding sources, becoming deeply familiar with a topic, and then distilling and communicating some of its essential features. The main point is that the categories that the researcher uses, and perhaps takes for granted at the beginning of the research process, usually undergo qualitative changes resulting from what is found. Becker describes how he tested hypotheses and let the jargon of the users develop into theoretical concepts. This happens over time while the study is being conducted, exemplifying what we mean by process.

In the research process, a pilot-study may be used to get a first glance of, for example, the field, how to approach it, and what methods can be used, after which the method and theory are chosen or refined before the main study begins. Thus, the empirical material is often central from the start of the project and frequently leads to adjustments by the researcher. Likewise, during the main study categories are not fixed; the empirical material is seen in light of the theory used, but it is also given the opportunity to kick back, thereby resisting attempts to apply theoretical straightjackets (Becker 1970 :43). In this process, coding and analysis are interwoven, and thus are often important steps for getting closer to the phenomenon and deciding what to focus on next. Becker began his research by interviewing musicians close to him, then asking them to refer him to other musicians, and later on doubling his original sample of about 25 to include individuals in other professions (Becker 1973:46). Additionally, he made use of some participant observation, documents, and interviews with opiate users made available to him by colleagues. As his inductive theory of deviance evolved, Becker expanded his sample in order to fine tune it, and test the accuracy and generality of his hypotheses. In addition, he introduced a negative case and discussed the null hypothesis ( 1963 :44). His phasic career model is thus based on a research design that embraces processual work. Typically, process means to move between “theory” and “material” but also to deal with negative cases, and Becker ( 1998 ) describes how discovering these negative cases impacted his research design and ultimately its findings.

Obviously, all research is process-oriented to some degree. The point is that the ideal-typical quantitative process does not imply change of the data, and iteration between data, evidence, hypotheses, empirical work, and theory. The data, quantified variables, are, in most cases fixed. Merging of data, which of course can be done in a quantitative research process, does not mean new data. New hypotheses are frequently tested, but the “raw data is often the “the same.” Obviously, over time new datasets are made available and put into use.

Another characteristic that is emphasized in our sample is that qualitative researchers – and in particular ethnographers – can, or as Goffman put it, ought to ( 1989 ), get closer to the phenomenon being studied and their data than quantitative researchers (for example, Silverman 2009 :85). Put differently, essentially because of their methods qualitative researchers get into direct close contact with those being investigated and/or the material, such as texts, being analyzed. Becker started out his interview study, as we noted, by talking to those he knew in the field of music to get closer to the phenomenon he was studying. By conducting interviews he got even closer. Had he done more observations, he would undoubtedly have got even closer to the field.

Additionally, ethnographers’ design enables researchers to follow the field over time, and the research they do is almost by definition longitudinal, though the time in the field is studied obviously differs between studies. The general characteristic of closeness over time maximizes the chances of unexpected events, new data (related, for example, to archival research as additional sources, and for ethnography for situations not necessarily previously thought of as instrumental – what Mannay and Morgan ( 2015 ) term the “waiting field”), serendipity (Merton and Barber 2004 ; Åkerström 2013 ), and possibly reactivity, as well as the opportunity to observe disrupted patterns that translate into exemplars of negative cases. Two classic examples of this are Becker’s finding of what medical students call “crocks” (Becker et al. 1961 :317), and Geertz’s ( 1973 ) study of “deep play” in Balinese society.

By getting and staying so close to their data – be it pictures, text or humans interacting (Becker was himself a musician) – for a long time, as the research progressively focuses, qualitative researchers are prompted to continually test their hunches, presuppositions and hypotheses. They test them against a reality that often (but certainly not always), and practically, as well as metaphorically, talks back, whether by validating them, or disqualifying their premises – correctly, as well as incorrectly (Fine 2003 ; Becker 1970 ). This testing nonetheless often leads to new directions for the research. Becker, for example, says that he was initially reading psychological theories, but when facing the data he develops a theory that looks at, you may say, everything but psychological dispositions to explain the use of marihuana. Especially researchers involved with ethnographic methods have a fairly unique opportunity to dig up and then test (in a circular, continuous and temporal way) new research questions and findings as the research progresses, and thereby to derive previously unimagined and uncharted distinctions by getting closer to the phenomenon under study.

Let us stress that getting close is by no means restricted to ethnography. The notion of hermeneutic circle and hermeneutics as a general way of understanding implies that we must get close to the details in order to get the big picture. This also means that qualitative researchers can literally also make use of details of pictures as evidence (cf. Harper 2002). Thus, researchers may get closer both when generating the material or when analyzing it.

Quantitative research, we maintain, in the ideal-typical representation cannot get closer to the data. The data is essentially numbers in tables making up the variables (Franzosi 2016 :138). The data may originally have been “qualitative,” but once reduced to numbers there can only be a type of “hermeneutics” about what the number may stand for. The numbers themselves, however, are non-ambiguous. Thus, in quantitative research, interpretation, if done, is not about the data itself—the numbers—but what the numbers stand for. It follows that the interpretation is essentially done in a more “speculative” mode without direct empirical evidence (cf. Becker 2017 ).

Improved Understanding

While distinction, process and getting closer refer to the qualitative work of the researcher, improved understanding refers to its conditions and outcome of this work. Understanding cuts deeper than explanation, which to some may mean a causally verified correlation between variables. The notion of explanation presupposes the notion of understanding since explanation does not include an idea of how knowledge is gained (Manicas 2006 : 15). Understanding, we argue, is the core concept of what we call the outcome of the process when research has made use of all the other elements that were integrated in the research. Understanding, then, has a special status in qualitative research since it refers both to the conditions of knowledge and the outcome of the process. Understanding can to some extent be seen as the condition of explanation and occurs in a process of interpretation, which naturally refers to meaning (Gadamer 1990 ). It is fundamentally connected to knowing, and to the knowing of how to do things (Heidegger [1927] 2001 ). Conceptually the term hermeneutics is used to account for this process. Heidegger ties hermeneutics to human being and not possible to separate from the understanding of being ( 1988 ). Here we use it in a broader sense, and more connected to method in general (cf. Seiffert 1992 ). The abovementioned aspects – for example, “objectivity” and “reflexivity” – of the approach are conditions of scientific understanding. Understanding is the result of a circular process and means that the parts are understood in light of the whole, and vice versa. Understanding presupposes pre-understanding, or in other words, some knowledge of the phenomenon studied. The pre-understanding, even in the form of prejudices, are in qualitative research process, which we see as iterative, questioned, which gradually or suddenly change due to the iteration of data, evidence and concepts. However, qualitative research generates understanding in the iterative process when the researcher gets closer to the data, e.g., by going back and forth between field and analysis in a process that generates new data that changes the evidence, and, ultimately, the findings. Questioning, to ask questions, and put what one assumes—prejudices and presumption—in question, is central to understand something (Heidegger [1927] 2001 ; Gadamer 1990 :368–384). We propose that this iterative process in which the process of understanding occurs is characteristic of qualitative research.

Improved understanding means that we obtain scientific knowledge of something that we as a scholarly community did not know before, or that we get to know something better. It means that we understand more about how parts are related to one another, and to other things we already understand (see also Fine and Hallett 2014 ). Understanding is an important condition for qualitative research. It is not enough to identify correlations, make distinctions, and work in a process in which one gets close to the field or phenomena. Understanding is accomplished when the elements are integrated in an iterative process.

It is, moreover, possible to understand many things, and researchers, just like children, may come to understand new things every day as they engage with the world. This subjective condition of understanding – namely, that a person gains a better understanding of something –is easily met. To be qualified as “scientific,” the understanding must be general and useful to many; it must be public. But even this generally accessible understanding is not enough in order to speak of “scientific understanding.” Though we as a collective can increase understanding of everything in virtually all potential directions as a result also of qualitative work, we refrain from this “objective” way of understanding, which has no means of discriminating between what we gain in understanding. Scientific understanding means that it is deemed relevant from the scientific horizon (compare Schütz 1962 : 35–38, 46, 63), and that it rests on the pre-understanding that the scientists have and must have in order to understand. In other words, the understanding gained must be deemed useful by other researchers, so that they can build on it. We thus see understanding from a pragmatic, rather than a subjective or objective perspective. Improved understanding is related to the question(s) at hand. Understanding, in order to represent an improvement, must be an improvement in relation to the existing body of knowledge of the scientific community (James [ 1907 ] 1955). Scientific understanding is, by definition, collective, as expressed in Weber’s famous note on objectivity, namely that scientific work aims at truths “which … can claim, even for a Chinese, the validity appropriate to an empirical analysis” ([1904] 1949 :59). By qualifying “improved understanding” we argue that it is a general defining characteristic of qualitative research. Becker‘s ( 1966 ) study and other research of deviant behavior increased our understanding of the social learning processes of how individuals start a behavior. And it also added new knowledge about the labeling of deviant behavior as a social process. Few studies, of course, make the same large contribution as Becker’s, but are nonetheless qualitative research.

Understanding in the phenomenological sense, which is a hallmark of qualitative research, we argue, requires meaning and this meaning is derived from the context, and above all the data being analyzed. The ideal-typical quantitative research operates with given variables with different numbers. This type of material is not enough to establish meaning at the level that truly justifies understanding. In other words, many social science explanations offer ideas about correlations or even causal relations, but this does not mean that the meaning at the level of the data analyzed, is understood. This leads us to say that there are indeed many explanations that meet the criteria of understanding, for example the explanation of how one becomes a marihuana smoker presented by Becker. However, we may also understand a phenomenon without explaining it, and we may have potential explanations, or better correlations, that are not really understood.

We may speak more generally of quantitative research and its data to clarify what we see as an important distinction. The “raw data” that quantitative research—as an idealtypical activity, refers to is not available for further analysis; the numbers, once created, are not to be questioned (Franzosi 2016 : 138). If the researcher is to do “more” or “change” something, this will be done by conjectures based on theoretical knowledge or based on the researcher’s lifeworld. Both qualitative and quantitative research is based on the lifeworld, and all researchers use prejudices and pre-understanding in the research process. This idea is present in the works of Heidegger ( 2001 ) and Heisenberg (cited in Franzosi 2010 :619). Qualitative research, as we argued, involves the interaction and questioning of concepts (theory), data, and evidence.

