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• Qualitative vs. Quantitative Research | Differences, Examples & Methods

Qualitative vs. Quantitative Research | Differences, Examples & Methods

Published on April 12, 2019 by Raimo Streefkerk . Revised on June 22, 2023.

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

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

Quantitative research is at risk for research biases including information bias , omitted variable bias , sampling bias , or selection bias . Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

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

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs. quantitative research, how to analyze qualitative and quantitative data, other interesting articles, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyze data, and they allow you to answer different kinds of research questions.

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

You survey 300 students at your university and ask them questions such as: “on a scale from 1-5, how satisfied are your with your professors?”

You can perform statistical analysis on the data and draw conclusions such as: “on average students rated their professors 4.4”.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: “How satisfied are you with your studies?”, “What is the most positive aspect of your study program?” and “What can be done to improve the study program?”

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

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

Analyzing quantitative data

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

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

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

Analyzing qualitative data

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

Some common approaches to analyzing qualitative data include:

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

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.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 .

A research project is an academic, scientific, or professional undertaking to answer a research question . Research projects can take many forms, such as qualitative or quantitative , descriptive , longitudinal , experimental , or correlational . What kind of research approach you choose will depend on your topic.

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Qualitative vs Quantitative Research Methods & Data Analysis

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On This Page:

What is the difference between quantitative and qualitative?

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

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

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

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

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

What Is Qualitative Research?

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

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

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

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

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

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

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

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

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

Qualitative Methods

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

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

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

Here are some examples of qualitative data:

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

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

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

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

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

Qualitative Data Analysis

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

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

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

Key Features

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

Limitations of Qualitative Research

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

Advantages of Qualitative Research

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

What Is Quantitative Research?

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

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

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

Quantitative Methods

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

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

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

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

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

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

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

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

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

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

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

Quantitative Data Analysis

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

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

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

Limitations of Quantitative Research

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

Advantages of Quantitative Research

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

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

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

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

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

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

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

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

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

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

Further Information

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

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Quantitative vs. Qualitative Research in Psychology

Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

Emily is a board-certified science editor who has worked with top digital publishing brands like Voices for Biodiversity, Study.com, GoodTherapy, Vox, and Verywell.

• Key Differences

Quantitative Research Methods

Qualitative research methods.

• How They Relate

In psychology and other social sciences, researchers are faced with an unresolved question: Can we measure concepts like love or racism the same way we can measure temperature or the weight of a star? Social phenomena⁠—things that happen because of and through human behavior⁠—are especially difficult to grasp with typical scientific models.

At a Glance

Psychologists rely on quantitative and quantitative research to better understand human thought and behavior.

• Qualitative research involves collecting and evaluating non-numerical data in order to understand concepts or subjective opinions.
• Quantitative research involves collecting and evaluating numerical data. 

This article discusses what qualitative and quantitative research are, how they are different, and how they are used in psychology research.

Qualitative Research vs. Quantitative Research

In order to understand qualitative and quantitative psychology research, it can be helpful to look at the methods that are used and when each type is most appropriate.

Psychologists rely on a few methods to measure behavior, attitudes, and feelings. These include:

• Self-reports , like surveys or questionnaires
• Observation (often used in experiments or fieldwork)
• Implicit attitude tests that measure timing in responding to prompts

Most of these are quantitative methods. The result is a number that can be used to assess differences between groups.

However, most of these methods are static, inflexible (you can't change a question because a participant doesn't understand it), and provide a "what" answer rather than a "why" answer.

Sometimes, researchers are more interested in the "why" and the "how." That's where qualitative methods come in.

Qualitative research is about speaking to people directly and hearing their words. It is grounded in the philosophy that the social world is ultimately unmeasurable, that no measure is truly ever "objective," and that how humans make meaning is just as important as how much they score on a standardized test.

Used to develop theories

Takes a broad, complex approach

Answers "why" and "how" questions

Explores patterns and themes

Used to test theories

Takes a narrow, specific approach

Answers "what" questions

Explores statistical relationships

Quantitative methods have existed ever since people have been able to count things. But it is only with the positivist philosophy of Auguste Comte (which maintains that factual knowledge obtained by observation is trustworthy) that it became a "scientific method."

The scientific method follows this general process. A researcher must:

• Generate a theory or hypothesis (i.e., predict what might happen in an experiment) and determine the variables needed to answer their question
• Develop instruments to measure the phenomenon (such as a survey, a thermometer, etc.)
• Develop experiments to manipulate the variables
• Collect empirical (measured) data
• Analyze data

Quantitative methods are about measuring phenomena, not explaining them.

Quantitative research compares two groups of people. There are all sorts of variables you could measure, and many kinds of experiments to run using quantitative methods.

These comparisons are generally explained using graphs, pie charts, and other visual representations that give the researcher a sense of how the various data points relate to one another.

Basic Assumptions

Quantitative methods assume:

• That the world is measurable
• That humans can observe objectively
• That we can know things for certain about the world from observation

In some fields, these assumptions hold true. Whether you measure the size of the sun 2000 years ago or now, it will always be the same. But when it comes to human behavior, it is not so simple.

As decades of cultural and social research have shown, people behave differently (and even think differently) based on historical context, cultural context, social context, and even identity-based contexts like gender , social class, or sexual orientation .

Therefore, quantitative methods applied to human behavior (as used in psychology and some areas of sociology) should always be rooted in their particular context. In other words: there are no, or very few, human universals.

Statistical information is the primary form of quantitative data used in human and social quantitative research. Statistics provide lots of information about tendencies across large groups of people, but they can never describe every case or every experience. In other words, there are always outliers.

Correlation and Causation

A basic principle of statistics is that correlation is not causation. Researchers can only claim a cause-and-effect relationship under certain conditions:

• The study was a true experiment.
• The independent variable can be manipulated (for example, researchers cannot manipulate gender, but they can change the primer a study subject sees, such as a picture of nature or of a building).
• The dependent variable can be measured through a ratio or a scale.

So when you read a report that "gender was linked to" something (like a behavior or an attitude), remember that gender is NOT a cause of the behavior or attitude. There is an apparent relationship, but the true cause of the difference is hidden.

Pitfalls of Quantitative Research

Quantitative methods are one way to approach the measurement and understanding of human and social phenomena. But what's missing from this picture?

As noted above, statistics do not tell us about personal, individual experiences and meanings. While surveys can give a general idea, respondents have to choose between only a few responses. This can make it difficult to understand the subtleties of different experiences.

Quantitative methods can be helpful when making objective comparisons between groups or when looking for relationships between variables. They can be analyzed statistically, which can be helpful when looking for patterns and relationships.

Qualitative data are not made out of numbers but rather of descriptions, metaphors, symbols, quotes, analysis, concepts, and characteristics. This approach uses interviews, written texts, art, photos, and other materials to make sense of human experiences and to understand what these experiences mean to people.

While quantitative methods ask "what" and "how much," qualitative methods ask "why" and "how."

Qualitative methods are about describing and analyzing phenomena from a human perspective. There are many different philosophical views on qualitative methods, but in general, they agree that some questions are too complex or impossible to answer with standardized instruments.

These methods also accept that it is impossible to be completely objective in observing phenomena. Researchers have their own thoughts, attitudes, experiences, and beliefs, and these always color how people interpret results.

Qualitative Approaches

There are many different approaches to qualitative research, with their own philosophical bases. Different approaches are best for different kinds of projects. For example:

• Case studies and narrative studies are best for single individuals. These involve studying every aspect of a person's life in great depth.
• Phenomenology aims to explain experiences. This type of work aims to describe and explore different events as they are consciously and subjectively experienced.
• Grounded theory develops models and describes processes. This approach allows researchers to construct a theory based on data that is collected, analyzed, and compared to reach new discoveries.
• Ethnography describes cultural groups. In this approach, researchers immerse themselves in a community or group in order to observe behavior.

Qualitative researchers must be aware of several different methods and know each thoroughly enough to produce valuable research.

Some researchers specialize in a single method, but others specialize in a topic or content area and use many different methods to explore the topic, providing different information and a variety of points of view.

There is not a single model or method that can be used for every qualitative project. Depending on the research question, the people participating, and the kind of information they want to produce, researchers will choose the appropriate approach.

Interpretation

Qualitative research does not look into causal relationships between variables, but rather into themes, values, interpretations, and meanings. As a rule, then, qualitative research is not generalizable (cannot be applied to people outside the research participants).

The insights gained from qualitative research can extend to other groups with proper attention to specific historical and social contexts.

Relationship Between Qualitative and Quantitative Research

It might sound like quantitative and qualitative research do not play well together. They have different philosophies, different data, and different outputs. However, this could not be further from the truth.

These two general methods complement each other. By using both, researchers can gain a fuller, more comprehensive understanding of a phenomenon.

For example, a psychologist wanting to develop a new survey instrument about sexuality might and ask a few dozen people questions about their sexual experiences (this is qualitative research). This gives the researcher some information to begin developing questions for their survey (which is a quantitative method).

After the survey, the same or other researchers might want to dig deeper into issues brought up by its data. Follow-up questions like "how does it feel when...?" or "what does this mean to you?" or "how did you experience this?" can only be answered by qualitative research.

By using both quantitative and qualitative data, researchers have a more holistic, well-rounded understanding of a particular topic or phenomenon.

Qualitative and quantitative methods both play an important role in psychology. Where quantitative methods can help answer questions about what is happening in a group and to what degree, qualitative methods can dig deeper into the reasons behind why it is happening. By using both strategies, psychology researchers can learn more about human thought and behavior.

Gough B, Madill A. Subjectivity in psychological science: From problem to prospect . Psychol Methods . 2012;17(3):374-384. doi:10.1037/a0029313

Pearce T. “Science organized”: Positivism and the metaphysical club, 1865–1875 . J Hist Ideas . 2015;76(3):441-465.

Adams G. Context in person, person in context: A cultural psychology approach to social-personality psychology . In: Deaux K, Snyder M, eds. The Oxford Handbook of Personality and Social Psychology . Oxford University Press; 2012:182-208.

Brady HE. Causation and explanation in social science . In: Goodin RE, ed. The Oxford Handbook of Political Science. Oxford University Press; 2011. doi:10.1093/oxfordhb/9780199604456.013.0049

Chun Tie Y, Birks M, Francis K. Grounded theory research: A design framework for novice researchers .  SAGE Open Med . 2019;7:2050312118822927. doi:10.1177/2050312118822927

Reeves S, Peller J, Goldman J, Kitto S. Ethnography in qualitative educational research: AMEE Guide No. 80 . Medical Teacher . 2013;35(8):e1365-e1379. doi:10.3109/0142159X.2013.804977

Salkind NJ, ed. Encyclopedia of Research Design . Sage Publishing.

Shaughnessy JJ, Zechmeister EB, Zechmeister JS.  Research Methods in Psychology . McGraw Hill Education.

By Anabelle Bernard Fournier Anabelle Bernard Fournier is a researcher of sexual and reproductive health at the University of Victoria as well as a freelance writer on various health topics.

• Submission Guidelines

qualitative and quantitative header

Learning Objective

Differentiate between qualitative and quantitative approaches.

Hong is a physical therapist who teaches injury assessment classes at the University of Utah. With the recent change to online for the remainder of the semester, Hong is interested in the impact on students’ skills acquisition for injury assessment. He wants to utilize both quantitative and qualitative approaches—he plans to compare previous student test scores to current student test scores. He also plans to interview current students about their experiences practicing injury assessment skills virtually. What specific study design methods will Hong use?

Making sense of the evidence

hen conducting a literature search and reviewing research articles, it is important to have a general understanding of the types of research and data you anticipate from different types of studies.

In this article, we review two broad categories of study methods, quantitative and qualitative, and discuss some of their subtypes, or designs, and the type of data that they generate.