Ragin ( 2004 :22) points out that “a good definition of qualitative research should be inclusive and should emphasize its key strengths and features, not what it lacks (for example, the use of sophisticated quantitative techniques).” We define qualitative research as an iterative process in which improved understanding to the scientific community is achieved by making new significant distinctions resulting from getting closer to the phenomenon studied. Qualitative research, as defined here, is consequently a combination of two criteria: (i) how to do things –namely, generating and analyzing empirical material, in an iterative process in which one gets closer by making distinctions, and (ii) the outcome –improved understanding novel to the scholarly community. Is our definition applicable to our own study? In this study we have closely read the empirical material that we generated, and the novel distinction of the notion “qualitative research” is the outcome of an iterative process in which both deduction and induction were involved, in which we identified the categories that we analyzed. We thus claim to meet the first criteria, “how to do things.” The second criteria cannot be judged but in a partial way by us, namely that the “outcome” —in concrete form the definition-improves our understanding to others in the scientific community.

We have defined qualitative research, or qualitative scientific work, in relation to quantitative scientific work. Given this definition, qualitative research is about questioning the pre-given (taken for granted) variables, but it is thus also about making new distinctions of any type of phenomenon, for example, by coining new concepts, including the identification of new variables. This process, as we have discussed, is carried out in relation to empirical material, previous research, and thus in relation to theory. Theory and previous research cannot be escaped or bracketed. According to hermeneutic principles all scientific work is grounded in the lifeworld, and as social scientists we can thus never fully bracket our pre-understanding.

We have proposed that quantitative research, as an idealtype, is concerned with pre-determined variables (Small 2008 ). Variables are epistemically fixed, but can vary in terms of dimensions, such as frequency or number. Age is an example; as a variable it can take on different numbers. In relation to quantitative research, qualitative research does not reduce its material to number and variables. If this is done the process of comes to a halt, the researcher gets more distanced from her data, and it makes it no longer possible to make new distinctions that increase our understanding. We have above discussed the components of our definition in relation to quantitative research. Our conclusion is that in the research that is called quantitative there are frequent and necessary qualitative elements.

Further, comparative empirical research on researchers primarily working with ”quantitative” approaches and those working with ”qualitative” approaches, we propose, would perhaps show that there are many similarities in practices of these two approaches. This is not to deny dissimilarities, or the different epistemic and ontic presuppositions that may be more or less strongly associated with the two different strands (see Goertz and Mahoney 2012 ). Our point is nonetheless that prejudices and preconceptions about researchers are unproductive, and that as other researchers have argued, differences may be exaggerated (e.g., Becker 1996 : 53, 2017 ; Marchel and Owens 2007 :303; Ragin 1994 ), and that a qualitative dimension is present in both kinds of work.

Several things follow from our findings. The most important result is the relation to quantitative research. In our analysis we have separated qualitative research from quantitative research. The point is not to label individual researchers, methods, projects, or works as either “quantitative” or “qualitative.” By analyzing, i.e., taking apart, the notions of quantitative and qualitative, we hope to have shown the elements of qualitative research. Our definition captures the elements, and how they, when combined in practice, generate understanding. As many of the quotations we have used suggest, one conclusion of our study holds that qualitative approaches are not inherently connected with a specific method. Put differently, none of the methods that are frequently labelled “qualitative,” such as interviews or participant observation, are inherently “qualitative.” What matters, given our definition, is whether one works qualitatively or quantitatively in the research process, until the results are produced. Consequently, our analysis also suggests that those researchers working with what in the literature and in jargon is often called “quantitative research” are almost bound to make use of what we have identified as qualitative elements in any research project. Our findings also suggest that many” quantitative” researchers, at least to some extent, are engaged with qualitative work, such as when research questions are developed, variables are constructed and combined, and hypotheses are formulated. Furthermore, a research project may hover between “qualitative” and “quantitative” or start out as “qualitative” and later move into a “quantitative” (a distinct strategy that is not similar to “mixed methods” or just simply combining induction and deduction). More generally speaking, the categories of “qualitative” and “quantitative,” unfortunately, often cover up practices, and it may lead to “camps” of researchers opposing one another. For example, regardless of the researcher is primarily oriented to “quantitative” or “qualitative” research, the role of theory is neglected (cf. Swedberg 2017 ). Our results open up for an interaction not characterized by differences, but by different emphasis, and similarities.

Let us take two examples to briefly indicate how qualitative elements can fruitfully be combined with quantitative. Franzosi ( 2010 ) has discussed the relations between quantitative and qualitative approaches, and more specifically the relation between words and numbers. He analyzes texts and argues that scientific meaning cannot be reduced to numbers. Put differently, the meaning of the numbers is to be understood by what is taken for granted, and what is part of the lifeworld (Schütz 1962 ). Franzosi shows how one can go about using qualitative and quantitative methods and data to address scientific questions analyzing violence in Italy at the time when fascism was rising (1919–1922). Aspers ( 2006 ) studied the meaning of fashion photographers. He uses an empirical phenomenological approach, and establishes meaning at the level of actors. In a second step this meaning, and the different ideal-typical photographers constructed as a result of participant observation and interviews, are tested using quantitative data from a database; in the first phase to verify the different ideal-types, in the second phase to use these types to establish new knowledge about the types. In both of these cases—and more examples can be found—authors move from qualitative data and try to keep the meaning established when using the quantitative data.

A second main result of our study is that a definition, and we provided one, offers a way for research to clarify, and even evaluate, what is done. Hence, our definition can guide researchers and students, informing them on how to think about concrete research problems they face, and to show what it means to get closer in a process in which new distinctions are made. The definition can also be used to evaluate the results, given that it is a standard of evaluation (cf. Hammersley 2007 ), to see whether new distinctions are made and whether this improves our understanding of what is researched, in addition to the evaluation of how the research was conducted. By making what is qualitative research explicit it becomes easier to communicate findings, and it is thereby much harder to fly under the radar with substandard research since there are standards of evaluation which make it easier to separate “good” from “not so good” qualitative research.

To conclude, our analysis, which ends with a definition of qualitative research can thus both address the “internal” issues of what is qualitative research, and the “external” critiques that make it harder to do qualitative research, to which both pressure from quantitative methods and general changes in society contribute.

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Financial Support for this research is given by the European Research Council, CEV (263699). The authors are grateful to Susann Krieglsteiner for assistance in collecting the data. The paper has benefitted from the many useful comments by the three reviewers and the editor, comments by members of the Uppsala Laboratory of Economic Sociology, as well as Jukka Gronow, Sebastian Kohl, Marcin Serafin, Richard Swedberg, Anders Vassenden and Turid Rødne.

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Aspers, P., Corte, U. What is Qualitative in Qualitative Research. Qual Sociol 42 , 139–160 (2019). https://doi.org/10.1007/s11133-019-9413-7

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Background: Shingles (herpes zoster), caused by reactivation of the varicella-zoster virus, is usually diagnosed and managed in primary care. The lifetime risk of shingles in the general population is approximately 30%, and it can have a detrimental effect on quality of life. There has been little qualitative research about patient experience and understanding of shingles. Design and Setting: Qualitative interviews with people recruited from primary care in England. Method: Qualitative semi-structured remote interviews were undertaken with 29 participants in a randomised controlled trial in primary care in England (ATHENA, ISRCTN14490832 ). Participants were aged >49 and were diagnosed within six days of shingles rash onset. Interviewees were sampled for diversity in terms of pain, intervention adherence, age, gender, and ethnicity. Data were analysed using reflexive thematic analysis. Findings: Participants’ understanding of shingles was limited, particularly pre-diagnosis. Television campaigns about the shingles vaccination programme helped some to recognise the rash. Shingles was understood as a disease with a variable prognosis, resulting in a sense of uncertainty about the significance when diagnosed. Participants reported a range of symptoms which impacted on everyday life. Some people thought their diagnosis was caused by poor mental health or challenging life circumstances, a perception sometimes reinforced by healthcare professionals. Many participants sought meaning in their diagnosis, reflecting upon, and sometimes changing, their life and circumstances. Conclusion: Primary care practitioners should be aware of the broad spectrum of patient knowledge, and the potential for better understanding to promote early attendance and treatment, to reduce the impact of shingles.

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The perceived relevance, utility and retention of basic sciences in general practice

• Faith O. Alele 1 , 2 ,
• Francis A. Albert 1 ,
• Emma Anderson 1 ,
• Abdul-Aziz Seidu 3 ,
• Hannah Mason 3 ,
• Paula Heggarty 1 ,
• Aaron Hollins 1 ,
• Tarun Sen Gupta 1 ,
• Lawrie McArthur 4 , 5 ,
• Richard B. Hays 1 &
• Bunmi S. Malau-Aduli 1 , 6  

BMC Medical Education volume  24 , Article number:  809 ( 2024 ) Cite this article

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Basic sciences are crucial for clinical medicine, yet studies focusing on their perceived utility among general practitioners (GPs) are sparse. Considering the broad scope of GPs’ practice, an in-depth understanding of basic sciences is fundamental for making informed clinical decisions. This study evaluated GP registrars’ retention and perceptions of the utility of basic sciences in clinical practice.

Using sequential explanatory mixed methods study design, knowledge retention was assessed by a multiple-choice question (MCQ) examination followed by interviews on the perception of the relevance and utility of basic sciences among GP registrars at James Cook University's (JCU) General Practice Training (GPT) program. Descriptive and inferential statistical analyses were conducted on the MCQ exam data, while thematic analysis was employed for the qualitative interview data.

Sixty-one GP registrars participated in the MCQ exam, while 11 of them were involved in the interviews. The highest mean score was obtained in biochemistry (75.1 ± 2.23) while the lowest mean score was in anatomy (56.07 ± 3.16). Key performance predictors included the formative clinical examination scores (β = 0.83, 95% CI: 0.45 to 1.2, p  < 0.001) and gender (β = -9.7, 95% CI: -17 to -2.3, p  = 0.011). The qualitative data analysis revealed five themes, including the backbone of clinical medicine, varying utility over time and by specialty, clinical synthesis integrates encapsulated knowledge, professional pressures hinder revisitation of knowledge and knowledge renewal enhances updates.

Basic sciences were considered relevant in clinical practice. Development of continuing professional development (CPDs) sessions and clinically relevant online resources were measures proposed to enhance the retention of knowledge. Future research could focus on innovative educational strategies for GPs.

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Introduction

Basic sciences are foundational to clinical medicine, significantly influencing medical education and practice [ 1 , 2 ]. Hence, despite a shift towards competency-based medical education, the importance of basic sciences persists [ 3 , 4 , 5 ]. Nonetheless, research indicates a decline in basic science retention post-graduation, with medical graduates retaining 67% to 75% of their knowledge in the first year, decreasing to below 50% by the second year [ 6 ]. This challenge is also documented among undergraduate medical students, particularly during the clinical years [ 4 , 5 , 6 , 7 , 8 ]. Contributing factors include the limited coverage of basic sciences in clinical textbooks and the vast number of clinical facts that need to be memorised [ 4 , 5 ].

Limited evidence among postgraduate medical trainees revealed poor knowledge retention rates, which was influenced by curriculum type, gender and age, suggesting that exposure to traditional curriculum, being female and shorter interval from graduation fostered basic science knowledge retention [ 9 ]. Despite low retention rates of basic sciences, medical practitioners often seek and apply this foundational knowledge when faced with complex medical cases [ 10 , 11 ]. This knowledge is utilised in patient communication, diagnosis, and treatment selection, with its perceived clinical relevance contributing to retention [ 7 ].

Existing studies on the retention of basic sciences among doctors have primarily focused on interns and surgeons, overlooking general practitioners (GPs), who are crucial in primary healthcare delivery [ 12 , 13 ]. GPs require a robust understanding of basic sciences to make informed clinical judgements, enhancing diagnostic precision and decision-making [ 1 , 13 , 14 , 15 ]. Hence, it is imperative to understand GPs' level of basic sciences knowledge retention and their perceived role of basic sciences in clinical practice. These insights can guide the development of strategies to improve patient care and inform the utility of basic sciences in general practice.

Therefore, this study addressed the following research questions:

What is the relationship between basic sciences knowledge retention and cultural background, age and gender?

What is the relationship between GP registrars’ performance in basic sciences exams and other assessments?

What are GP registrars’ perceptions of the relevance and application of basic sciences knowledge into their clinical practice?

Study context

The study was conducted within the General Practice Training (GPT) program at James Cook University (JCU), which was established in 2016 [ 16 ]. Designed to serve regional, rural, and remote communities, the program focuses on integrated education in general practice and rural generalist medicine, spanning from undergraduate to clinician years. Trainees begin hospital rotations in their Post Graduate Years 1 and 2 and commence general practice or rural hospital training in at least their third postgraduate year. The first year of training spans over two semesters, followed by a second year focused on clinical practice and fellowship examinations with either the Australian College of Rural and Remote Medicine or the Royal Australian College of General Practitioners. JCU’s GPT program incorporates an internal assessment schedule, enabling the evaluation of trainees' academic progress from the onset of orientation. This formative assessment process involves educational diagnosis to identify trainees' learning requirements, facilitating early remediation plans, and supporting their successful completion of the training [ 17 ].

Study design

Using a sequential explanatory mixed-methods approach [ 18 ], this study was conducted from June to December, 2023. The study commenced with the administration of a Basic Science Retention Examination (BSRE) which comprised 30 basic sciences MCQs to the registrars to assess their retention of basic sciences knowledge, in addition to the standard 65 MCQ clinical exam which is an integral component of JCU’s GPT program’s formative assessments. Insights from the quantitative data informed the development of the interview protocol and selection of participants for the qualitative phase. Subsequent individual interviews conducted via Microsoft Teams provided in-depth perspectives on the relevance and application of basic sciences in clinical practice. The integration of findings from both phases facilitated a comprehensive interpretation of the data, adhering to the Good Reporting of a Mixed-Methods Study (GRAMMS) standards [ 19 ]. The full checklist is included in Supplementary File 1.

Target population

This study involved general practice residents, also known as registrars, enrolled in the JCU GPT program. These healthcare professionals, often the first point of contact for patients in regional, rural and remote communities, provide a broad range of medical services. The 2023 cohort of JCU GPT registrars commencing their training (GPT 1) and those who had commenced eight months prior (GPT 2) were invited in August 2023 to participate in the BSRE, in addition to their routine formative assessment. There was a total of 82 registrars in this cohort.

Data collection

JCU GPT registrars were required to take a 65-question MCQ clinical examination in August 2023. For the quantitative part of this study, the BSRE, which comprised 30 basic sciences MCQs was administered to the registrars to assess their retention of basic sciences knowledge. The BSRE and clinical examination were combined into one, with a total of 95 MCQs and 110 minutes to complete the examination.

Quantitative data—test construction

All the 82 GPT 1 and GPT 2 registrars were invited to complete the combined BSRE (30 questions) and clinical (65 questions) examinations in August 2023. The registrars were informed 2 weeks prior about the duration of the examination, the online platform (testportal.net), the type of feedback that would be provided. Demographic variables of the participants were retrieved from the University Record System database. These variables include age, gender, years since graduation and graduate type—International Medical Graduates (IMGs) and Australian Medical Graduates (AMGs). Personalised feedback was offered as part of the routine formative assessments to enhance the registrars' engagement and their learning experience. The BSRE was designed to assess six core areas of basic sciences: anatomy, biochemistry, pathology, pharmacology, physiology, and social sciences, with five (5) questions per discipline. The basic sciences questions were sourced from the International Databases for Enhanced Assessments and Learning assessment item bank and were aligned with the basic science components of the curriculum [ 7 , 20 ]. Each question presented a brief clinical scenario and required a single correct answer, and the examination was administered electronically. The validity of the test items was vetted by content experts. For comparison purposes, all examination scores were expressed as percentages of correct answers in both the overall test and individual disciplines.

Qualitative data

For the qualitative phase of the study, 46 registrars consented to be contacted for interviews, and 11 were available to participate. These participants were selected based on their availability, reflecting the demanding nature of their work schedules. Each participant received AUD 50 grocery voucher as an appreciation for their time. Individual interviews were conducted remotely by one of the investigators (FOA), using the call functionality of Microsoft Teams to explore perceptions of the clinical relevance and utility of basic sciences in general practice. All interviews were conducted over a single call, lasted between 25 to 40 minutes and were structured around eight predefined questions (see Supplementary File 2). The interviews were recorded with the participants’ consent and transcribed verbatim.

Data analysis

Quantitative data analysis was conducted using R version 4.3.1 (Team, 2023). Categorical variables were presented as counts and percentages, while continuous variables were presented as medians and interquartile ranges (IQR). Comparative analyses employed Chi-square or Fisher’s exact tests for categorical variables and Kruskal–Wallis rank sum tests for continuous variables after checking for data normality. Pearson’s correlation test was conducted to assess the association between the different basic science disciplines, years since graduation, age, formative clinical exam score, and BSRE score. Multiple linear regression analysis was used to evaluate the influence of training background, age, and gender on the retention of basic science knowledge, while a multivariate linear model examined the factors affecting the observed score variations in the BSRE components.

Qualitative inductive thematic analysis [ 21 ] of the transcripts was conducted by three investigators (FOA, EA, and BSM-A), using NVivo 14. This involved the systematic identification of patterns, organisation into codes, and the development of themes [ 22 ], with discrepancies resolved through consensus meetings to ensure accuracy of the results. Integration of the quantitative and qualitative findings provided a comprehensive exploration of the results, enhancing the study’s depth and credibility.

Ethical considerations

This study was conducted in accordance with the Declaration of Helsinki [ 23 ]. Participants were required to provide electronic consent for the MCQ examination and verbal consent for the interviews, ensuring research integrity. Ethics approval (H9140) for the study was granted by the James Cook University Human Research Ethics Committee (HREC). During recruitment and data collection, participants were fully informed about the ethical clearance, the study’s objectives, their privacy rights, and potential benefits, ensuring all participants were well-informed and their rights respected throughout the study.

Quantitative findings

A total of 61 registrars (74% response rate) participated in the quantitative phase of the study. The median age was 32 years (IQR: 29 – 38), with 72% females and 59% AMG. The median years post-graduation was 5 years. The range of percent scores for the GP formative clinical exam and the BSRE were 41.5—76.9 and 20 – 90, respectively, while the median scores were 62 (IQR: 54 – 66) and 70 (IQR: 57 – 77), respectively (Table  1 ). As shown in Fig.  1 , scores varied across basic science disciplines, with the lowest in anatomy (56.07 ± 3.16), and the highest in biochemistry (75.08 ± 2.23).

Mean scores for the basic science disciplines

Figure  2 shows the correlation, between the basic science disciplines (anatomy, biochemistry, pathology, pharmacology, physiology, social sciences), years since graduation, age, formative clinical examination score and BSRE score. The correlation coefficients indicate a range of significant ( p  < 0.05) weak to strong associations. The overall BSRE score was positively and strongly correlated with pharmacology ( r  = 0.71, p  < 0.001) and social sciences ( r  = 0.67, p  < 0.001); it was moderately correlated with anatomy ( r  = 0.58, p  < 0.001), biochemistry ( r  = 0.45, p  < 0.001), pathology ( r  = 0.64, p  < 0.001), physiology ( r  = 0.45, p  < 0.001), and formative clinical examination ( r  = 0.47, p  < 0.001). The formative clinical examination was positively correlated with biochemistry ( r  = 0.39, p  < 0.01), pathology ( r  = 0.38, p  < 0.01), social sciences ( r  = 0.28, p  < 0.05) and pharmacology ( r  = 0.44, p  < 0.001). Additionally, BSRE (not significant) and the formative clinical examination were negatively correlated with years since graduation ( r  = -0.30, p  < 0.05) and age ( r  = -0.35, p  < 0.01). Intercorrelations between the basic science disciplines showed that Pharmacology was positively correlated with the other basic science disciplines: anatomy ( r = 0.30, p  < 0.05), biochemistry ( r  = 0.34, p  < 0.01), pathology ( r  = 0.32, p  < 0.05), physiology ( r  = 0.27, p  < 0.05), and social sciences ( r  = 0.29, p  < 0.05). Social sciences was positively correlated with anatomy ( r  = 0.32, p  < 0.05), pathology ( r  = 0.28, p  < 0.05), pharmacology ( r  = 0.29, p  < 0.05) and physiology ( r  = 0.32, p  < 0.05).