Quantitative vs. qualitative approaches

Quantitative is measurable. It is often associated with a more traditional scientific method of gathering data in an organized, objective manner so that findings can be generalized to other persons or populations. Quantitative designs are based on probabilities or likelihood—it utilizes ‘p’ values, power analysis, and other scientific methods to ensure the rigor and reproducibility of the results to other populations. Quantitative designs can be experimental, quasi-experimental, descriptive, or correlational.

Qualitative is usually more subjective , although like quantitative research, it also uses a systematic approach. Qualitative research is generally preferred when the clinical question centers around life experiences or meaning. Qualitative research explores the complexity, depth, and richness of a particular situation from the perspective of the informants—referring to the person or persons providing the information. This may be the patient, the patient’s caregivers, the patient’s family members, etc. The information may also come from the investigator’s or researcher’s observations. At the heart of qualitative research is the belief that reality is based on perceptions and can be different for each person, often changing over time.

Study design differences

Quantitative design methods.

Quantitative designs typically fall into four categories: experimental, quasi-experimental, descriptive, or correlational. Let’s talk about these different types. But before we begin, we need to briefly review the difference between independent and dependent variables.

The independent variable is the variable that is being manipulated, or the one that varies. It is sometimes called the ‘predictor’ or ‘treatment’ variable.

The dependent variable is the outcome (or response) variable. Changes in the dependent variables are presumed to be caused or influenced by the independent variable.

Experimental

In experimental designs, there are often treatment groups and control groups. This study design looks for cause and effect (if A, then B), so it requires having control over at least one of the independent, or treatment variables. Experimental design administers the treatment to some of the subjects (called the ‘experimental group’) and not to others (called the ‘control group’). Subjects are randomly assigned—meaning that they would have an equal chance of being assigned to the control group or the experimental group. This is the strongest design for testing cause and effect relationships because randomization reduces bias. In fact, most researchers believe that a randomized controlled trail is the only kind of research study where we can infer cause (if A, then B). The difficulty with a randomized controlled trial is that the results may not be generalizable in all circumstances with all patient populations, so as with any research study, you need to consider the application of the findings to your patients in your setting. 

Quasi-experimental

Quasi-Experimental studies also seek to identify a cause and effect (causal) relationship, although they are less powerful than experimental designs. This is because they lack one or more characteristics of a true experiment. For instance, they may not include random assignment or they may not have a control group. As is often the case in the ‘real world’, clinical care variables often cannot be controlled due to ethical, practical, or fiscal concerns. So, the quasi experimental approach is utilized when a randomized controlled trial is not possible. For example, if it was found that the new treatment stopped disease progression, it would no longer be ethical to withhold it from others by establishing a control group.

Descriptive

Descriptive studies give us an accurate account of the characteristics of a particular situation or group. They are often used to determine how often something occurs, the likelihood of something occurring, or to provide a way to categorize information. For example, let’s say we wanted to look at the visiting policy in the ICU and describe how implementing an open-visiting policy affected nurse satisfaction. We could use a research tool, such as a Likert scale (5 = very satisfied and 1 = very dissatisfied), to help us gain an understanding of how satisfied nurses are as a group with this policy.

Correlational

Correlational research involves the study of the relationship between two or more variables. The primary purpose is to explain the nature of the relationship, not to determine the cause and effect. For example, if you wanted to examine whether first-time moms who have an elective induction are more likely to have a cesarean birth than first-time moms who go into labor naturally, the independent variables would be ‘elective induction’ and ‘go into labor naturally’ (because they are the variables that ‘vary’) and the outcome variable is ‘cesarean section.’ Even if you find a strong relationship between elective inductions and an increased likelihood of cesarean birth, you cannot state that elective inductions ‘cause’ cesarean births because we have no control over the variables. We can only report an increased likelihood.   

Qualitative design methods

Qualitative methods delve deeply into experiences, social processes, and subcultures. Qualitative study generally falls under three types of designs: phenomenology, ethnography and grounded theory.

Phenomenology

In this approach, we want to understand and describe the lived experience or meaning of persons with a particular condition or situation. For example, phenomenological questions might ask “What is it like for an adolescent to have a younger sibling with a terminal illness?” or “What is the lived experience of caring for an older house-bound dependent parent?”

Ethnography

Ethnographic studies focus on the culture of a group of people. The assumption behind ethnographies is that groups of individuals evolve into a kind of ‘culture’ that guides the way members of that culture or group view the world. In this kind of study, the research focuses on participant observation, where the researcher becomes an active participant in that culture to understand its experiences. For example, nursing could be considered a professional culture, and the unit of a hospital can be viewed as a subculture. One example specific to nursing culture was a study done in 2006 by Deitrick and colleagues . They used ethnographic methods to examine problems related to answering patient call lights on one medical surgical inpatient unit. The single nursing unit was the ‘culture’ under study.

Grounded theory

Grounded theory research begins with a general research problem, selects persons most likely to clarify the initial understanding of the question, and uses a variety of techniques (interviewing, observation, document review to name a few) to discover and develop a theory. For example, one nurse researcher used a grounded theory approach to explain how African American women from different socioeconomic backgrounds make decisions about mammography screening. Because African American women historically have fewer mammograms (and therefore lower survival rates for later stage detection), understanding their decision-making process may help the provider support more effective health promotion efforts. 

Being able to identify the differences between qualitative and quantitative research and becoming familiar with the subtypes of each can make a literature search a little less daunting.

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This article originally appeared July 2, 2020. It was updated to reflect current practice on March 21, 2021.

Barbara Wilson

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Performing a rapid critical appraisal helps evaluate a study for its worth by ensuring validity, meaningful data, and significance to the patient. Contributors Barb Wilson, Mary Jean Austria, and Tallie Casucci share a checklist of questions to complete a rapid critical appraisal efficiently and effectively.

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Medical students Rachel Tsolinas and Sam Wilkinson, along with SOM professor Kathryn Moore, share a practical tool all health care professionals can use to broaden our understanding of how culture influences decisions and events.

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• Knowledge Base
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• Qualitative vs Quantitative Research | Examples & Methods

Qualitative vs Quantitative Research | Examples & Methods

Published on 4 April 2022 by Raimo Streefkerk . Revised on 8 May 2023.

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

Common quantitative methods include experiments, observations recorded as numbers, and surveys with closed-ended questions. Qualitative research Qualitative research is expressed in words . It is used to understand concepts, thoughts or experiences. This type of research enables you to gather in-depth insights on topics that are not well understood.

Table of contents

The differences between quantitative and qualitative research, data collection methods, when to use qualitative vs quantitative research, how to analyse qualitative and quantitative data, frequently asked questions about qualitative and quantitative research.

Quantitative and qualitative research use different research methods to collect and analyse data, and they allow you to answer different kinds of research questions.

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

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

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

Quantitative data collection methods

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

Qualitative data collection methods

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

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

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

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

Quantitative research approach

You survey 300 students at your university and ask them questions such as: ‘on a scale from 1-5, how satisfied are your with your professors?’

You can perform statistical analysis on the data and draw conclusions such as: ‘on average students rated their professors 4.4’.

Qualitative research approach

You conduct in-depth interviews with 15 students and ask them open-ended questions such as: ‘How satisfied are you with your studies?’, ‘What is the most positive aspect of your study program?’ and ‘What can be done to improve the study program?’

Based on the answers you get you can ask follow-up questions to clarify things. You transcribe all interviews using transcription software and try to find commonalities and patterns.

Mixed methods approach

You conduct interviews to find out how satisfied students are with their studies. Through open-ended questions you learn things you never thought about before and gain new insights. Later, you use a survey to test these insights on a larger scale.

It’s also possible to start with a survey to find out the overall trends, followed by interviews to better understand the reasons behind the trends.

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

Analysing quantitative data

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

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

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

Analysing qualitative data

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

Some common approaches to analysing qualitative data include:

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

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

Quantitative methods allow you to test a hypothesis by systematically collecting and analysing data, while qualitative methods allow you to explore ideas and experiences in depth.

In mixed methods research , you use both qualitative and quantitative data collection and analysis methods to answer your research question .

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts, and meanings, use qualitative methods .
• If you want to analyse a large amount of readily available data, use secondary data. If you want data specific to your purposes with control over how they are generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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 organisations.

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

• Prepare and organise 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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Raimo Streefkerk

Qualitative vs. Quantitative Research: Comparing the Methods and Strategies for Education Research

No matter the field of study, all research can be divided into two distinct methodologies: qualitative and quantitative research. Both methodologies offer education researchers important insights.

Education research assesses problems in policy, practices, and curriculum design, and it helps administrators identify solutions. Researchers can conduct small-scale studies to learn more about topics related to instruction or larger-scale ones to gain insight into school systems and investigate how to improve student outcomes.

Education research often relies on the quantitative methodology. Quantitative research in education provides numerical data that can prove or disprove a theory, and administrators can easily share the number-based results with other schools and districts. And while the research may speak to a relatively small sample size, educators and researchers can scale the results from quantifiable data to predict outcomes in larger student populations and groups.

Qualitative vs. Quantitative Research in Education: Definitions

Although there are many overlaps in the objectives of qualitative and quantitative research in education, researchers must understand the fundamental functions of each methodology in order to design and carry out an impactful research study. In addition, they must understand the differences that set qualitative and quantitative research apart in order to determine which methodology is better suited to specific education research topics.

Generate Hypotheses with Qualitative Research

Qualitative research focuses on thoughts, concepts, or experiences. The data collected often comes in narrative form and concentrates on unearthing insights that can lead to testable hypotheses. Educators use qualitative research in a study’s exploratory stages to uncover patterns or new angles.

Form Strong Conclusions with Quantitative Research

Quantitative research in education and other fields of inquiry is expressed in numbers and measurements. This type of research aims to find data to confirm or test a hypothesis.

Differences in Data Collection Methods

Keeping in mind the main distinction in qualitative vs. quantitative research—gathering descriptive information as opposed to numerical data—it stands to reason that there are different ways to acquire data for each research methodology. While certain approaches do overlap, the way researchers apply these collection techniques depends on their goal.

Interviews, for example, are common in both modes of research. An interview with students that features open-ended questions intended to reveal ideas and beliefs around attendance will provide qualitative data. This data may reveal a problem among students, such as a lack of access to transportation, that schools can help address.

An interview can also include questions posed to receive numerical answers. A case in point: how many days a week do students have trouble getting to school, and of those days, how often is a transportation-related issue the cause? In this example, qualitative and quantitative methodologies can lead to similar conclusions, but the research will differ in intent, design, and form.

Taking a look at behavioral observation, another common method used for both qualitative and quantitative research, qualitative data may consider a variety of factors, such as facial expressions, verbal responses, and body language.

On the other hand, a quantitative approach will create a coding scheme for certain predetermined behaviors and observe these in a quantifiable manner.

Qualitative Research Methods

• Case Studies : Researchers conduct in-depth investigations into an individual, group, event, or community, typically gathering data through observation and interviews.
• Focus Groups : A moderator (or researcher) guides conversation around a specific topic among a group of participants.
• Ethnography : Researchers interact with and observe a specific societal or ethnic group in their real-life environment.
• Interviews : Researchers ask participants questions to learn about their perspectives on a particular subject.

Quantitative Research Methods

• Questionnaires and Surveys : Participants receive a list of questions, either closed-ended or multiple choice, which are directed around a particular topic.
• Experiments : Researchers control and test variables to demonstrate cause-and-effect relationships.
• Observations : Researchers look at quantifiable patterns and behavior.
• Structured Interviews : Using a predetermined structure, researchers ask participants a fixed set of questions to acquire numerical data.

Choosing a Research Strategy

When choosing which research strategy to employ for a project or study, a number of considerations apply. One key piece of information to help determine whether to use a qualitative vs. quantitative research method is which phase of development the study is in.