Associations between basic science disciplines, formative clinical examination score, years since graduation and age

†Basic science retention examination score; ‡ Formative clinical examination score; §Years since graduation. * P  < 0.05; ** P  < 0.001; *** P  < 0.001

The relationship between the overall BSRE score and the demographic variables was assessed using both univariate and multivariable regression analyses. As shown in Table  2 , age, gender, and years since graduation were not significantly associated with the overall BSRE score in the univariate analysis. However, a statistically significant positive association was found between the formative clinical examination score and the overall BSRE score. Specifically, for every one-unit increase in the formative clinical examination score, there was an associated 0.79 units increase in the BSRE score (β = 0.79, 95% CI: 0.40—1.2, p  < 0.001). The IMGs scored 9.3 units lower on the BSRE compared to the AMGs (β = -9.3, 95% CI: -16 to -2.3, p  = 0.011). However, this association lost significance in the multivariable analysis (β = -8.8, 95% CI: -18 to 0.81, p  = 0.072). The only significant predictors of the BSRE score in the multivariable analysis were the formative clinical examination (β = 0.83, 95% CI: 0.45 to 1.2, p  < 0.001) and gender (β = -9.7, 95% CI: -17 to -2.3, p  = 0.011). High performance in the formative clinical examination and being male were significantly and positively associated with high BSRE score.

Table 3 presents the predictors of performance in each of the basic sciences disciplines. Gender was a significant predictor of performance in anatomy (β = -15, 95% CI: -31 to -1.8; p  = 0.028), indicating anatomy score for females was 15 units lower compared to males. Similarly, IMGs’ scores in physiology (β = -17, 95% CI: -30 to -2.6, p  = 0.021) and social sciences (β = -24, 95% CI: -40 to -7.9, p  = 0.004) were 17 and 24 units lower (respectively) than AMGs' scores. However, a positive association was observed in their performance in biochemistry with 14 units higher than AMGs (β = 14; 95% CI; 0.35 – 27; p  = 0.044). Formative clinical examination score showed a consistent and significant positive association with performance in biochemistry (β = 0.88; 95% CI; 0.34 to 1.4; p  = 0.002), pathology (β = 1.2; 95% CI; 0.52 to 1.9; p  = 0.001), pharmacology (β = 1.1; 95% CI 0.47 – 1.8; p  = 0.001) and social sciences (β = 0.78; 95% CI; 0.08 – 1.4; p  = 0.028).

Qualitative findings

The qualitative phase of this study explored GP registrars’ perceptions of the relevance, utility, and application of basic science in clinical practice. Of the eleven participants who consented to the interview, six were females, seven were AMGs, and six were recent graduates, within the last five years. Five themes emerged from the qualitative data, including the backbone of clinical medicine, varying utility over time and by specialty, clinical synthesis integrates encapsulated knowledge, professional pressures hinder revisitation of knowledge and knowledge renewal enhances updates.

Theme 1: the backbone of clinical medicine

The registrars perceived basic sciences as the ‘backbone of clinical medicine’, with the first year of medical training being the ‘gateway’ year that provides the foundational knowledge and the building blocks for the clinical concepts learned in later years. They acknowledged that basic science knowledge played a significant role in clinical reasoning and was applied in clinical diagnoses and decisions.

I think the main thing that I sort of believe in relation to this topic is that the basic sciences are required before you can have a good clinical understanding, as you would never be able to learn clinical medicine if you didn't have that. You know the backbone information. For example, if you have someone who comes in with acute gout in their toe, you examine the anatomy you explained to them. You know, this is the bone that's affected here. Basically, what happens is you have elevated urine, which forms crystals inside this joint, and that's why it feels like you are walking on glass because you're basically walking on uric acid crystals, glass shards. I think something like that would be an example of where you describe the anatomy of the patient and describe the pathophysiology of what's going on (Participant #1, Male, AMG).

For example, some patients come with abdominal pain. If you know the anatomy, then you can try to determine what would be the differential diagnosis according to the location and then you can work it out. So that's one, the other is chest pain, and you know the pathophysiology. So you know it depends on the characteristics of the pain, and you will know whether it is cardiac or from the lungs or whether it is musculoskeletal (Participant #10, Female, IMG).

Overall, registrars acknowledged that basic science knowledge was applied in clinical practice, often subconsciously. Anatomy and physiology were perceived as the most relevant basic sciences in general practice.

On a day-to-day basis in any GP practice, I guess it is a theoretical understanding of it [basic sciences] that you apply and draw on that you're not aware of necessarily. (Participant #6, Male, AMG).

They all are really, really very important and relevant in general practice. I think the most clinically relevant one is anatomy as I said, you need to know the human body to diagnose the disease source. If you don't know anatomy, you can't examine the patient. You don't know where exactly the pain is (Participant #7, Female IMG).

Probably physiology and anatomy together because it's an understanding of those two that gives you a good understanding of how and why different diseases present the way they do, and that also helps you keep communicating those things to the patient as well. The pharmacology and pathology, while there are elements that are important to know. I don't find like I'm drawing those skills quite as much as I would that for the other areas in my usual practice (Participant #4, Male, AMG).

Theme 2: varying utility over time and by specialty

The registrars acknowledged that the relevance of basic science knowledge varied over time and across different medical sub-specialities. Some disciplines, such as biochemistry, were seen as less relevant to GPs but crucial for intensive care unit (ICU) or emergency department (ED) physicians. Registrars relied heavily on pathologists’ reports and seldom used their pathology knowledge in practice. Similarly, they used guidelines and resources to inform drug choices, indicating less reliance on their pharmacology knowledge.

I think [biochemistry] is important, but because it is typically studied at the beginning of university and then studied less and less and it is emphasised less and less throughout university and then into your career, that it does start to fade into the past (Participant #2, Male AMG).

I can only speak from a generalist perspective. I'm not a cardiologist or a surgeon. And I'm sure that there will be different areas of the basic sciences that would be useful for different areas of medicine, which therein lies the challenge of what to feature in medical school, you know, to get to cover all bases (Participant #5, Male, AMG).

I think I always struggled with biochemistry itself as a subject in medical school. I still cannot remember the Kreb cycle or the exact cycle of the things we covered in biochemistry. I have no idea why it was important to me, but it is important to the ICU physicians because they manage patients based on those little molecular details, whereas it isn’t important to me (Participant #8, Female, IMG).

Now I know that I used to use biochemistry a lot more when I was working in emergency full time because you're dealing with derangements in potassium or sodium, whatever it is, or sugar, you know, all the time (Participant #6, Male, AMG).

Theme 3: clinical synthesis integrates encapsulated knowledge

Despite some basic science knowledge being perceived as forgotten or less utilised, participants indicated that this knowledge had become integrated and was being used subconsciously in clinical practice.

I think they’re probably to a certain level and there's this subconscious foundation that's all that other information that you've built along over the course of many years of study. You don't actively think about it all the time anymore because you don't find it quite as relevant to the patient in front of you. They are definitely things that I draw on, but I probably do not actively consciously think back to them (Participant #4, Male, AMG).

Furthermore, the participants stated that they had learned how to create patterns, filter information and relate basic science information to clinical cases.

I think I've filtered out all the tiny little details. I think I've also filtered to the point where I only consider things that are important in clinical practice (Participant #3, Female, AMG).

That's really tricky because the further you go through a career, you become less reliant on that side of things and are more reliant on what works and patterns. You know, for example, almost all clinicians utilise paracetamol every day. No one knows how it works, but we know it works (Participant #5, Male, AMG).

Theme 4: professional pressures hinder revisitation of knowledge

The registrars recognised the relevance and utility of basic sciences in clinical practice, but identified professional pressures, such as lack of time and heavy workload, as barriers to revisiting this knowledge. The vast scope of medical knowledge and lack of readily accessible resources further compounded these challenges.

It's probably a lack of time for me because there is so much to do, work and life, and then there is so much to learn at a higher level than just finding the time to go back to fundamentals I find hard. So, it would be time based, but also, I don't really know where to look. So, for anatomy, I will just Google image of things so that I can have a look at the anatomy, but I don't really know where I'd go to look for Physiology other than kind of try and go and find PDF's or hard copies of textbooks (Participant #2, Male, AMG).

I think one factor would probably be just a short consult length that we have, and the days are pretty busy and sort of, you know, maybe writing down that, oh, I might revisit that, or if you're not sure, I think like I would revisit it personally. But sometimes, if there's something that is not super important for the diagnosis or something, I make a mental note, but then I forget to go back to look it up (Participant #11, Female, AMG).

I think it's because, I mean, as a generalist, you need to know a little bit about absolutely everything and be able to draw on more than that little bit of knowledge that you need. When you don't know what's coming, it's very difficult. Whereas when you're a specialist, you know everything about one area, and so you sort of only have to draw on that particular bit of knowledge and a lot of the time in GP practice, you have to do, you don't know what you're going to be dealing with. So you don't have any time to prepare (Participant #6, Male, AMG).

Another layer of complexity identified by the participants related to dealing with high patient expectations.

Sometimes, if the patients appear like they have a very high expectation of a GP to know everything; I think it is a bit challenging for the junior GP to revisit their knowledge in front of the patient. I am sure all the GPs without the patient in their consulting room, they can revisit all of their memories, and find out all the exact things by themselves by looking at their guidelines for sure. But sometimes it's a bit challenging doing that in front of the patient (Participant #9, Female, IMG).

Theme 5: knowledge renewal enhances updates

The registrars suggested measures to support continuous updating of basic science knowledge. These included structured Continuous Professional Development (CPD) workshops on basic sciences, webinars, and easily accessible online resources. CPD workshops was the most favoured strategy.

I would say setting up CPD points for GPs is a good way of maintaining it. That's how you get a GP to attend all those teaching and learning activities (Participant #9, Female, IMG).

For example, webinars. People are doing a lot of webinars that they can sit at home and then attend the classes and they will get the educational hours (Participant #10, Female, IMG).

I mean, I think it would be really handy to have some sort of online resource that is very basic, very user-friendly. To teach, you know, like having pictures of anatomy that you can point out to say, this is what it looks like, or this is the procedure that I'm gonna do, and this is why. Just like very simple application of it all, which has all the useful information like frequently asked questions about the particular issue that you can sort of just go and find information (Participant #6, Male, AMG).

Triangulation of findings

In the quantitative findings, participants scored higher in biochemistry and lower in anatomy, indicating better retention of biochemistry. However, interviewees perceived anatomy and physiology as more relevant to clinical practice. This suggests that perceived relevance doesn’t equate to knowledge retention, possibly due to limited revision opportunities. Biochemistry and pathology, which were considered less relevant, may have been encapsulated and subconsciously integrated with clinical knowledge. This is likely due to regular revisits via readily accessible guidelines and pathologist reports, resulting in better performance and retention of knowledge. Furthermore, the AMGs outperformed the IMGs in most basic science disciplines, indicating higher retention of the basic science knowledge. This could have been because the AMGs, on average were younger with median age of 30 (IQR: 28-34) compared to median age of 36 (IQR: 32-40) for the IMGs and they had lesser years since graduation - median of 4 (IQR: 4-5) versus the IMGs' median of 11 (IQR: 8-16).

This study employed a mixed methods approach to investigate the influence of variables such as age, graduate type, years since graduation, and gender on GP registrars' retention and perceived utility of basic science knowledge in clinical practice. The findings identified gender and high scores in formative clinical examinations as predictors of basic science knowledge retention. Males outperformed females, particularly in anatomy, aligning with existing research that shows males generally excel in spatial tasks within this discipline [ 24 , 25 , 26 , 27 ]. Interestingly, performance in the formative clinical examination also forecasted competencies in biochemistry, pathology, pharmacology, and social sciences, underscoring the interplay between clinical acumen and basic science proficiency, suggesting a synergistic educational approach [ 28 ]. Further research is needed to fully understand these gender disparities and their implications in medical education.

Performance disparities in relation to cultural background were also evident, IMGs scored lower in physiology and social sciences but higher in biochemistry compared to AMGs. This variance could stem from the recency of basic science education among AMGs and the challenges IMGs face due to curriculum differences and adaptation to new medical systems. Most IMGs studied basic sciences early in their education without subsequent reinforcement, contributing to lower retention rates, as highlighted by Custers et al. [ 29 ]. Furthermore, the predominantly didactic and teacher-centred approaches in medical schools from the Gulf Countries, Asia, and Africa contrast with learner-centred strategies such as problem-based learning (PBL) which are employed in the UK, USA, Australia, and parts of Europe, to foster better integration of basic sciences with clinical sciences and promote lifelong learning [ 30 , 31 ]. Thus, enhancing continuous engagement with basic sciences through modern educational techniques is crucial for improving knowledge retention.

While registrars generally excelled in biochemistry despite its perceived low relevance, they struggled with anatomy, which was deemed the most applicable and essential discipline. This paradox may be attributed to the nature of the subjects. Biochemistry, though abstract and challenging, builds on foundational concepts that facilitate new learning and deeper understanding through a constructivist model. This model encourages the integration of new information with existing knowledge frameworks, enhancing retention over time [ 32 , 33 , 34 , 35 , 36 , 37 ]. Conversely, the direct application of anatomical knowledge in clinical practice highlights its critical role in understanding pathophysiology and guiding pharmacological interventions, underscoring the integrated nature of clinical reasoning, where biochemical and pathological knowledge are essential, albeit often subconsciously [ 38 , 39 ].

The learning process for anatomy involves both rote memorisation and deep learning strategies due to its extensive factual content and specialised vocabulary [ 40 , 41 , 42 ]. However, studies suggest that initial memorisation strategies do not support long-term retention, as students often rely on assessment-driven motivation rather than genuine understanding, which fails to promote sustained knowledge [ 43 ]. The relevance of anatomy is often only fully appreciated after significant clinical experience, indicating a need for educational strategies that encourage ongoing engagement with anatomical knowledge throughout medical training and practice [ 43 ]. This calls for re-evaluation of how anatomical knowledge is taught and revisited in clinical settings to bridge the gap between initial learning and practical application.

Despite the variation in perceptions across disciplines, registrars generally acknowledged the fundamental role of basic sciences in clinical reasoning and patient communication. Echoing this result, academic physicians affirmed the value of basic science knowledge in enhancing clinical diagnosis and improving patient interactions [ 44 ]. The study further highlighted that the ability to revisit or revise basic sciences in practice is often hindered by factors such as time constraints, workload, and the sheer breadth of medical knowledge. Challenges at the point of care include insufficient time, patient complexity, and information overload, with most inquiries going unresolved and only a small fraction of questions answered [ 45 , 46 , 47 ]. This underscores the importance of accessible and applicable resources that support continuous learning and knowledge retention at the point of care [ 48 ].

To enhance ongoing engagement with basic sciences, strategies such as development of CPD workshops, webinars, and provision of easily accessible online resources have been proposed. Continuous engagement through clinical practice, continued education, and teaching has been shown to enhance knowledge retention, with CPD programs often serving as the primary source for updating information [ 49 ]. Medical educators and training programs are advised to design interactive, multimodal CPD courses that focus on basic sciences to ensure knowledge enhancement and retention [ 50 ]. However, considering the busy schedules of registrars, training colleges and programs should integrate clinically relevant basic science courses into required CPDs. Additionally, there is a need for reliable information systems aligned with point-of-care needs that are relevant to practice, aiding clinical decision-making [ 51 , 52 , 53 ]. Collaborative efforts between information system designers and GPs could aid the development of resources tailored to specific basic science learning needs, enhancing the practical application of such knowledge in clinical settings [ 54 ].

Strengths and limitations

To the best of our knowledge this is the first study that examined knowledge retention and perceived relevance of basic sciences among GPs. The comprehensive mixed methods approach utilised incorporated quantitative and qualitative data to provide an understanding of basic science knowledge retention and its perceived utility in the GP setting. However, the study is not without limitations. The study assessed a sample of GPs located in one training program and results may not be generalisable to other clinical settings. Therefore, the findings need to be interpreted with caution. Whilst we ensured that the interviews included participants with different characteristics, there might have been bias introduced in the interviews based on recency of graduation with most of the participants graduating within the last five years. This selection may not have accurately representd the broader population of registrars who participated in the quantitative phase of the study.

Implications for practice and future research

Overall, the findings of the study highlight the need for medical curricula that better integrate basic sciences with clinical practice from undergraduate to postgraduate training years. It is evident that revision of the basic sciences should be continued even after the early undergraduate years as some concepts in the early years may not have been fully reinforced. Medical educators should consider the use of a spiral curriculum which involves iterative revisiting of topics with each encounter building on the previous knowledge [ 43 , 54 ]. This approach will enhance deep learning, reinforcing prior knowledge and stimulating integration of knowledge [ 44 , 54 ]. For GPs, the development of accessible, relevant, time-efficient continuing medical education resources could mitigate the barriers to knowledge retention [ 52 ]. Future research could explore longitudinal trends in basic science knowledge retention among GPs and investigate the efficacy of different educational interventions in enhancing the application of this knowledge in clinical practice. In addition, studies could also examine the specific challenges faced by IMGs in retaining and applying basic science knowledge, with the aim of developing targeted support mechanisms.

The findings of the study showed that basic science knowledge retention among GPs varied by discipline. Basic sciences were considered clinically relevant and identified as the backbone of clinical medicine. Nonetheless, time constraint, workload and vastness of medical information were barriers that limited revision of basic sciences in clinical practice. Strategies proposed include development of CPDs and clinically relevant online resources. The findings highlight the need to ensure that basic sciences education is not only retained but effectively integrated. Future research could explore innovative educational strategies that enhance GPs' retention of basic science knowledge in clinical practice.

Availability of data and materials

The data can be obtained from the corresponding author on reasonable request.

Abbreviations

Continuing Professional Development

General Practitioners

General Practice Training

Multiple-Choice Question

Post Graduate Year

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Acknowledgements

The authors are grateful to the study participants for the time committed to taking the exams and participating in the interviews. Ms Kimberley Martinsen is appreciated for assisting with the administration of the basic science exam. Associate Professor Oyelola Adegboye is acknowledged for assisting with the statistical analysis. The Australian College of Rural & Remote Medicine is also acknowledged for funding this project.

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Faith O. Alele, Francis A. Albert, Emma Anderson, Paula Heggarty, Aaron Hollins, Tarun Sen Gupta, Richard B. Hays & Bunmi S. Malau-Aduli

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All authors contributed to the conceptualisation and design of the study. FOA, EA, PH and BSM-A were responsible for data collection. FOA, EA and BSM-A conducted the qualitative data analysis. All authors were involved in the interpretation and triangulation of findings. The initial drafts of the manuscript were developed by FOA and BSM-A. All authors provided feedback on earlier versions of the manuscript and have read and approved the final version.

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Alele, F.O., Albert, F.A., Anderson, E. et al. The perceived relevance, utility and retention of basic sciences in general practice. BMC Med Educ 24 , 809 (2024). https://doi.org/10.1186/s12909-024-05750-2

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A Practical Guide to Writing Quantitative and Qualitative Research Questions and Hypotheses in Scholarly Articles

Edward barroga.