For example, if a project is in its early stages and requires more research to find a testable hypothesis, qualitative research methods might prove most helpful. On the other hand, if the research team has already established a hypothesis or theory, quantitative research methods will provide data that can validate the theory or refine it for further testing.

It’s also important to understand a project’s research goals. For instance, do researchers aim to produce findings that reveal how to best encourage student engagement in math? Or is the goal to determine how many students are passing geometry? These two scenarios require distinct sets of data, which will determine the best methodology to employ.

In some situations, studies will benefit from a mixed-methods approach. Using the goals in the above example, one set of data could find the percentage of students passing geometry, which would be quantitative. The research team could also lead a focus group with the students achieving success to discuss which techniques and teaching practices they find most helpful, which would produce qualitative data.

Learn How to Put Education Research into Action

Those with an interest in learning how to harness research to develop innovative ideas to improve education systems may want to consider pursuing a doctoral degree. American University’s School of Education Online offers a Doctor of Education (EdD) in Education Policy and Leadership that prepares future educators, school administrators, and other education professionals to become leaders who effect positive changes in schools. Courses such as Applied Research Methods I: Enacting Critical Research provides students with the techniques and research skills needed to begin conducting research exploring new ways to enhance education. Learn more about American’ University’s EdD in Education Policy and Leadership .

What’s the Difference Between Educational Equity and Equality?

EdD vs. PhD in Education: Requirements, Career Outlook, and Salary

Top Education Technology Jobs for Doctorate in Education Graduates

American University, EdD in Education Policy and Leadership

Edutopia, “2019 Education Research Highlights”

Formplus, “Qualitative vs. Quantitative Data: 15 Key Differences and Similarities”

iMotion, “Qualitative vs. Quantitative Research: What Is What?”

Scribbr, “Qualitative vs. Quantitative Research”

Simply Psychology, “What’s the Difference Between Quantitative and Qualitative Research?”

Typeform, “A Simple Guide to Qualitative and Quantitative Research”

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Research methods--quantitative, qualitative, and more: overview.

• Quantitative Research
• Qualitative Research
• Data Science Methods (Machine Learning, AI, Big Data)
• Text Mining and Computational Text Analysis
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About Research Methods

This guide provides an overview of research methods, how to choose and use them, and supports and resources at UC Berkeley. 

As Patten and Newhart note in the book Understanding Research Methods , "Research methods are the building blocks of the scientific enterprise. They are the "how" for building systematic knowledge. The accumulation of knowledge through research is by its nature a collective endeavor. Each well-designed study provides evidence that may support, amend, refute, or deepen the understanding of existing knowledge...Decisions are important throughout the practice of research and are designed to help researchers collect evidence that includes the full spectrum of the phenomenon under study, to maintain logical rules, and to mitigate or account for possible sources of bias. In many ways, learning research methods is learning how to see and make these decisions."

The choice of methods varies by discipline, by the kind of phenomenon being studied and the data being used to study it, by the technology available, and more.  This guide is an introduction, but if you don't see what you need here, always contact your subject librarian, and/or take a look to see if there's a library research guide that will answer your question. 

Suggestions for changes and additions to this guide are welcome! 

START HERE: SAGE Research Methods

Without question, the most comprehensive resource available from the library is SAGE Research Methods.  HERE IS THE ONLINE GUIDE  to this one-stop shopping collection, and some helpful links are below:

• SAGE Research Methods
• Little Green Books  (Quantitative Methods)
• Little Blue Books  (Qualitative Methods)
• Dictionaries and Encyclopedias  
• Case studies of real research projects
• Sample datasets for hands-on practice
• Streaming video--see methods come to life
• Methodspace- -a community for researchers
• SAGE Research Methods Course Mapping

Library Data Services at UC Berkeley

Library Data Services Program and Digital Scholarship Services

The LDSP offers a variety of services and tools !  From this link, check out pages for each of the following topics:  discovering data, managing data, collecting data, GIS data, text data mining, publishing data, digital scholarship, open science, and the Research Data Management Program.

Be sure also to check out the visual guide to where to seek assistance on campus with any research question you may have!

Library GIS Services

Other Data Services at Berkeley

D-Lab Supports Berkeley faculty, staff, and graduate students with research in data intensive social science, including a wide range of training and workshop offerings Dryad Dryad is a simple self-service tool for researchers to use in publishing their datasets. It provides tools for the effective publication of and access to research data. Geospatial Innovation Facility (GIF) Provides leadership and training across a broad array of integrated mapping technologies on campu Research Data Management A UC Berkeley guide and consulting service for research data management issues

General Research Methods Resources

Here are some general resources for assistance:

• Assistance from ICPSR (must create an account to access): Getting Help with Data , and Resources for Students
• Wiley Stats Ref for background information on statistics topics
• Survey Documentation and Analysis (SDA) .  Program for easy web-based analysis of survey data.

Consultants

• D-Lab/Data Science Discovery Consultants Request help with your research project from peer consultants.
• Research data (RDM) consulting Meet with RDM consultants before designing the data security, storage, and sharing aspects of your qualitative project.
• Statistics Department Consulting Services A service in which advanced graduate students, under faculty supervision, are available to consult during specified hours in the Fall and Spring semesters.

Related Resourcex

• IRB / CPHS Qualitative research projects with human subjects often require that you go through an ethics review.
• OURS (Office of Undergraduate Research and Scholarships) OURS supports undergraduates who want to embark on research projects and assistantships. In particular, check out their "Getting Started in Research" workshops
• Sponsored Projects Sponsored projects works with researchers applying for major external grants.
• Next: Quantitative Research >>
• Last Updated: Apr 3, 2023 3:14 PM
• URL: https://guides.lib.berkeley.edu/researchmethods

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Chapter 3: Developing a Research Question

3.5 Quantitative, Qualitative, & Mixed Methods Research Approaches

Generally speaking, qualitative and quantitative approaches are the most common methods utilized by researchers. While these two approaches are often presented as a dichotomy, in reality it is much more complicated. Certainly, there are researchers who fall on the more extreme ends of these two approaches, however most recognize the advantages and usefulness of combining both methods (mixed methods). In the following sections we look at quantitative, qualitative, and mixed methodological approaches to undertaking research. Table 2.3 synthesizes the differences between quantitative and qualitative research approaches.

Quantitative Research Approaches

A quantitative approach to research is probably the most familiar approach for the typical research student studying at the introductory level. Arising from the natural sciences, e.g., chemistry and biology), the quantitative approach is framed by the belief that there is one reality or truth that simply requires discovering, known as realism. Therefore, asking the “right” questions is key. Further, this perspective favours observable causes and effects and is therefore outcome-oriented. Typically, aggregate data is used to see patterns and “truth” about the phenomenon under study. True understanding is determined by the ability to predict the phenomenon.

Qualitative Research Approaches

On the other side of research approaches is the qualitative approach. This is generally considered to be the opposite of the quantitative approach. Qualitative researchers are considered phenomenologists, or human-centred researchers. Any research must account for the humanness, i.e., that they have thoughts, feelings, and experiences that they interpret of the participants. Instead of a realist perspective suggesting one reality or truth, qualitative researchers tend to favour the constructionist perspective: knowledge is created, not discovered, and there are multiple realities based on someone’s perspective. Specifically, a researcher needs to understand why, how and to whom a phenomenon applies. These aspects are usually unobservable since they are the thoughts, feelings and experiences of the person. Most importantly, they are a function of their perception of those things rather than what the outside researcher interprets them to be. As a result, there is no such thing as a neutral or objective outsider, as in the quantitative approach. Rather, the approach is generally process-oriented. True understanding, rather than information based on prediction, is based on understanding action and on the interpretive meaning of that action.

Table 3.3 Differences between quantitative and qualitative approaches (from Adjei, n.d).

Note: Researchers in emergency and safety professions are increasingly turning toward qualitative methods. Here is an interesting peer paper related to qualitative research in emergency care.

Qualitative Research in Emergency Care Part I: Research Principles and Common Applications by Choo, Garro, Ranney, Meisel, and Guthrie (2015)

Interview-based Qualitative Research in Emergency Care Part II: Data Collection, Analysis and Results Reporting.

Research Methods for the Social Sciences: An Introduction Copyright © 2020 by Valerie Sheppard is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License , except where otherwise noted.

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The differences between qualitative and quantitative research methods

Last updated

15 January 2023

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Two approaches to this systematic information gathering are qualitative and quantitative research. Each of these has its place in data collection, but each one approaches from a different direction. Here's what you need to know about qualitative and quantitative research.

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• The differences between quantitative and qualitative research

The main difference between these two approaches is the type of data you collect and how you interpret it. Qualitative research focuses on word-based data, aiming to define and understand ideas. This study allows researchers to collect information in an open-ended way through interviews, ethnography, and observation. You’ll study this information to determine patterns and the interplay of variables.

On the other hand, quantitative research focuses on numerical data and using it to determine relationships between variables. Researchers use easily quantifiable forms of data collection, such as experiments that measure the effect of one or several variables on one another.

• Qualitative vs. quantitative data collection

Focusing on different types of data means that the data collection methods vary. 

Quantitative data collection methods

As previously stated, quantitative data collection focuses on numbers. You gather information through experiments, database reports, or surveys with multiple-choice answers. The goal is to have data you can use in numerical analysis to determine relationships.

Qualitative data collection methods

On the other hand, the data collected for qualitative research is an exploration of a subject's attributes, thoughts, actions, or viewpoints. Researchers will typically conduct interviews , hold focus groups, or observe behavior in a natural setting to assemble this information. Other options include studying personal accounts or cultural records. 

• Qualitative vs. quantitative outcomes

The two approaches naturally produce different types of outcomes. Qualitative research gains a better understanding of the reason something happens. For example, researchers may comb through feedback and statements to ascertain the reasoning behind certain behaviors or actions.

On the other hand, quantitative research focuses on the numerical analysis of data, which may show cause-and-effect relationships. Put another way, qualitative research investigates why something happens, while quantitative research looks at what happens.

• How to analyze qualitative and quantitative data

Because the two research methods focus on different types of information, analyzing the data you've collected will look different, depending on your approach.

Analyzing quantitative data

As this data is often numerical, you’ll likely use statistical analysis to identify patterns. Researchers may use computer programs to generate data such as averages or rate changes, illustrating the results in tables or graphs.

Analyzing qualitative data

Qualitative data is more complex and time-consuming to process as it may include written texts, videos, or images to study. Finding patterns in thinking, actions, and beliefs is more nuanced and subject to interpretation. 

Researchers may use techniques such as thematic analysis , combing through the data to identify core themes or patterns. Another tool is discourse analysis , which studies how communication functions in different contexts.

• When to use qualitative vs. quantitative research

Choosing between the two approaches comes down to understanding what your goal is with the research.

Qualitative research approach

Qualitative research is useful for understanding a concept, such as what people think about certain experiences or how cultural beliefs affect perceptions of events. It can help you formulate a hypothesis or clarify general questions about the topic.

Quantitative research approach

On the other hand, quantitative research verifies or tests a hypothesis you've developed, or you can use it to find answers to those questions. 

Mixed methods approach

Often, researchers use elements of both types of research to provide complex and targeted information. This may look like a survey with multiple-choice and open-ended questions.

• Benefits and limitations

Of course, each type of research has drawbacks and strengths. It's essential to be aware of the pros and cons.

Qualitative studies: Pros and cons

This approach lets you consider your subject creatively and examine big-picture questions. It can advance your global understanding of topics that are challenging to quantify.

On the other hand, the wide-open possibilities of qualitative research can make it tricky to focus effectively on your subject of inquiry. It makes it easier for researchers to skew the data with social biases and personal assumptions. There’s also the tendency for people to behave differently under observation.