1 Department of General Education, Graduate School of Nursing Science, St. Luke’s International University, Tokyo, Japan.

Glafera Janet Matanguihan

2 Department of Biological Sciences, Messiah University, Mechanicsburg, PA, USA.

The development of research questions and the subsequent hypotheses are prerequisites to defining the main research purpose and specific objectives of a study. Consequently, these objectives determine the study design and research outcome. The development of research questions is a process based on knowledge of current trends, cutting-edge studies, and technological advances in the research field. Excellent research questions are focused and require a comprehensive literature search and in-depth understanding of the problem being investigated. Initially, research questions may be written as descriptive questions which could be developed into inferential questions. These questions must be specific and concise to provide a clear foundation for developing hypotheses. Hypotheses are more formal predictions about the research outcomes. These specify the possible results that may or may not be expected regarding the relationship between groups. Thus, research questions and hypotheses clarify the main purpose and specific objectives of the study, which in turn dictate the design of the study, its direction, and outcome. Studies developed from good research questions and hypotheses will have trustworthy outcomes with wide-ranging social and health implications.

INTRODUCTION

Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses. 1 , 2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results. 3 , 4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the inception of novel studies and the ethical testing of ideas. 5 , 6

It is crucial to have knowledge of both quantitative and qualitative research 2 as both types of research involve writing research questions and hypotheses. 7 However, these crucial elements of research are sometimes overlooked; if not overlooked, then framed without the forethought and meticulous attention it needs. Planning and careful consideration are needed when developing quantitative or qualitative research, particularly when conceptualizing research questions and hypotheses. 4

There is a continuing need to support researchers in the creation of innovative research questions and hypotheses, as well as for journal articles that carefully review these elements. 1 When research questions and hypotheses are not carefully thought of, unethical studies and poor outcomes usually ensue. Carefully formulated research questions and hypotheses define well-founded objectives, which in turn determine the appropriate design, course, and outcome of the study. This article then aims to discuss in detail the various aspects of crafting research questions and hypotheses, with the goal of guiding researchers as they develop their own. Examples from the authors and peer-reviewed scientific articles in the healthcare field are provided to illustrate key points.

DEFINITIONS AND RELATIONSHIP OF RESEARCH QUESTIONS AND HYPOTHESES

A research question is what a study aims to answer after data analysis and interpretation. The answer is written in length in the discussion section of the paper. Thus, the research question gives a preview of the different parts and variables of the study meant to address the problem posed in the research question. 1 An excellent research question clarifies the research writing while facilitating understanding of the research topic, objective, scope, and limitations of the study. 5

On the other hand, a research hypothesis is an educated statement of an expected outcome. This statement is based on background research and current knowledge. 8 , 9 The research hypothesis makes a specific prediction about a new phenomenon 10 or a formal statement on the expected relationship between an independent variable and a dependent variable. 3 , 11 It provides a tentative answer to the research question to be tested or explored. 4

Hypotheses employ reasoning to predict a theory-based outcome. 10 These can also be developed from theories by focusing on components of theories that have not yet been observed. 10 The validity of hypotheses is often based on the testability of the prediction made in a reproducible experiment. 8

Conversely, hypotheses can also be rephrased as research questions. Several hypotheses based on existing theories and knowledge may be needed to answer a research question. Developing ethical research questions and hypotheses creates a research design that has logical relationships among variables. These relationships serve as a solid foundation for the conduct of the study. 4 , 11 Haphazardly constructed research questions can result in poorly formulated hypotheses and improper study designs, leading to unreliable results. Thus, the formulations of relevant research questions and verifiable hypotheses are crucial when beginning research. 12

CHARACTERISTICS OF GOOD RESEARCH QUESTIONS AND HYPOTHESES

Excellent research questions are specific and focused. These integrate collective data and observations to confirm or refute the subsequent hypotheses. Well-constructed hypotheses are based on previous reports and verify the research context. These are realistic, in-depth, sufficiently complex, and reproducible. More importantly, these hypotheses can be addressed and tested. 13

There are several characteristics of well-developed hypotheses. Good hypotheses are 1) empirically testable 7 , 10 , 11 , 13 ; 2) backed by preliminary evidence 9 ; 3) testable by ethical research 7 , 9 ; 4) based on original ideas 9 ; 5) have evidenced-based logical reasoning 10 ; and 6) can be predicted. 11 Good hypotheses can infer ethical and positive implications, indicating the presence of a relationship or effect relevant to the research theme. 7 , 11 These are initially developed from a general theory and branch into specific hypotheses by deductive reasoning. In the absence of a theory to base the hypotheses, inductive reasoning based on specific observations or findings form more general hypotheses. 10

TYPES OF RESEARCH QUESTIONS AND HYPOTHESES

Research questions and hypotheses are developed according to the type of research, which can be broadly classified into quantitative and qualitative research. We provide a summary of the types of research questions and hypotheses under quantitative and qualitative research categories in Table 1 .

Research questions in quantitative research

In quantitative research, research questions inquire about the relationships among variables being investigated and are usually framed at the start of the study. These are precise and typically linked to the subject population, dependent and independent variables, and research design. 1 Research questions may also attempt to describe the behavior of a population in relation to one or more variables, or describe the characteristics of variables to be measured ( descriptive research questions ). 1 , 5 , 14 These questions may also aim to discover differences between groups within the context of an outcome variable ( comparative research questions ), 1 , 5 , 14 or elucidate trends and interactions among variables ( relationship research questions ). 1 , 5 We provide examples of descriptive, comparative, and relationship research questions in quantitative research in Table 2 .

Hypotheses in quantitative research

In quantitative research, hypotheses predict the expected relationships among variables. 15 Relationships among variables that can be predicted include 1) between a single dependent variable and a single independent variable ( simple hypothesis ) or 2) between two or more independent and dependent variables ( complex hypothesis ). 4 , 11 Hypotheses may also specify the expected direction to be followed and imply an intellectual commitment to a particular outcome ( directional hypothesis ) 4 . On the other hand, hypotheses may not predict the exact direction and are used in the absence of a theory, or when findings contradict previous studies ( non-directional hypothesis ). 4 In addition, hypotheses can 1) define interdependency between variables ( associative hypothesis ), 4 2) propose an effect on the dependent variable from manipulation of the independent variable ( causal hypothesis ), 4 3) state a negative relationship between two variables ( null hypothesis ), 4 , 11 , 15 4) replace the working hypothesis if rejected ( alternative hypothesis ), 15 explain the relationship of phenomena to possibly generate a theory ( working hypothesis ), 11 5) involve quantifiable variables that can be tested statistically ( statistical hypothesis ), 11 6) or express a relationship whose interlinks can be verified logically ( logical hypothesis ). 11 We provide examples of simple, complex, directional, non-directional, associative, causal, null, alternative, working, statistical, and logical hypotheses in quantitative research, as well as the definition of quantitative hypothesis-testing research in Table 3 .

Research questions in qualitative research

Unlike research questions in quantitative research, research questions in qualitative research are usually continuously reviewed and reformulated. The central question and associated subquestions are stated more than the hypotheses. 15 The central question broadly explores a complex set of factors surrounding the central phenomenon, aiming to present the varied perspectives of participants. 15

There are varied goals for which qualitative research questions are developed. These questions can function in several ways, such as to 1) identify and describe existing conditions ( contextual research question s); 2) describe a phenomenon ( descriptive research questions ); 3) assess the effectiveness of existing methods, protocols, theories, or procedures ( evaluation research questions ); 4) examine a phenomenon or analyze the reasons or relationships between subjects or phenomena ( explanatory research questions ); or 5) focus on unknown aspects of a particular topic ( exploratory research questions ). 5 In addition, some qualitative research questions provide new ideas for the development of theories and actions ( generative research questions ) or advance specific ideologies of a position ( ideological research questions ). 1 Other qualitative research questions may build on a body of existing literature and become working guidelines ( ethnographic research questions ). Research questions may also be broadly stated without specific reference to the existing literature or a typology of questions ( phenomenological research questions ), may be directed towards generating a theory of some process ( grounded theory questions ), or may address a description of the case and the emerging themes ( qualitative case study questions ). 15 We provide examples of contextual, descriptive, evaluation, explanatory, exploratory, generative, ideological, ethnographic, phenomenological, grounded theory, and qualitative case study research questions in qualitative research in Table 4 , and the definition of qualitative hypothesis-generating research in Table 5 .

Qualitative studies usually pose at least one central research question and several subquestions starting with How or What . These research questions use exploratory verbs such as explore or describe . These also focus on one central phenomenon of interest, and may mention the participants and research site. 15

Hypotheses in qualitative research

Hypotheses in qualitative research are stated in the form of a clear statement concerning the problem to be investigated. Unlike in quantitative research where hypotheses are usually developed to be tested, qualitative research can lead to both hypothesis-testing and hypothesis-generating outcomes. 2 When studies require both quantitative and qualitative research questions, this suggests an integrative process between both research methods wherein a single mixed-methods research question can be developed. 1

FRAMEWORKS FOR DEVELOPING RESEARCH QUESTIONS AND HYPOTHESES

Research questions followed by hypotheses should be developed before the start of the study. 1 , 12 , 14 It is crucial to develop feasible research questions on a topic that is interesting to both the researcher and the scientific community. This can be achieved by a meticulous review of previous and current studies to establish a novel topic. Specific areas are subsequently focused on to generate ethical research questions. The relevance of the research questions is evaluated in terms of clarity of the resulting data, specificity of the methodology, objectivity of the outcome, depth of the research, and impact of the study. 1 , 5 These aspects constitute the FINER criteria (i.e., Feasible, Interesting, Novel, Ethical, and Relevant). 1 Clarity and effectiveness are achieved if research questions meet the FINER criteria. In addition to the FINER criteria, Ratan et al. described focus, complexity, novelty, feasibility, and measurability for evaluating the effectiveness of research questions. 14

The PICOT and PEO frameworks are also used when developing research questions. 1 The following elements are addressed in these frameworks, PICOT: P-population/patients/problem, I-intervention or indicator being studied, C-comparison group, O-outcome of interest, and T-timeframe of the study; PEO: P-population being studied, E-exposure to preexisting conditions, and O-outcome of interest. 1 Research questions are also considered good if these meet the “FINERMAPS” framework: Feasible, Interesting, Novel, Ethical, Relevant, Manageable, Appropriate, Potential value/publishable, and Systematic. 14

As we indicated earlier, research questions and hypotheses that are not carefully formulated result in unethical studies or poor outcomes. To illustrate this, we provide some examples of ambiguous research question and hypotheses that result in unclear and weak research objectives in quantitative research ( Table 6 ) 16 and qualitative research ( Table 7 ) 17 , and how to transform these ambiguous research question(s) and hypothesis(es) into clear and good statements.

a These statements were composed for comparison and illustrative purposes only.

b These statements are direct quotes from Higashihara and Horiuchi. 16

a This statement is a direct quote from Shimoda et al. 17

The other statements were composed for comparison and illustrative purposes only.

CONSTRUCTING RESEARCH QUESTIONS AND HYPOTHESES

To construct effective research questions and hypotheses, it is very important to 1) clarify the background and 2) identify the research problem at the outset of the research, within a specific timeframe. 9 Then, 3) review or conduct preliminary research to collect all available knowledge about the possible research questions by studying theories and previous studies. 18 Afterwards, 4) construct research questions to investigate the research problem. Identify variables to be accessed from the research questions 4 and make operational definitions of constructs from the research problem and questions. Thereafter, 5) construct specific deductive or inductive predictions in the form of hypotheses. 4 Finally, 6) state the study aims . This general flow for constructing effective research questions and hypotheses prior to conducting research is shown in Fig. 1 .

Research questions are used more frequently in qualitative research than objectives or hypotheses. 3 These questions seek to discover, understand, explore or describe experiences by asking “What” or “How.” The questions are open-ended to elicit a description rather than to relate variables or compare groups. The questions are continually reviewed, reformulated, and changed during the qualitative study. 3 Research questions are also used more frequently in survey projects than hypotheses in experiments in quantitative research to compare variables and their relationships.

Hypotheses are constructed based on the variables identified and as an if-then statement, following the template, ‘If a specific action is taken, then a certain outcome is expected.’ At this stage, some ideas regarding expectations from the research to be conducted must be drawn. 18 Then, the variables to be manipulated (independent) and influenced (dependent) are defined. 4 Thereafter, the hypothesis is stated and refined, and reproducible data tailored to the hypothesis are identified, collected, and analyzed. 4 The hypotheses must be testable and specific, 18 and should describe the variables and their relationships, the specific group being studied, and the predicted research outcome. 18 Hypotheses construction involves a testable proposition to be deduced from theory, and independent and dependent variables to be separated and measured separately. 3 Therefore, good hypotheses must be based on good research questions constructed at the start of a study or trial. 12

In summary, research questions are constructed after establishing the background of the study. Hypotheses are then developed based on the research questions. Thus, it is crucial to have excellent research questions to generate superior hypotheses. In turn, these would determine the research objectives and the design of the study, and ultimately, the outcome of the research. 12 Algorithms for building research questions and hypotheses are shown in Fig. 2 for quantitative research and in Fig. 3 for qualitative research.

EXAMPLES OF RESEARCH QUESTIONS FROM PUBLISHED ARTICLES

• EXAMPLE 1. Descriptive research question (quantitative research)
• - Presents research variables to be assessed (distinct phenotypes and subphenotypes)
• “BACKGROUND: Since COVID-19 was identified, its clinical and biological heterogeneity has been recognized. Identifying COVID-19 phenotypes might help guide basic, clinical, and translational research efforts.
• RESEARCH QUESTION: Does the clinical spectrum of patients with COVID-19 contain distinct phenotypes and subphenotypes? ” 19
• EXAMPLE 2. Relationship research question (quantitative research)
• - Shows interactions between dependent variable (static postural control) and independent variable (peripheral visual field loss)
• “Background: Integration of visual, vestibular, and proprioceptive sensations contributes to postural control. People with peripheral visual field loss have serious postural instability. However, the directional specificity of postural stability and sensory reweighting caused by gradual peripheral visual field loss remain unclear.
• Research question: What are the effects of peripheral visual field loss on static postural control ?” 20
• EXAMPLE 3. Comparative research question (quantitative research)
• - Clarifies the difference among groups with an outcome variable (patients enrolled in COMPERA with moderate PH or severe PH in COPD) and another group without the outcome variable (patients with idiopathic pulmonary arterial hypertension (IPAH))
• “BACKGROUND: Pulmonary hypertension (PH) in COPD is a poorly investigated clinical condition.
• RESEARCH QUESTION: Which factors determine the outcome of PH in COPD?
• STUDY DESIGN AND METHODS: We analyzed the characteristics and outcome of patients enrolled in the Comparative, Prospective Registry of Newly Initiated Therapies for Pulmonary Hypertension (COMPERA) with moderate or severe PH in COPD as defined during the 6th PH World Symposium who received medical therapy for PH and compared them with patients with idiopathic pulmonary arterial hypertension (IPAH) .” 21
• EXAMPLE 4. Exploratory research question (qualitative research)
• - Explores areas that have not been fully investigated (perspectives of families and children who receive care in clinic-based child obesity treatment) to have a deeper understanding of the research problem
• “Problem: Interventions for children with obesity lead to only modest improvements in BMI and long-term outcomes, and data are limited on the perspectives of families of children with obesity in clinic-based treatment. This scoping review seeks to answer the question: What is known about the perspectives of families and children who receive care in clinic-based child obesity treatment? This review aims to explore the scope of perspectives reported by families of children with obesity who have received individualized outpatient clinic-based obesity treatment.” 22
• EXAMPLE 5. Relationship research question (quantitative research)
• - Defines interactions between dependent variable (use of ankle strategies) and independent variable (changes in muscle tone)
• “Background: To maintain an upright standing posture against external disturbances, the human body mainly employs two types of postural control strategies: “ankle strategy” and “hip strategy.” While it has been reported that the magnitude of the disturbance alters the use of postural control strategies, it has not been elucidated how the level of muscle tone, one of the crucial parameters of bodily function, determines the use of each strategy. We have previously confirmed using forward dynamics simulations of human musculoskeletal models that an increased muscle tone promotes the use of ankle strategies. The objective of the present study was to experimentally evaluate a hypothesis: an increased muscle tone promotes the use of ankle strategies. Research question: Do changes in the muscle tone affect the use of ankle strategies ?” 23

EXAMPLES OF HYPOTHESES IN PUBLISHED ARTICLES

• EXAMPLE 1. Working hypothesis (quantitative research)
• - A hypothesis that is initially accepted for further research to produce a feasible theory
• “As fever may have benefit in shortening the duration of viral illness, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response when taken during the early stages of COVID-19 illness .” 24
• “In conclusion, it is plausible to hypothesize that the antipyretic efficacy of ibuprofen may be hindering the benefits of a fever response . The difference in perceived safety of these agents in COVID-19 illness could be related to the more potent efficacy to reduce fever with ibuprofen compared to acetaminophen. Compelling data on the benefit of fever warrant further research and review to determine when to treat or withhold ibuprofen for early stage fever for COVID-19 and other related viral illnesses .” 24
• EXAMPLE 2. Exploratory hypothesis (qualitative research)
• - Explores particular areas deeper to clarify subjective experience and develop a formal hypothesis potentially testable in a future quantitative approach
• “We hypothesized that when thinking about a past experience of help-seeking, a self distancing prompt would cause increased help-seeking intentions and more favorable help-seeking outcome expectations .” 25
• “Conclusion
• Although a priori hypotheses were not supported, further research is warranted as results indicate the potential for using self-distancing approaches to increasing help-seeking among some people with depressive symptomatology.” 25
• EXAMPLE 3. Hypothesis-generating research to establish a framework for hypothesis testing (qualitative research)
• “We hypothesize that compassionate care is beneficial for patients (better outcomes), healthcare systems and payers (lower costs), and healthcare providers (lower burnout). ” 26
• Compassionomics is the branch of knowledge and scientific study of the effects of compassionate healthcare. Our main hypotheses are that compassionate healthcare is beneficial for (1) patients, by improving clinical outcomes, (2) healthcare systems and payers, by supporting financial sustainability, and (3) HCPs, by lowering burnout and promoting resilience and well-being. The purpose of this paper is to establish a scientific framework for testing the hypotheses above . If these hypotheses are confirmed through rigorous research, compassionomics will belong in the science of evidence-based medicine, with major implications for all healthcare domains.” 26
• EXAMPLE 4. Statistical hypothesis (quantitative research)
• - An assumption is made about the relationship among several population characteristics ( gender differences in sociodemographic and clinical characteristics of adults with ADHD ). Validity is tested by statistical experiment or analysis ( chi-square test, Students t-test, and logistic regression analysis)
• “Our research investigated gender differences in sociodemographic and clinical characteristics of adults with ADHD in a Japanese clinical sample. Due to unique Japanese cultural ideals and expectations of women's behavior that are in opposition to ADHD symptoms, we hypothesized that women with ADHD experience more difficulties and present more dysfunctions than men . We tested the following hypotheses: first, women with ADHD have more comorbidities than men with ADHD; second, women with ADHD experience more social hardships than men, such as having less full-time employment and being more likely to be divorced.” 27
• “Statistical Analysis
• ( text omitted ) Between-gender comparisons were made using the chi-squared test for categorical variables and Students t-test for continuous variables…( text omitted ). A logistic regression analysis was performed for employment status, marital status, and comorbidity to evaluate the independent effects of gender on these dependent variables.” 27