It can also be more difficult to get a large sample size because it's generally more complex and expensive to conduct qualitative research. The process usually takes longer, as well. 

Quantitative studies: Pros and cons

The quantitative methodology produces data you can communicate and present without bias. The methods are direct and generally easier to reproduce on a larger scale, enabling researchers to get accurate results. It can be instrumental in pinning down precise facts about a topic. 

It is also a restrictive form of inquiry. Researchers cannot add context to this type of data collection or expand their focus in a different direction within a single study. They must be alert for biases. Quantitative research is more susceptible to selection bias and omitting or incorrectly measuring variables.

• How to balance qualitative and quantitative research

Although people tend to gravitate to one form of inquiry over another, each has its place in studying a subject. Both approaches can identify patterns illustrating the connection between multiple elements, and they can each advance your understanding of subjects in important ways. 

Understanding how each option will serve you will help you decide how and when to use each. Generally, qualitative research can help you develop and refine questions, while quantitative research helps you get targeted answers to those questions. Which element do you need to advance your study of the subject? Can both of them hone your knowledge?

Open-ended vs. close-ended questions

One way to use techniques from both approaches is with open-ended and close-ended questions in surveys. Because quantitative analysis requires defined sets of data that you can represent numerically, the questions must be close-ended. On the other hand, qualitative inquiry is naturally open-ended, allowing room for complex ideas.

An example of this is a survey on the impact of inflation. You could include both multiple-choice questions and open-response questions:

1. How do you compensate for higher prices at the grocery store? (Select all that apply)

A. Purchase fewer items

B. Opt for less expensive choices

C. Take money from other parts of the budget

D. Use a food bank or other charity to fill the gaps

E. Make more food from scratch

2. How do rising prices affect your grocery shopping habits? (Write your answer)

We need qualitative and quantitative forms of research to advance our understanding of the world. Neither is the "right" way to go, but one may be better for you depending on your needs. 

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Qualitative Vs. Quantitative Research — A step-wise guide to conduct research

A research study includes the collection and analysis of data. In quantitative research, the data are analyzed with numbers and statistics, and in qualitative research, the data analyzed are non-numerical and perceive the meaning of social reality.

What Is Qualitative Research?

Qualitative research observes and describes a phenomenon to gain a deeper understanding of a subject. It is also used to generate hypotheses for further studies. In general, qualitative research is explanatory and helps understands how an individual perceives non-numerical data, like video, photographs, or audio recordings. The qualitative data is collected from diary accounts or interviews and analyzed by grounded theory or thematic analysis.

When to Use Qualitative Research?

Qualitative research is used when the outcome of the research study is to disseminate knowledge and understand concepts, thoughts, and experiences. This type of research focuses on creating ideas and formulating theories or hypotheses .

Benefits of Qualitative Research

• Unlike quantitative research, which relies on numerical data, qualitative research relies on data collected from interviews, observations, and written texts.
• It is often used in fields such as sociology and anthropology, where the goal is to understand complex social phenomena.
• Qualitative research is considered to be more flexible and adaptive, as it is used to study a wide range of social aspects.
• Additionally, qualitative research often leads to deeper insights into the research study. This helps researchers and scholars in designing their research methods .

Qualitative Research Example

In research, to understand the culture of a pharma company, one could take an ethnographic approach. With an experience in the company, one could gather data based on the —

• Field notes with observations, and reflections on one’s experiences of the company’s culture
• Open-ended surveys for employees across all the company’s departments via email to find out variations in culture across teams and departments
• Interview sessions with employees and gather information about their experiences and perspectives.

What Is Quantitative Research?

Quantitative research is for testing hypotheses and measuring relationships between variables. It follows the process of objectively collecting data and analyzing it numerically, to determine and control variables of interest. This type of research aims to test causal relationships between variables and provide generalized results. These results determine if the theory proposed for the research study could be accepted or rejected.

When to Use Quantitative Research?

Quantitative research is used when a research study needs to confirm or test a theory or a hypothesis. When a research study is focused on measuring and quantifying data, using a quantitative approach is appropriate. It is often used in fields such as economics, marketing, or biology, where researchers are interested in studying trends and relationships between variables .

Benefits of Quantitative Research

• Quantitative data is interpreted with statistical analysis . The type of statistical study is based on the principles of mathematics and it provides a fast, focused, scientific and relatable approach.
• Quantitative research creates an ability to replicate the test and results of research. This approach makes the data more reliable and less open to argument.
• After collecting the quantitative data, expected results define which statistical tests are applicable and results provide a quantifiable conclusion for the research hypothesis
• Research with complex statistical analysis is considered valuable and impressive. Quantitative research is associated with technical advancements like computer modeling and data-based decisions.

Quantitative Research Example

An organization wishes to conduct a customer satisfaction (CSAT) survey by using a survey template. From the survey, the organization can acquire quantitative data and metrics on the brand or the organization based on the customer’s experience. Various parameters such as product quality, pricing, customer experience, etc. could be used to generate data in the form of numbers that is statistically analyzed.

Data Collection Methods

1. qualitative data collection methods.

Qualitative data is collected from interview sessions, discussions with focus groups, case studies, and ethnography (scientific description of people and cultures with their customs and habits). The collection methods involve understanding and interpreting social interactions.

Qualitative research data also includes respondents’ opinions and feelings, which is conducted face-to-face mostly in focus groups. Respondents are asked open-ended questions either verbally or through discussion among a group of people, related to the research topic implemented to collect opinions for further research.

2. Quantitative Data Collection Methods

Quantitative research data is acquired from surveys, experiments, observations, probability sampling, questionnaire observation, and content review. Surveys usually contain a list of questions with multiple-choice responses relevant to the research topic under study. With the availability of online survey tools, researchers can conduct a web-based survey for quantitative research.

Quantitative data is also assimilated from research experiments. While conducting experiments, researchers focus on exploring one or more independent variables and studying their effect on one or more dependent variables.

A Step-wise Guide to Conduct Qualitative and Quantitative Research

• Understand the difference between types of research — qualitative, quantitative, or mixed-methods-based research.
• Develop a research question or hypothesis. This research approach will define which type of research one could choose.
• Choose a method for data collection. Depending on the process of data collection, the type of research could be determined.
• Analyze and interpret the collected data. Based on the analyzed data, results are reported.
• If observed results are not equivalent to expected results, consider using an unbiased research approach or choose both qualitative and quantitative research methods for preferred results.

Qualitative Vs. Quantitative Research – A Comparison

With an awareness of qualitative vs. quantitative research and the different data collection methods , researchers could use one or both types of research approaches depending on their preferred results. Moreover, to implement unbiased research and acquire meaningful insights from the research study, it is advisable to consider both qualitative and quantitative research methods .

Through this article, you would have understood the comparison between qualitative and quantitative research. However, if you have any queries related to qualitative vs. quantitative research, do comment below or email us.

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Psychological Research

Quantitative and qualitative approaches to research.

When designing a study, typically, researchers choose a quantitative or qualitative research design. In some cases, a mixed-method approach may be appropriate. Which approach used will develop on the research question and the type of information sought. Quantitative methods may be better for understanding what is happening, while qualitative methods may be better for understanding the hows and why of a phenomenon.

Video 2.3.1.  Types of Research explains the difference between qualitative and quantitative research. A closed-captioned version of this video is available here .

Quantitative Research

Quantitative research typically starts with a focused research question or hypothesis, collects a small amount of data from each of a large number of individuals, describes the resulting data using statistical techniques, and draws general conclusions about some large population. The strength of quantitative research is its ability to provide precise answers to specific research questions and to draw general conclusions about human behavior; however, it is not nearly as good at  generating   novel and interesting research questions. Likewise, while quantitative research is good at drawing general conclusions about human behavior, it is not nearly as good at providing detailed descriptions of the behavior of particular groups in particular situations. And it is not very good at all at communicating what it is actually like to be a member of a particular group in a particular situation. But the relative weaknesses of quantitative research are the relative strengths of qualitative research.

Qualitative Research

Although this is by far the most common approach to conducting empirical research in psychology, there is a vital alternative called qualitative research . Qualitative research can help researchers to generate new and interesting research questions and hypotheses. Qualitative researchers generally begin with a less focused research question, collect large amounts of relatively “unfiltered” data from a relatively small number of individuals, and describe their data using nonstatistical techniques. They are usually less concerned with drawing general conclusions about human behavior than with understanding in detail the  experience   of their research participants. Qualitative research can also provide rich and detailed descriptions of human behavior in the real-world contexts in which it occurs. Similarly, qualitative research can convey a sense of what it is actually like to be a member of a particular group or in a particular situation—what qualitative researchers often refer to as the ‘lived experience’ of the research participants.

Mixed-Methods

Given their differences, it may come as no surprise that quantitative and qualitative research do not coexist in complete harmony. Some quantitative researchers criticize that qualitative methods lack objectivity, are challenging to evaluate, and do not allow generalization to other people or situations. At the same time, some qualitative researchers criticize that quantitative methods overlook the richness of behavior and experience, and instead answer simple questions about easily quantifiable variables. However, many researchers from both camps now agree that the two approaches can and should be combined into what has come to be called mixed-methods research (Todd, Nerlich, McKeown, & Clarke, 2004). One approach to combining quantitative and qualitative research is to use qualitative research for hypothesis generation and quantitative research for hypothesis testing. A second approach to combining quantitative and qualitative research is referred to as triangulation. The idea is to use both quantitative and qualitative methods simultaneously to study the same general questions and to compare the results. If the results of the quantitative and qualitative methods converge on the same general conclusion, they reinforce and enrich each other. If the results diverge, then they suggest an interesting new question: Why do the results diverge, and how can they be reconciled?

Video 2.3.2.  What are Qualitative and Quantitative Variables explains the difference between quantitative and qualitative variables that may be used in research.

Becoming Familiar with Research

An excellent way to become more familiar with these research approaches, both quantitative and qualitative, is to look at journal articles, which are written in sections that follow these steps in the scientific process. Most psychological articles and many papers in the social sciences follow the writing guidelines and format dictated by the American Psychological Association (APA). In general, the structure follows: abstract (summary of the article), introduction or literature review, methods explaining how the study was conducted, results of the study, discussion and interpretation of findings, and references.

The aftermath of teenage suicide: a qualitative study of the psychosocial consequences for the supervising family

Per Lindqvist and his colleagues (2008), wanted to learn how the families of teenage suicide victims cope with their loss. They did not have a specific research question or hypothesis, such as, what percentage of family members join suicide support groups? Instead, they wanted to understand the variety of reactions that families had, with a focus on what it is like from  their  perspectives. To do this, they interviewed the families of 10 teenage suicide victims in their homes in rural Sweden. The interviews were relatively unstructured, beginning with a general request for the families to talk about the victim and ending with an invitation to talk about anything else that they wanted to tell the interviewer. One of the most important themes that emerged from these interviews was that even as life returned to “normal,” the families continued to struggle with the question of why their loved one committed suicide. This struggle appeared to be especially difficult for families in which the suicide was most unexpected. This relationship can now be explored using quantitative research. But it is unclear whether this question would have arisen at all without the researchers sitting down with the families and listening to what they themselves wanted to say about their experience.

• Quantitative and Qualitative Approaches to Research. Authored by : Nicole Arduini-Van Hoose. Provided by : Hudson Valley Community College. Located at : https://courses.lumenlearning.com/adolescent/chapter/quantitative-and-qualitative-approaches-to-research/ . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
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Research Approaches: Quantitative and Qualitative Research Approaches

Owing to the confusion surrounding research designs and approaches, this guide briefly explores two major research paradigms, namely qualitative and quantitative along with a ‘tier approach’, namely Mixed Method Approach (Creswell, 2003). It should be noted that all the research designs fall either under qualitative or quantitative approach or both, that is mixed research approach.  

Quantitative Research Approach

Quantitative research approach is driven by the researchers with the need to quantify data. It involves a numeric or statistical approach to research design. It is specific in its surveying and experimentation, as it builds upon existing theories. The methodology of a quantitative research maintains the assumption of an empiricist paradigm (Creswell, 2003). The research itself is independent of the researcher. As a result, data is used to objectively measure reality. Quantitative research creates meaning through objectivity uncovered in the collected data. Quantitative researchers seek explanations and predictions that can be generalized to other persons and places. The intent of this approach is to establish, confirm, or validate relationships and to develop generalizations that can contribute to theory (Leedy & Ormrod, 2001, p. 102).

Quantitative research begins with a problem statement and involves the formation of a hypothesis, a literature review, and a quantitative data analysis. Creswell (2003) states that quantitative research “employ strategies of inquiry such as experimental and surveys, and collect data on predetermined instruments that yield statistical data” (p. 18). The findings from quantitative research can be predictive, explanatory, and confirming.

Qualitative Research Approach

Qualitative research is an unfolding model that occurs in a natural setting that enables the researcher to develop a level of detail from high involvement in the actual experiences (Creswell, 1994). One identifier of a qualitative research is the social phenomenon being investigated from the participant’s viewpoint. There are different types of research designs that use qualitative research approach. These include case study, ethnography study, phenomenological study, grounded theory study, and content analysis. These five areas are representative of research that is built upon inductive reasoning and associated methodologies.

What constitutes qualitative research involves purposeful use for describing, explaining, and interpreting the collected data. Leedy and Ormrod (2001) allege that qualitative research is less structured in description because it formulates and builds new theories. It is an effective model that occurs in a natural setting that enables the researcher to develop a level of detail from being highly involved in the actual experiences (Creswell, 2003).

Conclusion:  Quantitative and qualitative research methods investigate and explore the different claims to knowledge and both methods are designed to address a specific type of research question. While the quantitative method provides an objective measure of reality, the qualitative method allows the researcher to explore and better understand the complexity of a phenomenon in a subjective manner. Although each approach seeks to validate sensory knowledge as truth, neither is absolute in its form and neither is superior to the other.

Creswell, J. W. (1994). Research design: Qualitative and quantitative approaches . Thousand Oaks, CA: SAGE Publications.

___________ (2003). Research design: Qualitative, quantitative and mixed methods approaches (2nd ed.). Thousand Oaks, CA: SAGE Publications.

Leedy, P. & Ormrod, J. (2001). Practical research: Planning and design (7th ed.). Upper Saddle River, NJ: Merrill Prentice Hall. Thousand Oaks: SAGE Publications.

Johnson, R. B. & Onwuegbuzie, A. J. (2004). Mixed methods research: A research paradigm whose time has come. Educational Researcher, 33(7), 14-26.

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From Qualitative to Quantitative | Online Guide to Combining Q&A with Other Research Methods Article

Anh Vu • 09 Apr 2024 • 5 min read

Are you frustrated with the limitations of your research methods? Many methods have their drawbacks, resulting in incomplete insights.  But there’s an innovative approach that combines qualitative and quantitative methods with Q&A sessions. This article will demonstrate how combining these methods can help you access more data and insights.

Table of Contents

Understanding qualitative and quantitative research, steps to combine q&a with qualitative research methods, steps to combine q&a with quantitative research methods, common challenges when holding q&a sessions, enriching your research with q&a.

Qualitative vs. quantitative research methods differ in the type of questions they help you answer. Qualitative research, like interviews and observations, offers rich insights into people’s thoughts and behaviors. It’s all about understanding the “why” behind actions. 

Conversely, quantitative research focuses on numbers and measurements, giving us clear statistical trends and patterns to answer questions like “what” or “when.” Surveys and experiments fall into this category.

Each method has its limitations, which a Q&A session can help with. Results and conclusions from qualitative methods might only apply to some because of the small sample size. Q&A can help by getting more opinions from a wider group. On the other hand, quantitative methods give you numbers, but they might miss the details.

With Q&A, you can dig deeper into those details and understand them better. Blending qualitative and quantitative methods with Q&A helps you see the whole picture better, providing unique insights you wouldn’t have otherwise.

Picture yourself investigating customer satisfaction in a restaurant for your master degree . Alongside interviews and observations, you organize a Q&A session. Merging Q&A insights with qualitative findings can lead to detailed insights for informed decision-making, such as optimizing staffing during busy hours. Here’s an example of how you do it:

• Plan your Q&A session: Choose the timing, location, and participants for your session. For instance, consider holding it during quiet times in the restaurant, inviting regular and occasional customers to share feedback. You can also have a virtual session. However, remember that attendees may only be engaged for part of the session, which can impact the quality of their responses.
• Conduct the Q&A session: Encourage a welcoming atmosphere to boost participation. Start with a warm introduction, express gratitude for attendance, and explain how their input will improve the restaurant experience.
• Document responses: Take detailed notes during the session to capture critical points and noteworthy quotes. Document customer comments about specific menu items or praises for staff friendliness.
• Analyze Q&A data: Review your notes and recordings, searching for recurring themes or observations. Compare these insights with your previous research to spot patterns, like common complaints about long wait times during peak hours.
• Integrate findings: Combine Q&A insights with other research data to gain a better understanding. Identify connections between data sources, such as Q&A feedback confirming survey responses about service speed dissatisfaction.
• Draw conclusions and make recommendations: Summarize your findings and propose actionable steps. For instance, suggest adjusting staffing levels or implementing a reservation system to address the issues.

Now, let’s shift to another scenario. Imagine you’re exploring factors influencing online shopping behavior to refine marketing strategies as part of your online executive MBA requirements. Alongside a questionnaire with effective survey questions , you add Q&A sessions to your method for deeper insights. Here’s how to combine Q&A with quantitative methods:

• Plan your research design: Determine how Q&A sessions align with your quantitative objectives. Schedule sessions to complement survey data collection, perhaps before or after distributing online surveys.
• Structure Q&A sessions: Craft questions to gather qualitative insights alongside quantitative data. Use a mix of open-ended questions to explore motivations and closed-ended queries for statistical analysis.
• Administer surveys: To collect numerical data, you must send surveys to a broader audience. A study on response rates found that sending online surveys can generate a 44.1% response rate. To increase this response rate, refine your population. Ensure the survey questions align with research objectives and are related to the qualitative insights from Q&A sessions.
• Analyze combined data: Combine Q&A insights with survey data to see shopping trends. Find connections between qualitative feedback on user preferences and quantitative data on purchasing habits. For example, dark roast coffee lovers from your Q&A session might indicate in their surveys that they buy more coffee bags per month than your medium roast lovers.
• Interpret and report findings: Present results clearly, highlighting critical insights from qualitative and quantitative perspectives. Use visuals like charts or graphs to show trends effectively.
• Draw implications and recommendations: Based on combined qualitative and quantitative data analysis, provide practical suggestions that can be implemented. For example, recommend customized marketer strategies that attract your medium roast coffee lovers and drive profit.

Hosting Q&A sessions can be tricky, but technology offers solutions to make them smoother. For example, the global presentation software market is expected to grow by 13.5% from 2024 to 2031, emphasizing its growing importance. Here are some common hurdles you might face, along with how technology can help:

• Limited Participation: Encouraging everyone to join in can take time and effort. Here, virtual Q&A sessions can help, allowing participants to ask questions via their phones and the internet, making involvement easy. You can also offer incentives or rewards, or use an AI presentation maker to create engaging slides.
• Managing Time Effectively: Balancing time while covering all topics is a challenge. You can address this issue with tools that allow you to approve or deny questions before they appear. You can also set a time limit for discussions.
• Handling Difficult Questions: Tough questions need careful handling. Allowing anonymity is an effective strategy for this challenge. It helps people feel safer asking difficult questions, promoting honest discussions without fear of judgment.
• Ensuring Quality Responses: Getting informative responses is vital to a productive Q&A session. Likewise, customizing the Q&A slide with bright backgrounds and fonts keeps participants engaged and ensures effective communication.
• Navigating Technical Issues: Technical issues can interrupt sessions. Some tools offer helpful features to help you avoid this issue. Allowing participants to upvote questions, for example, can help you prioritize important questions. You could also prepare backup devices for audio and video recordings so you don’t have to worry about losing your data.

Throughout this article, we’ve seen how combining Q&A with other research methods can unlock a wealth of insights that may not be possible through a single method. Whether you’re using Q&A to supplement qualitative research or combining it with quantitative research, the approach can help you gain a more comprehensive understanding of your topic.

Remember to communicate openly, listen attentively, and stay flexible. Following the steps outlined in this article, you can integrate Q&A sessions into your research design and emerge with better, more detailed insights. 

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Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review

• LIFE CYCLE SUSTAINABILITY ASSESSMENT
• Published: 02 March 2022

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• Cecilia Silvestri   ORCID: orcid.org/0000-0003-2528-601X 1 ,
• Luca Silvestri   ORCID: orcid.org/0000-0002-6754-899X 2 ,
• Michela Piccarozzi   ORCID: orcid.org/0000-0001-9717-9462 1 &
• Alessandro Ruggieri 1  

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A Correction to this article was published on 24 March 2022

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The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector.

Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes.

The analysis of the results allowed us to classify the sample of articles in three main clusters (“Assessment-LCA,” “Best practice,” and “Decision-making”) and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability.

Conclusions

The sample analysis facilitated the identification of new categories of impact that deserve attention, such as the cooperation among stakeholders in the supply chain and eco-innovation.

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Source: Authors’ elaboration. Notes: The graph shows the temporal distribution of the articles under analysis

Source: Authors’ elaborations. Notes: The graph shows the time distribution of articles from the three major journals

Source: Authors’ elaboration. Notes: The graph shows the composition of the sample according to the three clusters identified by the analysis

Source: Authors’ elaboration. Notes: The graph shows the distribution of articles over time by cluster

Source: Authors’ elaboration. Notes: The graph shows the network visualization

Source: Authors’ elaboration. Notes: The graph shows the overlay visualization

Source: Authors’ elaboration. Notes: The graph shows the classification of articles by scientific field

Source: Authors’ elaboration. Notes: Article classification based on their cluster to which they belong and scientific field

Source: Authors’ elaboration

Source: Authors’ elaboration. Notes: The graph shows the distribution of items over time based on TBL

Source: Authors’ elaboration. Notes: The graph shows the Pareto diagram highlighting the most used indicators in literature for measuring sustainability in the agri-food sector

Source: Authors’ elaboration. Notes: The graph shows the distribution over time of articles divided into conceptual and empirical

Source: Authors’ elaboration. Notes: The graph shows the classification of articles, divided into conceptual and empirical, in-depth analysis

Source: Authors’ elaboration. Notes: The graph shows the geographical distribution of the authors

Source: Authors’ elaboration. Notes: The graph shows the distribution of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: The graph shows the time distribution of publication of authors according to the continent from which they originate

Source: Authors’ elaboration. Notes: Sustainability measurement indicators and impact categories of LCA, S-LCA, and LCC tools should be integrated in order to provide stakeholders with best practices as guidelines and tools to support both decision-making and measurement, according to the circular economy approach

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A Correction to this paper has been published: https://doi.org/10.1007/s11367-022-02038-9

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Silvestri, C., Silvestri, L., Piccarozzi, M. et al. Toward a framework for selecting indicators of measuring sustainability and circular economy in the agri-food sector: a systematic literature review. Int J Life Cycle Assess (2022). https://doi.org/10.1007/s11367-022-02032-1

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Choosing a Qualitative Research Approach

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Editor's Note: The online version of this article contains a list of further reading resources and the authors' professional information .