EXAMPLES OF HYPOTHESIS AS WRITTEN IN PUBLISHED ARTICLES IN RELATION TO OTHER PARTS

• EXAMPLE 1. Background, hypotheses, and aims are provided
• “Pregnant women need skilled care during pregnancy and childbirth, but that skilled care is often delayed in some countries …( text omitted ). The focused antenatal care (FANC) model of WHO recommends that nurses provide information or counseling to all pregnant women …( text omitted ). Job aids are visual support materials that provide the right kind of information using graphics and words in a simple and yet effective manner. When nurses are not highly trained or have many work details to attend to, these job aids can serve as a content reminder for the nurses and can be used for educating their patients (Jennings, Yebadokpo, Affo, & Agbogbe, 2010) ( text omitted ). Importantly, additional evidence is needed to confirm how job aids can further improve the quality of ANC counseling by health workers in maternal care …( text omitted )” 28
• “ This has led us to hypothesize that the quality of ANC counseling would be better if supported by job aids. Consequently, a better quality of ANC counseling is expected to produce higher levels of awareness concerning the danger signs of pregnancy and a more favorable impression of the caring behavior of nurses .” 28
• “This study aimed to examine the differences in the responses of pregnant women to a job aid-supported intervention during ANC visit in terms of 1) their understanding of the danger signs of pregnancy and 2) their impression of the caring behaviors of nurses to pregnant women in rural Tanzania.” 28
• EXAMPLE 2. Background, hypotheses, and aims are provided
• “We conducted a two-arm randomized controlled trial (RCT) to evaluate and compare changes in salivary cortisol and oxytocin levels of first-time pregnant women between experimental and control groups. The women in the experimental group touched and held an infant for 30 min (experimental intervention protocol), whereas those in the control group watched a DVD movie of an infant (control intervention protocol). The primary outcome was salivary cortisol level and the secondary outcome was salivary oxytocin level.” 29
• “ We hypothesize that at 30 min after touching and holding an infant, the salivary cortisol level will significantly decrease and the salivary oxytocin level will increase in the experimental group compared with the control group .” 29
• EXAMPLE 3. Background, aim, and hypothesis are provided
• “In countries where the maternal mortality ratio remains high, antenatal education to increase Birth Preparedness and Complication Readiness (BPCR) is considered one of the top priorities [1]. BPCR includes birth plans during the antenatal period, such as the birthplace, birth attendant, transportation, health facility for complications, expenses, and birth materials, as well as family coordination to achieve such birth plans. In Tanzania, although increasing, only about half of all pregnant women attend an antenatal clinic more than four times [4]. Moreover, the information provided during antenatal care (ANC) is insufficient. In the resource-poor settings, antenatal group education is a potential approach because of the limited time for individual counseling at antenatal clinics.” 30
• “This study aimed to evaluate an antenatal group education program among pregnant women and their families with respect to birth-preparedness and maternal and infant outcomes in rural villages of Tanzania.” 30
• “ The study hypothesis was if Tanzanian pregnant women and their families received a family-oriented antenatal group education, they would (1) have a higher level of BPCR, (2) attend antenatal clinic four or more times, (3) give birth in a health facility, (4) have less complications of women at birth, and (5) have less complications and deaths of infants than those who did not receive the education .” 30

Research questions and hypotheses are crucial components to any type of research, whether quantitative or qualitative. These questions should be developed at the very beginning of the study. Excellent research questions lead to superior hypotheses, which, like a compass, set the direction of research, and can often determine the successful conduct of the study. Many research studies have floundered because the development of research questions and subsequent hypotheses was not given the thought and meticulous attention needed. The development of research questions and hypotheses is an iterative process based on extensive knowledge of the literature and insightful grasp of the knowledge gap. Focused, concise, and specific research questions provide a strong foundation for constructing hypotheses which serve as formal predictions about the research outcomes. Research questions and hypotheses are crucial elements of research that should not be overlooked. They should be carefully thought of and constructed when planning research. This avoids unethical studies and poor outcomes by defining well-founded objectives that determine the design, course, and outcome of the study.

Disclosure: The authors have no potential conflicts of interest to disclose.

Author Contributions:

• Conceptualization: Barroga E, Matanguihan GJ.
• Methodology: Barroga E, Matanguihan GJ.
• Writing - original draft: Barroga E, Matanguihan GJ.
• Writing - review & editing: Barroga E, Matanguihan GJ.

Understanding Quantitative Research: Definition, Collection methods, Design, Analysis and Reporting

Quantitative studies play an essential role in scientific and academic research. By enabling numerical data to be measured and analyzed with precision, quantitative surveys provide objective and generalizable results , often unattainable by qualitative methods . A student who undertakes a quantitative survey as part of his or her dissertation or thesis acquires crucial skills such as analytical rigor, mastery of statistical techniques and the ability to interpret numerical data. In this way, they can make a significant contribution to their field of research.  

Contents What is a quantitative study? What are the data collection methods for a quantitative study? How to design and plan a quantitative study? How can quantitative data be successfully analyzed and reported?

What is quantitative research?

Definition, objectives and benefits of quantitative research  .

“Quantitative research is a methodology that provides support when you need to draw general conclusions from your research and predict outcomes. These methods are designed to collect numerical data that can be used to measure variables. ” Survey Monkey, Qualitative vs. quantitative research: What's the difference?

The advantages of this method include : 

• the possibility of obtaining objective, reliable data, 
• the application of rigorous statistical models
• the ability to make comparisons on a large scale and over time,
• the ability to reveal causal relationships between variables, thus providing a basis for decision-making.

Differences between qualitative and quantitative research

Which data collection methods for a quantitative study?

Primary collection using surveys/questionnaires .

Primary data collection means that the researcher collects data directly from the sample, without relying on data collected in previous quantitative surveys. Questionnaires are the most common method used in quantitative research. They can be administered online, by telephone or in person to large population samples. Standardized questionnaires guarantee uniform data collection, delivering statistically significant results.

Primary data collection in longitudinal studies

Longitudinal studies follow the same participants over a long period, offering insights into evolutions and trends over time. They are particularly useful for studying changes in behavior, attitudes or conditions over different phases.

Primary collection by experimental research

Primary collection by experimental research involves the deliberate creation and manipulation of variables in a controlled environment to observe their direct effects on other variables . This method enables researchers to test specific hypotheses and establish cause-and-effect relationships with great precision. 

“In this method, the theory being studied has not yet been proven; it is merely speculation. Thus, an experiment is carried out to prove or disprove the theory.” Voxco, Quantitative research: Definition, methods and examples

This approach is particularly useful for studies where internal validity and methodological rigor are crucial.

Secondary analysis of quantitative data

Secondary data analysis uses existing databases to re-analyze information and answer new research questions. This method is effective in fully exploiting available data, and can reveal additional insights without the need for new data collection.

How do you design and plan a quantitative study?

Defining research objectives.

The first step in designing a quantitative study is to clarify the research objectives . This involves determining what the quanti study seeks to achieve and the specific questions it aims to answer. These objectives will guide the entire research process.

Choosing the data collection method

Depending on your research objectives, choose the most appropriate data collection method . This is what we developed in the previous section.

Select the sample

The representativeness of the results will depend on the selection of the sample. Determine the necessary size and the sampling method (random, stratified, etc.) to ensure that the sample accurately reflects the target population.

Design measurement instruments

Design measurement instruments, such as questionnaires or experimental protocols, t hat are clear, precise and adapted to the study objective. Questions should be formulated in such a way as to minimize bias (mostly closed-ended questions ) and easily yield usable figures.

Planning data analysis

Before collecting data, plan how it will be analyzed. This includes selecting appropriate statistical techniques and using data analysis software. Advance planning ensures that the data collected will answer the research questions validly and reliably. In a professional setting, incorporating task management tools into your planning process can streamline data collection and analysis, ensuring that each step is executed efficiently and effectively.

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How to successfully analyze and report quantitative data?

Checking and cleaning quantitative data

Before starting analysis, it's important to ensure that data is complete and error-free. Identify and manage missing data, correct anomalies and eliminate duplicates, while guaranteeing the integrity of the information.  

Initial exploration of quantitative data

Perform initial data exploration. Analyze measures of central tendency (mean, median) and dispersion (standard deviation, variance). Use visualizations such as histograms, whisker boxes and scatter plots to detect trends, distributions and possible anomalies.

Selecting statistical methods

Select the appropriate statistical methods according to your research objectives and the nature of the data. 

• For comparisons between groups, use tests such as the T-test or ANOVA . 
• To analyze relationships between variables, consider regression techniques .  

Analysis and interpretation of results

Interpret the results in the context of your study. Relate the findings to the original hypotheses and discuss their relevance to the research question. Consider the practical and theoretical implications of the results, as well as their limitations and potential implications for future research.

Clear, visual presentation of quantitative results

Use tables and graphs to illustrate your quantitative results in a concise and accessible way. Make sure visualizations are well-labeled, understandable and directly linked to key findings. The visual aspect helps to communicate results effectively and convincingly. 

It's also vital to document each stage of the analysis in detail in a quantitative survey report. Include :

• a methodological description
• analysis results
• visualizations
• your interpretations .

A well-structured report validates the rigor of your analysis and makes it easier for other researchers to understand and reproduce your results.  

Quantitative studies represent a fundamental pillar in the world of research, offering powerful tools for the collection and analysis of objective data. Using rigorous methods and advanced statistical techniques, they deliver reliable, generalizable results that are invaluable for decision-making. Whether designing surveys, analyzing data or presenting results, a well-planned and executed quantitative approach can not only strengthen the validity of findings, but also enrich the overall understanding of the research field. By mastering these skills, researchers and students make a significant contribution to the advancement of scientific knowledge.

Discover other practical guides to conducting effective quantitative research: 

Types of quantitative research , Lyssna Quantitative Research Methods,  Nova Southeastern University A Guide To Conducting Great Quantitative Research, EngageSpark

Information: This informative article was written in part with the help of ChatGPT. The content generated by AI has been reworked to check the veracity of the information, the relevance of the instructions and to add clarifications.

What are the quantitative studies?

What's the difference between qualitative and quantitative research?

Why conduct a quantitative study?

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