The Challenge

Educators often pose questions about qualitative research. For example, a program director might say: “I collect data from my residents about their learning experiences in a new longitudinal clinical rotation. If I want to know about their learning experiences, should I use qualitative methods? I have been told that there are many approaches from which to choose. Someone suggested that I use grounded theory, but how do I know this is the best approach? Are there others?”

What Is Known

Qualitative research is the systematic inquiry into social phenomena in natural settings. These phenomena can include, but are not limited to, how people experience aspects of their lives, how individuals and/or groups behave, how organizations function, and how interactions shape relationships. In qualitative research, the researcher is the main data collection instrument. The researcher examines why events occur, what happens, and what those events mean to the participants studied. 1 , 2

Qualitative research starts from a fundamentally different set of beliefs—or paradigms—than those that underpin quantitative research. Quantitative research is based on positivist beliefs that there is a singular reality that can be discovered with the appropriate experimental methods. Post-positivist researchers agree with the positivist paradigm, but believe that environmental and individual differences, such as the learning culture or the learners' capacity to learn, influence this reality, and that these differences are important. Constructivist researchers believe that there is no single reality, but that the researcher elicits participants' views of reality. 3 Qualitative research generally draws on post-positivist or constructivist beliefs.

Qualitative scholars develop their work from these beliefs—usually post-positivist or constructivist—using different approaches to conduct their research. In this Rip Out, we describe 3 different qualitative research approaches commonly used in medical education: grounded theory, ethnography, and phenomenology. Each acts as a pivotal frame that shapes the research question(s), the method(s) of data collection, and how data are analyzed. 4 , 5

Choosing a Qualitative Approach

Before engaging in any qualitative study, consider how your views about what is possible to study will affect your approach. Then select an appropriate approach within which to work. Alignment between the belief system underpinning the research approach, the research question, and the research approach itself is a prerequisite for rigorous qualitative research. To enhance the understanding of how different approaches frame qualitative research, we use this introductory challenge as an illustrative example.

The clinic rotation in a program director's training program was recently redesigned as a longitudinal clinical experience. Resident satisfaction with this rotation improved significantly following implementation of the new longitudinal experience. The program director wants to understand how the changes made in the clinic rotation translated into changes in learning experiences for the residents.

Qualitative research can support this program director's efforts. Qualitative research focuses on the events that transpire and on outcomes of those events from the perspectives of those involved. In this case, the program director can use qualitative research to understand the impact of the new clinic rotation on the learning experiences of residents. The next step is to decide which approach to use as a frame for the study.

The table lists the purpose of 3 commonly used approaches to frame qualitative research. For each frame, we provide an example of a research question that could direct the study and delineate what outcomes might be gained by using that particular approach.

Methodology Overview

How You Can Start TODAY

• 1 Examine the foundations of the existing literature: As part of the literature review, make note of what is known about the topic and which approaches have been used in prior studies. A decision should be made to determine the extent to which the new study is exploratory and the extent to which findings will advance what is already known about the topic.
• 2 Find a qualitatively skilled collaborator: If you are interested in doing qualitative research, you should consult with a qualitative expert. Be prepared to talk to the qualitative scholar about what you would like to study and why . Furthermore, be ready to describe the literature to date on the topic (remember, you are asking for this person's expertise regarding qualitative approaches—he or she won't necessarily have content expertise). Qualitative research must be designed and conducted with rigor (rigor will be discussed in Rip Out No. 8 of this series). Input from a qualitative expert will ensure that rigor is employed from the study's inception.
• 3 Consider the approach: With a literature review completed and a qualitatively skilled collaborator secured, it is time to decide which approach would be best suited to answering the research question. Questions to consider when weighing approaches might include the following:
• • Will my findings contribute to the creation of a theoretical model to better understand the area of study? ( grounded theory )
• • Will I need to spend an extended amount of time trying to understand the culture and process of a particular group of learners in their natural context? ( ethnography )
• • Is there a particular phenomenon I want to better understand/describe? ( phenomenology )

What You Can Do LONG TERM

• 1 Develop your qualitative research knowledge and skills : A basic qualitative research textbook is a valuable investment to learn about qualitative research (further reading is provided as online supplemental material). A novice qualitative researcher will also benefit from participating in a massive online open course or a mini-course (often offered by professional organizations or conferences) that provides an introduction to qualitative research. Most of all, collaborating with a qualitative researcher can provide the support necessary to design, execute, and report on the study.
• 2 Undertake a pilot study: After learning about qualitative methodology, the next best way to gain expertise in qualitative research is to try it in a small scale pilot study with the support of a qualitative expert. Such application provides an appreciation for the thought processes that go into designing a study, analyzing the data, and reporting on the findings. Alternatively, if you have the opportunity to work on a study led by a qualitative expert, take it! The experience will provide invaluable opportunities for learning how to engage in qualitative research.

Supplementary Material

The views expressed in this article are those of the authors and do not necessarily reflect the official policy or position of the Uniformed Services University of the Health Sciences, the Department of the Navy, the Department of Defense, or the US government.

References and Resources for Further Reading

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This paper is in the following e-collection/theme issue:

Published on 11.4.2024 in Vol 26 (2024)

Patients’ Experiences With Digitalization in the Health Care System: Qualitative Interview Study

Authors of this article:

Original Paper

• Christian Gybel Jensen 1 * , MA   ; 
• Frederik Gybel Jensen 1 * , MA   ; 
• Mia Ingerslev Loft 1, 2 * , MSc, PhD  

1 Department of Neurology, Rigshospitalet, Copenhagen, Denmark

2 Institute for People and Technology, Roskilde University, Roskilde, Denmark

*all authors contributed equally

Corresponding Author:

Mia Ingerslev Loft, MSc, PhD

Department of Neurology

Rigshospitalet

Inge Lehmanns Vej 8

Phone: 45 35457076

Email: [email protected]

Background: The digitalization of public and health sectors worldwide is fundamentally changing health systems. With the implementation of digital health services in health institutions, a focus on digital health literacy and the use of digital health services have become more evident. In Denmark, public institutions use digital tools for different purposes, aiming to create a universal public digital sector for everyone. However, this digitalization risks reducing equity in health and further marginalizing citizens who are disadvantaged. Therefore, more knowledge is needed regarding patients’ digital practices and experiences with digital health services.

Objective: This study aims to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients in the neurology field and address the following research questions: (1) How do patients use digital services and digital tools? (2) How do they experience them?

Methods: We used a qualitative design with a hermeneutic approach. We conducted 31 semistructured interviews with patients who were hospitalized or formerly hospitalized at the department of neurology in a hospital in Denmark. The interviews were audio recorded and subsequently transcribed. The text from each transcribed interview was analyzed using manifest content analysis.

Results: The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools. Our findings show that digital tools were experienced differently, and specific conditions were important for the possibility of engaging in digital practices, including having access to social resources; possessing physical, cognitive, and communicative capabilities; and feeling motivated, secure, and comfortable. These prerequisites were necessary for participants to have positive experiences using digital tools in the health care system. Those who did not have these prerequisites experienced challenges and, in some cases, felt left out.

Conclusions: Experiences with digital practices and digital health services are complex and multifaceted. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges. To ensure that digitalization does not create inequities in health, it is necessary for developers and institutions to be aware of the differences in digital health literacy, focus on simplifying communication with patients and next of kin, and find flexible solutions for citizens who are disadvantaged.

Introduction

In 2022, the fourth most googled question in Denmark was, “Why does MitID not work?” [ 1 ]. MitID (My ID) is a digital access tool that Danes use to enter several different private and public digital services, from bank accounts to mail from their municipality or the state. MitID is a part of many Danish citizens’ everyday lives because the public sector in Denmark is digitalized in many areas. In recent decades, digitalization has changed how governments and people interact and has demonstrated the potential to change the core functions of public sectors and delivery of public policies and services [ 2 ]. When public sectors worldwide become increasingly digitalized, this transformation extends to the public health sectors as well, and some studies argue that we are moving toward a “digital public health era” that is already impacting the health systems and will fundamentally change the future of health systems [ 3 ]. While health systems are becoming more digitalized, it is important that both patients and digitalized systems adapt to changes in accordance with each other. Digital practices of people can be understood as what people do with and through digital technologies and how people relate to technology [ 4 ]. Therefore, it is relevant to investigate digital practices and how patients perceive and experience their own use of digital tools and services, especially in relation to existing digital health services. In our study, we highlight a broad perspective on experiences with digital practices and particularly add insight into the challenges with digital practices faced by patients who have acute or chronic illness, with some of them also experiencing physical, communicative, or cognitive difficulties.

An international Organization for Economic Cooperation and Development report indicates that countries are digitalized to different extents and in different ways; however, this does not mean that countries do not share common challenges and insights into the implementation of digital services [ 2 ].

In its global Digital Government Index, Denmark is presented as one of the leading countries when it comes to public digitalization [ 2 ]. Recent statistics indicate that approximately 97% of Danish families have access to the internet at home [ 5 ]. The Danish health sector already offers many different digital services, including web-based delivery of medicine, e-consultations, patient-related outcome questionnaires, and seeking one’s own health journal or getting test results through; “Sundhed” [ 6 ] (the national health portal) and “Sundhedsjournalen” (the electronic patient record); or the apps “Medicinkortet” (the shared medication record), “Minlæge” (My Doctor, consisting of, eg, communication with the general practitioner), or “MinSP” (My Health Platform, consisting of, eg, communication with health care staff in hospitals) [ 6 - 8 ].

The Danish Digital Health Strategy from 2018 aims to create a coherent and user-friendly digital public sector for everyone [ 9 ], but statistics indicate that certain groups in society are not as digitalized as others. In particular, the older population uses digital services the least, with 5% of people aged 65 to 75 years and 18% of those aged 75 to 89 years having never used the internet in 2020 [ 5 ]. In parts of the literature, it has been problematized how the digitalization of the welfare state is related to the marginalization of older citizens who are socially disadvantaged [ 10 ]. However, statistics also indicate that the probability of using digital tools increases significantly as a person’s experience of using digital tools increases, regardless of their age or education level [ 5 ].

Understanding the digital practices of patients is important because they can use digital tools to engage with the health system and follow their own health course. Researching experiences with digital practices can be a way to better understand potential possibilities and barriers when patients use digital health services. With patients becoming more involved in their own health course and treatment, the importance of patients’ health literacy is being increasingly recognized [ 11 ]. The World Health Organization defines health literacy as the “achievement of a level of knowledge, personal skills and confidence to take action to improve personal and community health by changing personal lifestyles and living conditions” [ 12 ]. Furthermore, health literacy can be described as “a person’s knowledge and competencies to meet complex demands of health in modern society, ” and it is viewed as a critical step toward patient empowerment [ 11 , 12 ]. In a digitalized health care system, this also includes the knowledge, capabilities, and resources that individuals require to use and benefit from eHealth services, that is, “digital health literacy (eHealth literacy)” [ 13 ]. An eHealth literacy framework created by Norgaard et al [ 13 ] identified that different aspects, for example, the ability to process information and actively engage with digital services, can be viewed as important facets of digital health literacy. This argument is supported by studies that demonstrate how patients with cognitive and communicative challenges experience barriers to the use of digital tools and require different approaches in the design of digital solutions in the health sector [ 14 , 15 ]. Access to digital services and digital literacy is becoming increasingly important determinants of health, as people with digital literacy and access to digital services can facilitate improvement of health and involvement in their own health course [ 16 ].

The need for a better understanding of eHealth literacy and patients’ capabilities to meet public digital services’ demands as well as engage in their own health calls for a deeper investigation into digital practices and the use of digital tools and services from the perspective of patients with varying digital capabilities. Important focus areas to better understand digital practices and related challenges have already been highlighted in various studies. They indicate that social support, assessment of value in digital services, and systemic assessment of digital capabilities are important in the use and implementation of digital tools, and they call for better insight into complex experiences with digital services [ 13 , 17 , 18 ]. Therefore, we aimed to examine digital practices and experiences with public digital health services and digital tools from the perspective of patients, addressing the following research questions: how do patients use digital services and digital tools, and how do they experience them?

We aimed to investigate digital practices and experiences with digital health services and digital tools; therefore, we used a qualitative design and adopted a hermeneutic approach as the point of departure, which means including preexisting knowledge of digital practices but also providing room for new comprehension [ 19 ]. Our interpretive approach is underpinned by the philosophical hermeneutic approach by Gadamer et al [ 19 ], in which they described the interpretation process as a “hermeneutic circle,” where the researcher enters the interpretation process with an open mind and historical awareness of a phenomenon (preknowledge). We conducted semistructured interviews using an interview guide. This study followed the COREQ (Consolidated Criteria for Reporting Qualitative Research) checklist [ 20 ].

Setting and Participants

To gain a broad understanding of experiences with public digital health services, a purposive sampling strategy was used. All 31 participants were hospitalized or formerly hospitalized patients in a large neurological department in the capital of Denmark ( Table 1 ). We assessed whether including patients from the neurological field would give us a broad insight into the experiences of digital practices from different perspectives. The department consisted of, among others, 8 inpatient units covering, for example, acute neurology and stroke units, from which the patients were recruited. Patients admitted to a neurological department can have both acute and transient neurological diseases, such as infections in the brain, stroke, or blood clot in the brain from which they can recover completely or have persistent physical and mental difficulties, or experience chronic neurological and progressive disorders such as Parkinson disease and dementia. Some patients hospitalized in neurological care will have communicative and cognitive difficulties because of their neurological disorders. Nursing staff from the respective units helped the researchers (CGJ, FGJ, and MIL) identify patients who differed in terms of gender, age, and severity of neurological illness. Some patients (6/31, 19%) had language difficulties; however, a speech therapist assessed them as suitable participants. We excluded patients with severe cognitive difficulties and those who were not able to speak the Danish language. Including patients from the field of neurology provided an opportunity to study the experience of digital health practice from various perspectives. Hence, the sampling strategy enabled the identification and selection of information-rich participants relevant to this study [ 21 ], which is the aim of qualitative research. The participants were invited to participate by either the first (CGJ) or last author (MIL), and all invited participants (31/31, 100%) chose to participate.

All 31 participants were aged between 40 to 99 years, with an average age of 71.75 years ( Table 1 ). Out of the 31 participants, 10 (32%) had physical disabilities or had cognitive or communicative difficulties due to sequela in relation to neurological illness or other physical conditions.

Data Collection

The 31 patient interviews were conducted over a 2-month period between September and November 2022. Of the 31 patients, 20 (65%) were interviewed face-to-face at the hospital in their patient room upon admission and 11 (35%) were interviewed on the phone after being discharged. The interviews had a mean length of 20.48 minutes.

We developed a semistructured interview guide ( Table 2 ). The interview questions were developed based on the research aim, findings from our preliminary covering of literature in the field presented in the Introduction section, and identified gaps that we needed to elaborate on to be able to answer our research question [ 22 ]. The semistructured interview guide was designed to support the development of a trusting relationship and ensure the relevance of the interviews’ content [ 22 ]. The questions served as a prompt for the participants and were further supported by questions such as “please tell me more” and “please elaborate” throughout the interview, both to heighten the level of detail and to verify our understanding of the issues at play. If the participant had cognitive or communicative difficulties, communication was supported using a method called Supported Communication for Adults with Aphasia [ 23 ] during the interview.

The interviews were performed by all authors (CGJ, FGJ, and MIL individually), who were skilled in conducting interviews and qualitative research. The interviewers are not part of daily clinical practice but are employed in the department of neurology from where the patients were recruited. All interviews were audio recorded and subsequently transcribed verbatim by all 3 authors individually.

a PRO: patient-related outcome.

Data Analysis

The text from each transcribed interview was analyzed using manifest content analysis, as described by Graneheim and Lundman [ 24 ]. Content analysis is a method of analyzing written, verbal, and visual communication in a systematic way [ 25 ]. Qualitative content analysis is a structured but nonlinear process that requires researchers to move back and forth between the original text and parts of the text during the analysis. Manifest analysis is the descriptive level at which the surface structure of the text central to the phenomenon and the research question is described. The analysis was conducted as a collaborative effort between the first (CGJ) and last authors (MIL); hence, in this inductive circular process, to achieve consistency in the interpretation of the text, there was continued discussion and reflection between the researchers. The transcriptions were initially read several times to gain a sense of the whole context, and we analyzed each interview. The text was initially divided into domains that reflected the lowest degree of interpretation, as a rough structure was created in which the text had a specific area in common. The structure roughly reflected the interview guide’s themes, as guided by Graneheim and Lundman [ 24 ]. Thereafter, the text was divided into meaning units, condensed into text-near descriptions, and then abstracted and labeled further with codes. The codes were categorized based on similarities and differences. During this process, we discussed the findings to reach a consensus on the content, resulting in the final 4 categories presented in this paper.

Ethical Considerations

The interviewees received oral and written information about the study and its voluntary nature before the interviews. Written informed consent was obtained from all participants. Participants were able to opt of the study at any time. Data were anonymized and stored electronically on locked and secured servers. The Ethics Committee of the Capitol Region in Denmark was contacted before the start of the study. This study was registered and approved by the ethics committee and registered under the Danish Data Protection Agency (number P2021-839). Furthermore, the ethical principles of the Declaration of Helsinki were followed for this study.

The analysis provided insights into 4 different categories regarding digital practices and experiences of using digital tools and services in health care systems: social resources as a digital lifeline, possessing the necessary capabilities, big feelings as facilitators or barriers, and life without digital tools.

Social Resources as a Digital Lifeline

Throughout the analysis, it became evident that access to both material and social resources was of great importance when using digital tools. Most participants already possessed and had easy access to a computer, smartphone, or tablet. The few participants who did not own the necessary digital tools told us that they did not have the skills needed to use these tools. For these participants, the lack of material resources was tied particularly to a lack of knowledge and know-how, as they expressed that they would not know where to start after buying a computer—how to set it up, connect it to the internet, and use its many systems.

However, possessing the necessary material resources did not mean that the participants possessed the knowledge and skill to use digital tools. Furthermore, access to material resources was also a question of having access to assistance when needed. Some participants who had access to a computer, smartphone, and tablet and knew how to use these tools still had to obtain help when setting up hardware, updating software, or getting a new device. These participants were confident in their own ability to use digital devices but also relied on family, friends, and neighbors in their everyday use of these tools. Certain participants were explicitly aware of their own use of social resources when expressing their thoughts on digital services in health care systems:

I think it is a blessing and a curse. I think it is both. I would say that if I did not have someone around me in my family who was almost born into the digital world, then I think I would be in trouble. But I feel sorry for those who do not have that opportunity, and I know quite a few who do not. They get upset, and it’s really frustrating. [Woman, age 82 years]

The participants’ use of social resources indicates that learning skills and using digital tools are not solely individual tasks but rather continuously involve engagement with other people, particularly whenever a new unforeseen problem arises or when the participants want a deeper understanding of the tools they are using:

If tomorrow I have to get a new ipad...and it was like that when I got this one, then I had to get XXX to come and help me move stuff and he was sweet to help with all the practical stuff. I think I would have cursed a couple of times (if he hadn’t been there), but he is always helpful, but at the same time he is also pedagogic so I hope that next time he showed me something I will be able to do it. [Man, age 71 years]

For some participants, obtaining assistance from a more experienced family member was experienced as an opportunity to learn, whereas for other participants, their use of public digital services was even tied directly to assistance from a spouse or family member:

My wife, she has access to mine, so if something comes up, she can just go in and read, and we can talk about it afterwards what (it is). [Man, age 85 years]

The participants used social resources to navigate digital systems and understand and interpret communication from the health care system through digital devices. Another example of this was the participants who needed assistance to find, answer, and understand questionnaires from the health care department. Furthermore, social resources were viewed as a support system that made participants feel more comfortable and safer when operating digital tools. The social resources were particularly important when overcoming unforeseen and new challenges and when learning new skills related to the use of digital tools. Participants with physical, cognitive, and communicative challenges also explained how social resources were of great importance in their ability to use digital tools.

Possessing the Necessary Capabilities

The findings indicated that possessing the desire and knowing how to use digital tools are not always enough to engage with digital services successfully. Different health issues can carry consequences for motor skills and mobility. Some of these consequences were visibly affecting how our participants interacted with digital devices, and these challenges were somewhat easy to discover. However, our participants revealed hidden challenges that posed difficulties. In some specific cases, cognitive and communicative inabilities can make it difficult to use digital tools, and this might not always be clear until the individual tries to use a device’s more complex functions. An example of this is that some participants found it easy to turn on a computer and use it to write but difficult to go through security measures on digital services or interpret and understand digital language. Remembering passwords and logging on to systems created challenges, particularly for those experiencing health issues that directly affect memory and cognitive abilities, who expressed concerns about what they were able to do through digital tools:

I think it is very challenging because I would like to use it how I used to before my stroke; (I) wish that everything (digital skills) was transferred, but it just isn’t. [Man, age 80 years]

Despite these challenges, the participants demonstrated great interest in using digital tools, particularly regarding health care services and their own well-being. However, sometimes, the challenges that they experienced could not be conquered merely by motivation and good intentions. Another aspect of these challenges was the amount of extra time and energy that the participants had to spend on digital services. A patient diagnosed with Parkinson disease described how her symptoms created challenges that changed her digital practices:

Well it could for example be something like following a line in the device. And right now it is very limited what I can do with this (iPhone). Now I am almost only using it as a phone, and that is a little sad because I also like to text and stuff, but I also find that difficult (...) I think it is difficult to get an overview. [Woman, age 62 years]

Some participants said that after they were discharged from the hospital, they did not use the computer anymore because it was too difficult and too exhausting , which contributed to them giving up . Using digital tools already demanded a certain amount of concentration and awareness, and some diseases and health conditions affected these abilities further.

Big Feelings as Facilitators or Barriers

The findings revealed a wide range of digital practices in which digital tools were used as a communication device, as an entertainment device, and as a practical and informative tool for ordering medicine, booking consultations, asking health-related questions, or receiving email from public institutions. Despite these different digital practices, repeating patterns and arguments appeared when the participants were asked why they learned to use digital tools or wanted to improve their skills. A repeating argument was that they wanted to “follow the times, ” or as a participant who was still not satisfied with her digital skills stated:

We should not go against the future. [Woman, age 89 years]

The participants expressed a positive view of the technological developments and possibilities that digital devices offered, and they wanted to improve their knowledge and skills related to digital practice. For some participants, this was challenging, and they expressed frustration over how technological developments “moved too fast ,” but some participants interpreted these challenges as a way to “keep their mind sharp. ”

Another recurring pattern was that the participants expressed great interest in using digital services related to the health care system and other public institutions. The importance of being able to navigate digital services was explicitly clear when talking about finding test answers, written electronic messages, and questionnaires from the hospital or other public institutions. Keeping up with developments, communicating with public institutions, and taking an interest in their own health and well-being were described as good reasons to learn to use digital tools.

However, other aspects also affected these learning facilitators. Some participants felt alienated while using digital tools and described the practice as something related to feelings of anxiety, fear, and stupidity as well as something that demanded “a certain amount of courage. ” Some participants felt frustrated with the digital challenges they experienced, especially when the challenges were difficult to overcome because of their physical conditions:

I get sad because of it (digital challenges) and I get very frustrated and it takes a lot of time because I have difficulty seeing when I look away from the computer and have to turn back again to find out where I was and continue there (...) It pains me that I have to use so much time on it. [Man, age 71 years]

Fear of making mistakes, particularly when communicating with public institutions, for example, the health care system, was a common pattern. Another pattern was the fear of misinterpreting the sender and the need to ensure that the written electronic messages were actually from the described sender. Some participants felt that they were forced to learn about digital tools because they cared a lot about the services. Furthermore, fears of digital services replacing human interaction were a recurring concern among the participants. Despite these initial and recurring feelings, some participants learned how to navigate the digital services that they deemed relevant. Another recurring pattern in this learning process was repetition, the practice of digital skills, and consistent assistance from other people. One participant expressed the need to use the services often to remember the necessary skills:

Now I can figure it out because now I’ve had it shown 10 times. But then three months still pass... and then I think...how was it now? Then I get sweat on my forehead (feel nervous) and think; I’m not an idiot. [Woman, age 82 years]

For some participants, learning how to use digital tools demanded time and patience, as challenges had to be overcome more than once because they reappeared until the use of digital tools was more automatized into their everyday lives. Using digital tools and health services was viewed as easier and less stressful when part of everyday routines.

Life Without Digital Tools: Not a Free Choice

Even though some participants used digital tools daily, other participants expressed that it was “too late for them.” These participants did not view it as a free choice but as something they had to accept that they could not do. They wished that they could have learned it earlier in life but did not view it as a possibility in the future. Furthermore, they saw potential in digital services, including digital health care services, but they did not know exactly what services they were missing out on. Despite this lack of knowledge, they still felt sad about the position they were in. One participant expressed what she thought regarding the use of digital tools in public institutions:

Well, I feel alright about it, but it is very, very difficult for those of us who do not have it. Sometimes you can feel left out—outside of society. And when you do not have one of those (computers)...A reference is always made to w and w (www.) and then you can read on. But you cannot do that. [Woman, age 94 years]

The feeling of being left out of society was consistent among the participants who did not use digital tools. To them, digital systems seemed to provide unfair treatment based on something outside of their own power. Participants who were heavily affected by their medical conditions and could not use digital services also felt left out because they saw the advantages of using digital tools. Furthermore, a participant described the feelings connected to the use of digital tools in public institutions:

It is more annoying that it does not seem to work out in my favour. [Woman, age 62 years]

These statements indicated that it is possible for individuals to want to use digital tools and simultaneously find them too challenging. These participants were aware that there are consequences of not using digital tools, and that saddens them, as they feel like they are not receiving the same treatment as other people in society and the health care system.

Principal Findings

The insights from our findings demonstrated that our participants had different digital practices and different experiences with digital tools and services; however, the analysis also highlighted patterns related to how digital services and tools were used. Specific conditions were important for the possibility of digital practice, including having access to social resources; possessing the necessary capabilities; and feeling motivated, secure, and comfortable . These prerequisites were necessary to have positive experiences using digital tools in the health care system, although some participants who lived up to these prerequisites were still skeptical toward digital solutions. Others who did not live up to these prerequisites experienced challenges and even though they were aware of opportunities, this awareness made them feel left out. A few participants even viewed the digital tools as a threat to their participation in society. This supports the notion of Norgaard et al [ 13 ] that the attention paid to digital capability demands from eHealth systems is very important. Furthermore, our findings supported the argument of Hjeltholt and Papazu [ 17 ] that it is important to better understand experiences related to digital services. In our study, we accommodate this request and bring forth a broad perspective on experiences with digital practices; we particularly add insight into the challenges with digital practices for patients who also have acute or chronic illness, with some of them also experiencing physical, communicative, and cognitive difficulties. To our knowledge, there is limited existing literature focusing on digital practices that do not have a limited scope, for example, a focus on perspectives on eHealth literacy in the use of apps [ 26 ] or intervention studies with a focus on experiences with digital solutions, for example, telemedicine during the COVID-19 pandemic [ 27 ]. As mentioned by Hjeltholt et al [ 10 ], certain citizens are dependent on their own social networks in the process of using and learning digital tools. Rasi et al [ 28 ] and Airola et al [ 29 ] argued that digital health literacy is situated and should include the capabilities of the individual’s social network. Our findings support these arguments that access to social resources is an important condition; however, the findings also highlight that these resources can be particularly crucial in the use of digital health services, for example, when interpreting and understanding digital and written electronic messages related to one’s own health course or when dealing with physical, cognitive, and communicative disadvantages. Therefore, we argue that the awareness of the disadvantages is important if we want to understand patients’ digital capabilities, and the inclusion of the next of kin can be evident in unveiling challenges that are unknown and not easily visible or when trying to reach patients with digital challenges through digital means.

Studies by Kayser et al [ 30 ] and Kanoe et al [ 31 ] indicated that patients’ abilities to interpret and understand digital health–related services and their benefits are important for the successful implementation of eHealth services—an argument that our findings support. Health literacy in both digital and physical contexts is important if we want to understand how to better design and implement services. Our participants’ statements support the argument that communication through digital means cannot be viewed as similar to face-to-face communication and that an emphasis on digital health literacy demonstrates how health systems are demanding different capabilities from the patients [ 13 ]. We argue that it is important to communicate the purposes of digital services so that both the patient and their next of kin know why they participate and how it can benefit them. Therefore, it is important to make it as clear as possible that digital health services can benefit the patient and that these services are developed to support information, communication, and dialogue between patients and health professionals. However, our findings suggest that even after interpreting and understanding the purposes of digital health services, some patients may still experience challenges when using digital tools.

Therefore, it is important to understand how and why patients learn digital skills, particularly because both experience with digital devices and estimation of the value of digital tools have been highlighted as key factors for digital practices [ 5 , 18 ]. Our findings indicate that a combination of these factors is important, as recognizing the value of digital tools was not enough to facilitate the necessary learning process for some of our participants. Instead, our participants described the use of digital tools as complex and continuous processes in which automation of skills, assistance from others, and time to relearn forgotten knowledge were necessary and important facilitators for learning and understanding digital tools as well as becoming more comfortable and confident in the use of digital health services. This was particularly important, as it was more encouraging for our participants to learn digital tools when they felt secure, instead of feeling afraid and anxious, a point that Bailey et al [ 18 ] also highlighted. The value of digital solutions and the will to learn were greater when challenges were viewed as something to overcome and learn from instead of something that created a feeling of being stupid. This calls for attention on how to simplify and explain digital tools and services so that users do not feel alienated. Our findings also support the argument that digital health literacy should take into account emotional well-being related to digital practice [ 32 ].

The various perspectives that our participants provided regarding the use of digital tools in the health care system indicate that patients are affected by the use of digital health services and their own capabilities to use digital tools. Murray et al [ 33 ] argued that the use of digital tools in health sectors has the potential to improve health and health delivery by improving efficacy, efficiency, accessibility, safety, and personalization, and our participants also highlighted these positive aspects. However, different studies found that some patients, particularly older adults considered socially vulnerable, have lower digital health literacy [ 10 , 34 , 35 ], which is an important determinant of health and may widen disparities and inequity in health care [ 16 ]. Studies on older adult populations’ adaptation to information and communication technology show that engaging with this technology can be limited by the usability of technology, feelings of anxiety and concern, self-perception of technology use, and the need for assistance and inclusive design [ 36 ]. Our participants’ experiences with digital practices support the importance of these focus areas, especially when primarily older patients are admitted to hospitals. Furthermore, our findings indicate that some older patients who used to view themselves as being engaged in their own health care felt more distanced from the health care system because of digital services, and some who did not have the capabilities to use digital tools felt that they were treated differently compared to the rest of society. They did not necessarily view themselves as vulnerable but felt vulnerable in the specific experience of trying to use digital services because they wished that they were more capable. Moreover, this was the case for patients with physical and cognitive difficulties, as they were not necessarily aware of the challenges before experiencing them. Drawing on the phenomenological and feministic approach by Ahmed [ 37 ], these challenges that make patients feel vulnerable are not necessarily visible to others but can instead be viewed as invisible institutional “walls” that do not present themselves before the patient runs into them. Some participants had to experience how their physical, cognitive, or communicative difficulties affected their digital practice to realize that they were not as digitally capable as they once were or as others in society. Furthermore, viewed from this perspective, our findings could be used to argue that digital capabilities should be viewed as a privilege tied to users’ physical bodies and that digital services in the health care system are indirectly making patients without this privilege vulnerable. This calls for more attention to the inequities that digital tools and services create in health care systems and awareness that those who do not use digital tools are not necessarily indifferent about the consequences. Particularly, in a context such as the Danish one, in which the digital strategy is to create an intertwined and user-friendly public digital sector for everyone, it needs to be understood that patients have different digital capabilities and needs. Although some have not yet had a challenging experience that made them feel vulnerable, others are very aware that they receive different treatment and feel that they are on their own or that the rest of the society does not care about them. Inequities in digital health care, such as these, can and should be mitigated or prevented, and our investigation into the experiences with digital practices can help to show that we are creating standards and infrastructures that deliberately exclude the perspectives of those who are most in need of the services offered by the digital health care system [ 8 ]. Therefore, our findings support the notions that flexibility is important in the implementation of universal public digital services [ 17 ]; that it is important to adjust systems in accordance with patients’ eHealth literacy and not only improve the capabilities of individuals [ 38 ]; and that the development and improvement of digital health literacy are not solely an individual responsibility but are also tied to ways in which institutions organize, design, and implement digital tools and services [ 39 ].

Limitations

This qualitative study provided novel insights into the experiences with public digital health services from the perspective of patients in the Danish context, enabling a deeper understanding of how digital health services and digital tools are experienced and used. This helps build a solid foundation for future interventions aimed at digital health literacy and digital health interventions. However, this study has some limitations. First, the study was conducted in a country where digitalization is progressing quickly, and people, therefore, are accustomed to this pace. Therefore, readers must be aware of this. Second, the study included patients with different neurological conditions; some of their digital challenges were caused or worsened by these neurological conditions and are, therefore, not applicable to all patients in the health system. However, the findings provided insights into the patients’ digital practices before their conditions and other challenges not connected to neurological conditions shared by patients. Third, the study was broad, and although a large number of informants was included, from a qualitative research perspective, we would recommend additional research in this field to develop interventions that target digital health literacy and the use of digital health services.

Conclusions

Experiences with digital tools and digital health services are complex and multifaceted. The advantages in communication, finding information, or navigating through one’s own health course work as facilitators for engaging with digital tools and digital health services. However, this is not enough on its own. Furthermore, feeling secure and motivated and having time to relearn and practice skills are important facilitators. Engagement in digital practices for the examined population requires access to continuous assistance from their social network. If patients do not meet requirements, digital health services can be experienced as exclusionary and a source of concern. Physical, cognitive, and communicative difficulties might make it impossible to use digital tools or create more challenges that require assistance. Digitalization of the health care system means that patients do not have the choice to opt out of using digital services without having consequences, resulting in them receiving a different treatment than others. To ensure digitalization does not create inequities in health, it is necessary for developers and the health institutions that create, design, and implement digital services to be aware of differences in digital health literacy and to focus on simplifying communication with patients and next of kin through and about digital services. It is important to focus on helping individuals meet the necessary conditions and finding flexible solutions for those who do not have the same privileges as others if the public digital sector is to work for everyone.

Acknowledgments

The authors would like to thank all the people who gave their time to be interviewed for the study, the clinical nurse specialists who facilitated interviewing patients, and the other nurses on shift who assisted in recruiting participants.

Conflicts of Interest

None declared.

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Abbreviations

Edited by A Mavragani; submitted 14.03.23; peer-reviewed by G Myreteg, J Eriksen, M Siermann; comments to author 18.09.23; revised version received 09.10.23; accepted 27.02.24; published 11.04.24.

©Christian Gybel Jensen, Frederik Gybel Jensen, Mia Ingerslev Loft. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 11.04.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.